diff --git a/pr-2308/CMakeLists.txt b/pr-2308/CMakeLists.txt
deleted file mode 100644
index ce8cd35572..0000000000
--- a/pr-2308/CMakeLists.txt
+++ /dev/null
@@ -1,153 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# Add nvq++ compile + execution test of code examples
-# Args:
-#   TEST_NAME: name of the test executable. Test name is prefixed with "nvqpp"
-#   SOURCE_LOCATION: location of the source file (relative to 'sphinx' directory by default) 
-# Optional keyword args:
-#   TARGET <TARGET_NAME>: name of the target to use
-#   TARGET_OPTION <Option>: extra option for the target
-#   SOURCE_DIR <DIR>: the directory that SOURCE_LOCATION is relative to (if not the default)
-#   LAUNCH_COMMAND <COMMAND>: the command to launch the test (e.g., mpirun)
-function(add_nvqpp_test TEST_NAME SOURCE_LOCATION)
-  cmake_parse_arguments(PARSED_ARGS "" "TARGET;LABELS;SOURCE_DIR;LAUNCH_COMMAND;APPLICATION_ARGS;TARGET_OPTION" "" ${ARGN})
-  set(NVQPP_COMPILE_ARGS "--library-mode ")
-  if(PARSED_ARGS_TARGET)
-    set(NVQPP_COMPILE_ARGS "${NVQPP_COMPILE_ARGS} --target ${PARSED_ARGS_TARGET}")
-    if (PARSED_ARGS_TARGET_OPTION)
-      set(NVQPP_COMPILE_ARGS "${NVQPP_COMPILE_ARGS} --${PARSED_ARGS_TARGET}-option ${PARSED_ARGS_TARGET_OPTION}")
-    endif()
-  endif()
-  if (NOT PARSED_ARGS_SOURCE_DIR)
-    set(PARSED_ARGS_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/sphinx")
-  endif()
-  add_test(
-  NAME
-    nvqpp_${TEST_NAME}
-  COMMAND
-    bash -c "rm -f ${TEST_NAME}; ${CMAKE_BINARY_DIR}/bin/nvq++ ${NVQPP_COMPILE_ARGS} ${PARSED_ARGS_SOURCE_DIR}/${SOURCE_LOCATION} -o ${TEST_NAME} && \
-              ${PARSED_ARGS_LAUNCH_COMMAND} ${CMAKE_CURRENT_BINARY_DIR}/${TEST_NAME} ${PARSED_ARGS_APPLICATION_ARGS}"
-  )
-  if(PARSED_ARGS_LABELS)
-    set_tests_properties(nvqpp_${TEST_NAME} PROPERTIES LABELS "${PARSED_ARGS_LABELS}")
-  endif()
-endfunction()
-
-add_nvqpp_test(GHZ examples/cpp/basics/static_kernel.cpp)
-add_nvqpp_test(MultiControlOps examples/cpp/basics/multi_controlled_operations.cpp)
-add_nvqpp_test(ExpVals examples/cpp/basics/expectation_values.cpp)
-add_nvqpp_test(MidCircuitMeasurements examples/cpp/basics/mid_circuit_measurement.cpp)
-add_nvqpp_test(PhaseEstimation applications/cpp/phase_estimation.cpp)
-add_nvqpp_test(Grover applications/cpp/grover.cpp)
-add_nvqpp_test(QAOA applications/cpp/qaoa_maxcut.cpp)
-add_nvqpp_test(VQEH2 applications/cpp/vqe_h2.cpp)
-add_nvqpp_test(AmplitudeEstimation applications/cpp/amplitude_estimation.cpp)
-add_nvqpp_test(Builder examples/cpp/other/builder/builder.cpp)
-add_nvqpp_test(QAOABuilder examples/cpp/other/builder/qaoa_maxcut_builder.cpp)
-add_nvqpp_test(VQEH2Builder examples/cpp/other/builder/vqe_h2_builder.cpp)
-add_nvqpp_test(ComputeAction examples/cpp/other/compute_actions.cpp)
-add_nvqpp_test(Gradients examples/cpp/other/gradients.cpp)
-add_nvqpp_test(IterativePhaseEstimation applications/cpp/iterative_qpe.cpp)
-add_nvqpp_test(RandomWalkPhaseEstimation applications/cpp/random_walk_qpe.cpp)
-
-if (CUSTATEVEC_ROOT AND CUDA_FOUND) 
-  add_nvqpp_test(CuQuantumGHZ examples/cpp/basics/cuquantum_backends.cpp TARGET nvidia LABELS gpu_required)  
-  add_nvqpp_test(CuQuantumBernsteinVazirani applications/cpp/bernstein_vazirani.cpp TARGET nvidia LABELS gpu_required)  
-endif()
-
-# code snippets in docs
-add_nvqpp_test(QuickStart_default quick_start.cpp SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-add_nvqpp_test(FirstObserve using/first_observe.cpp SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-add_nvqpp_test(FirstSample using/first_sample.cpp SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-add_nvqpp_test(Timing using/time.cpp SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp APPLICATION_ARGS "10")
-
-set(NGPUS 0)
-if (CUSTATEVEC_ROOT AND CUDA_FOUND) 
-  add_nvqpp_test(QuickStart_nvidia quick_start.cpp TARGET nvidia LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-
-  # mqpu snippets need custatevec backend and optionally MPI
-  add_nvqpp_test(SampleAsync using/cudaq/platform/sample_async.cpp TARGET nvidia TARGET_OPTION mqpu LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-  add_nvqpp_test(ObserveMQPU using/cudaq/platform/observe_mqpu.cpp TARGET nvidia TARGET_OPTION mqpu LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-  add_nvqpp_test(StateAsyncMQPU using/cudaq/platform/get_state_async.cpp TARGET nvidia TARGET_OPTION mqpu LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-
-  # Legacy check for the `nvidia-mqpu` target
-  add_nvqpp_test(LegacySampleAsync using/cudaq/platform/sample_async.cpp TARGET nvidia-mqpu LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-  add_nvqpp_test(LegacyObserveMQPU using/cudaq/platform/observe_mqpu.cpp TARGET nvidia-mqpu LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-  add_nvqpp_test(LegacyStateAsyncMQPU using/cudaq/platform/get_state_async.cpp TARGET nvidia-mqpu LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-
-  # Add the MPI test if MPI was found and there are more than 2 GPUs
-  if (MPI_CXX_FOUND)
-    # Count the number of GPUs
-    find_program(NVIDIA_SMI "nvidia-smi")
-    if(NVIDIA_SMI)
-      execute_process(COMMAND bash -c "nvidia-smi --list-gpus | wc -l" OUTPUT_VARIABLE NGPUS)
-      # Only build this test if we have more than 1 GPU
-      if (${NGPUS} GREATER_EQUAL 2)
-        add_nvqpp_test(ObserveMQPU_MPI using/cudaq/platform/observe_mqpu_mpi.cpp
-                        TARGET nvidia 
-                        TARGET_OPTION mqpu 
-                        SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp 
-                        LAUNCH_COMMAND "${MPIEXEC} --allow-run-as-root -np 2")
-        add_nvqpp_test(LegacyObserveMQPU_MPI using/cudaq/platform/observe_mqpu_mpi.cpp
-                        TARGET nvidia-mqpu 
-                        SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp 
-                        LAUNCH_COMMAND "${MPIEXEC} --allow-run-as-root -np 2")
-      endif()
-    endif(NVIDIA_SMI)  
-  endif()   
-endif()
-
-add_nvqpp_test(photonics_sim targets/cpp/photonics.cpp TARGET orca-photonics)
-add_nvqpp_test(SampleAsyncRemote using/cudaq/platform/sample_async_remote.cpp TARGET remote-mqpu SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/cpp)
-set_tests_properties(
-  nvqpp_SampleAsyncRemote
-    PROPERTIES
-      ENVIRONMENT_MODIFICATION PATH=path_list_append:${CMAKE_BINARY_DIR}/bin
-)
-
-# Only add the python tests if we built the python API
-if (CUDAQ_ENABLE_PYTHON)
-  function(add_pycudaq_test TEST_NAME SOURCE_LOCATION)
-    cmake_parse_arguments(PARSED_ARGS "" "LABELS;SOURCE_DIR;LAUNCH_COMMAND" "" ${ARGN}) 
-    if (NOT PARSED_ARGS_SOURCE_DIR)
-      set(PARSED_ARGS_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/sphinx/examples/python")
-    endif()
-    add_test(
-    NAME
-      pycudaq_${TEST_NAME}
-    COMMAND
-      bash -c "${PARSED_ARGS_LAUNCH_COMMAND} ${Python_EXECUTABLE} ${PARSED_ARGS_SOURCE_DIR}/${SOURCE_LOCATION}"
-    )
-    set_tests_properties(pycudaq_${TEST_NAME} PROPERTIES ENVIRONMENT "PYTHONPATH=${CMAKE_BINARY_DIR}/python")
-    if(PARSED_ARGS_LABELS)
-      set_tests_properties(pycudaq_${TEST_NAME} PROPERTIES LABELS "${PARSED_ARGS_LABELS}")
-    endif()
-  endfunction()
-
-  add_pycudaq_test(Intro intro.py)
-  if (CUDA_FOUND)
-    add_pycudaq_test(EvolveDynamics using/backends/dynamics.py LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/python)
-  endif()
-
-  if (CUTENSORNET_ROOT AND CUDA_FOUND) 
-    # This example uses tensornet backend.
-    add_pycudaq_test(SampleAsyncRemote using/cudaq/platform/sample_async_remote.py SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/python)
-  endif()
-  
-  if (CUSTATEVEC_ROOT AND CUDA_FOUND) 
-    add_pycudaq_test(SampleAsync using/cudaq/platform/sample_async.py LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/python)
-    add_pycudaq_test(ObserveMQPU using/cudaq/platform/observe_mqpu.py LABELS gpu_required SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/python)
-    if (MPI_CXX_FOUND AND ${NGPUS} GREATER_EQUAL 2)
-      add_pycudaq_test(ObserveMQPU_MPI using/cudaq/platform/observe_mqpu_mpi.py 
-                        LABELS "gpu_required;mgpus_required"
-                        SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sphinx/snippets/python
-                        LAUNCH_COMMAND "${MPIEXEC} --allow-run-as-root -np 2")
-    endif()
-  endif()
-endif()
diff --git a/pr-2308/Doxyfile.in b/pr-2308/Doxyfile.in
deleted file mode 100644
index 8fa75de397..0000000000
--- a/pr-2308/Doxyfile.in
+++ /dev/null
@@ -1,2704 +0,0 @@
-# Doxyfile 1.9.7
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-#
-# Note:
-#
-# Use doxygen to compare the used configuration file with the template
-# configuration file:
-# doxygen -x [configFile]
-# Use doxygen to compare the used configuration file with the template
-# configuration file without replacing the environment variables or CMake type
-# replacement variables:
-# doxygen -x_noenv [configFile]
-
-#---------------------------------------------------------------------------
-# Project related configuration options
-#---------------------------------------------------------------------------
-
-# This tag specifies the encoding used for all characters in the configuration
-# file that follow. The default is UTF-8 which is also the encoding used for all
-# text before the first occurrence of this tag. Doxygen uses libiconv (or the
-# iconv built into libc) for the transcoding. See
-# https://www.gnu.org/software/libiconv/ for the list of possible encodings.
-# The default value is: UTF-8.
-
-DOXYFILE_ENCODING      = UTF-8
-
-# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
-# double-quotes, unless you are using Doxywizard) that should identify the
-# project for which the documentation is generated. This name is used in the
-# title of most generated pages and in a few other places.
-# The default value is: My Project.
-
-PROJECT_NAME           = "CUDA-Q"
-
-# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
-# could be handy for archiving the generated documentation or if some version
-# control system is used.
-
-PROJECT_NUMBER         =
-
-# Using the PROJECT_BRIEF tag one can provide an optional one line description
-# for a project that appears at the top of each page and should give viewer a
-# quick idea about the purpose of the project. Keep the description short.
-
-PROJECT_BRIEF          =
-
-# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
-# in the documentation. The maximum height of the logo should not exceed 55
-# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
-# the logo to the output directory.
-
-PROJECT_LOGO           =
-
-# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
-# into which the generated documentation will be written. If a relative path is
-# entered, it will be relative to the location where doxygen was started. If
-# left blank the current directory will be used.
-
-OUTPUT_DIRECTORY       = "${DOXYGEN_OUTPUT_PREFIX}"
-
-# If the CREATE_SUBDIRS tag is set to YES then doxygen will create up to 4096
-# sub-directories (in 2 levels) under the output directory of each output format
-# and will distribute the generated files over these directories. Enabling this
-# option can be useful when feeding doxygen a huge amount of source files, where
-# putting all generated files in the same directory would otherwise causes
-# performance problems for the file system. Adapt CREATE_SUBDIRS_LEVEL to
-# control the number of sub-directories.
-# The default value is: NO.
-
-CREATE_SUBDIRS         = NO
-
-# Controls the number of sub-directories that will be created when
-# CREATE_SUBDIRS tag is set to YES. Level 0 represents 16 directories, and every
-# level increment doubles the number of directories, resulting in 4096
-# directories at level 8 which is the default and also the maximum value. The
-# sub-directories are organized in 2 levels, the first level always has a fixed
-# number of 16 directories.
-# Minimum value: 0, maximum value: 8, default value: 8.
-# This tag requires that the tag CREATE_SUBDIRS is set to YES.
-
-CREATE_SUBDIRS_LEVEL   = 8
-
-# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
-# characters to appear in the names of generated files. If set to NO, non-ASCII
-# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
-# U+3044.
-# The default value is: NO.
-
-ALLOW_UNICODE_NAMES    = NO
-
-# The OUTPUT_LANGUAGE tag is used to specify the language in which all
-# documentation generated by doxygen is written. Doxygen will use this
-# information to generate all constant output in the proper language.
-# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Bulgarian,
-# Catalan, Chinese, Chinese-Traditional, Croatian, Czech, Danish, Dutch, English
-# (United States), Esperanto, Farsi (Persian), Finnish, French, German, Greek,
-# Hindi, Hungarian, Indonesian, Italian, Japanese, Japanese-en (Japanese with
-# English messages), Korean, Korean-en (Korean with English messages), Latvian,
-# Lithuanian, Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese,
-# Romanian, Russian, Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish,
-# Swedish, Turkish, Ukrainian and Vietnamese.
-# The default value is: English.
-
-OUTPUT_LANGUAGE        = English
-
-# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member
-# descriptions after the members that are listed in the file and class
-# documentation (similar to Javadoc). Set to NO to disable this.
-# The default value is: YES.
-
-BRIEF_MEMBER_DESC      = YES
-
-# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief
-# description of a member or function before the detailed description
-#
-# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
-# brief descriptions will be completely suppressed.
-# The default value is: YES.
-
-REPEAT_BRIEF           = YES
-
-# This tag implements a quasi-intelligent brief description abbreviator that is
-# used to form the text in various listings. Each string in this list, if found
-# as the leading text of the brief description, will be stripped from the text
-# and the result, after processing the whole list, is used as the annotated
-# text. Otherwise, the brief description is used as-is. If left blank, the
-# following values are used ($name is automatically replaced with the name of
-# the entity):The $name class, The $name widget, The $name file, is, provides,
-# specifies, contains, represents, a, an and the.
-
-ABBREVIATE_BRIEF       = "The $name class" \
-                         "The $name widget" \
-                         "The $name file" \
-                         is \
-                         provides \
-                         specifies \
-                         contains \
-                         represents \
-                         a \
-                         an \
-                         the
-
-# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
-# doxygen will generate a detailed section even if there is only a brief
-# description.
-# The default value is: NO.
-
-ALWAYS_DETAILED_SEC    = NO
-
-# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
-# inherited members of a class in the documentation of that class as if those
-# members were ordinary class members. Constructors, destructors and assignment
-# operators of the base classes will not be shown.
-# The default value is: NO.
-
-INLINE_INHERITED_MEMB  = NO
-
-# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
-# before files name in the file list and in the header files. If set to NO the
-# shortest path that makes the file name unique will be used
-# The default value is: YES.
-
-FULL_PATH_NAMES        = YES
-
-# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
-# Stripping is only done if one of the specified strings matches the left-hand
-# part of the path. The tag can be used to show relative paths in the file list.
-# If left blank the directory from which doxygen is run is used as the path to
-# strip.
-#
-# Note that you can specify absolute paths here, but also relative paths, which
-# will be relative from the directory where doxygen is started.
-# This tag requires that the tag FULL_PATH_NAMES is set to YES.
-
-STRIP_FROM_PATH        =
-
-# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
-# path mentioned in the documentation of a class, which tells the reader which
-# header file to include in order to use a class. If left blank only the name of
-# the header file containing the class definition is used. Otherwise one should
-# specify the list of include paths that are normally passed to the compiler
-# using the -I flag.
-
-STRIP_FROM_INC_PATH    =
-
-# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
-# less readable) file names. This can be useful is your file systems doesn't
-# support long names like on DOS, Mac, or CD-ROM.
-# The default value is: NO.
-
-SHORT_NAMES            = NO
-
-# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
-# first line (until the first dot) of a Javadoc-style comment as the brief
-# description. If set to NO, the Javadoc-style will behave just like regular Qt-
-# style comments (thus requiring an explicit @brief command for a brief
-# description.)
-# The default value is: NO.
-
-JAVADOC_AUTOBRIEF      = NO
-
-# If the JAVADOC_BANNER tag is set to YES then doxygen will interpret a line
-# such as
-# /***************
-# as being the beginning of a Javadoc-style comment "banner". If set to NO, the
-# Javadoc-style will behave just like regular comments and it will not be
-# interpreted by doxygen.
-# The default value is: NO.
-
-JAVADOC_BANNER         = NO
-
-# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
-# line (until the first dot) of a Qt-style comment as the brief description. If
-# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
-# requiring an explicit \brief command for a brief description.)
-# The default value is: NO.
-
-QT_AUTOBRIEF           = NO
-
-# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
-# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
-# a brief description. This used to be the default behavior. The new default is
-# to treat a multi-line C++ comment block as a detailed description. Set this
-# tag to YES if you prefer the old behavior instead.
-#
-# Note that setting this tag to YES also means that rational rose comments are
-# not recognized any more.
-# The default value is: NO.
-
-MULTILINE_CPP_IS_BRIEF = NO
-
-# By default Python docstrings are displayed as preformatted text and doxygen's
-# special commands cannot be used. By setting PYTHON_DOCSTRING to NO the
-# doxygen's special commands can be used and the contents of the docstring
-# documentation blocks is shown as doxygen documentation.
-# The default value is: YES.
-
-PYTHON_DOCSTRING       = YES
-
-# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
-# documentation from any documented member that it re-implements.
-# The default value is: YES.
-
-INHERIT_DOCS           = YES
-
-# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
-# page for each member. If set to NO, the documentation of a member will be part
-# of the file/class/namespace that contains it.
-# The default value is: NO.
-
-SEPARATE_MEMBER_PAGES  = NO
-
-# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
-# uses this value to replace tabs by spaces in code fragments.
-# Minimum value: 1, maximum value: 16, default value: 4.
-
-TAB_SIZE               = 4
-
-# This tag can be used to specify a number of aliases that act as commands in
-# the documentation. An alias has the form:
-# name=value
-# For example adding
-# "sideeffect=@par Side Effects:^^"
-# will allow you to put the command \sideeffect (or @sideeffect) in the
-# documentation, which will result in a user-defined paragraph with heading
-# "Side Effects:". Note that you cannot put \n's in the value part of an alias
-# to insert newlines (in the resulting output). You can put ^^ in the value part
-# of an alias to insert a newline as if a physical newline was in the original
-# file. When you need a literal { or } or , in the value part of an alias you
-# have to escape them by means of a backslash (\), this can lead to conflicts
-# with the commands \{ and \} for these it is advised to use the version @{ and
-# @} or use a double escape (\\{ and \\})
-
-ALIASES                =
-
-# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
-# only. Doxygen will then generate output that is more tailored for C. For
-# instance, some of the names that are used will be different. The list of all
-# members will be omitted, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_FOR_C  = YES
-
-# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
-# Python sources only. Doxygen will then generate output that is more tailored
-# for that language. For instance, namespaces will be presented as packages,
-# qualified scopes will look different, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_JAVA   = NO
-
-# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
-# sources. Doxygen will then generate output that is tailored for Fortran.
-# The default value is: NO.
-
-OPTIMIZE_FOR_FORTRAN   = NO
-
-# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
-# sources. Doxygen will then generate output that is tailored for VHDL.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_VHDL   = NO
-
-# Set the OPTIMIZE_OUTPUT_SLICE tag to YES if your project consists of Slice
-# sources only. Doxygen will then generate output that is more tailored for that
-# language. For instance, namespaces will be presented as modules, types will be
-# separated into more groups, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_SLICE  = NO
-
-# Doxygen selects the parser to use depending on the extension of the files it
-# parses. With this tag you can assign which parser to use for a given
-# extension. Doxygen has a built-in mapping, but you can override or extend it
-# using this tag. The format is ext=language, where ext is a file extension, and
-# language is one of the parsers supported by doxygen: IDL, Java, JavaScript,
-# Csharp (C#), C, C++, Lex, D, PHP, md (Markdown), Objective-C, Python, Slice,
-# VHDL, Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:
-# FortranFree, unknown formatted Fortran: Fortran. In the later case the parser
-# tries to guess whether the code is fixed or free formatted code, this is the
-# default for Fortran type files). For instance to make doxygen treat .inc files
-# as Fortran files (default is PHP), and .f files as C (default is Fortran),
-# use: inc=Fortran f=C.
-#
-# Note: For files without extension you can use no_extension as a placeholder.
-#
-# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
-# the files are not read by doxygen. When specifying no_extension you should add
-# * to the FILE_PATTERNS.
-#
-# Note see also the list of default file extension mappings.
-
-EXTENSION_MAPPING      =
-
-# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
-# according to the Markdown format, which allows for more readable
-# documentation. See https://daringfireball.net/projects/markdown/ for details.
-# The output of markdown processing is further processed by doxygen, so you can
-# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
-# case of backward compatibilities issues.
-# The default value is: YES.
-
-MARKDOWN_SUPPORT       = YES
-
-# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up
-# to that level are automatically included in the table of contents, even if
-# they do not have an id attribute.
-# Note: This feature currently applies only to Markdown headings.
-# Minimum value: 0, maximum value: 99, default value: 5.
-# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
-
-TOC_INCLUDE_HEADINGS   = 3
-
-# When enabled doxygen tries to link words that correspond to documented
-# classes, or namespaces to their corresponding documentation. Such a link can
-# be prevented in individual cases by putting a % sign in front of the word or
-# globally by setting AUTOLINK_SUPPORT to NO.
-# The default value is: YES.
-
-AUTOLINK_SUPPORT       = YES
-
-# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
-# to include (a tag file for) the STL sources as input, then you should set this
-# tag to YES in order to let doxygen match functions declarations and
-# definitions whose arguments contain STL classes (e.g. func(std::string);
-# versus func(std::string) {}). This also make the inheritance and collaboration
-# diagrams that involve STL classes more complete and accurate.
-# The default value is: NO.
-
-BUILTIN_STL_SUPPORT    = NO
-
-# If you use Microsoft's C++/CLI language, you should set this option to YES to
-# enable parsing support.
-# The default value is: NO.
-
-CPP_CLI_SUPPORT        = NO
-
-# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
-# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen
-# will parse them like normal C++ but will assume all classes use public instead
-# of private inheritance when no explicit protection keyword is present.
-# The default value is: NO.
-
-SIP_SUPPORT            = NO
-
-# For Microsoft's IDL there are propget and propput attributes to indicate
-# getter and setter methods for a property. Setting this option to YES will make
-# doxygen to replace the get and set methods by a property in the documentation.
-# This will only work if the methods are indeed getting or setting a simple
-# type. If this is not the case, or you want to show the methods anyway, you
-# should set this option to NO.
-# The default value is: YES.
-
-IDL_PROPERTY_SUPPORT   = YES
-
-# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
-# tag is set to YES then doxygen will reuse the documentation of the first
-# member in the group (if any) for the other members of the group. By default
-# all members of a group must be documented explicitly.
-# The default value is: NO.
-
-DISTRIBUTE_GROUP_DOC   = NO
-
-# If one adds a struct or class to a group and this option is enabled, then also
-# any nested class or struct is added to the same group. By default this option
-# is disabled and one has to add nested compounds explicitly via \ingroup.
-# The default value is: NO.
-
-GROUP_NESTED_COMPOUNDS = NO
-
-# Set the SUBGROUPING tag to YES to allow class member groups of the same type
-# (for instance a group of public functions) to be put as a subgroup of that
-# type (e.g. under the Public Functions section). Set it to NO to prevent
-# subgrouping. Alternatively, this can be done per class using the
-# \nosubgrouping command.
-# The default value is: YES.
-
-SUBGROUPING            = YES
-
-# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
-# are shown inside the group in which they are included (e.g. using \ingroup)
-# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
-# and RTF).
-#
-# Note that this feature does not work in combination with
-# SEPARATE_MEMBER_PAGES.
-# The default value is: NO.
-
-INLINE_GROUPED_CLASSES = NO
-
-# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
-# with only public data fields or simple typedef fields will be shown inline in
-# the documentation of the scope in which they are defined (i.e. file,
-# namespace, or group documentation), provided this scope is documented. If set
-# to NO, structs, classes, and unions are shown on a separate page (for HTML and
-# Man pages) or section (for LaTeX and RTF).
-# The default value is: NO.
-
-INLINE_SIMPLE_STRUCTS  = NO
-
-# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
-# enum is documented as struct, union, or enum with the name of the typedef. So
-# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
-# with name TypeT. When disabled the typedef will appear as a member of a file,
-# namespace, or class. And the struct will be named TypeS. This can typically be
-# useful for C code in case the coding convention dictates that all compound
-# types are typedef'ed and only the typedef is referenced, never the tag name.
-# The default value is: NO.
-
-TYPEDEF_HIDES_STRUCT   = NO
-
-# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
-# cache is used to resolve symbols given their name and scope. Since this can be
-# an expensive process and often the same symbol appears multiple times in the
-# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
-# doxygen will become slower. If the cache is too large, memory is wasted. The
-# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
-# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
-# symbols. At the end of a run doxygen will report the cache usage and suggest
-# the optimal cache size from a speed point of view.
-# Minimum value: 0, maximum value: 9, default value: 0.
-
-LOOKUP_CACHE_SIZE      = 0
-
-# The NUM_PROC_THREADS specifies the number of threads doxygen is allowed to use
-# during processing. When set to 0 doxygen will based this on the number of
-# cores available in the system. You can set it explicitly to a value larger
-# than 0 to get more control over the balance between CPU load and processing
-# speed. At this moment only the input processing can be done using multiple
-# threads. Since this is still an experimental feature the default is set to 1,
-# which effectively disables parallel processing. Please report any issues you
-# encounter. Generating dot graphs in parallel is controlled by the
-# DOT_NUM_THREADS setting.
-# Minimum value: 0, maximum value: 32, default value: 1.
-
-NUM_PROC_THREADS       = 0
-
-#---------------------------------------------------------------------------
-# Build related configuration options
-#---------------------------------------------------------------------------
-
-# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
-# documentation are documented, even if no documentation was available. Private
-# class members and static file members will be hidden unless the
-# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
-# Note: This will also disable the warnings about undocumented members that are
-# normally produced when WARNINGS is set to YES.
-# The default value is: NO.
-
-EXTRACT_ALL            = NO
-
-# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
-# be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PRIVATE        = NO
-
-# If the EXTRACT_PRIV_VIRTUAL tag is set to YES, documented private virtual
-# methods of a class will be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PRIV_VIRTUAL   = NO
-
-# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
-# scope will be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PACKAGE        = NO
-
-# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
-# included in the documentation.
-# The default value is: NO.
-
-EXTRACT_STATIC         = NO
-
-# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
-# locally in source files will be included in the documentation. If set to NO,
-# only classes defined in header files are included. Does not have any effect
-# for Java sources.
-# The default value is: YES.
-
-EXTRACT_LOCAL_CLASSES  = YES
-
-# This flag is only useful for Objective-C code. If set to YES, local methods,
-# which are defined in the implementation section but not in the interface are
-# included in the documentation. If set to NO, only methods in the interface are
-# included.
-# The default value is: NO.
-
-EXTRACT_LOCAL_METHODS  = NO
-
-# If this flag is set to YES, the members of anonymous namespaces will be
-# extracted and appear in the documentation as a namespace called
-# 'anonymous_namespace{file}', where file will be replaced with the base name of
-# the file that contains the anonymous namespace. By default anonymous namespace
-# are hidden.
-# The default value is: NO.
-
-EXTRACT_ANON_NSPACES   = NO
-
-# If this flag is set to YES, the name of an unnamed parameter in a declaration
-# will be determined by the corresponding definition. By default unnamed
-# parameters remain unnamed in the output.
-# The default value is: YES.
-
-RESOLVE_UNNAMED_PARAMS = YES
-
-# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
-# undocumented members inside documented classes or files. If set to NO these
-# members will be included in the various overviews, but no documentation
-# section is generated. This option has no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_MEMBERS     = NO
-
-# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
-# undocumented classes that are normally visible in the class hierarchy. If set
-# to NO, these classes will be included in the various overviews. This option
-# will also hide undocumented C++ concepts if enabled. This option has no effect
-# if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_CLASSES     = NO
-
-# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
-# declarations. If set to NO, these declarations will be included in the
-# documentation.
-# The default value is: NO.
-
-HIDE_FRIEND_COMPOUNDS  = NO
-
-# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
-# documentation blocks found inside the body of a function. If set to NO, these
-# blocks will be appended to the function's detailed documentation block.
-# The default value is: NO.
-
-HIDE_IN_BODY_DOCS      = NO
-
-# The INTERNAL_DOCS tag determines if documentation that is typed after a
-# \internal command is included. If the tag is set to NO then the documentation
-# will be excluded. Set it to YES to include the internal documentation.
-# The default value is: NO.
-
-INTERNAL_DOCS          = NO
-
-# With the correct setting of option CASE_SENSE_NAMES doxygen will better be
-# able to match the capabilities of the underlying filesystem. In case the
-# filesystem is case sensitive (i.e. it supports files in the same directory
-# whose names only differ in casing), the option must be set to YES to properly
-# deal with such files in case they appear in the input. For filesystems that
-# are not case sensitive the option should be set to NO to properly deal with
-# output files written for symbols that only differ in casing, such as for two
-# classes, one named CLASS and the other named Class, and to also support
-# references to files without having to specify the exact matching casing. On
-# Windows (including Cygwin) and MacOS, users should typically set this option
-# to NO, whereas on Linux or other Unix flavors it should typically be set to
-# YES.
-# Possible values are: SYSTEM, NO and YES.
-# The default value is: SYSTEM.
-
-CASE_SENSE_NAMES       = YES
-
-# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
-# their full class and namespace scopes in the documentation. If set to YES, the
-# scope will be hidden.
-# The default value is: NO.
-
-HIDE_SCOPE_NAMES       = NO
-
-# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
-# append additional text to a page's title, such as Class Reference. If set to
-# YES the compound reference will be hidden.
-# The default value is: NO.
-
-HIDE_COMPOUND_REFERENCE= NO
-
-# If the SHOW_HEADERFILE tag is set to YES then the documentation for a class
-# will show which file needs to be included to use the class.
-# The default value is: YES.
-
-SHOW_HEADERFILE        = YES
-
-# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
-# the files that are included by a file in the documentation of that file.
-# The default value is: YES.
-
-SHOW_INCLUDE_FILES     = YES
-
-# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
-# grouped member an include statement to the documentation, telling the reader
-# which file to include in order to use the member.
-# The default value is: NO.
-
-SHOW_GROUPED_MEMB_INC  = NO
-
-# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
-# files with double quotes in the documentation rather than with sharp brackets.
-# The default value is: NO.
-
-FORCE_LOCAL_INCLUDES   = NO
-
-# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
-# documentation for inline members.
-# The default value is: YES.
-
-INLINE_INFO            = YES
-
-# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
-# (detailed) documentation of file and class members alphabetically by member
-# name. If set to NO, the members will appear in declaration order.
-# The default value is: YES.
-
-SORT_MEMBER_DOCS       = NO
-
-# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
-# descriptions of file, namespace and class members alphabetically by member
-# name. If set to NO, the members will appear in declaration order. Note that
-# this will also influence the order of the classes in the class list.
-# The default value is: NO.
-
-SORT_BRIEF_DOCS        = NO
-
-# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
-# (brief and detailed) documentation of class members so that constructors and
-# destructors are listed first. If set to NO the constructors will appear in the
-# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
-# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
-# member documentation.
-# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
-# detailed member documentation.
-# The default value is: NO.
-
-SORT_MEMBERS_CTORS_1ST = NO
-
-# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
-# of group names into alphabetical order. If set to NO the group names will
-# appear in their defined order.
-# The default value is: NO.
-
-SORT_GROUP_NAMES       = NO
-
-# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
-# fully-qualified names, including namespaces. If set to NO, the class list will
-# be sorted only by class name, not including the namespace part.
-# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
-# Note: This option applies only to the class list, not to the alphabetical
-# list.
-# The default value is: NO.
-
-SORT_BY_SCOPE_NAME     = NO
-
-# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
-# type resolution of all parameters of a function it will reject a match between
-# the prototype and the implementation of a member function even if there is
-# only one candidate or it is obvious which candidate to choose by doing a
-# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
-# accept a match between prototype and implementation in such cases.
-# The default value is: NO.
-
-STRICT_PROTO_MATCHING  = NO
-
-# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
-# list. This list is created by putting \todo commands in the documentation.
-# The default value is: YES.
-
-GENERATE_TODOLIST      = YES
-
-# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
-# list. This list is created by putting \test commands in the documentation.
-# The default value is: YES.
-
-GENERATE_TESTLIST      = YES
-
-# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
-# list. This list is created by putting \bug commands in the documentation.
-# The default value is: YES.
-
-GENERATE_BUGLIST       = YES
-
-# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
-# the deprecated list. This list is created by putting \deprecated commands in
-# the documentation.
-# The default value is: YES.
-
-GENERATE_DEPRECATEDLIST= YES
-
-# The ENABLED_SECTIONS tag can be used to enable conditional documentation
-# sections, marked by \if <section_label> ... \endif and \cond <section_label>
-# ... \endcond blocks.
-
-ENABLED_SECTIONS       =
-
-# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
-# initial value of a variable or macro / define can have for it to appear in the
-# documentation. If the initializer consists of more lines than specified here
-# it will be hidden. Use a value of 0 to hide initializers completely. The
-# appearance of the value of individual variables and macros / defines can be
-# controlled using \showinitializer or \hideinitializer command in the
-# documentation regardless of this setting.
-# Minimum value: 0, maximum value: 10000, default value: 30.
-
-MAX_INITIALIZER_LINES  = 30
-
-# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
-# the bottom of the documentation of classes and structs. If set to YES, the
-# list will mention the files that were used to generate the documentation.
-# The default value is: YES.
-
-SHOW_USED_FILES        = YES
-
-# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
-# will remove the Files entry from the Quick Index and from the Folder Tree View
-# (if specified).
-# The default value is: YES.
-
-SHOW_FILES             = YES
-
-# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
-# page. This will remove the Namespaces entry from the Quick Index and from the
-# Folder Tree View (if specified).
-# The default value is: YES.
-
-SHOW_NAMESPACES        = YES
-
-# The FILE_VERSION_FILTER tag can be used to specify a program or script that
-# doxygen should invoke to get the current version for each file (typically from
-# the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command command input-file, where command is the value of the
-# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
-# by doxygen. Whatever the program writes to standard output is used as the file
-# version. For an example see the documentation.
-
-FILE_VERSION_FILTER    =
-
-# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
-# by doxygen. The layout file controls the global structure of the generated
-# output files in an output format independent way. To create the layout file
-# that represents doxygen's defaults, run doxygen with the -l option. You can
-# optionally specify a file name after the option, if omitted DoxygenLayout.xml
-# will be used as the name of the layout file. See also section "Changing the
-# layout of pages" for information.
-#
-# Note that if you run doxygen from a directory containing a file called
-# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
-# tag is left empty.
-
-LAYOUT_FILE            =
-
-# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
-# the reference definitions. This must be a list of .bib files. The .bib
-# extension is automatically appended if omitted. This requires the bibtex tool
-# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.
-# For LaTeX the style of the bibliography can be controlled using
-# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
-# search path. See also \cite for info how to create references.
-
-CITE_BIB_FILES         =
-
-#---------------------------------------------------------------------------
-# Configuration options related to warning and progress messages
-#---------------------------------------------------------------------------
-
-# The QUIET tag can be used to turn on/off the messages that are generated to
-# standard output by doxygen. If QUIET is set to YES this implies that the
-# messages are off.
-# The default value is: NO.
-
-QUIET                  = NO
-
-# The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
-# this implies that the warnings are on.
-#
-# Tip: Turn warnings on while writing the documentation.
-# The default value is: YES.
-
-WARNINGS               = YES
-
-# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
-# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
-# will automatically be disabled.
-# The default value is: YES.
-
-WARN_IF_UNDOCUMENTED   = NO
-
-# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as documenting some parameters in
-# a documented function twice, or documenting parameters that don't exist or
-# using markup commands wrongly.
-# The default value is: YES.
-
-WARN_IF_DOC_ERROR      = YES
-
-# If WARN_IF_INCOMPLETE_DOC is set to YES, doxygen will warn about incomplete
-# function parameter documentation. If set to NO, doxygen will accept that some
-# parameters have no documentation without warning.
-# The default value is: YES.
-
-WARN_IF_INCOMPLETE_DOC = NO
-
-# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
-# are documented, but have no documentation for their parameters or return
-# value. If set to NO, doxygen will only warn about wrong parameter
-# documentation, but not about the absence of documentation. If EXTRACT_ALL is
-# set to YES then this flag will automatically be disabled. See also
-# WARN_IF_INCOMPLETE_DOC
-# The default value is: NO.
-
-WARN_NO_PARAMDOC       = NO
-
-# If WARN_IF_UNDOC_ENUM_VAL option is set to YES, doxygen will warn about
-# undocumented enumeration values. If set to NO, doxygen will accept
-# undocumented enumeration values. If EXTRACT_ALL is set to YES then this flag
-# will automatically be disabled.
-# The default value is: NO.
-
-WARN_IF_UNDOC_ENUM_VAL = NO
-
-# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
-# a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS
-# then doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but
-# at the end of the doxygen process doxygen will return with a non-zero status.
-# If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS_PRINT then doxygen behaves
-# like FAIL_ON_WARNINGS but in case no WARN_LOGFILE is defined doxygen will not
-# write the warning messages in between other messages but write them at the end
-# of a run, in case a WARN_LOGFILE is defined the warning messages will be
-# besides being in the defined file also be shown at the end of a run, unless
-# the WARN_LOGFILE is defined as - i.e. standard output (stdout) in that case
-# the behavior will remain as with the setting FAIL_ON_WARNINGS.
-# Possible values are: NO, YES, FAIL_ON_WARNINGS and FAIL_ON_WARNINGS_PRINT.
-# The default value is: NO.
-
-WARN_AS_ERROR          = FAIL_ON_WARNINGS
-
-# The WARN_FORMAT tag determines the format of the warning messages that doxygen
-# can produce. The string should contain the $file, $line, and $text tags, which
-# will be replaced by the file and line number from which the warning originated
-# and the warning text. Optionally the format may contain $version, which will
-# be replaced by the version of the file (if it could be obtained via
-# FILE_VERSION_FILTER)
-# See also: WARN_LINE_FORMAT
-# The default value is: $file:$line: $text.
-
-WARN_FORMAT            = "$file:$line: $text"
-
-# In the $text part of the WARN_FORMAT command it is possible that a reference
-# to a more specific place is given. To make it easier to jump to this place
-# (outside of doxygen) the user can define a custom "cut" / "paste" string.
-# Example:
-# WARN_LINE_FORMAT = "'vi $file +$line'"
-# See also: WARN_FORMAT
-# The default value is: at line $line of file $file.
-
-WARN_LINE_FORMAT       = "at line $line of file $file"
-
-# The WARN_LOGFILE tag can be used to specify a file to which warning and error
-# messages should be written. If left blank the output is written to standard
-# error (stderr). In case the file specified cannot be opened for writing the
-# warning and error messages are written to standard error. When as file - is
-# specified the warning and error messages are written to standard output
-# (stdout).
-
-WARN_LOGFILE           =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the input files
-#---------------------------------------------------------------------------
-
-# The INPUT tag is used to specify the files and/or directories that contain
-# documented source files. You may enter file names like myfile.cpp or
-# directories like /usr/src/myproject. Separate the files or directories with
-# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
-# Note: If this tag is empty the current directory is searched.
-
-INPUT                  = "${CUDAQ_REPO_ROOT}/runtime"
-
-# This tag can be used to specify the character encoding of the source files
-# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
-# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see:
-# https://www.gnu.org/software/libiconv/) for the list of possible encodings.
-# See also: INPUT_FILE_ENCODING
-# The default value is: UTF-8.
-
-INPUT_ENCODING         = UTF-8
-
-# This tag can be used to specify the character encoding of the source files
-# that doxygen parses The INPUT_FILE_ENCODING tag can be used to specify
-# character encoding on a per file pattern basis. Doxygen will compare the file
-# name with each pattern and apply the encoding instead of the default
-# INPUT_ENCODING) if there is a match. The character encodings are a list of the
-# form: pattern=encoding (like *.php=ISO-8859-1). See cfg_input_encoding
-# "INPUT_ENCODING" for further information on supported encodings.
-
-INPUT_FILE_ENCODING    =
-
-# If the value of the INPUT tag contains directories, you can use the
-# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
-# *.h) to filter out the source-files in the directories.
-#
-# Note that for custom extensions or not directly supported extensions you also
-# need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# read by doxygen.
-#
-# Note the list of default checked file patterns might differ from the list of
-# default file extension mappings.
-#
-# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
-# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
-# *.hh, *.hxx, *.hpp, *.h++, *.l, *.cs, *.d, *.php, *.php4, *.php5, *.phtml,
-# *.inc, *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C
-# comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f18, *.f, *.for, *.vhd,
-# *.vhdl, *.ucf, *.qsf and *.ice.
-
-FILE_PATTERNS          = *.h \
-                         *.hpp \
-                         *.inc
-
-# The RECURSIVE tag can be used to specify whether or not subdirectories should
-# be searched for input files as well.
-# The default value is: NO.
-
-RECURSIVE              = YES
-
-# The EXCLUDE tag can be used to specify files and/or directories that should be
-# excluded from the INPUT source files. This way you can easily exclude a
-# subdirectory from a directory tree whose root is specified with the INPUT tag.
-#
-# Note that relative paths are relative to the directory from which doxygen is
-# run.
-
-EXCLUDE                = "${CUDAQ_REPO_ROOT}/runtime/cudaq/algorithms/optimizers"
-
-# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
-# directories that are symbolic links (a Unix file system feature) are excluded
-# from the input.
-# The default value is: NO.
-
-EXCLUDE_SYMLINKS       = NO
-
-# If the value of the INPUT tag contains directories, you can use the
-# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
-# certain files from those directories.
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories for example use the pattern */test/*
-
-EXCLUDE_PATTERNS       = "Convert*"
-
-# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
-# (namespaces, classes, functions, etc.) that should be excluded from the
-# output. The symbol name can be a fully qualified name, a word, or if the
-# wildcard * is used, a substring. Examples: ANamespace, AClass,
-# ANamespace::AClass, ANamespace::*Test
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories use the pattern */test/*
-
-EXCLUDE_SYMBOLS        = "classcudaq_1_1*"
-
-# The EXAMPLE_PATH tag can be used to specify one or more files or directories
-# that contain example code fragments that are included (see the \include
-# command).
-
-EXAMPLE_PATH           =
-
-# If the value of the EXAMPLE_PATH tag contains directories, you can use the
-# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank all
-# files are included.
-
-EXAMPLE_PATTERNS       = *
-
-# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
-# searched for input files to be used with the \include or \dontinclude commands
-# irrespective of the value of the RECURSIVE tag.
-# The default value is: NO.
-
-EXAMPLE_RECURSIVE      = NO
-
-# The IMAGE_PATH tag can be used to specify one or more files or directories
-# that contain images that are to be included in the documentation (see the
-# \image command).
-
-IMAGE_PATH             =
-
-# The INPUT_FILTER tag can be used to specify a program that doxygen should
-# invoke to filter for each input file. Doxygen will invoke the filter program
-# by executing (via popen()) the command:
-#
-# <filter> <input-file>
-#
-# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
-# name of an input file. Doxygen will then use the output that the filter
-# program writes to standard output. If FILTER_PATTERNS is specified, this tag
-# will be ignored.
-#
-# Note that the filter must not add or remove lines; it is applied before the
-# code is scanned, but not when the output code is generated. If lines are added
-# or removed, the anchors will not be placed correctly.
-#
-# Note that doxygen will use the data processed and written to standard output
-# for further processing, therefore nothing else, like debug statements or used
-# commands (so in case of a Windows batch file always use @echo OFF), should be
-# written to standard output.
-#
-# Note that for custom extensions or not directly supported extensions you also
-# need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# properly processed by doxygen.
-
-INPUT_FILTER           =
-
-# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
-# basis. Doxygen will compare the file name with each pattern and apply the
-# filter if there is a match. The filters are a list of the form: pattern=filter
-# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
-# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
-# patterns match the file name, INPUT_FILTER is applied.
-#
-# Note that for custom extensions or not directly supported extensions you also
-# need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# properly processed by doxygen.
-
-FILTER_PATTERNS        =
-
-# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
-# INPUT_FILTER) will also be used to filter the input files that are used for
-# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
-# The default value is: NO.
-
-FILTER_SOURCE_FILES    = NO
-
-# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
-# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
-# it is also possible to disable source filtering for a specific pattern using
-# *.ext= (so without naming a filter).
-# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
-
-FILTER_SOURCE_PATTERNS =
-
-# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
-# is part of the input, its contents will be placed on the main page
-# (index.html). This can be useful if you have a project on for instance GitHub
-# and want to reuse the introduction page also for the doxygen output.
-
-USE_MDFILE_AS_MAINPAGE =
-
-# The Fortran standard specifies that for fixed formatted Fortran code all
-# characters from position 72 are to be considered as comment. A common
-# extension is to allow longer lines before the automatic comment starts. The
-# setting FORTRAN_COMMENT_AFTER will also make it possible that longer lines can
-# be processed before the automatic comment starts.
-# Minimum value: 7, maximum value: 10000, default value: 72.
-
-FORTRAN_COMMENT_AFTER  = 72
-
-#---------------------------------------------------------------------------
-# Configuration options related to source browsing
-#---------------------------------------------------------------------------
-
-# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
-# generated. Documented entities will be cross-referenced with these sources.
-#
-# Note: To get rid of all source code in the generated output, make sure that
-# also VERBATIM_HEADERS is set to NO.
-# The default value is: NO.
-
-SOURCE_BROWSER         = NO
-
-# Setting the INLINE_SOURCES tag to YES will include the body of functions,
-# classes and enums directly into the documentation.
-# The default value is: NO.
-
-INLINE_SOURCES         = NO
-
-# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
-# special comment blocks from generated source code fragments. Normal C, C++ and
-# Fortran comments will always remain visible.
-# The default value is: YES.
-
-STRIP_CODE_COMMENTS    = YES
-
-# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
-# entity all documented functions referencing it will be listed.
-# The default value is: NO.
-
-REFERENCED_BY_RELATION = NO
-
-# If the REFERENCES_RELATION tag is set to YES then for each documented function
-# all documented entities called/used by that function will be listed.
-# The default value is: NO.
-
-REFERENCES_RELATION    = NO
-
-# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
-# to YES then the hyperlinks from functions in REFERENCES_RELATION and
-# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
-# link to the documentation.
-# The default value is: YES.
-
-REFERENCES_LINK_SOURCE = YES
-
-# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
-# source code will show a tooltip with additional information such as prototype,
-# brief description and links to the definition and documentation. Since this
-# will make the HTML file larger and loading of large files a bit slower, you
-# can opt to disable this feature.
-# The default value is: YES.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-SOURCE_TOOLTIPS        = YES
-
-# If the USE_HTAGS tag is set to YES then the references to source code will
-# point to the HTML generated by the htags(1) tool instead of doxygen built-in
-# source browser. The htags tool is part of GNU's global source tagging system
-# (see https://www.gnu.org/software/global/global.html). You will need version
-# 4.8.6 or higher.
-#
-# To use it do the following:
-# - Install the latest version of global
-# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file
-# - Make sure the INPUT points to the root of the source tree
-# - Run doxygen as normal
-#
-# Doxygen will invoke htags (and that will in turn invoke gtags), so these
-# tools must be available from the command line (i.e. in the search path).
-#
-# The result: instead of the source browser generated by doxygen, the links to
-# source code will now point to the output of htags.
-# The default value is: NO.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-USE_HTAGS              = NO
-
-# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
-# verbatim copy of the header file for each class for which an include is
-# specified. Set to NO to disable this.
-# See also: Section \class.
-# The default value is: YES.
-
-VERBATIM_HEADERS       = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the alphabetical class index
-#---------------------------------------------------------------------------
-
-# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
-# compounds will be generated. Enable this if the project contains a lot of
-# classes, structs, unions or interfaces.
-# The default value is: YES.
-
-ALPHABETICAL_INDEX     = YES
-
-# The IGNORE_PREFIX tag can be used to specify a prefix (or a list of prefixes)
-# that should be ignored while generating the index headers. The IGNORE_PREFIX
-# tag works for classes, function and member names. The entity will be placed in
-# the alphabetical list under the first letter of the entity name that remains
-# after removing the prefix.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-IGNORE_PREFIX          =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the HTML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
-# The default value is: YES.
-
-GENERATE_HTML          = NO
-
-# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_OUTPUT            = "${DOXYGEN_OUTPUT_PREFIX}/html"
-
-# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
-# generated HTML page (for example: .htm, .php, .asp).
-# The default value is: .html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FILE_EXTENSION    = .html
-
-# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
-# each generated HTML page. If the tag is left blank doxygen will generate a
-# standard header.
-#
-# To get valid HTML the header file that includes any scripts and style sheets
-# that doxygen needs, which is dependent on the configuration options used (e.g.
-# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
-# default header using
-# doxygen -w html new_header.html new_footer.html new_stylesheet.css
-# YourConfigFile
-# and then modify the file new_header.html. See also section "Doxygen usage"
-# for information on how to generate the default header that doxygen normally
-# uses.
-# Note: The header is subject to change so you typically have to regenerate the
-# default header when upgrading to a newer version of doxygen. For a description
-# of the possible markers and block names see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_HEADER            =
-
-# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
-# generated HTML page. If the tag is left blank doxygen will generate a standard
-# footer. See HTML_HEADER for more information on how to generate a default
-# footer and what special commands can be used inside the footer. See also
-# section "Doxygen usage" for information on how to generate the default footer
-# that doxygen normally uses.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FOOTER            =
-
-# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
-# sheet that is used by each HTML page. It can be used to fine-tune the look of
-# the HTML output. If left blank doxygen will generate a default style sheet.
-# See also section "Doxygen usage" for information on how to generate the style
-# sheet that doxygen normally uses.
-# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
-# it is more robust and this tag (HTML_STYLESHEET) will in the future become
-# obsolete.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_STYLESHEET        =
-
-# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
-# cascading style sheets that are included after the standard style sheets
-# created by doxygen. Using this option one can overrule certain style aspects.
-# This is preferred over using HTML_STYLESHEET since it does not replace the
-# standard style sheet and is therefore more robust against future updates.
-# Doxygen will copy the style sheet files to the output directory.
-# Note: The order of the extra style sheet files is of importance (e.g. the last
-# style sheet in the list overrules the setting of the previous ones in the
-# list).
-# Note: Since the styling of scrollbars can currently not be overruled in
-# Webkit/Chromium, the styling will be left out of the default doxygen.css if
-# one or more extra stylesheets have been specified. So if scrollbar
-# customization is desired it has to be added explicitly. For an example see the
-# documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_EXTRA_STYLESHEET  =
-
-# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the HTML output directory. Note
-# that these files will be copied to the base HTML output directory. Use the
-# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
-# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
-# files will be copied as-is; there are no commands or markers available.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_EXTRA_FILES       =
-
-# The HTML_COLORSTYLE tag can be used to specify if the generated HTML output
-# should be rendered with a dark or light theme.
-# Possible values are: LIGHT always generate light mode output, DARK always
-# generate dark mode output, AUTO_LIGHT automatically set the mode according to
-# the user preference, use light mode if no preference is set (the default),
-# AUTO_DARK automatically set the mode according to the user preference, use
-# dark mode if no preference is set and TOGGLE allow to user to switch between
-# light and dark mode via a button.
-# The default value is: AUTO_LIGHT.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE        = AUTO_LIGHT
-
-# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
-# will adjust the colors in the style sheet and background images according to
-# this color. Hue is specified as an angle on a color-wheel, see
-# https://en.wikipedia.org/wiki/Hue for more information. For instance the value
-# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
-# purple, and 360 is red again.
-# Minimum value: 0, maximum value: 359, default value: 220.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_HUE    = 220
-
-# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
-# in the HTML output. For a value of 0 the output will use gray-scales only. A
-# value of 255 will produce the most vivid colors.
-# Minimum value: 0, maximum value: 255, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_SAT    = 100
-
-# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
-# luminance component of the colors in the HTML output. Values below 100
-# gradually make the output lighter, whereas values above 100 make the output
-# darker. The value divided by 100 is the actual gamma applied, so 80 represents
-# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
-# change the gamma.
-# Minimum value: 40, maximum value: 240, default value: 80.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_GAMMA  = 80
-
-# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting this
-# to YES can help to show when doxygen was last run and thus if the
-# documentation is up to date.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_TIMESTAMP         = NO
-
-# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
-# documentation will contain a main index with vertical navigation menus that
-# are dynamically created via JavaScript. If disabled, the navigation index will
-# consists of multiple levels of tabs that are statically embedded in every HTML
-# page. Disable this option to support browsers that do not have JavaScript,
-# like the Qt help browser.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_DYNAMIC_MENUS     = YES
-
-# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
-# documentation will contain sections that can be hidden and shown after the
-# page has loaded.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_DYNAMIC_SECTIONS  = NO
-
-# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
-# shown in the various tree structured indices initially; the user can expand
-# and collapse entries dynamically later on. Doxygen will expand the tree to
-# such a level that at most the specified number of entries are visible (unless
-# a fully collapsed tree already exceeds this amount). So setting the number of
-# entries 1 will produce a full collapsed tree by default. 0 is a special value
-# representing an infinite number of entries and will result in a full expanded
-# tree by default.
-# Minimum value: 0, maximum value: 9999, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_INDEX_NUM_ENTRIES = 100
-
-# If the GENERATE_DOCSET tag is set to YES, additional index files will be
-# generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see:
-# https://developer.apple.com/xcode/), introduced with OSX 10.5 (Leopard). To
-# create a documentation set, doxygen will generate a Makefile in the HTML
-# output directory. Running make will produce the docset in that directory and
-# running make install will install the docset in
-# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
-# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
-# genXcode/_index.html for more information.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_DOCSET        = NO
-
-# This tag determines the name of the docset feed. A documentation feed provides
-# an umbrella under which multiple documentation sets from a single provider
-# (such as a company or product suite) can be grouped.
-# The default value is: Doxygen generated docs.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_FEEDNAME        = "Doxygen generated docs"
-
-# This tag determines the URL of the docset feed. A documentation feed provides
-# an umbrella under which multiple documentation sets from a single provider
-# (such as a company or product suite) can be grouped.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_FEEDURL         =
-
-# This tag specifies a string that should uniquely identify the documentation
-# set bundle. This should be a reverse domain-name style string, e.g.
-# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_BUNDLE_ID       = org.doxygen.Project
-
-# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
-# the documentation publisher. This should be a reverse domain-name style
-# string, e.g. com.mycompany.MyDocSet.documentation.
-# The default value is: org.doxygen.Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
-
-# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
-# The default value is: Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_NAME  = Publisher
-
-# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
-# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
-# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
-# on Windows. In the beginning of 2021 Microsoft took the original page, with
-# a.o. the download links, offline the HTML help workshop was already many years
-# in maintenance mode). You can download the HTML help workshop from the web
-# archives at Installation executable (see:
-# http://web.archive.org/web/20160201063255/http://download.microsoft.com/downlo
-# ad/0/A/9/0A939EF6-E31C-430F-A3DF-DFAE7960D564/htmlhelp.exe).
-#
-# The HTML Help Workshop contains a compiler that can convert all HTML output
-# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
-# files are now used as the Windows 98 help format, and will replace the old
-# Windows help format (.hlp) on all Windows platforms in the future. Compressed
-# HTML files also contain an index, a table of contents, and you can search for
-# words in the documentation. The HTML workshop also contains a viewer for
-# compressed HTML files.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_HTMLHELP      = NO
-
-# The CHM_FILE tag can be used to specify the file name of the resulting .chm
-# file. You can add a path in front of the file if the result should not be
-# written to the html output directory.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_FILE               =
-
-# The HHC_LOCATION tag can be used to specify the location (absolute path
-# including file name) of the HTML help compiler (hhc.exe). If non-empty,
-# doxygen will try to run the HTML help compiler on the generated index.hhp.
-# The file has to be specified with full path.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-HHC_LOCATION           =
-
-# The GENERATE_CHI flag controls if a separate .chi index file is generated
-# (YES) or that it should be included in the main .chm file (NO).
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-GENERATE_CHI           = NO
-
-# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
-# and project file content.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_INDEX_ENCODING     =
-
-# The BINARY_TOC flag controls whether a binary table of contents is generated
-# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
-# enables the Previous and Next buttons.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-BINARY_TOC             = NO
-
-# The TOC_EXPAND flag can be set to YES to add extra items for group members to
-# the table of contents of the HTML help documentation and to the tree view.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-TOC_EXPAND             = NO
-
-# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
-# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
-# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
-# (.qch) of the generated HTML documentation.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_QHP           = NO
-
-# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
-# the file name of the resulting .qch file. The path specified is relative to
-# the HTML output folder.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QCH_FILE               =
-
-# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
-# Project output. For more information please see Qt Help Project / Namespace
-# (see:
-# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_NAMESPACE          = org.doxygen.Project
-
-# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
-# Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see:
-# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-folders).
-# The default value is: doc.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_VIRTUAL_FOLDER     = doc
-
-# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
-# filter to add. For more information please see Qt Help Project / Custom
-# Filters (see:
-# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_NAME   =
-
-# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
-# custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see:
-# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_ATTRS  =
-
-# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
-# project's filter section matches. Qt Help Project / Filter Attributes (see:
-# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_SECT_FILTER_ATTRS  =
-
-# The QHG_LOCATION tag can be used to specify the location (absolute path
-# including file name) of Qt's qhelpgenerator. If non-empty doxygen will try to
-# run qhelpgenerator on the generated .qhp file.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHG_LOCATION           =
-
-# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
-# generated, together with the HTML files, they form an Eclipse help plugin. To
-# install this plugin and make it available under the help contents menu in
-# Eclipse, the contents of the directory containing the HTML and XML files needs
-# to be copied into the plugins directory of eclipse. The name of the directory
-# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
-# After copying Eclipse needs to be restarted before the help appears.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_ECLIPSEHELP   = NO
-
-# A unique identifier for the Eclipse help plugin. When installing the plugin
-# the directory name containing the HTML and XML files should also have this
-# name. Each documentation set should have its own identifier.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
-
-ECLIPSE_DOC_ID         = org.doxygen.Project
-
-# If you want full control over the layout of the generated HTML pages it might
-# be necessary to disable the index and replace it with your own. The
-# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
-# of each HTML page. A value of NO enables the index and the value YES disables
-# it. Since the tabs in the index contain the same information as the navigation
-# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-DISABLE_INDEX          = NO
-
-# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
-# structure should be generated to display hierarchical information. If the tag
-# value is set to YES, a side panel will be generated containing a tree-like
-# index structure (just like the one that is generated for HTML Help). For this
-# to work a browser that supports JavaScript, DHTML, CSS and frames is required
-# (i.e. any modern browser). Windows users are probably better off using the
-# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
-# further fine tune the look of the index (see "Fine-tuning the output"). As an
-# example, the default style sheet generated by doxygen has an example that
-# shows how to put an image at the root of the tree instead of the PROJECT_NAME.
-# Since the tree basically has the same information as the tab index, you could
-# consider setting DISABLE_INDEX to YES when enabling this option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_TREEVIEW      = NO
-
-# When both GENERATE_TREEVIEW and DISABLE_INDEX are set to YES, then the
-# FULL_SIDEBAR option determines if the side bar is limited to only the treeview
-# area (value NO) or if it should extend to the full height of the window (value
-# YES). Setting this to YES gives a layout similar to
-# https://docs.readthedocs.io with more room for contents, but less room for the
-# project logo, title, and description. If either GENERATE_TREEVIEW or
-# DISABLE_INDEX is set to NO, this option has no effect.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FULL_SIDEBAR           = NO
-
-# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
-# doxygen will group on one line in the generated HTML documentation.
-#
-# Note that a value of 0 will completely suppress the enum values from appearing
-# in the overview section.
-# Minimum value: 0, maximum value: 20, default value: 4.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-ENUM_VALUES_PER_LINE   = 4
-
-# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
-# to set the initial width (in pixels) of the frame in which the tree is shown.
-# Minimum value: 0, maximum value: 1500, default value: 250.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-TREEVIEW_WIDTH         = 250
-
-# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
-# external symbols imported via tag files in a separate window.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-EXT_LINKS_IN_WINDOW    = NO
-
-# If the OBFUSCATE_EMAILS tag is set to YES, doxygen will obfuscate email
-# addresses.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-OBFUSCATE_EMAILS       = YES
-
-# If the HTML_FORMULA_FORMAT option is set to svg, doxygen will use the pdf2svg
-# tool (see https://github.com/dawbarton/pdf2svg) or inkscape (see
-# https://inkscape.org) to generate formulas as SVG images instead of PNGs for
-# the HTML output. These images will generally look nicer at scaled resolutions.
-# Possible values are: png (the default) and svg (looks nicer but requires the
-# pdf2svg or inkscape tool).
-# The default value is: png.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FORMULA_FORMAT    = png
-
-# Use this tag to change the font size of LaTeX formulas included as images in
-# the HTML documentation. When you change the font size after a successful
-# doxygen run you need to manually remove any form_*.png images from the HTML
-# output directory to force them to be regenerated.
-# Minimum value: 8, maximum value: 50, default value: 10.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_FONTSIZE       = 10
-
-# The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
-# to create new LaTeX commands to be used in formulas as building blocks. See
-# the section "Including formulas" for details.
-
-FORMULA_MACROFILE      =
-
-# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
-# https://www.mathjax.org) which uses client side JavaScript for the rendering
-# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
-# installed or if you want to formulas look prettier in the HTML output. When
-# enabled you may also need to install MathJax separately and configure the path
-# to it using the MATHJAX_RELPATH option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-USE_MATHJAX            = NO
-
-# With MATHJAX_VERSION it is possible to specify the MathJax version to be used.
-# Note that the different versions of MathJax have different requirements with
-# regards to the different settings, so it is possible that also other MathJax
-# settings have to be changed when switching between the different MathJax
-# versions.
-# Possible values are: MathJax_2 and MathJax_3.
-# The default value is: MathJax_2.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_VERSION        = MathJax_2
-
-# When MathJax is enabled you can set the default output format to be used for
-# the MathJax output. For more details about the output format see MathJax
-# version 2 (see:
-# http://docs.mathjax.org/en/v2.7-latest/output.html) and MathJax version 3
-# (see:
-# http://docs.mathjax.org/en/latest/web/components/output.html).
-# Possible values are: HTML-CSS (which is slower, but has the best
-# compatibility. This is the name for Mathjax version 2, for MathJax version 3
-# this will be translated into chtml), NativeMML (i.e. MathML. Only supported
-# for NathJax 2. For MathJax version 3 chtml will be used instead.), chtml (This
-# is the name for Mathjax version 3, for MathJax version 2 this will be
-# translated into HTML-CSS) and SVG.
-# The default value is: HTML-CSS.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_FORMAT         = HTML-CSS
-
-# When MathJax is enabled you need to specify the location relative to the HTML
-# output directory using the MATHJAX_RELPATH option. The destination directory
-# should contain the MathJax.js script. For instance, if the mathjax directory
-# is located at the same level as the HTML output directory, then
-# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
-# Content Delivery Network so you can quickly see the result without installing
-# MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from https://www.mathjax.org before deployment. The default value is:
-# - in case of MathJax version 2: https://cdn.jsdelivr.net/npm/mathjax@2
-# - in case of MathJax version 3: https://cdn.jsdelivr.net/npm/mathjax@3
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest
-
-# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
-# extension names that should be enabled during MathJax rendering. For example
-# for MathJax version 2 (see
-# https://docs.mathjax.org/en/v2.7-latest/tex.html#tex-and-latex-extensions):
-# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
-# For example for MathJax version 3 (see
-# http://docs.mathjax.org/en/latest/input/tex/extensions/index.html):
-# MATHJAX_EXTENSIONS = ams
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_EXTENSIONS     =
-
-# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
-# of code that will be used on startup of the MathJax code. See the MathJax site
-# (see:
-# http://docs.mathjax.org/en/v2.7-latest/output.html) for more details. For an
-# example see the documentation.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_CODEFILE       =
-
-# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
-# the HTML output. The underlying search engine uses javascript and DHTML and
-# should work on any modern browser. Note that when using HTML help
-# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
-# there is already a search function so this one should typically be disabled.
-# For large projects the javascript based search engine can be slow, then
-# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
-# search using the keyboard; to jump to the search box use <access key> + S
-# (what the <access key> is depends on the OS and browser, but it is typically
-# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
-# key> to jump into the search results window, the results can be navigated
-# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
-# the search. The filter options can be selected when the cursor is inside the
-# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
-# to select a filter and <Enter> or <escape> to activate or cancel the filter
-# option.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-SEARCHENGINE           = YES
-
-# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
-# implemented using a web server instead of a web client using JavaScript. There
-# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
-# setting. When disabled, doxygen will generate a PHP script for searching and
-# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
-# and searching needs to be provided by external tools. See the section
-# "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SERVER_BASED_SEARCH    = NO
-
-# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
-# script for searching. Instead the search results are written to an XML file
-# which needs to be processed by an external indexer. Doxygen will invoke an
-# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
-# search results.
-#
-# Doxygen ships with an example indexer (doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see:
-# https://xapian.org/).
-#
-# See the section "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-EXTERNAL_SEARCH        = NO
-
-# The SEARCHENGINE_URL should point to a search engine hosted by a web server
-# which will return the search results when EXTERNAL_SEARCH is enabled.
-#
-# Doxygen ships with an example indexer (doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see:
-# https://xapian.org/). See the section "External Indexing and Searching" for
-# details.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SEARCHENGINE_URL       =
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
-# search data is written to a file for indexing by an external tool. With the
-# SEARCHDATA_FILE tag the name of this file can be specified.
-# The default file is: searchdata.xml.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SEARCHDATA_FILE        = searchdata.xml
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
-# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
-# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
-# projects and redirect the results back to the right project.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-EXTERNAL_SEARCH_ID     =
-
-# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
-# projects other than the one defined by this configuration file, but that are
-# all added to the same external search index. Each project needs to have a
-# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
-# to a relative location where the documentation can be found. The format is:
-# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-EXTRA_SEARCH_MAPPINGS  =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the LaTeX output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
-# The default value is: YES.
-
-GENERATE_LATEX         = NO
-
-# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_OUTPUT           = "${DOXYGEN_OUTPUT_PREFIX}/latex"
-
-# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
-# invoked.
-#
-# Note that when not enabling USE_PDFLATEX the default is latex when enabling
-# USE_PDFLATEX the default is pdflatex and when in the later case latex is
-# chosen this is overwritten by pdflatex. For specific output languages the
-# default can have been set differently, this depends on the implementation of
-# the output language.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_CMD_NAME         = latex
-
-# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
-# index for LaTeX.
-# Note: This tag is used in the Makefile / make.bat.
-# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file
-# (.tex).
-# The default file is: makeindex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-MAKEINDEX_CMD_NAME     = makeindex
-
-# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to
-# generate index for LaTeX. In case there is no backslash (\) as first character
-# it will be automatically added in the LaTeX code.
-# Note: This tag is used in the generated output file (.tex).
-# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.
-# The default value is: makeindex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_MAKEINDEX_CMD    = makeindex
-
-# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-COMPACT_LATEX          = NO
-
-# The PAPER_TYPE tag can be used to set the paper type that is used by the
-# printer.
-# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
-# 14 inches) and executive (7.25 x 10.5 inches).
-# The default value is: a4.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PAPER_TYPE             = a4
-
-# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
-# that should be included in the LaTeX output. The package can be specified just
-# by its name or with the correct syntax as to be used with the LaTeX
-# \usepackage command. To get the times font for instance you can specify :
-# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
-# To use the option intlimits with the amsmath package you can specify:
-# EXTRA_PACKAGES=[intlimits]{amsmath}
-# If left blank no extra packages will be included.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-EXTRA_PACKAGES         =
-
-# The LATEX_HEADER tag can be used to specify a user-defined LaTeX header for
-# the generated LaTeX document. The header should contain everything until the
-# first chapter. If it is left blank doxygen will generate a standard header. It
-# is highly recommended to start with a default header using
-# doxygen -w latex new_header.tex new_footer.tex new_stylesheet.sty
-# and then modify the file new_header.tex. See also section "Doxygen usage" for
-# information on how to generate the default header that doxygen normally uses.
-#
-# Note: Only use a user-defined header if you know what you are doing!
-# Note: The header is subject to change so you typically have to regenerate the
-# default header when upgrading to a newer version of doxygen. The following
-# commands have a special meaning inside the header (and footer): For a
-# description of the possible markers and block names see the documentation.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HEADER           =
-
-# The LATEX_FOOTER tag can be used to specify a user-defined LaTeX footer for
-# the generated LaTeX document. The footer should contain everything after the
-# last chapter. If it is left blank doxygen will generate a standard footer. See
-# LATEX_HEADER for more information on how to generate a default footer and what
-# special commands can be used inside the footer. See also section "Doxygen
-# usage" for information on how to generate the default footer that doxygen
-# normally uses. Note: Only use a user-defined footer if you know what you are
-# doing!
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_FOOTER           =
-
-# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
-# LaTeX style sheets that are included after the standard style sheets created
-# by doxygen. Using this option one can overrule certain style aspects. Doxygen
-# will copy the style sheet files to the output directory.
-# Note: The order of the extra style sheet files is of importance (e.g. the last
-# style sheet in the list overrules the setting of the previous ones in the
-# list).
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_EXTRA_STYLESHEET =
-
-# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the LATEX_OUTPUT output
-# directory. Note that the files will be copied as-is; there are no commands or
-# markers available.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_EXTRA_FILES      =
-
-# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
-# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
-# contain links (just like the HTML output) instead of page references. This
-# makes the output suitable for online browsing using a PDF viewer.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PDF_HYPERLINKS         = YES
-
-# If the USE_PDFLATEX tag is set to YES, doxygen will use the engine as
-# specified with LATEX_CMD_NAME to generate the PDF file directly from the LaTeX
-# files. Set this option to YES, to get a higher quality PDF documentation.
-#
-# See also section LATEX_CMD_NAME for selecting the engine.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-USE_PDFLATEX           = YES
-
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
-# command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BATCHMODE        = NO
-
-# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
-# index chapters (such as File Index, Compound Index, etc.) in the output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HIDE_INDICES     = NO
-
-# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
-# bibliography, e.g. plainnat, or ieeetr. See
-# https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
-# The default value is: plain.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BIB_STYLE        = plain
-
-# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
-# page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_TIMESTAMP        = NO
-
-# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
-# path from which the emoji images will be read. If a relative path is entered,
-# it will be relative to the LATEX_OUTPUT directory. If left blank the
-# LATEX_OUTPUT directory will be used.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_EMOJI_DIRECTORY  =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the RTF output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
-# RTF output is optimized for Word 97 and may not look too pretty with other RTF
-# readers/editors.
-# The default value is: NO.
-
-GENERATE_RTF           = YES
-
-# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: rtf.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_OUTPUT             = "${DOXYGEN_OUTPUT_PREFIX}/rtf"
-
-# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-COMPACT_RTF            = NO
-
-# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
-# contain hyperlink fields. The RTF file will contain links (just like the HTML
-# output) instead of page references. This makes the output suitable for online
-# browsing using Word or some other Word compatible readers that support those
-# fields.
-#
-# Note: WordPad (write) and others do not support links.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_HYPERLINKS         = NO
-
-# Load stylesheet definitions from file. Syntax is similar to doxygen's
-# configuration file, i.e. a series of assignments. You only have to provide
-# replacements, missing definitions are set to their default value.
-#
-# See also section "Doxygen usage" for information on how to generate the
-# default style sheet that doxygen normally uses.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_STYLESHEET_FILE    =
-
-# Set optional variables used in the generation of an RTF document. Syntax is
-# similar to doxygen's configuration file. A template extensions file can be
-# generated using doxygen -e rtf extensionFile.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_EXTENSIONS_FILE    =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the man page output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
-# classes and files.
-# The default value is: NO.
-
-GENERATE_MAN           = NO
-
-# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it. A directory man3 will be created inside the directory specified by
-# MAN_OUTPUT.
-# The default directory is: man.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_OUTPUT             = "${DOXYGEN_OUTPUT_PREFIX}/man"
-
-# The MAN_EXTENSION tag determines the extension that is added to the generated
-# man pages. In case the manual section does not start with a number, the number
-# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
-# optional.
-# The default value is: .3.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_EXTENSION          = .3
-
-# The MAN_SUBDIR tag determines the name of the directory created within
-# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
-# MAN_EXTENSION with the initial . removed.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_SUBDIR             =
-
-# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
-# will generate one additional man file for each entity documented in the real
-# man page(s). These additional files only source the real man page, but without
-# them the man command would be unable to find the correct page.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_LINKS              = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the XML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
-# captures the structure of the code including all documentation.
-# The default value is: NO.
-
-GENERATE_XML           = YES
-
-# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: xml.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_OUTPUT             = "${DOXYGEN_OUTPUT_PREFIX}/xml"
-
-# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
-# listings (including syntax highlighting and cross-referencing information) to
-# the XML output. Note that enabling this will significantly increase the size
-# of the XML output.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_PROGRAMLISTING     = YES
-
-# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
-# namespace members in file scope as well, matching the HTML output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_NS_MEMB_FILE_SCOPE = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the DOCBOOK output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
-# that can be used to generate PDF.
-# The default value is: NO.
-
-GENERATE_DOCBOOK       = NO
-
-# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
-# front of it.
-# The default directory is: docbook.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-DOCBOOK_OUTPUT         = "${DOXYGEN_OUTPUT_PREFIX}/docbook"
-
-#---------------------------------------------------------------------------
-# Configuration options for the AutoGen Definitions output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
-# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
-# the structure of the code including all documentation. Note that this feature
-# is still experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_AUTOGEN_DEF   = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the Perl module output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
-# file that captures the structure of the code including all documentation.
-#
-# Note that this feature is still experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_PERLMOD       = NO
-
-# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
-# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
-# output from the Perl module output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_LATEX          = NO
-
-# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
-# formatted so it can be parsed by a human reader. This is useful if you want to
-# understand what is going on. On the other hand, if this tag is set to NO, the
-# size of the Perl module output will be much smaller and Perl will parse it
-# just the same.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_PRETTY         = YES
-
-# The names of the make variables in the generated doxyrules.make file are
-# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
-# so different doxyrules.make files included by the same Makefile don't
-# overwrite each other's variables.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_MAKEVAR_PREFIX =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the preprocessor
-#---------------------------------------------------------------------------
-
-# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
-# C-preprocessor directives found in the sources and include files.
-# The default value is: YES.
-
-ENABLE_PREPROCESSING   = YES
-
-# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
-# in the source code. If set to NO, only conditional compilation will be
-# performed. Macro expansion can be done in a controlled way by setting
-# EXPAND_ONLY_PREDEF to YES.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-MACRO_EXPANSION        = YES
-
-# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
-# the macro expansion is limited to the macros specified with the PREDEFINED and
-# EXPAND_AS_DEFINED tags.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-EXPAND_ONLY_PREDEF     = YES
-
-# If the SEARCH_INCLUDES tag is set to YES, the include files in the
-# INCLUDE_PATH will be searched if a #include is found.
-# The default value is: YES.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-SEARCH_INCLUDES        = YES
-
-# The INCLUDE_PATH tag can be used to specify one or more directories that
-# contain include files that are not input files but should be processed by the
-# preprocessor. Note that the INCLUDE_PATH is not recursive, so the setting of
-# RECURSIVE has no effect here.
-# This tag requires that the tag SEARCH_INCLUDES is set to YES.
-
-INCLUDE_PATH           = "${CUDAQ_REPO_ROOT}/build/include" \
-                         "${CUDAQ_REPO_ROOT}/build/include/cudaq/Optimizer/CodeGen" \
-                         "${CUDAQ_REPO_ROOT}/build/include/cudaq/Optimizer/Transforms" \
-                         "${CUDAQ_REPO_ROOT}/build/include/cudaq/Optimizer/Dialect/CC" \
-                         "${CUDAQ_REPO_ROOT}/build/include/cudaq/Optimizer/Dialect/Quake"
-
-# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
-# patterns (like *.h and *.hpp) to filter out the header-files in the
-# directories. If left blank, the patterns specified with FILE_PATTERNS will be
-# used.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-INCLUDE_FILE_PATTERNS  =
-
-# The PREDEFINED tag can be used to specify one or more macro names that are
-# defined before the preprocessor is started (similar to the -D option of e.g.
-# gcc). The argument of the tag is a list of macros of the form: name or
-# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
-# is assumed. To prevent a macro definition from being undefined via #undef or
-# recursively expanded use the := operator instead of the = operator.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-PREDEFINED             = __attribute__(x)=
-
-# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
-# tag can be used to specify a list of macro names that should be expanded. The
-# macro definition that is found in the sources will be used. Use the PREDEFINED
-# tag if you want to use a different macro definition that overrules the
-# definition found in the source code.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-EXPAND_AS_DEFINED      =
-
-# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
-# remove all references to function-like macros that are alone on a line, have
-# an all uppercase name, and do not end with a semicolon. Such function macros
-# are typically used for boiler-plate code, and will confuse the parser if not
-# removed.
-# The default value is: YES.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-SKIP_FUNCTION_MACROS   = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to external references
-#---------------------------------------------------------------------------
-
-# The TAGFILES tag can be used to specify one or more tag files. For each tag
-# file the location of the external documentation should be added. The format of
-# a tag file without this location is as follows:
-# TAGFILES = file1 file2 ...
-# Adding location for the tag files is done as follows:
-# TAGFILES = file1=loc1 "file2 = loc2" ...
-# where loc1 and loc2 can be relative or absolute paths or URLs. See the
-# section "Linking to external documentation" for more information about the use
-# of tag files.
-# Note: Each tag file must have a unique name (where the name does NOT include
-# the path). If a tag file is not located in the directory in which doxygen is
-# run, you must also specify the path to the tagfile here.
-
-TAGFILES               =
-
-# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
-# tag file that is based on the input files it reads. See section "Linking to
-# external documentation" for more information about the usage of tag files.
-
-GENERATE_TAGFILE       =
-
-# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
-# the class index. If set to NO, only the inherited external classes will be
-# listed.
-# The default value is: NO.
-
-ALLEXTERNALS           = NO
-
-# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
-# in the modules index. If set to NO, only the current project's groups will be
-# listed.
-# The default value is: YES.
-
-EXTERNAL_GROUPS        = YES
-
-# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
-# the related pages index. If set to NO, only the current project's pages will
-# be listed.
-# The default value is: YES.
-
-EXTERNAL_PAGES         = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the dot tool
-#---------------------------------------------------------------------------
-
-# You can include diagrams made with dia in doxygen documentation. Doxygen will
-# then run dia to produce the diagram and insert it in the documentation. The
-# DIA_PATH tag allows you to specify the directory where the dia binary resides.
-# If left empty dia is assumed to be found in the default search path.
-
-DIA_PATH               =
-
-# If set to YES the inheritance and collaboration graphs will hide inheritance
-# and usage relations if the target is undocumented or is not a class.
-# The default value is: YES.
-
-HIDE_UNDOC_RELATIONS   = YES
-
-# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
-# available from the path. This tool is part of Graphviz (see:
-# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
-# Bell Labs. The other options in this section have no effect if this option is
-# set to NO
-# The default value is: NO.
-
-HAVE_DOT               = NO
-
-# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
-# to run in parallel. When set to 0 doxygen will base this on the number of
-# processors available in the system. You can set it explicitly to a value
-# larger than 0 to get control over the balance between CPU load and processing
-# speed.
-# Minimum value: 0, maximum value: 32, default value: 0.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_NUM_THREADS        = 0
-
-# DOT_COMMON_ATTR is common attributes for nodes, edges and labels of
-# subgraphs. When you want a differently looking font in the dot files that
-# doxygen generates you can specify fontname, fontcolor and fontsize attributes.
-# For details please see <a href=https://graphviz.org/doc/info/attrs.html>Node,
-# Edge and Graph Attributes specification</a> You need to make sure dot is able
-# to find the font, which can be done by putting it in a standard location or by
-# setting the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the
-# directory containing the font. Default graphviz fontsize is 14.
-# The default value is: fontname=Helvetica,fontsize=10.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_COMMON_ATTR        = "fontname=Helvetica,fontsize=10"
-
-# DOT_EDGE_ATTR is concatenated with DOT_COMMON_ATTR. For elegant style you can
-# add 'arrowhead=open, arrowtail=open, arrowsize=0.5'. <a
-# href=https://graphviz.org/doc/info/arrows.html>Complete documentation about
-# arrows shapes.</a>
-# The default value is: labelfontname=Helvetica,labelfontsize=10.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_EDGE_ATTR          = "labelfontname=Helvetica,labelfontsize=10"
-
-# DOT_NODE_ATTR is concatenated with DOT_COMMON_ATTR. For view without boxes
-# around nodes set 'shape=plain' or 'shape=plaintext' <a
-# href=https://www.graphviz.org/doc/info/shapes.html>Shapes specification</a>
-# The default value is: shape=box,height=0.2,width=0.4.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_NODE_ATTR          = "shape=box,height=0.2,width=0.4"
-
-# You can set the path where dot can find font specified with fontname in
-# DOT_COMMON_ATTR and others dot attributes.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTPATH           =
-
-# If the CLASS_GRAPH tag is set to YES (or GRAPH) then doxygen will generate a
-# graph for each documented class showing the direct and indirect inheritance
-# relations. In case HAVE_DOT is set as well dot will be used to draw the graph,
-# otherwise the built-in generator will be used. If the CLASS_GRAPH tag is set
-# to TEXT the direct and indirect inheritance relations will be shown as texts /
-# links.
-# Possible values are: NO, YES, TEXT and GRAPH.
-# The default value is: YES.
-
-CLASS_GRAPH            = TEXT
-
-# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
-# graph for each documented class showing the direct and indirect implementation
-# dependencies (inheritance, containment, and class references variables) of the
-# class with other documented classes.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-COLLABORATION_GRAPH    = YES
-
-# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
-# groups, showing the direct groups dependencies. See also the chapter Grouping
-# in the manual.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GROUP_GRAPHS           = YES
-
-# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
-# collaboration diagrams in a style similar to the OMG's Unified Modeling
-# Language.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-UML_LOOK               = NO
-
-# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
-# class node. If there are many fields or methods and many nodes the graph may
-# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
-# number of items for each type to make the size more manageable. Set this to 0
-# for no limit. Note that the threshold may be exceeded by 50% before the limit
-# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
-# but if the number exceeds 15, the total amount of fields shown is limited to
-# 10.
-# Minimum value: 0, maximum value: 100, default value: 10.
-# This tag requires that the tag UML_LOOK is set to YES.
-
-UML_LIMIT_NUM_FIELDS   = 10
-
-# If the DOT_UML_DETAILS tag is set to NO, doxygen will show attributes and
-# methods without types and arguments in the UML graphs. If the DOT_UML_DETAILS
-# tag is set to YES, doxygen will add type and arguments for attributes and
-# methods in the UML graphs. If the DOT_UML_DETAILS tag is set to NONE, doxygen
-# will not generate fields with class member information in the UML graphs. The
-# class diagrams will look similar to the default class diagrams but using UML
-# notation for the relationships.
-# Possible values are: NO, YES and NONE.
-# The default value is: NO.
-# This tag requires that the tag UML_LOOK is set to YES.
-
-DOT_UML_DETAILS        = NO
-
-# The DOT_WRAP_THRESHOLD tag can be used to set the maximum number of characters
-# to display on a single line. If the actual line length exceeds this threshold
-# significantly it will wrapped across multiple lines. Some heuristics are apply
-# to avoid ugly line breaks.
-# Minimum value: 0, maximum value: 1000, default value: 17.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_WRAP_THRESHOLD     = 17
-
-# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
-# collaboration graphs will show the relations between templates and their
-# instances.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-TEMPLATE_RELATIONS     = NO
-
-# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
-# YES then doxygen will generate a graph for each documented file showing the
-# direct and indirect include dependencies of the file with other documented
-# files.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INCLUDE_GRAPH          = YES
-
-# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
-# set to YES then doxygen will generate a graph for each documented file showing
-# the direct and indirect include dependencies of the file with other documented
-# files.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INCLUDED_BY_GRAPH      = YES
-
-# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
-# dependency graph for every global function or class method.
-#
-# Note that enabling this option will significantly increase the time of a run.
-# So in most cases it will be better to enable call graphs for selected
-# functions only using the \callgraph command. Disabling a call graph can be
-# accomplished by means of the command \hidecallgraph.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CALL_GRAPH             = NO
-
-# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
-# dependency graph for every global function or class method.
-#
-# Note that enabling this option will significantly increase the time of a run.
-# So in most cases it will be better to enable caller graphs for selected
-# functions only using the \callergraph command. Disabling a caller graph can be
-# accomplished by means of the command \hidecallergraph.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CALLER_GRAPH           = NO
-
-# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
-# hierarchy of all classes instead of a textual one.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GRAPHICAL_HIERARCHY    = YES
-
-# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
-# dependencies a directory has on other directories in a graphical way. The
-# dependency relations are determined by the #include relations between the
-# files in the directories.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DIRECTORY_GRAPH        = YES
-
-# The DIR_GRAPH_MAX_DEPTH tag can be used to limit the maximum number of levels
-# of child directories generated in directory dependency graphs by dot.
-# Minimum value: 1, maximum value: 25, default value: 1.
-# This tag requires that the tag DIRECTORY_GRAPH is set to YES.
-
-DIR_GRAPH_MAX_DEPTH    = 1
-
-# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
-# generated by dot. For an explanation of the image formats see the section
-# output formats in the documentation of the dot tool (Graphviz (see:
-# http://www.graphviz.org/)).
-# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
-# to make the SVG files visible in IE 9+ (other browsers do not have this
-# requirement).
-# Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
-# png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus and
-# png:gdiplus:gdiplus.
-# The default value is: png.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_IMAGE_FORMAT       = png
-
-# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
-# enable generation of interactive SVG images that allow zooming and panning.
-#
-# Note that this requires a modern browser other than Internet Explorer. Tested
-# and working are Firefox, Chrome, Safari, and Opera.
-# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
-# the SVG files visible. Older versions of IE do not have SVG support.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INTERACTIVE_SVG        = NO
-
-# The DOT_PATH tag can be used to specify the path where the dot tool can be
-# found. If left blank, it is assumed the dot tool can be found in the path.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_PATH               =
-
-# The DOTFILE_DIRS tag can be used to specify one or more directories that
-# contain dot files that are included in the documentation (see the \dotfile
-# command).
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOTFILE_DIRS           =
-
-# The MSCFILE_DIRS tag can be used to specify one or more directories that
-# contain msc files that are included in the documentation (see the \mscfile
-# command).
-
-MSCFILE_DIRS           =
-
-# The DIAFILE_DIRS tag can be used to specify one or more directories that
-# contain dia files that are included in the documentation (see the \diafile
-# command).
-
-DIAFILE_DIRS           =
-
-# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
-# path where java can find the plantuml.jar file or to the filename of jar file
-# to be used. If left blank, it is assumed PlantUML is not used or called during
-# a preprocessing step. Doxygen will generate a warning when it encounters a
-# \startuml command in this case and will not generate output for the diagram.
-
-PLANTUML_JAR_PATH      =
-
-# When using plantuml, the PLANTUML_CFG_FILE tag can be used to specify a
-# configuration file for plantuml.
-
-PLANTUML_CFG_FILE      =
-
-# When using plantuml, the specified paths are searched for files specified by
-# the !include statement in a plantuml block.
-
-PLANTUML_INCLUDE_PATH  =
-
-# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
-# that will be shown in the graph. If the number of nodes in a graph becomes
-# larger than this value, doxygen will truncate the graph, which is visualized
-# by representing a node as a red box. Note that doxygen if the number of direct
-# children of the root node in a graph is already larger than
-# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
-# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
-# Minimum value: 0, maximum value: 10000, default value: 50.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_GRAPH_MAX_NODES    = 50
-
-# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
-# generated by dot. A depth value of 3 means that only nodes reachable from the
-# root by following a path via at most 3 edges will be shown. Nodes that lay
-# further from the root node will be omitted. Note that setting this option to 1
-# or 2 may greatly reduce the computation time needed for large code bases. Also
-# note that the size of a graph can be further restricted by
-# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
-# Minimum value: 0, maximum value: 1000, default value: 0.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
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-
-# Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output
-# files in one run (i.e. multiple -o and -T options on the command line). This
-# makes dot run faster, but since only newer versions of dot (>1.8.10) support
-# this, this feature is disabled by default.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
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diff --git a/pr-2308/README.md b/pr-2308/README.md
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--- a/pr-2308/README.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# CUDA-Q Documentation
-
-This folder contains tools and content to build the CUDA-Q documentation.
-The [script for building docs](../scripts/build_docs.sh) can be used to build
-the complete CUDA-Q documentation. Please see the comment in that script
-for more detail.
-
-We use [Sphinx](https://www.sphinx-doc.org/) to produce documentation in HTML
-format. This documentation includes conceptual documentation in the form of
-Markdown or reStructuredText format, API documentation generated based on doc
-comments in the source code, as well as potentially source code examples and
-snippets. Unfortunately, syntax highlighting for MLIR code snippets is currently
-not available in [Pygments](https://pygments.org/languages/).
-
-## API Documentation
-
-We use [Sphinx](https://www.sphinx-doc.org/) to include documentation defined in
-the form of doc comments in the source code for all of our APIs. The build is
-configured by the settings in the [sphinx/conf.py](./sphinx/conf.py) file.
-
-- **C++ source code**: <br/>
-  As part of the build [Doxygen](https://www.doxygen.org/) is used to generated
-  documentation based on doc comments. The documentation generation is
-  configured in the [Doxyfile.in](./Doxyfile.in) file - see the manual for
-  [possible configurations](https://www.doxygen.nl/manual/config.html). Our
-  build replaces the environment variables used in that file to produce the
-  final `Doxyfile` with which `doxygen` is invoked. We use the [Breathe
-  extension](https://breathe.readthedocs.io/) for Sphinx to incorporate content
-  from the generated XML files in our docs.
-
-- **TableGen files**: <br/>
-  Our CMake files define the `cudaq-doc` target that produces Markdown files
-  containing documentation for
-  [TableGen](https://llvm.org/docs/TableGen/index.html) files. We use the
-  [MyST-Parser extension](https://myst-parser.readthedocs.io/) for Sphinx to
-  incorporate content from the generated Markdown files in our docs.
-
-- **Python bindings**: <br/>
-  We use [pybind11](https://github.com/pybind/pybind11) to define Python
-  bindings for the CUDA-Q API. Doc comments are defined as part of
-  defining these bindings in C++. To incorporate the API documentation, the
-  `cudaq` Python package needs to be built and installed prior to generating the
-  CUDA-Q documentation. The [build_docs.sh](../scripts/build_docs.sh)
-  script will automatically do that if necessary. This project generates
-  ReStructuredText documentation from Python `docstrings` using `autodoc`.
-  The generated `.rst` files are in `docs/source/apidoc`. The files are
-  generated automatically by `doc-build` and `doc-clean` will remove them.
-
-## Sphinx Extensions
-
-The extensions we use to generate API docs are outlined and linked in the
-section above. The full list of built-in Sphinx tensions can be found
-[here](https://www.sphinx-doc.org/en/master/usage/extensions/index.html). The
-list of extensions that are enabled for building CUDA-Q documentation is
-defined by the value of the `extensions` configuration in
-[conf.py](./sphinx/conf.py).
-
-## References
-
-Additional links that may be helpful that are not listed above:
-
-- [References and automatic link generation in
-  Doxygen](https://www.star.bnl.gov/public/comp/sofi/doxygen/autolink.html)
-- [Using Napoleon style for Python doc
-  comments](https://docs.softwareheritage.org/devel/contributing/sphinx.html)
-- [Cross-referencing Python
-  objects](https://www.sphinx-doc.org/en/master/usage/restructuredtext/domains.html#cross-referencing-python-objects)
-- [Cross-referencing C++
-  objects](https://www.sphinx-doc.org/en/master/usage/restructuredtext/domains.html#cross-referencing)
-- [Sphinx configuration
-  options](https://www.sphinx-doc.org/en/master/usage/configuration.html)
-- [Test examples in Python
-  documentation](https://docs.python.org/3/library/doctest.html)
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-Code documentation
-******************
-
-.. toctree::
-  :caption: CUDA-Q API
-  :maxdepth: 1
-
-  C++ API <languages/cpp_api.rst>
-  Python API <languages/python_api.rst>
-  Quantum Operations <default_ops.rst>
\ No newline at end of file
diff --git a/pr-2308/_sources/api/default_ops.rst.txt b/pr-2308/_sources/api/default_ops.rst.txt
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-Quantum Operations
-******************************
-
-CUDA-Q provides a default set of quantum operations on qubits. 
-These operations can be used to define custom kernels and libraries.
-Since the set of quantum intrinsic operations natively supported on a specific target 
-depends on the backends architecture, the :code:`nvq++` compiler automatically
-decomposes the default operations into the appropriate set of intrinsic operations 
-for that target.
-
-The sections `Unitary Operations on Qubits`_ and `Measurements on Qubits`_ list the default set of quantum operations on qubits.
-
-Operations that implement unitary transformations of the quantum state are templated.
-The template argument allows to invoke the adjoint and controlled version of the quantum transformation, see the section on `Adjoint and Controlled Operations`_.
-
-CUDA-Q additionally provides overloads to support broadcasting of
-single-qubit operations across a vector of qubits.  For example,
-:code:`x(cudaq::qvector<>&)` flips the state of each qubit in the provided
-:code:`cudaq::qvector`. 
-
-
-Unitary Operations on Qubits
-=============================
-
-:code:`x`
----------------------
-
-This operation implements the transformation defined by the Pauli-X matrix. It is also known as the quantum version of a `NOT`-gate.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # X = | 0  1 |
-        #     | 1  0 |
-        x(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // X = | 0  1 |
-        //     | 1  0 |
-        x(qubit);
-
-:code:`y`
----------------------
-
-This operation implements the transformation defined by the Pauli-Y matrix.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Y = | 0  -i |
-        #     | i   0 |
-        y(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Y = | 0  -i |
-        //     | i   0 |
-        y(qubit);
-
-:code:`z`
----------------------
-
-This operation implements the transformation defined by the Pauli-Z matrix.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Z = | 1   0 |
-        #     | 0  -1 |
-        z(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Z = | 1   0 |
-        //     | 0  -1 |
-        z(qubit);
-
-:code:`h`
----------------------
-
-This operation is a rotation by π about the X+Z axis, and 
-enables one to create a superposition of computational basis states.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # H = (1 / sqrt(2)) * | 1   1 |
-        #                     | 1  -1 |
-        h(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // H = (1 / sqrt(2)) * | 1   1 |
-        //                     | 1  -1 |
-        h(qubit);
-
-:code:`r1`
----------------------
-
-This operation is an arbitrary rotation about the :code:`|1>` state.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # R1(λ) = | 1     0    |
-        #         | 0  exp(iλ) |
-        r1(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // R1(λ) = | 1     0    |
-        //         | 0  exp(iλ) |
-        r1(std::numbers::pi, qubit);
-
-:code:`rx`
----------------------
-
-This operation is an arbitrary rotation about the X axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Rx(θ) = |  cos(θ/2)  -isin(θ/2) |
-        #         | -isin(θ/2)  cos(θ/2)  |
-        rx(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Rx(θ) = |  cos(θ/2)  -isin(θ/2) |
-        //         | -isin(θ/2)  cos(θ/2)  |
-        rx(std::numbers::pi, qubit);
-
-:code:`ry`
----------------------
-
-This operation is an arbitrary rotation about the Y axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Ry(θ) = | cos(θ/2)  -sin(θ/2) |
-        #         | sin(θ/2)   cos(θ/2) |
-        ry(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Ry(θ) = | cos(θ/2)  -sin(θ/2) |
-        //         | sin(θ/2)   cos(θ/2) |
-        ry(std::numbers::pi, qubit);
-
-:code:`rz`
----------------------
-
-This operation is an arbitrary rotation about the Z axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Rz(λ) = | exp(-iλ/2)      0     |
-        #         |     0       exp(iλ/2) |
-        rz(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Rz(λ) = | exp(-iλ/2)      0     |
-        //         |     0       exp(iλ/2) |
-        rz(std::numbers::pi, qubit);
-
-:code:`s`
----------------------
-
-This operation applies to its target a rotation by π/2 about the Z axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # S = | 1   0 |
-        #     | 0   i |
-        s(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // S = | 1   0 |
-        //     | 0   i |
-        s(qubit);
-
-:code:`t`
----------------------
-
-This operation applies to its target a π/4 rotation about the Z axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # T = | 1      0     |
-        #     | 0  exp(iπ/4) |
-        t(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // T = | 1      0     |
-        //     | 0  exp(iπ/4) |
-        t(qubit);
-
-:code:`swap`
----------------------
-
-This operation swaps the states of two qubits.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit_1, qubit_2 = cudaq.qubit(), cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Swap = | 1 0 0 0 |
-        #        | 0 0 1 0 |
-        #        | 0 1 0 0 |
-        #        | 0 0 0 1 |
-        swap(qubit_1, qubit_2)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit_1, qubit_2;
-
-        // Apply the unitary transformation
-        // Swap = | 1 0 0 0 |
-        //        | 0 0 1 0 |
-        //        | 0 1 0 0 |
-        //        | 0 0 0 1 |
-        swap(qubit_1, qubit_2);
-
-:code:`u3`
----------------------
-
-This operation applies the universal three-parameters operator to target qubit. The three parameters are Euler angles - theta (θ), phi (φ), and lambda (λ).
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # U3(θ,φ,λ) = | cos(θ/2)            -exp(iλ) * sin(θ/2)       |
-        #             | exp(iφ) * sin(θ/2)   exp(i(λ + φ)) * cos(θ/2) |
-        u3(np.pi, np.pi, np.pi / 2, q)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // U3(θ,φ,λ) = | cos(θ/2)            -exp(iλ) * sin(θ/2)       |
-        //             | exp(iφ) * sin(θ/2)   exp(i(λ + φ)) * cos(θ/2) |
-        u3(M_PI, M_PI, M_PI_2, q);
-
-
-Adjoint and Controlled Operations
-==================================
-
-.. tab:: Python
-
-    The :code:`adj` method of any gate can be used to invoke the 
-    `adjoint <https://en.wikipedia.org/wiki/Conjugate_transpose>`__ transformation:
-
-    .. code-block:: python
-
-        # Create a kernel and allocate a qubit in a |0> state.
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation defined by the matrix
-        # T = | 1      0     |
-        #     | 0  exp(iπ/4) |
-        # to the state of the qubit `q`:
-        t(qubit)
-
-        # Apply its adjoint transformation defined by the matrix
-        # T† = | 1      0     |
-        #      | 0  exp(-iπ/4) |
-        t.adj(qubit)
-        # `qubit` is now again in the initial state |0>.
-
-    The :code:`ctrl` method of any gate can be used to apply the transformation
-    conditional on the state of one or more control qubits, see also this 
-    `Wikipedia entry <https://en.wikipedia.org/wiki/Quantum_logic_gate#Controlled_gates>`__.
-
-    .. code-block:: python
-
-        # Create a kernel and allocate qubits in a |0> state.
-        ctrl_1, ctrl_2, target = cudaq.qubit(), cudaq.qubit(), cudaq.qubit()
-        # Create a superposition.
-        h(ctrl_1)
-        # `ctrl_1` is now in a state (|0> + |1>) / √2.
-
-        # Apply the unitary transformation
-        # | 1  0  0  0 |
-        # | 0  1  0  0 |
-        # | 0  0  0  1 |
-        # | 0  0  1  0 |
-        x.ctrl(ctrl_1, ctrl_2)
-        # `ctrl_1` and `ctrl_2` are in a state (|00> + |11>) / √2.
-
-        # Set the state of `target` to |1>:
-        x(target)
-        # Apply the transformation T only if both 
-        # control qubits are in a |1> state:
-        t.ctrl([ctrl_1, ctrl_2], target)
-        # The qubits ctrl_1, ctrl_2, and target are now in a state
-        # (|000> + exp(iπ/4)|111>) / √2.
-
-.. tab:: C++
-
-    The template argument :code:`cudaq::adj` can be used to invoke the 
-    `adjoint <https://en.wikipedia.org/wiki/Conjugate_transpose>`__ transformation:
-
-    .. code-block:: cpp
-
-        // Allocate a qubit in a |0> state.
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation defined by the matrix
-        // T = | 1      0     |
-        //     | 0  exp(iπ/4) |
-        // to the state of the qubit `q`:
-        t(qubit);
-
-        // Apply its adjoint transformation defined by the matrix
-        // T† = | 1      0     |
-        //      | 0  exp(-iπ/4) |
-        t<cudaq::adj>(qubit);
-        // Qubit `q` is now again in the initial state |0>.
-
-    The template argument :code:`cudaq::ctrl` can be used to apply the transformation
-    conditional on the state of one or more control qubits, see also this 
-    `Wikipedia entry <https://en.wikipedia.org/wiki/Quantum_logic_gate#Controlled_gates>`__.
-
-    .. code-block:: cpp
-
-        // Allocate qubits in a |0> state.
-        cudaq::qubit ctrl_1, ctrl_2, target;
-        // Create a superposition.
-        h(ctrl_1);
-        // Qubit ctrl_1 is now in a state (|0> + |1>) / √2.
-
-        // Apply the unitary transformation
-        // | 1  0  0  0 |
-        // | 0  1  0  0 |
-        // | 0  0  0  1 |
-        // | 0  0  1  0 |
-        x<cudaq::ctrl>(ctrl_1, ctrl_2);
-        // The qubits ctrl_1 and ctrl_2 are in a state (|00> + |11>) / √2.
-
-        // Set the state of `target` to |1>:
-        x(target);
-        // Apply the transformation T only if both 
-        // control qubits are in a |1> state:
-        t<cudaq::ctrl>(ctrl_1, ctrl_2, target);
-        // The qubits ctrl_1, ctrl_2, and target are now in a state
-        // (|000> + exp(iπ/4)|111>) / √2.
-
-
-Following common convention, by default the transformation is applied to the target qubit(s)
-if all control qubits are in a :code:`|1>` state. 
-However, that behavior can be changed to instead apply the transformation when a control qubit is in 
-a :code:`|0>` state by negating the polarity of the control qubit.
-The syntax for negating the polarity is the not-operator preceding the
-control qubit: 
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit c, q;
-        h(c);
-        x<cudaq::ctrl>(!c, q);
-        // The qubits c and q are in a state (|01> + |10>) / √2.
-
-This notation is only supported in the context of applying a controlled operation and is only valid for control qubits. For example, negating either of the target qubits in the
-:code:`swap` operation is not allowed.
-Negating the polarity of control qubits is similarly supported when using :code:`cudaq::control` to conditionally apply a custom quantum kernel.
-
-
-Measurements on Qubits
-=============================
-
-:code:`mz`
----------------------
-
-This operation measures a qubit with respect to the computational basis, 
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-Z matrix.
-This is a non-linear transformation, and no template overloads are available.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-        mz(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-        mz(qubit);
-
-:code:`mx`
----------------------
-
-This operation measures a qubit with respect to the Pauli-X basis, 
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-X matrix.
-This is a non-linear transformation, and no template overloads are available.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-        mx(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-        mx(qubit);
-
-:code:`my`
----------------------
-
-This operation measures a qubit with respect to the Pauli-Y basis, 
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-Y matrix.
-This is a non-linear transformation, and no template overloads are available.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-        kernel.my(qubit)
-        
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-        my(qubit);
-
-
-User-Defined Custom Operations
-==============================
-
-Users can define a custom quantum operation by its unitary matrix. First use 
-the API to register a custom operation, outside of a CUDA-Q kernel. Then the 
-operation can be used within a CUDA-Q kernel like any of the built-in operations
-defined above.
-Custom operations are supported on qubits only (`qudit` with `level = 2`).
-
-.. tab:: Python
-
-    The :code:`cudaq.register_operation` API accepts an identifier string for 
-    the custom operation and its unitary matrix. The matrix can be a `list` or
-    `numpy` array of complex numbers. A 1D matrix is interpreted as row-major.
-    
-
-    .. code-block:: python
-
-        import cudaq
-        import numpy as np
-
-        cudaq.register_operation("custom_h", 1. / np.sqrt(2.) * np.array([1, 1, 1, -1]))
-
-        cudaq.register_operation("custom_x", np.array([0, 1, 1, 0]))
-
-        @cudaq.kernel
-        def bell():
-            qubits = cudaq.qvector(2)
-            custom_h(qubits[0])
-            custom_x.ctrl(qubits[0], qubits[1])
-
-        cudaq.sample(bell).dump()
-
-        
-.. tab:: C++
-
-    The macro :code:`CUDAQ_REGISTER_OPERATION` accepts a unique name for the 
-    operation, the number of target qubits, the number of rotation parameters 
-    (can be 0), and the unitary matrix as a 1D row-major `std::vector<complex>` 
-    representation.
-    
-    .. code-block:: cpp
-
-        #include <cudaq.h>
-
-        CUDAQ_REGISTER_OPERATION(custom_h, 1, 0,
-                                {M_SQRT1_2, M_SQRT1_2, M_SQRT1_2, -M_SQRT1_2})
-
-        CUDAQ_REGISTER_OPERATION(custom_x, 1, 0, {0, 1, 1, 0})
-
-        __qpu__ void bell_pair() {
-            cudaq::qubit q, r;
-            custom_h(q);
-            custom_x<cudaq::ctrl>(q, r);
-        }
-
-        int main() {
-            auto counts = cudaq::sample(bell_pair);
-            for (auto &[bits, count] : counts) {
-                printf("%s\n", bits.data());
-            }
-        }
-
-
-For multi-qubit operations, the matrix is interpreted with MSB qubit ordering,
-i.e. big-endian convention. The following example shows two different custom
-operations, each operating on 2 qubits.
-
-
-.. tab:: Python
-
-    .. literalinclude:: ../snippets/python/using/examples/two_qubit_custom_op.py
-      :language: python
-      :start-after: [Begin Docs]
-      :end-before: [End Docs]
-
-
-.. tab:: C++
-
-    .. literalinclude:: ../snippets/cpp/using/two_qubit_custom_op.cpp
-      :language: cpp
-      :start-after: [Begin Docs]
-      :end-before: [End Docs]
-
-
-.. note:: 
-
-  When a custom operation is used on hardware backends, it is synthesized to a
-  set of native quantum operations. Currently, only 1-qubit and 2-qubit custom 
-  operations are supported on hardware backends.
-
-
-Photonic Operations on Qudits
-=============================
-
-These operations are valid only on the `orca-photonics` target which does not support
-the quantum operations above.
-
-:code:`create`
----------------------
-
-This operation increments the number of photons in a qumode up to a maximum value
-defined by the qudit level that represents the qumode. If it is applied to a qumode
-where the number of photons is already at the maximum value, the operation has no
-effect.
-
-:math:`U|0\rangle → |1\rangle, U|1\rangle → |2\rangle, U|2\rangle → |3\rangle, \cdots, U|d\rangle → |d\rangle`
-where :math:`d` is the qudit level.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = qudit(3)
-        create(q)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> q(1);
-        create(q[0]);
-
-:code:`annihilate`
----------------------
-
-This operation reduces the number of photons in a qumode up to a minimum value of
-0 representing the vacuum state. If it is applied to a qumode where the number of
-photons is already at the minimum value 0, the operation has no effect.
-
-:math:`U|0\rangle → |0\rangle, U|1\rangle → |0\rangle, U|2\rangle → |1\rangle, \cdots, U|d\rangle → |d-1\rangle`
-where :math:`d` is the qudit level.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = qudit(3)
-        annihilate(q)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> q(1);
-        annihilate(q[0]);
-
-:code:`phase_shift`
----------------------
-
-A phase shifter adds a phase :math:`\phi` on a qumode. For the annihilation (:math:`a_1`)
-and creation operators (:math:`a_1^\dagger`) of a qumode, the phase shift operator
-is defined  by
-
-.. math::
-    P(\phi) = \exp\left(i \phi a_1^\dagger a_1  \right)
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = qudit(4)
-        phase_shift(q, 0.17)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<4> q(1);
-        phase_shift(q[0], 0.17);
-
-:code:`beam_splitter`
----------------------
-
-Beam splitters act on two qumodes together and it is parameterized by a single angle 
-:math:`\theta`, relating to reflectivity.
-For the annihilation (:math:`a_1` and :math:`a_2`) and creation operators (:math:`a_1^\dagger`
-and :math:`a_2^\dagger`) of two qumodes, the beam splitter operator is defined by
-
-.. math::
-    B(\theta) = \exp\left[i \theta (a_1^\dagger a_2 + a_1 a_2^\dagger) \right]
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = [qudit(3) for _ in range(2)]
-        beam_splitter(q[0], q[1], 0.34)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> q(2);
-        beam_splitter(q[0], q[1], 0.34);
-
-:code:`mz`
----------------------
-
-This operation returns the measurement results of the input qumode(s).
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qumodes = [qudit(3) for _ in range(2)]
-        mz(qumodes)
-
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> qumodes(2);
-        mz(qumodes);
diff --git a/pr-2308/_sources/api/languages/cpp_api.rst.txt b/pr-2308/_sources/api/languages/cpp_api.rst.txt
deleted file mode 100644
index 34487dbefb..0000000000
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@@ -1,266 +0,0 @@
-CUDA-Q C++ API
-******************************
-
-Operators 
-=============
-
-.. doxygenclass:: cudaq::spin_op
-    :members:
-
-Quantum
-=========
-
-.. doxygenvariable:: cudaq::dyn
-
-.. doxygenclass:: cudaq::qudit
-    :members:
-
-.. doxygenclass:: cudaq::qreg
-    :members:
-
-.. doxygenclass:: cudaq::qvector
-    :members:
-
-.. doxygenclass:: cudaq::qspan
-    :members:
-
-.. doxygenclass:: cudaq::qview
-    :members:
-
-.. doxygentypedef:: cudaq::qubit
-    
-Common
-=========
-
-.. doxygenclass:: cudaq::observe_result
-    :members:
-
-.. doxygenstruct:: cudaq::observe_options
-    :members:
-
-.. doxygenfunction:: cudaq::observe(const observe_options &options, QuantumKernel &&kernel, spin_op H, Args &&...args)
-.. doxygenfunction:: cudaq::observe(std::size_t shots, QuantumKernel &&kernel, spin_op H, Args &&...args)
-.. doxygenfunction:: cudaq::observe(QuantumKernel &&kernel, spin_op H, Args &&...args)
-.. doxygenfunction:: cudaq::observe(QuantumKernel &&kernel, const SpinOpContainer &termList, Args &&...args)
-
-.. doxygenclass:: cudaq::ExecutionContext
-    :members:
-
-.. doxygenclass:: cudaq::details::future
-    :members:
-
-.. doxygenclass:: cudaq::async_result
-    :members:
-
-.. doxygentypedef:: async_sample_result
-
-
-.. doxygenstruct:: cudaq::ExecutionResult
-    :members:
-
-.. doxygenclass:: cudaq::sample_result
-    :members:
-
-.. doxygenstruct:: cudaq::sample_options
-    :members:
-
-.. doxygenfunction:: cudaq::sample(const sample_options &options, QuantumKernel &&kernel, Args &&...args)
-.. doxygenfunction:: cudaq::sample(std::size_t shots, QuantumKernel &&kernel, Args &&...args)
-.. doxygenfunction:: cudaq::sample(QuantumKernel &&kernel, Args&&... args)
-
-.. doxygenclass:: cudaq::SimulationState
-
-.. doxygenstruct:: cudaq::SimulationState::Tensor
-    :members:
-
-.. doxygenenum:: cudaq::SimulationState::precision
-
-.. doxygenenum:: cudaq::simulation_precision
-
-.. doxygentypedef:: cudaq::tensor
-
-.. doxygentypedef:: cudaq::TensorStateData
-
-.. doxygentypedef:: cudaq::state_data
-
-.. doxygenclass:: cudaq::CusvState
-
-.. doxygenclass:: nvqir::MPSSimulationState
-
-.. doxygenclass:: nvqir::TensorNetSimulationState
-
-.. doxygenclass:: cudaq::RemoteSimulationState
-
-.. doxygenclass:: cudaq::registry::RegisteredType
-    :members:
-
-.. doxygenclass:: cudaq::complex_matrix
-    :members:
-
-.. doxygenclass:: cudaq::Trace
-
-.. doxygenfunction:: cudaq::range(ElementType total)
-.. doxygenfunction:: cudaq::range(ElementType begin, ElementType end, ElementType step)
-
-.. doxygenfunction:: cudaq::draw(QuantumKernel &&kernel, Args&&... args)
-
-.. doxygenfunction:: cudaq::get_state(QuantumKernel &&kernel, Args&&... args)
-
-.. doxygenclass:: cudaq::Resources
-
-.. doxygentypedef:: cudaq::complex_matrix::value_type
-
-Noise Modeling 
-================
-
-.. doxygenstruct:: cudaq::kraus_op
-    :members:
-
-.. doxygenclass:: cudaq::kraus_channel
-    :members:
-
-.. doxygenclass:: cudaq::amplitude_damping_channel
-    :members:
-
-.. doxygenclass:: cudaq::bit_flip_channel
-    :members:
-
-.. doxygenclass:: cudaq::phase_flip_channel
-    :members:
-
-.. doxygenclass:: cudaq::depolarization_channel
-    :members:
-
-.. doxygenclass:: cudaq::noise_model
-    :members:
-
-.. doxygenenum:: cudaq::noise_model_type
-
-Kernel Builder
-===============
-
-.. doxygenclass:: cudaq::kernel_builder
-    :members:
-
-.. doxygenclass:: cudaq::QuakeValue
-    :members:
-
-.. doxygenclass:: cudaq::details::kernel_builder_base
-    :members:
-
-.. doxygenclass:: cudaq::details::KernelBuilderType
-    :members:
-
-Algorithms
-===========
-
-.. doxygenclass:: cudaq::optimizer
-    :members:
-
-.. doxygenclass:: cudaq::optimizable_function
-    :members:
-
-.. doxygentypedef:: cudaq::optimization_result
-
-.. doxygenclass:: cudaq::state
-    :members:
-
-.. doxygenclass:: cudaq::gradient
-    :members:
-
-.. doxygenclass:: cudaq::gradients::central_difference
-    :members:
-
-.. doxygenclass:: cudaq::gradients::parameter_shift
-    :members:
-
-.. doxygenclass:: cudaq::gradients::forward_difference
-    :members:
-
-Platform
-=========
-
-.. doxygenclass:: cudaq::QPU
-    :members:
-
-.. doxygenclass:: cudaq::BaseRemoteRESTQPU
-
-.. doxygenclass:: cudaq::BaseRemoteSimulatorQPU
-
-.. doxygenclass:: cudaq::BaseNvcfSimulatorQPU
-
-.. doxygenclass:: cudaq::FermioniqBaseQPU
-
-.. doxygenclass:: cudaq::OrcaRemoteRESTQPU 
-
-.. doxygenclass:: cudaq::QuEraBaseQPU
-
-.. doxygenclass:: cudaq::quantum_platform
-    :members:
-
-.. doxygenstruct:: cudaq::RemoteCapabilities
-    :members:
-
-.. doxygenclass:: cudaq::SerializedCodeExecutionContext
-
-.. doxygentypedef:: cudaq::QuantumTask
-
-.. doxygentypedef:: cudaq::QubitConnectivity
-
-.. doxygentypedef:: cudaq::QubitEdge
-
-.. doxygentypedef:: cudaq::KernelExecutionTask
-
-.. doxygenstruct:: cudaq::KernelThunkResultType
-
-.. doxygentypedef:: cudaq::KernelThunkType
-
-Utilities
-=========
-
-.. doxygentypedef:: cudaq::complex
-
-.. doxygentypedef:: cudaq::real 
-
-.. doxygenfunction:: cudaq::range(std::size_t)
-    
-Namespaces 
-===========
-
-.. doxygennamespace:: cudaq
-    :desc-only:
-
-.. doxygenfunction:: cudaq::num_available_gpus
-.. doxygenfunction:: cudaq::set_random_seed
-.. doxygenfunction:: cudaq::set_noise
-.. doxygenfunction:: cudaq::unset_noise
-
-.. doxygennamespace:: cudaq::details
-    :desc-only:
-
-.. doxygennamespace:: cudaq::registry
-    :desc-only:
-
-.. doxygennamespace:: cudaq::mpi
-    :desc-only:
-
-.. doxygenfunction:: cudaq::mpi::initialize()
-.. doxygenfunction:: cudaq::mpi::initialize(int argc, char **argv)
-.. doxygenfunction:: cudaq::mpi::is_initialized
-.. doxygenfunction:: cudaq::mpi::finalize
-.. doxygenfunction:: cudaq::mpi::rank
-.. doxygenfunction:: cudaq::mpi::num_ranks
-.. doxygenfunction:: cudaq::mpi::all_gather(std::vector<double> &global, const std::vector<double> &local)
-.. doxygenfunction:: cudaq::mpi::all_gather(std::vector<int> &global, const std::vector<int> &local)
-.. doxygenfunction:: cudaq::mpi::all_reduce(const T&, const Func&)
-.. doxygenfunction:: cudaq::mpi::all_reduce(const T &localValue, const BinaryFunction &function)
-.. doxygenfunction:: cudaq::mpi::broadcast(std::vector<double> &data, int rootRank)
-.. doxygenfunction:: cudaq::mpi::broadcast(std::string &data, int rootRank)
-
-.. doxygennamespace:: cudaq::orca
-    :desc-only:
-
-.. doxygenfunction:: cudaq::orca::sample(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, int n_samples = 10000, std::size_t qpu_id = 0)
-.. doxygenfunction:: cudaq::orca::sample(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, std::vector<double> &ps_angles, int n_samples = 10000, std::size_t qpu_id = 0)
-.. doxygenfunction:: cudaq::orca::sample_async(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, int n_samples = 10000, std::size_t qpu_id = 0)
-.. doxygenfunction:: cudaq::orca::sample_async(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, std::vector<double> &ps_angles, int n_samples = 10000, std::size_t qpu_id = 0)
diff --git a/pr-2308/_sources/api/languages/python_api.rst.txt b/pr-2308/_sources/api/languages/python_api.rst.txt
deleted file mode 100644
index d84ddc427c..0000000000
--- a/pr-2308/_sources/api/languages/python_api.rst.txt
+++ /dev/null
@@ -1,258 +0,0 @@
-CUDA-Q Python API
-******************************
-
-.. automodule:: cudaq
-
-Program Construction
-=============================
-
-.. autofunction:: cudaq::make_kernel
-.. [SKIP_TEST]: Reason - AttributeError: module 'cudaq' has no attribute 'from_state'
-.. .. autofunction:: cudaq::from_state
-
-.. autoclass:: cudaq::PyKernel
-.. autoclass:: cudaq::Kernel
-
-    .. automethod:: qalloc
-    .. automethod:: __str__
-    .. automethod:: __call__
-
-    .. automethod:: x
-    .. automethod:: cx
-    .. automethod:: y
-    .. automethod:: cy
-    .. automethod:: z
-    .. automethod:: cz
-    .. automethod:: h
-    .. automethod:: ch
-    .. automethod:: s
-    .. automethod:: sdg
-    .. automethod:: cs
-    .. automethod:: t
-    .. automethod:: tdg
-    .. automethod:: ct
-    .. automethod:: rx
-    .. automethod:: crx
-    .. automethod:: ry
-    .. automethod:: cry
-    .. automethod:: rz
-    .. automethod:: crz
-    .. automethod:: r1
-    .. automethod:: cr1
-    .. automethod:: swap
-    .. automethod:: cswap
-    .. automethod:: exp_pauli
-    .. automethod:: mx
-    .. automethod:: my
-    .. automethod:: mz
-    .. automethod:: c_if
-    .. automethod:: for_loop
-    .. automethod:: adjoint
-    .. automethod:: control
-    .. automethod:: apply_call
-    .. automethod:: u3
-
-.. autoclass:: cudaq::PyKernelDecorator
-    :members:
-    :special-members: __str__, __call__
-
-.. autofunction:: kernel
-    
-Kernel Execution
-=============================
-
-.. autofunction:: cudaq::sample
-.. autofunction:: cudaq::sample_async
-.. autofunction:: cudaq::observe
-.. autofunction:: cudaq::observe_async
-.. autofunction:: cudaq::get_state
-.. autofunction:: cudaq::get_state_async
-.. autofunction:: cudaq::vqe
-.. autofunction:: cudaq::draw
-.. autofunction:: cudaq::translate
-
-Dynamics
-=============================
-
-.. autofunction:: cudaq::evolve
-.. autofunction:: cudaq::evolve_async
-
-Backend Configuration
-=============================
-
-.. autofunction:: cudaq::has_target
-.. autofunction:: cudaq::get_target
-.. autofunction:: cudaq::get_targets
-.. autofunction:: cudaq::set_target
-.. autofunction:: cudaq::reset_target
-.. autofunction:: cudaq::set_noise
-.. autofunction:: cudaq::unset_noise
-.. automethod:: cudaq::initialize_cudaq
-.. automethod:: cudaq::num_available_gpus
-.. automethod:: cudaq::set_random_seed
-
-Data Types
-=============================
-
-.. autoclass:: cudaq::SimulationPrecision
-    :members:
-    
-.. autoclass:: cudaq::Target
-    :members:
-
-.. autoclass:: cudaq::State
-    :members:
-
-.. autoclass:: cudaq::Tensor
-
-.. autoclass:: cudaq::QuakeValue
-
-    .. automethod:: __add__
-    .. automethod:: __radd__
-    .. automethod:: __sub__
-    .. automethod:: __rsub__
-    .. automethod:: __neg__
-    .. automethod:: __mul__
-    .. automethod:: __rmul__
-    .. automethod:: __getitem__
-    .. automethod:: slice
-
-.. autoclass:: cudaq::qubit
-.. autoclass:: cudaq::qreg
-.. autoclass:: cudaq::qvector
-
-.. autoclass:: cudaq::ComplexMatrix
-    :members:
-    :special-members: __getitem__, __str__
-
-.. autoclass:: cudaq::Schedule
-.. autoclass:: cudaq.operator.integrator.BaseIntegrator
-.. autoclass:: cudaq::EvolveResult
-    :members:
-
-.. autoclass:: cudaq::AsyncEvolveResult
-    :members:
-
-.. autoclass:: cudaq::SpinOperator
-.. autoclass:: cudaq.operator.expressions.OperatorSum
-
-.. autoclass:: cudaq.operator.expressions.ElementaryOperator
-    
-    .. automethod:: define
-
-.. autoclass:: cudaq.operator.expressions.ProductOperator
-
-.. autoclass:: cudaq.operator.expressions.RydbergHamiltonian
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq.operator.expressions.ScalarOperator
-.. autoclass:: cudaq.operator.definitions.SpinOperator
-
-.. autofunction:: cudaq::spin.i
-.. autofunction:: cudaq::spin.x
-.. autofunction:: cudaq::spin.y
-.. autofunction:: cudaq::spin.z
-
-.. autoclass:: cudaq.operator.cudm_state.CuDensityMatState
-    :members:
-
-.. autofunction:: cudaq.operator.cudm_state.to_cupy_array
-
-.. autoclass:: cudaq::SampleResult
-    :members:
-    :special-members: __getitem__, __len__, __iter__
-
-.. autoclass:: cudaq::AsyncSampleResult
-    :members:
-
-.. autoclass:: cudaq::ObserveResult
-    :members:
-
-.. autoclass:: cudaq::AsyncObserveResult
-    :members:
-
-.. autoclass:: cudaq::AsyncStateResult
-    :members:
-
-.. autoclass:: cudaq::OptimizationResult
-    :members:
-
-
-Optimizers
------------------
-
-.. autoclass:: cudaq.optimizers::optimizer
-
-.. autoclass:: cudaq.optimizers::GradientDescent
-    :members:
-
-.. autoclass:: cudaq.optimizers::COBYLA
-    :members:
-
-.. autoclass:: cudaq.optimizers::NelderMead
-    :members:
-
-.. autoclass:: cudaq.optimizers::LBFGS
-    :members:
-
-Gradients
------------------
-
-.. autoclass:: cudaq.gradients::gradient
-    :members:
-
-.. autoclass:: cudaq.gradients::CentralDifference
-    :members:
-
-.. autoclass:: cudaq.gradients::ForwardDifference
-    :members:
-
-.. autoclass:: cudaq.gradients::ParameterShift
-    :members:
-
-Noisy Simulation
------------------
-
-.. autoclass:: cudaq::NoiseModel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::BitFlipChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::PhaseFlipChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::DepolarizationChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::AmplitudeDampingChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::KrausChannel
-    :members:
-    :special-members: __getitem__
-
-.. autoclass:: cudaq::KrausOperator
-    :members:
-
-MPI Submodule
-=============================
-
-.. automethod:: cudaq.mpi::initialize
-.. automethod:: cudaq.mpi::rank
-.. automethod:: cudaq.mpi::num_ranks
-.. automethod:: cudaq.mpi::all_gather
-.. automethod:: cudaq.mpi::broadcast
-.. automethod:: cudaq.mpi::is_initialized
-.. automethod:: cudaq.mpi::finalize
-
-ORCA Submodule
-=============================
-
-.. automethod:: cudaq.orca::sample
diff --git a/pr-2308/_sources/applications/python/afqmc.ipynb.txt b/pr-2308/_sources/applications/python/afqmc.ipynb.txt
deleted file mode 100644
index 6f150dca1d..0000000000
--- a/pr-2308/_sources/applications/python/afqmc.ipynb.txt
+++ /dev/null
@@ -1,533 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quantum Enhanced Auxiliary Field Quantum Monte Carlo\n",
-    "\n",
-    "This work was done in collaboration with the Next Generation Computing team at [BASF](https://www.basf.com/global/en.html).\n",
-    "\n",
-    "In this tutorial we implement a quantum-classical hybrid workflow for computing the ground state energies of a strongly interacting molecular system. The algorithm consists of two parts:\n",
-    "\n",
-    "\n",
-    "1. A variational quantum eigensolver that uses the quantum-number-preserving ansatz proposed by [Anselmetti et al. (2021)](https://doi.org/10.1088/1367-2630/ac2cb3) to generate a quantum trial wave function $|\\Psi_T\\rangle$ using CUDA Quantum.\n",
-    "\n",
-    "2. An Auxiliary-Field Quantum Monte Carlo simulation that realizes a classical imaginary time evolution and collects the ground state energy estimates.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "# Package installs\n",
-    "!pip install pyscf==2.6.2 openfermion==1.6.1 ipie==0.7.1 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (>= 3.0.0) is not supported.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Relevant imports\n",
-    "\n",
-    "import cudaq\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from pyscf import gto, scf, ao2mo, mcscf\n",
-    "\n",
-    "from afqmc_src.vqe_cudaq_qnp import VQE, get_cudaq_hamiltonian\n",
-    "from afqmc_src.utils_ipie import get_coeff_wf, gen_ipie_input_from_pyscf_chk\n",
-    "\n",
-    "from ipie.hamiltonians.generic import Generic as HamGeneric\n",
-    "from ipie.qmc.afqmc import AFQMC\n",
-    "from ipie.systems.generic import Generic\n",
-    "from ipie.trial_wavefunction.particle_hole import ParticleHole\n",
-    "from ipie.analysis.extraction import extract_observable\n",
-    "\n",
-    "from ipie.config import config\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "\n",
-    "# Ipie has recently added GPU support however this remains a bit tricky to use as it requires manual installation of several packages.\n",
-    "# Once this is streamlined, we can set the GPU option to True in the tutorial.\n",
-    "config.update_option(\"use_gpu\", False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start by defining the structure of the molecule, the basis set, and its spin. We build the molecule object with PySCF and run a preliminary Hartree-Fock computation. Here we choose as an example a [chelating agent](https://doi.org/10.1021/acs.jctc.3c01375) representing a relevant class of substances industrially produced at large scales. Their use ranges, among the others, from water softeners in cleaning applications, modulators of redox behaviour in oxidative bleaching, scale suppressants, soil remediation and ligands for catalysts. In particular we focus here in a Fe(III)-NTA complex whose structure is given in the file imported below.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the molecular structure and the basis set for the Fenta molecule.\n",
-    "\n",
-    "atom = \"afqmc_src/geo_fenta.xyz\"\n",
-    "basis = \"cc-pVTZ\"\n",
-    "spin = 1\n",
-    "num_active_orbitals = 5\n",
-    "num_active_electrons = 5\n",
-    "\n",
-    "# You can swap to O3 which is a smaller system and takes less computational resources and time to run.\n",
-    "\n",
-    "atom = \"afqmc_src/geo_o3.xyz\"\n",
-    "basis = \"cc-pVTZ\"\n",
-    "spin = 0\n",
-    "num_active_orbitals = 9\n",
-    "num_active_electrons = 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-224.34048064812222"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# PYSCF helps to build the molecule and run Hartree-Fock.\n",
-    "\n",
-    "# Define the molecule.\n",
-    "molecule = gto.M(atom=atom, spin=spin, basis=basis, verbose=0)\n",
-    "\n",
-    "# Restriced open shell HF.\n",
-    "hartee_fock = scf.ROHF(molecule)\n",
-    "hartee_fock.chkfile = \"afqmc_src/output.chk\"\n",
-    "\n",
-    "# Run Hartree-Fock.\n",
-    "hartee_fock.kernel()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hamiltonian preparation for VQE\n",
-    "\n",
-    "Since this molecule contains of around 600 orbitals (which would correspond to 1200 qubits) and 143 total electrons, it is impossible to perform a full VQE with full statevector simulation. Therefore, we need to identify an active space with fewer orbitals and electrons that contribute to the strongly interacting part of the whole molecule. We then run a post Hartree-Fock computation with the PySCF's built-in CASCI method in order to obtain the one-body ($t_{pq}$) and two-body \n",
-    "($V_{prqs}$) integrals that define the molecular Hamiltonian in the active space:\n",
-    "\n",
-    "$$ H= \\sum_{pq}t_{pq}\\hat{a}_{p}^\\dagger \\hat {a}_{q}+\\sum_{pqrs}  V_{prqs}\\hat a_{p}^\\dagger \\hat a_{q}^\\dagger \\hat a_{s}\\hat a_{r} \\tag{1}$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from openfermion.transforms import jordan_wigner\n",
-    "from openfermion import generate_hamiltonian\n",
-    "\n",
-    "# Run a CASCI simulation for computing the Hamiltonian in the active space.\n",
-    "casci = mcscf.CASCI(hartee_fock, num_active_orbitals, num_active_electrons)\n",
-    "casci.fix_spin_(ss=(molecule.spin / 2 * (molecule.spin / 2 + 1)))\n",
-    "\n",
-    "# Executes the kernel to compute the hamiltonian in the active space.\n",
-    "casci.kernel()\n",
-    "\n",
-    "# Compute the one-body (h1) and two-body integrals (tbi) as shown in equation 1.\n",
-    "h1, energy_core = casci.get_h1eff()\n",
-    "\n",
-    "h2 = casci.get_h2eff()\n",
-    "h2_no_symmetry = ao2mo.restore('1', h2, num_active_orbitals)\n",
-    "\n",
-    "# V_pqrs terms in H.\n",
-    "tbi = np.asarray(h2_no_symmetry.transpose(0, 2, 3, 1), order='C')\n",
-    "\n",
-    "# Compute the hamiltonian and convert it to a CUDA-Q operator.\n",
-    "mol_ham = generate_hamiltonian(h1, tbi, energy_core.item())\n",
-    "jw_hamiltonian = jordan_wigner(mol_ham)\n",
-    "hamiltonian, constant_term = get_cudaq_hamiltonian(jw_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run VQE with CUDA-Q\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now execute the VQE algorithm using the quantum number preserving ansatz. At the end of the VQE, we store the final statevector that will be used in the classical AFQMC computation as an initial guess.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Using cudaq optimizer\n",
-      "# Num Params: 16\n",
-      "# Qubits: 18\n",
-      "# N_layers: 1\n",
-      "# Energy after the VQE: -224.3881035525103\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define some options for the VQE.\n",
-    "options = {\n",
-    "    'n_vqe_layers': 1,\n",
-    "    'maxiter': 100,\n",
-    "    'energy_core': constant_term,\n",
-    "    'return_final_state_vec': True\n",
-    "}\n",
-    "\n",
-    "n_qubits = 2 * num_active_orbitals\n",
-    "\n",
-    "vqe = VQE(n_qubits=n_qubits,\n",
-    "          num_active_electrons=num_active_electrons,\n",
-    "          spin=spin,\n",
-    "          options=options)\n",
-    "\n",
-    "results = vqe.execute(hamiltonian)\n",
-    "\n",
-    "# Best energy from VQE.\n",
-    "optimized_energy = results['energy_optimized']\n",
-    "\n",
-    "# Final state vector.\n",
-    "final_state_vector = results[\"state_vec\"]\n",
-    "\n",
-    "# Energies during the optimization loop.\n",
-    "vqe_energies = results[\"callback_energies\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Auxiliary Field Quantum Monte Carlo (AFQMC)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "AFQMC is a numerical method for computing relevant properties of strongly interacting molecules. AFQMC is a type of Quantum Monte Carlo method that combines the use of random walks with an auxiliary field to simulate the imaginary-time evolution of a quantum system and drive it to the lowest energy state. This method can provide accurate results for ground-state properties of a wide range of physical systems, including atoms, molecules, and solids. Here we summarize the main features of AFQMC while a detailed introduction to can be found [here](https://www.cond-mat.de/events/correl13/manuscripts/zhang.pdf).\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We consider the electronic Hamiltonian in the second quantization\n",
-    "\\begin{equation}\n",
-    "H = {H}_1 + {H}_2 \n",
-    "=\\sum_{pq} h_{pq} {a}_{p}^{\\dagger} {a}_{q} + \\frac{1}{2} \\sum_{pqrs} v_{pqrs}{a}_{p}^{\\dagger} {a}_r {a}^{\\dagger}_{q} {a}_s \\tag{2}\n",
-    "\\end{equation}\n",
-    "where ${a}_{p}^{\\dagger}$ and ${a}_{q}$ are fermionic creation and annihilation operators of orbitals $p$ and $q$, respectively. The terms $h_{pq} $ and \n",
-    "$v_{pqrs}$ are the matrix elements of the one-body, $H_1$, and two-body, $H_2$, interactions of $H$, respectively. Here, we omit the spin indices for simplicity.\n",
-    "\n",
-    "AFQMC realizes an imaginary time propagation of an initial state (chosen as a Slater determinant) $\\ket{\\Psi_{I}}$ towards the ground state $\\ket{\\Psi_0}$ of a given hamiltonian, $H$, with\n",
-    "\\begin{equation}\n",
-    "\\ket{\\Psi_0} \\sim\\lim_{n \\to \\infty} \\left[ e^{-\\Delta\\tau H  }  \\right]^{n} \\ket{\\Psi_{I}}\n",
-    "\\tag{3}\n",
-    "\\end{equation} \n",
-    "where $\\Delta\\tau$ is the imaginary time step.\n",
-    "\n",
-    "AFQMC relies on decomposing the two-body interactions $H_2$ in terms of sum of squares of one-body operators ${v}_\\gamma$ such that the Hamiltonian ${H}$ becomes\n",
-    "\\begin{equation}\n",
-    "H = v_0 - \\frac{1}{2}\\sum_{\\gamma=1}^{N_\\gamma} {v}_\\gamma^2\n",
-    "\\tag{4}\n",
-    "\\end{equation}\n",
-    "with ${v}_0 = {H}_1 $ and $\n",
-    "{v}_\\gamma = i \\sum_{pq} L^{\\gamma}_{pq} {a}_{p}^{\\dagger}{a}_{q}.\n",
-    "$\n",
-    "The $N_\\gamma$ matrices $L^{\\gamma}_{pq}$ are called Cholesky vectors as they are  obtained via a Cholesky decomposition of the two-body matrix elements \n",
-    "$v_{pqrs}$ via $v_{pqrs} = \\sum_{\\gamma=1}^{N_\\gamma} L^{\\gamma}_{pr} L^{\\gamma}_{qs}$.\n",
-    "\n",
-    "The imaginary time propagation evolves an ensemble of walkers $\\{\\phi^{(n)}\\}$ (that are Slater determinants) and allows one to access observables of the system. For example, the local energy\n",
-    "\\begin{equation}\n",
-    "\\mathcal{E}_{\\text{loc}}(\\phi^{(n)}) = \\frac{\\bra{\\Psi_\\mathrm{T}}H\\ket{\\phi^{(n)}}}{\\braket{\\Psi_\\mathrm{T}| \\phi^{(n)}}}\n",
-    "\\tag{5}\n",
-    "\\end{equation}\n",
-    "defined as the mixed expectation value of the Hamiltonian with the trial wave function $\\ket{\\Psi_\\mathrm{T}}$.\n",
-    "\n",
-    "\n",
-    "The trial wavefunction can be in general a single or a multi-Slater determinant coming from VQE for example. This might help in achieving more accurate ground state energy estimates.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The implementation of AFQMC we use here is from [ipie](https://github.com/JoonhoLee-Group/ipie) that supports both CPUs and GPUs and requires the following steps:\n",
-    "\n",
-    "\n",
-    "1. Preparation of the molecular Hamiltonian by performing the Cholesky decomposition\n",
-    "\n",
-    "2. Preparation of the trial state from the VQE wavefunction\n",
-    "\n",
-    "3. Executing AFQMC\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparation of the molecular Hamiltonian\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Number of electrons in simulation: (12, 12)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# AFQMC.\n",
-    "\n",
-    "# Generate the input Hamiltonian for ipie from the checkpoint file from pyscf.\n",
-    "ipie_hamiltonian = gen_ipie_input_from_pyscf_chk(hartee_fock.chkfile,\n",
-    "                                                 mcscf=True,\n",
-    "                                                 chol_cut=1e-5)\n",
-    "\n",
-    "h1e, cholesky_vectors, e0 = ipie_hamiltonian\n",
-    "\n",
-    "num_basis = cholesky_vectors.shape[1]\n",
-    "num_chol = cholesky_vectors.shape[0]\n",
-    "\n",
-    "system = Generic(nelec=molecule.nelec)\n",
-    "\n",
-    "afqmc_hamiltonian = HamGeneric(\n",
-    "    np.array([h1e, h1e]),\n",
-    "    cholesky_vectors.transpose((1, 2, 0)).reshape(\n",
-    "        (num_basis * num_basis, num_chol)), e0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparation of the trial wave function\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build the trial wavefunction from the state vector computed via VQE.\n",
-    "wavefunction = get_coeff_wf(final_state_vector,\n",
-    "                            n_active_elec=num_active_electrons,\n",
-    "                            spin=spin)\n",
-    "\n",
-    "trial = ParticleHole(wavefunction,\n",
-    "                     molecule.nelec,\n",
-    "                     num_basis,\n",
-    "                     num_dets_for_props=len(wavefunction[0]),\n",
-    "                     verbose=False)\n",
-    "\n",
-    "trial.compute_trial_energy = True\n",
-    "trial.build()\n",
-    "trial.half_rotate(afqmc_hamiltonian)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup of the AFQMC parameters\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we can choose the input options like the timestep $\\Delta\\tau$, the total number of walkers `num_walkers` and the total number of AFQMC iterations `num_blocks`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# random seed is 96264512\n",
-      "            Block                   Weight            WeightFactor            HybridEnergy                  ENumer                  EDenom                  ETotal                  E1Body                  E2Body\n",
-      "                0   1.0000000000000000e+02  1.0000000000000000e+02  0.0000000000000000e+00 -2.2437583763935545e+04  1.0000000000000000e+02 -2.2437583763935547e+02 -3.7639365190228011e+02  1.5201781426292453e+02\n",
-      "                1   4.2276634193515412e+02  1.4127560668989827e+03 -1.1711742028818304e+02 -2.2473358126540003e+04  9.9999999999999986e+01 -2.2473358126540006e+02 -3.7646854013277283e+02  1.5173495886737268e+02\n",
-      "                2   1.0031922288872407e+02  3.8320523739865604e+02 -1.1743088014788954e+02 -2.2489226882493567e+04  1.0000000000000001e+02 -2.2489226882493563e+02 -3.7650504938463922e+02  1.5161278055970348e+02\n",
-      "                3   9.9900990681040355e+01  1.0008400623205630e+02 -1.1736864885170948e+02 -2.2495677577437204e+04  9.9999999999999972e+01 -2.2495677577437212e+02 -3.7659644834889821e+02  1.5163967257452603e+02\n",
-      "                4   1.0009188692360159e+02  1.0005173726372723e+02 -1.1748969527283802e+02 -2.2495531836556856e+04  1.0000000000000001e+02 -2.2495531836556853e+02 -3.7675907314082951e+02  1.5180375477526098e+02\n",
-      "                5   9.9997269300807844e+01  1.0010618465796188e+02 -1.1752703012577417e+02 -2.2502732667629320e+04  1.0000000000000001e+02 -2.2502732667629317e+02 -3.7663343013337044e+02  1.5160610345707727e+02\n",
-      "                6   1.0012131352337956e+02  1.0019003056579172e+02 -1.1770170647504112e+02 -2.2513369839216481e+04  1.0000000000000000e+02 -2.2513369839216480e+02 -3.7660812717909516e+02  1.5147442878693036e+02\n",
-      "                7   9.9936984461419740e+01  9.9929966800671224e+01 -1.1765353928750643e+02 -2.2516138533920657e+04  1.0000000000000000e+02 -2.2516138533920659e+02 -3.7660292355465600e+02  1.5144153821544941e+02\n",
-      "                8   9.9902337463172714e+01  9.9910800755312891e+01 -1.1761532255317621e+02 -2.2518524275281430e+04  9.9999999999999986e+01 -2.2518524275281433e+02 -3.7674246483479845e+02  1.5155722208198404e+02\n",
-      "                9   1.0012943675389775e+02  1.0013880643723378e+02 -1.1780913595074867e+02 -2.2512465963277762e+04  1.0000000000000000e+02 -2.2512465963277762e+02 -3.7677999264623367e+02  1.5165533301345607e+02\n",
-      "               10   9.9628730363609819e+01  9.9223106824565718e+01 -1.1749814144939067e+02 -2.2517668156221851e+04  1.0000000000000000e+02 -2.2517668156221850e+02 -3.7688306341863290e+02  1.5170638185641434e+02\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Setup the AFQMC parameters.\n",
-    "afqmc_msd = AFQMC.build(molecule.nelec,\n",
-    "                        afqmc_hamiltonian,\n",
-    "                        trial,\n",
-    "                        num_walkers=100,\n",
-    "                        num_steps_per_block=25,\n",
-    "                        num_blocks=10,\n",
-    "                        timestep=0.005,\n",
-    "                        stabilize_freq=5,\n",
-    "                        seed=96264512,\n",
-    "                        pop_control_freq=5,\n",
-    "                        verbose=False)\n",
-    "\n",
-    "# Run the AFQMC.\n",
-    "afqmc_msd.run(estimator_filename='afqmc_src/estimates.0.h5')\n",
-    "afqmc_msd.finalise(verbose=False)\n",
-    "\n",
-    "# Extract the energies.\n",
-    "qmc_data = extract_observable(afqmc_msd.estimators.filename, \"energy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fea847f35e0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the energies.\n",
-    "\n",
-    "vqe_y = vqe_energies\n",
-    "vqe_x = list(range(len(vqe_y)))\n",
-    "plt.plot(vqe_x, vqe_y, label=\"VQE\")\n",
-    "\n",
-    "afqmc_y = list(qmc_data[\"ETotal\"])\n",
-    "afqmc_x = [i + vqe_x[-1] for i in list(range(len(afqmc_y)))]\n",
-    "plt.plot(afqmc_x, afqmc_y, label=\"AFQMC\")\n",
-    "\n",
-    "plt.xlabel(\"Optimization steps\")\n",
-    "plt.ylabel(\"Energy [Ha]\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/bernstein_vazirani.ipynb.txt b/pr-2308/_sources/applications/python/bernstein_vazirani.ipynb.txt
deleted file mode 100644
index b0333b5cf8..0000000000
--- a/pr-2308/_sources/applications/python/bernstein_vazirani.ipynb.txt
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "29b3f948-cc6a-4776-ad75-1dc259e0c298",
-   "metadata": {},
-   "source": [
-    "# Bernstein-Vazirani Algorithm\n",
-    "\n",
-    "The Bernstein-Vazirani (BV) algorithm demonstrates an exponential speedup over classical methods for the particular task: \n",
-    "\n",
-    "Suppose we have an oracle that implements the function $f: \\{0,1\\}^n \\longrightarrow \\{0,1\\}$. \n",
-    "\n",
-    "$f(x)$ is promised to be the dot product between $x$ and a secret $n$-bit binary string $s$:\n",
-    "\n",
-    "$f(x) = x \\cdot s (mod \\space 2) =  (x_1s_1 + x_2s_2 + ... + x_ns_n) (mod \\space 2)$.\n",
-    "\n",
-    "Our goal is to find the secret string $s$.\n",
-    "\n",
-    "### Classical case\n",
-    "\n",
-    "For the case of $n=3$ consider $s=101$, which is a secret string and hence we dont have access to it. \n",
-    "\n",
-    "We have access to the function: $f(x) = x\\cdot s (mod \\space 2) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \\space 2)$\n",
-    "\n",
-    "Let us strategically query the function to determine $s$. \n",
-    "\n",
-    "$1^{st}$ query: $f(001) = 001 \\cdot s (mod \\space 2) = (0 \\cdot 1 + 0 \\cdot 0 + 1 \\cdot 1)(mod \\space 2) = 1 (mod \\space 2) = 1$\n",
-    "\n",
-    "$2^{nd}$ query: $f(010) = 010 \\cdot s (mod \\space 2) = (0 \\cdot 1 + 1 \\cdot 0 + 0 \\cdot 1)(mod \\space 2) = 0 (mod \\space 2) = 0$\n",
-    "\n",
-    "$3^{rd}$ query: $f(100) = 100 \\cdot s (mod \\space 2) = (1 \\cdot 1 + 0 \\cdot 0 + 0 \\cdot 1)(mod \\space 2) = 1 (mod \\space 2) = 1$\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Remember that from a user perspective, you only see $f(001) = 1$ since the inner mechanisms of the oracle and the value of $s$ is hidden from you. \n",
-    "\n",
-    "Why did we query $f(001)$ and not $f(011)$?\n",
-    "\n",
-    "For $f(001) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \\space 2) = (0 + 0 + x_3s_3) (mod \\space 2) = (x_3s_3) (mod \\space 2) = 1 \\cdot s_3 (mod \\space 2) = s_3 (mod \\space 2) = 1$\n",
-    "\n",
-    "We have now isolated $s_3$ and know that $s_3 (mod \\space 2) = 1$. $s_3$ can only be 0 or 1 and in this case it has to be $1$ since $0 (mod \\space 2) = 0$. \n",
-    "\n",
-    "For $f(011) = (x_2s_2 + x_3s_3) (mod \\space 2)$, we will be left with $s_2$ and $s_3$ each of which will be more difficult to isolate via a combination of linear equations. Hence why we have to strategically query the function. \n",
-    "\n",
-    "In the classical case, we see that the secret string $s$ can be calculated in 3 queries. More generally speaking, it would take $n$ queries to generate enough information to determine $s$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebdfe818",
-   "metadata": {},
-   "source": [
-    "### Quantum case\n",
-    "\n",
-    "The BV quantum algorithm can take advantage of superposition and entanglement to encode the problem and produce an answer with a single oracle (black box function) query. The setup is a register of $n$ qubits in the $\\ket{0}$ state and a single auxiliary qubit in the $\\ket{1}$ state. The auxiliary qubit enables the phase kickback. Consider the register of $n$ qubits initialized here:\n",
-    "\n",
-    "$$ \\ket{0} $$\n",
-    "\n",
-    "Application of a Hadamard gate to each qubit results in:\n",
-    "$$ H \\ket{0}  = \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} \\ket{x}$$\n",
-    "\n",
-    "Next, the oracle, $U_f$, performs maps $\\ket{x}$ to $(-1)^{f(x)}\\ket{x}$ resulting in:\n",
-    "\n",
-    "$$ U_f \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} \\ket{x} = \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{f(x)}\\ket{x} =\\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{a*x}\\ket{x} $$\n",
-    "\n",
-    "Applying Hadamard gates again returns the following state:\n",
-    "$$ H \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{a*x}\\ket{x}  =  \\frac{1}{\\sqrt{2^n}}\\sum_{x,y}^{n-1} (-1)^{a*x} (-1)^{y*x} \\ket{y}  = \\frac{1}{\\sqrt{2^n}}\\sum_{y}^{n-1} \\sum_{x}^{n-1} (-1)^{(a \\oplus y)*x}\\ket{y} $$\n",
-    "\n",
-    "In the case of $a \\neq y$, the entire term becomes zero. In the case of $a = y$, the state $\\ket{a}$ is returned with an amplitude of 1.  This means there is a 100% chance of the measurement result being the hidden bitstring $a$ with only one call to the oracle!\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fdbba1e-8eab-42a2-8964-6d7e86db0ad4",
-   "metadata": {},
-   "source": [
-    "### Implementing in CUDA-Q\n",
-    "\n",
-    "The cell below generates a random bitstring of length $n$. If you are running this on your CPU, keep the qubit count small and increase to around 30 if you are running on a GPU with the `nvidia` backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "883762a1-d271-4692-adad-d64daa20805d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "#cudaq.set_target('nvidia')  # GPU backend which enables scaling to large problem sizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d72225e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qubit_count = 5  # Set to around 30 qubits if you have GPU access\n",
-    "\n",
-    "secret_string = [1, 1, 0, 1,\n",
-    "                 0]  # Change the secret string to whatever you prefer\n",
-    "\n",
-    "assert qubit_count == len(secret_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3df178fe-ccf3-41aa-b5f7-037a1a1d4a69",
-   "metadata": {},
-   "source": [
-    "Next, the oracle kernel is defined. This will be used inside of the primary BV kernel, so it needs to take both the main register and auxiliary qubit as inputs as well as the secret bitstring. The oracle loops through the bits and applies a CNOT on the auxiliary qubit if the register qubit is a 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "91dbde75-486b-4015-9627-9ff404bdb475",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def oracle(register: cudaq.qview, auxiliary_qubit: cudaq.qubit,\n",
-    "           secret_string: List[int]):\n",
-    "\n",
-    "    for index, bit in enumerate(secret_string):\n",
-    "        if bit == 1:\n",
-    "            x.ctrl(register[index], auxiliary_qubit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "685bb02e-afdc-4b69-afb4-81af9c2fafbd",
-   "metadata": {},
-   "source": [
-    "The code below performs the steps described above for the BV algorithm, calling the oracle kernel as needed. You can see the circuit created below and verify that the procedure guessed the correct bitstring. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d93cb1db-b5c6-45ef-9efc-c426895aac18",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮          ╭───╮          \n",
-      "q0 : ┤ h ├───────●──┤ h ├──────────\n",
-      "     ├───┤       │  ╰───╯╭───╮     \n",
-      "q1 : ┤ h ├───────┼────●──┤ h ├─────\n",
-      "     ├───┤       │    │  ├───┤     \n",
-      "q2 : ┤ h ├───────┼────┼──┤ h ├─────\n",
-      "     ├───┤       │    │  ╰───╯╭───╮\n",
-      "q3 : ┤ h ├───────┼────┼────●──┤ h ├\n",
-      "     ├───┤       │    │    │  ├───┤\n",
-      "q4 : ┤ h ├───────┼────┼────┼──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╭─┴─╮╭─┴─╮╰───╯\n",
-      "q5 : ┤ x ├┤ h ├┤ x ├┤ x ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯╰───╯╰───╯     \n",
-      "\n",
-      "secret bitstring = [1, 1, 0, 1, 0]\n",
-      "measured state = 11010\n",
-      "Were we successful? True\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def bernstein_vazirani(secret_string: List[int]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(len(secret_string))  # register of size n\n",
-    "    auxiliary_qubit = cudaq.qubit()  # auxiliary qubit\n",
-    "\n",
-    "    # Prepare the auxillary qubit.\n",
-    "    x(auxiliary_qubit)\n",
-    "    h(auxiliary_qubit)\n",
-    "\n",
-    "    # Place the rest of the register in a superposition state.\n",
-    "    h(qubits)\n",
-    "\n",
-    "    # Query the oracle.\n",
-    "    oracle(qubits, auxiliary_qubit, secret_string)\n",
-    "\n",
-    "    # Apply another set of Hadamards to the register.\n",
-    "    h(qubits)\n",
-    "\n",
-    "    mz(qubits)  # measures only the main register\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(bernstein_vazirani, secret_string))\n",
-    "result = cudaq.sample(bernstein_vazirani, secret_string)\n",
-    "\n",
-    "print(f\"secret bitstring = {secret_string}\")\n",
-    "print(f\"measured state = {result.most_probable()}\")\n",
-    "print(\n",
-    "    f\"Were we successful? {''.join([str(i) for i in secret_string]) == result.most_probable()}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "388592de-3b47-45f4-b687-72abed6fdf8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/cost_minimization.ipynb.txt b/pr-2308/_sources/applications/python/cost_minimization.ipynb.txt
deleted file mode 100644
index 824ef41237..0000000000
--- a/pr-2308/_sources/applications/python/cost_minimization.ipynb.txt
+++ /dev/null
@@ -1,207 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cost Minimization\n",
-    "\n",
-    "Below we start with a basic example of a hybrid variational algorithm which involves flipping the Bloch vector of a qubit from the $\\ket{0}$ to the $\\ket{1}$ state. First we import the relevant packages and set our backend to simulate our workflow on NVIDIA GPUs. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───────╮╭───────╮\n",
-      "q0 : ┤ rx(0) ├┤ ry(0) ├\n",
-      "     ╰───────╯╰───────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Initialize a kernel/ ansatz and variational parameters.\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: List[float]):\n",
-    "    # Allocate a qubit that is initialized to the |0> state.\n",
-    "    qubit = cudaq.qubit()\n",
-    "    # Define gates and the qubits they act upon.\n",
-    "    rx(angles[0], qubit)\n",
-    "    ry(angles[1], qubit)\n",
-    "\n",
-    "\n",
-    "# Our Hamiltonian will be the Z expectation value of our qubit.\n",
-    "hamiltonian = cudaq.spin.z(0)\n",
-    "\n",
-    "# Initial gate parameters which intialize the qubit in the zero state\n",
-    "initial_parameters = [0, 0]\n",
-    "\n",
-    "print(cudaq.draw(kernel, initial_parameters))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We build our cost function such that its minimal value corresponds to the qubit being in the $\\ket{1}$ state. The observe call below allows us to simulate our statevector $\\ket{\\psi}$, and calculate $\\bra{\\psi}Z\\ket{\\psi}$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cost_values = []\n",
-    "\n",
-    "\n",
-    "def cost(parameters):\n",
-    "    \"\"\"Returns the expectation value as our cost.\"\"\"\n",
-    "    expectation_value = cudaq.observe(kernel, hamiltonian,\n",
-    "                                      parameters).expectation()\n",
-    "    cost_values.append(expectation_value)\n",
-    "    return expectation_value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# We see that the initial value of our cost function is one, demonstrating that our qubit is in the zero state\n",
-    "initial_cost_value = cost(initial_parameters)\n",
-    "print(initial_cost_value)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we use our built-in optimization suite to minimize the cost function. We will be using the gradient-free COBYLA alogrithm. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define a CUDA-Q optimizer.\n",
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "optimizer.initial_parameters = initial_parameters\n",
-    "\n",
-    "result = optimizer.optimize(dimensions=2, function=cost)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cost Value')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting how the value of the cost function decreases during the minimization procedure.\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x_values = list(range(len(cost_values)))\n",
-    "y_values = cost_values\n",
-    "\n",
-    "plt.plot(x_values, y_values)\n",
-    "\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Cost Value\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the final value or our cost function, $\\bra{\\psi}Z\\ket{\\psi} = -1$ demonstrating that the qubit is in the $\\ket{1}$ state."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/deutschs_algorithm.ipynb.txt b/pr-2308/_sources/applications/python/deutschs_algorithm.ipynb.txt
deleted file mode 100644
index feaacacd9d..0000000000
--- a/pr-2308/_sources/applications/python/deutschs_algorithm.ipynb.txt
+++ /dev/null
@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deutsch's Algorithm "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a function which takes in a bit and outputs a bit. This can be represented as $f: \\{0,1\\} \\longrightarrow \\{0,1\\}$. \n",
-    "\n",
-    "The function $f$ has a property; either it is constant or balanced. \n",
-    "\n",
-    "If constant, the outputs are the same regardless of the inputs, i.e., $f(0) = f(1) = 0$ or $f(0) = f(1) = 1$.\n",
-    "\n",
-    "If balanced, the ouputs are balanced across their possibilities, i.e,  if $f(0) = 0$ then $f(1) = 1$ or if $f(0) = 1$ then $f(1) = 0$.\n",
-    "\n",
-    "The question we would like to answer is if the function is constant or balanced. \n",
-    " \n",
-    "Classically, if we are given a function $f$, we can solve to find its property via the code below: \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Treat the code snippet below like a black box, i.e., you don't have access to the value of the property argument and can just query f with different inputs.\n",
-    "\n",
-    "\n",
-    "def f(x, property='constant'):\n",
-    "\n",
-    "    if property == 'constant':\n",
-    "\n",
-    "        # The output is a 1 regardless of the input; we can also make the output to be 0.\n",
-    "\n",
-    "        if x == 0:\n",
-    "            return 1\n",
-    "        elif x == 1:\n",
-    "            return 1\n",
-    "\n",
-    "    if property == 'balanced':\n",
-    "\n",
-    "        # The output depends on the input.\n",
-    "\n",
-    "        if x == 0:\n",
-    "            return 1\n",
-    "        elif x == 1:\n",
-    "            return 0"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us now classically find out if the function we defined above is constant or balanced. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The function is constant\n"
-     ]
-    }
-   ],
-   "source": [
-    "if f(0) == 0:\n",
-    "    if f(1) == 0:\n",
-    "        print('The function is constant')\n",
-    "    elif f(1) == 1:\n",
-    "        print('The function is balanced')\n",
-    "\n",
-    "elif f(0) == 1:\n",
-    "    if f(1) == 1:\n",
-    "        print('The function is constant')\n",
-    "    elif f(1) == 0:\n",
-    "        print('The function is balanced')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you step through the `if` statements above, one can see that we require 2 calls to the function to determine its property. That is, we have to query $f$ twice.\n",
-    "\n",
-    "The claim is that Deutsch's algorithm can solve for this property with 1 function evalulation, demonstrating quantum advantage. \n",
-    "\n",
-    "Below we first go through the math and then the implementation in CUDA Quantum. \n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## XOR $\\oplus$\n",
-    "\n",
-    "You may have seen the $\\oplus$ symbol floating around. This is the exclusive OR or the XOR operation. It follows the rules of addition modulo 2. For example, \n",
-    "\n",
-    "\n",
-    "1. $3\\oplus5 = (3+5)_{\\%2} = 8_{\\%2} = 0$\n",
-    "\n",
-    "2. $5\\oplus4 = (5+4)_{\\%2} = 9_{\\%2} = 1$\n",
-    "\n",
-    "A quick way to perform the $\\oplus$ calculation is if the result of the addition is an even number, the result is 0 otherwise 1. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum oracles\n",
-    "\n",
-    "<img src=\"images/oracle.png\"  width=\"300\" height=\"150\">\n",
-    "\n",
-    "Suppose we have $f(x): \\{0,1\\} \\longrightarrow \\{0,1\\}$. We can compute this function on a quantum computer using oracles which we treat as black box functions that yield the output with an appropriate sequence of logic gates. \n",
-    "\n",
-    "Above you see an oracle represented as $U_f$ which allows us to transform the state $\\ket{x}\\ket{y}$ into: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{x}\\ket{y}  =  \\ket{x}\\ket{y \\oplus f(x)}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "If $y = 0$, then $U_f\\ket{x}\\ket{y} = U_f\\ket{x}\\ket{0}  =   \\ket{x}\\ket{0 \\oplus f(x)} =  \\ket{x}\\ket{f(x)}$ since $f(x)$ can either be $0/1$ and $0 \\oplus 0 = 0$ and $0 \\oplus 1 = 1$.\n",
-    "\n",
-    "This is remarkable because by setting $\\ket{y} = \\ket{0}$, we can extract the value of $f(x)$ by measuring the value of the second qubit. \n",
-    " \n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phase oracle\n",
-    "\n",
-    "\n",
-    "Since $f(x)$ can be either $0$ or $1$, we have that $0 \\oplus f(x) = 0 \\oplus 0 =  0 = f(x)$ or $0 \\oplus f(x) = 0 \\oplus 1 =  1 = f(x)$. \n",
-    "\n",
-    "Similarly, $1 \\oplus f(x) = 1 \\oplus 0 =  1 = \\overline{f(x)}$ or $1 \\oplus f(x) = 1 \\oplus 1 =  0 = \\overline{f(x)}$ where the bar on top of $f(x)$ denotes 'not $f(x)$'. \n",
-    "\n",
-    "\n",
-    "Let us now prove a result which we shall use later: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{x}\\ket{-}  &=   U_f \\ket{x} \\tfrac{1}{\\sqrt{2}}(\\ket{0} - \\ket{1}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (U_f\\ket{x}\\ket{0} - U_f\\ket{x}\\ket{1}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{0 \\oplus f(x)} - \\ket{x}\\ket{1 \\oplus f(x)}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{f(x)} - \\ket{x}\\ket{\\overline{f(x)}}) \\\\\n",
-    "&= \n",
-    "\\begin{cases}\n",
-    "      \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{0} - \\ket{x}\\ket{1}) = \\ket{x}\\ket{-} &   f(x) = 0 \\\\\n",
-    "      \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{1} - \\ket{x}\\ket{0}) = -\\ket{x}\\ket{-} &   f(x) = 1 \\\\\n",
-    "\\end{cases} \\\\\n",
-    "&= (-1)^{f(x)}\\ket{x}\\ket{-}    \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "In summary, $U_f\\ket{x}\\ket{-} = (-1)^{f(x)}\\ket{x}\\ket{-}$ where the $\\ket{-}$ qubit was left unchanged and a phase was applied to $\\ket{x}$. \n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Quantum parallelism\n",
-    "\n",
-    "Consider: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{+}\\ket{0} &= U_f \\tfrac{1}{\\sqrt{2}}(\\ket{0}+\\ket{1}) \\ket{0} \\\\\n",
-    "&= U_f \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{0}+\\ket{1}\\ket{0}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}(U_f \\ket{0}\\ket{0} + U_f \\ket{1}\\ket{0})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(U_f \\ket{0}\\ket{0} + U_f \\ket{1}\\ket{0})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{0 \\oplus f(0)} + \\ket{1}\\ket{0 \\oplus f(1)})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{f(0)} + \\ket{1}\\ket{f(1)})  \\\\   \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We have calculated information about both $f(0)$ and $f(1)$ simultaneously. Quantum mechanics allows for parallelism by exploiting the ability of superposition states.  "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deutschs' Algorithm: \n",
-    "\n",
-    "Our aim is to find out if $f: \\{0,1\\} \\longrightarrow \\{0,1\\}$ is a constant or a balanced function? If constant, $f(0) = f(1)$, and if balanced, $f(0) \\neq f(1)$.\n",
-    "\n",
-    "We step through the circuit diagram below and follow the math after the application of each gate.\n",
-    "\n",
-    "<img src=\"images/deutsch.png\"  width=\"500\" height=\"210\">\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\ket{\\psi_0}  =  \\ket{01}\n",
-    "\\tag{1}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\\ket{\\psi_1}  =   H_1H_0\\ket{\\psi_0} = H_1H_0\\ket{01} = \\ket{+-} = \\frac{1}{\\sqrt{2}} (\\ket{0}+\\ket{1})\\ket{-}\n",
-    "\\tag{2}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\ket{\\psi_2}  =   U_f\\ket{\\psi_1} = \\frac{1}{\\sqrt{2}} (U_f\\ket{0}\\ket{-} + U_f\\ket{1}\\ket{-})\n",
-    "\\tag{3}\n",
-    "$$\n",
-    "\n",
-    "using $U_f\\ket{x}\\ket{-} = (-1)^{f(x)}\\ket{x}\\ket{-}$ which we proved in the phase oracle section above: \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_2}  =  \\frac{1}{\\sqrt{2}} ((-1)^{f(0)}\\ket{0}\\ket{-} + (-1)^{f(1)}\\ket{1}\\ket{-}) \\\\\n",
-    "= \\frac{1}{\\sqrt{2}} ((-1)^{f(0)}\\ket{0} + (-1)^{f(1)}\\ket{1}) \\ket{-} \\\\\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We now drop the second, qubit in the $\\ket{-}$ state from our derivations as the figure above has no further gate operations acting on it. Remember our aim is to find out if $f(x)$ is a constant or a balanced function. We have the following options: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\ket{\\psi_{2}}=\n",
-    "\\begin{cases}\n",
-    "      \\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1})  &   f(0) = 0, f(1) = 0, f(x)  \\text{ is constant} \\\\\n",
-    "      -\\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1})  &   f(0) = 1, f(1) = 1, f(x)  \\text{ is constant} \\\\\n",
-    "      \\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})  &   f(0) = 0, f(1) = 1, f(x)  \\text{ is balanced} \\\\\n",
-    "      -\\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})  &   f(0) = 1, f(1) = 0, f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} =\n",
-    "\\begin{cases}\n",
-    "      \\pm \\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1}) = \\pm \\ket{+}  &    f(x)  \\text{ is constant} \\\\\n",
-    "      \\pm \\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})= \\pm \\ket{-}  &    f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{3}}= H\\ket{\\psi_{2}}\n",
-    "\\begin{cases}\n",
-    "      \\pm H\\ket{+} =  \\pm \\ket{0} &    f(x)  \\text{ is constant} \\\\\n",
-    "      \\pm H\\ket{-}  = \\pm \\ket{1}  &   f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We can now measure the first qubit to yield either a $0$ or a $1$ to determine if $f(x)$ is constant or balanced. \n",
-    "\n",
-    "Deutsch's algorithm may not be practically useful but does demonstrate quantum advantage since it uses one function call to determine the property of $f$ in comparison to two for classical methods. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (>= 3.0.0) is not supported.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import the CUDA-Q package and set the target to run on NVIDIA GPUs.\n",
-    "\n",
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here we input the values of [f(0), f(1)] which allows us to represent constant or balanced functions.\n",
-    "\n",
-    "fx = [0, 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮          ╭───╮\n",
-      "q0 : ┤ h ├───────●──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╰───╯\n",
-      "q1 : ┤ x ├┤ h ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯     \n",
-      "\n",
-      "f(x) is a balanced function\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let us now code up the circuit shown above following the state Psi after each step.\n",
-    "\n",
-    "qubit_count = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(fx: List[int]):\n",
-    "    qubit_0 = cudaq.qubit()\n",
-    "    qubit_1 = cudaq.qubit()\n",
-    "\n",
-    "    # Psi 0\n",
-    "    x(qubit_1)\n",
-    "\n",
-    "    # Psi 1\n",
-    "    h(qubit_0)\n",
-    "    h(qubit_1)\n",
-    "\n",
-    "    # Psi 2 - oracle\n",
-    "    if fx[0] == 1:\n",
-    "        x.ctrl(qubit_0, qubit_1)\n",
-    "        x(qubit_1)\n",
-    "\n",
-    "    if fx[1] == 1:\n",
-    "        x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "    # Psi 3\n",
-    "    h(qubit_0)\n",
-    "\n",
-    "    # Measure the qubit to yield if the function is constant or balanced.\n",
-    "    mz(qubit_0)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, fx))\n",
-    "\n",
-    "result = cudaq.sample(kernel, fx, shots_count=1)\n",
-    "\n",
-    "if np.array(result)[0] == '0':\n",
-    "    print('f(x) is a constant function')\n",
-    "elif np.array(result)[0] == '1':\n",
-    "    print('f(x) is a balanced function')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
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-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
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-  },
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-  "vscode": {
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-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/digitized_counterdiabatic_qaoa.ipynb.txt b/pr-2308/_sources/applications/python/digitized_counterdiabatic_qaoa.ipynb.txt
deleted file mode 100644
index a3043b7a1b..0000000000
--- a/pr-2308/_sources/applications/python/digitized_counterdiabatic_qaoa.ipynb.txt
+++ /dev/null
@@ -1,427 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Molecular docking via DC-QAOA\n",
-    "\n",
-    "Drugs often work by binding to an active site of a protein, inhibiting or activating its function for some therapeutic purpose. Finding new candidate drugs is extremely difficult. The study of molecular docking helps guide this search and involves the prediction of how strongly a certain ligand (drug) will bind to its target (usually a protein).  \n",
-    "\n",
-    "One of the primary challenges to molecular docking arises from the many geometric degrees of freedom present in proteins and ligands, making it difficult to predict the optimal orientation and assess if the drug is a good candidate or not. One solution is to formulate the problem as a mathematical optimization problem where the optimal solution corresponds to the most likely ligand-protein configuration. This optimization problem can be solved on a quantum computer using methods like the Quantum Approximate Optimization Algorithm (QAOA). This tutorial demonstrates how this [paper](https://arxiv.org/pdf/2308.04098) used digitized-counteradiabatic (DC) QAOA to study molecular docking.  This tutorial assumes you have an understanding of QAOA, if not, please see the CUDA-Q MaxCut tutorial found [here](https://nvidia.github.io/cuda-quantum/latest/applications/python/qaoa.html).\n",
-    "\n",
-    "The next section provides more detail on the problem setup followed by CUDA-Q implementations below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setting up the Molecular Docking Problem\n",
-    "\n",
-    "The figure from the [paper](https://arxiv.org/pdf/2308.04098) provides a helpful diagram for understanding the workflow.\n",
-    "\n",
-    "![docking](./images/docking.png)\n",
-    "\n",
-    "\n",
-    "There are 6 key steps:\n",
-    "1.  The experimental protein and ligand structures are determined and used to select pharmacores, or an important chemical group that will govern the chemical interactions.\n",
-    "2. Two labeled distance graphs (LAGs) of size $N$ and $M$ represent the protein and the ligand, respectively. Each node corresponds to a pharmacore and each edge weight corresponds to the distance between pharmacores.\n",
-    "3.  A $M*N$ node binding interaction graph (BIG) is created from the LAGs. Each node in the BIG graph corresponds to a pair of pharmacores, one from the ligand and the other from the protein. The existence of edges between nodes in the BIG graph are determined from the LAGs and correspond to interactions that can feesibly coexist. Therefore, cliques in the graph correspond to mutually possible interactions. \n",
-    "4. The problem is mapped to a QAOA circuit and corresponding Hamiltonian. From there, the ground state solution is determined.\n",
-    "5.  The ground state will produce the maximum weighted clique which corresponds to the best (most strongly bound) orientation of the ligand and protein.\n",
-    "6.  The predicted docking structure is interpreted from the QAOA result and is used for further analysis.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### CUDA-Q Implementation\n",
-    "\n",
-    "First, the appropriate libraries are imported and the `nvidia` backend is selected to run on GPUs if available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "import numpy as np\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The block below defines two of the BIG data sets from the paper. The first is a smaller example, but it can be swapped with the commented out example below at your discretion. The weights are specified for each node based on the nature of the ligand and protein pharmacores represented by the node."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Edges:  [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4], [3, 4], [3, 5], [4, 5]]\n",
-      "Non-Edges:  [[0, 3], [1, 4], [2, 5]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The two graph inputs from the paper\n",
-    "\n",
-    "# BIG 1\n",
-    "\n",
-    "nodes = [0, 1, 2, 3, 4, 5]\n",
-    "qubit_num = len(nodes)\n",
-    "edges = [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4],\n",
-    "         [3, 4], [3, 5], [4, 5]]\n",
-    "non_edges = [\n",
-    "    [u, v] for u in nodes for v in nodes if u < v and [u, v] not in edges\n",
-    "]\n",
-    "\n",
-    "print('Edges: ', edges)\n",
-    "print('Non-Edges: ', non_edges)\n",
-    "\n",
-    "weights = [0.6686, 0.6686, 0.6686, 0.1453, 0.1453, 0.1453]\n",
-    "penalty = 6.0\n",
-    "num_layers = 3\n",
-    "\n",
-    "# BIG 2 (More expensive simulation)\n",
-    "#nodes=[0,1,2,3,4,5,6,7]\n",
-    "#qubit_num=len(nodes)\n",
-    "#edges=[[0,1],[0,2],[0,5],[0,6],[0,7],[1,2],[1,4],[1,6],[1,7],[2,4],[2,5],[2,7],[3,4],[3,5],[3,6],\\\n",
-    "#    [4,5],[4,6],[5,6]]\n",
-    "#non_edges=[[u,v] for u in nodes for v in nodes if u<v and [u,v] not in edges]\n",
-    "#print('Edges: ', edges)\n",
-    "#print('Non-edges: ', non_edges)\n",
-    "#weights=[0.6686,0.6686,0.6886,0.1091,0.0770,0.0770,0.0770,0.0770]\n",
-    "#penalty=8.0\n",
-    "#num_layers=8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the Hamiltonian is constructed: \n",
-    "\n",
-    "$$H = \\frac{1}{2}\\sum_{i \\in V}w_i(\\sigma^z_i - 1) + \\frac{P}{4} \\sum_{(i,j) \\notin E, i \\neq j} (\\sigma^z_i -1)(\\sigma^z_j - 1) $$\n",
-    "\n",
-    "\n",
-    "The first term concerns the vertices and the weights of the given pharmacores.  The second term is a penalty term that penalizes edges of the graph with no interactions.  The penalty $P$ is set by the user and is defined as 6 in the cell above. The function below returns the Hamiltonian as a CUDA-Q `spin_op` object.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate the Hamiltonian\n",
-    "def ham_clique(penalty, nodes, weights, non_edges) -> cudaq.SpinOperator:\n",
-    "\n",
-    "    spin_ham = 0.0\n",
-    "    for wt, node in zip(weights, nodes):\n",
-    "        #print(wt,node)\n",
-    "        spin_ham += 0.5 * wt * spin.z(node)\n",
-    "        spin_ham -= 0.5 * wt * spin.i(node)\n",
-    "\n",
-    "    for non_edge in non_edges:\n",
-    "        u, v = (non_edge[0], non_edge[1])\n",
-    "        #print(u,v)\n",
-    "        spin_ham += penalty / 4.0 * (spin.z(u) * spin.z(v) - spin.z(u) -\n",
-    "                                     spin.z(v) + spin.i(u) * spin.i(v))\n",
-    "\n",
-    "    return spin_ham"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below strips the Hamiltonian into a list of coefficients and corresponding Pauli words which can be passed into a quantum kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.5+0j] IIZIIZ\n",
-      "[1.5+0j] ZIIZII\n",
-      "[-1.1657+0j] IZIIII\n",
-      "[1.5+0j] IZIIZI\n",
-      "[-1.42735+0j] IIIZII\n",
-      "[3.2791499999999996+0j] IIIIII\n",
-      "[-1.1657+0j] IIZIII\n",
-      "[-1.42735+0j] IIIIIZ\n",
-      "[-1.1657+0j] ZIIIII\n",
-      "[-1.42735+0j] IIIIZI\n",
-      "\n",
-      "[(1.5+0j), (1.5+0j), (-1.1657+0j), (1.5+0j), (-1.42735+0j), (3.2791499999999996+0j), (-1.1657+0j), (-1.42735+0j), (-1.1657+0j), (-1.42735+0j)]\n",
-      "['IIZIIZ', 'ZIIZII', 'IZIIII', 'IZIIZI', 'IIIZII', 'IIIIII', 'IIZIII', 'IIIIIZ', 'ZIIIII', 'IIIIZI']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Collect coefficients from a spin operator so we can pass them to a kernel\n",
-    "def term_coefficients(ham: cudaq.SpinOperator) -> list[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "    # Collect Pauli words from a spin operator so we can pass them to a kernel\n",
-    "\n",
-    "\n",
-    "def term_words(ham: cudaq.SpinOperator) -> list[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "ham = ham_clique(penalty, nodes, weights, non_edges)\n",
-    "print(ham)\n",
-    "\n",
-    "coef = term_coefficients(ham)\n",
-    "words = term_words(ham)\n",
-    "\n",
-    "print(term_coefficients(ham))\n",
-    "print(term_words(ham))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The kernel below defines a DC-QAOA circuit.  What makes the approach \"DC\" is the inclusion of additional counteradiabatic terms to better drive the optimization to the ground state. These terms are digitized and applied as additional operations following each QAOA layer.  The increase in parameters is hopefully offset by requiring fewer layers. In this example, the DC terms are the additional parameterized $Y$ operations applied to each qubit. These can be commented out to run conventional QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def dc_qaoa(qubit_num:int, num_layers:int, thetas:list[float],\\\n",
-    "    coef:list[complex], words:list[cudaq.pauli_word]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    h(qubits)\n",
-    "\n",
-    "    count = 0\n",
-    "    for p in range(num_layers):\n",
-    "\n",
-    "        for i in range(len(coef)):\n",
-    "            exp_pauli(thetas[count] * coef[i].real, qubits, words[i])\n",
-    "            count += 1\n",
-    "\n",
-    "        for j in range(qubit_num):\n",
-    "            rx(thetas[count], qubits[j])\n",
-    "            count += 1\n",
-    "\n",
-    "        #Comment out this for loop for conventional QAOA\n",
-    "        for k in range(qubit_num):\n",
-    "            ry(thetas[count], qubits[k])\n",
-    "            count += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The classical optimizer for the QAOA procedure can be specified as one of the built-in CUDA-Q optimizers, in this case Nelder Mead. The parameter count is defined for DC-QAOA, but can be swapped with the commented line below for conventional QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of parameters:  66\n",
-      "Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064, 0.37118004688049144, -0.03656087558321203, 0.08564174998504231, 0.21639801853794682, 0.11122286088634259, 0.1743727097033635, -0.36518146001762486, -0.15829741539542244, -0.3467434780387345, 0.28043500852894776, -0.09986021299050934, 0.14125225086023052, -0.19141728018199775, -0.11970943368650361, -0.3853063093646483, -0.1112643868789806, 0.3527177454825464, -0.22156160012057186, -0.1418496891385843, 0.32811766468303116, -0.367642000671186, -0.34158180583996006, 0.10196745745501312, 0.29359239180502594, -0.3858537615546677, 0.19366130907065582, 0.24570488114056754, -0.3332307385378807, 0.12287973244618389, 0.007274514934614895, -0.015799547372526146, 0.3578070967202224, -0.39268963055535144, -0.19872246354138554, 0.16668715544467982, -0.13777293592446055, -0.17514665212709513, 0.15350249947988204, 0.32872977428061945, -0.20068831419712105, -0.032919322131134854, -0.19399909325771983, -0.09477141125241506, 0.08210460401106645, 0.21392577760158515, -0.3393568044538389, 0.14615087942938465, 0.03790339186006314, -0.2843250892879255, -0.3151384847055956, -0.19983741137121905, -0.27348611567665115, 0.33457528180906904, 0.14145414847455462, -0.20604220093940323, 0.05410235084309195, 0.04447870918600966, -0.3355714098595045, 0.266806440171265, -0.07436189654442632, -0.2789176729721685, -0.2427508182662484]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Specify the optimizer and its initial parameters.\n",
-    "optimizer = cudaq.optimizers.NelderMead()\n",
-    "\n",
-    "#Specify random seeds\n",
-    "np.random.seed(13)\n",
-    "cudaq.set_random_seed(13)\n",
-    "\n",
-    "# if dc_qaoa used\n",
-    "parameter_count = (2 * qubit_num + len(coef)) * num_layers\n",
-    "\n",
-    "# if qaoa used\n",
-    "# parameter_count=(qubit_num+len(coef))*num_layers\n",
-    "\n",
-    "print('Total number of parameters: ', parameter_count)\n",
-    "optimizer.initial_parameters = np.random.uniform(-np.pi / 8, np.pi / 8,\n",
-    "                                                 parameter_count)\n",
-    "print(\"Initial parameters = \", optimizer.initial_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A cost function is specified which computes the expectation value of the DC-QAOA circuit and the Hamiltonian using the `observe` function. Running the optimization returns the minimized expectation value and the optimal parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "optimal_expectation = -2.0057493966746804\n",
-      "optimal_parameters = [2.0508763934174787, 0.013930789730781493, 0.5793211220774144, 0.878009560684498, 0.5277129177248182, 0.4404810513078178, 0.5755552245467919, 0.14125558672355468, 0.3724262117066903, 0.1318978057007808, -1.1228708513911436, 0.932342804955409, -0.8478237950658537, 0.46345886313018125, -0.5809397306340341, 0.2408342488137229, 0.11216088888484882, -0.009704173265255175, 0.4757346661223584, -0.7281211610985926, 0.06051951319169091, -0.7794512146826196, 0.09249435261907034, 0.09998378319110682, 1.255349350720572, 1.2607038244228248, 0.2060124032311757, 0.13991934581192997, 0.9874814082082164, -0.1591291464755939, 0.30815482837046393, -0.9701804681517978, -0.002609462845755913, 0.43533533568363353, 0.642630110681613, 0.6137063363954748, -0.7204687246344496, 0.08390768435524378, 0.5480630700433249, -0.38905723227347905, -0.6837811162838194, -0.17239016898719284, 0.1649341118754853, -0.46771209183422724, -0.008565327035838663, 1.982230359328883, -0.4232972687799105, 0.22765896988428905, 0.04207923928239914, -0.36758378917672285, -0.01825447063622079, -0.059755059728027485, -0.6849697218162497, 0.2711684382411018, -0.2904257415666667, -0.16359529445017368, -0.09168623367396612, 0.5786087806926155, -0.3476755367718726, 0.1209273564533628, 0.605136043801364, -0.19128215816141694, 0.16756583092588012, 1.0715488214105267, -0.5269641128095075, -0.3029128369198704]\n"
-     ]
-    }
-   ],
-   "source": [
-    "cost_values = []\n",
-    "\n",
-    "\n",
-    "def objective(parameters):\n",
-    "\n",
-    "    cost = cudaq.observe(dc_qaoa, ham, qubit_num, num_layers, parameters, coef,\n",
-    "                         words).expectation()\n",
-    "    cost_values.append(cost)\n",
-    "    return cost\n",
-    "\n",
-    "\n",
-    "# Optimize!\n",
-    "optimal_expectation, optimal_parameters = optimizer.optimize(\n",
-    "    dimensions=parameter_count, function=objective)\n",
-    "\n",
-    "print('optimal_expectation =', optimal_expectation)\n",
-    "print('optimal_parameters =', optimal_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sampling the circuit with the optimal parameters allows for the `most_probable` command to reveal the bitsting corresponding to the ideal graph partitioning solution. This indicates what sort of interactions are present in the ideal docking configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 110001:16 011100:4 111000:199979 011000:1 }\n",
-      "\n",
-      "The MVWCP is given by the partition:  111000\n"
-     ]
-    }
-   ],
-   "source": [
-    "shots = 200000\n",
-    "\n",
-    "counts = cudaq.sample(dc_qaoa,\n",
-    "                      qubit_num,\n",
-    "                      num_layers,\n",
-    "                      optimal_parameters,\n",
-    "                      coef,\n",
-    "                      words,\n",
-    "                      shots_count=shots)\n",
-    "print(counts)\n",
-    "\n",
-    "print('The MVWCP is given by the partition: ', counts.most_probable())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"./images/partition.png\" alt=\"dockin\" width=\"300\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The convergence of the optimization can be plotted below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x_values = list(range(len(cost_values)))\n",
-    "y_values = cost_values\n",
-    "\n",
-    "plt.plot(x_values, y_values)\n",
-    "\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Cost Value\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/_sources/applications/python/divisive_clustering_coresets.ipynb.txt b/pr-2308/_sources/applications/python/divisive_clustering_coresets.ipynb.txt
deleted file mode 100644
index 0cffbd7110..0000000000
--- a/pr-2308/_sources/applications/python/divisive_clustering_coresets.ipynb.txt
+++ /dev/null
@@ -1,1097 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Divisive Clustering With Coresets Using CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial will explore a CUDA-Q implementation of recent research (ArXiv Paper: https://arxiv.org/pdf/2402.01529.pdf) performed by a team from the University of Edinburgh. This tutorial was jointly developed by NVIDIA and the authors so users can better understand their method and explore how CUDA-Q removed barriers to scaling. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code for this tutorial is based off the MIT licensed code found here: https://github.com/Boniface316/bigdata_vqa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Clustering is a common unsupervised learning technique aimed at grouping data with similar characteristics. The unique properties of quantum computers could allow for enhanced pattern finding in clustering applications and enable more reliable data analysis. However, quantum computers today are severely limited by qubit count and noise.  Performing practical clustering applications would require far too many qubits.  The Edinburgh team developed a new method (extending the work of Harrow) to leverage coresets for clustering applications on quantum computers and use far fewer qubits.  This tutorial will walk through an example using this approach for divisive clustering and emphasize the utility of CUDA-Q for scaling quantum simulations.\n",
-    "\n",
-    "The goal of divisive clustering is to begin with all data points as one set, and iteratively bipartition the data until each point is its own cluster.  The branching behavior of this process can be used to understand similarities in the data points.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If you are running outside of a CUDA-Q container or CUDA-Q directory tree, you may need to uncomment these lines to fetch the files.\n",
-    "# If you are running inside a CUDA-Q tree, then this step can be skipped.\n",
-    "# !mkdir divisive_clustering_src\n",
-    "# !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py\n",
-    "# !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "!pip install mpi4py==3.1.6 networkx==2.8.8 pandas==2.2.2 scikit-learn==1.4.2 tqdm==4.66.2 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "# Auxillary Imports\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import warnings\n",
-    "from typing import Tuple\n",
-    "from divisive_clustering_src.divisive_clustering import Coreset, DivisiveClustering, Dendrogram, Voironi_Tessalation\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The settings below are global parameters for the quantum simulation and can be toggled by the user.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "circuit_depth = 1\n",
-    "max_iterations = 75\n",
-    "max_shots = 1000\n",
-    "np.random.seed(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Given a data set $X = (x_1, x_2, \\cdots, x_N)$, a coreset is weighted data set of much smaller size ($X', w$) that represents $X$ enough such that analysis of ($X', w$) can allow us to draw reasonable approximate conclusions about $X$. There are various approaches to build coresets. They can be found in Practical Coreset Construction for Machine Learning (https://arxiv.org/pdf/1703.06476.pdf) and New Streaming Algorithms for Coresets in Machine Learning (https://arxiv.org/pdf/1703.06476.pdf).\n",
-    "\n",
-    "\n",
-    "Essentially, coreset construction boils down to finding the optimal coreset size and weights given some error tolerance.  Given the constraints of a quantum computer, in this work, a coreset size is selected $a$ $priori$, and the error is determined for each model.\n",
-    "\n",
-    "\n",
-    "The following is an example $M=10$ coreset constructed from a 1000-point data set and loaded into a pandas data frame. See the image below where the coreset is represented by the black stars, the size of which corresponds to the weights.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "          X         Y     weights Name\n",
-      "0  7.028364  1.669787  234.230716    A\n",
-      "1  7.167441  0.354792  101.319288    B\n",
-      "2  1.022889 -0.921443  125.158339    C\n",
-      "3  2.706134 -2.636852   13.650774    D\n",
-      "4  6.998497  0.455847  116.758239    E\n",
-      "5  7.507918  0.630311  120.727176    F\n",
-      "6 -2.102508  2.297727   53.294127    G\n",
-      "7  5.722463  1.400433   77.415840    H\n",
-      "8 -1.425868  2.341136   42.847985    I\n",
-      "9  7.985373 -0.063209  240.116237    J\n"
-     ]
-    }
-   ],
-   "source": [
-    "raw_data = Coreset.create_dataset(1000)\n",
-    "coreset = Coreset(\n",
-    "    raw_data=raw_data,\n",
-    "    number_of_sampling_for_centroids=10,\n",
-    "    coreset_size=10,\n",
-    "    number_of_coresets_to_evaluate=4,\n",
-    "    coreset_method=\"BFL2\",\n",
-    ")\n",
-    "\n",
-    "coreset_vectors, coreset_weights = coreset.get_best_coresets()\n",
-    "\n",
-    "coreset_df = pd.DataFrame({\n",
-    "    \"X\": coreset_vectors[:, 0],\n",
-    "    \"Y\": coreset_vectors[:, 1],\n",
-    "    \"weights\": coreset_weights\n",
-    "})\n",
-    "coreset_df[\"Name\"] = [chr(i + 65) for i in coreset_df.index]\n",
-    "print(coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(raw_data[:, 0], raw_data[:, 1], label=\"Raw Data\", c=\"#7eba00\")\n",
-    "plt.scatter(\n",
-    "    coreset_df[\"X\"],\n",
-    "    coreset_df[\"Y\"],\n",
-    "    s=coreset_df[\"weights\"],\n",
-    "    label=\"Coreset\",\n",
-    "    color=\"black\",\n",
-    "    marker=\"*\",\n",
-    ")\n",
-    "plt.xlabel(\"X\")\n",
-    "plt.ylabel(\"Y\")\n",
-    "plt.title(\"Raw data and its best 10 coreset using BFL2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to cluster data on a quantum computer, the task needs to be cast into the form of a binary optimization problem.  Each qubit represents a coreset point, and the quantum algorithm determines how to bipartition the coreset points at each iteration of the divisive clustering routine. \n",
-    "\n",
-    "The first step is to convert coreset points into a fully connected graph. The edge weight is calculated by:\n",
-    "\n",
-    "$e_{ij} = w_iw_jd_{ij}$ where $d_{ij}$ is the Euclidean distance between points $i$ and $j$. \n",
-    "\n",
-    "This process is handled by `Coreset.coreset_to_graph()`. The function returns a fully connected graph $G$ with edge weights."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The divisive clustering problem will be implemented on a quantum computer using a variational quantum algorithm (VQA) approach.  A VQA takes a Hamiltonian (encoded with the optimization problem) and a parameterized ansatz and evaluates expectation values (quantum computer) that inform updates to the ansatz parameters (classical computer).  The graph $G$ (Code in the \"src\" file) is used to construct the Hamiltonian, derived specifically for the divisive clustering problem, and motivated by a max-cut Hamiltonian.  The `spin.z(i)` method in CUDA-Q adds a Pauli Z operation that acts on qubit $i$ to the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_K2_Hamiltonian(G: nx.Graph) -> cudaq.SpinOperator:\n",
-    "    \"\"\"Returns the K2 Hamiltonian for the given graph G\n",
-    "\n",
-    "    Args:\n",
-    "        G (nx.Graph): Weighted graph\n",
-    "    \"\"\"\n",
-    "    H = 0\n",
-    "\n",
-    "    for i, j in G.edges():\n",
-    "        weight = G[i][j][\"weight\"]\n",
-    "        H += weight * (spin.z(i) * spin.z(j))\n",
-    "\n",
-    "    return H"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below constructs a quantum kernel, defining the circuit which will serve as an ansatz.  The structure of the circuit is a hardware efficient ansatz consisting of layers of parameterized $R_Z$ and $R_Y$ gate acting on each qubit, followed by a linear cascade of CNOT gates, and two more rotation gates.\n",
-    "\n",
-    "The `@cudaq.kernel` decorator allows us to define a quantum circuit in the new kernel mode syntax which provides performance benefits to JIT compilation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_VQE_circuit(number_of_qubits: int, circuit_depth: int) -> cudaq.Kernel:\n",
-    "    \"\"\"Returns the VQE circuit for the given number of qubits and circuit depth\n",
-    "\n",
-    "    Args:\n",
-    "        number_of_qubits (int): Number of qubits\n",
-    "        circuit_depth (int): Circuit depth\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq.Kernel: VQE Circuit\n",
-    "    \"\"\"\n",
-    "\n",
-    "    @cudaq.kernel\n",
-    "    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):\n",
-    "        \"\"\"VQE Circuit\n",
-    "\n",
-    "        Args:\n",
-    "            thetas (list[float]): List of parameters\n",
-    "            number_of_qubits (int): Number of qubits\n",
-    "            circuit_depth (int): Circuit depth\n",
-    "        \"\"\"\n",
-    "        qubits = cudaq.qvector(number_of_qubits)\n",
-    "\n",
-    "        theta_position = 0\n",
-    "\n",
-    "        for i in range(circuit_depth):\n",
-    "            for j in range(number_of_qubits):\n",
-    "                ry(thetas[theta_position], qubits[j])\n",
-    "                rz(thetas[theta_position + 1], qubits[j])\n",
-    "\n",
-    "                theta_position += 2\n",
-    "\n",
-    "            for j in range(number_of_qubits - 1):\n",
-    "                cx(qubits[j], qubits[j + 1])\n",
-    "\n",
-    "            for j in range(number_of_qubits):\n",
-    "                ry(thetas[theta_position], qubits[j])\n",
-    "                rz(thetas[theta_position + 1], qubits[j])\n",
-    "\n",
-    "                theta_position += 2\n",
-    "\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualize the circuit using the `cudaq.draw()` method. Below, we are drawing the circuit for 5 qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭────────────╮ ╭────────────╮      ╭────────────╮╭────────────╮»\n",
-      "q0 : ┤ ry(0.8904) ├─┤ rz(0.7335) ├───●──┤ ry(0.4343) ├┤ rz(0.2236) ├»\n",
-      "     ├────────────┤ ├────────────┤ ╭─┴─╮╰────────────╯├────────────┤»\n",
-      "q1 : ┤ ry(0.7937) ├─┤ rz(0.9981) ├─┤ x ├──────●───────┤ ry(0.3945) ├»\n",
-      "     ├───────────┬╯ ├────────────┤ ╰───╯    ╭─┴─╮     ╰────────────╯»\n",
-      "q2 : ┤ ry(0.696) ├──┤ rz(0.3352) ├──────────┤ x ├───────────●───────»\n",
-      "     ├───────────┴╮╭┴────────────┤          ╰───╯         ╭─┴─╮     »\n",
-      "q3 : ┤ ry(0.6658) ├┤ rz(0.05277) ├────────────────────────┤ x ├─────»\n",
-      "     ├───────────┬╯├─────────────┴╮                       ╰───╯     »\n",
-      "q4 : ┤ ry(0.791) ├─┤ rz(0.003569) ├─────────────────────────────────»\n",
-      "     ╰───────────╯ ╰──────────────╯                                 »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                                             \n",
-      "─────────────────────────────────────────────\n",
-      "╭────────────╮                               \n",
-      "┤ rz(0.4119) ├───────────────────────────────\n",
-      "├────────────┤╭────────────╮                 \n",
-      "┤ ry(0.3205) ├┤ rz(0.3504) ├─────────────────\n",
-      "╰────────────╯├────────────┤ ╭────────────╮  \n",
-      "──────●───────┤ ry(0.3913) ├─┤ rz(0.7392) ├──\n",
-      "    ╭─┴─╮     ├────────────┤╭┴────────────┴─╮\n",
-      "────┤ x ├─────┤ ry(0.3171) ├┤ rz(0.0008056) ├\n",
-      "    ╰───╯     ╰────────────╯╰───────────────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "parameter_count = 4 * circuit_depth * 5\n",
-    "parameters = np.random.rand(parameter_count)\n",
-    "\n",
-    "circuit = get_VQE_circuit(5, circuit_depth)\n",
-    "print(cudaq.draw(circuit, parameters, 5, circuit_depth))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next step is to select a classical optimizer. There are multiple [optimizers](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#optimizers) built-in to CUDA-Q that can be selected. The code below returns the optimizer with the proper number of initial parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,\n",
-    "                  **kwargs) -> Tuple[cudaq.optimizers.optimizer, int]:\n",
-    "    \"\"\"Returns the optimizer with the given parameters\n",
-    "\n",
-    "    Args:\n",
-    "        optimizer (cudaq.optimizers.optimizer): Optimizer\n",
-    "        max_iterations (int): Maximum number of iterations\n",
-    "        **kwargs: Additional arguments\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count\n",
-    "    \"\"\"\n",
-    "    parameter_count = 4 * kwargs[\"circuit_depth\"] * kwargs[\"qubits\"]\n",
-    "    initial_params = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,\n",
-    "                                       parameter_count)\n",
-    "    optimizer.initial_parameters = initial_params\n",
-    "\n",
-    "    optimizer.max_iterations = max_iterations\n",
-    "    return optimizer, parameter_count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Divisive Clustering Function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `DivisiveClusteringVQA` class enables the procedure to iteratively bipartition the coreset points until each is its own cluster. If you wish to develop on top of this or see how the underlying code works, please see the `divisive_clustering.py` file in the src directory. \n",
-    "\n",
-    "`run_divisive_clustering`, takes the current iteration's coreset points that will be bipartitioned as inputs, extracts the appropriate weights, and builds a graph $G$. The graph is then an input into the `get_counts_from_simulation` function. \n",
-    "\n",
-    "\n",
-    "`get_counts_from_simulation` handles preparation and execution of the quantum simulation. First, it takes $G$ and from it builds a spin Hamiltonian. Second, it defines a cost function, which in this case is a lambda function that returns the expectation value of our parameterized quantum circuit and the Hamiltonian. This value is obtained using the CUDA-Q `observe` command, accelerated by GPUs.  After the expectation value is minimized, the quantum circuit corresponding to the optimal parameters is sampled using the CUDA-Q `sample` function. The bitstrings and their associated counts are returned by `get_counts_from_simulation`.\n",
-    "\n",
-    "A subset of these counts is evaluated to compute their exact cost. The best bitstring is returned and later used to assign the coreset points to one of two clusters.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class DivisiveClusteringVQA(DivisiveClustering):\n",
-    "\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        **kwargs,\n",
-    "    ):\n",
-    "        super().__init__(**kwargs)\n",
-    "\n",
-    "    def run_divisive_clustering(\n",
-    "        self,\n",
-    "        coreset_vectors_df_for_iteration: pd.DataFrame,\n",
-    "    ):\n",
-    "        \"\"\"Runs the Divisive Clustering algorithm\n",
-    "\n",
-    "        Args:\n",
-    "            coreset_vectors_df_for_iteration (pd.DataFrame): Coreset vectors for the iteration\n",
-    "\n",
-    "        Returns:\n",
-    "            str: Best bitstring\n",
-    "\n",
-    "        \"\"\"\n",
-    "        coreset_vectors_for_iteration_np, coreset_weights_for_iteration_np = (\n",
-    "            self._get_iteration_coreset_vectors_and_weights(\n",
-    "                coreset_vectors_df_for_iteration))\n",
-    "\n",
-    "        G = Coreset.coreset_to_graph(\n",
-    "            coreset_vectors_for_iteration_np,\n",
-    "            coreset_weights_for_iteration_np,\n",
-    "            metric=self.coreset_to_graph_metric,\n",
-    "        )\n",
-    "\n",
-    "        counts = self.get_counts_from_simulation(\n",
-    "            G,\n",
-    "            self.circuit_depth,\n",
-    "            self.max_iterations,\n",
-    "            self.max_shots,\n",
-    "        )\n",
-    "\n",
-    "        return self._get_best_bitstring(counts, G)\n",
-    "\n",
-    "    def get_counts_from_simulation(self, G: nx.graph, circuit_depth: int,\n",
-    "                                   max_iterations: int,\n",
-    "                                   max_shots: int) -> cudaq.SampleResult:\n",
-    "        \"\"\"\n",
-    "        Runs the VQA simulation\n",
-    "\n",
-    "        Args:\n",
-    "            G (nx.graph): Graph\n",
-    "            circuit_depth (int): Circuit depth\n",
-    "            max_iterations (int): Maximum number of iterations\n",
-    "            max_shots (int): Maximum number of shots\n",
-    "\n",
-    "        Returns:\n",
-    "            cudaq.SampleResult: Measurement from the experiment\n",
-    "        \"\"\"\n",
-    "\n",
-    "        qubits = len(G.nodes)\n",
-    "        Hamiltonian = self.create_Hamiltonian(G)\n",
-    "        optimizer, parameter_count = self.optimizer_function(\n",
-    "            self.optimizer,\n",
-    "            max_iterations,\n",
-    "            qubits=qubits,\n",
-    "            circuit_depth=circuit_depth)\n",
-    "\n",
-    "        kernel = self.create_circuit(qubits, circuit_depth)\n",
-    "\n",
-    "        def objective_function(\n",
-    "            parameter_vector: list[float],\n",
-    "            hamiltonian: cudaq.SpinOperator = Hamiltonian,\n",
-    "            kernel: cudaq.Kernel = kernel,\n",
-    "        ) -> float:\n",
-    "            \"\"\"\n",
-    "\n",
-    "            Objective function that returns the cost of the simulation\n",
-    "\n",
-    "            Args:\n",
-    "                parameter_vector (List[float]):\n",
-    "                hamiltonian (cudaq.SpinOperator): Circuit parameter values as a vector\n",
-    "                kernel (cudaq.Kernel) : Circuit configuration\n",
-    "\n",
-    "            Returns:\n",
-    "                float: Expectation value of the circuit\n",
-    "\n",
-    "            \"\"\"\n",
-    "\n",
-    "            get_result = lambda parameter_vector: cudaq.observe(\n",
-    "                kernel, hamiltonian, parameter_vector, qubits, circuit_depth\n",
-    "            ).expectation()\n",
-    "\n",
-    "            cost = get_result(parameter_vector)\n",
-    "\n",
-    "            return cost\n",
-    "\n",
-    "        energy, optimal_parameters = optimizer.optimize(\n",
-    "            dimensions=parameter_count, function=objective_function)\n",
-    "\n",
-    "        counts = cudaq.sample(kernel,\n",
-    "                              optimal_parameters,\n",
-    "                              qubits,\n",
-    "                              circuit_depth,\n",
-    "                              shots_count=max_shots)\n",
-    "\n",
-    "        return counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An instance of the `DivisiveClusteringVQA` class is mostly constructed from variables previously discussed like the functions for building the Hamiltonians and quantum circuits. Parameters related to the quantum simulation can also be specified here such as `circuit_depth` and `max_shots`.  The ` threshold_for_max_cut` parameter specifies what percent of the sample results from the quantum computer are checked for the best bitstring value.\n",
-    "\n",
-    "The other options specify advanced features like if the data is normalized and how the graph weights are computed.\n",
-    "\n",
-    "\n",
-    "Finally, the `get_divisive_sequence` method performs the iterations and produces the clustering data which we will analyze below. Note that this postprocessing code is not exposed in this tutorial but can be found in the source code. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 129/129 [00:00<00:00, 12075.19it/s]\n",
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-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15827.56it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 13617.87it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "divisive_clustering = DivisiveClusteringVQA(\n",
-    "    circuit_depth=circuit_depth,\n",
-    "    max_iterations=max_iterations,\n",
-    "    max_shots=max_shots,\n",
-    "    threshold_for_max_cut=0.75,\n",
-    "    create_Hamiltonian=get_K2_Hamiltonian,\n",
-    "    optimizer=optimizer,\n",
-    "    optimizer_function=get_optimizer,\n",
-    "    create_circuit=get_VQE_circuit,\n",
-    "    normalize_vectors=True,\n",
-    "    sort_by_descending=True,\n",
-    "    coreset_to_graph_metric=\"dist\",\n",
-    ")\n",
-    "\n",
-    "hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(\n",
-    "    coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data can be nicely visualized with a Dendrogram which maps where the bipartitionings occurred. Early splits generally mark divisions between the least similar data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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99ddHq1atol69ejFy5MgqB5FHbPw9qI33Zks0b9482rVrF++8806NHlfdQerVqe49LC8vj+985ztxxRVXVPuYffbZJyK++sb4zTffjKeffjqmTZsW06ZNi/vvvz+GDh0aDzzwQI3WVVMbe33rR2rFcg8//HC88sor8fjjj8fTTz8d5513Xtx8883xyiuvRNOmTTf5PNvj46Bhw4YxaNCguOeee2LhwoXxt7/9LWbPnh033nhjpeUOOOCAiIh46623YuDAgdWu66233oqIiC5dumyz+YCdk2gBtovf/e53ERG5QKn4JqVhw4bb7CeqHTt2jIivflq+/jdBn332WW5rzOau44MPPqi0jtWrV8e8efNys1YsN2fOnOjTp09uubVr18b8+fM3+6Dshx9+OPr06RP33ntvpduXLFkSu+6662atY0ut/1qPP/74Svd98MEHuftrasCAAXH33XfHyy+/HEcfffTXzlBeXh6zZ8+O/fffP3f7woULY8mSJZs1w1577RXLly/frI+jwsLCOPnkk+Pkk0+O8vLyuOiii+Kuu+6Kq666Krp27brZ69rcyKpQscVjyZIllU4bveGWpApHHXVUHHXUUfGP//iPMWnSpBgyZEhMnjw5zj///Bo974Yq3u958+ZV2iI5Z86cGq1nyJAh8dvf/jYeeuihmDdvXhQUFMTgwYMrLdOzZ89o2bJlTJo0KX75y19WG1T/9m//FhERZ5xxxha8GqAucUwLUOumT58e119/fXTu3Dm3z3vbtm2jd+/ecdddd8Unn3xS5TEbnsp4c/Tt2zcaNmwYt99+e6WfLFecPWpz11FYWBi33XZbpXXce++9UVJSkjvLUo8ePaJ169Zxzz33VDpuZOLEiZsdSBFf/WR8w5+CT506tcrxPLWhR48e0bZt2/jtb39b6fTC06ZNi/fee2+zzyi1oSuuuCKaNGkS559/fixcuLDK/XPnzs2dZvikk06KiKr/RrfccktExGbNMGjQoHj55Zfj6aefrnLfkiVLcv8+ixcvrnRfvXr1cnFZ8fo3d12NGzfO3bY5Ks62tv4xMStWrMht4anw5ZdfVvl4OOSQQyrNuDUqfmhw5513Vrr99ttvr9F6evbsGZ06dYoHH3wwHnrooejVq1eVM+Y1btw4rrjiivjggw/il7/8ZZV1PPnkkzFhwoQ4+eST46CDDqrhKwHqGltagG1q2rRp8f7778fatWtj4cKFMX369HjmmWeiY8eO8dhjj1U64PuOO+6IY489Ng466KAYPnx4dOnSJRYuXBgvv/xyfPTRR1V+V8nXadOmTYwaNSrGjh0bAwYMiJNOOineeOONmDZt2mZvtWjTpk2MHj06xowZE9/97nfjlFNOiQ8++CDuvPPOOPzww3O/8K6wsDCuvfbauOSSS+L444+PQYMGxfz582PChAmx1157bfZP4gcMGBDXXXddDBs2LI455ph4++23Y+LEidtld5mGDRvGDTfcEMOGDYtevXrF4MGDc6c87tSpU/zsZz/bovXutddeMWnSpDjzzDNj//33j6FDh0a3bt1i9erV8dJLL8XUqVNzx0J07949zjnnnLj77rtjyZIl0atXr3j11VfjgQceiIEDB1bairUxl19+eTz22GMxYMCAOPfcc+Owww6LFStWxNtvvx0PP/xwzJ8/P3bdddc4//zz44svvojjjz8+2rdvHx9++GHcfvvtccghh+S28mzuuoqLi+OAAw6Ihx56KPbZZ59o1apVdOvWLbp161btjCeccELsueee8aMf/Sguv/zyqF+/ftx3333Rpk2b+Nvf/pZb7oEHHog777wzTj311Nhrr71i2bJlcc8990Tz5s1zgbc1DjvssPj+978ft956ayxevDh3yuO//vWvEbH5W5AKCgrirLPOil//+tcREXHddddVu9wVV1wRb775Ztxwww3x8ssvx/e///0oLi6OF154IR588ME48MADY8KECVUeV1JSUuV3OlXwSyehjsrbecuAnUrFqVsrLoWFhdluu+2Wfec738nGjRuXLV26tNrHzZ07Nxs6dGi22267ZQ0bNsz22GOPbMCAAdnDDz9cZd0bnga24jTA//Vf/5W7bd26ddmYMWOy3XffPSsuLs569+6dvfPOO1nHjh2rPeXxxk4t+y//8i/ZfvvtlzVs2DD7xje+kV144YXZl19+WWW52267LevYsWNWVFSUHXHEEdmLL76YHXbYYdl3v/vdKnNOnTq1yuNXrVqV/fznP8/N27Nnz+zll1/OevXqVe1pkzdcx8ZeR8UpcD/77LNqX9/6HnrooezQQw/NioqKslatWmVDhgzJPvroo816nk3561//mg0fPjzr1KlTVlhYmDVr1izr2bNndvvtt1c6zfWaNWuyMWPGZJ07d84aNmyYdejQIRs9enSlZbLsq1Me9+/fv9rnWrZsWTZ69Oisa9euWWFhYbbrrrtmxxxzTPZP//RPuVNEP/zww9kJJ5yQtW3bNissLMz23HPP7IILLsg++eSTGq8ry7LspZdeyg477LCssLBws04XPHPmzOzII4/MPfctt9xS5ZTHs2bNygYPHpztueeeWVFRUda2bdtswIAB2euvv15pXRs+38b+vTdcf5Zl2YoVK7IRI0ZkrVq1ypo2bZoNHDgw++CDD7KIyH7zm99s8jWs7913380iIisqKqr2/0aF8vLybMKECVnPnj2zZs2a5T5H9O3bNysrK6uy/KZOeezbFqi7CrJsOx+lCbATKy8vjzZt2sRpp50W99xzT77Hgc3y5ptvxqGHHhoPPvhgpdMW14Y1a9bEySefHM8991w8/vjjlc7eBrAxjmkB2EKrVq2qcvzBv/3bv8UXX3wRvXv3zs9Q8DVKS0ur3HbrrbdGvXr14lvf+latP3/Dhg3j97//fRxyyCFxxhlnVPv7iAA2ZEsLwBaaMWNG/OxnP4szzjgjWrduHbNmzYp777039t9//5g5c2YUFhbme0SoYsyYMTFz5szo06dPNGjQIHf65x//+Mdx11135Xs8gGqJFoAtNH/+/PjpT38ar776anzxxRfRqlWrOOmkk+I3v/lNlV+WB6l45plnYsyYMfGXv/wlli9fHnvuuWf88Ic/jF/+8pfRoIHz8wBpEi0AAEDSHNMCAAAkTbQAAABJ2+47r5aXl8ff//73aNas2Wb/EisAAGDnk2VZLFu2LNq1axf16m18e8p2j5a///3v0aFDh+39tAAAQKIWLFgQ7du33+j92z1amjVrFhFfDda8efPt/fQAAEAili5dGh06dMg1wsZs92ip2CWsefPmogUAAPjaw0YciA8AACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQtAb5HgDyKcuyKF2zLt9jAMB2U9ywfhQUFOR7DKgR0UKdlWVZnP7bl2Pmh1/mexQA2G56dNwlpv7kaOHCDsXuYdRZpWvWCRYA6pzXP/zSXgbscGxpgYh4/R/6RuPC+vkeAwBqzcrV66LHr57N9xiwRUQLRETjwvrRuNB/BwCAFNk9DAAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJJWo2i59tpro6CgoNJlv/32q63ZAAAAokFNH3DggQfGs88++38raFDjVQAAAGy2GhdHgwYNYrfddquNWQAAAKqo8TEts2fPjnbt2kWXLl1iyJAh8be//a025gIAAIiIGm5pOfLII2PChAmx7777xieffBJjxoyJ4447Lt55551o1qxZtY8pKyuLsrKy3PWlS5du3cQAAECdUqNo6devX+7vBx98cBx55JHRsWPHmDJlSvzoRz+q9jFjx46NMWPGbN2UAABAnbVVpzxu2bJl7LPPPjFnzpyNLjN69OgoKSnJXRYsWLA1TwkAANQxWxUty5cvj7lz58buu+++0WWKioqiefPmlS4AAACbq0bRMmrUqHj++edj/vz58dJLL8Wpp54a9evXj8GDB9fWfAAAQB1Xo2NaPvrooxg8eHAsXrw42rRpE8cee2y88sor0aZNm9qaDwAAqONqFC2TJ0+urTkAAACqtVXHtAAAANQ20QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkLQG+R6Ar5dlWZSuWZfvMXY6K1evrfbvbDvFDetHQUFBvscAAHZwoiVxWZbF6b99OWZ++GW+R9mp9fjVc/keYafUo+MuMfUnRwsXAGCr2D0scaVr1gkWdlivf/ilrYQAwFazpWUH8vo/9I3GhfXzPQZ8rZWr10WPXz2b7zEAgJ2EaNmBNC6sH40L/ZMBAFC32D0MAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACStlXR8pvf/CYKCgpi5MiR22gcAACAyrY4Wl577bW466674uCDD96W8wAAAFSyRdGyfPnyGDJkSNxzzz2xyy67bOuZAAAAcrYoWkaMGBH9+/ePvn37but5AAAAKmlQ0wdMnjw5Zs2aFa+99tpmLV9WVhZlZWW560uXLq3pUwIAAHVYjba0LFiwIC699NKYOHFiNGrUaLMeM3bs2GjRokXu0qFDhy0aFAAAqJtqFC0zZ86MRYsWxTe/+c1o0KBBNGjQIJ5//vm47bbbokGDBrFu3boqjxk9enSUlJTkLgsWLNhmwwMAADu/Gu0e9u1vfzvefvvtSrcNGzYs9ttvv/h//+//Rf369as8pqioKIqKirZuSgAAoM6qUbQ0a9YsunXrVum2Jk2aROvWravcDgAAsC1s1S+XBAAAqG01PnvYhmbMmLENxgAAAKieLS0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJq1G0jB8/Pg4++OBo3rx5NG/ePI4++uiYNm1abc0GAABQs2hp3759/OY3v4mZM2fG66+/Hscff3x873vfi3fffbe25gMAAOq4BjVZ+OSTT650/R//8R9j/Pjx8corr8SBBx64TQcDAACIqGG0rG/dunUxderUWLFiRRx99NHbciYAAICcGkfL22+/HUcffXSsWrUqmjZtGo8++mgccMABG12+rKwsysrKcteXLl26ZZMCAAB1Uo3PHrbvvvvGm2++GX/+85/jwgsvjHPOOSf+8pe/bHT5sWPHRosWLXKXDh06bNXAAABA3VLjaCksLIyuXbvGYYcdFmPHjo3u3bvHuHHjNrr86NGjo6SkJHdZsGDBVg0MAADULVt8TEuF8vLySrt/baioqCiKioq29mkAAIA6qkbRMnr06OjXr1/sueeesWzZspg0aVLMmDEjnn766dqaDwAAqONqFC2LFi2KoUOHxieffBItWrSIgw8+OJ5++un4zne+U1vzAQAAdVyNouXee++trTkAAACqVeMD8QEAALYn0QIAACRtq88eBtRMlmVRurY032PUqpVr1q3399KIgvp5nGb7KG5QHAUFBfkeAwB2SqIFtqMsy2LotKHx5mdv5nuUWpWVN4yI6yMioveUXlFQb01+B9oODm17aDzw3QeECwDUAtEC21Hp2tKdPlgiIgrqrYlm+1+Z7zG2qzcWvRGla0ujccPG+R4FAHY6ogXyZMagGVHcoDjfY7CVSteWRu8pvfM9BgDs1EQL5Elxg2I/lQcA2AzOHgYAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASatRtIwdOzYOP/zwaNasWbRt2zYGDhwYH3zwQW3NBgAAULNoef7552PEiBHxyiuvxDPPPBNr1qyJE044IVasWFFb8wEAAHVcg5os/NRTT1W6PmHChGjbtm3MnDkzvvWtb23TwQAAACJqGC0bKikpiYiIVq1abXSZsrKyKCsry11funTp1jwlAABQx2zxgfjl5eUxcuTI6NmzZ3Tr1m2jy40dOzZatGiRu3To0GFLnxIAAKiDtjhaRowYEe+8805Mnjx5k8uNHj06SkpKcpcFCxZs6VMCAAB10BbtHnbxxRfHE088EX/605+iffv2m1y2qKgoioqKtmg4AACAGkVLlmVxySWXxKOPPhozZsyIzp0719ZcAAAAEVHDaBkxYkRMmjQp/uM//iOaNWsWn376aUREtGjRIoqLi2tlQAAAoG6r0TEt48ePj5KSkujdu3fsvvvuuctDDz1UW/MBAAB1XI13DwPIhyzLonRtab7HqGL9mVKcr7hBcRQUFOR7DADYKlv1e1oAtocsy2LotKHx5mdv5nuUTeo9pXe+R6ji0LaHxgPffUC4ALBD2+JTHgNsL6VrS5MPllS9seiNJLcAAUBN2NIC7FBmDJoRxQ2c+OPrlK4tTXLLDwBsCdEC7FCKGxRH44aN8z0GALAd2T0MAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACS1iDfAyQjyyLWrMz3FFWtXrfe31dGRP28jbJRDRtHFBTkewoAAHZSoiXiq2C578SIBX/O9yRVZUURcf9Xf7+pa0RBWV7HqVaHoyLOe0q4AABQK0RLxFdbWFIMlohoXFAW8xudle8xNm3BK1+9h4VN8j0JAAA7IdGyoVFzIgob53uKHcPqlRH/1DXfUwAAsJMTLRsqbGyLAQAAJMTZwwAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABImmgBAACSJloAAICkiRYAACBpogUAAEiaaAEAAJImWgAAgKSJFgAAIGmiBQAASJpoAQAAkiZaAACApIkWAAAgaaIFAABIWoN8DwAAsL4syyIrLc33GDud8tXr/u/vK0ujfG39PE6zcyooLo6CgoJ8j7FTEi0AQDKyLIsPzxoSpW+8ke9Rdjqr6hdGnPzriIiY3fPYaLRudZ4n2vkUf/Ob0XHig8KlFogWACAZWWmpYKkljdatjml/GJXvMXZqpbNmRVZaGgWNG+d7lJ2OaAEAkrT3iy9EveLifI8BX6u8tDRm9zw232Ps1EQLAJCkesXFUc9PrIFw9jAAACBxNY6WP/3pT3HyySdHu3btoqCgIP7whz/UwlgAAABfqXG0rFixIrp37x533HFHbcwDAABQSY2PaenXr1/069evNmYBAACootYPxC8rK4uysrLc9aVLl9b2UwIAADuRWj8Qf+zYsdGiRYvcpUOHDrX9lAAAwE6k1qNl9OjRUVJSkrssWLCgtp8SAADYidT67mFFRUVRVFRU208DAADspPyeFgAAIGk13tKyfPnymDNnTu76vHnz4s0334xWrVrFnnvuuU2HAwAAqHG0vP7669GnT5/c9csuuywiIs4555yYMGHCNhsMAAAgYguipXfv3pFlWW3MAgAAUIVjWgAAgKTV+tnDAABgW8iyLLLS0nyPUUX5ejOVJzhfRERBcXEUFBTke4wtJloAAEhelmXx4VlDovSNN/I9yibN7nlsvkeoVvE3vxkdJz64w4aL3cMAAEheVlqafLCkrHTWrCS3Um0uW1oAANih7P3iC1GvuDjfY+wQyktLk936UxOiBQCAHUq94uKo17hxvsdgO7J7GAAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJEy0AAEDSRAsAAJA00QIAACRNtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASWuQ7wEAgM2TZVlkpaX5HqNWla/3+sp38tcaEVFQXBwFBQX5HgOSJ1oAYAeQZVl8eNaQKH3jjXyPst3M7nlsvkeodcXf/GZ0nPigcIGvYfcwANgBZKWldSpY6orSWbN2+q1nsC3Y0gIAO5i9X3wh6hUX53sMtkJ5aWmd2JIE24poAYAdTL3i4qjXuHG+xwDYbuweBgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJc8pjAFhPlmVJ/rK/8vVmKk9wvoLiYr/VHag1ogUA/leWZfHhWUOS/83zKf5SwuJvfjM6TnxQuAC1wu5hAPC/stLS5IMlVaWzZiW5hQrYOdjSAgDV2PvFF6JecXG+x0heeWlpklt+gJ3LFkXLHXfcETfddFN8+umn0b1797j99tvjiCOO2NazAUDe1CsujnqNG+d7DABiC3YPe+ihh+Kyyy6La665JmbNmhXdu3ePE088MRYtWlQb8wEAAHVcjaPllltuieHDh8ewYcPigAMOiN/+9rfRuHHjuO+++2pjPgAAoI6r0e5hq1evjpkzZ8bo0aNzt9WrVy/69u0bL7/8crWPKSsri7Kystz1kpKSiIhYunTplsxbO1aviCjLvvr70qURhevyO8+OwvtWYyvXrIx1pV+9T0uXLo21DdfmeaIdg/et5rxnW6Z85cpYvu7/3rd6a71vX8d7tmW8bzXnPdsyqb9vFU2QZdmmF8xq4OOPP84iInvppZcq3X755ZdnRxxxRLWPueaaa7KIcHFxcXFxcXFxcXFxqfayYMGCTXZIrZ89bPTo0XHZZZflrpeXl8cXX3wRrVu3di53AACow7Isi2XLlkW7du02uVyNomXXXXeN+vXrx8KFCyvdvnDhwthtt92qfUxRUVEUFRVVuq1ly5Y1eVoAAGAn1aJFi69dpkYH4hcWFsZhhx0Wzz33XO628vLyeO655+Loo4+u+YQAAABfo8a7h1122WVxzjnnRI8ePeKII46IW2+9NVasWBHDhg2rjfkAAIA6rsbRcuaZZ8Znn30WV199dXz66adxyCGHxFNPPRXf+MY3amM+AACgjivIvvb8YgAAAPlT418uCQAAsD2JFgAAIGmiBQAASJpoAQAAkiZa/tedd94ZBQUFceSRR+Z7lORNmDAhCgoKKl3atm0bffr0iWnTpuV7vKTNnTs3LrjggujSpUs0atQomjdvHj179oxx48ZFaWlpvsdLSsXH2euvv17t/b17945u3bpt56l2PF/3PvJ/qvvcVnG58sor8z1e0nyc1cy8efPi4osvjn322ScaN24cjRs3jgMOOCBGjBgRb731Vr7HS86G/zcbNWoU7dq1ixNPPDFuu+22WLZsWb5HTNKmPqcVFBTEK6+8ku8Ra6TGpzzeWU2cODE6deoUr776asyZMye6du2a75GSd91110Xnzp0jy7JYuHBhTJgwIU466aR4/PHHY8CAAfkeLzlPPvlknHHGGVFUVBRDhw6Nbt26xerVq+OFF16Iyy+/PN599924++678z0m1HkVn9vWJ5DZVp544ok488wzo0GDBjFkyJDo3r171KtXL95///145JFHYvz48TFv3rzo2LFjvkdNTsX/zTVr1sSnn34aM2bMiJEjR8Ytt9wSjz32WBx88MH5HjFJ1X1Oi4gd7ntd0RJf/cTjpZdeikceeSQuuOCCmDhxYlxzzTX5Hit5/fr1ix49euSu/+hHP4pvfOMb8e///u+iZQPz5s2LH/zgB9GxY8eYPn167L777rn7RowYEXPmzIknn3wyjxMCFTb83Abbyty5c3NfC5577rlKXwsiIm644Ya48847o149O8JUZ8P/m6NHj47p06fHgAED4pRTTon33nsviouL8zhhmnaWz2n+V8RXW1l22WWX6N+/f5x++ukxceLEfI+0Q2rZsmUUFxdHgwZaeEM33nhjLF++PO69994qX6Qivvppx6WXXpqHyQDYXm688cZYsWJF3H///dV+LWjQoEH89Kc/jQ4dOuRhuh3T8ccfH1dddVV8+OGH8eCDD+Z7HGqRaImvouW0006LwsLCGDx4cMyePTtee+21fI+VvJKSkvj888/js88+i3fffTcuvPDCWL58eZx99tn5Hi05jz/+eHTp0iWOOeaYfI+yw6n4ONvwsmbNmnyPxk6quo852BaeeOKJ6Nq1q+Nnt7Ef/vCHERHxxz/+Mc+TpKm6z2mLFy/O91g1Vud/JD5z5sx4//334/bbb4+IiGOPPTbat28fEydOjMMPPzzP06Wtb9++la4XFRXFfffdF9/5znfyNFGali5dGh9//HF873vfy/coO6QNP87Wd+CBB27HSagrqvuYy7IsD5OwM1m6dGn8/e9/j4EDB1a5b8mSJbF27drc9SZNmtjNqQbat28fLVq0iLlz5+Z7lCRV9zmtqKgoVq1alYdptlydj5aJEyfGN77xjejTp09ERBQUFMSZZ54ZDz74YNx8881Rv379PE+YrjvuuCP22WefiIhYuHBhPPjgg3H++edHs2bN4rTTTsvzdOlYunRpREQ0a9Ysz5PsmNb/OFvfz3/+81i3bl0eJmJnt7GPOdgaFV8LmjZtWuW+3r17x//8z//krt90000xatSo7TbbzqBp06bOIrYR1X1O2xG/v63T0bJu3bqYPHly9OnTJ+bNm5e7/cgjj4ybb745nnvuuTjhhBPyOGHajjjiiEoHdg0ePDgOPfTQuPjii2PAgAFRWFiYx+nS0bx584gIn0y30IYfZxV22WUXu+1QKzb2MQdbo+IHV8uXL69y31133RXLli2LhQsX2sV6Cy1fvjzatm2b7zGStLN8TqvT0TJ9+vT45JNPYvLkyTF58uQq90+cOFG01EC9evWiT58+MW7cuJg9e7Zdd/5X8+bNo127dvHOO+/kexQA8qRFixax++67V/u1oOIYl/nz52/nqXYOH330UZSUlOxwp/ClZur0gfgTJ06Mtm3bxtSpU6tcBg8eHI8++qhf+FdDFfvkVveTpLpswIABMXfu3Hj55ZfzPQoAedK/f/+YM2dOvPrqq/keZafyu9/9LiIiTjzxxDxPQm2qs9FSWloajzzySAwYMCBOP/30KpeLL744li1bFo899li+R91hrFmzJv74xz9GYWFh7L///vkeJylXXHFFNGnSJM4///xYuHBhlfvnzp0b48aNy8NkAGwvV1xxRTRu3DjOO++8ar8WOOFDzU2fPj2uv/766Ny5cwwZMiTf41CL6uzuYY899lgsW7YsTjnllGrvP+qoo6JNmzYxceLEOPPMM7fzdDuGadOmxfvvvx8REYsWLYpJkybF7Nmz48orr8wdx8FX9tprr5g0aVKceeaZsf/++8fQoUOjW7dusXr16njppZdi6tSpce655+Z7TABq0d577x2TJk2KwYMHx7777htDhgyJ7t27R5ZlMW/evJg0aVLUq1cv2rdvn+9Rk1TxfcfatWtj4cKFMX369HjmmWeiY8eO8dhjj0WjRo3yPWKS1v9+bX3HHHNMdOnSJQ8TbZk6Gy0TJ06MRo0abfT0vPXq1Yv+/fvHxIkTY/HixdG6devtPGH6rr766tzfGzVqFPvtt1+MHz8+LrjggjxOla5TTjkl3nrrrbjpppviP/7jP2L8+PFRVFQUBx98cNx8880xfPjwfI8IsEUqthDsiGck2t6+973vxdtvvx0333xz/PGPf4z77rsvCgoKomPHjtG/f//4yU9+Et27d8/3mEmq+L6jsLAwWrVqFQcddFDceuutMWzYMGfo3IT1v19b3/33379DRUtBZlskALAVbrvttrj00ktjzpw5sddee+V7HGAnVGePaQEAto3XXnstmjRpEh07dsz3KMBOqs7uHgYAbJ3f//73MWPGjJg4cWKcf/750aCBbyuA2mH3MABgi3Tu3DmWLVsWp556atx6663RpEmTfI8E7KRECwAAkDTHtAAAAEkTLQAAQNJECwAAkDTRAgAAJE20AAAASRMtAABA0kQLAACQNNECAAAkTbQAAABJ+/9NVsdwRnDnBwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dendo = Dendrogram(coreset_df, hierarchial_clustering_sequence)\n",
-    "dendo.plot_dendrogram(plot_title=\"Dendrogram of Coreset using VQE\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each branch point in the dendrogram aboves corresponds to one of the plots below. Notice the first iterations are the most complicated, and the final iterations become trivial bipartitioning of two points. Occasionally, especially in the first iteration, the partitioning might be puzzling at first glance. The data might seem to naturally cluster into two groups. However, there are cases where a stray point seems to belong in the wrong cluster.  There are two explanations for this.  1) The quantum sampling is approximate and stochastic. It is possible that too few shots were taken to sample the ground state of the problem. 2) It is important to remember that we are clustering coresets and not data points. There can be cases where it is optimal to pay a penalty by excluding a point based on proximity if the weights are small enough that the penalty has less impact. Usually, if a point looks unusually clustered and you go look at the original coresets plotted above, that point will have a small weight."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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f//rXuvrqqxUZGalPP/1UM2fO1KFDh/TWW285x/1hQUqS83V942ZmZurJJ5+81OkAAAAAAAC0el4tSs2aNUvPPfdcvTEHDhy45PEnT57s/He/fv3UuXNnDR06VF988YWuueaaSx43PT1daWlpztd2u11RUVGXPB4AAAAAAEBr49Wi1IwZMzRhwoR6Y7p37y6r1apTp065tFdWVurs2bN13i+qNnFxcZKkw4cP65prrpHValV+fr5LTHFxsSTVO67ZbJbZbG7w+wIAAAAAAMCVV4tSYWFhCgsLu2hcfHy8SkpKVFBQoNjYWEnS1q1bVV1d7Sw0NURhYaEkqXPnzs5xn376aZ06dcp5eWBOTo4sFov69OnTyNkAAAAAAACgoXziRue9e/dWUlKSUlJSlJ+fr48++kipqakaO3as88l7J06cUK9evZxnPn3xxReaN2+eCgoKdPToUW3atEnJycm6+eab1b9/f0nSsGHD1KdPH/3mN7/RP/7xD73zzjt6/PHHNWXKFM6EAoAWZPv27br99tsVGRkpk8mkjRs31hv/1ltv6Re/+IXCwsJksVgUHx+vd955xzPJAgAAAK2ETxSlpO+eoterVy8NHTpUI0aM0E033aSlS5c611dUVOjQoUPOp+sFBgbqvffe07Bhw9SrVy/NmDFDo0eP1ttvv+3s4+/vr82bN8vf31/x8fG65557lJycrLlz53p8fgAA9ykvL1d0dLSWLFnSoPjt27frF7/4hbZs2aKCggLdeuutuv322/XJJ5+4OVMAAACg9TAZhmF4OwlfZ7fbFRwcrNLSUlksFm+nAwCN0tr2YSaTSRs2bNCoUaMa1e+6667TmDFjlJGR0aD41rZdAbQ87Mfcg+0KwNc15X7Mq/eUAgDAF1RXV+vcuXMKDQ2tM8bhcMjhcDhf2+12T6QGAAAA+CyfuXwPAABvWbBggcrKyvSrX/2qzpjMzEwFBwc7l6ioKA9mCAAAAPgeilIAANRj7dq1evLJJ/XGG284n9Ram/T0dJWWljqX48ePezBLAAAAwPdw+R4AAHVYt26dfvvb32r9+vVKSEioN9ZsNvPkVgAAAKAROFMKAIBa/PnPf9bEiRP15z//WSNHjvR2OgAAAECLQ1EKANDilZWVqbCwUIWFhZKkI0eOqLCwUMeOHZP03aV3ycnJzvi1a9cqOTlZL7zwguLi4mSz2WSz2VRaWuqN9AEAl+Ds2bMaN26cLBaLQkJCNGnSJJWVldUbP3XqVPXs2VNt27ZVly5dNG3atBr7fpPJVGNZt26du6cDAC0SRSkAaGEmTJjg/CW5TZs2ioiI0C9+8QutWLFC1dXV3k7PK3bv3q2YmBjFxMRIktLS0hQTE6OMjAxJUlFRkbNAJUlLly5VZWWlpkyZos6dOzuX6dOnezTvCRMmaNSoUR59TwBoKcaNG6f9+/crJydHmzdv1vbt2zV58uQ640+ePKmTJ09qwYIF2rdvn1atWqXs7GxNmjSpRuzKlStVVFTkXNhXA/BlP/z74YfL4cOH3f7e3FMKAFqgpKQkrVy5UlVVVSouLlZ2dramT5+uN998U5s2bVJAQOva/Q8ZMkSGYdS5ftWqVS6vt23b5t6EAABudeDAAWVnZ2vXrl0aOHCgJGnx4sUaMWKEFixYoMjIyBp9+vbtq7/85S/O19dcc42efvpp3XPPPaqsrHQ5doaEhMhqtbp/IgDgId///fBDYWFhbn9fzpQCgBbIbDbLarXqqquu0vXXX6/HHntMf/3rX/X3v/+9RgEGAICWJi8vTyEhIc6ClCQlJCTIz89PO3fubPA4paWlslgsNb7MmTJlijp16qRBgwZpxYoV9X7x4XA4ZLfbXRYAaG6+//vhh4u/v7/b35eiFAC0Erfddpuio6P11ltveTsVAADcymazKTw83KUtICBAoaGhstlsDRrj9OnTmjdvXo1L/ubOnas33nhDOTk5Gj16tB544AEtXry4znEyMzMVHBzsXKKioho/IQBooShKAUAr0qtXLx09etTbaQAAcElmzZpV631PfrgcPHjwst/Hbrdr5MiR6tOnj+bMmeOy7oknntDgwYMVExOjmTNn6tFHH9X8+fPrHCs9PV2lpaXO5fjx45edHwA0tc2bN6t9+/bO5a677vLI+7aum4oAQAt1ZO9X2r/jn/LzM+nf5/5dZ5xhGDKZTB7MDI1hVJ+THNskwy75Xy2p7stBAKA1mjFjhiZMmFBvTPfu3WW1WnXq1CmX9srKSp09e/ai94I6d+6ckpKS1KFDB23YsEFt2rSpNz4uLk7z5s2Tw+GQ2Wyusd5sNtfaDgDeYhiGDuz8XF98ckRtggL17XmHbr31Vr3yyivOmCuuuMIjuVCUAgAfdur4aT3z6z9o/0f/+VZ4v7FLHaxX6Ny/ytShY3uX+AMHDqhbt26eThMXYRjVMspeksqXSXJIMkkyZHxbKlX/1MvZAUDzERYW1qAb78bHx6ukpEQFBQWKjY2VJG3dulXV1dWKi4urs5/dbldiYqLMZrM2bdqkoKCgi75XYWGhOnbsSOEJgE84su+YMsf9QUf2Hvv+V059pt3qGGVR1FVRMrf17L6My/cAwEed+1eZ0m7O0IGd/6yx7l/FJZr5i3mquFDhbNu6dav27t2r0aNHezJNNIBR9qJU/pK+K0hJzjOkjG9lXCiUcWGXlzIDAN/Uu3dvJSUlKSUlRfn5+froo4+UmpqqsWPHOp+8d+LECfXq1Uv5+fmSvitIDRs2TOXl5Vq+fLnsdrtsNptsNpuqqqokSW+//bb+9Kc/ad++fTp8+LBeeeUVPfPMM5o6darX5goADWU7ekppN2foq8++/q7h+185DUOnjp3WU2N+X++DG9yBM6UAwEf9bel7OnX8tIzqmgeOaqNK+wo+04Y//U09buii7OxsZWZm6pe//KWSk5O9kC3qYlSd+d8zpGpd+93/nlso05V/9lxSANACrFmzRqmpqRo6dKj8/Pw0evRoLVq0yLm+oqJChw4d0vnz5yVJe/bscT6Zr0ePHi5jHTlyRF27dlWbNm20ZMkSPfTQQzIMQz169NDChQuVkpLiuYkBwCV6Y/4mnS/7t6qrqmtd//HmAn2W909dd2NPj+VEUQoAfNS7q96vtSAlSWdUrP/RZn2UukVXdrpS0dHRWrRokcaPHy8/P06SbVa+/buk2n8x+I4hVRTIqCqSyb+zp7ICAJ8XGhqqtWvX1rm+a9euLmcEDBky5KJnCCQlJSkpKanJcgQATzEMQzmvblN1Zd2/d/oH+Ou9//cBRSkAwMWVfGOvtf060890nX4mSbpmQFdl7an7iUDwPqP6rCR/SZU11q38ww9uxlt9VqIoBQAAgEtQWVGpb8sdta67zvTd3w7VVdUqPV373xjuwtflAOCjwrt0Un0P0vPz91PnbuGeSwiX5Luzn2oWpH4UJflFeCIdAAAAtEABbQLUIbR9vTF+/iaFR3XyUEb/+54efTcAQJMZmZKg+i4yqK6q1vDfJngsH1yioOGS6nvKib9kHiKTv2d/QQAAAEDLYTKZNDIlQX7+dZeBqiqrlXjvbR7MiqIUAPisYROG6KfXd6/1wGLyM+mGX8ZqYGK0FzJDY5j82stkSa9jrZ9kMsvU/mGP5gQAAICW579n3K6wqCvrLEz91wOJ6ta3i0dzoigFAD7K3Nas59/L0C+Sb1FAG///tLcz678f+qUy3pzBTc19hKndr2UKfl7y+9E9o9rEyhT6ukxtrvVOYgAAAGgxgjtZ9IePntaNd/xMJr//3AekfccrdO/Tv9aURfd6PCeTcbFHTOCi7Ha7goODVVpaKovF4u10ALRC9jPn9PmeL+Xn76deg3qobfu2De/LPswtLmW7Gka1VLFXMuySfxeZAq52c5YAUDeOD+7BdgXQHJw+eVZH9x1XYFAb9Yq7VoHmNg3u25T7MZ6+BwAtgOXKDor9BZfq+TqTyU8K5P9HAAAAuFenyFB1igz1dhpcvgcAAAAAAADPoygFAAAAAAAAj6MoBQAAAAAAAI+jKAUAAAAAAACPoygFAAAAAAAAj6MoBQAAAAAAAI+jKAUAAAAAAACPC/B2Ai2BYRiSJLvd7uVMAKDxvt93fb8vQ9Pg2ADA13F8cA+ODwB8XVMeHyhKNYFz585JkqKiorycCQBcunPnzik4ONjbabQYHBsAtBQcH5oWxwcALUVTHB9MBl99XLbq6mqdPHlSHTp00Llz5xQVFaXjx4/LYrF4OzWfYLfb2WaNxDZrPLZZ3QzD0Llz5xQZGSk/P67qbio/PDaYTCaPvjef98ZhezUe26zxfHGbcXxwD28eH2rji5/Ny8F8W77WNmdvzLcpjw+cKdUE/Pz89JOf/ESSnAcWi8XSKn4AmhLbrPHYZo3HNqsd34A3vR8eG7yFz3vjsL0aj23WeL62zTg+NL3mcHyoja99Ni8X8235WtucPT3fpjo+8JUHAAAAAAAAPI6iFAAAAAAAADyOolQTM5vNmj17tsxms7dT8Rlss8ZjmzUe2wytCZ/3xmF7NR7brPHYZmiuWttnk/m2fK1tzr4+X250DgAAAAAAAI/jTCkAAAAAAAB4HEUpAAAAAAAAeBxFKQAAAAAAAHgcRakmtmTJEnXt2lVBQUGKi4tTfn6+t1NqtjIzM/Wzn/1MHTp0UHh4uEaNGqVDhw55Oy2f8eyzz8pkMunBBx/0dirN3okTJ3TPPffoyiuvVNu2bdWvXz/t3r3b22kBl+xSPtNr1qxRdHS02rVrp86dO+vee+/VmTNnPJSx93Tt2lUmk6nGMmXKlDr7rF+/Xr169VJQUJD69eunLVu2eDBj72vsNlu2bJl+/vOfq2PHjurYsaMSEhJa3e8/l/I5+966detkMpk0atQo9yeKVuns2bMaN26cLBaLQkJCNGnSJJWVldUbP3XqVPXs2VNt27ZVly5dNG3aNJWWlrrE1faZX7dunbun0yDumvOxY8c0cuRItWvXTuHh4XrkkUdUWVnp7ul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-      "text/plain": [
-       "<Figure size 1200x1200 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Dendrogram.plot_hierarchial_split(hierarchial_clustering_sequence, coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The hierarchical clustering can be converted to flat clustering by drawing a line perpendicular to the branches. Any data point that intersects the line is considered to be in the same cluster. The function below performs this task at threshold height of 1.5. If you want to use the number of clusters instead of height, you can use `dendo.get_clusters_using_k()` method. You pass the number of desired clusters as an argument. The figure below shows the clusters that are formed at threshold height of 1.5."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "threshold_height = 1\n",
-    "clusters = dendo.get_clusters_using_height(threshold_height)\n",
-    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\"]\n",
-    "dendo.plot_dendrogram(\n",
-    "    plot_title=\"Dendrogram of Coreset using VQE\",\n",
-    "    colors=colors,\n",
-    "    clusters=clusters,\n",
-    "    color_threshold=threshold_height,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can visualize the flat clusters using `dendo.plot_clusters()` method. The function takes the clusters and colors as arguments. The clusters are represented by different colors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gGRkZ9n4LFixg5syZ/O53v6NevXqcPn2aefPm4efnx0MPPQTkh4XGjRvz8ccf07BhQ2rUqEHTpk1p2rQpM2bMoEOHDjRr1oyBAwcSERHBsWPH2Lp1K4cPH+a///3vVd/n6dOnCQsLo3fv3vZHCX/++ed8/fXX1xwtnDBhAmvWrKFDhw688MILVKpUiTlz5pCbm8vf//73kn/Yl6lcuTIJCQl06dKFe++9l8cff5z27dtTuXJldu3axcKFCwkICGDixIlUrlyZSZMm8fTTT9OpUyeefPJJ+1JndevWZfjw4de8XqdOnfjjH/9IfHw8KSkpPPDAA1SuXJl9+/bxySefMG3aNHr37l2i76mk5wKIiopi1qxZTJgwgfr16xMUFMR9991XZI3+/v489thjTJ8+HcMwqFevHitWrCg0h/iHH37g/vvv5/HHH6dx48ZUqlSJZcuWcezYMX7/+99f1/dxOS8vL8aOHcuQIUO47777ePzxx0lPT2f+/PnUq1evVKPZffv25f3332fVqlX06dOnyIeNdOjQgX/+858MGzaM5s2bM2DAAEJCQtizZw/z5s3Dw8OD5cuXF7l82ZIlS4qcMtS1a9cyW1ZORMTOpWtNiJSxH374wRw4cKBZt25d08vLy/T19TXbt29vTp8+3WF5sSuXOjNN09yxY4fZpk0b08vLy6xdu7b5j3/8o9BSZ8nJyeaTTz5p1q5d2/5QhEceecT85ptvHM715ZdfmlFRUaaXl1ehZc/2799v9uvXz6xZs6ZZuXJlMzQ01HzkkUfMJUuW2PsUXPfKpbpyc3PNP/3pT2aLFi3sD6po0aKFOXPmzBJ9PsnJyWa3bt3MatWqmVWrVjWjo6PNL7/80qFPaZY6K3Dy5EnzlVdeMZs1a2ZWrVrVrFKlitm0aVNz9OjRZmZmpkPfjz/+2GzZsqVpsVjMGjVqXPUhF8WZO3euGRUVZXp7e5u+vr5ms2bNzJdfftk8evSo/X2W5HsqyblM0zSzsrLMhx9+2PT19S3RMmHHjx83Y2NjzapVq5oBAQHmH//4R/P77793WOrs559/NuPi4sxGjRqZPj4+pr+/v9mmTRtz8eLFDucqbqmzTz75xKFfUQ/RME3TfOutt8w6deqYFovFbN26tfnFF1+YUVFRZkxMzFXfw+UuXrxohoSEmID52WefXbXv5s2bzR49epi33367aRiGCZhBQUGF/h6Y5tWXOqOI5eJERMqCYZplfHeEiIjctPLy8ggMDKRXr17MmzfP6dcbP348r7zyCn/961+ZMGGC068nInItmvYgIlJB/frrr1gsFocpDu+//z4nTpygc+fO5VLDmDFjOHr0KBMnTqR27do899xz5XJdEZHiaORXRKSC2rBhA8OHD+exxx7jtttuIzk5mXfffZfIyEh27Njh8JAMERF3oZFfEZEKqm7dulitVt566y1OnDhBjRo16NevH2+88YaCr4i4LY38ioiIiIjb0Dq/IiIiIuI2FH5FRERExG1UuDm/eXl5HD16FF9f31It4i4iIiKuY5omp0+fplatWnh4aGxOnKfChd+jR49itVpdXYaIiIhch0OHDhEWFubqMqQCq3Dh19fXF8j/j8fPz8/F1YiIiEhJ5OTkYLVa7T/HRZylwoXfgqkOfn5+Cr8iIiK3GE1ZFGfTpBoRERERcRsKvyIiIiLiNhR+RURERMRtVLg5vyIiIiLXyzRNLl68iM1mc3UpUgqVK1fG09OzRH0VfkVEbjJbt26lQ4cOxMTEkJiY6OpyRNzG+fPnyczM5Ny5c64uRUrJMAzCwsKoVq3aNfsq/IqI3GTeffddhgwZwrvvvsvRo0epVauWq0sSqfDy8vI4cOAAnp6e1KpVCy8vL608cYswTZPjx49z+PBhGjRocM0RYIVfEZGbyJkzZ/j444/55ptvyMrKYv78+fzlL39xdVkiFd758+fJy8vDarVStWpVV5cjpRQYGEh6ejoXLly4ZvjVDW8iIjeRxYsX06hRI+644w769u3Lv/71L0zTdHVZIm5Dj1a+NZVmlF4jvyIirmSzwebNkJkJISG8+8479O3bF4CYmBiys7PZuHEjnTt3dm2dIiIVhP55IyLiKgkJULcuREfDH/7A3uhotm/dypOXnk5ZqVIlnnjiCd59913X1ikiUoEo/IqIuEJCAvTuDYcP25veBS4Ctf7v/6jk6UmlSpWYNWsWS5cuJTs722WlisitzzAMli9f7uoybgoKvyIi5c1mg6FD4bK5vBeB94EpQAqQEhhIyo4d/Pe//6VWrVosWrTINbWKyE0vKyuLIUOGEBERgcViwWq10r17d9auXeuU623YsAHDMDh16pRTzg9w4sQJ+vTpg5+fH9WrV+eZZ57hzJkzZXJuhV8RkfK2ebPDiC/ACuAk8AzQFGh67BhNT56kadOmxMbGauqDyK3CZoMNG2DRovw/nfywjPT0dKKioli3bh2TJ09m586dJCUlER0dTVxcnFOvfaMKHihSlD59+rBr1y7WrFnDihUr2LRpE88991yZXFfhV0SkvGVmFmp6F+gC+BfRLzY2lm+++YbvvvuuPKoTket1xTx+oqPzXyckOO2SL7zwAoZhsH37dmJjY2nYsCFNmjRhxIgRbNu2rchjihq5TUlJwTAM0tPTATh48CDdu3cnICAAHx8fmjRpwmeffUZ6ejrR0dEABAQEYBgGAwYMAPLXSo6Pjyc8PBxvb29atGjBkiVLCl135cqVREVFYbFY2LJlS6H6UlNTSUpK4p133qFNmzZ06NCB6dOn89FHH3H06NEb/sycGn5nzZpF8+bN8fPzw8/Pj7Zt27Jy5cqrHvPJJ5/QqFEjqlSpQrNmzfjss8+cWaKISPkLCSnU9B+g0LPcLvVr3bo1pmnSvHlzp5cmItepiHn8ABw5kt/uhAB84sQJkpKSiIuLw8fHp9D+6tWrX/e54+LiyM3NZdOmTezcuZNJkyZRrVo1rFYrS5cuBWDv3r1kZmYybdo0AOLj43n//feZPXs2u3btYvjw4fTt25eNGzc6nHvUqFG88cYbpKamFvn/a1u3bqV69eq0atXK3talSxc8PDz46quvrvs9FXDqUmdhYWG88cYbNGjQANM0WbBgAT169ODbb7+lSZMmhfp/+eWXPPnkk8THx/PII4+wcOFCevbsSXJyMk2bNnVmqSIi5adjRwgLy/+hWNQavoaRv79jx/KvTURKr4h5/Hammf/f9LBh0KMHXOMBDKWRlpaGaZo0atSozM5ZICMjg9jYWJo1awZARESEfV+NGjUACAoKsgfs3NxcXn/9dT7//HPatm1rP2bLli3MmTOHTp062Y9/7bXX6Nq1a7HXzsrKIigoyKGtUqVK1KhRg6ysrBt+b04d+e3evTsPPfQQDRo0oGHDhkycOJFq1aoVOww/bdo0YmJi+NOf/kRkZCTjx4/nrrvu4u2333ZmmSIi5cvTEy6NlHDlwuwFr6dOLdMfkiLiREXM43dgmnDoUH6/MuTMB+C8+OKLTJgwgfbt2/Pqq69ec9pVWloa586do2vXrlSrVs2+vf/+++zfv9+h7+Ujuq5QbnN+bTYbH330EWfPnrX/i+BKW7dupUuXLg5t3bp1Y+vWrcWeNzc3l5ycHIdNROSm16sXLFkCoaGO7WFh+e29ermmLhEpvSLm8d9QvxJq0KABhmGwZ8+eUh1X8BS7y8PzhQsXHPo8++yz/Pjjjzz11FPs3LmTVq1aMX369GLPWbASQ2JiIikpKfZt9+7dDvN+gSKnaFyuZs2a/PTTTw5tFy9e5MSJE9SsWfPab/AanB5+d+7cSbVq1bBYLAwaNIhly5bRuHHjIvtmZWURHBzs0BYcHHzVIe74+Hj8/f3tm9VqLdP6RUScplcvSE+H9eth4cL8Pw8cUPAVudUUMY//hvqVUI0aNejWrRszZszg7NmzhfYXtxRZYGAgAJmXhfGUlJRC/axWK4MGDSIhIYGRI0cyb948ALy8vID8gc0CjRs3xmKxkJGRQf369R220maztm3bcurUKXbs2GFvW7duHXl5ebRp06ZU5yqK08PvHXfcQUpKCl999RXPP/88/fv3Z/fu3WV2/tGjR5OdnW3fDh06VGbnFhFxOk9P6NwZnnwy/09NdRC59RTM479yGlMBwwCr1Snz+GfMmIHNZqN169YsXbqUffv2kZqayltvvVXsb9oLAunYsWPZt28fiYmJTJkyxaHPsGHDWLVqFQcOHCA5OZn169cTGRkJQJ06dTAMgxUrVnD8+HHOnDmDr68vL730EsOHD2fBggXs37+f5ORkpk+fzoIFC0r1niIjI4mJiWHgwIFs376dL774gsGDB/P73/+eWrVqXd8HdRmnh18vLy/q169PVFQU8fHxtGjRwn5X4JVq1qzJsWPHHNqOHTt21SFui8ViX02iYBMREREpNy6cxx8REUFycjLR0dGMHDmSpk2b0rVrV9auXcusWbOKPKZy5cosWrSIPXv20Lx5cyZNmsSECRMc+thsNuLi4uxBtGHDhsycOROA0NBQxo0bx6hRowgODmbw4MEAjB8/njFjxhAfH28/LjExkfDw8FK/rw8//JBGjRpx//3389BDD9GhQwfmzp1b6vMUxTCdOVu6CPfddx+1a9dm/vz5hfY98cQTnDt3jv/85z/2tnbt2tG8eXNmz55dovPn5OTg7+9Pdna2grCIiMgtwtU/v3/99VcOHDhAeHg4VapUub6TJCTkr/pw+c1vVmt+8NV0Jqcqzffn1KXORo8ezYMPPkjt2rU5ffo0CxcuZMOGDaxatQqAfv36ERoaSnx8PABDhw6lU6dOTJkyhYcffpiPPvqIb775psySvoiIiIjT9OqVv5zZ5s35N7eFhORPddB0ppuKU8PvTz/9RL9+/cjMzMTf35/mzZuzatUq+9puGRkZ9jsOIX+Ud+HChfztb3/jL3/5Cw0aNGD58uVa41dERERuDQXz+OWmVe7THpzN1b82ERERkdJz9c/vMpn2IC5Tmu+v3Nb5FRERERFxNYVfEREREXEbCr8iIiIi4jYUfkVERETEbSj8ioiIiIjbUPgVEREREbeh8CsiIiJSwRmGwfLly11dxk1B4VdERESkjNhssGEDLFqU/6fN5vxrZmVlMWTIECIiIrBYLFitVrp3787atWudcr0NGzZgGAanTp1yyvkBJk6cSLt27ahatSrVq1cv03M79QlvIiIiIu4iIQGGDoXDh39rCwuDadPyn3zsDOnp6bRv357q1aszefJkmjVrxoULF1i1ahVxcXHs2bPHORcuA6ZpYrPZqFSpcBw9f/48jz32GG3btuXdd98t0+tq5FdERETkBiUkQO/ejsEX4MiR/PaEBOdc94UXXsAwDLZv305sbCwNGzakSZMmjBgxgm3bthV5TFEjtykpKRiGQXp6OgAHDx6ke/fuBAQE4OPjQ5MmTfjss89IT08nOjoagICAAAzDYMCAAQDk5eURHx9PeHg43t7etGjRgiVLlhS67sqVK4mKisJisbBly5Yiaxw3bhzDhw+nWbNmN/4hXUEjvyIiIiI3wGbLH/E1zcL7TBMMA4YNgx49wNOz7K574sQJkpKSmDhxIj4+PoX238h0gbi4OM6fP8+mTZvw8fFh9+7dVKtWDavVytKlS4mNjWXv3r34+fnh7e0NQHx8PB988AGzZ8+mQYMGbNq0ib59+xIYGEinTp3s5x41ahRvvvkmERERBAQEXHeN10vhV0REROQGbN5ceMT3cqYJhw7l9+vcueyum5aWhmmaNGrUqOxOeklGRgaxsbH2kdeIiAj7vho1agAQFBRkD9i5ubm8/vrrfP7557Rt29Z+zJYtW5gzZ45D+H3ttdfo2rVrmddcUgq/IiIiIjcgM7Ns+5WUWdRQcxl58cUXef7551m9ejVdunQhNjaW5s2bF9s/LS2Nc+fOFQq158+fp2XLlg5trVq1ckrNJaXwKyIiInIDQkLKtl9JNWjQAMMwSn1Tm4dH/i1fl4fnCxcuOPR59tln6datG4mJiaxevZr4+HimTJnCkCFDijznmTNnAEhMTCQ0NNRhn8VicXhd1BSN8qQb3kRERERuQMeO+as6GEbR+w0DrNb8fmWpRo0adOvWjRkzZnD27NlC+4tbiiwwMBCAzMuGolNSUgr1s1qtDBo0iISEBEaOHMm8efMA8PLyAsB22TpujRs3xmKxkJGRQf369R02q9V6vW/RKRR+RURERG6Ap2f+cmZQOAAXvJ46tWxvdiswY8YMbDYbrVu3ZunSpezbt4/U1FTeeust+9zbKxUE0rFjx7Jv3z4SExOZMmWKQ59hw4axatUqDhw4QHJyMuvXrycyMhKAOnXqYBgGK1as4Pjx45w5cwZfX19eeuklhg8fzoIFC9i/fz/JyclMnz6dBQsWlPp9ZWRkkJKSQkZGBjabjZSUFFJSUuwjzDfErGCys7NNwMzOznZ1KSIiIlJCrv75/csvv5i7d+82f/nll+s+x9KlphkWZpr5t7jlb1ZrfrszHT161IyLizPr1Kljenl5maGhoeajjz5qrl+/3t4HMJctW2Z/vWXLFrNZs2ZmlSpVzI4dO5qffPKJCZgHDhwwTdM0Bw8ebNarV8+0WCxmYGCg+dRTT5k///yz/fjXXnvNrFmzpmkYhtm/f3/TNE0zLy/PnDp1qnnHHXeYlStXNgMDA81u3bqZGzduNE3TNNevX28C5smTJ6/5nvr3728ChbbL39PlSvP9GZc+kAojJycHf39/srOz8fPzc3U5IiIiUgKu/vn966+/cuDAAcLDw6lSpcp1n8dmy1/VITMzf45vx47OGfEVR6X5/nTDm4iIiEgZ8fQs2+XMpOxpzq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4VdERERE3IbCr4iIiEgFZxgGy5cvd3UZNwWFXxEREZEyYsuzsSF9A4t2LmJD+gZseTanXzMrK4shQ4YQERGBxWLBarXSvXt31q5d65TrbdiwAcMwOHXqlFPOn56ezjPPPEN4eDje3t7Uq1ePV199lfPnz5fJ+fV4YxEREZEykJCawNCkoRzOOWxvC/MLY1rMNHpF9nLKNdPT02nfvj3Vq1dn8uTJNGvWjAsXLrBq1Sri4uLYs2ePU65bFkzTxGazUamSYxzds2cPeXl5zJkzh/r16/P9998zcOBAzp49y5tvvnnD19XIr4iIiMgNSkhNoPfi3g7BF+BIzhF6L+5NQmqCU677wgsvYBgG27dvJzY2loYNG9KkSRNGjBjBtm3bijymqJHblJQUDMMgPT0dgIMHD9K9e3cCAgLw8fGhSZMmfPbZZ6SnpxMdHQ1AQEAAhmEwYMAAAPLy8oiPj7eP2LZo0YIlS5YUuu7KlSuJiorCYrGwZcuWQvXFxMTw3nvv8cADDxAREcGjjz7KSy+9REJC2XyGGvkVERERuQG2PBtDk4ZiYhbaZ2JiYDAsaRg97uiBp4dnmV33xIkTJCUlMXHiRHx8fArtr169+nWfOy4ujvPnz7Np0yZ8fHzYvXs31apVw2q1snTpUmJjY9m7dy9+fn54e3sDEB8fzwcffMDs2bNp0KABmzZtom/fvgQGBtKpUyf7uUeNGsWbb75JREQEAQEBJaonOzubGjVqXPf7uZzCr4iIiMgN2JyxudCI7+VMTA7lHGJzxmY61+1cZtdNS0vDNE0aNWpUZucskJGRQWxsLM2aNQMgIiLCvq8ghAYFBdkDdm5uLq+//jqff/45bdu2tR+zZcsW5syZ4xB+X3vtNbp27VriWtLS0pg+fXqZTHkAhV8RERGRG5J5OrNM+5WUaRYeaS4rL774Is8//zyrV6+mS5cuxMbG0rx582L7p6Wlce7cuUKh9vz587Rs2dKhrVWrViWu48iRI8TExPDYY48xcODA0r2JYjh1zm98fDx33303vr6+BAUF0bNnT/bu3XvVY+bPn49hGA5blSpVnFmmiIiIyHUL8Q0p034l1aBBAwzDKPVNbR4e+fHv8vB84cIFhz7PPvssP/74I0899RQ7d+6kVatWTJ8+vdhznjlzBoDExERSUlLs2+7dux3m/QJFTtEoytGjR4mOjqZdu3bMnTu3RMeUhFPD78aNG4mLi2Pbtm2sWbOGCxcu8MADD3D27NmrHufn50dmZqZ9O3jwoDPLFBEREbluHWt3JMwvDAOjyP0GBlY/Kx1rdyzT69aoUYNu3boxY8aMIrNVcUuRBQYGApCZ+dtIdEpKSqF+VquVQYMGkZCQwMiRI5k3bx4AXl5eANhsvy3j1rhxYywWCxkZGdSvX99hs1qtpX5vR44coXPnzkRFRfHee+/ZA3tZcOq0h6SkJIfX8+fPJygoiB07dnDvvfcWe5xhGNSsWdOZpYmIiIiUCU8PT6bFTKP34t4YGA43vhUE4qkxU8v0ZrcCM2bMoH379rRu3ZrXXnuN5s2bc/HiRdasWcOsWbNITU0tdExBIB07diwTJ07khx9+YMqUKQ59hg0bxoMPPkjDhg05efIk69evJzIyEoA6depgGAYrVqzgoYcewtvbG19fX1566SWGDx9OXl4eHTp0IDs7my+++AI/Pz/69+9f4vdUEHzr1KnDm2++yfHjx+37yiIflutSZ9nZ2QDXvFvvzJkz1KlTB6vVSo8ePdi1a1exfXNzc8nJyXHYRERERMpTr8heLHl8CaF+oQ7tYX5hLHl8idPW+Y2IiCA5OZno6GhGjhxJ06ZN6dq1K2vXrmXWrFlFHlO5cmUWLVrEnj17aN68OZMmTWLChAkOfWw2G3FxcURGRhITE0PDhg2ZOXMmAKGhoYwbN45Ro0YRHBzM4MGDARg/fjxjxowhPj7eflxiYiLh4eGlek9r1qwhLS2NtWvXEhYWRkhIiH0rC4bpzNnSl8nLy+PRRx/l1KlTRa7pVmDr1q3s27eP5s2bk52dzZtvvsmmTZvYtWsXYWFhhfqPHTuWcePGFWrPzs7Gz8+vTN+DiIiIOEdOTg7+/v4u+/n966+/cuDAAcLDw2/oXiNbno3NGZvJPJ1JiG8IHWt3dMqIrzgqzfdXbuH3+eefZ+XKlWzZsqXIEFucCxcuEBkZyZNPPsn48eML7c/NzSU3N9f+OicnB6vVqvArIiJyC6ko4VdcozTfX7ksdTZ48GBWrFjBpk2bShV8IX9ovmXLlqSlpRW532KxYLFYyqJMEREREangnDrn1zRNBg8ezLJly1i3bl2p53xA/pyTnTt3ltk8DxERERFxX04d+Y2Li2PhwoV8+umn+Pr6kpWVBYC/v7/9UXj9+vUjNDSU+Ph4IP+pH/fccw/169fn1KlTTJ48mYMHD/Lss886s1QRERERcQNODb8Fdxl27tzZof29995jwIABQP7j8y5fu+3kyZMMHDiQrKwsAgICiIqK4ssvv6Rx48bOLFVERERE3EC53fBWXlw9YV5ERERKz9U/v3XD262tNN9fua7zKyIiIiLiSgq/IiIiIuI2FH5FRERExG0o/IqIiIhUcIZhsHz5cleXcVNQ+BUREREpIzabjQ0bNrBo0SI2bNiAzWZz+jWzsrIYMmQIERERWCwWrFYr3bt3Z+3atU653oYNGzAMg1OnTjnl/ACPPvootWvXpkqVKoSEhPDUU09x9OjRMjm3wq+IiIhIGUhISKBu3bpER0fzhz/8gejoaOrWrUtCQoLTrpmenk5UVBTr1q1j8uTJ7Ny5k6SkJKKjo4mLi3PadcuCaZpcvHixyH3R0dEsXryYvXv3snTpUvbv30/v3r3L5LoKvyIiIiI3KCEhgd69e3P48GGH9iNHjtC7d2+nBeAXXngBwzDYvn07sbGxNGzYkCZNmjBixAi2bdtW5DFFjdympKRgGAbp6ekAHDx4kO7duxMQEICPjw9NmjThs88+Iz09nejoaAACAgIwDMP+7Ia8vDzi4+MJDw/H29ubFi1asGTJkkLXXblyJVFRUVgsFrZs2VJkjcOHD+eee+6hTp06tGvXjlGjRrFt2zYuXLhww5+ZUx9yISIiIlLR2Ww2hg4dSlGPTjBNE8MwGDZsGD169MDT07PMrnvixAmSkpKYOHEiPj4+hfZXr179us8dFxfH+fPn2bRpEz4+PuzevZtq1aphtVpZunQpsbGx7N27Fz8/P/tTe+Pj4/nggw+YPXs2DRo0YNOmTfTt25fAwEA6depkP/eoUaN48803iYiIICAgoETv88MPP6Rdu3ZUrlz5ut9TAYVfERERkRuwefPmQiO+lzNNk0OHDrF58+ZCT729EWlpaZimSaNGjcrsnAUyMjKIjY2lWbNmAERERNj31ahRA4CgoCB7wM7NzeX111/n888/p23btvZjtmzZwpw5cxzC72uvvUbXrl2vWcOf//xn3n77bc6dO8c999zDihUryuS9adqDiIiIyA3IzMws034l5cyH9L744otMmDCB9u3b8+qrr/Ldd99dtX9aWhrnzp2ja9euVKtWzb69//777N+/36Fvq1atSlTDn/70J7799ltWr16Np6cn/fr1K5P3rJFfERERkRsQEhJSpv1KqkGDBhiGwZ49e0p1nIdH/tjn5UHyyrm0zz77LN26dSMxMZHVq1cTHx/PlClTGDJkSJHnPHPmDACJiYmEhoY67LNYLA6vi5qiUZTbb7+d22+/nYYNGxIZGYnVamXbtm32keXrpZFfERERkRvQsWNHwsLCMAyjyP2GYWC1WunYsWOZXrdGjRp069aNGTNmcPbs2UL7i1uKLDAwEHAciU5JSSnUz2q1MmjQIBISEhg5ciTz5s0DwMvLC8BhGbfGjRtjsVjIyMigfv36DpvVar3et2iXl5cH5E+vuFEKvyIiIiI3wNPTk2nTpgEUCsAFr6dOnVqmN7sVmDFjBjabjdatW7N06VL27dtHamoqb731VrEjpAWBdOzYsezbt4/ExESmTJni0GfYsGGsWrWKAwcOkJyczPr164mMjASgTp06GIbBihUrOH78OGfOnMHX15eXXnqJ4cOHs2DBAvbv309ycjLTp09nwYIFpXpPX331FW+//TYpKSkcPHiQdevW8eSTT1KvXr0bHvUFhV8RERGRG9arVy+WLFlS6Ff+YWFhLFmyhF69ejnluhERESQnJxMdHc3IkSNp2rQpXbt2Ze3atcyaNavIYypXrsyiRYvYs2cPzZs3Z9KkSUyYMMGhj81mIy4ujsjISGJiYmjYsCEzZ84EIDQ0lHHjxjFq1CiCg4MZPHgwAOPHj2fMmDHEx8fbj0tMTCQ8PLxU76lq1aokJCRw//33c8cdd/DMM8/QvHlzNm7cWGgKxfUwTGfOlnaBnJwc/P39yc7Oxs/Pz9XliIiISAm4+uf3r7/+yoEDBwgPD6dKlSrXfR6bzcbmzZvJzMwkJCSEjh07OmXEVxyV5vvTDW8iIiIiZcTT07NMlzOTsqdpDyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIibGTBgAD179izUvmHDBgzDKPaxuCIVgcKviIiIiLgNhV8RERGRCs4wDJYvX+7qMm4KCr8iIiIiZSTPlkf6hnR2LtpJ+oZ08mx5Tr9mVlYWQ4YMISIiAovFgtVqpXv37qxdu9Yp1yvP6TG5ubnceeedGIZBSkpKmZxTjzcWERFxA7Y8G5szNpN5OpOsM1l8vuJzqlWr5tjHZnNRdRVDakIqSUOTyDmcY2/zC/MjZloMkb0inXLN9PR02rdvT/Xq1Zk8eTLNmjXjwoULrFq1iri4OPbs2eOU65YF0zSx2WxUqlR8HH355ZepVasW//3vf8vsuhr5FRERqeASUhOoO60u0Qui+UPCH1iVtopK9SoxackkUlJS7Ns777zj6lJvWakJqSzuvdgh+ALkHMlhce/FpCakOuW6L7zwAoZhsH37dmJjY2nYsCFNmjRhxIgRbNu2rchjihq5TUlJwTAM0tPTATh48CDdu3cnICAAHx8fmjRpwmeffUZ6ejrR0dEABAQEYBgGAwYMACAvL4/4+HjCw8Px9vamRYsWLFmypNB1V65cSVRUFBaLhS1bthT73lauXMnq1at58803b+xDuoJGfkVERCqwhNQEei/ujYnp0J5r5DLkqyEsqbOEXpG9ADh8+LArSrzl5dnySBqaxBUfcT4TMCBpWBJ39LgDD8+yG3c8ceIESUlJTJw4ER8fn0L7q1evft3njouL4/z582zatAkfHx92795NtWrVsFqtLF26lNjYWPbu3Yufnx/e3t4AxMfH88EHHzB79mwaNGjApk2b6Nu3L4GBgXTq1Ml+7lGjRvHmm28SERFBQEBAkdc/duwYAwcOZPny5VStWvW630dRFH5FREQqKFuejaFJQwsF38sNSxpGjzt64OnhWY6VVSwZmzMKjfg6MCHnUA4ZmzOo27lumV03LS0N0zRp1KhRmZ2zQEZGBrGxsTRr1gyAiIgI+74aNWoAEBQUZA/Yubm5vP7663z++ee0bdvWfsyWLVuYM2eOQ/h97bXX6Nq1a7HXNk2TAQMGMGjQIFq1amUfjS4rCr8iIiIV1OaMzRzOKX4018TkUM4hNmdspnPdzuVXWAVzOvN0mfYrKdMs/h81N+rFF1/k+eefZ/Xq1XTp0oXY2FiaN29ebP+0tDTOnTtXKNSeP3+eli1bOrS1atXqqteePn06p0+fZvTo0df/Bq5Cc35FREQqqMzTmWXaT4rmG+Jbpv1KqkGDBhiGUeqb2jw88uPf5eH5woULDn2effZZfvzxR5566il27txJq1atmD59erHnPHPmDACJiYkO88h3797tMO8XKHKKxuXWrVvH1q1bsVgsVKpUifr16wP5obl///4lf6PFUPgVERGpoEJ8Q4re8TvgycL9OnfujGmaNzRX1B3V7lgbvzA/MIrpYICf1Y/aHWuX6XVr1KhBt27dmDFjBmfPni20v7ilyAIDAwHIzPztHz1FLSNmtVoZNGgQCQkJjBw5knnz5gHg5eUFOK4O0rhxYywWCxkZGdSvX99hs1qtpXpfb731Fv/973/tAfqzzz4D4OOPP2bixImlOldRFH5FREQqqI61OxLmF4ZRTCozMLD6WelYu2M5V1axeHh6EDMtJv/FlR/1pdcxU2PK9Ga3AjNmzMBms9G6dWuWLl3Kvn37SE1N5a233rLPvb1SQSAdO3Ys+/btIzExkSlTpjj0GTZsGKtWreLAgQMkJyezfv16IiPzl2urU6cOhmGwYsUKjh8/zpkzZ/D19eWll15i+PDhLFiwgP3795OcnMz06dNZsGBBqd5T7dq1adq0qX1r2LAhAPXq1SMsLOw6PiVHCr8iIiIVlKeHJ9NipgEUCsAFr6fGTNXNbmUgslckjy95HL9QP4d2vzA/Hl/yuNPW+Y2IiCA5OZno6GhGjhxJ06ZN6dq1K2vXrmXWrFlFHlO5cmUWLVrEnj17aN68OZMmTWLChAkOfWw2G3FxcURGRhITE0PDhg2ZOXMmAKGhoYwbN45Ro0YRHBzM4MGDARg/fjxjxowhPj7eflxiYiLh4eFOee/XyzCdOFs6Pj6ehIQE9uzZg7e3N+3atWPSpEnccccdVz3uk08+YcyYMaSnp9OgQQMmTZrEQw89VKJr5uTk4O/vT3Z2Nn5+ftc+QEREpIJLSE1gaNJQh5vfrH5WpsZMtS9z5mqu/vn966+/cuDAAcLDw6lSpcp1nyfPlkfG5gxOZ57GN8SX2h1rO2XEVxyV5vtz6moPGzduJC4ujrvvvpuLFy/yl7/8hQceeIDdu3cXO9n5yy+/5MknnyQ+Pp5HHnmEhQsX0rNnT5KTk2natKkzyxUREamQekX2oscdPexPeAvxDaFj7Y4a8XUCD0+PMl3OTMqeU0d+r3T8+HGCgoLYuHEj9957b5F9nnjiCc6ePcuKFSvsbffccw933nkns2fPvuY1XP0vRxERESk9V//8LquRX3GN0nx/5ToOn52dDfy2OHJRtm7dSpcuXRzaunXrxtatW4vsn5ubS05OjsMmIiIiIlKUcgu/eXl5DBs2jPbt2191+kJWVhbBwcEObcHBwWRlZRXZPz4+Hn9/f/tW2uU0RERERMR9lFv4jYuL4/vvv+ejjz4q0/OOHj2a7Oxs+3bo0KEyPb+IiIiIVBzl8njjwYMHs2LFCjZt2nTN9dlq1qzJsWPHHNqOHTtGzZo1i+xvsViwWCxlVquIiIiIVFxOHfk1TZPBgwezbNky1q1bV6J13tq2bcvatWsd2tasWVPsQs0iIiIiIiXl1JHfuLg4Fi5cyKeffoqvr6993q6/vz/e3t4A9OvXj9DQUOLj4wEYOnQonTp1YsqUKTz88MN89NFHfPPNN8ydO9eZpYqIiIiIG3DqyO+sWbPIzs6mc+fOhISE2LePP/7Y3icjI8Ph2dLt2rVj4cKFzJ07lxYtWrBkyRKWL1+uNX5FRERE5IY5deS3JEsIb9iwoVDbY489xmOPPeaEikRERETcj2EYLFu2jJ49e7q6FJfT8/ZEREREykqeDY5tgPRF+X/m2Zx+yaysLIYMGUJERAQWiwWr1Ur37t0L3UNVVjZs2IBhGJw6dcop5weoW7cuhmE4bG+88UaZnLtcVnsQERERqfAOJcCOoXDu8G9tVcMgahpYeznlkunp6bRv357q1aszefJkmjVrxoULF1i1ahVxcXHs2bPHKdctC6ZpYrPZqFSp6Dj62muvMXDgQPtrX1/fMrmuRn5FREREbtShBNjc2zH4Apw7kt9+KMEpl33hhRcwDIPt27cTGxtLw4YNadKkCSNGjGDbtm1FHlPUyG1KSgqGYZCeng7AwYMH6d69OwEBAfj4+NCkSRM+++wz0tPTiY6OBiAgIADDMBgwYACQ/0Cz+Ph4wsPD8fb2tt+7deV1V65cSVRUFBaLhS1bthT73nx9falZs6Z98/HxubEP6xKFXxEREZEbkWfLH/GlqHudLrXtGFbmUyBOnDhBUlIScXFxRQbD6tWrX/e54+LiyM3NZdOmTezcuZNJkyZRrVo1rFYrS5cuBWDv3r1kZmYybdo0IP+pu++//z6zZ89m165dDB8+nL59+7Jx40aHc48aNYo33niD1NRUmjdvXmwNb7zxBrfddhstW7Zk8uTJXLx48brfz+U07UFERETkRhzfXHjE14EJ5w7l9wvuXGaXTUtLwzRNGjVqVGbnLJCRkUFsbCzNmjUDICIiwr6vRo0aAAQFBdkDdm5uLq+//jqff/65/dkMERERbNmyhTlz5tCpUyf78a+99hpdu3a96vVffPFF7rrrLmrUqMGXX37J6NGjyczM5B//+McNvzeFXxEREZEb8UvmtfuUpl8JlWRVrev14osv8vzzz7N69Wq6dOlCbGzsVUdp09LSOHfuXKFQe/78eVq2bOnQ1qpVq2tef8SIEfb/3bx5c7y8vPjjH/9IfHz8DT/ZV9MeRERERG6Ed0jZ9iuhBg0aYBhGqW9q8/DIj3+Xh+cLFy449Hn22Wf58ccfeeqpp9i5cyetWrVi+vTpxZ7zzJkzACQmJpKSkmLfdu/e7TDvF7iuubtt2rTh4sWL9jnJN0LhV0RERORGBHbMX9UBo5gOBlS15vcrQzVq1KBbt27MmDGDs2fPFtpf3FJkgYGBAA4PGUtJSSnUz2q1MmjQIBISEhg5ciTz5s0DwMvLCwCb7bc5zI0bN8ZisZCRkUH9+vUdNqvVer1v0aE+Dw8PgoKCbvhcCr8iIiIiN8LDM385M6BwAL70Ompqfr8yNmPGDGw2G61bt2bp0qXs27eP1NRU3nrrLfvc2ysVBNKxY8eyb98+EhMTmTJlikOfYcOGsWrVKg4cOEBycjLr168nMjISgDp16mAYBitWrOD48eOcOXMGX19fXnrpJYYPH86CBQvYv38/ycnJTJ8+nQULFpTqPW3dupWpU6fy3//+lx9//JEPP/zQfvNcQEDA9X1Ql1H4FREREblR1l7QcQlUDXVsrxqW3+6kdX4jIiJITk4mOjqakSNH0rRpU7p27cratWuZNWtWkcdUrlyZRYsWsWfPHpo3b86kSZOYMGGCQx+bzUZcXByRkZHExMTQsGFDZs6cCUBoaCjjxo1j1KhRBAcHM3jwYADGjx/PmDFjiI+Ptx+XmJhIeHh4qd6TxWLho48+olOnTjRp0oSJEycyfPhw5s6dex2fUGGG6czZ0i6Qk5ODv78/2dnZ+Pn5ubocERERKQFX//z+9ddfOXDgAOHh4VSpUuX6T5Rny1/V4ZfM/Dm+gR2dMuIrjkrz/Wm1BxEREZGy4uFZpsuZSdnTtAcRERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIhIBWcYBsuXL3d1GTcFhV8RERGRW1hWVhZDhgwhIiICi8WC1Wqle/furF271inX27BhA4ZhcOrUKaecv0BiYiJt2rTB29ubgIAAevbsWSbn1RPeRERERMqMDdgMZAIhQEfAeY83Tk9Pp3379lSvXp3JkyfTrFkzLly4wKpVq4iLi2PPnj1Ou/aNMk0Tm81GpUqF4+jSpUsZOHAgr7/+Ovfddx8XL17k+++/L5PrauRXREREpEwkAHWBaOAPl/6se6ndOV544QUMw2D79u3ExsbSsGFDmjRpwogRI9i2bVuRxxQ1cpuSkoJhGKSnpwNw8OBBunfvTkBAAD4+PjRp0oTPPvuM9PR0oqOjAQgICMAwDAYMGABAXl4e8fHxhIeH4+3tTYsWLViyZEmh665cuZKoqCgsFgtbtmwpVN/FixcZOnQokydPZtCgQTRs2JDGjRvz+OOPl8lnppFfERERkRuWAPQGzCvaj1xqXwL0KtMrnjhxgqSkJCZOnIiPj0+h/dWrV7/uc8fFxXH+/Hk2bdqEj48Pu3fvplq1alitVpYuXUpsbCx79+7Fz88Pb29vAOLj4/nggw+YPXs2DRo0YNOmTfTt25fAwEA6depkP/eoUaN48803iYiIICAgoNC1k5OTOXLkCB4eHrRs2ZKsrCzuvPNOJk+eTNOmTa/7PRVQ+BURERG5ITZgKIWDL5faDGAY0IOynAKRlpaGaZo0atSozM5ZICMjg9jYWJo1awZARESEfV+NGjUACAoKsgfs3NxcXn/9dT7//HPatm1rP2bLli3MmTPHIfy+9tprdO3atdhr//jjjwCMHTuWf/zjH9StW5cpU6bQuXNnfvjhB/v1r5emPYiIiIjckM3A4avsN4FDl/qVHdMsKmyXjRdffJEJEybQvn17Xn31Vb777rur9k9LS+PcuXN07dqVatWq2bf333+f/fv3O/Rt1arVVc+Vl5cHwF//+ldiY2OJiorivffewzAMPvnkkxt7Y2jkV0REROQGZZZxv5Jp0KABhmGU+qY2D4/8sc/Lw/OFCxcc+jz77LN069aNxMREVq9eTXx8PFOmTGHIkCFFnvPMmTNA/goNoaGhDvssFovD66KmaFwuJCQEgMaNGzucIyIigoyMjKseWxIa+RURERG5ISFl3K9katSoQbdu3ZgxYwZnz54ttL+4pcgCAwMByMz8LYynpKQU6me1Whk0aBAJCQmMHDmSefPmAeDl5QWAzWaz923cuDEWi4WMjAzq16/vsFmt1lK9r4Kb4fbu3Wtvu3DhAunp6dSpU6dU5yqKwq+IiIjIDekIhJE/t7coBmC91K9szZgxA5vNRuvWrVm6dCn79u0jNTWVt956yz739koFgXTs2LHs27ePxMREpkyZ4tBn2LBhrFq1igMHDpCcnMz69euJjIwEoE6dOhiGwYoVKzh+/DhnzpzB19eXl156ieHDh7NgwQL2799PcnIy06dPZ8GCBaV6T35+fgwaNIhXX32V1atXs3fvXp5//nkAHnvssev4lBwp/IqIiIjcEE9g2qX/fWUALng9FWes9xsREUFycjLR0dGMHDmSpk2b0rVrV9auXcusWbOKPKZy5cosWrSIPXv20Lx5cyZNmsSECRMc+thsNuLi4oiMjCQmJoaGDRsyc+ZMAEJDQxk3bhyjRo0iODiYwYMHAzB+/HjGjBlDfHy8/bjExETCw8NL/b4mT57M73//e5566inuvvtuDh48yLp164pcHaK0DNOZs6VdICcnB39/f7Kzs/Hz83N1OSIiIlICrv75/euvv3LgwAHCw8OpUqXKdZ4lgfxVHy6/+c1KfvAt22XOxFFpvj/d8CYiIiJSJnqRv5xZ+T3hTUpP4VdERESkzHgCnV1dhFyF5vyKiIiIiNtwavjdtGkT3bt3p1atWhiGwfLly6/av+CZz1duWVlZzixTRERERNyEU8Pv2bNnadGiBTNmzCjVcXv37iUzM9O+BQUFOalCEREREXEnTp3z++CDD/Lggw+W+rjLnxUtIiIiUl4KHq0rt5bSLF52U97wduedd5Kbm0vTpk0ZO3Ys7du3L7Zvbm4uubm59tc5OTnlUaKIiIhUIF5eXnh4eHD06FECAwPx8vLCMIp7aIXcTEzT5Pjx4xiGQeXKla/Z/6YKvyEhIcyePZtWrVqRm5vLO++8Q+fOnfnqq6+46667ijwmPj6ecePGlXOlIiIiUpF4eHgQHh5OZmYmR48edXU5UkqGYRAWFoan57WXlSu3h1wYhsGyZcvo2bNnqY7r1KkTtWvX5v/9v/9X5P6iRn6tVqseciEiInILcfVDLgqYpsnFixex2Wwuq0FKr3LlyiUKvnCTjfwWpXXr1mzZsqXY/RaLBYvFUo4ViYiISEVV8Kvzkvz6XG5NN/06vykpKYSEhLi6DBERERGpAJw68nvmzBnS0tLsrw8cOEBKSgo1atSgdu3ajB49miNHjvD+++8DMHXqVMLDw2nSpAm//vor77zzDuvWrWP16tXOLFNERERE3IRTw+8333xDdHS0/fWIESMA6N+/P/PnzyczM5OMjAz7/vPnzzNy5EiOHDlC1apVad68OZ9//rnDOURERERErle53fBWXm6WCfMiIiJScvr5LeXlpp/zKyIiIiJSVhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXymRAQMGYBhGoS0mJsbVpYmIiIiUmFOf8CYVS0xMDO+9955Dm8VicVE1IiIiIqWn8CslZrFYqFmzpqvLEBEREblumvYgIiI3jSunWN12223ExMTw3Xffubo0EakgFH7lKmzABmARkMWKFSuoVq2aw/b666+7tkQRqXBiYmLIzMwkMzOTtWvXUqlSJR555BFXlyUiFYSmPUgxEoChwGF7S3S0hVmzJgHd7G01atQo98pEpGK7fIpVzZo1GTVqFB07duT48eMEBga6uDoRudUp/EoREoDegOnQ6uOTS/36Q4AlQC8X1CUi7ubMmTN88MEH1K9fn9tuu83V5YhIBaDwK1ewkT/ia16lzzCgB+BZHgWJiJspmGIFcPbsWUJCQlixYgUeHpqpJyI3TuFXrrCZy6c6XC43F7KyTOAQ8CnQjkqVKnH77beXY30iUtHYbLB5M2RmQlYWdO4czezZswA4efIkM2fO5MEHH2T79u3UqVPHxdWW3oABAzh16hTLly93dSkigsKvFJJZ7J6kJAgJKXgVC8Add9zBnj17nF+WiFRICQkwdCgcvuzf3FWq+PDdd/XpdWl21TvvvIO/vz/z5s1jwoQJrilURCoM/Q5JrhBSZOv8+WCal2/rMU1TwVdErltCAvTu7Rh8AX79Nb89ISH/tWEYeHh48Msvv5R/kSJS4WjkV67QEQgDjlD0vF/j0v6O5VmUiFQwNlv+iK9Z5O0FuZhmFkOGQMOGJ5k1623OnDlD9+7dy7tMEamAFH7lCp7ANPJXezBwDMDGpT+nopvdRORGbN5ceMT3N0lACEePQps2vjRp0ohPPvmEzp07l1+BIlJhKfxKEXqRv5yZ4zq/+SO+U9EyZyJyozKLvb1g/qUt3zvvwJNPOr+esmSz2di8eTOZmZmEhIRgFj28LSIuovArxehF/nJmm8m/CS6E/KkOGvEVkRsXUvTtBdfd72aRkJDA0KFDOXzZsHbVqlVp0qSJC6sSkcvphje5Ck+gM/DkpT8VfEWkbHTsCGFhYBhF7zcMsFrz+90qEhIS6N27t0PwBTh37hxff/01CQV38ImISyn8iohIufP0hGnT8v/3lQG44PXUqfn9bgU2m42hQ4dedYrDsGHDsNls5ViViBRF4VdERFyiVy9YsgRCQx3bw8Ly23vdQrcXbN68udCI75UOHTrE5s2by6kiESmOwq+IiLhMr16Qng7r18PChfl/HjhwawVfgMzi7+C7rn63iqysLIYMGUJERAQWiwWr1Ur37t1Zu3atq0sTKZZueBMREZfy9IRbfRWzkBLemVfSfreC9PR02rdvT/Xq1Zk8eTLNmjXjwoULrFq1iri4OD0ESW5ahlnB1mDJycnB39+f7Oxs/Pz8XF2OiIi4AZvNRt26dTly5EiR834NwyAsLIwDBw7geatMZL6Ghx56iO+++469e/fi4+PjsO/UqVNUr169VOfTz28pL5r2ICIicoM8PT2ZdukOPuOKO/gKXk+dOrXCBN8TJ06QlJREXFxcoeALlDr4ipQnhV8REZEy0KtXL5YsWULoFXfwhYWFsWTJEnrdahOZi5BnyyN9QzpJM5IwTZOGDRu6uiSRUtOcXxERkTLSq1cvevTo4fCEt44dO1aIEd/UhFSShiaRcziHw5ee/rnijytobDQmsleki6sTKTmFXxERkTLk6elJ51v9Dr4rpCaksrj3Yrg0nbkGNQA49L9DLO69mMeXPK4ALLcMTXsQERGRYuXZ8kgammQPvgBVqUp96rOd7Zw3z5M0LIk8W559/6lTp8q/UJESUvgVERGRYmVsziDncE6h9od4CBOTecxj26FtbFy0kdTUVN566y3atm3rgkpFSkbTHkRERKRYpzNPF9legxr8kT+yiU2sYhXLnl5GUHAQUVFRzJo1q5yrFCk5p478btq0ie7du1OrVi0Mw2D58uXXPGbDhg3cddddWCwW6tevz/z5851ZooiIiFyFb4hv8fvw5WEeZjjD+WHNDxw+fJhPP/20ws15lorFqeH37NmztGjRghkzZpSo/4EDB3j44YeJjo4mJSWFYcOG8eyzz7Jq1SpnlikiIiLFqN2xNn5hfmAU08EAP6sftTvWLte6RK6XU6c9PPjggzz44IMl7j979mzCw8OZMmUKAJGRkWzZsoV//vOfdOvWrchjcnNzyc3Ntb/OySk8L0lERESuj4enBzHTYvJXezBwuPGtIBDHTI3Bw1O3Ecmt4ab6m7p161a6dOni0NatWze2bt1a7DHx8fH4+/vbN6vV6uwyRURE3Epkr0geX/I4fqGOjx32C/PTMmdyy7mpbnjLysoiODjYoS04OJicnBx++eUXvL29Cx0zevRoRowYYX+dk5OjACwiIlLGIntFckePO8jYnMHpzNP4hvhSu2NtjfjKLeemCr/Xw2KxYLFYXF2GiIhIhefh6UHdznVdXYbIDbmp/rlWs2ZNjh075tB27Ngx/Pz8ihz1FREREREpjZsq/LZt25a1a9c6tK1Zs0aLZYuIiIhImXBq+D1z5gwpKSmkpKQA+UuZpaSkkJGRAeTP1+3Xr5+9/6BBg/jxxx95+eWX2bNnDzNnzmTx4sUMHz7cmWWKiIiIiJtwavj95ptvaNmyJS1btgRgxIgRtGzZkldeeQWAzMxMexAGCA8PJzExkTVr1tCiRQumTJnCO++8U+wyZyIiIiIipWGYpmleu9utIycnB39/f7Kzs/Hz87v2ASIiIuJy+vkt5eWmmvMrIiIiIuJMCr8iIiIi4jYUfkVERETEbSj8ioiIiIjbUPgVEREREbeh8CsiIiIibkPhV0RERETchsKviIiIiLgNhV8RERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4VdERERE3IbCr4iIiIi4DYVfEREREXEbCr8iIiIi4jYUfkVERETEbSj8ioiIiIjbUPgVEREREbeh8CsiIiIibkPhV0RERETchsKviIiIiLgNhV8RERERcRsKvyIiIiLiNsol/M6YMYO6detSpUoV2rRpw/bt24vtO3/+fAzDcNiqVKlSHmWKiIiISAXn9PD78ccfM2LECF599VWSk5Np0aIF3bp146effir2GD8/PzIzM+3bwYMHnV2miIiIiLgBp4fff/zjHwwcOJCnn36axo0bM3v2bKpWrcq//vWvYo8xDIOaNWvat+DgYGeXKSIiIiJuwKnh9/z58+zYsYMuXbr8dkEPD7p06cLWrVuLPe7MmTPUqVMHq9VKjx492LVrV7F9c3NzycnJcdhERERERIri1PD7888/Y7PZCo3cBgcHk5WVVeQxd9xxB//617/49NNP+eCDD8jLy6Ndu3YcPny4yP7x8fH4+/vbN6vVWubvQ0REREQqhptutYe2bdvSr18/7rzzTjp16kRCQgKBgYHMmTOnyP6jR48mOzvbvh06dKicKxYRERGRW0UlZ5789ttvx9PTk2PHjjm0Hzt2jJo1a5boHJUrV6Zly5akpaUVud9isWCxWG64VhERERGp+Jw68uvl5UVUVBRr1661t+Xl5bF27Vratm1bonPYbDZ27txJSEiIs8oUERERETfh1JFfgBEjRtC/f39atWpF69atmTp1KmfPnuXpp58GoF+/foSGhhIfHw/Aa6+9xj333EP9+vU5deoUkydP5uDBgzz77LPOLlVEREREKjinh98nnniC48eP88orr5CVlcWdd95JUlKS/Sa4jIwMPDx+G4A+efIkAwcOJCsri4CAAKKiovjyyy9p3Lixs0sVERERkQrOME3TdHURZSknJwd/f3+ys7Px8/NzdTkiIiJSAvr5LeXlplvtQURERETEWRR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4fcGZGVlMXToUOrXr0+VKlUIDg6mffv2zJo1i3Pnzrm6PBERERG5QiVXF3Cr+vHHH2nfvj3Vq1fn9ddfp1mzZlgsFnbu3MncuXMJDQ3l0UcfdXWZIiIiInIZwzRN09VFlKWcnBz8/f3Jzs7Gz8/PadeJiYlh165d7NmzBx8fn0L7TdPEMAynXV9ERKQiKa+f3yKa9nAd/ve//7F69Wri4uKKDL6Agq+IiIjITUjh9zqkpaVhmiZ33HGHQ/vtt99OtWrVqFatGn/+859dVJ2IiIiIFEdzfksqzwbHN8MvmXDiZJFdtm/fTl5eHn369CE3N7ecCxQRERGRa1H4LYlDCbBjKJw7DED902AYsPerpfC739m7RUREAODt7e30kgYMGMCpU6dYvny5068lIiIiUlFo2sO1HEqAzb3twRfgNl/o2hTenvshZ/cudGFxIiIiIlIaCr9Xk2fLH/Gl8IIYM5+Gi3nQ6v7+fLxoEampqezdu5cPPviAPXv24OnpWf71ioiIiMhVadrD1Rzf7DDie7l6wfDtRHj93xcZPWokhzN/xmKx0LhxY1566SVeeOGFci5WRERERK5F4fdqfsm86u6QAJjeH6a3mwJ1n3R+PZffdPdLFpgW519TREREpAJR+L0a75Cy7Xcjrrjpjkwgt0p+u7WX868vIiIiUgFozu/VBHaEqmFAcQ+sMKCqNb+fMxVx0x0Atl/z2w8lOPf6IiIiIhWEwu/VeHhC1LRLL64MwJdeR03N7+csV7npzm7HsPx+IiIiInJVCr/XYu0FHZdA1VDH9qph+e3OnnJwlZvu8plw7lB+PxERERG5Ks35LQlrLwjt8dvNZt4h+VMdnDniW6CYm+7yTKjkce1+IiIiIvIbhd+S8vCE4M7lf91ibqb7KQfqB1+7n4iIiIj8RtMebnZX3HR38iysSIYNqdClKZTbTXciIiIiFYDC783uipvu/m8uDPoXjHwIekRdanb2TXciIiIiFYSmPdwKCm662zGUZcMvu/mtqjU/+GqdXxEREZESUfi9VbjypjsRERGRCqJcpj3MmDGDunXrUqVKFdq0acP27duv2v+TTz6hUaNGVKlShWbNmvHZZ5+VR5k3v4Kb7uo+mf+ngq+IiIhIqTg9/H788ceMGDGCV199leTkZFq0aEG3bt346aefiuz/5Zdf8uSTT/LMM8/w7bff0rNnT3r27Mn333/v7FJFREREpIIzTNO8yqPDblybNm24++67efvttwHIy8vDarUyZMgQRo0aVaj/E088wdmzZ1mxYoW97Z577uHOO+9k9uzZ17xeTk4O/v7+ZGdn4+fnV3ZvRERERJxGP7+lvDh15Pf8+fPs2LGDLl26/HZBDw+6dOnC1q1bizxm69atDv0BunXrVmz/3NxccnJyHDYRERERkaI4Nfz+/PPP2Gw2goODHdqDg4PJysoq8pisrKxS9Y+Pj8ff39++Wa3WsileRERERCqcW36d39GjR5OdnW3fDh065OqSREREROQm5dSlzm6//XY8PT05duyYQ/uxY8eoWbNmkcfUrFmzVP0tFgsWi6VsChYRERGRCs2pI79eXl5ERUWxdu1ae1teXh5r166lbdu2RR7Ttm1bh/4Aa9asKba/iIiIiEhJOf0hFyNGjKB///60atWK1q1bM3XqVM6ePcvTTz8NQL9+/QgNDSU+Ph6AoUOH0qlTJ6ZMmcLDDz/MRx99xDfffMPcuXOdXaqIiIiIVHBOD79PPPEEx48f55VXXiErK4s777yTpKQk+01tGRkZeHj8NgDdrl07Fi5cyN/+9jf+8pe/0KBBA5YvX07Tpk2dXaqIiIiIVHBOX+e3vGmdQBERkVuPfn5LebnlV3sQERERESkphV8RERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+HXTQwYMADDMAptaWlpri5NREREpNw4/QlvcvOIiYnhvffec2gLDAx0UTUiIiIi5U/h141YLBZq1qzp6jJEREREXEbTHkRERETEbSj8upEVK1ZQrVo1+/bYY4+5uiQRERGRcqVpDxVZng2Ob4ZfMuGXLKI7d2bW7Nn23T4+Pi4sTkRERKT8KfxWVIcSYMdQOHc4/3Um+ORWob7lO7D2cm1tIiIiIi6iaQ8V0aEE2Nz7t+BbwPZrfvuhBNfUJSIiIuJiCr8VTZ4tf8QXs/g+O4bl9xMRERFxMwq/Fc3xzYVHfB2YcO5Qfj8RERERN6M5vxXNL5lFNs8fVLJ+IiIiIhWZRn4rGu+Qsu0nIiIiUoEo/FY0gR2hahhgFNPBgKrW/H4iIiIibkbht6Lx8ISoaZdeXBmAL72OmprfT0RERMTNKPxWRNZe0HEJVA11bK8alt+udX5FRETETemGt4rK2gtCe/z2hDfvkPypDhrxFRERETem8FuReXhCcGdXVyEiIiJy09C0BxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4VdERERE3IbCr4iIiIi4DYVfEREREXEbCr8iIiIi4jYUfkVERETEbTgt/J44cYI+ffrg5+dH9erVeeaZZzhz5sxVj+ncuTOGYThsgwYNclaJIiIiIuJmKjnrxH369CEzM5M1a9Zw4cIFnn76aZ577jkWLlx41eMGDhzIa6+9Zn9dtWpVZ5UoIiIiIm7GKeE3NTWVpKQkvv76a1q1agXA9OnTeeihh3jzzTepVatWscdWrVqVmjVrOqMsEREREXFzTpn2sHXrVqpXr24PvgBdunTBw8ODr7766qrHfvjhh9x+++00bdqU0aNHc+7cuav2z83NJScnx2ETERERESmKU0Z+s7KyCAoKcrxQpUrUqFGDrKysYo/7wx/+QJ06dahVqxbfffcdf/7zn9m7dy8JCQnFHhMfH8+4cePKrHYRERERqbhKFX5HjRrFpEmTrtonNTX1uot57rnn7P+7WbNmhISEcP/997N//37q1atX5DGjR49mxIgR9tc5OTlYrdbrrkFEREREKq5Shd+RI0cyYMCAq/aJiIigZs2a/PTTTw7tFy9e5MSJE6Waz9umTRsA0tLSig2/FosFi8VS4nOKiIiIiPsqVfgNDAwkMDDwmv3atm3LqVOn2LFjB1FRUQCsW7eOvLw8e6AtiZSUFABCQkJKU6aIiIiISJGccsNbZGQkMTExDBw4kO3bt/PFF18wePBgfv/739tXejhy5AiNGjVi+/btAOzfv5/x48ezY8cO0tPT+fe//02/fv249957ad68uTPKFBERcUsDBgywr6dfuXJlgoOD6dq1K//617/Iy8tzdXkiTuW0h1x8+OGHNGrUiPvvv5+HHnqIDh06MHfuXPv+CxcusHfvXvtqDl5eXnz++ec88MADNGrUiJEjRxIbG8t//vMfZ5UoIiLitmJiYsjMzCQ9PZ2VK1cSHR3N0KFDeeSRR7h48aKryxNxGsM0TdPVRZSlnJwc/P39yc7Oxs/Pz9XliIiI3HQGDBjAqVOnWL58uUP7unXruP/++5k3bx7PPvtsudakn99SXpw28isiIiK3lvvuu48WLVpcdYlRkVud0x5vLCIiIjcTG7AZyASygKJXSmrUqBHfffddOdYlUr4UfkVERCq8BGAocPiytiqX2ns59DRNE8Mwyq80kXKmaQ8iIiIVWgLQG8fgC/DrpXbHKQ6pqamEh4eXT2kiLqDwKyIiUmHZyB/xvdq97cMu9cu/4W3nzp3ExsY6vzQRF9G0BxERkQprM4VHfPPl5kJWlonNdohjx94lKeln4uPjeeSRR+jXr1/5lilSjhR+RUREKqzMYvckJUFICFSqBAEBf6JFi9a89dZb9O/fHw8P/WJYKi6FXxERkQorpMjW+fPzt998CnR2ejUiNwP9005ERKTC6giEAcWt3mAA1kv9RNyDwq+IiEiF5QlMu/S/rwzABa+nXuon4h4UfkVERCq0XsASIPSK9rBL7b0KHSFSkWnOr4iISIXXC+jBb094CyF/qoNGfMX9KPyKiIi4BU90U5uIpj2IiIiIiBtR+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI26hwT3gzTROAnJwcF1ciIiIiJVXwc7vg57iIs1S48Hv69GkArFariysRERGR0jp9+jT+/v6uLkMqMMOsYP/EysvL4+jRo/j6+mIYhqvLuWnk5ORgtVo5dOgQfn5+ri5HLqPv5uam7+fmpe/m5lba78c0TU6fPk2tWrXw8NCsTHGeCjfy6+HhQVhYmKvLuGn5+fnph8RNSt/NzU3fz81L383NrTTfj0Z8pTzon1YiIiIi4jYUfkVERETEbSj8ugmLxcKrr76KxWJxdSlyBX03Nzd9PzcvfTc3N30/crOqcDe8iYiIiIgURyO/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4dcNzJgxg7p161KlShXatGnD9u3bXV2SAPHx8dx99934+voSFBREz5492bt3r6vLkiK88cYbGIbBsGHDXF2KXHLkyBH69u3Lbbfdhre3N82aNeObb75xdVluz2azMWbMGMLDw/H29qZevXqMHz8eLSwlNxOF3wru448/ZsSIEbz66qskJyfTokULunXrxk8//eTq0tzexo0biYuLY9u2baxZs4YLFy7wwAMPcPbsWVeXJpf5+uuvmTNnDs2bN3d1KXLJyZMnad++PZUrV2blypXs3r2bKVOmEBAQ4OrS3N6kSZOYNWsWb7/9NqmpqUyaNIm///3vTJ8+3dWlidhpnd8Krk2bNtx99928/fbbAOTl5WG1WhkyZAijRo1ycXVyuePHjxMUFMTGjRu59957XV2OAGfOnOGuu+5i5syZTJgwgTvvvJOpU6e6uiy3N2rUKL744gs2b97s6lLkCo888gjBwcG8++679rbY2Fi8vb354IMPXFiZyG808luBnT9/nh07dtClSxd7m4eHB126dGHr1q0urEyKkp2dDUCNGjVcXIkUiIuL4+GHH3b4b0hc79///jetWrXiscceIygoiJYtWzJv3jxXlyVAu3btWLt2LT/88AMA//3vf9myZQsPPvigiysT+U0lVxcgzvPzzz9js9kIDg52aA8ODmbPnj0uqkqKkpeXx7Bhw2jfvj1NmzZ1dTkCfPTRRyQnJ/P111+7uhS5wo8//sisWbMYMWIEf/nLX/j666958cUX8fLyon///q4uz62NGjWKnJwcGjVqhKenJzabjYkTJ9KnTx9XlyZip/ArchOIi4vj+++/Z8uWLa4uRYBDhw4xdOhQ1qxZQ5UqVVxdjlwhLy+PVq1a8frrrwPQsmVLvv/+e2bPnq3w62KLFy/mww8/ZOHChTRp0oSUlBSGDRtGrVq19N3ITUPhtwK7/fbb8fT05NixYw7tx44do2bNmi6qSq40ePBgVqxYwaZNmwgLC3N1OQLs2LGDn376ibvuusveZrPZ2LRpE2+//Ta5ubl4enq6sEL3FhISQuPGjR3aIiMjWbp0qYsqkgJ/+tOfGDVqFL///e8BaNasGQcPHiQ+Pl7hV24amvNbgXl5eREVFcXatWvtbXl5eaxdu5a2bdu6sDIBME2TwYMHs2zZMtatW0d4eLirS5JL7r//fnbu3ElKSop9a9WqFX369CElJUXB18Xat29faFnAH374gTp16rioIilw7tw5PDwco4Wnpyd5eXkuqkikMI38VnAjRoygf//+tGrVitatWzN16lTOnj3L008/7erS3F5cXBwLFy7k008/xdfXl6ysLAD8/f3x9vZ2cXXuzdfXt9Dcax8fH2677TbNyb4JDB8+nHbt2vH666/z+OOPs337dubOncvcuXNdXZrb6969OxMnTqR27do0adKEb7/9ln/84x/83//9n6tLE7HTUmdu4O2332by5MlkZWVx55138tZbb9GmTRtXl+X2DMMosv29995jwIAB5VuMXFPnzp211NlNZMWKFYwePZp9+/YRHh7OiBEjGDhwoKvLcnunT59mzJgxLFu2jJ9++olatWrx5JNP8sorr+Dl5eXq8kQAhV8RERERcSOa8ysiIiIibkPhV0RERETchsKviIiIiLgNhV8RERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuI3/D41GRMsQk+8TAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dendo.plot_clusters(clusters,\n",
-    "                    colors,\n",
-    "                    plot_title=\"Clusters of Coreset using VQE\",\n",
-    "                    show_annotation=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function below uses the `dendo.get_voronoi_tessalation()` method to convert the clusters into regions. `coreset_df`, `clusters` and `colors` need to be passed as the arguments to create the regions. This function creates a region for each coreset point separately and then colors them according to the clusters with colors passed as arguments. Another option is to create regions using the centroids of the clusters. You need to pass `tesslation_by_cluster=True` to the function to perform this task.\n",
-    "\n",
-    "Once the region creation is complete, you can use `plot_voironi()` method to plot the regions. The function takes the clusters and colors as arguments. \n",
-    "\n",
-    "Remembering that these regions were based on coresets, they can overlay the original data set and be used to cluster the data based on the coreset analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "vt = Voironi_Tessalation(coreset_df,\n",
-    "                         clusters,\n",
-    "                         colors,\n",
-    "                         tesslation_by_cluster=False)\n",
-    "vt.plot_voironi(plot_title=\"Voironi Tessalation of Coreset using VQE\",\n",
-    "                show_annotation=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## QAOA Implementation\n",
-    "\n",
-    "CUDA-Q is designed to be a flexible tool for developers so they can test different implementations of the same code. For example, one can perform the same analysis, instead using a QAOA approach. Only the kernel needs to be changed as is done below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                             »\n",
-      "q0 : ──●──────────────────●──────────────────────────────────────────────────»\n",
-      "     ╭─┴─╮╭────────────╮╭─┴─╮                                                »\n",
-      "q1 : ┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──────────────────────────»\n",
-      "     ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮                        »\n",
-      "q2 : ────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──»\n",
-      "                             ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮»\n",
-      "q3 : ────────────────────────────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├»\n",
-      "                                                     ╰───╯╰────────────╯╰───╯»\n",
-      "q4 : ────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                             »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                        ╭───╮╭────────────╮╭───╮╭───────────╮\n",
-      "────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├┤ rx(1.111) ├\n",
-      "                        ╰─┬─╯╰────────────╯╰─┬─╯├───────────┤\n",
-      "──────────────────────────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "                          │                  │  ├───────────┤\n",
-      "──────────────────────────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "                          │                  │  ├───────────┤\n",
-      "──●──────────────────●────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "╭─┴─╮╭────────────╮╭─┴─╮  │                  │  ├───────────┤\n",
-      "┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──┤ rx(1.111) ├\n",
-      "╰───╯╰────────────╯╰───╯                        ╰───────────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_QAOA_circuit(number_of_qubits, circuit_depth) -> cudaq.Kernel:\n",
-    "    \"\"\"Returns the QAOA circuit for the given number of qubits and circuit depth\n",
-    "\n",
-    "\n",
-    "    Args:\n",
-    "        number_of_qubits (int): Number of qubits\n",
-    "        circuit_depth (int): Circuit depth\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq.Kernel: QAOA Circuit\n",
-    "    \"\"\"\n",
-    "\n",
-    "    @cudaq.kernel\n",
-    "    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):\n",
-    "        qubits = cudaq.qvector(number_of_qubits)\n",
-    "\n",
-    "        layers = circuit_depth\n",
-    "\n",
-    "        for layer in range(layers):\n",
-    "            for qubit in range(number_of_qubits):\n",
-    "                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])\n",
-    "                rz(2.0 * thetas[layer], qubits[(qubit + 1) % number_of_qubits])\n",
-    "                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])\n",
-    "\n",
-    "            rx(2.0 * thetas[layer + layers], qubits)\n",
-    "\n",
-    "    return kernel\n",
-    "\n",
-    "\n",
-    "circuit = get_QAOA_circuit(5, circuit_depth)\n",
-    "\n",
-    "print(cudaq.draw(circuit, np.random.rand(2 * circuit_depth), 5, circuit_depth))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,\n",
-    "                  **kwargs) -> Tuple[cudaq.optimizers.optimizer, int]:\n",
-    "    \"\"\"\n",
-    "    Returns the optimizer with the given parameters\n",
-    "\n",
-    "    Args:\n",
-    "        optimizer (cudaq.optimizers.optimizer): Optimizer\n",
-    "        max_iterations (int): Maximum number of iterations\n",
-    "        **kwargs: Additional arguments\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count\n",
-    "    \"\"\"\n",
-    "\n",
-    "    parameter_count = 2 * kwargs[\"circuit_depth\"]\n",
-    "    optimizer.initial_parameters = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,\n",
-    "                                                     parameter_count)\n",
-    "    optimizer.max_iterations = max_iterations\n",
-    "    return optimizer, parameter_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 484/484 [00:00<00:00, 12163.89it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 52703.30it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 48/48 [00:00<00:00, 31987.07it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 36393.09it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 37957.50it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 42473.96it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "divisive_clustering = DivisiveClusteringVQA(\n",
-    "    circuit_depth=circuit_depth,\n",
-    "    max_iterations=max_iterations,\n",
-    "    max_shots=max_shots,\n",
-    "    threshold_for_max_cut=0.75,\n",
-    "    create_Hamiltonian=get_K2_Hamiltonian,\n",
-    "    optimizer=optimizer,\n",
-    "    optimizer_function=get_optimizer,\n",
-    "    create_circuit=get_QAOA_circuit,\n",
-    "    normalize_vectors=True,\n",
-    "    sort_by_descending=True,\n",
-    "    coreset_to_graph_metric=\"dist\",\n",
-    ")\n",
-    "\n",
-    "hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(\n",
-    "    coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scaling simulations with CUDA-Q\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The University of Edinburgh team quickly encountered scaling challenges when they were developing this method. By developing with CUDA-Q they were able to port their code to an HPC environment once GPUs became available. GPUs massively accelerated their development and testing and allowed them to produce the 25 qubit experiments presented in their publication. If you have a GPU available, run the following code examples and see how the times compare on your device. Each call executes the divisive clustering procedure using the QAOA method, 100000 simulated shots for each VQE loop, and maximum 75 VQE iterations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, try a slightly larger N=18 problem using the CPU (`qpp-cpu`) backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Uncomment the following line if you want to explicitly execute this step on your system.\n",
-    "# !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now try the N=18 example on the GPU backend (`nvidia`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "100%|███████████████████████████████████████| 751/751 [00:00<00:00, 3460.26it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 42771.74it/s]\n",
-      "100%|█████████████████████████████████████| 4064/4064 [00:00<00:00, 6862.37it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 56871.92it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 44979.13it/s]\n",
-      "100%|██████████████████████████████████████| 128/128 [00:00<00:00, 19366.94it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 53773.13it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 54648.91it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 51941.85it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 56111.09it/s]\n",
-      "Total time for the execution: 461.866833317\n",
-      "Total time spent on CUDA-Q: 10.452308367999706\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Scaling up to N=25, the task becomes even more onerous on a CPU. Depending on your device, the simulation may not even run. Try it and feel free to interrupt after your patience has worn out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# target = 'qpp-cpu'\n",
-    "# Uncomment the following line if you want to explicitly execute this step on your system.\n",
-    "# !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "N=25 can still easily be completed by a single GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "100%|█████████████████████████████████████| 7352/7352 [00:03<00:00, 2063.82it/s]\n",
-      "100%|███████████████████████████████████| 16492/16492 [00:03<00:00, 4739.44it/s]\n",
-      "100%|██████████████████████████████████████| 256/256 [00:00<00:00, 15185.58it/s]\n",
-      "100%|████████████████████████████████████████| 64/64 [00:00<00:00, 23728.05it/s]\n",
-      "100%|██████████████████████████████████████| 256/256 [00:00<00:00, 15437.97it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 50840.05it/s]\n",
-      "100%|████████████████████████████████████████| 32/32 [00:00<00:00, 33562.82it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 54120.05it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 54560.05it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 55924.05it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 42717.29it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 55007.27it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 53601.33it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 47127.01it/s]\n",
-      "Total time for the execution: 67.61674502899999\n",
-      "Total time spent on CUDA-Q: 21.439895901\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia'\n",
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to push the simulation to an $N=34$ coreset, a single GPU (assuming A100) will run out of memory.  Run the code below to see for yourself. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "RuntimeError: NLOpt runtime error: nlopt failure\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia'\n",
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 34"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To compute a problem with 34 qubits, we need to pool the memory of multiple GPUs.  If you have multiple GPUs available, try the code below to run the same computation on 4 GPUs. You do not need to wait for the code to finish, just note how it does not fail immediately due to memory issues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "^C\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia-mgpu'\n",
-    "gpu_count = !nvidia-smi -L | wc -l\n",
-    "try:\n",
-    "    gpu_count = int(gpu_count[0])\n",
-    "except:\n",
-    "    gpu_count = 0\n",
-    "if gpu_count >= 4:\n",
-    "    !mpirun -np 4 python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia-mgpu --M 34\n",
-    "else:\n",
-    "    print(\n",
-    "        f'Not enough GPUs found on this system ({gpu_count}) to run this step')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/_sources/applications/python/hadamard_test.ipynb.txt b/pr-2308/_sources/applications/python/hadamard_test.ipynb.txt
deleted file mode 100644
index 1d1f781d59..0000000000
--- a/pr-2308/_sources/applications/python/hadamard_test.ipynb.txt
+++ /dev/null
@@ -1,472 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements\n",
-    "\n",
-    "The Hadamard test is a quantum algorithm for estimating expectation values and is a useful subroutine for a number of quantum applications ranging from estimation of molecular ground state energies to quantum semidefinite programming. This tutorial will briefly introduce the Hadamard test, demonstrate how it can be implemented in CUDA-Q, and then parallelized for a Quantum Krylov Subspace Diagonalization application.\n",
-    "\n",
-    "The Hadamard test is performed using a register with an ancilla qubit in the $\\ket{0}$ state and a prepared quantum state $\\ket{\\psi}$, the following circuit can be used to extract the expectation value from measurement of the ancilla.\n",
-    "\n",
-    "\n",
-    "![Htest](./images/htest.png)\n",
-    "\n",
-    "The key insight is that $$P(0) = \\frac{1}{2} \\left[ I + Re \\bra{\\psi} O \\ket{\\phi} \\right]$$ and $$P(1) = \\frac{1}{2} \\left[ I - Re \\bra{\\psi} O \\ket{\\phi} \\right]$$ so their difference is equal to $$P(0)-P(1) = Re \\bra{\\psi} O \\ket{\\phi}.$$\n",
-    "\n",
-    "\n",
-    "More details and a short derivation can be found [here](https://en.wikipedia.org/wiki/Hadamard_test)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if you want to perform the Hadamard test to compute an expectation value like $\\bra{\\psi} O \\ket{\\phi}$, where $\\ket{\\psi}$ and $\\ket{\\phi}$ are different states and $O$ is a Pauli Operator? This is a common subroutine for the QKSD, where matrix elements are determined by computing expectation values between different states.\n",
-    "\n",
-    "Defining $O$ as \n",
-    "$$O = X_1X_2,$$\n",
-    "\n",
-    "and given the fact that\n",
-    "$$\\ket{\\psi} = U_{\\psi}\\ket{0} \\qquad \\ket{\\phi} = U_{\\phi}\\ket{0},$$\n",
-    "\n",
-    "we can combine the state preparation steps into the operator resulting in\n",
-    "$$\\bra{\\psi}O\\ket{\\phi} = \\bra{0}U_\\psi^\\dagger O U_\\phi\\ket{0},$$\n",
-    "which corresponds to the following circuit.\n",
-    "![Htest2](./images/htestfactored.png)\n",
-    "\n",
-    "By preparing this circuit, and repeatedly measuring the ancilla qubit, we estimate the expectation value as $$P(0)-P(1) = Re \\bra{\\psi} O \\ket{\\phi}.$$\n",
-    "\n",
-    "\n",
-    "The following sections demonstrate how this can be performed in CUDA-Q."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical result as a reference: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before performing the Hadamard test, let's determine the exact expectation value by performing the matrix multiplications explicitly. The code below builds two CUDA-Q kernels corresponding to $\\ket{\\psi} = \\frac{1}{\\sqrt{2}}\\begin{pmatrix}1 \\\\ 0 \\\\ 1 \\\\ 0\\end{pmatrix}$ and $\\ket{\\phi} = \\begin{pmatrix}0 \\\\ 1 \\\\ 0 \\\\ 0\\end{pmatrix}.$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "from functools import reduce\n",
-    "\n",
-    "cudaq.set_target('nvidia')\n",
-    "\n",
-    "qubit_num = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def psi(num: int):\n",
-    "    q = cudaq.qvector(num)\n",
-    "    h(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def phi(n: int):\n",
-    "    q = cudaq.qvector(n)\n",
-    "    x(q[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The state vectors can be accessed using the `get_state` command and printed as numpy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Psi state:  SV: [(0.707107,0), (0,0), (0.707107,0), (0,0)]\n",
-      "\n",
-      "Phi state:  SV: [(0,0), (1,0), (0,0), (0,0)]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "psi_state = cudaq.get_state(psi, qubit_num)\n",
-    "print('Psi state: ', psi_state)\n",
-    "\n",
-    "phi_state = cudaq.get_state(phi, qubit_num)\n",
-    "print('Phi state: ', phi_state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian operator ($O$ in the derivation above) is defined as a CUDA-Q spin operator and converted to a matrix with `to_matrix`. The following line of code performs the explicit matrix multiplications to produce the exact expectation value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hamiltonian:  [[0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n",
-      " [0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n",
-      " [0.+0.j 1.+0.j 0.+0.j 0.+0.j]\n",
-      " [1.+0.j 0.+0.j 0.+0.j 0.+0.j]] \n",
-      "\n",
-      "Numerical expectation value:  (0.7071067690849304+0j)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ham = cudaq.spin.x(0) * cudaq.spin.x(1)\n",
-    "ham_matrix = ham.to_matrix()\n",
-    "print('hamiltonian: ', np.array(ham_matrix), '\\n')\n",
-    "\n",
-    "exp_val = reduce(np.dot, (np.array(psi_state).conj().T, ham_matrix, phi_state))\n",
-    "\n",
-    "print('Numerical expectation value: ', exp_val)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `Sample` to perform the Hadamard test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Three CUDA-Q kernels are constructed below corresponding to $\\ket{\\psi}$, $\\ket{\\phi}$, and the Hamiltonian. A fourth kernel constructs the Hadamard test circuit and completes with a measurement of the ancilla qubit in the computational basis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "cudaq.set_target('nvidia')\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_psi(q: cudaq.qview):\n",
-    "    h(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_phi(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def ham_cir(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "    x(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(n: int):\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    q = cudaq.qvector(n)\n",
-    "    h(ancilla)\n",
-    "    cudaq.control(U_phi, ancilla, q)\n",
-    "    cudaq.control(ham_cir, ancilla, q)\n",
-    "    cudaq.control(U_psi, ancilla, q)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    mz(ancilla)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CUDA-Q `sample` method computes 100000 sample ancilla measurements, and from them we can estimate the expectation value. The standard error is provided as well. Try increasing the sample size and note the convergence of the expectation value and the standard error towards the numerical result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 0:85281 1:14719 }\n",
-      "\n",
-      "Observable QC:  0.70562 + - 0.0015844563982640861\n",
-      "Numerical result 0.7071067690849304\n"
-     ]
-    }
-   ],
-   "source": [
-    "shots = 100000\n",
-    "qubit_num = 2\n",
-    "count = cudaq.sample(kernel, qubit_num, shots_count=shots)\n",
-    "print(count)\n",
-    "\n",
-    "mean_val = (count['0'] - count['1']) / shots\n",
-    "error = np.sqrt(2 * count['0'] * count['1'] / shots) / shots\n",
-    "print('Observable QC: ', mean_val, '+ -', error)\n",
-    "print('Numerical result', np.real(exp_val))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test\n",
-    "\n",
-    "This example is small, but a more practical application of the Hadamard test such as QKSD will require much larger circuits. The QKSD method works by reducing the exponential $2^N$ Hilbert space into an exponentially smaller subspace using a set of non-orthogonal states which are easy to prepare on a quantum computer. The Hadamard test is used to compute the matrix elements of this smaller subspace which is then diagonalized using a classical method to produce the eigenvalues. [This paper](https://www.osti.gov/servlets/purl/1962060) described the method in more detail and is the source of the figure below.\n",
-    "\n",
-    "![Htest3](./images/QKSD.png)\n",
-    "\n",
-    "This method can be easily parallelized, and multiple QPUs, if available, could compute the matrix elements asynchronously.  The CUDA-Q `mqpu` backend allows you to simulate a computation across multiple simulated QPUs. The code below demonstrates how.\n",
-    "\n",
-    "First, the Hadamard test circuit is defined, but this time the $\\ket{\\psi}$ and $\\ket{\\phi}$ states contain parameterized rotations so that multiple states can be quickly generated, for the sake of example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_psi(q: cudaq.qview, theta: float):\n",
-    "    ry(theta, q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_phi(q: cudaq.qview, theta: float):\n",
-    "    rx(theta, q[0])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def ham_cir(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "    x(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(n: int, angle: float, theta: float):\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    q = cudaq.qvector(n)\n",
-    "    h(ancilla)\n",
-    "    cudaq.control(U_phi, ancilla, q, theta)\n",
-    "    cudaq.control(ham_cir, ancilla, q)\n",
-    "    cudaq.control(U_psi, ancilla, q, angle)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    mz(ancilla)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the `nvidia-mqpu` backend is specified and the number of GPUs available is determined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of QPUs: 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "cudaq.set_target(\"nvidia\", option=\"mqpu\")\n",
-    "\n",
-    "target = cudaq.get_target()\n",
-    "qpu_count = target.num_qpus()\n",
-    "print(\"Number of QPUs:\", qpu_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `sample_async` command is then used to distribute the Hadamard test computations across multiple simulated QPUs. The results are saved in a list and accessed below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shots = 100000\n",
-    "angle = [0.0, 1.5, 3.14, 0.7]\n",
-    "theta = [0.6, 1.2, 2.2, 3.0]\n",
-    "qubit_num = 2\n",
-    "\n",
-    "result = []\n",
-    "for i in range(4):\n",
-    "    count = cudaq.sample_async(kernel,\n",
-    "                               qubit_num,\n",
-    "                               angle[i],\n",
-    "                               theta[i],\n",
-    "                               shots_count=shots,\n",
-    "                               qpu_id=i % qpu_count)\n",
-    "    result.append(count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The four matrix elements are shown below and can be classically processed to produce the eigenvalues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "{ 0:49865 1:50135 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.0027 + - 0.0022360598270171573\n",
-      "1\n",
-      "{ 0:49796 1:50204 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.00408 + - 0.002236049366181346\n",
-      "2\n",
-      "{ 0:49695 1:50305 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.0061 + - 0.002236026375068058\n",
-      "3\n",
-      "{ 0:49972 1:50028 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.00056 + - 0.002236067626884303\n"
-     ]
-    }
-   ],
-   "source": [
-    "mean_val = np.zeros(len(angle))\n",
-    "i = 0\n",
-    "for count in result:\n",
-    "    print(i)\n",
-    "    i_result = count.get()\n",
-    "    print(i_result)\n",
-    "    mean_val[i] = (i_result['0'] - i_result['1']) / shots\n",
-    "    error = np.sqrt(2 * i_result['0'] * i_result['1'] / shots) / shots\n",
-    "    print('QKSD Matrix Element: ', mean_val[i], '+ -', error)\n",
-    "    i += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classically Diagonalize the Subspace Matrix\n",
-    "\n",
-    "For a problem of this size, numpy can be used to diagonalize the subspace and produce the eigenvalues and eigenvectors in the basis of non-orthogonal states. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[-0.0027  -0.00408]\n",
-      " [-0.0061  -0.00056]]\n",
-      "Eigenvalues: \n",
-      "[-0.00782313  0.00456313]\n",
-      "Eigenvector: \n",
-      "[[-0.76575845  0.64312829]\n",
-      " [-0.64312829 -0.76575845]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "my_mat = np.zeros((2, 2), dtype=float)\n",
-    "m = 0\n",
-    "for k in range(2):\n",
-    "    for j in range(2):\n",
-    "        my_mat[k, j] = mean_val[m]\n",
-    "        m += 1\n",
-    "\n",
-    "print(my_mat)\n",
-    "\n",
-    "E, V = np.linalg.eigh(my_mat)\n",
-    "\n",
-    "print('Eigenvalues: ')\n",
-    "print(E)\n",
-    "\n",
-    "print('Eigenvector: ')\n",
-    "print(V)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/_sources/applications/python/hybrid_qnns.ipynb.txt b/pr-2308/_sources/applications/python/hybrid_qnns.ipynb.txt
deleted file mode 100644
index cc3f054e61..0000000000
--- a/pr-2308/_sources/applications/python/hybrid_qnns.ipynb.txt
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hybrid Quantum Neural Networks\n",
-    "\n",
-    "The example below highlights a hybrid quantum neural network workflow with CUDA-Q and PyTorch where both layers can GPU accelerated to maximise performance."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"images/hybrid.png\" alt=\"hybrid\" width=\"600\">\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We perform binary classification on the MNIST dataset where data flows through the neural network architecture to the quantum circuit whose output is used to classify hand written digits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "\n",
-    "!pip install matplotlib==3.8.4 torch==2.0.1+cu118 torchvision==0.15.2+cu118 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu118"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the relevant libraries\n",
-    "\n",
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch\n",
-    "from torch.autograd import Function\n",
-    "from torchvision import datasets, transforms\n",
-    "import torch.optim as optim\n",
-    "import torch.nn as nn\n",
-    "import torchvision\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "torch.manual_seed(22)\n",
-    "cudaq.set_random_seed(44)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set CUDAQ and PyTorch to run on either CPU or GPU.\n",
-    "\n",
-    "device = torch.device('cpu')\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "#cudaq.set_target(\"nvidia\")\n",
-    "#device = torch.device(\"cuda:0\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def prepare_data(target_digits, sample_count, test_size):\n",
-    "    \"\"\"Load and prepare the MNIST dataset to be used \n",
-    "    \n",
-    "    Args: \n",
-    "        target_digits (list): digits to perform classification of \n",
-    "        sample_count (int): total number of images to be used\n",
-    "        test_size (float): percentage of sample_count to be used as test set, the remainder is the training set\n",
-    "        \n",
-    "    Returns: \n",
-    "        dataset in train, test format with targets \n",
-    "    \n",
-    "    \"\"\"\n",
-    "\n",
-    "    transform = transforms.Compose(\n",
-    "        [transforms.ToTensor(),\n",
-    "         transforms.Normalize((0.1307), (0.3081))])\n",
-    "\n",
-    "    dataset = datasets.MNIST(\"./data\",\n",
-    "                             train=True,\n",
-    "                             download=True,\n",
-    "                             transform=transform)\n",
-    "\n",
-    "    # Filter out the required labels.\n",
-    "    idx = (dataset.targets == target_digits[0]) | (dataset.targets\n",
-    "                                                   == target_digits[1])\n",
-    "    dataset.data = dataset.data[idx]\n",
-    "    dataset.targets = dataset.targets[idx]\n",
-    "\n",
-    "    # Select a subset based on number of datapoints specified by sample_count.\n",
-    "    subset_indices = torch.randperm(dataset.data.size(0))[:sample_count]\n",
-    "\n",
-    "    x = dataset.data[subset_indices].float().unsqueeze(1).to(device)\n",
-    "\n",
-    "    y = dataset.targets[subset_indices].to(device).float().to(device)\n",
-    "\n",
-    "    # Relabel the targets as a 0 or a 1.\n",
-    "    y = torch.where(y == min(target_digits), 0.0, 1.0)\n",
-    "\n",
-    "    x_train, x_test, y_train, y_test = train_test_split(x,\n",
-    "                                                        y,\n",
-    "                                                        test_size=test_size /\n",
-    "                                                        100,\n",
-    "                                                        shuffle=True,\n",
-    "                                                        random_state=42)\n",
-    "\n",
-    "    return x_train, x_test, y_train, y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Classical parameters.\n",
-    "\n",
-    "sample_count = 1000  # Total number of images to use.\n",
-    "target_digits = [5, 6]  # Hand written digits to classify.\n",
-    "test_size = 30  # Percentage of dataset to be used for testing.\n",
-    "classification_threshold = 0.5  # Classification boundary used to measure accuracy.\n",
-    "epochs = 1000  # Number of epochs to train for.\n",
-    "\n",
-    "# Quantum parmeters.\n",
-    "\n",
-    "qubit_count = 1\n",
-    "hamiltonian = spin.z(0)  # Measurement operator.\n",
-    "shift = torch.tensor(torch.pi / 2)  # Magnitude of parameter shift."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train, x_test, y_train, y_test = prepare_data(target_digits, sample_count,\n",
-    "                                                test_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Xrl3bcqFChSx/9dVXlocNGxZ4DZ3h7vMmNwCA+EaZAAAAJIvOAAAAnovLMoFuHpGUlGS5WLFiYa/fvXu35blz51p+6aWXLJ86dSrN2gcAQGZBmQAAACSLzgAAAJ6LyzIBAACIDmUCAACQLDoDAAB4js4AAACeozMAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5+gMAADgOToDAAB4js4AAACeozMAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5+gMAADguWszugHwQ548eQKPFy1aZLlWrVqW77//fssbNmyIebsA+GHFihWW69SpY/nEiROB6+rWrWt506ZNsW9YJsHIAAAAnqMzAACA5ygTIF306NEj8LhmzZqWL126lN7NwRVUrlzZ8vLlyy0fO3YscF2jRo0s79q1K+bt8tlNN91kedWqVZZ37NhhuVWrVunapnigJUj9zLl8+bJl/R13zrlt27bFvF2ZESMDAAB4js4AAACe865McM01/9f/efLJJy0PGjQocF3hwoWTfa5Ro0ZZ7tatm+WJEydaPn36dOCe4cOHR/xeomnbtq3lgQMHRrxu6tSplrdu3RrTNiE8fX969uxp+YYbbgibnXNuzpw5lsuXLx+7xsFNmTLFcpkyZSyvXLkyI5oTNxo3bmw5a9aslg8dOmR58ODBgXvOnTsX+4ZlQowMAADgOToDAAB4zosyQalSpSy/9NJLlh9//HHLSUlJgXvGjh2b7PNqOaBIkSKWv/vuu4j3PP3005ZHjhyZ7GvEGy3DNG/e3HL27NkD1+nM9C5dusS8XfiJbv5UvXp1y/oe5M2b17LOug4VWjZA2unQoUPgsQ53q7lz56ZDa+LXXXfdFfbrBQsWtFyxYsXA97Zv3x7TNmVWjAwAAOA5OgMAAHjOizLB6NGjLRctWtRyr169LO/evTtwz8KFC1P0Gq1bt05d4xJMmzZtLDdr1izidR9++GE6tAahXnvtNctdu3ZN0b1nz54NPH7rrbfSpE34pdDPkyxZslieMGGC5XXr1qVbm+KRlgPUqVOnLB88eDC9mpOpMTIAAIDn6AwAAOC5hC0T3HrrrZbLli1r+bnnnrO8YMGC9GySF5o0aRLVdYsXL45xS/CzChUqWI72/QkndHOWESNGpPq58Ev9+vWzXL9+/cD39u3bZ3nmzJmWL168GPuGJaA9e/ZYDj2bwFeMDAAA4Dk6AwAAeC7uywQFChSwnDt3bst6VoCWDJ599lnLlAnSXtWqVcN+/V//+lfg8dKlS9OjOV7SI4idCw6D6oZCkejGUbqZ1v79+9OgdVAPPvig5eeff97y5s2bA9c1bNjQ8uHDh2PfsAQ3a9asjG5CpsPIAAAAnqMzAACA5+gMAADguUw7Z0DPni5RokTge4888ojlzp07W9Zap561/uc//9ny0aNH07SdCNaVr7/++rDXfPPNN4HHZ86ciWmbfKaHYTkXPFDoSgcP/axHjx6W2Z0t7ennlB6cpu9TgwYNAvccOXIk5u3yyY4dOzK6CZkOIwMAAHiOzgAAAJ7LVGUCLQ3cfffdlvVwFeecq1WrlmVdNnU1u6sh9e677z7Lv/rVr8JeE3rIDdKWLplt37594HvRlAbGjh1refz48Zbz589vOXRXvD/+8Y9hn6t3796WN23aZPncuXPJtsMH1apVs6xLCz/55BPLlAWQ3hgZAADAc3QGAADwXIaXCXRmre7ANXDgQMvffvtt4J5BgwZZDi0hIHOaNGlSRjch4egqm+7du0d1j5ZrRo0aZfmdd96xXLRoUcsTJkywrLvgXcnatWstDx06NGz2rWyUK1cuy/q3oD+H/v37p2ubAMXIAAAAnqMzAACA5zK8THDddddZfuWVV8Jeky9fvsDjd99913KfPn0sr1mzxvKuXbssMzM3tlq1ahWT59VDj0aPHm15586dlrt06RK458KFCzFpS2Y0bNgwy6VKlYrqHj0AR8ttv/3tby3Pnz/fcjQrEa6kX79+lnW1kG4E5gPdyKl06dKWBw8ebHnLli0R77///vstt2zZ0vLJkycta9nnxx9/TH1j49wdd9xh+ZZbbrGcJUuWjGhO3GBkAAAAz9EZAADAcxleJnjxxReTvUY3PnHOuVWrVlm+/fbbw96zbNkyyzNnzrTcoUOHqNo1efJkyxs2bLCcI0cOy0lJSVE9V6K76aab0uy59Oe7YMECywUKFLBcpUqVsNc751zr1q3TrC2ZkZZOdLhY6Qod55y7dOmS5ddff92y7os/YMCAsPfrvdGKdL9usOMbfa/Onz9v+dChQ5ZHjhxp+YEHHgjcrxsVKR36rlOnjmU9v+XEiROpaHH8uuuuuyxrmWDjxo2WP/roozR7vY4dO1rWz6x4OweHkQEAADxHZwAAAM9leJlgyJAhlnWzEl0lEGrJkiWW9+zZY7lcuXKWq1evblmHQ0OP0v2ZDkM751zPnj0t69GiWrL44osvLLdo0cIyqxdST8szoe9JOJGGTxNV+fLlLUea6R86tD9t2jTLWg645557wj6X3q9fP3z4cOB5P/3007Dt0tncev/VrkyIN7oCplKlSpZ1aF/PhNDVFnpOgXPOderUKexraOnl97//vWU9L2TFihUpaXbC0nNTtHyg/0OuRM/i6Nu3r+Wbb77Zctu2bS0/9thjgfu///776BubARgZAADAc3QGAADwHJ0BAAA8l+FzBrQ+qbWt1NS5dFmHKly4sOX9+/eHvUbrSc4Fz15v2rSp5bfeestyjRo1LJctW9ay7oTog9mzZ1uuVatW2Gt0uY9zzq1bty7sdSVLlgz7dd118t5777UcurRUl1dpTTtRtGvXLsX36Hty2223pejeTZs2Wdbd8pxzbuXKlZanTp1qWecM+KxevXqWQ5d7/kyXuOl8meXLlweu088jpUsTdc6AziVgzsBPihcvblnncITOGdD5L3qPzrcJXe7+M/1be++99wLfe+KJJyzr+5ZZMDIAAIDn6AwAAOC5DC8TpIdIpQF1pd2idLhHz3ofPny45blz51rWg0iOHz8edTvj1WeffWb5hx9+sJwnTx7LOnzvXPBMd/2ZatYS0gcffGBZSzJ60JVzzuXNmzclTY8LuutgNAcS6dJb5365m11yZs2aZblbt26WQ5cs9u/f33LoMqpwfPhbuPPOOy3r54B67rnnLE+YMMHy2bNnU/x6tWvXDvt1n5c36+eRHmpWpkwZy71797bcqFGjwP16aJfurqrlhM8//zzsaxcsWNBy6GeeLn3PjCVMRgYAAPAcnQEAADznRZkgLR08eDDs13W3PN1JzAe7du2yHKlMELpToK4u0J+pnsN+4MABy19++aXlYsWKXV2D40yRIkUs58uXL9nrCxUqFHicLVu2FL1e+/btLVeoUMFy6K6g0ZQGXn75Zcu6EidRNW/e3LKWsPSwoA8//NByakoDuXLlCvt6Z86csbx48eIUP2+i0JLvyZMnw16jpTfNzjl38eJFy/r7qwfeffXVV2GfV98PLR0759yTTz5pmTIBAADIdOgMAADgOcoESFPbt2+3rIeB6MZPzjn397//3bJuzvLQQw9Z1pKDbmSjKwZChwEjHUQVz/RgG82R6KZMzv1yFUByIh1aFK0tW7ZYHjNmjGUfVhPkzJkz7Nd1w6ZoD8aJRFcx6aZbgwYNsqxlNZ+lZnOfiRMnWtZD7qKhqwlC6QZGmREjAwAAeI7OAAAAnqNMkEI6uxq/1KxZM8urV6+2XKVKlcB1Wjb4z3/+Y/n999+3rEOgej670jMLnIu8GUg802F7zZGEDu1Hc0+k+6O9V3/udevWtexDaSAaV3teyVNPPWW5e/fulrUsNn78+Kt6jUTUpUsXy8uWLbMcelaK0s2BQsub4bzwwguW27ZtG/G60NUFmQ0jAwAAeI7OAAAAnqNMEIHO/OzVq5flrl27hr1+2rRplo8dOxarZmV6uomK7muvM6Cdc65hw4aW9XhXPeYzEj1romfPnqlpZlzRDUr03IGHH344Xduhqzucc65v376WdZMbSgO/pOdpbNy40bJuzKXHd+tnjnPOtW7d2rKuRtC/o8OHD6dNYxOIblym50Do55Fu4uRc8Gh6/Vnr51SkVTYXLlywrKtqnAse9Z4ZMTIAAIDn6AwAAOC5LJejnC4czWYn6aV+/fqW+/XrZ3ns2LGWFyxYYFlnuEfa175jx46Bx3pd7ty5k22T3j9jxoxkr/eNDoc651zTpk0tN2nSxHKrVq0snz592vKUKVMs6/HHusmRDypXrmxZf8f1PILQv9Vo/sS1tKUbOQ0ZMsTy119/Hbhn1apVyT6vb3TDJy2d6EZZ//jHPyzr+5k/f37L58+fDzzvvHnzLGt5Zu/evVfZYj/pzzq0JKOrA1SkMoGWHDZv3mx5/vz5V93OtBLNZwAjAwAAeI7OAAAAnqMzAACA5+JyzkD27Nktt2zZ0rKeS61nsufIkcNy1qxZr+q1dbe8Ro0aWT548KDl1BzuAqSUzsPQs9Jr1qwZuE7nZ0TSu3dvy3q4EFLv17/+tWWd25IvXz7LOg9jzpw5lseNGxd4rn379sWiifAEcwYAAECy6AwAAOC5uCwTRKJtvPba/9tcUXfvWr9+vWUdTi1atGjguSZPnmxZSwD649LdpgAAyIwoEwAAgGTRGQAAwHMJVSYAAABBlAkAAECy6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5+gMAADgOToDAAB4js4AAACeozMAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA566N9sLLly/Hsh0AACCDMDIAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5/4f5kKbg4Vh3lIAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot some images from the training set to visualise.\n",
-    "if device != 'cpu':\n",
-    "    sample_to_plot = x_train[:10].to(torch.device('cpu'))\n",
-    "else:\n",
-    "    sample_to_plot = x_train[:10]\n",
-    "\n",
-    "grid_img = torchvision.utils.make_grid(sample_to_plot,\n",
-    "                                       nrow=5,\n",
-    "                                       padding=3,\n",
-    "                                       normalize=True)\n",
-    "plt.imshow(grid_img.permute(1, 2, 0))\n",
-    "plt.axis('off')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class QuantumFunction(Function):\n",
-    "    \"\"\"Allows the quantum circuit to input data, output expectation values \n",
-    "    and calculate gradients of variational parameters via finite difference\"\"\"\n",
-    "\n",
-    "    def __init__(self, qubit_count: int, hamiltonian: cudaq.SpinOperator):\n",
-    "        \"\"\"Define the quantum circuit in CUDA Quantum\"\"\"\n",
-    "\n",
-    "        @cudaq.kernel\n",
-    "        def kernel(qubit_count: int, thetas: np.ndarray):\n",
-    "\n",
-    "            qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "            ry(thetas[0], qubits[0])\n",
-    "            rx(thetas[1], qubits[0])\n",
-    "\n",
-    "        self.kernel = kernel\n",
-    "        self.qubit_count = qubit_count\n",
-    "        self.hamiltonian = hamiltonian\n",
-    "\n",
-    "    def run(self, theta_vals: torch.tensor) -> torch.tensor:\n",
-    "        \"\"\"Excetute the quantum circuit to output an expectation value\"\"\"\n",
-    "\n",
-    "        #If running on GPU, thetas is a torch.tensor that will live on GPU memory. The observe function calls a .tolist() method on inputs which moves thetas from GPU to CPU.\n",
-    "\n",
-    "        qubit_count = [self.qubit_count for _ in range(theta_vals.shape[0])]\n",
-    "\n",
-    "        results = cudaq.observe(self.kernel, self.hamiltonian, qubit_count,\n",
-    "                                theta_vals)\n",
-    "\n",
-    "        exp_vals = [results[i].expectation() for i in range(len(results))]\n",
-    "        exp_vals = torch.Tensor(exp_vals).to(device)\n",
-    "\n",
-    "        return exp_vals\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def forward(ctx, thetas: torch.tensor, quantum_circuit,\n",
-    "                shift) -> torch.tensor:\n",
-    "\n",
-    "        # Save shift and quantum_circuit in context to use in backward.\n",
-    "        ctx.shift = shift\n",
-    "        ctx.quantum_circuit = quantum_circuit\n",
-    "\n",
-    "        # Calculate expectation value.\n",
-    "        exp_vals = ctx.quantum_circuit.run(thetas).reshape(-1, 1)\n",
-    "\n",
-    "        ctx.save_for_backward(thetas, exp_vals)\n",
-    "\n",
-    "        return exp_vals\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def backward(ctx, grad_output):\n",
-    "        \"\"\"Backward pass computation via finite difference\"\"\"\n",
-    "\n",
-    "        thetas, _ = ctx.saved_tensors\n",
-    "\n",
-    "        gradients = torch.zeros(thetas.shape, device=device)\n",
-    "\n",
-    "        for i in range(thetas.shape[1]):\n",
-    "\n",
-    "            thetas_plus = thetas.clone()\n",
-    "            thetas_plus[:, i] += ctx.shift\n",
-    "            exp_vals_plus = ctx.quantum_circuit.run(thetas_plus)\n",
-    "\n",
-    "            thetas_minus = thetas.clone()\n",
-    "            thetas_minus[:, i] -= ctx.shift\n",
-    "            exp_vals_minus = ctx.quantum_circuit.run(thetas_minus)\n",
-    "\n",
-    "            gradients[:, i] = (exp_vals_plus - exp_vals_minus) / (2 * ctx.shift)\n",
-    "\n",
-    "        gradients = torch.mul(grad_output, gradients)\n",
-    "\n",
-    "        return gradients, None, None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class QuantumLayer(nn.Module):\n",
-    "    \"\"\"Encapsulates a quantum circuit into a quantum layer adhering PyTorch convention\"\"\"\n",
-    "\n",
-    "    def __init__(self, qubit_count: int, hamiltonian, shift: torch.tensor):\n",
-    "        super(QuantumLayer, self).__init__()\n",
-    "\n",
-    "        self.quantum_circuit = QuantumFunction(qubit_count, hamiltonian)\n",
-    "        self.shift = shift\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "\n",
-    "        result = QuantumFunction.apply(input, self.quantum_circuit, self.shift)\n",
-    "\n",
-    "        return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Hybrid_QNN(nn.Module):\n",
-    "    \"\"\"Structure of the hybrid neural network with classical fully connected layers and quantum layers\"\"\"\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        super(Hybrid_QNN, self).__init__()\n",
-    "\n",
-    "        self.fc1 = nn.Linear(28 * 28, 256)\n",
-    "        self.fc2 = nn.Linear(256, 128)\n",
-    "        self.dropout = nn.Dropout(0.25)\n",
-    "\n",
-    "        self.fc3 = nn.Linear(128, 64)\n",
-    "        self.fc4 = nn.Linear(64, 32)\n",
-    "        self.fc5 = nn.Linear(32, 2)\n",
-    "        self.dropout = nn.Dropout(0.25)\n",
-    "\n",
-    "        # The 2 outputs from PyTorch fc5 layer feed into the 2 variational gates in the quantum circuit.\n",
-    "        self.quantum = QuantumLayer(qubit_count, hamiltonian, shift)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "\n",
-    "        x = x.view(-1, 28 * 28)  # Turns images into vectors.\n",
-    "\n",
-    "        x = torch.relu(self.fc1(x))\n",
-    "        x = torch.relu(self.fc2(x))\n",
-    "        x = self.dropout(x)\n",
-    "\n",
-    "        x = torch.relu(self.fc3(x))\n",
-    "        x = torch.relu(self.fc4(x))\n",
-    "        x = torch.relu(self.fc5(x))\n",
-    "        x = self.dropout(x)\n",
-    "\n",
-    "        # Quantum circuit outputs an expectation value which is fed into the sigmoid activation function to perform classification.\n",
-    "        x = torch.sigmoid(self.quantum(x))\n",
-    "\n",
-    "        return x.view(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def accuracy_score(y, y_hat):\n",
-    "    return sum((y == (y_hat >= classification_threshold))) / len(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hybrid_model = Hybrid_QNN().to(device)\n",
-    "\n",
-    "optimizer = optim.Adadelta(hybrid_model.parameters(),\n",
-    "                           lr=0.001,\n",
-    "                           weight_decay=0.8)\n",
-    "\n",
-    "loss_function = nn.BCELoss().to(device)\n",
-    "\n",
-    "training_cost = []\n",
-    "testing_cost = []\n",
-    "training_accuracy = []\n",
-    "testing_accuracy = []\n",
-    "\n",
-    "hybrid_model.train()\n",
-    "for epoch in range(epochs):\n",
-    "\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "    y_hat_train = hybrid_model(x_train).to(device)\n",
-    "\n",
-    "    train_cost = loss_function(y_hat_train, y_train).to(device)\n",
-    "\n",
-    "    train_cost.backward()\n",
-    "\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    training_accuracy.append(accuracy_score(y_train, y_hat_train))\n",
-    "    training_cost.append(train_cost.item())\n",
-    "\n",
-    "    hybrid_model.eval()\n",
-    "    with torch.no_grad():\n",
-    "\n",
-    "        y_hat_test = hybrid_model(x_test).to(device)\n",
-    "\n",
-    "        test_cost = loss_function(y_hat_test, y_test).to(device)\n",
-    "\n",
-    "        testing_accuracy.append(accuracy_score(y_test, y_hat_test))\n",
-    "        testing_cost.append(test_cost.item())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ly4uOHTvy9ttvO48tLS3loYceIiIiAi8vL9q0acOMGTMAiI6OBuCGG27AZDI5H4tUhb6X0hSVlttY+vNRpn623dm24ZkEnru+KxufSWD9U1ez/qmr2TJtmIptafDUw93AmEwm/nxTD26d9RA9Sh+l5fEDsHIGDH/R6Ggi0pTZ7VBWZMxru/vARYblXsw//vEPpk2bxltvvUWvXr346aefmDBhAr6+vtxzzz288cYbLF26lE8++YTWrVuTlpZGWloaABs2bCA0NJT333+fESNGYLFYXPGuxBWM+l664DsJ+l5Kw2e12Z2Tnv3ako2Hef3rPc7HL9/UDU83x3c0uJlnneUTqQsquBugFr4ePHXzQJ5ecC9/93gV+9rZmLreDJE9jY4mIk1VWRG8FGnMaz91FDx8L+kU06dP5/XXX+fGG28EICYmhh07dvDOO+9wzz33kJqaSvv27RkyZAgmk4k2bdo4jw0JCQEgICCA8PDwS8ohLmbU99IF30nQ91IaNrvdzk1/W8OWtLzz7tMjKoCh7YK5VbOJSyOmIeUN1JVxofh1u5b/WAdgstuwL/0dWMuNjiUi0uAUFhayb98+7rvvPpo1a+bcXnzxRfbt2wfAuHHj2LJlC3FxcTz88MN89dVXBqeWxk7fS2no5q0+4Cy23S2mc7boIB/m39OXxxLjLjp5oEhDph7uBmzaqM7cuuc+LrNtxT99K2ycB/EPGh1LRJoidx9Hr55Rr30JTp48CcDcuXOJj4+v8NyZYbi9e/fmwIEDfPHFF3zzzTfceuutJCQksGTJkkt6ballRn0vL/E7CfpeSsNWUFzGi8t2AjAwNoiPHxhgcCIR46jgbsCCm3kyPjGeV/8zhhfd38e28s+Yu48B7wCjo4lIU2MyuWQIrRHCwsKIjIxk//793Hnnnefdz8/PjzFjxjBmzBhuvvlmRowYQW5uLoGBgbi7u2O1WuswtVSJvpf6Xkq1pOUWkV5w7jXX1bVk42Hn/Tdu73XJ5xNpyFRwN3C394ti5I/XsTf/K9qfOgI/vA7DXzA6lohIg/Lcc8/x8MMP4+/vz4gRIygpKWHjxo0cP36cyZMnM3PmTCIiIujVqxdms5nFixcTHh5OQEAA4JgROikpicGDB+Pp6UmLFi2MfUPSKOh7KXXpYHYhw/6yijKr3WXnHNM3ipDmmgRNmjYV3A2cm8XMlN905aUFd/C+x6vY18/B1O9+aNHm4geLiAgA999/Pz4+Prz66qs8/vjj+Pr60q1bN37/+98D0Lx5c1555RX27t2LxWKhX79+LF++HLPZMRXK66+/zuTJk5k7dy4tW7bk4MGDxr0ZaTT0vZS6dMfcdZRZ7TT3ciPEBTOFRwX6MP26zi5IJrXCbod/3Qd7GtjcD6UnHLcezWt+jkd+Bt+6W27OZLfbXfdnrEaioKAAf39/8vPz8fPzMzpOldw5dy2/Tf0Dgy2/QJ9xMOqvRkcSkUasuLiYAwcOEBMTg5eXl9FxGrQLfZYN8feoNl3o89B30rX0eTZ8OSdLmPn1HgqKLz6prtVmY/m2dMAxBPy6HgatOiF159RxeDna6BTGeHwf+AZf0imq8/usHu5G4pGEOF559yYGW37B/tM/MF32OPi3MjqWiIiIiBhg8abD/GN9arWO6RkVoGK7qcg7/d3wCYb7vzY2S1WdyID3RzjuXz0NutxQs/N41+3lNSq4G4n+MYFYogex9nBnBrIDVs+Ca18zOpaIiIiIGCAl0zHTfUKnMAa3u/jwWTezieFdtGZ7vbPsMdj2ievPe2Y54RbREBjr+vPXBv/WZ+/7tWwwuetFwT179mxeffVV0tPT6dGjB2+++Sb9+/evdN8rrriCVatWndM+cuRIli1bBjjWpVywYEGF5xMTE1mxYoXrw9cjj1zdnjfm38BAyw7smz/ANPQP4BdhdCwRERERqWP7sxwF9/U9IxmlXuuGyVoOG+eDvRZXG4gZWnvndjWLG8RcBkd/hvbDjU5TZYYX3IsWLWLy5MnMmTOH+Ph4Zs2aRWJiIrt37yY0NPSc/T/99FNKS0udj3NycujRowe33HJLhf1GjBjB+++/73zs6dn4Z0gc2DaIlyMHkZwZR392w9q3IPFPRscSERERkTr06pe72JyaB0DXlv7GhpGaKzjiKLYtHjBxDWBy7fktbhDQwCZavuvfUF4Mns2MTlJlhhfcM2fOZMKECYwfPx6AOXPmsGzZMubPn8+TTz55zv6BgYEVHi9cuBAfH59zCm5PT0/Cw5vWsBiTycS9Q2J4+5Pr6O/xqqOX+4onwfMSZvETEbkAzbt56RryZ1idEWplZWXMmDGDBQsWcOTIEeLi4nj55ZcZMWKESzM15M+zPtHn2DBtO5zP1zszmP3dPgA6hjcnJrhhrkXfKB34Hj77LZQWVm1/2+mebf8oCG5fe7kaEosbWBpOsQ1gNvLFS0tL2bRpEwkJCc42s9lMQkICa9eurdI55s2bx2233Yavb8X/mKxcuZLQ0FDi4uKYOHEiOTk55z1HSUkJBQUFFbaGamS3CHb59mefLQJTSQFs+djoSCLSCFksFoAKI46kZoqKigBwd3c3OEn1nBmhNn36dDZv3kyPHj1ITEwkMzOz0v2feeYZ3nnnHd5880127NjB//3f/3HDDTfw008/uSTPmc/vzOcpl6ahfi+bMpvNzv0fbOCNpL3Otn/cH29gIjnHtsWQnwancqu2leQ7josebGxuuSSG9nBnZ2djtVoJCwur0B4WFsauXbsuenxycjLbt29n3rx5FdpHjBjBjTfeSExMDPv27eOpp57immuuYe3atc5/JP7ajBkzeO655y7tzdQT7hYzdw+K5e/fJPKC+e9n1+U2G/q3FRFpZNzc3PDx8SErKwt3d3fnur9SdXa7naKiIjIzMwkICKj096k+q+4ItQ8//JCnn36akSNHAjBx4kS++eYbXn/9dT766KNLzmOxWAgICHAW/D4+PphMLh5+2QQ09O9lU7b1SD4ZBSX4eFi4uU8rbu0bRZAL1tMWFzp+yHGb8Cx0uKZqx5gtENi21iJJ7TN8SPmlmDdvHt26dTtn+Nptt93mvN+tWze6d+9O27ZtWblyJVdfffU555kyZQqTJ092Pi4oKCAqKqr2gtey2/u35qqkK3jc/gl+ufsg5WvokGh0LBFpREwmExERERw4cIBDhw4ZHadBCwgIaHCXQJ0ZoTZlyhRn28VGqJWUlJyznrO3tzerV68+7+uUlJRQUlLifHyxEWhnPsfz9bJL1TXE72VT9+3ODACuiAvh+eu7Gpymidr3HfznYSg7VfnzRbmO21b9IbRj3eUSQxlacAcHB2OxWMjIyKjQnpGRcdH/yBcWFrJw4UKef/75i75ObGwswcHBpKSkVFpwe3p6NqpJ1QJ9PbisSzQLf7mSB9yWwbq/qeAWEZfz8PCgffv2GlZ+Cdzd3RtkD2JNRqglJiYyc+ZMLrvsMtq2bUtSUhKffvopVuv5Z9+t7gi0M38ICg0NpaysrMrHSUUN9XvZFH247hBb0/IA+GFvNgBXdQy7wBFSq7YuOru+9fl4+kNYl7rJI/WCoQW3h4cHffr0ISkpidGjRwNgs9lISkrioYceuuCxixcvpqSkhLvuuuuir3P48GFycnKIiGg6S2Td1i+KJ7YO537Lcsz7v4PcAxAYY3QsEWlkzGbzOb2WIpX561//yoQJE+jYsSMmk4m2bdsyfvx45s+ff95jajoCzWKxqGCURm/9/hymfra9Qpu7xcQVcSEGJRLnkPFhL0C7czv5AMf60d4BdRZJjGf4kPLJkydzzz330LdvX/r378+sWbMoLCx0XhM2duxYWrZsyYwZMyocN2/ePEaPHk1QUFCF9pMnT/Lcc89x0003ER4ezr59+3jiiSdo164diYlNp5d3QGwQlsA2rC7oymWWbfDTR3D1VKNjiYhII1CTEWohISF89tlnFBcXk5OTQ2RkJE8++SSxsbHnfZ3GNgJNxJUeXbQFcMxEfn3PlgD0aOVPsK7brhvlpfDxGMjYcbatMMtx23qgerHFyfCCe8yYMWRlZTFt2jTS09Pp2bMnK1ascA5TS01NPWcynt27d7N69Wq++uqrc85nsVjYunUrCxYsIC8vj8jISIYPH84LL7zQpH60zWYTt/aN4uOvr3IU3Fv+AVdMcUylLyIicgkuZYSal5cXLVu2pKysjH/961/ceuutdZBYpHFJzSniaH4xAM9c25kh7YMNTtQEpW+Ffd+e2+4VACFxdR5H6q96UX099NBD5/2BXrly5TltcXFx510f0tvbmy+//NKV8Rqsm/u04q9f9SHH3pygE8cg5RuIc+16pyIi0jRVd4Ta+vXrOXLkCD179uTIkSM8++yz2Gw2nnjiCSPfhkiD9PtFjuX0BsQGqtg2yvGDjtuInnDdm2fbA1qDl58RiaSeqhcFt9SOMD8v+rUN418HL3NMnrb5AxXcIiLiEtUdoVZcXMwzzzzD/v37adasGSNHjuTDDz8kICDAoHcgUv9ZbXY+3XyY3MKzk1OWWW1sTs0D4JquTWd+Ipf74o+w8z81P770pOM2pCNEdHdNJmmUTPbzdRU3YQUFBfj7+5Ofn4+fX8P+C9UnG9J459MvSPJ8HLvJgmnyDmiuZT5ERBqCxvR75Ar6PKQpyS0s5d8/HeGF/+6o9HkvdzPbn03EzWKu9Hm5gLJT8CcX/Xv4N3+Bvve65lzSYFTn90g93I3ciG7hPPN5FJtt7ehtToFf/g0DJhodS0RERETOY1/WSa6Z9QOlVhsAPaICaBvi63zezWzi7gHRKrZrKi/NcevRDMb9t+bn8WgGQe1ck0kaLRXcjZyflztXxYXy+a7BjoJ722IV3CIiIiL1xMmScu5fsIG03FPOtqLSckqtNjzdzLRq4c1bt/ciKtDHwJR1wGaDJePgyE+1/1rljgnnCGgDkb1q//WkSVPB3QRc3zOSqb8MYJr7h1iObIKcfRDU1uhYIiIiIk3aR+sO8e2uTNbtz630+Zm39uTa7k3kOu3sPbDj87p9zaj+dft60iSp4G4CruwYSolnED9auziWCNv+L7hcs8KKiIiIGGX7kXye+Wy78/HYgW24sXcr5+PmXm7EBvtWdmjjlJfquA1qDze8U/uvZ7ZAeLfafx1p8lRwNwFe7hau7hTK51sHOwrubYvhssfBZDI6moiIiEiTlLQzE4AukX6M7tmSO+Jb4+vZRP5pvvZtSH4X+NXczSWnZ/0O7gCt+hgSS6Q2NJH/V0til3Ae39KXl3DHM3sPpG/TEgYiIiIitWj7kXzumLuOguLy8+4zdmAbxvRrXYep6oE1b8CJY5U/F9WvbrOI1DIV3E3E5XEhlLk1I8nai5GWZMewchXcIiIiIi5TZrXxwn93kJpbBMChnKILFtvBzTwY1rmJLddaVny22L7rX+D5qyWV3Lw0zFsaHRXcTYSPhxuXdQhh+a54R8G98z+Q8KyGlYuIiIhcopTMk6zZl83+rEI+WHvonOfn3NWHftEtzmlv7uWOh1sdLe1VWgSL7oTjh8A3GG75O/hFnn3+q6mwa1nt57Cd/gOEuy+0vVr/FpVGTwV3E5LYJZzpO3pSihseufsgaxeEdjI6loiIiEiDZLfbOZJ3invmJ3Mk7+yyXgmdwhjR1dFzHdrck8s6hBgV8ayDq2Hft477uftg538h/gHH47JTjmHedalVHxXb0iSo4G5CEjqF8kezDz9Yu3G15SdHL7cKbhEREZEaeWThFpb+fBQAL3czV3cMw9fTwh+GxxHm52Vwuv+Rd+j8j/MPO249msGdS2o/i8kE4bq0UZoGFdxNSICPBwNiA1lxoN/pgnuplgcTERERqYEf9mY5i+1mnm7cNySGR4d1qLsA69+BDfOoMNP3hRTlOG5NZrDbYPMHsPcrR1vZ6d75gDbQZqDLo4o0ZSq4m5iETmG8kdIbm7sZc/o2OH4QWkQbHUtERESkQZm/+gAAgb4ebJ46rO4D/DATTqZX/7j4ibBuNpQUOLZf03JcIi6ngruJuTIulOf+48d6W0cGmnc4rt8Z9JDRsUREREQajLyiUr7bnQXAu3cbUKSWnTpbbN/xCXj4Vu04L38I6wp9x8PJjIrPmd0gsrdrc4qICu6mJjrYl9hgX7443s9RcO9SwS0iIiJSHb9ftAWAlgHe9Glz7uzjl6y0EBaPh/y0yp+3ljluPZpB++HVn3wsuL1jE5Fap4K7CbqyYyhfrO7D8+4LIG09FOWCT6DRsURERETqvWeX/sLK073bt/aNwlQbM23vXwV7v7z4fi17a6ZvkXpOBXcTdGVcKPNWB5NCa9rZUx1LRHS72ehYIiIiIvXW6r3Z/DP5EF/94hiKHRPsy++ualc7L5aX6riNHgqXPV75PiaThoCLNAAquJugfjEt8PWw8HV5D9q5pcKeFSq4RURERCqRV1TKt7syef2rPc61toObefL1o5dhNruwd/lUHvzrPjiRcfb67MieEHu5615DROqcCu4myNPNwuB2wXy7sxcT3f4DKd+AzQpmi9HRRERERAxXUFxGcZkVgGmf/cKKXxwFsIfFzLRRnbm8QwhuFrNrX3TPl45/k/2aerBFGjwV3E3UVR1DeXpHe06YmtH81HE4vAFaDzA6loiIiIihVmxP57f/2ITtf5a3Hto+mFE9Irm1b1TtvHDeIcdt++EwYCJ4BUBkr9p5LRGpMyq4m6grO4ZixcJ35d24zrLW8VdVFdwiIiLSxK3ak+ksts+MGB/cLpgP7u3vugnSvnwaDv5QsS3/iOO2VT9oe5VrXkdEDKeCu4kK8/OiY3hzvs3s5Si4934FCdONjiUiIiJiGLvdzsfJjqW4Zo3pyeheLV3/IiczYe1b539ew8hFGhUV3E3YkHbB/Cu9OzZMmDO2Q8FR8Is0OpaIiIiIIZZsOuy83y60We28yPHTQ8ebhcH1b1d8zjcIInrWzuuKiCFUcDdhg9sH895qP3aZ2tLZngL7V0LPO4yOJSIiIlLnisusPL5kKwCR/l50ifSr2YlyD8B/H4Xi/PO80On2oHbQPqFmryEiDYYK7iasf3Qg7hYT35Z1obNbCuz7TgW3iIiINCn/WH+ImV/tobTc5mz74L74ml+vvXUR7P/u4vupJ1ukSVDB3YT5errRq3ULfjjYnYfcPnf8ONhsYHbxMhciIiIi9cwnG9NYk5LNqj1ZHC8qc7ZPurLtpQ0nPzNkvOed0Pn6yvexeECbQTV/DRFpMFRwN3FD2gXz5oH2FJu88CrMgsxfILyb0bFEREREas3ejBM8cXr4OICvh4V//XYQvh5utGrhXbOTFmbD5w9B6hrH49groUOiC9KKSEOmrswmbnC7YMpwI9neydGwrwpDoEREREQaqNJyGw9+uAmA6CAfnrm2E/+YMICO4X5EBfrUfCj5js9hzxdnr9GO6OGixCLSkKngbuJ6tPKnmacbK8u6Ohr2fWtsIBEREZFacjC7kB7PfcX+7EIAHh3WgfuHxtIzKuDST553eih53LXwf6shpMOln1NEGjwNKW/i3CxmBsQG8f2u08PIU9dCWTG4exkbTERERMSF0nKLuOK1lc7HwzqHcX3PKqyzXXISPp8EJ9IvvF/uPsdt9GBdniciTiq4hSHtgvhmZ0tyzUEEluc4iu62VxodS0RERMRl3v/xoPP+a7f04OY+rap24N4vYcdnVX8hzT4uIr+iglsY3C4YMLGyvAs3mr93zFaugltEREQakW93ZQAwblA0N/WuQs/2GccPOm5jLof+Ey68b7MwaNWvZgFFpFFSwS20C21GkK8Hq0515UaP7x3XcQ973uhYIiIiIi6xP+skB3OKcLeYeCwxrnoTo+WlOm5bD4BOo2onoIg0Wpo0TTCZTAyIDeJH2+nrjdK3OZa2EBEREWkEvt2VCcCA2CCaeVazv+nMutoBrV2cSkSaAhXcAsCA2ECy8SfVLcbRcHC1sYFEREREXCRpp6PgvqpjaPUPPtPDHdDGhYlEpKnQkHIBYGDbIABWlsYx1nzAUXB3GW1sKBEREZEaOlVqZd3+HE6VWdlwMBeAa5rvhw//ANbSqp/ozDXc6uEWkRpQwS0AtA1pRnAzD34s6sRYjxVw8AejI4mIiIjU2IvLdvCP9anOx+1CmxG+9U3Yl1T9k3kHgl81JloTETlNBbcAjuu442OD+HFrR0dD1i44mQXNQowNJiIiIlJNVpudL7Y71s3uEumHr6cbDwyNhaSDjh0ufxJCOlT9hBE9waJ/NotI9dWLa7hnz55NdHQ0Xl5exMfHk5ycfN59r7jiCkwm0znbtdde69zHbrczbdo0IiIi8Pb2JiEhgb1799bFW2nQBsQGkUdzDjmv41Yvt4iIiDQ8Px/OI7ewlOZebnw2aTCfPDiQhE6hkJ/m2KHHbdD1pqpvQW2NfUMi0mAZXnAvWrSIyZMnM336dDZv3kyPHj1ITEwkMzOz0v0//fRTjh075ty2b9+OxWLhlltuce7zyiuv8MYbbzBnzhzWr1+Pr68viYmJFBcX19XbapAGxgYCsLL0dC+3Jk4TERGRBqaguIzf/fMnAC7rEIK75fQ/d09mQHkxmMzg38rAhCLSlBhecM+cOZMJEyYwfvx4OnfuzJw5c/Dx8WH+/PmV7h8YGEh4eLhz+/rrr/Hx8XEW3Ha7nVmzZvHMM89w/fXX0717dz744AOOHj3KZ599VofvrOFxXsddroJbREREGharzc6B7EJmLN/JkbxTAFz961nJz8w27tcSLO4GJBSRpsjQi1FKS0vZtGkTU6ZMcbaZzWYSEhJYu3Ztlc4xb948brvtNnx9fQE4cOAA6enpJCQkOPfx9/cnPj6etWvXctttt51zjpKSEkpKSpyPCwoKavqWGrQz13Gv3toJOyZM2bvhRAY0DzM6moiIiMgF3b9gA9/tznI+vjH0GKN3fQi/nJ6RvPD0c1reS0TqkKE93NnZ2VitVsLCKhZ0YWFhpKenX/T45ORktm/fzv333+9sO3Ncdc45Y8YM/P39nVtUVFR130qjMSA2iHyaccgt1tFwSL3cIiIiUn8dyTvF8L+schbbfl5u9I8O5NWI7zDv+cIxK/m+JEjf6jggvJuBaUWkqWnQ0y3OmzePbt260b9//0s6z5QpU5g8ebLzcUFBQZMtus9ex92BceZ9cOAHx2QhIiIiIvXQF9uOsSfjJAADYgNZ+MBAxxNzHnfcDnoYwro47ls8oP0wA1KKSFNlaMEdHByMxWIhIyOjQntGRgbh4eEXPLawsJCFCxfy/PPPV2g/c1xGRgYREREVztmzZ89Kz+Xp6Ymnp2cN3kHjc+Y67tVFnRnn8YWu4xYREZF6bdUeR8/2oLZBzLun39knjp++ZrvnHRDayYBkIiIGDyn38PCgT58+JCUlOdtsNhtJSUkMHDjwgscuXryYkpIS7rrrrgrtMTExhIeHVzhnQUEB69evv+g55ex13Mm2OOyYIGcvnLj48H4RERGRupaeX8wPe7MBuLF3K7w9LI4nTuVBSb7jfkBrY8KJiFAPZimfPHkyc+fOZcGCBezcuZOJEydSWFjI+PHjARg7dmyFSdXOmDdvHqNHjyYoKKhCu8lk4ve//z0vvvgiS5cuZdu2bYwdO5bIyEhGjx5dF2+pwRsYG0QBzTjofnrNSfVyi4iISD007v1k5/1hnX41f0/eIcetTzB4+NZxKhGRswy/hnvMmDFkZWUxbdo00tPT6dmzJytWrHBOepaamorZXPHvArt372b16tV89dVXlZ7ziSeeoLCwkAceeIC8vDyGDBnCihUr8PLyqvX30xgMiHX8EeO7kjhizClw8AfodrPBqURERERgd/oJ3kjaS3GZlV3pBUx2W8yNkXn4f/bh2Z3OzEjeQjOSi4ixTHa73W50iPqmoKAAf39/8vPz8fPzMzpOnbPb7fT7UxI9itYwz+N1CGwLD282OpaISJPT1H+P/pc+j6btQHYha/Zl869Nh9mcmgdAe9NhvvZ84vwH9R4L171ZNwFFpMmozu+R4T3cUv+YTCYGxAby/daOjvW4c/dpPW4RERExjN1uZ9z7yRzKKXK2PZ4YR/eiXNgAtIiGoX+oeJDFAzok1mlOEZH/pYJbKjUgNoj/bj3GIbcYosv3Q+oa6HKD0bFERESkCdqVfoJDOUV4uJl5OmITQ2wbiM3wxZR3eiby8O6O3mwRkXpGBbdUasDp9bh/KG1PtHk/HFqrgltEREQM8e2uTACuaNeCe9L+CtZSyPnVDmFdjQkmInIRKrilUm1DmhHk68G6U3Hc7fGlo4dbREREpI4Vl1l59cvdAIyMtsPBUsdw8Wtecezg4QsdrzUwoYjI+anglkqZTCb6xwSSvD3O0ZC+HYrzwcvf2GAiIiLSJGQWFHMgu5BvdmY424YGFzru+EdB3/EGJRMRqToV3HJe/WMC+WJ7CzLcIgkrPwppydB+mNGxREREpJErKC4jYeYqCorLnW1XxoUQVLbH8SCgtUHJRESqx3zxXaSpio9xrMe9pqy9o+GQhpWLiIhI7Vqx/Rjdn/2KguJyvN0txIb4MiA2kJm39oQzk6RpfW0RaSDUwy3nFRfeHD8vN9aUxXGD+ypIXWt0JBEREWnkPt18xHn/4avaMdH0L8jaCcuBoz85nlAPt4g0ECq45bwsZsd13Bt2nb6O+8gmKCsGdy9jg4mIiEijdSDbcZ32Kzd359aWx+Gdl87dKbRLHacSEakZFdxyQf1jAvlmZzh5lkACrLmOojt6sNGxREREpBEqLrNyKKcIgIGxQZC+2fFEixgY8FvHfd9gaD/coIQiItWjglsuyHEdt4n11g4kss6xPJgKbhEREakF6/bnUGYtZ7rvZ7T69lPI3ed4omVviH/A2HAiIjWgglsuqEukH74eFtaUxZHovg4O6TpuERERqR07jhXQ17SH8dYlsP1XT4R2MiyTiMilUMEtF+RmMdMnOpANe09fx52WDDYrmC3GBhMREZFG52B2IdHmdMeDkE7Q5x7w8IUuNxobTESkhlRwy0XFxwTy+p7WnDL74l16AtK3QWRPo2OJiIhII3IsbR9jtk2gj/tuR0PrATBgorGhREQukdbhlovqHxOIDTObbB0cDVoeTERERFzol6P5pM69gz6m3WcbNYxcRBoBFdxyUd1b+ePpZmZN2emC+9AaYwOJiIhIo/LfrceIN+8629Dvfug91rhAIiIuoiHlclGebhZ6tQ4g+cDp67gPrQG7HUwmY4OJiIhIw1V2ClbOgJNZDNibVfG5q54Bd29jcomIuJAKbqmS+Jgg/ra/LWUmd9yLsiEnBYLbGx1LREREGqqd/4Uf/wrA5f/7nFdAXacREakVGlIuVRIfE0gp7mzjdJGtYeUiIiJyKXL3O25a9GBG2e3MKLud4p7j4YFVGkUnIo2GCm6pkl6tW+BuMfFjmSZOExERERfIOwTAFu/+vGMdxcfuN+A1epZWQhGRRkUFt1SJt4eF7q0C2GD71XXcIiIiIjWVlwrA0oPuAEwZqVnJRaTxUcEtVRYfE8hmW3tsmB1/lS44anQkERERaaiOO3q40+whAHRr6W9kGhGRWqFJ06TK+scE8vZKH/aaoomz73f0cne72ehYIiIiUl8dPwjr/uaYkfx/2AsOYwIO20N4cXRXuqrgFpFGSAW3VFmfNi0wm+DHsg7Eue13XMetgltERETO54fXYfMHlT5lAgrsPpibh3NnfOu6zSUiUkdUcEuVNfdyp2tLf5KPduReVkDqOqMjiYiISH2W45iJnK43QcjZa7R3phewbOsx1ti68NsR7TFpVnIRaaRUcEu19I8O5PPDp2cqz/gFivPBS0PAREREpBKnZyKn/4PQOt7Z/O6iLfzbeoQHL4/l7oHRxmQTEakDmjRNqiU+NogsAjhiCgfskLbB6EgiIiJSH1nLoOCI436LNs7m1Jwi/v2To/3qjmFGJBMRqTMquKVa+kW3wGSCdeXtHQ1pGlYuIiIilSg4AnYbWDzBNxSA4jIrCX9ZBYC/tzu9WwcYGFBEpPap4JZqCfDxIC6s+dn1uHUdt4iIiFTm9LJfBLQGs+OfnGv35VBabgPg6ZGdcLPon6Ii0rjpv3JSbfExgWy0nb6O+/BGx5AxERERkV87M5zcv5WzKWlXBgB3DWjNrf2ijEglIlKnVHBLtcXHBrHPHkmBqTmUn4L0rUZHEhERkfrmVJ7j1ifI2bThwHEALmsfYkAgEZG6p4Jbqq1fdCB2zGwob+do0LByERER+V/F+Y7b06uZ2Gx2DuQUAhAX3tyoVCIidUoFt1RbSHNP2ob4slHXcYuINGmzZ88mOjoaLy8v4uPjSU5OvuD+s2bNIi4uDm9vb6Kionj00UcpLi6uo7RS54rzHLenC+6j+acoLbfhbjHRMsDbuFwiInVIBbfUSP+YIDacuY47dR3Y7cYGEhGROrVo0SImT57M9OnT2bx5Mz169CAxMZHMzMxK9//nP//Jk08+yfTp09m5cyfz5s1j0aJFPPXUU3WcXOrM//RwH8wuAqB1oI8mSxORJkP/tZMaiY8JZJs9ljLcoDATjh8wOpKIiNShmTNnMmHCBMaPH0/nzp2ZM2cOPj4+zJ8/v9L916xZw+DBg7njjjuIjo5m+PDh3H777RfsFS8pKaGgoKDCJg3ImYLbOwCAA9knAYgJbmZQIBGRuqeCW2qkf0wgJXjwsy3W0aBh5SIiTUZpaSmbNm0iISHB2WY2m0lISGDt2rWVHjNo0CA2bdrkLLD379/P8uXLGTly5HlfZ8aMGfj7+zu3qCjNat2gFOU6br0CADiY4+jhjg7yMSiQiEjdU8EtNRIZ4E1UoPfZ5cFUcIuINBnZ2dlYrVbCwsIqtIeFhZGenl7pMXfccQfPP/88Q4YMwd3dnbZt23LFFVdccEj5lClTyM/Pd25paWkufR9Sy/JSHbf+jj+UZBQ4rtcP9/cyKpGISJ1TwS01Fh8TxKYzBXfaemPDiIhIvbZy5Upeeukl3n77bTZv3synn37KsmXLeOGFF857jKenJ35+fhU2aSBOpMOJo477Aa0ByDxRAkCYnwpuEWk63IwOIA1X/5hA/rzpdMGdtcsxdMwn0NhQIiJS64KDg7FYLGRkZFRoz8jIIDw8vNJjpk6dyt133839998PQLdu3SgsLOSBBx7g6aefxmxWH0CjsmHe2fu+wQBknS64Q5t7GpFIRMQQhv+6VXdJkby8PCZNmkRERASenp506NCB5cuXO59/9tlnMZlMFbaOHTvW9ttokgbEBJGLH/vskY6GtAv/byciIo2Dh4cHffr0ISkpydlms9lISkpi4MCBlR5TVFR0TlFtsVgAsGuli8an5ITjtlU/MJlYuy+HA9mONbgjtSSYiDQhhvZwn1lSZM6cOcTHxzNr1iwSExPZvXs3oaGh5+xfWlrKsGHDCA0NZcmSJbRs2ZJDhw4REBBQYb8uXbrwzTffOB+7uakjvzZEBXoT7ufFhqIOtHU7CqlrIW6E0bFERKQOTJ48mXvuuYe+ffvSv39/Zs2aRWFhIePHjwdg7NixtGzZkhkzZgAwatQoZs6cSa9evYiPjyclJYWpU6cyatQoZ+EtjUiZo7imfSLZJ0u4fa5jrpfWgT60aqGCW0SaDkMr0V8vKQIwZ84cli1bxvz583nyySfP2X/+/Pnk5uayZs0a3N3dAYiOjj5nPzc3t/MOaRPXMZlMxMcGsmlbB25jpa7jFhFpQsaMGUNWVhbTpk0jPT2dnj17smLFCudEaqmpqRV6tJ955hlMJhPPPPMMR44cISQkhFGjRvGnP/3JqLcgtanUMSM5Hj58t+vs2uwvjO6KyWQyKJSISN0zbEh5TZYUWbp0KQMHDmTSpEmEhYXRtWtXXnrpJaxWa4X99u7dS2RkJLGxsdx5552kpqZeMIvW+ay5/jGBbLTFOR4c2QzlJcYGEhGROvPQQw9x6NAhSkpKWL9+PfHx8c7nVq5cyd///nfnYzc3N6ZPn05KSgqnTp0iNTWV2bNnnzNKTRqJMkfBXWr24vElWwF4+Or2XN4hxMhUIiJ1zrCCuyZLiuzfv58lS5ZgtVpZvnw5U6dO5fXXX+fFF1907hMfH8/f//53VqxYwd/+9jcOHDjA0KFDOXHixHmzaJ3PmouPCeSAPZwcux9YS+DoFqMjiYiIiNFKHUPKt2WWOZtGdtPoQxFpegyfNK06bDYboaGhvPvuu/Tp04cxY8bw9NNPM2fOHOc+11xzDbfccgvdu3cnMTGR5cuXk5eXxyeffHLe82qdz5prG9KMIF/Ps+txp2k9bhERkSav7BQAP6WXApDYJYyO4VrWTUSaHsMK7posKRIREUGHDh0qTK7SqVMn0tPTKS0trfSYgIAAOnToQEpKynmzaJ3PmjOZTPSPCWTDmWHlqSq4RUREmrzTQ8o3HnVcajZhaKyRaUREDGNYwV2TJUUGDx5MSkoKNpvN2bZnzx4iIiLw8PCo9JiTJ0+yb98+IiIiXPsGxCk+JpBNZ3q4U9eBlncRERFpuux2yNgOQFaxBYvZRPdWAcZmEhExiKFDyidPnszcuXNZsGABO3fuZOLEiecsKTJlyhTn/hMnTiQ3N5dHHnmEPXv2sGzZMl566SUmTZrk3Oexxx5j1apVHDx4kDVr1nDDDTdgsVi4/fbb6/z9NRX9Y4LYbo+h2O4Op3Ihe6/RkURERMQoB39w3j1Oc6JaeOPh1qCuYhQRcRlDlwWr7pIiUVFRfPnllzz66KN0796dli1b8sgjj/DHP/7Ruc/hw4e5/fbbycnJISQkhCFDhrBu3TpCQjQrZm3pGN4cby8vfra1Jd60y3Edd0gHo2OJiIiIEbJ2O+/ut0dydUgzA8OIiBjL0IIbHEuKPPTQQ5U+t3LlynPaBg4cyLp1579OeOHCha6KJlVkNjuu4964twPx5l2Quh56jzU6loiIiNS1zF2w/DEAPve6HophSPtgg0OJiBhH43vEJeJjgn41cVrl66iLiIhII7f0bCfKxhMtALiqY6hRaUREDKeCW1yif0wgm23tHQ9y98HJLGMDiYiISN2y253DyVdY+/Fv6xASOoXSJsjX4GAiIsZRwS0u0SXSD6uHP7ttrRwNWo9bRESkaTl1HEoKAPh92W+5tm8c793Tz+BQIiLGUsEtLuFmMdMnOpCNWo9bRESkaco/7LgxB1CMJx3CmxscSETEeCq4xWXiYwLZeGY97rT1xoYRERGRunXqOABZVses5ANjg4xMIyJSL6jgFpeJjwlkg93Rw20/ugXKThkbSEREROpOcT4AeXYf3C0mOqqHW0REBbe4TrdW/mRZwsiwB2CylcGRTUZHEhERkbpyuuAusPsQ2twLs9lkcCAREeOp4BaX8XSz0Lv1r4eVJxsbSEREROrOmYIbH0KaexocRkSkflDBLS5VYXmwwxuMDSMiIiJ1pzgPgAK7rwpuEZHTVHCLS8XHBrL5dA+3PW29Y01OERERafTsRY5J0wrw4Yq4EIPTiIjUDyq4xaV6RbVgjzmGErsbpqIcyN1vdCQRERGpA2W5qQActQdzU+9WBqcREakfVHCLS3l7WOjUKoRt9lhHg67jFhERafz2r8Jj/1cAlDZrhZe7xeBAIiL1gwpucbmK13Gr4BYREWnUCnPgoxudDyNjOxsYRkSkflHBLS7XPyaQTWcKbvVwi4iING45KWArB+CFsjtpqYJbRMRJBbe4XN/oQLbYHQW3PXMHFBcYnEhERERqzb4kADabuzLPei0xwc0MDiQiUn+o4BaXa+bpRljLaA7bgzHZbXBkk9GRREREpLasfweA1DJ/AFoH+hiZRkSkXlHBLbUiPiaQTaeXB9N63CIiIo2UzQqlJwFYZL0CkwmCm3kYHEpEpP5QwS21on9M0NmJ09LWGxtGREREasfu5WArx2Z2Z72tE0G+nrhZ9M9LEZEz9F9EqRX9owPZfPo6blvaBrDZDE4kIiIiLmUtg0V3AVDsE4ENM2F+ngaHEhGpX1RwS63w93HHFtKFU3YPzCX5kL3H6EgiIiLiSvlpzrvLWz0KQGyIJkwTEfk1FdxSa/q1DeNne1vHA63HLSIi0riknr5kLLgD76W3A+CqjiEGBhIRqX9UcEut6R8T+KvruFVwi4iINCpr3gSg1DuEXeknMJng8g6hBocSEalfVHBLrekXHcim0wW3NVUTp4mIiDQqtnIA0kOGAhDVwodAX81QLiLyayq4pdaENPfkeIseAFhy9sCp4wYnEhEREZcpzgfgjYOtAIgJ9jUyjYhIvaSCW2pVXNtY9tvCHQ8ObzQ2jIhIExcdHc3zzz9Pamqq0VGkMSjOA2DdMSsAnSP9DAwjIlI/qeCWWjUgNpCf7LqOW0SkPvj973/Pp59+SmxsLMOGDWPhwoWUlJQYHUsaorJiKC8GoMDuA8Bjw+OMTCQiUi+p4JZa1T8mkE22DgCUH1pncBoRkabt97//PVu2bCE5OZlOnTrxu9/9joiICB566CE2b95sdDxpSEoKALDZTZzAm88mDcZiNhkcSkSk/lHBLbUqwt+bo827OR4c2QQ2q7GBRESE3r1788Ybb3D06FGmT5/Oe++9R79+/ejZsyfz58/HbrcbHVHqu9PzspzAm4lXtKdnVICxeURE6ikV3FLrQmJ7cMLujVt5IWTuMDqOiEiTV1ZWxieffMJ1113HH/7wB/r27ct7773HTTfdxFNPPcWdd95pdESp7/LTADhmD2JQ22CDw4iI1F9uRgeQxq9/bAhbtrVlqGW74zru8G5GRxIRaZI2b97M+++/z8cff4zZbGbs2LH85S9/oWPHjs59brjhBvr162dgSmkIrLkHsQBp9hC6hmp2chGR81EPt9S6+JggNp+eOK38kNbjFhExSr9+/di7dy9/+9vfOHLkCK+99lqFYhsgJiaG2267zaCE0lDkHd0HQLoplLDmXganERGpv9TDLbUuKtCbg95doOzflB1apy+diIhB9u/fT5s2bS64j6+vL++//34dJZKGKu9oCkGAObANZk2WJiJyXurhllpnMpnwio4HwPvEITiZZXAiEZGmKTMzk/Xrzx1ptH79ejZu3GhAImmo3Aoc13C3aNnO4CQiIvWbCm6pE93aRbPH1tLx4PAGY8OIiDRRkyZNIi0t7Zz2I0eOMGnSJAMSSUNks9nxPXUEAL9wFdwiIheiglvqRHxsIJttp6/jTtV13CIiRtixYwe9e/c+p71Xr17s2KFVJKRqvv55P8EmxzrckdEdDE4jIlK/qeCWOhEb7Msej84AFKX8aHAaEZGmydPTk4yMjHPajx07hpubZtiQqknf9B8ATuJLbFQrg9OIiNRvKrilTphMJmjtuI7bJ3srlJcanEhEpOkZPnw4U6ZMIT8/39mWl5fHU089xbBhwwxMJg1GyUnuOTwdAFvAhSfgExERzVIudSimQw9yDzQj0HYSjv0MUVrnVUSkLr322mtcdtlltGnThl69egGwZcsWwsLC+PDDDw1OJw2BPXc/Z+YkPznoCfwMTSMiUv+ph1vqTHzbYDbZ4gAoP7jG4DQiIk1Py5Yt2bp1K6+88gqdO3emT58+/PWvf2Xbtm1ERUUZHU8agPxjKQD8bIslqPd1BqcREan/DC+4Z8+eTXR0NF5eXsTHx5OcnHzB/fPy8pg0aRIRERF4enrSoUMHli9ffknnlLrRLqQZ2906AXBi72qD04iINE2+vr488MADzJ49m9dee42xY8fi7u5udCxpIPKP7gMgxz0cTzeLwWlEROq/GhXczz//PEVFRee0nzp1iueff77K51m0aBGTJ09m+vTpbN68mR49epCYmEhmZmal+5eWljJs2DAOHjzIkiVL2L17N3PnzqVly5Y1PqfUHbPZRGlkfwC8jiWD3W5wIhGRpmnHjh2sWLGCpUuXVthELqY4+wAAp3w0WZqISFWY7PbqVz0Wi4Vjx44RGhpaoT0nJ4fQ0FCsVmuVzhMfH0+/fv146623ALDZbERFRfG73/2OJ5988pz958yZw6uvvsquXbvO+9f46p6zMgUFBfj7+5Ofn4+fn65OcqW/f7+b25MG42kqg4c2QnB7oyOJiNRbrv492r9/PzfccAPbtm3DZDJx5p8AJpPjqtyq/n4bRb/Pxtv22ki6nfyRr6KfYPi4p42OIyJiiOr8HtWoh9tutzt/nH/t559/JjAwsErnKC0tZdOmTSQkJJwNYzaTkJDA2rVrKz1m6dKlDBw4kEmTJhEWFkbXrl156aWXnP9AqMk5AUpKSigoKKiwSe3o1y6cLfa2AFgPnf9/ExERcb1HHnmEmJgYMjMz8fHx4ZdffuH777+nb9++rFy50uh4Us+VW214nDwMQEz7TganERFpGKpVcLdo0YLAwEBMJhMdOnQgMDDQufn7+zNs2DBuvfXWKp0rOzsbq9VKWFhYhfawsDDS09MrPWb//v0sWbIEq9XK8uXLmTp1Kq+//jovvvhijc8JMGPGDPz9/Z2bJo6pPR3D/dhm7ghA/q7vDU4jItK0rF27lueff57g4GDMZjNms5khQ4YwY8YMHn74YaPjST2382gBkXbHJXqx7bsYnEZEpGGo1rJgs2bNwm63c++99/Lcc8/h7+/vfM7Dw4Po6GgGDhzo8pBn2Gw2QkNDeffdd7FYLPTp04cjR47w6quvMn369Bqfd8qUKUyePNn5uKCgQEV3LbGYTRSG9YWMzzAf0WR2IiJ1yWq10rx5cwCCg4M5evQocXFxtGnTht27dxucTuq7U4e30Nx0CgBLi9YGpxERaRiqVXDfc889AMTExDB48GDc3Gq+jHdwcDAWi4WMjIwK7RkZGYSHh1d6TEREBO7u7lgsZ2fF7NSpE+np6ZSWltbonACenp54enrW+L1I9fh3GAIZEFB0CE5mQbMQoyOJiDQJXbt25eeffyYmJob4+HheeeUVPDw8ePfdd4mNjTU6ntRndjt9vr4ZgHxzAP7u3gYHEhFpGGp0DXfz5s3ZuXOn8/Hnn3/O6NGjeeqppygtLa3SOTw8POjTpw9JSUnONpvNRlJS0nl7yQcPHkxKSgo2m83ZtmfPHiIiIvDw8KjROaXu9eoQwy6bYwSBNVXXcYuI1JVnnnnG+Rv6/PPPc+DAAYYOHcry5ct54403DE4n9VphFhZbGQBfhNxrcBgRkYajRgX3gw8+yJ49ewDHddVjxozBx8eHxYsX88QTT1T5PJMnT2bu3LksWLCAnTt3MnHiRAoLCxk/fjwAY8eOZcqUKc79J06cSG5uLo888gh79uxh2bJlvPTSS0yaNKnK5xTjdYn0Y6spDoDjO38wOI2ISNORmJjIjTfeCEC7du3YtWsX2dnZZGZmctVVVxmcTuq1fd8CcMQexI7ImwwOIyLScNRoTPiePXvo2bMnAIsXL+byyy/nn//8Jz/++CO33XYbs2bNqtJ5xowZQ1ZWFtOmTSM9PZ2ePXuyYsUK56RnqampmM1n/yYQFRXFl19+yaOPPkr37t1p2bIljzzyCH/84x+rfE4xnpvFzPHgPpDzDTbNVC4iUifKysrw9vZmy5YtdO3a1dle1dVFpAkryoV/PwjAYXsIAd6VL80qIiLnqlHBbbfbnUPSvvnmG37zm98AjoI4Ozu7Wud66KGHeOihhyp9rrIlSgYOHMi6detqfE6pH3zaDYYcCCrYAaVF4OFjdCQRkUbN3d2d1q1b1/u1tqUeyjo7od7fykcxsoV+s0VEqqpGQ8r79u3Liy++yIcffsiqVau49tprAThw4IB6kqVKOnfqRrq9BRas2A5vMjqOiEiT8PTTT/PUU0+Rm5trdBRpSI79DMAaa2dW2noRE+JrcCARkYajRj3cs2bN4s477+Szzz7j6aefpl27dgAsWbKEQYMGuTSgNE7dWgXwjT2OkaZ15Oz8npDYoUZHEhFp9N566y1SUlKIjIykTZs2+PpWLJw2b95sUDKp1755FoBjBAHQIbS5gWFERBqWGhXc3bt3Z9u2bee0v/rqqxWW7BI5Hw83M9ktekH+Okr2/2h0HBGRJmH06NFGR5AGyGa2YAZWWntwR3xr/H10DbeISFXVfCFtYNOmTc7lwTp37kzv3r1dEkqaBs+2g2Hz3wg6vgVsVjDrjzUiIrVp+vTpRkeQhsRuh8MbMZeeBGC1rStrru1scCgRkYalRgV3ZmYmY8aMYdWqVQQEBACQl5fHlVdeycKFCwkJCXFlRmmk2ncfwIlN3jS3FWI7tg1zy55GRxIREZEzdi+HhXc4H8Z3isHbQ38cFxGpjhpNmva73/2OkydP8ssvv5Cbm0tubi7bt2+noKCAhx9+2NUZpZHqHhXEFhzrcWds/9bgNCIijZ/ZbMZisZx3E6kgJ6XCwzYh/gYFERFpuGrUw71ixQq++eYbOnXq5Gzr3Lkzs2fPZvjw4S4LJ42bm8VMeos+kLeF4pQfIHGy0ZFERBq1f//73xUel5WV8dNPP7FgwQKee+45g1JJvVV2qsLDmGDNTi4iUl01KrhtNhvu7udOmOHu7u5cn1ukKjzaDoFN8wjO2ei4VsxkMjqSiEijdf3115/TdvPNN9OlSxcWLVrEfffdZ0AqqbdKC513bXaTCm4RkRqo0ZDyq666ikceeYSjR486244cOcKjjz7K1Vdf7bJw0vi16zGUU3YPmtsKKE/fYXQcEZEmacCAASQlJRkdQ+qbsiLn3cfLHtT62yIiNVCjgvutt96ioKCA6Oho2rZtS9u2bYmJiaGgoIA333zT1RmlEevYKpifTY7ruI9t1T/2RETq2qlTp3jjjTdo2bKl0VGkvil1FNwzym7nS/erCGnmaXAgEZGGp0ZDyqOioti8eTPffPMNu3btAqBTp04kJCS4NJw0fhaziYwWveH4ttPXcWvSPRGR2tKiRQtMv7p0x263c+LECXx8fPjoo48MTCb1ka20EDNQhCdRgT4VvjsiIlI11Sq4v/32Wx566CHWrVuHn58fw4YNY9iwYQDk5+fTpUsX5syZw9ChQ2slrDROHrFDYdMCQnQdt4hIrfrLX/5SoWgym82EhIQQHx9PixYtDEwm9dGRzGyigGI8eHaU1t8WEamJahXcs2bNYsKECfj5+Z3znL+/Pw8++CAzZ85UwS3V0rbX5ZRsdCPAlktp1l48QjsYHUlEpFEaN26c0RGkgcgrKuVoVg5RZggODCQ+NsjoSCIiDVK1ruH++eefGTFixHmfHz58OJs2bbrkUNK0tG8Zwi+m9gAc+ekbg9OIiDRe77//PosXLz6nffHixSxYsMCARFJfbUnLw5sSAB64qqvBaUREGq5qFdwZGRmVLgd2hpubG1lZWZccSpoWk8lERmBfAIpTvjc4jYhI4zVjxgyCg4PPaQ8NDeWll14yIJHUVweyCwk3HQegRVgrg9OIiDRc1Sq4W7Zsyfbt28/7/NatW4mIiLjkUNL0eLQdAkBwrkZIiIjUltTUVGJiYs5pb9OmDampqQYkkvoqLTOXUFOe40FAG0OziIg0ZNUquEeOHMnUqVMpLi4+57lTp04xffp0fvOb37gsnDQdbXtfTZndQog1k5KsA0bHERFplEJDQ9m6des57T///DNBQbpGV84qzNgPQJmbL3hrQj0RkZqqVsH9zDPPkJubS4cOHXjllVf4/PPP+fzzz3n55ZeJi4sjNzeXp59+uraySiPWJjyY3eZYAA799LXBaUREGqfbb7+dhx9+mO+++w6r1YrVauXbb7/lkUce4bbbbjM6ntQTR/NOkZG6B4Cy5lFaPURE5BJUa5bysLAw1qxZw8SJE5kyZQp2ux1wXIObmJjI7NmzCQsLq5Wg0riZTCYyA/tCzl6K934Pwx8wOpKISKPzwgsvcPDgQa6++mrc3Bz/BLDZbIwdO1bXcIvTpk3riDfvBMAjONrYMCIiDVy1Cm5wXOe1fPlyjh8/TkpKCna7nfbt22v9TrlkbrFDIedjQnI3Gh1FRKRR8vDwYNGiRbz44ots2bIFb29vunXrRps2ukZXTtu2hFE/3Of8F6JbYLShcUREGrpqF9xntGjRgn79+rkyizRxbftcjTXZRIT1GIXZqfgGtzY6kohIo9S+fXvat29vdAypjzIck+MW2H0o94sisIcuNRARuRTVuoZbpDa1DA8nxeyYPffgJl3HLSLiajfddBMvv/zyOe2vvPIKt9xyiwGJpL4pL8wDYL51BFl3JkFkL2MDiYg0cCq4pV7JDOwDQMm+HwxOIiLS+Hz//feMHDnynPZrrrmG77//vtrnmz17NtHR0Xh5eREfH09ycvJ5973iiiswmUznbNdee221X1dqT3ZOJgAmrwA6hDUzOI2ISMOnglvqFY+2QwEIztF13CIirnby5Ek8PDzOaXd3d6egoKBa51q0aBGTJ09m+vTpbN68mR49epCYmEhmZmal+3/66accO3bMuW3fvh2LxaKe9XrEbrdz8PBRAKIiwzFpdnIRkUumglvqlZg+w7DZTbS2plGQedjoOCIijUq3bt1YtGjROe0LFy6kc+fO1TrXzJkzmTBhAuPHj6dz587MmTMHHx8f5s+fX+n+gYGBhIeHO7evv/4aHx8fFdz1yC87f8G/PAeADm1aGZxGRKRxqPGkaSK1ITQskj2WWDrY9nFw43K6j9TyYCIirjJ16lRuvPFG9u3bx1VXXQVAUlIS//znP1myZEmVz1NaWsqmTZuYMmWKs81sNpOQkMDatWurdI558+Zx22234evre959SkpKKCkpcT6ubi+8VMPBH+n6yUhnV0znGE1cKiLiCurhlnonIzgeAGvKdwYnERFpXEaNGsVnn31GSkoKv/3tb/nDH/7AkSNH+Pbbb2nXrl2Vz5OdnY3VaiUsLKxCe1hYGOnp6Rc9Pjk5me3bt3P//fdfcL8ZM2bg7+/v3KKioqqcUarHfnp28lN2Dw406405qq/BiUREGgcV3FLveLR39Lq0PJ4MdrvBaUREGpdrr72WH3/8kcLCQvbv38+tt97KY489Ro8ePeosw7x58+jWrRv9+/e/4H5TpkwhPz/fuaWlpdVRwqYn53geAMtsA9g94mNw9zY2kIhII6GCW+qddn0TKLG7EWrP5njaTqPjiIg0Ot9//z333HMPkZGRvP7661x11VWsW7euyscHBwdjsVjIyMio0J6RkUF4ePgFjy0sLGThwoXcd999F30dT09P/Pz8KmxSOzalHAHAy6c5I7pe+H9DERGpOhXcUu8EtWjBTnfH5D1HNn1hcBoRkcYhPT2dP//5z7Rv355bbrkFPz8/SkpK+Oyzz/jzn/9Mv379qnwuDw8P+vTpQ1JSkrPNZrORlJTEwIEDL3js4sWLKSkp4a677qrxexHXSsst4lB6NgAxEcEGpxERaVxUcEu9lBs6wHHn4Cpjg4iINAKjRo0iLi6OrVu3MmvWLI4ePcqbb755SeecPHkyc+fOZcGCBezcuZOJEydSWFjI+PHjARg7dmyFSdXOmDdvHqNHjyYoKOiSXl9cJ2lnBj4UA9CuZajBaUREGhfNUi71km/HBDj6Lm3yN4LNCmaL0ZFERBqsL774gocffpiJEyfSvn17l5xzzJgxZGVlMW3aNNLT0+nZsycrVqxwTqSWmpqK2Vzx7/q7d+9m9erVfPXVVy7JIK6xJ/MkfUyO2eA9fZobnEZEpHFRwS31Uqe+l1GQ5IOfqZCM3esJ6zTI6EgiIg3W6tWrmTdvHn369KFTp07cfffd3HbbbZd83oceeoiHHnqo0udWrlx5TltcXBx2TYZZ72QWlODD6eXX3H2MDSMi0shoSLnUS34+3uzycsyYe+wnXcctInIpBgwYwNy5czl27BgPPvggCxcuJDIyEpvNxtdff82JEyeMjigG2p1RcLbg9jj/uugiIlJ9Kril3jrVaggAnmmrDU4iItI4+Pr6cu+997J69Wq2bdvGH/7wB/785z8TGhrKddddZ3Q8MUBK5knSck/hZSp1NGg5MBERl1LBLfVWeK8RAMQWbaOs+KTBaUREGpe4uDheeeUVDh8+zMcff2x0HDHIz2l5APhR5Gjw1NJrIiKupIJb6q32nXpzjGA8TWXs3/Cl0XFERBoli8XC6NGjWbp0qdFRxAAZJxyzk4d5nh5S7hVgXBgRkUZIBbfUW2aLmX3+jsnSCrcvNziNiIhI45NZ4Ci0fWynR5J5+RuYRkSk8VHBLfWaucNwAFpm/QCa2VZERMSlMgqKMWPDy1roaFDBLSLiUvWi4J49ezbR0dF4eXkRHx9PcnLyeff9+9//jslkqrB5eXlV2GfcuHHn7DNixIjafhtSC9oNHEmJ3Y0wWwbHU38xOo6IiEijciiniGZnrt8GFdwiIi5meMG9aNEiJk+ezPTp09m8eTM9evQgMTGRzMzM8x7j5+fHsWPHnNuhQ4fO2WfEiBEV9tGEMA1TaGAQ2927AXB4g64vFBERcZX0/GJ2HCvA33S6d9vdB9w8jA0lItLIGF5wz5w5kwkTJjB+/Hg6d+7MnDlz8PHxYf78+ec9xmQyER4e7tzCwsLO2cfT07PCPi1atKjNtyG16Hjk5QB4HPjG4CQiIiKNx8R/bAIgnOOOhmahBqYREWmcDC24S0tL2bRpEwkJCc42s9lMQkICa9euPe9xJ0+epE2bNkRFRXH99dfzyy/nDjVeuXIloaGhxMXFMXHiRHJycs57vpKSEgoKCipsUn8E9voNALGFP2MrPmFwGhERkYZvb8YJfkrNA2BC19ONAW0MyyMi0lgZWnBnZ2djtVrP6aEOCwsjPT290mPi4uKYP38+n3/+OR999BE2m41BgwZx+PBh5z4jRozggw8+ICkpiZdffplVq1ZxzTXXYLVaKz3njBkz8Pf3d25RUVGue5Nyybp27U0qYbhTzv5kzVYuIiJyqX45erZzYdi+GY47Aa0NSiMi0ngZPqS8ugYOHMjYsWPp2bMnl19+OZ9++ikhISG88847zn1uu+02rrvuOrp168bo0aP573//y4YNG1i5cmWl55wyZQr5+fnOLS0trY7ejVSFh7uF/QGDATi5fZnBaURERBq+/dmO67Zv6xeFyeLuaGzV18BEIiKNk6EFd3BwMBaLhYyMjArtGRkZhIeHV+kc7u7u9OrVi5SUlPPuExsbS3Bw8Hn38fT0xM/Pr8Im9Ytn55EAtM5aBbbKRyqIiIhI1Rw+7piZvE2AO5SdnqW88/UGJhIRaZwMLbg9PDzo06cPSUlJzjabzUZSUhIDBw6s0jmsVivbtm0jIiLivPscPnyYnJycC+4j9VvnQSPJt/sSaM/j6PZVRscRERFp0DILSgCI8i492+ipDgcREVczfEj55MmTmTt3LgsWLGDnzp1MnDiRwsJCxo8fD8DYsWOZMmWKc//nn3+er776iv3797N582buuusuDh06xP333w84JlR7/PHHWbduHQcPHiQpKYnrr7+edu3akZiYaMh7lEvn38yXrb6OP8LkbFhicBoREZGGLfNEMQDhno5bPP3BbDEwkYhI4+RmdIAxY8aQlZXFtGnTSE9Pp2fPnqxYscI5kVpqaipm89m/Cxw/fpwJEyaQnp5OixYt6NOnD2vWrKFz584AWCwWtm7dyoIFC8jLyyMyMpLhw4fzwgsv4Onpach7FNco6/Ab2PIN4Ue+BrsdTCajI4mIiDQ4+7NOsifjJACh7qcLbi9/AxOJiDReJrvdbjc6RH1TUFCAv78/+fn5up67HjmclUOLtzrhayoh/+6v8W/b3+hIIiK1Sr9HFenzuDRlVht//mIXP6ZksyvdsczmnnHueCy8BcK6wcTVBicUEWkYqvN7ZPiQcpGqahUSxGbPfgAcWbPI4DQiIiINyw97s5i3+oCz2J41picetjNDypsZmExEpPFSwS0NSlFbx2zlLQ6tcAwrFxERkSpJyXQMI+8RFcDsO3pzfc9IKHdMnoabLrsTEakNKrilQel02U2U2N2IKD9M3sGfjY4jIiLSIGw9nMdLy3cBcHn7YK7tHoHJZILyU44d3LwMTCci0nip4JYGpXVEOD979AYgbfU/DU4jIiLSMMz8eo/zfpeWv5ogTT3cIiK1SgW3NDgF7UcDEHrwcw0rFxERqQI389mVPYa0Cz77RPnpa7jVwy0iUitUcEuD0/HyMZy0exFmTSd3t2ZUFRERuZgD2YUAvHRDN3w9f7UqrLPgVg+3iEhtUMEtDU6rsGCSvQYDkPXjBwanERERqd+O5J1iX1YhFrOJa7tHVHzSOaRcPdwiIrVBBbc0SCWdbgYg4vAKKC81OI2IiEj9dSjH0bvdJsgHf2/3ik+qh1tEpFap4JYGqcdl15FpD8DPXkD2z8uNjiMiIlJvZRY4erHDmlfSi60ebhGRWqWCWxqkyMBmbGx2FQDH135kcBoREZH6a3PqcQBC/SrpxVYPt4hIrVLBLQ2WR+/bAWidvQr7qeMGpxEREal/rDY7H6w9BEC4XyW92IU5jlv1cIuI1AoV3NJgDRx8JXvsUXhSStoqTZ4mIiLyv35KPfsH6Vv6tqr45H8nw+5ljvsquEVEaoUKbmmwfL3c2R5xAwDuWxZoTW4REZFfSc0p4uY5awG4rkck7UKbV9xh53/O3o+Kr8NkIiJNhwpuadAih46j2O5ORPE+Sg4lGx1HRESk3vhkY5rz/shu4RWfLDsFhZmO+4/vg4judZhMRKTpUMEtDVr/TrF8ZxkEQMZ37xicRkREpP7Yn30SgGGdw0js8j8F97q/OW49/cAnqI6TiYg0HSq4pUEzm00c73QHAGGp/4XiAoMTiYiI1A/7sxzrb9/ePwqTyVTxyWNbHLeefvC/z4mIiMuo4JYGb8iVo9hra4mnvYScNZo8TURExGazczDHUXDHBDdzNFrLoazYsR0/6Ggb+YoxAUVEmggV3NLgtQ72ZW2QY/I0kt8Bm83YQCIiIgZLLyimuMyGm9lEqxbecGQzvBwNfwpzbMd+duwY0MbQnCIijZ0KbmkUWl1xHwV2H4KKUynd/ZXRcURERAx1KKcIgFYtvHG3mCHlGyg9UXGn4DgIbm9AOhGRpkMFtzQKl3eL4b9uCQDkfftXg9OIiIgYK/NEMQDh/qfX1z4zhPyyx+HJNMf223Xg5mlMQBGRJkIFtzQKFrOJkt73Y7WbCM1aA5k7jY4kIiJimMyCEgBCm3tByQnY8g/HE0HtwcvPsZn1z0ARkdqm/9JKo3Ht0Hi+tvcD4Ph3bxqcRkRExDhnerhDm3vC0Z/OPtFmkEGJRESaJhXc0miE+nmxs7VjibBmuxbDySyDE4mIiBgj84SjhzvMzwuOH3I0Rg+FgCgDU4mIND0quKVRuXzY9WyxxeJuL6Xwe13LLSIiTVNGgaOHO9qSBUsfcjQGtTUwkYhI06SCWxqV3m0C+aLF3QC4bZoPp44bnEhERKTunenhjjmx4WxjuwSD0oiINF0quKXR6Xn1GHbaovC0FlK2Zo7RcUREROpc1ulJ04JKjzkaetwBnUYZmEhEpGlSwS2NzvCukSz0uhUA27q/OWZnFRERaSLyT5VxoqQcC1ZabH7L0RjaydhQIiJNlApuaXQsZhMxl93BPlsEnmX52JLnGR1JRESkzhzMLgSgb/Pcs40xlxmURkSkaVPBLY3SLf2iWWC5AYCyH/4CxQUGJxIREakbezNPAtCz2enfvsBYiOxpXCARkSZMBbc0Sr6eboQOHkuKLRLP0jzsP75hdCQREZE6sWf7RjZ4TuTJ49McDSEaTi4iYhQV3NJojR3SjrfMtwNgXTMbTmYanEhERKT2BR9bRYgpHxN2R0O7q4wNJCLShKnglkbLz8udNoPGsMXWFjdrEfZVrxgdSUREpFaVltvwPJkGQG7X8fDEAeh3v8GpRESaLhXc0qjdOySWv5ruBMC+8e+Qe8DYQCIiIrXoteVbucftawA8IrqAT6DBiUREmjYV3NKo+fu402XQb/je2g2zvQz7V1ONjiQiIlJrkteuct73aa+ZyUVEjKaCWxq9CUNjmWW5h3K7GdOu/8C+74yOJCIi4nJpuUW0MmUBcNizHebQOIMTiYiICm5p9Px93Em88io+tA4DwPbFH8FaZnAqERER1/p2VyZRpwvuVnF9DU4jIiKggluaiHsGRfOxz53k2Jtjzt4NyXONjiQiIuJSa/ZlO3u4CWhtbBgREQFUcEsT4eVu4f5hvXm1fAwA9u9egoJjBqcSERFxnX1ZhSq4RUTqGRXc0mTc2LslmwOvZYstFlPpCVj+mNGRREREXMJqs1OYc5TLLVsdDS3aGBtIRESAelJwz549m+joaLy8vIiPjyc5Ofm8+/7973/HZDJV2Ly8vCrsY7fbmTZtGhEREXh7e5OQkMDevXtr+21IPedmMfP0qG48WfYAZXYL7Pov7FhqdCwREZFLllNYQn/71rMN4d2MCyMiIk6GF9yLFi1i8uTJTJ8+nc2bN9OjRw8SExPJzMw87zF+fn4cO3bMuR06dKjC86+88gpvvPEGc+bMYf369fj6+pKYmEhxcXFtvx2p5y7vEEJUp37MsY4CwL78MTh13OBUIiIilyavqMw5YRpdbgDvFsYGEhERoB4U3DNnzmTChAmMHz+ezp07M2fOHHx8fJg/f/55jzGZTISHhzu3sLAw53N2u51Zs2bxzDPPcP3119O9e3c++OADjh49ymeffVYH70jqu6nXduYdbmSfLQLTyQz48mmjI4mIiFyS4ydLeMx9seNBSCdjw4iIiJOhBXdpaSmbNm0iISHB2WY2m0lISGDt2rXnPe7kyZO0adOGqKgorr/+en755RfncwcOHCA9Pb3COf39/YmPjz/vOUtKSigoKKiwSePVOsiH8Zd15I9lE7Bhgi3/gB2fGx1LRESkxk5l/2q0X5tBxgUREZEKDC24s7OzsVqtFXqoAcLCwkhPT6/0mLi4OObPn8/nn3/ORx99hM1mY9CgQRw+fBjAeVx1zjljxgz8/f2dW1RU1KW+NannfntFO4769WROuWNoOUsfhoKjxoYSERGpIWvuQQBOmptDzFBjw4iIiJPhQ8qra+DAgYwdO5aePXty+eWX8+mnnxISEsI777xT43NOmTKF/Px855aWlubCxFIfeXtYmH5dF/5SfjPbbDFQnAf//j+w2YyOJiIiUnV5afBWfwaunwTAMZ+OBgcSEZFfM7TgDg4OxmKxkJGRUaE9IyOD8PDwKp3D3d2dXr16kZKSAuA8rjrn9PT0xM/Pr8ImjV9il3ASurbikbJJFOMJB1bBj7OMjiUiIlJ1e7+E7N34cAoAe5vBBgcSEZFfM7Tg9vDwoE+fPiQlJTnbbDYbSUlJDBw4sErnsFqtbNu2jYiICABiYmIIDw+vcM6CggLWr19f5XNK0/HcdV3I8mzN9LKxjoZvX4AD3xsbSkREpKqOO67d/pd1KFeX/ZW2N043OJCIiPya4UPKJ0+ezNy5c1mwYAE7d+5k4sSJFBYWMn78eADGjh3LlClTnPs///zzfPXVV+zfv5/Nmzdz1113cejQIe6//37AMYP573//e1588UWWLl3Ktm3bGDt2LJGRkYwePdqItyj1WKifF0+N7MQi6xX823Y52G2w5F4oOGZ0NBERkYvLSwXgF1s0tyVehsVi+D/tRETkV9yMDjBmzBiysrKYNm0a6enp9OzZkxUrVjgnPUtNTcVsPvvjcfz4cSZMmEB6ejotWrSgT58+rFmzhs6dOzv3eeKJJygsLOSBBx4gLy+PIUOGsGLFCry8vOr8/Un9N6ZvFJ/9dIQpB8bRq1kq0YUHYPE4GPdfsLgbHU9EROT88hw93IftwXTy0W+WiEh9Y7Lb7XajQ9Q3BQUF+Pv7k5+fr+u5m4jUnCKu+ev3BJcd4SufaXhaT0Lf++Da18FkMjqeiDRR+j2qSJ9HJV6JhaIcrimZwR/uvomEzmEXP0ZERC5JdX6PNO5IBMfa3NNGdeaQPZyHSx7Ejgk2zoPkd42OJiIiUrmSk1CUA8BhewgtfNXDLSJS36jgFjnt1r5RDOscxpflfXjPa5yjccWTsPdrQ3OJiIhU6vT12/l2X07gg7+3h8GBRETkf6ngFjnNZDIx48ZuBDfz4E95CWwO/I1jErXF4yHjF6PjiYiIVHS64E6zhwAQ5udpZBoREamECm6RXwlu5smrN/cATIw5eis5wf2g9AR8eCMcP2h0PBERkbNOT5iWZg8hpLknzb00pFxEpL5RwS3yP67sGMqDl8dShhvXZU2kNKgjnEyHD0bDyUyj44mIiACQn5kGQLo9kHYhzQxOIyIilVHBLVKJx4bH0bdNC46UeHGfdQo2/9Zw/ICjp/tUntHxREREOJaRDkA+vkwZ2dHgNCIiUhkV3CKVcLeYefOOXgT6evBDujt/iXgFfEMgYxt8dBMU5xsdUUREmrjiE7kA9Gjfhu6tAowNIyIilVLBLXIeEf7ezLy1BwBvbrHxRa+3wbsFHNnoGF6unm4RETGIzWYnLzcbAP+AYIPTiIjI+ajgFrmAK+JCeWx4BwAe/q6cbQkfgXcgHN0MH1wPRbkGJxQRkaZo/YFc/EyFAISEhhmcRkREzkcFt8hFTLqyHdd2j6DMamfc8lOk37gEfILh2Bb44Do4kWF0RBERaWJCV/2R3uYUAKIiIgxOIyIi56OCW+QiTCYTr97cnc4RfuQUlnLv8iJO3fm545ru9G0wbxjk7DM6poiINBWFObRNXQyAFQsEtTc4kIiInI8KbpEq8PFwY+49fQny9WDHsQImfX2K8nEroEW0Yx3UecPhyGajY4qISFOQd9B5d07vpdBcQ8pFROorFdwiVdQywJt3x/bF083Mt7symfpDEfZ7v4KIHlCUDX//DaR8Y3RMERFp5Ky5hwDYaOuAf2iUwWlERORCVHCLVEOfNi144/ZemE3wcXIab6wvgHHLIPYKKCuEf9wK698Bu93oqCIitW727NlER0fj5eVFfHw8ycnJF9w/Ly+PSZMmERERgaenJx06dGD58uV1lLbxWLPpJwDS7CEMba8ZykVE6jMV3CLVlNglnOeu7wrAX77Zwydb8+COxdDjDrBb4Ysn4D+PQHmpsUFFRGrRokWLmDx5MtOnT2fz5s306NGDxMREMjMzK92/tLSUYcOGcfDgQZYsWcLu3buZO3cuLVu2rOPkDdsnG9M4mLIDgLJmrWgT5GtwIhERuRA3owOINER3D2jDsbxTvL1yH1P+vQ0/bzdGjH4bwjrDV1Nh8wLISYFbPwBf9T6ISOMzc+ZMJkyYwPjx4wGYM2cOy5YtY/78+Tz55JPn7D9//nxyc3NZs2YN7u7uAERHR1/wNUpKSigpKXE+LigocN0baKB8177GrW6Oy5cSB/c3OI2IiFyMerhFaujxxDhu7tMKq83O7z7+ie92Z8Gg38Edn4CnHxz6Ed65DNI2GB1VRMSlSktL2bRpEwkJCc42s9lMQkICa9eurfSYpUuXMnDgQCZNmkRYWBhdu3blpZdewmq1nvd1ZsyYgb+/v3OLimri1yvbrAzP+cj50L/dAAPDiIhIVajgFqkhk8nEyzd15zen1+h+8KNN/JiSDR2Gw31fQ1A7KDgC74+AdX/Tdd0i0mhkZ2djtVoJC6s4O3ZYWBjp6emVHrN//36WLFmC1Wpl+fLlTJ06lddff50XX3zxvK8zZcoU8vPznVtaWppL30eDc+IY7pQDcOj2VRDe1eBAIiJyMSq4RS6BxWziL2N6MqxzGKXlNu5fsJHkA7kQ2hEeWAldbgBbOax4EhbfA8X5RkcWETGEzWYjNDSUd999lz59+jBmzBiefvpp5syZc95jPD098fPzq7A1Zacy9wNw0BZGULSKbRGRhkAFt8glcreYeeuOXlzWIYRTZVbGv5/M2n054Nkcbn4frnkFzO6w43P422A48IPRkUVELklwcDAWi4WMjIwK7RkZGYSHh1d6TEREBB06dMBisTjbOnXqRHp6OqWlmmSyKk5mOAruY6YQmnlqGh4RkYZABbeIC3i6WXjnrj4MbhdEYamVce8ns3J3JphMEP8g3LsCWkRDfhosGAVfPg1lxUbHFhGpEQ8PD/r06UNSUpKzzWazkZSUxMCBAys9ZvDgwaSkpGCz2Zxte/bsISIiAg8Pj1rP3BiUZDkK7lz3yv+oISIi9Y8KbhEX8fawMO+eflzVMZSSchsTPtjIiu2nr2Vs1Rf+70fofQ9gh7Vvwdwr4djPhmYWEampyZMnM3fuXBYsWMDOnTuZOHEihYWFzlnLx44dy5QpU5z7T5w4kdzcXB555BH27NnDsmXLeOmll5g0aZJRb6HBseYeAuCEl5ZSExFpKFRwi7iQl7uFOXf14dpujonUJv1zM59uPux40rMZXPcG3L4IfEMgcwe8eyV89QyUFhobXESkmsaMGcNrr73GtGnT6NmzJ1u2bGHFihXOidRSU1M5duyYc/+oqCi+/PJLNmzYQPfu3Xn44Yd55JFHKl1CTM7jhOPzNAeo4BYRaShMdrumTv5fBQUF+Pv7k5+f3+QnaJGasdrs/PFfW1myyVFsP54Yx2+vaIvJZHLsUJgNyx+HXz51PA5oDdf+BdonnOeMItIU6feooqb8efywN4vmHw6np3k/K7r9hRE33Wt0JBGRJqs6v0fq4RapBRaziVdu6s6EoTEAvPrlbp7+bDvl1tPXLvoGwy3vwx2LwT8K8lLhHzfBkvvgRMYFziwiIk3NqVIrd89Lxo8iAFpGRBicSEREqkoFt0gtMZtNPH1tZ54d1RmTCf65PpUHPtxEYUn52Z06DIffroOBD4HJDNuXwJt94Mc3oFyz9oqICHz5i2M+ED+To+DuGN3KyDgiIlINKrhFatm4wTH87c4+eLqZ+XZXJjf9bQ2pOUVnd/BsBol/ggnfQmRvKD0BX0+Fvw2EPV8ZF1xEROqFBWsPAnb8Txfc7r6BhuYREZGqU8EtUgdGdA3n4wcGENzMk13pJxj11mpW7cmquFNkL7g/Ca6fDb6hkJMC/7wFProZMn4xJriIiBhq1Z4sfkrNo6MpDXdOj5Dy8jc2lIiIVJkKbpE60rt1C/77uyH0jAog/1QZ495P5u2VKVSYt9Bshl53we82waDfgdkdUr6Gvw2GTx+E4wcNyy8iInWrzGrjnvnJAIx2X3/2CQ9fgxKJiEh1qeAWqUPh/l4senAAt/WLwm6HV1bs5v4FG8kt/J/rtb38YPiLjuu7O48G7LB1IbzZF774I5zMquz0IiLSiOzJOOG8f1uH03cG/BbOrHghIiL1ngpukTrm6Wbhzzd156UbuuHhZiZpVybX/PV71u7LOXfn4HZw6wKY8B3EXgG2Mlg/B/7aA759EYpy6zy/iIjUjcyCEoLI5yOfmQSkfetobNXX2FAiIlItKrhFDHJHfGs+++1g2ob4klFQwh3vreP1r3afXTrs11r2hrGfw92fOa71LiuE71+FWd3g6+nq8RYRaYQyTxRzjSWZIbaNUJIPmCC8h9GxRESkGlRwixioc6Qf//ndEMb0dQwxf/PbFG57dx1puUWVH9D2Skdv960fQlg3KD0JP85yFN4rnoIT6XWaX0REak9mQQlRpkzHg46/gf9b7Rj5JCIiDYYKbhGD+Xi48fLN3Xnj9l4093Rj46HjjJj1PR8np1acUO0Mkwk6Xwf/9wPcvtCxlFj5KVg3G2Z1h2V/gLy0un8jIiLiUmm5hTzotszxIHoohHc1NpCIiFSbCm6ReuK6HpEsf2Qo/aMDKSy1MuXTbYx7fwPp+cWVH2AyQdw1jvW77/oXRA0AawlseM9xjfe/JkD6trp9EyIi4jIlGXvOPojQUHIRkYZIBbdIPRIV6MPHDwzgmWs74eFmZtWeLIb/ZRWfbEyrvLcbHIV3uwS4dwXc8x+IuRzsVtj2CcwZAh+Mhn3fwvmOFxGReunE8YyzD9oMNC6IiIjUmApukXrGYjZx/9BYlj88hB6t/CkoLueJJVu57d117Ms6ef4DTSaIuQzuWQoPrIKuN4HJAvu/gw9vgHeGwtZPoLz0/OcQEZF6If9UGbZT+QBYNVGaiEiDpYJbpJ5qF9qcf00cxJRrOuLtbmH9gVyumfUDs77ZQ0m59cIHR/aEm+fDwz9B/P+Bu49jePmnE+AvXRxLiuUfqZP3ISIi1XcwuxA/HBNoWrz9DU4jIiI1pYJbpB5zs5h58PK2fPXoZVwRF0Kp1casb/ZyzV9/qHzd7v/Vog1c8zI8+gtc9Qw0C4fCzLNLii26Gw58r+HmIiL1zMZDx/EznV6xwksFt4hIQ1UvCu7Zs2cTHR2Nl5cX8fHxJCcnV+m4hQsXYjKZGD16dIX2cePGYTKZKmwjRoyoheQidSMq0If3x/Xjzdt7EdzMk/1Zhdw+dx2//cem8y8h9ms+gXDZ4/Dodrj5fWgz2HGd986lsGAUvD0A1r8Lp47X/psREZGL+mFnKi+6v+94oIJbRKTBMrzgXrRoEZMnT2b69Ols3ryZHj16kJiYSGZm5gWPO3jwII899hhDhw6t9PkRI0Zw7Ngx5/bxxx/XRnyROmMymRjVI5KkP1zOXQNaYzbB8m3pXD1zFa+s2MXJkvKLn8TiDl1vhPHLYeIa6HsvuPtC1i744nF4LQ6W3Af7vgObrfbflIiInONEcRluB3842xDY1rgwIiJySQwvuGfOnMmECRMYP348nTt3Zs6cOfj4+DB//vzzHmO1Wrnzzjt57rnniI2NrXQfT09PwsPDnVuLFi1q6y2I1Cl/b3deHN2N5Y8MZXC7IErLbby9ch9XvraSxRvTsNmqODw8rAv85i/wh51wzSsQ2sWxrNj2JfDhaMfSYt/NgOOHavX9iIhIRav3ZuNnP3G2YeAk48KIiMglMbTgLi0tZdOmTSQkJDjbzGYzCQkJrF279rzHPf/884SGhnLfffedd5+VK1cSGhpKXFwcEydOJCfn/Ne7lpSUUFBQUGETqe86hvvx0X3xvHt3H9oE+ZB1ooTHl2zlutmr+WFvVtVP5OUP8Q/CxB/hgZXQ737w9If8VFj1Z/hrd/j7b+Cnj6A4v9bej4iIOGw/mo+/qdDxoMuN4OZpbCAREakxQwvu7OxsrFYrYWFhFdrDwsJIT0+v9JjVq1czb9485s6de97zjhgxgg8++ICkpCRefvllVq1axTXXXIPVWvnMzjNmzMDf39+5RUVF1fxNidQhk8nE8C7hfPXoZTw1siPNPd3YfqSAu+clc+d76/g5La86J4PIXnDt6/DYbrhpHsRe4Xju4A/w+SR4rQMsHge7v9DyYiIiteRgdpFzhnJdvy0i0rC5GR2gOk6cOMHdd9/N3LlzCQ4OPu9+t912m/N+t27d6N69O23btmXlypVcffXV5+w/ZcoUJk+e7HxcUFCgolsaFE83Cw9c1paberfire9S+Me6VH5MyeH6lB8Z2S2cPwyPo21Is6qf0N0but3s2PJSHet3b10E2Xvgl387Nu9A6HIDdB8DUf0dBbuIiFyynekF9D4zQ7l3gKFZRETk0hhacAcHB2OxWMjIyKjQnpGRQXh4+Dn779u3j4MHDzJq1Chnm+30xE5ubm7s3r2btm3PnVgkNjaW4OBgUlJSKi24PT098fTUcC1p+IKaeTJ9VBfuHRzDrG/28ulPh1m+LZ0vf8nglj6teOiqdrRq4VO9kwa0hsseg6F/gGM/O4rv7UvgZAZsnOfYWkRDt1uh600Q2rFW3puISFOQmlPE/qxC7vP6wtGgHm4RkQbN0CHlHh4e9OnTh6SkJGebzWYjKSmJgQMHnrN/x44d2bZtG1u2bHFu1113HVdeeSVbtmw5b6/04cOHycnJISIiotbei0h9EhXow+u39mDFI5eR0CkMq83Owg1pXPHqSqZ8urVqS4n9L5MJInvCiJdg8k64+9/Q43bHLOfHD8L3r8Db8TA73jHZWuZOV78tEZFG79tdGQTyq7lkWsQYF0ZERC6ZyW63V3FK49qxaNEi7rnnHt555x369+/PrFmz+OSTT9i1axdhYWGMHTuWli1bMmPGjEqPHzduHHl5eXz22WcAnDx5kueee46bbrqJ8PBw9u3bxxNPPMGJEyfYtm1blXqyCwoK8Pf3Jz8/Hz8/P1e+XRFDbDqUy1++3svqlGwA3Mwmbunbit9e0Y6owGr2eP+v0kLHNd1bP4F934Kt7OxzwXHQZTR0Hg2hnTTsXKSa9HtUUVP4PO6et56ClHV87jnN0TAtF8wWY0OJiEgF1fk9Mvwa7jFjxpCVlcW0adNIT0+nZ8+erFixwjmRWmpqKmZz1TviLRYLW7duZcGCBeTl5REZGcnw4cN54YUXNGxcmqw+bQL56P54NhzM5a/fOArvj5PTWLzx8KUX3h6+Z6/3PpXnKL53fOYovrN3w6qXHVtwB+g0CuJGQmRvqMb/r0VEmgK73U7ygVz+5PaNo6H1QBXbIiINnOE93PVRU/gLujRtGw/m8tekvfyw92yP96gekdw/NIYukS66XrA431F8//IZ7EsC669mNfcNhbgR0OEax0zoHpfYyy7SSOn3qKLG/nkcLyyl1wtfs9LjUaLNGdDpOhjzodGxRETkf1Tn90gFdyUa+w+6yBn/W3gDDG4XxP1DY7miQwgmVw0BLy6APV/C7uWQ8g2U/Or6RDcviL0S4q6B9sPBT3MtiJyh36OKGvvnsTv9BCNmrWS31zg8KIf/+xHCuxodS0RE/keDGlIuIsbpGx3Ih/fFs/VwHnN/OMDybcf4MSWHH1NyaB/ajAlDY7m+VySebpc4pNHLD7rf4tjKS+HQj47e791fQH4q7PnCsQGEdYV2V0Pbq6H1AHDTpSAi0jRknigmhHxHsW2yQIhWfRARaejUw12Jxv4XdJHzOXy8iPd/PMjC5FQKS60ABPl6MKZfFHcOaEPLAG/XvqDdDhm/OArvPV/Akc3Ar/6T5O4LMZc5CvB2V0NgrGtfX6Se0+9RRY3981i0IZVPPl3CvzyfcyzJ+PttRkcSEZFKqIdbRGqkVQsfpv6mMw9f3Z6Fyam8/+NB0guKeXvlPuas2sfVncK4e0AbhrQLxmx2wXBzk8kxXDK8K1z+OBTmwP7vHMPOU5KgMLNi73dgLLRLgLZXQZvBjp5zEZFG4kB2Ea1MWY4HAW2MDSMiIi6hgltEzuHv7c6Dl7fl3iExfLMjgw/XHWLNvhy+3pHB1zsyiAn25c741tzSJwp/H3fXvbBv0NkZz202yNh+tvhOWwe5+yH5XcdmskCrvo7rv2OvcNy3uDCLiEgdO5B9kvYquEVEGhUV3CJyXu4WM9d0i+CabhGkZJ7go3Wp/GvTYQ5kF/Lisp289tVuru/Rktv6R9EzKsB1k6yBY9mwiO6Obehkx8RrB753zHi+f6Wj+E5b79hW/Rk8mjl6vdueLsBDOmrdbxFpUA5mF3Gls+BubWwYERFxCRXcIlIl7UKb8+x1XXg8MY7Pthzhw7WH2JV+gkUb01i0MY0OYc24tW8UN/ZuRaCvh+sDePlBp984NoDjhxyF95ntVC7s/dKxATQLdxTeZzbNfi4i9djPaXnszjhBlLsKbhGRxkSTplWisU/KIuIKdrudDQePszA5lWXbjlFSbgPA3WJiWOcwxvRrzZB2wVhcca33xdhskLEN9n3nKL5T10J5ccV9Qjo6Cu82g6H1QGgWUvu5RC6Rfo8qarSfh7WcudPvpqUpmyHmbfiZTsH4L6DNIKOTiYhIJbQO9yVqtD/oIrUk/1QZS38+yicb0th2JN/ZHunvxc19o7ilTyuiAn3qLlBZseOa7zO930e3UGH2c4Cgdo7Cu80gx22LaA1Bl3pHv0cVNdbPo3jX13gtvPlsg9kNJu/SHwZFROopFdyXqLH+oIvUhR1HC/hkYxr//ukI+afKAEcdO6htEKN7tiSxazh+XnU8uVlRruP67wOrIHUdZO44d5/mERUL8NDOjuvIRQyk36OKGuvnceybt4hY/TTbbdE0H3wfbTr1h9YDjI4lIiLnoYL7EjXWH3SRulRcZuWrHRl8siGN1SnZznYPNzMJnUK5rkdLrogLwcvdUvfhinIdk60dWuMYfn70J7CVV9zH0x9ax0Or/hDVDyJ7axkyqXP6PaqosX4eJ//ckWbFx1juO5qRjy8wOo6IiFyE1uEWEcN5uVu4rkck1/WIJC23iM+3HOGzLUdJyTzJ8m3pLN+WTnMvN67pGs51PVoSHxuIu6WOepR9AiHuGscGUFoERzY5iu9DayAtGUryYe9Xjg0Ak6PXu1VfiOoPrfpBUHv1govIpSk5SbPiYwD4tuxscBgREXE19XBXorH+BV3EaHa7nR3HCli65Sj/+fkoR/PPTmwW4ONOQqcwRnQJZ0j7YGN6vs+wlkP6Vkcv+OENkLYB8lPP3c/LH1r2dRTfZ3rBfQLrPq80Wvo9qqhRfh4ZO+BvAwFYecsvXNGllcGBRETkYjSk/BI1yh90kXrGZrOz8dBxPttyhC+3p5NTWOp8ztfDwhUdQxnRJZwrO4bSzLMeDMY5kQ6HN8LhZMftkc1Qfurc/QJaQ0SP01tPx22z0DqPK42Dfo8qaoyfh23ncsyLbmebLZrmD68hOtjX6EgiInIRGlIuIvWe2Wyif0wg/WMCeeH6rmw8mMsX29P58pd0juUXs2zrMZZtPYaH2/+3d+fhTVX5/8DfSdpsbZO0TZu00A0oZZNFCrUs+igdEf064OC4TMWKjvxAcEBcGUXUGQa+Oo+DOgwuP9H5fXHEYR7EZQS/WBwFLDsUCqXIUsrSpGuapEu65Pz+uCUltEADTdPl/Xqe+9zk3JPLuUfbTz+5554jx4QBRmQMNmHS4GiYdOrANDjM7L0OeFMDYD0s3QG/sFWcBGxF0pb/1UWfjb0oCW/edLGcFZ2IUFt6EiEAzoooTDJoAt0cIiLqYEy4iSjgFHIZ0vpFIq1fJJbcPQQHz1Zh02ELNuVZcKqsGluOlmDL0RLgc2B4Xz0mDZKS76GxOsgClbQqgoHYkdI29nGprNYGWA4BxblA8QFpX/Yz4Dgvbcc2tnw+JKp1Em5IYBJO1MvUlZ5CCICyIDOUQZwTgoiop+GQ8jb0xCFrRN2REALHrE58l2/F5iNW5J614eLfWLF6NW4bHI2MwSbc1C8ysM99X47LCVjzmpPwXGlN8NKjgGhqXVdtAGKGA6ZhQPRgIHooED0IUHKIaW/FeOStx/WHEMCrBgDASs1szH3+vwPbHiIiahcOKSeiHkEmkyHFHIYUcxjm3joAJY46fH+0BN/ll2Dbz2U4X1WHNTuKsGZHEbRKBSYmS0PPbxsUjchQVaCbL1GFSuvpXrymbkOtNFHShbvgxbnS2uB1tub1wn+86AQyIDwRMA2VZkk3DZES8Yh+gIK/wom6tfLjnpdVYQMC2BAiIvIX/rVGRN1GdJga94+Jx/1j4lHX0ISfTpThu/wSZOdbYbW78O1hK749bIVMBozoa8AtA6NwS0oURvQ1QCHvQkO1gzVA39HSdkFjPVCaDxQflJJv62FpX10KVJ6StqNft9RXqICoFOlOuHGg9NqYAkQkScPdiajLq6k4D23z6/hRvwhoW4iIyD84pLwNPW7IGlEPJ4RA3jk7vsu34rt8Kw6ft3sdN2iDMTE5CrcMjMLNA42IDgvQxGvXwlkqJd4XJ+El+UBDTdv15cHS3e+ogVICHpUiJeTGZA5N74YYj7z1pP5oaHLjr6vexlNlS3BYloyhS/YEuklERNROHFJORL2KTCbDDX31uKGvHk/9YiAsVXX48VgpfjhWiq0/l8JW04CvcqW1vwFgSIwOE5ONGDfAiDGJ4dAqu/CvwtAoIPQWoN8tLWVuN2ArlIallx4Fyo4BpQXSBG0N1UBZgbThK+9zhcUCkf2lhDyyPxDRX9qHJwHB3ehLCKIeYPGGPNSfLwaUQJNKH+jmEBGRn3ThvzKJiK6NWa/GfWPicN+YODQ2uZF71oYfCqQE/OC5KhwptuNIsR3v/XgSwQoZRsWFY9yASIwfYMSIvoauP1OwXC4lzRH9WpYpA6RE3H5OSrZLj3nva8pbZksv3HrJCWWAvm/rRDyiv/T8eJCyM6+OqMerrK7H2t1n8IiieaSKmgk3EVFPxSHlbehJQ9aIyFu504Vtx8uw/XgZth8vxzlbrddxrVKBMYkRGNdfSsCHxOgg70rPf1+rmgppnfDyE0DFiYv2JwFX1eU/J5MD+rjWiXhkf6mcybhfMR5567b9ceBT4PQ26TGPcU/i8wPn8Nxne/Gz+mEAQNWQh6C/b2WAG0lERO3FIeVERJcRGarC1JF9MHVkHwghUFRRg+3Hy/HTiTLknChHeXU9fmgejg5Iz3+PTYzAmMQIjEmKwNBYHYIVXfwOeFu0EdLWN9W7XAjp7nerRPyElKDXOwHbaWk7scX7szI5oOsLhCdId8I9W5K010ZwXXEilxP4Yq5nKcD6fpPw1GeFmCzf76miNycGqHFERORvTLiJqNeSyWRIiAxBQmQIfpMWD7dboMDqwE8nyvHT8TLsPFUBW00D/veIFf97xAoA0AQrMCregNTECIxNjMCoeANCVN34V6lMBoQYpS0+zfuYEICzpO1EvOKkNHFbVZG0tRqmDkAZ6p2I6+MAQ5y01/cFNOFMyKnnq630JNsAsPfIzwCCkSiztNQZ+386v11ERNQpuvFfiUREHUsul2FwjA6DY3R4bEISGprcOHSuCrtOVWBPYQV2F1aiqrZBSshPlAMAFHIZhsToMCYxAjcmGDAyzoA+Bg1kPSGRlMmAMJO0JYzzPnYhGa8sbL3ZTkvPktc7AWuetLVFGdqSfBua9/r4ltdhMYBc4ddLJOowx74Fjv5beh2XBozKlF7XeT+y4d77d/wpSGCCphBoADDxGUDdjYbHExGRT/gMdxu67TNiRORXbrfA8VKnVwJ+6TPgAGAMVWJEXwNGxDVvffUwaHvZs84NdUDVmUsS8SKprOqstL741ciDpJnVdTFAqElKwMMu7M1AqFna9+A75YxH3rpsfwgBvJ4k3c0GAMiA505Kj1UUbgM+vuvyn/3V/wWG/7pTmklERB2Dz3ATEfmBXC7DQFMYBprC8NBNCQCA87Za7C6swO7CCuSeqUJ+sR1lznpkHy1B9tESz2cTI7UYGdeShA+J0UEd3IPv3garpbW/jcltH2+olRLvqjOA7UxLIn7htf0c4G5sGbJ+JQqVlIhfSMAvJOahJiAkWlpaLSQaCIniJG/kH7WVLcm2Wi/d1a44hZ+K3fhm3Xb88ZLqLhGMoFufgyLUCAyZ2unNJSKizsM73G3ost+gE1GXV9fQhCPFduSesUnb2SqcKqtuVU8uAwZEh2JYrB5DYnUY1kfa69TBAWh1F+RuAhwWKQl3WgCHFXAUS2VOi7R3WIDaCt/OqzYAodHNCbix5fWFpDy0OTEPjQaCNX65NF8wHnnrcv0hBPDTO8D5/cDh9dKXPOFJwJkdwIAMbC/TQpSfwATFYa+P1en7Q/3UvgA1moiIrhfvcBMRBYg6WIEb48NxY3y4p8xWU4+DZ6uaE3AbDpypQpnThWNWJ45ZnVi//5ynbkKk1isJHxqrgzFUFYhLCSy5AtD3kbYraagDnFZpcxRfkphbgeoSwFkqDWEXTUCdTdrKjl29Dcqwi5LyKO9EXRsplYUYpb0mnM+b90bn9gKbF7e8Nw6U1rM/swM4/h3GA0Ab/1uoYwZ3VguJiCjAmHATEfmZQavEzQOjcPPAKE9Zib0OeeerkHfOjsPN+3O2Wpwur8Hp8hr8+1Cxp65Zp8bgmDCkmHUYZA5DijkM/aNCoQzqhsuTdbRgdfOyZAlXrud2S4m2s6Q5CS+RknDP+1LvfVM9UO+QtspT7WiITHpeNyQK0DbP+n7vaibhPV35cWkf0Q8Y+RtgyD2AKhSI6Iev953CMasTADBjQjKiUn8FHP8OaKwFht0bwEYTEVFnYsJNRBQA0To1btOpcdsgk6essroeR4rtyDtXhcPn7cg7Lw1Ht9jrYLHX4fuClonGguQy9IsKaUnCTVIi3je8h8yQ3tHk8pa1yDHoynWFkJ7BrS5rIzkvBWrKmo+VSe/rbACa1zOvkWavhzKMyXZvsPtDaZ8wDrj5WU/xVlMm5p3fBQCYmGzEU3eMlSb2ixoYiFYSEVEAMeEmIuoiwkOUGD/AiPEDjJ6yalcj8ovtyLc4UGCxo8DiwFGLA466Rs+Q9K9yW84RqgpCsikUKSbpLnj/6BD0jwpF33AtFHIm4u0ikwEag7QZB1y9flMDUFPhnYw31vm7lRRoTY3AWSmpXncyGF+t3uU5dKJEurOtDJLj/z06ll+CERH1Yky4iYi6sBBVEFITI5CaGOEpE0KguKrOk3wXWOw4anHgRKkTTlcj9hfZsL/I5nUeZZAcSZEhGBAdiv5RIegfHYr+UaFIMoYgRMVQcF0UwS3rlVPvYW+Ze+E163g4rK2Xuls/ZxyTbSKiXo5/ZRERdTMymQyxBg1iDRrcOijaU97Q5MbJ0moctdhxosSJE6XVOFHqxMmyatQ3ulFgdaDA6mh1PpNOhYTIECRFhiDBqEViZAgSIrVIiAxBKJNx6qEqqutRUe1CjF7j25dObjeQ81fUnd4DNYBTbjMc0OKP04ZBc9FSf3ERWgzro+/4hhMRUbfCv6SIiHqIYIUcKc2Tql2syS1wrrIWJ0qdLVuJlIyXV9fDanfBandh16nWS2wZQ1VIMkrJd2Lkhb2UmHMJM+rO7nsvB8dLnPjHb9Mw7qLHOK7q1H+AzYuhbn57XMRCpw5CZlo872YTEVErTLiJiHo4hVyG+Egt4iO1XnfEAWmittMVNSgsq0ZheTVOl9d49hXV9ShzulDmdGF3YWWr80aGKJEQeeGOeAjiIzWIC9ciLkKLqFAV5HxmnLqwcK30hVFlTYNvHyw/AQA45u6DTSIdG+U34zdpCUy2iYioTUy4iYh6sfAQJcJDlBgZZ2h1rKqmAacrqlFYXoPTZc37cmlf5nShvLoe5dX12HfJ8+KA9Mx43/ALCXhLIh4foUVcuBZ6Le+OU2AZtEoAQGVN/eUr2YqAPR8BjS4ICBwtdiCsbB/6AtjqHo6Ee1/DxpFXWSueiIh6NSbcRETUJr02GMO1Bgzva2h1zFHX4FkzvLC8GoVl1ThTWYMzFbUorqpFfaP0PPnJ0uo2zx2mDvIk4zF6DWINasQaWl5Hh6k5qzr51YU73LYrJdw//Dewfw0AQAZg8EWHTohYjL/k8Q0iIqJLdYmEe+XKlXjjjTdgsVgwYsQIvPPOOxg7duxVP7d27Vo8+OCDmDp1KjZs2OApF0JgyZIl+OCDD2Cz2TB+/HisWrUKycnJfrwKIqLeI0wdjGF99G1OCtXQ5Eaxra45Aa/xJOJFFTU4W1mDMmc9HHWNOFJsx5Fie5vnV8hlMIWpEGPQIEavRp/mfYxBg1i9BjEGNSJDlBzGS9cs3HOH+wpDysuOAwDE4F/ivTzALYCoUBWM0SakDs3CILOuM5pKRETdWMAT7s8++wwLFy7Eu+++i7S0NKxYsQKTJ09GQUEBoqOjL/u5wsJCPPPMM5g4cWKrY6+//jrefvtt/P3vf0dSUhIWL16MyZMn48iRI1Cr1W2cjYiIOkqwQu55ZrwtNfWNOFtZizMVNThbWYvzVbUottWhuKoW5211sNjr0OQWOF9Vh/NVl1/POlghQ3SYGlFhKkSHqRCtUyE6TN3qdWSoinfLqZULQ8pXbz+F/8k57SmPgwX3yv+DYDTiPvlB6GTA9ENjsa+xHwDgy4fGtznqg4iIqC0yIYQIZAPS0tIwZswY/PWvfwUAuN1uxMXF4cknn8QLL7zQ5meamppw880349FHH8XWrVths9k8d7iFEIiNjcXTTz+NZ555BgBQVVUFk8mEjz/+GA888MBV22S326HX61FVVQWdjt9eExF1pia3QKnD1SoRL66qxfmqOhTbalHqdKG90UsuAyJDm5PysOZEXCe9jmpOyCNClDCGqKDTBHWpu+aMR946sj92F1bgNx/sQEOT9/9IK4NX4C7FLs/7RiFHqmsVbAhDVnoCXp067Lr+XSIi6v58iUcBvcNdX1+PvXv3YtGiRZ4yuVyOjIwM5OTkXPZzr732GqKjo/HYY49h69atXsdOnToFi8WCjIwMT5ler0daWhpycnLaTLhdLhdcLpfnvd3e9hBHIiLyP4VcBrNeDbNeDcS3Xae+0Y1Spwsl9jqUOFwocbhQetHrEkcdrHYXyp0uuAVQ6nCh1OHC4av828EKGSJClIgMUSEyVInIECUiQ6XXxgtloarmciW0yoAPFKNrNCYxAnsX/wLVrkavcuP//AEoA2pTpqEpNBYNMTdiY/J/QSGTISpMFaDWEhFRdxXQvxTKysrQ1NQEk8nkVW4ymXD06NE2P7Nt2zZ8+OGHOHDgQJvHLRaL5xyXnvPCsUstW7YMr776qo+tJyKiQFEGydHHoEEfg+aK9ZrcAuXOliS8xO79ury6HuVOF8qd9XC4GtHQJDzrkreHOliOCK0003tEiBLhWmkfEaLE3FsHcCh7V7bzPejs59HqvoStEACg+cVLgJFzvxAR0fXpVl/NOxwOzJgxAx988AGMRmOHnXfRokVYuHCh573dbkdcXFyHnZ+IiAJDIZchWqdGtE4NoPUEbxera2hCRXU9yp31KK92ee3LLi5zulBWXY/6RjfqGtxtPmuuDpbjd5OYrHVpBz4BinPbPqZQAXr+HUBERNcvoAm30WiEQqGA1Wr1KrdarTCbza3qnzhxAoWFhbj77rs9ZW63GwAQFBSEgoICz+esVitiYmK8zjly5Mg226FSqaBScZgYEVFvpg5WINagQexV7poD0nwhTlcjbDUNqKiuR0VNPSqc9aisqUdFdT2a3AGdHoXaY/j9QGLriVcBAIkTgGBOskpERNcvoAm3UqnE6NGjkZ2djWnTpgGQEujs7GzMmzevVf1Bgwbh0KFDXmUvvfQSHA4H3nrrLcTFxSE4OBhmsxnZ2dmeBNtut2Pnzp2YM2eOvy+JiIh6AZlMhjB1MMLUwYiLaHs2duri0ucGugVERNQLBHxI+cKFC5GVlYXU1FSMHTsWK1asQHV1NWbOnAkAePjhh9GnTx8sW7YMarUaw4Z5zw5qMBgAwKt8wYIF+OMf/4jk5GTPsmCxsbGepJ6IiIiIiIjI3wKecN9///0oLS3Fyy+/DIvFgpEjR2LTpk2eSc+Kioogl8t9Oudzzz2H6upqzJo1CzabDRMmTMCmTZu4BjcRERERERF1moCvw90Vcd1TIiLqChiPvLE/iIioK/AlHvl265iIiIiIiIiI2oUJNxEREREREZEfMOEmIiIiIiIi8gMm3ERERHRNVq5cicTERKjVaqSlpWHXrl2Xrfvxxx9DJpN5bZzMlIiIejom3EREROSzzz77DAsXLsSSJUuwb98+jBgxApMnT0ZJScllP6PT6VBcXOzZTp8+3YktJiIi6nxMuImIiMhnb775Jh5//HHMnDkTQ4YMwbvvvgutVovVq1df9jMymQxms9mzXVgClIiIqKdiwk1EREQ+qa+vx969e5GRkeEpk8vlyMjIQE5OzmU/53Q6kZCQgLi4OEydOhWHDx++4r/jcrlgt9u9NiIiou6ECTcRERH5pKysDE1NTa3uUJtMJlgsljY/k5KSgtWrV+OLL77AmjVr4Ha7MW7cOJw9e/ay/86yZcug1+s9W1xcXIdeBxERkb8x4SYiIiK/S09Px8MPP4yRI0filltuwfr16xEVFYX33nvvsp9ZtGgRqqqqPNuZM2c6scVERETXLyjQDSAiIqLuxWg0QqFQwGq1epVbrVaYzeZ2nSM4OBijRo3C8ePHL1tHpVJBpVJdV1uJiIgCiXe4iYiIyCdKpRKjR49Gdna2p8ztdiM7Oxvp6entOkdTUxMOHTqEmJgYfzWTiIgo4HiHm4iIiHy2cOFCZGVlITU1FWPHjsWKFStQXV2NmTNnAgAefvhh9OnTB8uWLQMAvPbaa7jpppswYMAA2Gw2vPHGGzh9+jR++9vfBvIyiIiI/IoJNxEREfns/vvvR2lpKV5++WVYLBaMHDkSmzZt8kykVlRUBLm8ZSBdZWUlHn/8cVgsFoSHh2P06NH46aefMGTIkEBdAhERkd/JhBAi0I3oaqqqqmAwGHDmzBnodLpAN4eIiHopu92OuLg42Gw26PX6QDcn4BifiYioK/AlPvMOdxscDgcAcPkRIiLqEhwOBxNuMD4TEVHX0p74zDvcbXC73Th//jzCwsIgk8mu61wXvv3gt/Htxz7zDfvLN+wv37HPfNOR/SWEgMPhQGxsrNfw7N6K8Tmw2Ge+YX/5jn3mG/aXbwIVn3mHuw1yuRx9+/bt0HPqdDr+IPiIfeYb9pdv2F++Y5/5pqP6i3e2WzA+dw3sM9+wv3zHPvMN+8s3nR2f+XU5ERERERERkR8w4SYiIiIiIiLyAybcfqZSqbBkyRKoVKpAN6XbYJ/5hv3lG/aX79hnvmF/dQ/87+Q79plv2F++Y5/5hv3lm0D1FydNIyIiIiIiIvID3uEmIiIiIiIi8gMm3ERERERERER+wISbiIiIiIiIyA+YcBMRERERERH5ARNuP1u5ciUSExOhVquRlpaGXbt2BbpJnW7ZsmUYM2YMwsLCEB0djWnTpqGgoMCrTl1dHebOnYvIyEiEhoZi+vTpsFqtXnWKiopw1113QavVIjo6Gs8++ywaGxs781ICZvny5ZDJZFiwYIGnjH3m7dy5c3jooYcQGRkJjUaDG264AXv27PEcF0Lg5ZdfRkxMDDQaDTIyMvDzzz97naOiogKZmZnQ6XQwGAx47LHH4HQ6O/tSOkVTUxMWL16MpKQkaDQa9O/fH3/4wx9w8TyavbnPfvzxR9x9992IjY2FTCbDhg0bvI53VN8cPHgQEydOhFqtRlxcHF5//XV/Xxo1Y3yWMEZfH8bnq2N89g3j85V1y/gsyG/Wrl0rlEqlWL16tTh8+LB4/PHHhcFgEFarNdBN61STJ08WH330kcjLyxMHDhwQd955p4iPjxdOp9NTZ/bs2SIuLk5kZ2eLPXv2iJtuukmMGzfOc7yxsVEMGzZMZGRkiP3794tvvvlGGI1GsWjRokBcUqfatWuXSExMFMOHDxfz58/3lLPPWlRUVIiEhATxyCOPiJ07d4qTJ0+Kb7/9Vhw/ftxTZ/ny5UKv14sNGzaI3Nxc8ctf/lIkJSWJ2tpaT5077rhDjBgxQuzYsUNs3bpVDBgwQDz44IOBuCS/W7p0qYiMjBRff/21OHXqlFi3bp0IDQ0Vb731lqdOb+6zb775Rrz44oti/fr1AoD4/PPPvY53RN9UVVUJk8kkMjMzRV5envj000+FRqMR7733XmddZq/F+NyCMfraMT5fHeOz7xifr6w7xmcm3H40duxYMXfuXM/7pqYmERsbK5YtWxbAVgVeSUmJACB++OEHIYQQNptNBAcHi3Xr1nnq5OfnCwAiJydHCCH9cMnlcmGxWDx1Vq1aJXQ6nXC5XJ17AZ3I4XCI5ORksXnzZnHLLbd4Ajr7zNvzzz8vJkyYcNnjbrdbmM1m8cYbb3jKbDabUKlU4tNPPxVCCHHkyBEBQOzevdtTZ+PGjUImk4lz5875r/EBctddd4lHH33Uq+xXv/qVyMzMFEKwzy52aUDvqL7529/+JsLDw71+Hp9//nmRkpLi5ysixufLY4xuH8bn9mF89h3jc/t1l/jMIeV+Ul9fj7179yIjI8NTJpfLkZGRgZycnAC2LPCqqqoAABEREQCAvXv3oqGhwauvBg0ahPj4eE9f5eTk4IYbboDJZPLUmTx5Mux2Ow4fPtyJre9cc+fOxV133eXVNwD77FJffvklUlNT8etf/xrR0dEYNWoUPvjgA8/xU6dOwWKxePWXXq9HWlqaV38ZDAakpqZ66mRkZEAul2Pnzp2ddzGdZNy4ccjOzsaxY8cAALm5udi2bRumTJkCgH12JR3VNzk5Obj55puhVCo9dSZPnoyCggJUVlZ20tX0PozPV8YY3T6Mz+3D+Ow7xudr11Xjc9C1XhBdWVlZGZqamrx+mQKAyWTC0aNHA9SqwHO73ViwYAHGjx+PYcOGAQAsFguUSiUMBoNXXZPJBIvF4qnTVl9eONYTrV27Fvv27cPu3btbHWOfeTt58iRWrVqFhQsX4ve//z12796N3/3ud1AqlcjKyvJcb1v9cXF/RUdHex0PCgpCREREj+svAHjhhRdgt9sxaNAgKBQKNDU1YenSpcjMzAQA9tkVdFTfWCwWJCUltTrHhWPh4eF+aX9vx/h8eYzR7cP43H6Mz75jfL52XTU+M+GmTjV37lzk5eVh27ZtgW5Kl3bmzBnMnz8fmzdvhlqtDnRzujy3243U1FT86U9/AgCMGjUKeXl5ePfdd5GVlRXg1nVN//znP/HJJ5/gH//4B4YOHYoDBw5gwYIFiI2NZZ8R9VKM0VfH+OwbxmffMT73PBxS7idGoxEKhaLVrJRWqxVmszlArQqsefPm4euvv8b333+Pvn37esrNZjPq6+ths9m86l/cV2azuc2+vHCsp9m7dy9KSkpw4403IigoCEFBQfjhhx/w9ttvIygoCCaTiX12kZiYGAwZMsSrbPDgwSgqKgLQcr1X+nk0m80oKSnxOt7Y2IiKiooe118A8Oyzz+KFF17AAw88gBtuuAEzZszAU089hWXLlgFgn11JR/VNb/oZ7UoYn9vGGN0+jM++YXz2HePzteuq8ZkJt58olUqMHj0a2dnZnjK3243s7Gykp6cHsGWdTwiBefPm4fPPP8eWLVtaDdEYPXo0goODvfqqoKAARUVFnr5KT0/HoUOHvH5ANm/eDJ1O1+oXeU8wadIkHDp0CAcOHPBsqampyMzM9Lxmn7UYP358q2Vsjh07hoSEBABAUlISzGazV3/Z7Xbs3LnTq79sNhv27t3rqbNlyxa43W6kpaV1wlV0rpqaGsjl3iFAoVDA7XYDYJ9dSUf1TXp6On788Uc0NDR46mzevBkpKSkcTu5HjM/eGKN9w/jsG8Zn3zE+X7suG5+vaao1ape1a9cKlUolPv74Y3HkyBExa9YsYTAYvGal7A3mzJkj9Hq9+M9//iOKi4s9W01NjafO7NmzRXx8vNiyZYvYs2ePSE9PF+np6Z7jF5bQuP3228WBAwfEpk2bRFRUVI9cQuNyLp4FVQj22cV27dolgoKCxNKlS8XPP/8sPvnkE6HVasWaNWs8dZYvXy4MBoP44osvxMGDB8XUqVPbXCZi1KhRYufOnWLbtm0iOTm5Ryyh0ZasrCzRp08fz7Ij69evF0ajUTz33HOeOr25zxwOh9i/f7/Yv3+/ACDefPNNsX//fnH69GkhRMf0jc1mEyaTScyYMUPk5eWJtWvXCq1Wy2XBOgHjcwvG6OvH+Hx5jM++Y3y+su4Yn5lw+9k777wj4uPjhVKpFGPHjhU7duwIdJM6HYA2t48++shTp7a2VjzxxBMiPDxcaLVacc8994ji4mKv8xQWFoopU6YIjUYjjEajePrpp0VDQ0MnX03gXBrQ2WfevvrqKzFs2DChUqnEoEGDxPvvv+913O12i8WLFwuTySRUKpWYNGmSKCgo8KpTXl4uHnzwQREaGip0Op2YOXOmcDgcnXkZncZut4v58+eL+Ph4oVarRb9+/cSLL77otQRGb+6z77//vs3fW1lZWUKIjuub3NxcMWHCBKFSqUSfPn3E8uXLO+sSez3GZwlj9PVjfL4yxmffMD5fWXeMzzIhhPD9vjgRERERERERXQmf4SYiIiIiIiLyAybcRERERERERH7AhJuIiIiIiIjID5hwExEREREREfkBE24iIiIiIiIiP2DCTUREREREROQHTLiJiIiIiIiI/IAJNxEREREREZEfMOEmooCTyWTYsGFDoJtBREREF2F8Jrp+TLiJerlHHnkEMpms1XbHHXcEumlERES9FuMzUc8QFOgGEFHg3XHHHfjoo4+8ylQqVYBaQ0RERADjM1FPwDvcRASVSgWz2ey1hYeHA5CGk61atQpTpkyBRqNBv3798K9//cvr84cOHcJtt90GjUaDyMhIzJo1C06n06vO6tWrMXToUKhUKsTExGDevHlex8vKynDPPfdAq9UiOTkZX375pedYZWUlMjMzERUVBY1Gg+Tk5FZ/gBAREfU0jM9E3R8TbiK6qsWLF2P69OnIzc1FZmYmHnjgAeTn5wMAqqurMXnyZISHh2P37t1Yt24dvvvuO6+AvWrVKsydOxezZs3CoUOH8OWXX2LAgAFe/8arr76K++67DwcPHsSdd96JzMxMVFRUeP79I0eOYOPGjcjPz8eqVatgNBo7rwOIiIi6IMZnom5AEFGvlpWVJRQKhQgJCfHali5dKoQQAoCYPXu212fS0tLEnDlzhBBCvP/++yI8PFw4nU7P8X//+99CLpcLi8UihBAiNjZWvPjii5dtAwDx0ksved47nU4BQGzcuFEIIcTdd98tZs6c2TEXTERE1A0wPhP1DHyGm4hw6623YtWqVV5lERERntfp6elex9LT03HgwAEAQH5+PkaMGIGQkBDP8fHjx8PtdqOgoAAymQznz5/HpEmTrtiG4cOHe16HhIRAp9OhpKQEADBnzhxMnz4d+/btw+23345p06Zh3Lhx13StRERE3QXjM1H3x4SbiBASEtJqCFlH0Wg07aoXHBzs9V4mk8HtdgMApkyZgtOnT+Obb77B5s2bMWnSJMydOxd//vOfO7y9REREXQXjM1H3x2e4ieiqduzY0er94MGDAQCDBw9Gbm4uqqurPce3b98OuVyOlJQUhIWFITExEdnZ2dfVhqioKGRlZWHNmjVYsWIF3n///es6HxERUXfH+EzU9fEONxHB5XLBYrF4lQUFBXkmPlm3bh1SU1MxYcIEfPLJJ9i1axc+/PBDAEBmZiaWLFmCrKwsvPLKKygtLcWTTz6JGTNmwGQyAQBeeeUVzJ49G9HR0ZgyZQocDge2b9+OJ598sl3te/nllzF69GgMHToULpcLX3/9tecPCiIiop6K8Zmo+2PCTUTYtGkTYmJivMpSUlJw9OhRANIMpWvXrsUTTzyBmJgYfPrppxgyZAgAQKvV4ttvv8X8+fMxZswYaLVaTJ8+HW+++abnXFlZWairq8Nf/vIXPPPMMzAajbj33nvb3T6lUolFixahsLAQGo0GEydOxNq1azvgyomIiLouxmei7k8mhBCBbgQRdV0ymQyff/45pk2bFuimEBERUTPGZ6Lugc9wExEREREREfkBE24iIiIiIiIiP+CQciIiIiIiIiI/4B1uIiIiIiIiIj9gwk1ERERERETkB0y4iYiIiIiIiPyACTcRERERERGRHzDhJiIiIiIiIvIDJtxEREREREREfsCEm4iIiIiIiMgPmHATERERERER+cH/B/BtLuNzYNYMAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(training_cost, label='Train')\n",
-    "plt.plot(testing_cost, label='Test')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Cost')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(training_accuracy, label='Train')\n",
-    "plt.plot(testing_accuracy, label='Test')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/krylov.ipynb.txt b/pr-2308/_sources/applications/python/krylov.ipynb.txt
deleted file mode 100644
index 183a13d691..0000000000
--- a/pr-2308/_sources/applications/python/krylov.ipynb.txt
+++ /dev/null
@@ -1,634 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "16e3036d-b1be-43b8-8838-95dfc14cad60",
-   "metadata": {},
-   "source": [
-    "# Multi-reference Quantum Krylov Algorithm - $H_2$ Molecule\n",
-    "\n",
-    "The multireference selected quantum Krylov (MRSQK) algorithm is defined in [this paper](https://arxiv.org/pdf/1911.05163) and was developed as a low-cost alternative to quantum phase estimation. This tutorial will demonstrate how this algorithm can be implemented in CUDA-Q and accelerated using multiple GPUs. The [CUDA-Q Hadamard test tutorial](https://nvidia.github.io/cuda-quantum/latest/applications/python/hadamard_test.html) might provide helpful background information for understanding this tutorial.\n",
-    "\n",
-    "The algorithm works by preparing an initial state, and then defining this state in a smaller subspace constructed with a basis that corresponds to Trotter steps of the initial state. This subspace can be diagonalized to produce an approximate energy for the system without variational optimization of any parameters.\n",
-    "\n",
-    "In the example below, the initial guess is the ground state of the diagonalized Hamiltonian for demonstration purposes.  In practice one could use a number of heuristics to prepare the ground state such as Hartree Fock or CISD. A very promising ground state preparation method which can leverage quantum computers is the linear combination of unitaries (LCU). LCU would allow for the state preparation to occur completely on the quantum computer and avoid storing an inputting the exponentially large state vector.\n",
-    "\n",
-    "\n",
-    "Regardless of the method used for state preparation, the procedure begins by selecting a $d$-dimensional basis of reference states ${\\Phi_0 \\cdots \\Phi_d},$ where each is a linear combination of Slater determinants: \n",
-    "\n",
-    "$$ \\ket{\\Phi_I}  =  \\sum_{\\mu} d_{\\mu I}\\ket{\\phi_{\\mu}}. $$\n",
-    "\n",
-    "\n",
-    "From this, a non-orthogonal Krylov Space $\\mathcal{K} = \\{\\psi_{0} \\cdots \\psi_{N}\\}$ is constructed by applying a family of $s$ unitary operators on each of the $d$ reference states resulting in $d*s = N$ elements in the Krylov space where \n",
-    "$$ \\ket{\\psi_{\\alpha}} \\equiv \\ket{\\psi_I^{(n)}} = \\hat{U}_n\\ket{\\Phi_I}, $$\n",
-    "\n",
-    "Therefore, the general quantum state that we originally set out to describe is\n",
-    "\n",
-    "$$ \\ket{\\Psi} = \\sum_{\\alpha} c_{\\alpha}\\ket{\\psi_{\\alpha}} = \\sum_{I=0}^d \\sum_{n=0}^s c_I^{(n)}\\hat{U}_n\\ket{\\Phi_I}.  $$\n",
-    "\n",
-    "The energy of this state can be obtained by solving the generalized eigenvalue problem\n",
-    "$$ \\boldsymbol{Hc}=\\boldsymbol{Sc}E, $$\n",
-    "\n",
-    "where the elements of the overlap are\n",
-    "\n",
-    "$$S_{\\alpha \\beta} = \\braket{\\psi_{\\alpha}|\\psi_{\\beta}} =   \\braket{\\Phi_I|\\hat{U}_m^{\\dagger}\\hat{U}_n|\\Phi_J}$$ \n",
-    "\n",
-    "and Hamiltonian matrix is\n",
-    "\n",
-    "$$H_{\\alpha \\beta} = \\braket{\\psi_{\\alpha}|\\hat{H}|\\psi_{\\beta}} =   \\braket{\\Phi_I|\\hat{U}_m^{\\dagger}\\hat{H}\\hat{U}_n|\\Phi_J}.$$\n",
-    "\n",
-    "The matrix elements for $S$ are computed with the Hadamard test with a circuit shown below for the case of the overlap matrix elements. \n",
-    "\n",
-    "![Htest](./images/krylovcircuit.png)\n",
-    "\n",
-    "The $2\\sigma_+$ term refers to measurement of the expectation value of this circuit with the $X+iY$ operator.\n",
-    "\n",
-    "The Hamiltonian matrix elements are computed with a circuit that includes controlled application of the Hamiltonian. Once the $H$ and $S$ matrices are constructed, the diagonalization is performed classically to produce an estimate for the ground state in question.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b90de056-4dc5-473f-bf50-29c7d8b2ad05",
-   "metadata": {},
-   "source": [
-    "### Setup\n",
-    "\n",
-    "This cell installs the necessary packages. This application can be parallelized, so please uncomment the `mgpu` specification below if you would like to run on more than one GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba61665c-dc3b-4e43-b1cf-340855ea68fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "import scipy\n",
-    "\n",
-    "!pip install openfermion==1.6.1 pyscf==2.6.2 openfermionpyscf==0.5 -q\n",
-    "\n",
-    "# Single-node, single gpu\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "multi_gpu = False\n",
-    "\n",
-    "# Single-node, multi-GPU\n",
-    "#cudaq.set_target(\"nvidia\", option='mqpu,fp64')\n",
-    "#multi_gpu = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ba6e94e-a3e6-48aa-8392-e598429b0be7",
-   "metadata": {},
-   "source": [
-    "The molecule is defined below and its Hamiltonian is extracted as a matrix. The matrix is diagonalized to produce the ground state. The corresponding state vector will be used as the initial state to ensure good results for this demonstration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fb4192c-054a-4bff-b86f-a8d58cf8bac6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define H2 molecule\n",
-    "geometry = [('H', (0.0, 0.0, 0.0)), ('H', (0.0, 0.0, 0.7474))]\n",
-    "\n",
-    "hamiltonian, data = cudaq.chemistry.create_molecular_hamiltonian(\n",
-    "    geometry, 'sto-3g', 1, 0)\n",
-    "\n",
-    "electron_count = data.n_electrons\n",
-    "qubits_num = 2 * data.n_orbitals\n",
-    "\n",
-    "# Diagonalize Hamiltonian\n",
-    "spin_ham_matrix = hamiltonian.to_matrix()\n",
-    "e, c = np.linalg.eig(spin_ham_matrix)\n",
-    "\n",
-    "# Find the ground state energy and the corresponding eigenvector\n",
-    "print('Ground state energy (classical simulation)= ', np.min(e), ', index= ',\n",
-    "      np.argmin(e))\n",
-    "min_indices = np.argmin(e)\n",
-    "\n",
-    "# Eigenvector can be used to initialize the qubits\n",
-    "vec = c[:, min_indices]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "562f795e-cf4c-4e43-a0d9-3a3ae346277d",
-   "metadata": {},
-   "source": [
-    "The functions below take the original Hamiltonian defined above, and strip it into a list of Pauli words and a list of coefficients which will be uses later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "014e72eb-ee66-48f5-bb58-73210e621fad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Collect coefficients from a spin operator so they can pass to a kernel\n",
-    "def termCoefficients(ham: cudaq.SpinOperator) -> list[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Collect Pauli words from a spin operator so they can pass to a kernel\n",
-    "def termWords(ham: cudaq.SpinOperator) -> list[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Build the lists of coefficients and Pauli Words from the H2 Hamiltonian\n",
-    "coefficient = termCoefficients(hamiltonian)\n",
-    "pauli_string = termWords(hamiltonian)\n",
-    "\n",
-    "print(coefficient)\n",
-    "print(pauli_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74b4c079-8837-47ae-a484-52ec5f4a160e",
-   "metadata": {},
-   "source": [
-    "In this example, the unitary operators that build the Krylov subspace are first-order Trotter operations at different time steps. The performance here could potentially be improved by increasing the size of the time step, using a higher order Trotter approximation, or using other sorts of approximations. The CUDA-Q kernels below define the unitary operations that construct the $\\psi$ basis.  Each receives the target qubits, the time step, and components of the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c98db2b7-eaaf-4e3a-83bb-4f7ad0730471",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Applies Unitary operation corresponding to Um\n",
-    "@cudaq.kernel\n",
-    "def U_m(qubits: cudaq.qview, dt: float, coefficients: list[complex],\n",
-    "        words: list[cudaq.pauli_word]):\n",
-    "    # Compute U_m = exp(-i m dt H)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(dt * coefficients[i].real, qubits, words[i])\n",
-    "\n",
-    "\n",
-    "# Applies Unitary operation corresponding to Un\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_n(qubits: cudaq.qview, dt: float, coefficients: list[complex],\n",
-    "        words: list[cudaq.pauli_word]):\n",
-    "    # Compute U_n = exp(-i n dt H)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(dt * coefficients[i].real, qubits, words[i])\n",
-    "\n",
-    "\n",
-    "# Applies the gate operations for a Hamiltonian Pauli Word\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def apply_pauli(qubits: cudaq.qview, word: list[int]):\n",
-    "    # Add H (Hamiltonian operator)\n",
-    "    for i in range(len(word)):\n",
-    "        if word[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "        if word[i] == 2:\n",
-    "            y(qubits[i])\n",
-    "        if word[i] == 3:\n",
-    "            z(qubits[i])\n",
-    "\n",
-    "\n",
-    "# Performs Hadamard test circuit which determines matrix elements of S and H of subspace\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qfd_kernel(dt_alpha: float, dt_beta: float, coefficients: list[complex],\n",
-    "               words: list[cudaq.pauli_word], word_list: list[int],\n",
-    "               vec: list[complex]):\n",
-    "\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    qreg = cudaq.qvector(vec)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    x(ancilla)\n",
-    "    cudaq.control(U_m, ancilla, qreg, dt_alpha, coefficients, words)\n",
-    "    x(ancilla)\n",
-    "\n",
-    "    cudaq.control(apply_pauli, ancilla, qreg, word_list)\n",
-    "    cudaq.control(U_n, ancilla, qreg, dt_beta, coefficients, words)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c8f9fb4-4974-44bf-aa8c-c9e86ffb0dd4",
-   "metadata": {},
-   "source": [
-    "The auxillary function below takes a Pauli word, and converts it to a list of integers which informs applications of this word to a circuit with the `apply_pauli` kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5bed072a-9fb6-4394-9759-b45ba292d0a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pauli_str(pauli_word, qubits_num):\n",
-    "\n",
-    "    my_list = []\n",
-    "    for i in range(qubits_num):\n",
-    "        if str(pauli_word[i]) == 'I':\n",
-    "            my_list.append(0)\n",
-    "        if str(pauli_word[i]) == 'X':\n",
-    "            my_list.append(1)\n",
-    "        if str(pauli_word[i]) == 'Y':\n",
-    "            my_list.append(2)\n",
-    "        if str(pauli_word[i]) == 'Z':\n",
-    "            my_list.append(3)\n",
-    "    return my_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "999675e2-a5b0-43d1-ba80-827f1c81b3dc",
-   "metadata": {},
-   "source": [
-    "The spin operators necessary for the Hadamard test are defined below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "297dbc29-e48a-411b-bc42-4fabe180a641",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the spin-op x for real component and y for the imaginary component.\n",
-    "x_0 = cudaq.spin.x(0)\n",
-    "y_0 = cudaq.spin.y(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1880e50d-6d27-44e4-b27e-329d8c499bfb",
-   "metadata": {},
-   "source": [
-    "Finally, the time step for the unitary operations that build the Krylov space is defined as well as the dimension of the Krylov space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3ae6b210-13c4-40ba-be73-4fb906a1f7ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Define parameters for the quantum Krylov space\n",
-    "dt = 0.5\n",
-    "\n",
-    "# Dimension of the Krylov space\n",
-    "m_qfd = 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d59f13b-e9f8-44e6-b270-be4fc6a80c01",
-   "metadata": {},
-   "source": [
-    "### Computing the matrix elements \n",
-    "\n",
-    "The cell below computes the overlap matrix. This can be done in serial or in parallel, depending on the `multi_gpu` specification.  First, an operator is built to apply the identity to the overlap matrix circuit when `apply_pauli` is called. Next, the `wf_overlap` array is constructed which will hold the matrix elements. \n",
-    "\n",
-    "Next, a pair of nested loops iterate over the time steps defined by the dimension of the subspace.  Each m,n combination corresponds to computation of an off-diagonal matrix element of the overlap matrix $S$ using the Hadamard test.  This is accomplished by calling the CUDA-Q `observe` function with the X and Y operators, along with the time steps, the components of the Hamiltonian matrix, and the initial state vector `vec`.\n",
-    "\n",
-    "The observe function broadcasts over the two provided operators $X$ and $Y$ and returns a list of results. The `expectation` function returns the expectation values which are summed and stored in the matrix.\n",
-    "\n",
-    "The multi-gpu case completes the same steps, except the `observe_async` command is used. This allows for the $X$ and $Y$ observables to be evaluated at the same time on two different simulated QPUs. In this case, the results are stored in lists corresponding to the real and imaginary parts. These are then accessed later with the `get` command to build $S$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "875124e8-dba8-4ace-828b-d4b03352f9d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Single GPU:\n",
-    "if not multi_gpu:\n",
-    "\n",
-    "    # Add identity operator to compute overlap of basis\n",
-    "    observe_op = 1.0\n",
-    "    for m in range(qubits_num):\n",
-    "        observe_op *= cudaq.spin.i(m)\n",
-    "    identity_word = observe_op.to_string(False)\n",
-    "    pauli_list = pauli_str(identity_word, qubits_num)\n",
-    "\n",
-    "    # Empty overlap matrix S\n",
-    "    wf_overlap = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loop to solve for S matrix elements\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "            results = cudaq.observe(qfd_kernel, [x_0, y_0], dt_m, dt_n,\n",
-    "                                    coefficient, pauli_string, pauli_list, vec)\n",
-    "            temp = [result.expectation() for result in results]\n",
-    "            wf_overlap[m, n] = temp[0] + temp[1] * 1j\n",
-    "            if n != m:\n",
-    "                wf_overlap[n, m] = np.conj(wf_overlap[m, n])\n",
-    "\n",
-    "else:\n",
-    "\n",
-    "    ## Multi-GPU\n",
-    "\n",
-    "    # Add identity operator to compute overlap of basis\n",
-    "    observe_op = 1.0\n",
-    "\n",
-    "    for m in range(qubits_num):\n",
-    "        observe_op *= cudaq.spin.i(m)\n",
-    "\n",
-    "    identity_word = observe_op.to_string(False)\n",
-    "    pauli_list = pauli_str(identity_word, qubits_num)\n",
-    "\n",
-    "    # Empty overlap matrix S\n",
-    "    wf_overlap = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Empty lists to store results\n",
-    "    collect_overlap_real = []\n",
-    "    collect_overlap_img = []\n",
-    "\n",
-    "    # Loop to compute matrix elements of S\n",
-    "    count = 0\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            # The count variable determines which (simulated) QPU the computation is sent to\n",
-    "            # The results are stored in a list corresponding to the real and imaginary parts\n",
-    "            count_id = count % 2\n",
-    "            collect_overlap_real.append(\n",
-    "                cudaq.observe_async(qfd_kernel,\n",
-    "                                    x_0,\n",
-    "                                    dt_m,\n",
-    "                                    dt_n,\n",
-    "                                    coefficient,\n",
-    "                                    pauli_string,\n",
-    "                                    pauli_list,\n",
-    "                                    vec,\n",
-    "                                    qpu_id=count_id))\n",
-    "\n",
-    "            collect_overlap_img.append(\n",
-    "                cudaq.observe_async(qfd_kernel,\n",
-    "                                    y_0,\n",
-    "                                    dt_m,\n",
-    "                                    dt_n,\n",
-    "                                    coefficient,\n",
-    "                                    pauli_string,\n",
-    "                                    pauli_list,\n",
-    "                                    vec,\n",
-    "                                    qpu_id=count_id + 2))\n",
-    "            count += 1\n",
-    "\n",
-    "    tot_dim = 0\n",
-    "\n",
-    "    # A second loop takes the lists of results and, using `get`, populates the matrix elements\n",
-    "    for n in range(m_qfd):\n",
-    "        for m in range(n, m_qfd):\n",
-    "            observe_result = collect_overlap_real[tot_dim].get()\n",
-    "            real_val = observe_result.expectation()\n",
-    "\n",
-    "            observe_result = collect_overlap_img[tot_dim].get()\n",
-    "            img_val = observe_result.expectation()\n",
-    "\n",
-    "            wf_overlap[m, n] = real_val + img_val * 1j\n",
-    "            if n != m:\n",
-    "                wf_overlap[n, m] = np.conj(wf_overlap[m, n])\n",
-    "\n",
-    "            tot_dim += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fa0e0f1-0705-4dd6-a354-30db1526e266",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian matrix elements are computed in the same way, except this time the Hamiltonian is applied as part of the circuit. This is accomplished with the extra for loop, which iterates through the terms in the Hamiltonian, computing an expectation value for each one and then summing the results to produce one matrix element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "562a6063-b0fe-4d69-a4f4-c7d5309436da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Single GPU:\n",
-    "if not multi_gpu:\n",
-    "\n",
-    "    # Empty H\n",
-    "    ham_matrx = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loops over H matrix terms\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            # 2 entry array that stores real and imaginary part of matrix element\n",
-    "            tot_e = np.zeros(2)\n",
-    "\n",
-    "            # Loops over the terms in the Hamiltonian, computing expectation values\n",
-    "            for coef, word in zip(coefficient, pauli_string):\n",
-    "                #print(coef,word)\n",
-    "\n",
-    "                pauli_list = pauli_str(word, qubits_num)\n",
-    "                #print(pauli_list)\n",
-    "\n",
-    "                results = cudaq.observe(qfd_kernel, [x_0, y_0], dt_m, dt_n,\n",
-    "                                        coefficient, pauli_string, pauli_list,\n",
-    "                                        vec)\n",
-    "\n",
-    "                temp = [result.expectation() for result in results]\n",
-    "\n",
-    "                # Multiplies result by coefficient corresponding to Pauli Word\n",
-    "                temp[0] = coef.real * temp[0]\n",
-    "                temp[1] = coef.imag * temp[1]\n",
-    "\n",
-    "                # Accumulates results for each Pauli Word\n",
-    "                tot_e[0] += temp[0]\n",
-    "                tot_e[1] += temp[1]\n",
-    "\n",
-    "            # Sums real and imaginary totals to specify Hamiltonian entry\n",
-    "            ham_matrx[m, n] = tot_e[0] + tot_e[1] * 1j\n",
-    "            if n != m:\n",
-    "                ham_matrx[n, m] = np.conj(ham_matrx[m, n])\n",
-    "else:\n",
-    "\n",
-    "    ## Multi-GPU\n",
-    "\n",
-    "    # Empty H\n",
-    "    ham_matrx = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loops over H matrix terms\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            #List storing results from real and imaginary circuit evaluations for each Pauli word\n",
-    "            ham_matrix_real = []\n",
-    "            ham_matrix_imag = []\n",
-    "\n",
-    "            # Loop to asynchronously compute real and imaginary parts for each Pauli word on two GPUs\n",
-    "            count = 0\n",
-    "            tot_e = np.zeros(2)\n",
-    "            for coef, word in zip(coefficient, pauli_string):\n",
-    "                count_id = count % 2\n",
-    "                #print(coef,word)\n",
-    "\n",
-    "                pauli_list = pauli_str(word, qubits_num)\n",
-    "                #print(pauli_list)\n",
-    "\n",
-    "                ham_matrix_real.append(\n",
-    "                    cudaq.observe_async(qfd_kernel,\n",
-    "                                        x_0,\n",
-    "                                        dt_m,\n",
-    "                                        dt_n,\n",
-    "                                        coefficient,\n",
-    "                                        pauli_string,\n",
-    "                                        pauli_list,\n",
-    "                                        vec,\n",
-    "                                        qpu_id=count_id))\n",
-    "                ham_matrix_imag.append(\n",
-    "                    cudaq.observe_async(qfd_kernel,\n",
-    "                                        y_0,\n",
-    "                                        dt_m,\n",
-    "                                        dt_n,\n",
-    "                                        coefficient,\n",
-    "                                        pauli_string,\n",
-    "                                        pauli_list,\n",
-    "                                        vec,\n",
-    "                                        qpu_id=count_id + 2))\n",
-    "\n",
-    "                count += 1\n",
-    "\n",
-    "            # Loops through coefficients to sum the real and imaginary parts of H matrix element\n",
-    "            i = 0\n",
-    "            for coef in coefficient:\n",
-    "\n",
-    "                observe_result = ham_matrix_real[i].get()\n",
-    "                real_val = observe_result.expectation()\n",
-    "\n",
-    "                observe_result = ham_matrix_imag[i].get()\n",
-    "                img_val = observe_result.expectation()\n",
-    "\n",
-    "                tot_e[0] += real_val * coef.real\n",
-    "                tot_e[1] += img_val * coef.imag\n",
-    "\n",
-    "                i += 1\n",
-    "\n",
-    "            # Enter final H matrix element\n",
-    "            ham_matrx[m, n] = tot_e[0] + tot_e[1] * 1j\n",
-    "            if n != m:\n",
-    "                ham_matrx[n, m] = np.conj(ham_matrx[m, n])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1917bd58-4ce3-4e3e-9ccf-096577c0da14",
-   "metadata": {},
-   "source": [
-    "### Determining the ground state energy of the subspace\n",
-    "\n",
-    "The final step is to solve the generalized eigenvaulue problem with the overlap and Hamiltonian matrices constructed using the quantum computer. The procedure begins by diagonalizing  $S$ with the transform $$S = U\\Sigma U^{\\dagger}$$\n",
-    "\n",
-    "The eigenvectors $v$ and eigenvalues $s$ are used to construct a new matrix $X':$\n",
-    "\n",
-    "$$ X' = S ^{\\frac{-1}{2}} = \\sum_k v_{ki} \\frac{1}{\\sqrt{s_k}} v_{kj}.$$\n",
-    "\n",
-    "The matrix $X'$ diagonalizes $H:$\n",
-    "\n",
-    "$$ X'^{\\dagger}HX' = ES^{\\frac{1}{2}}C.$$\n",
-    "\n",
-    "Using the eigenvectors of $H'$, ($^{\\frac{1}{2}}C$), the original eigenvectors to the problem can be found by left multiplying by $S^{\\frac{-1}{2}}C.$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8b281533-bf3d-4aed-823b-9d2f926dfe3e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def eig(H, s):\n",
-    "    #Solver for generalized eigenvalue problem\n",
-    "    # HC = SCE\n",
-    "\n",
-    "    THRESHOLD = 1e-20\n",
-    "    s_diag, u = scipy.linalg.eig(s)\n",
-    "    s_prime = []\n",
-    "    for sii in s_diag:\n",
-    "        if np.imag(sii) > 1e-7:\n",
-    "            raise ValueError(\n",
-    "                \"S may not be hermitian, large imag. eval component.\")\n",
-    "        if np.real(sii) > THRESHOLD:\n",
-    "            s_prime.append(np.real(sii))\n",
-    "\n",
-    "    X_prime = np.zeros((len(s_diag), len(s_prime)), dtype=complex)\n",
-    "\n",
-    "    for i in range(len(s_diag)):\n",
-    "        for j in range(len(s_prime)):\n",
-    "            X_prime[i][j] = u[i][j] / np.sqrt(s_prime[j])\n",
-    "\n",
-    "    H_prime = (((X_prime.conjugate()).transpose()).dot(H)).dot(X_prime)\n",
-    "    e_prime, C_prime = scipy.linalg.eig(H_prime)\n",
-    "    C = X_prime.dot(C_prime)\n",
-    "\n",
-    "    return e_prime, C"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74c1fa51-4fae-412b-b90d-262e0b3aaf53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eigen_value, eigen_vect = eig(ham_matrx[0:m_qfd, 0:m_qfd], wf_overlap[0:m_qfd,\n",
-    "                                                                      0:m_qfd])\n",
-    "print('Energy from QFD:')\n",
-    "print(np.min(eigen_value))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/logical_aim_sqale.ipynb.txt b/pr-2308/_sources/applications/python/logical_aim_sqale.ipynb.txt
deleted file mode 100644
index f51bd10294..0000000000
--- a/pr-2308/_sources/applications/python/logical_aim_sqale.ipynb.txt
+++ /dev/null
@@ -1,1356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Anderson Impurity Model ground state solver on Infleqtion's Sqale"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ground state quantum chemistry—computing total energies of molecular configurations to within chemical accuracy—is perhaps the most highly-touted industrial application of fault-tolerant quantum computers. Strongly correlated materials, for example, are particularly interesting, and tools like dynamical mean-field theory (DMFT) allow one to account for the effect of their strong, localized electronic correlations. These DMFT models help predict material properties by approximating the system as a single site impurity inside a “bath” that encompasses the rest of the system. Simulating such dynamics can be a tough task using classical methods, but can be done efficiently on a quantum computer via quantum simulation.\n",
-    "\n",
-    "In this notebook, we showcase a workflow for preparing the ground state of the minimal single-impurity Anderson model (SIAM) using the Hamiltonian Variational Ansatz for a range of realistic parameters. As a first step towards running DMFT on a fault-tolerant quantum computer, we will use logical qubits encoded in the `[[4, 2, 2]]` code. Using this workflow, we will obtain the ground state energy estimates via noisy simulation, and then also execute the corresponding optimized circuits on Infleqtion's gate-based neutral-atom quantum computer, making the benefits of logical qubits apparent. More details can be found in our [paper](https://arxiv.org/abs/2412.07670)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This demo notebook uses CUDA-Q (`cudaq`) and a CUDA-QX library, `cudaq-solvers`; let us first begin by importing (and installing as needed) these packages:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    import cudaq_solvers as solvers\n",
-    "    import cudaq\n",
-    "    import matplotlib.pyplot as plt\n",
-    "except ImportError:\n",
-    "    print(\"Installing required packages...\")\n",
-    "    %pip install --quiet 'cudaq-solvers' 'matplotlib'\n",
-    "    print(\"Installed `cudaq`, `cudaq-solvers`, and `matplotlib` packages.\")\n",
-    "    print(\"You may need to restart the kernel to import newly installed packages.\")\n",
-    "    import cudaq_solvers as solvers\n",
-    "    import cudaq\n",
-    "    import matplotlib.pyplot as plt\n",
-    "\n",
-    "from collections.abc import Mapping, Sequence\n",
-    "import numpy as np\n",
-    "from scipy.optimize import minimize\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To prepare our ground state quantum Anderson impurity model circuits (referred to as AIM circuits in this notebook for short), we use VQE to train an ansatz to minimize a Hamiltonian and obtain optimal angles that can be used to set the AIM circuits. As described in our [paper](https://arxiv.org/abs/2412.07670), the associated restricted Hamiltonian for our SIAM can be reduced to,\n",
-    "$$  \n",
-    "\\begin{equation}\n",
-    "H_{(U, V)} = U (Z_0 Z_2 - 1) / 4 + V (X_0 + X_2),\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "where $U$ is the Coulomb interaction and $V$ the hybridization strength. In this notebook workflow, we will optimize over a 2-dimensional grid of Hamiltonian parameter values, namely $U\\in \\{1, 5, 9\\}$ and $V\\in \\{-9, -1, 7\\}$ (with all values assumed to be in units of eV), to ensure that the ansatz is generally trainable and expressive, and obtain 9 different circuit layers identified by the key $(U, V)$. We will simulate the VQE on GPU (or optionally on CPU if you do not have GPU access), enabled by CUDA-Q, in the absence of noise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if cudaq.num_available_gpus() == 0:\n",
-    "    cudaq.set_target(\"qpp-cpu\", option=\"fp64\")\n",
-    "else:\n",
-    "    cudaq.set_target(\"nvidia\", option=\"fp64\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This workflow can be easily defined in CUDA-Q as shown in the cell below, using the CUDA-QX Solvers library (which accelerates quantum algorithms like the VQE):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def ansatz(n_qubits: int) -> cudaq.Kernel:\n",
-    "    # Create a CUDA-Q parameterized kernel\n",
-    "    paramterized_ansatz, variational_angles = cudaq.make_kernel(list)\n",
-    "    qubits = paramterized_ansatz.qalloc(n_qubits)\n",
-    "\n",
-    "    # Using |+> as the initial state:\n",
-    "    paramterized_ansatz.h(qubits[0])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    paramterized_ansatz.rx(variational_angles[0], qubits[0])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "    paramterized_ansatz.rz(variational_angles[1], qubits[1])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "    return paramterized_ansatz\n",
-    "\n",
-    "\n",
-    "def run_logical_vqe(cudaq_hamiltonian: cudaq.SpinOperator) -> tuple[float, list[float]]:\n",
-    "    # Set seed for easier reproduction\n",
-    "    np.random.seed(42)\n",
-    "\n",
-    "    # Initial angles for the optimizer\n",
-    "    init_angles = np.random.random(2) * 1e-1\n",
-    "\n",
-    "    # Obtain CUDA-Q Ansatz\n",
-    "    num_qubits = cudaq_hamiltonian.get_qubit_count()\n",
-    "    variational_kernel = ansatz(num_qubits)\n",
-    "\n",
-    "    # Perform VQE optimization\n",
-    "    energy, params, _ = solvers.vqe(\n",
-    "        variational_kernel,\n",
-    "        cudaq_hamiltonian,\n",
-    "        init_angles,\n",
-    "        optimizer=minimize,\n",
-    "        method=\"SLSQP\",\n",
-    "        tol=1e-10,\n",
-    "    )\n",
-    "    return energy, params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing circuits in the `[[4,2,2]]` encoding"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `[[4,2,2]]` code is a quantum error detection code that uses four physical qubits to encode two logical qubits. In this notebook, we will construct two variants of quantum circuits: physical (bare, unencoded) and logical (encoded). These circuits will be informed by the Hamiltonian Variational Ansatz described earlier. To measure all the terms in our Hamiltonian, we will measure the data qubits in both the $Z$- and $X$-basis, as allowed by the `[[4,2,2]]` logical gateset. Full details on the circuit constructions are outlined in our [paper](https://arxiv.org/abs/2412.07670)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we create functions to build our CUDA-Q AIM circuits, both physical and logical versions. As we consider noisy simulation in this notebook, we will include some noisy gates. Here, for simplicity, we will just register a custom identity gate -- to be later used as a noisy operation to model readout error: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.register_operation(\"meas_id\", np.identity(2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def aim_physical_circuit(\n",
-    "    angles: list[float], basis: str, *, ignore_meas_id: bool = False\n",
-    ") -> cudaq.Kernel:\n",
-    "    kernel = cudaq.make_kernel()\n",
-    "    qubits = kernel.qalloc(2)\n",
-    "\n",
-    "    # Bell state prep\n",
-    "    kernel.h(qubits[0])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    # Rx Gate\n",
-    "    kernel.rx(angles[0], qubits[0])\n",
-    "\n",
-    "    # ZZ rotation\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.rz(angles[1], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    if basis == \"z_basis\":\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=2, function=lambda q_idx: getattr(kernel, \"meas_id\")(qubits[q_idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    elif basis == \"x_basis\":\n",
-    "        kernel.h(qubits)\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=2, function=lambda q_idx: getattr(kernel, \"meas_id\")(qubits[q_idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    else:\n",
-    "        raise ValueError(\"Unsupported basis provided:\", basis)\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def aim_logical_circuit(\n",
-    "    angles: list[float], basis: str, *, ignore_meas_id: bool = False\n",
-    ") -> cudaq.Kernel:\n",
-    "    kernel = cudaq.make_kernel()\n",
-    "    qubits = kernel.qalloc(6)\n",
-    "\n",
-    "    kernel.for_loop(start=0, stop=3, function=lambda idx: kernel.h(qubits[idx]))\n",
-    "    kernel.cx(qubits[1], qubits[4])\n",
-    "    kernel.cx(qubits[2], qubits[3])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[3])\n",
-    "\n",
-    "    # Rx teleportation\n",
-    "    kernel.rx(angles[0], qubits[0])\n",
-    "\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[3])\n",
-    "    kernel.h(qubits[0])\n",
-    "\n",
-    "    if basis == \"z_basis\":\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=5, function=lambda idx: getattr(kernel, \"meas_id\")(qubits[idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    elif basis == \"x_basis\":\n",
-    "        # ZZ rotation and teleportation\n",
-    "        kernel.cx(qubits[3], qubits[5])\n",
-    "        kernel.cx(qubits[2], qubits[5])\n",
-    "        kernel.rz(angles[1], qubits[5])\n",
-    "        kernel.cx(qubits[1], qubits[5])\n",
-    "        kernel.cx(qubits[4], qubits[5])\n",
-    "        kernel.for_loop(start=1, stop=5, function=lambda idx: kernel.h(qubits[idx]))\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=6, function=lambda idx: getattr(kernel, \"meas_id\")(qubits[idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    else:\n",
-    "        raise ValueError(\"Unsupported basis provided:\", basis)\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the circuit definitions above, we can now define a function that automatically runs the VQE and constructs a dictionary containing all the AIM circuits we want to submit to hardware (or noisily simulate):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_circuit_set(ignore_meas_id: bool = False) -> object:\n",
-    "    u_vals = [1, 5, 9]\n",
-    "    v_vals = [-9, -1, 7]\n",
-    "    circuit_dict = {}\n",
-    "    for u in u_vals:\n",
-    "        for v in v_vals:\n",
-    "            qubit_hamiltonian = (\n",
-    "                0.25 * u * cudaq.spin.z(0) * cudaq.spin.z(1)\n",
-    "                - 0.25 * u\n",
-    "                + v * cudaq.spin.x(0)\n",
-    "                + v * cudaq.spin.x(1)\n",
-    "            )\n",
-    "            _, opt_params = run_logical_vqe(qubit_hamiltonian)\n",
-    "            angles = [float(angle) for angle in opt_params]\n",
-    "            print(f\"Computed optimal angles={angles} for U={u}, V={v}\")\n",
-    "\n",
-    "            tmp_physical_dict = {}\n",
-    "            tmp_logical_dict = {}\n",
-    "            for basis in (\"z_basis\", \"x_basis\"):\n",
-    "                tmp_physical_dict[basis] = aim_physical_circuit(\n",
-    "                    angles, basis, ignore_meas_id=ignore_meas_id\n",
-    "                )\n",
-    "                tmp_logical_dict[basis] = aim_logical_circuit(\n",
-    "                    angles, basis, ignore_meas_id=ignore_meas_id\n",
-    "                )\n",
-    "\n",
-    "            circuit_dict[f\"{u}:{v}\"] = {\n",
-    "                \"physical\": tmp_physical_dict,\n",
-    "                \"logical\": tmp_logical_dict,\n",
-    "            }\n",
-    "    print(\"\\nFinished building optimized circuits!\")\n",
-    "    return circuit_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9\n",
-      "Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1\n",
-      "Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7\n",
-      "Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9\n",
-      "Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1\n",
-      "Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7\n",
-      "Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9\n",
-      "Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1\n",
-      "Computed optimal angles=[-1.7301462729177735, 1.570796033796985] for U=9, V=7\n",
-      "\n",
-      "Finished building optimized circuits!\n"
-     ]
-    }
-   ],
-   "source": [
-    "sim_circuit_dict = generate_circuit_set()\n",
-    "circuit_layers = sim_circuit_dict.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up submission and decoding workflow "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we define various helper functions that will play a role in generating the associated energies of the AIM circuits based on the circuit samples (in the different bases), as well as decode the logical circuits with post-selection informed by the `[[4,2,2]]` code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _num_qubits(counts: Mapping[str, float]) -> int:\n",
-    "    for key in counts:\n",
-    "        if key.isdecimal():\n",
-    "            return len(key)\n",
-    "    return 0\n",
-    "\n",
-    "\n",
-    "def process_counts(\n",
-    "    counts: Mapping[str, float],\n",
-    "    data_qubits: Sequence[int],\n",
-    "    flag_qubits: Sequence[int] = (),\n",
-    ") -> dict[str, float]:\n",
-    "    new_data: dict[str, float] = {}\n",
-    "    for key, val in counts.items():\n",
-    "        if not all(key[i] == \"0\" for i in flag_qubits):\n",
-    "            continue\n",
-    "\n",
-    "        new_key = \"\".join(key[i] for i in data_qubits)\n",
-    "\n",
-    "        if not set(\"01\").issuperset(new_key):\n",
-    "            continue\n",
-    "\n",
-    "        new_data.setdefault(new_key, 0)\n",
-    "        new_data[new_key] += val\n",
-    "\n",
-    "    return new_data\n",
-    "\n",
-    "\n",
-    "def decode(counts: Mapping[str, float]) -> dict[str, float]:\n",
-    "    \"\"\"Decode physical counts into logical counts. Should be called after `process_counts`.\"\"\"\n",
-    "\n",
-    "    if not counts:\n",
-    "        return {}\n",
-    "\n",
-    "    num_qubits = _num_qubits(counts)\n",
-    "    assert num_qubits % 4 == 0\n",
-    "\n",
-    "    physical_to_logical = {\n",
-    "        \"0000\": \"00\",\n",
-    "        \"1111\": \"00\",\n",
-    "        \"0011\": \"01\",\n",
-    "        \"1100\": \"01\",\n",
-    "        \"0101\": \"10\",\n",
-    "        \"1010\": \"10\",\n",
-    "        \"0110\": \"11\",\n",
-    "        \"1001\": \"11\",\n",
-    "    }\n",
-    "\n",
-    "    new_data: dict[str, float] = {}\n",
-    "    for key, val in counts.items():\n",
-    "        physical_keys = [key[i : i + 4] for i in range(0, num_qubits, 4)]\n",
-    "        logical_keys = [physical_to_logical.get(physical_key) for physical_key in physical_keys]\n",
-    "        if None not in logical_keys:\n",
-    "            new_key = \"\".join(logical_keys)\n",
-    "            new_data.setdefault(new_key, 0)\n",
-    "            new_data[new_key] += val\n",
-    "\n",
-    "    return new_data\n",
-    "\n",
-    "\n",
-    "def ev_x(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * ((-1) ** int(k[0]) + (-1) ** int(k[1]))\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def ev_xx(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * (-1) ** k.count(\"1\")\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def ev_zz(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * (-1) ** k.count(\"1\")\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def aim_logical_energies(\n",
-    "    data_ordering: object, counts_list: Sequence[dict[str, float]]\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    counts_data = {\n",
-    "        data_ordering[i]: decode(\n",
-    "            process_counts(\n",
-    "                counts,\n",
-    "                data_qubits=[1, 2, 3, 4],\n",
-    "                flag_qubits=[0, 5],\n",
-    "            )\n",
-    "        )\n",
-    "        for i, counts in enumerate(counts_list)\n",
-    "    }\n",
-    "    return _aim_energies(counts_data)\n",
-    "\n",
-    "\n",
-    "def aim_physical_energies(\n",
-    "    data_ordering: object, counts_list: Sequence[dict[str, float]]\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    counts_data = {\n",
-    "        data_ordering[i]: process_counts(\n",
-    "            counts,\n",
-    "            data_qubits=[0, 1],\n",
-    "        )\n",
-    "        for i, counts in enumerate(counts_list)\n",
-    "    }\n",
-    "    return _aim_energies(counts_data)\n",
-    "\n",
-    "\n",
-    "def _aim_energies(\n",
-    "    counts_data: Mapping[tuple[int, int, str], dict[str, float]],\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    evxs: dict[tuple[int, int], float] = {}\n",
-    "    evxxs: dict[tuple[int, int], float] = {}\n",
-    "    evzzs: dict[tuple[int, int], float] = {}\n",
-    "    totals: dict[tuple[int, int], float] = {}\n",
-    "\n",
-    "    for key, counts in counts_data.items():\n",
-    "        h_params, basis = key\n",
-    "        key_a, key_b = h_params.split(\":\")\n",
-    "        u, v = int(key_a), int(key_b)\n",
-    "        if basis.startswith(\"x\"):\n",
-    "            evxs[u, v] = ev_x(counts)\n",
-    "            evxxs[u, v] = ev_xx(counts)\n",
-    "        else:\n",
-    "            evzzs[u, v] = ev_zz(counts)\n",
-    "\n",
-    "        totals.setdefault((u, v), 0)\n",
-    "        totals[u, v] += sum(counts.values())\n",
-    "\n",
-    "    energies = {}\n",
-    "    uncertainties = {}\n",
-    "    for u, v in evxs.keys() & evzzs.keys():\n",
-    "        string_key = f\"{u}:{v}\"\n",
-    "        energies[string_key] = u * (evzzs[u, v] - 1) / 4 + v * evxs[u, v]\n",
-    "\n",
-    "        uncertainty_xx = 2 * v**2 * (1 + evxxs[u, v]) - u * v * evxs[u, v] / 2\n",
-    "        uncertainty_zz = u**2 * (1 - evzzs[u, v]) / 2\n",
-    "\n",
-    "        uncertainties[string_key] = np.sqrt(\n",
-    "            (uncertainty_zz + uncertainty_xx - energies[string_key] ** 2) / (totals[u, v] / 2)\n",
-    "        )\n",
-    "\n",
-    "    return energies, uncertainties\n",
-    "\n",
-    "\n",
-    "def _get_energy_diff(\n",
-    "    bf_energies: dict[str, float],\n",
-    "    physical_energies: dict[str, float],\n",
-    "    logical_energies: dict[str, float],\n",
-    ") -> tuple[list[float], list[float]]:\n",
-    "    physical_energy_diff = []\n",
-    "    logical_energy_diff = []\n",
-    "\n",
-    "    # Data ordering following `bf_energies` keys\n",
-    "    for layer in bf_energies.keys():\n",
-    "        physical_sim_energy = physical_energies[layer]\n",
-    "        logical_sim_energy = logical_energies[layer]\n",
-    "        true_energy = bf_energies[layer]\n",
-    "        u, v = layer.split(\":\")\n",
-    "        print(f\"Layer=({u}, {v}) has brute-force energy of: {true_energy}\")\n",
-    "        print(f\"Physical circuit of layer=({u}, {v}) got an energy of: {physical_sim_energy}\")\n",
-    "        print(f\"Logical circuit of layer=({u}, {v}) got an energy of: {logical_sim_energy}\")\n",
-    "        print(\"-\" * 72)\n",
-    "\n",
-    "        if logical_sim_energy < physical_sim_energy:\n",
-    "            print(\"Logical circuit achieved the lower energy!\")\n",
-    "        else:\n",
-    "            print(\"Physical circuit achieved the lower energy\")\n",
-    "        print(\"-\" * 72, \"\\n\")\n",
-    "\n",
-    "        physical_energy_diff.append(\n",
-    "            -1 * (true_energy - physical_sim_energy)\n",
-    "        )  # Multiply by -1 since negative energies\n",
-    "        logical_energy_diff.append(-1 * (true_energy - logical_sim_energy))\n",
-    "    return physical_energy_diff, logical_energy_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def submit_aim_circuits(\n",
-    "    circuit_dict: object,\n",
-    "    *,\n",
-    "    folder_path: str = \"future_aim_results\",\n",
-    "    shots_count: int = 1000,\n",
-    "    noise_model: cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel | None = None,\n",
-    "    run_async: bool = False,\n",
-    ") -> dict[str, list[dict[str, int]]] | None:\n",
-    "    if run_async:\n",
-    "        os.makedirs(folder_path, exist_ok=True)\n",
-    "    else:\n",
-    "        aim_results = {\"physical\": [], \"logical\": []}\n",
-    "\n",
-    "    for layer in circuit_dict.keys():\n",
-    "        if run_async:\n",
-    "            print(f\"Posting circuits associated with layer=('{layer}')\")\n",
-    "        else:\n",
-    "            print(f\"Running circuits associated with layer=('{layer}')\")\n",
-    "\n",
-    "        for basis in (\"z_basis\", \"x_basis\"):\n",
-    "            if run_async:\n",
-    "                u, v = layer.split(\":\")\n",
-    "\n",
-    "                tmp_physical_results = cudaq.sample_async(\n",
-    "                    circuit_dict[layer][\"physical\"][basis], shots_count=shots_count\n",
-    "                )\n",
-    "                file = open(f\"{folder_path}/physical_{basis}_job_u={u}_v={v}_result.txt\", \"w\")\n",
-    "                file.write(str(tmp_physical_results))\n",
-    "                file.close()\n",
-    "\n",
-    "                tmp_logical_results = cudaq.sample_async(\n",
-    "                    circuit_dict[layer][\"logical\"][basis], shots_count=shots_count\n",
-    "                )\n",
-    "                file = open(f\"{folder_path}/logical_{basis}_job_u={u}_v={v}_result.txt\", \"w\")\n",
-    "                file.write(str(tmp_logical_results))\n",
-    "                file.close()\n",
-    "            else:\n",
-    "                tmp_physical_results = cudaq.sample(\n",
-    "                    circuit_dict[layer][\"physical\"][basis],\n",
-    "                    shots_count=shots_count,\n",
-    "                    noise_model=noise_model,\n",
-    "                )\n",
-    "                tmp_logical_results = cudaq.sample(\n",
-    "                    circuit_dict[layer][\"logical\"][basis],\n",
-    "                    shots_count=shots_count,\n",
-    "                    noise_model=noise_model,\n",
-    "                )\n",
-    "                aim_results[\"physical\"].append({k: v for k, v in tmp_physical_results.items()})\n",
-    "                aim_results[\"logical\"].append({k: v for k, v in tmp_logical_results.items()})\n",
-    "    if not run_async:\n",
-    "        print(\"\\nCompleted all circuit sampling!\")\n",
-    "        return aim_results\n",
-    "    else:\n",
-    "        print(\"\\nAll circuits submitted for async sampling!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _get_async_results(\n",
-    "    layers: object, *, folder_path: str = \"future_aim_results\"\n",
-    ") -> dict[str, list[dict[str, int]]]:\n",
-    "    aim_results = {\"physical\": [], \"logical\": []}\n",
-    "    for layer in layers:\n",
-    "        print(f\"Retrieving all circuits counts associated with layer=('{layer}')\")\n",
-    "        u, v = layer.split(\":\")\n",
-    "        for basis in (\"z_basis\", \"x_basis\"):\n",
-    "            file = open(f\"{folder_path}/physical_{basis}_job_u={u}_v={v}_result.txt\", \"r\")\n",
-    "            tmp_physical_results = cudaq.AsyncSampleResult(str(file.read()))\n",
-    "            physical_counts = tmp_physical_results.get()\n",
-    "\n",
-    "            file = open(f\"{folder_path}/logical_{basis}_job_u={u}_v={v}_result.txt\", \"r\")\n",
-    "            tmp_logical_results = cudaq.AsyncSampleResult(str(file.read()))\n",
-    "            logical_counts = tmp_logical_results.get()\n",
-    "\n",
-    "            aim_results[\"physical\"].append({k: v for k, v in physical_counts.items()})\n",
-    "            aim_results[\"logical\"].append({k: v for k, v in logical_counts.items()})\n",
-    "\n",
-    "    print(\"\\nObtained all circuit samples!\")\n",
-    "    return aim_results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running a CUDA-Q noisy simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we will first explore the performance of the physical and logical circuits under the influence of a device noise model. This will help us predict experimental results, as well as understand the dominant error sources at play. Such a simulation can be achieved via CUDA-Q's density matrix simulator: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "cudaq.set_target(\"density-matrix-cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_device_noise(\n",
-    "    depolar_prob_1q: float,\n",
-    "    depolar_prob_2q: float,\n",
-    "    *,\n",
-    "    readout_error_prob: float | None = None,\n",
-    "    custom_gates: list[str] | None = None,\n",
-    ") -> cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel:\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "    depolar_noise = cudaq.DepolarizationChannel(depolar_prob_1q)\n",
-    "\n",
-    "    noisy_ops = [\"z\", \"s\", \"x\", \"h\", \"rx\", \"rz\"]\n",
-    "    for op in noisy_ops:\n",
-    "        noise.add_all_qubit_channel(op, depolar_noise)\n",
-    "\n",
-    "    if custom_gates:\n",
-    "        custom_depolar_channel = cudaq.DepolarizationChannel(depolar_prob_1q)\n",
-    "        for op in custom_gates:\n",
-    "            noise.add_all_qubit_channel(op, custom_depolar_channel)\n",
-    "\n",
-    "    # Two qubit depolarization error\n",
-    "    p_0 = 1 - depolar_prob_2q\n",
-    "    p_1 = np.sqrt((1 - p_0**2) / 3)\n",
-    "\n",
-    "    k0 = np.array(\n",
-    "        [[p_0, 0.0, 0.0, 0.0], [0.0, p_0, 0.0, 0.0], [0.0, 0.0, p_0, 0.0], [0.0, 0.0, 0.0, p_0]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k1 = np.array(\n",
-    "        [[0.0, 0.0, p_1, 0.0], [0.0, 0.0, 0.0, p_1], [p_1, 0.0, 0.0, 0.0], [0.0, p_1, 0.0, 0.0]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k2 = np.array(\n",
-    "        [\n",
-    "            [0.0, 0.0, -1j * p_1, 0.0],\n",
-    "            [0.0, 0.0, 0.0, -1j * p_1],\n",
-    "            [1j * p_1, 0.0, 0.0, 0.0],\n",
-    "            [0.0, 1j * p_1, 0.0, 0.0],\n",
-    "        ],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k3 = np.array(\n",
-    "        [[p_1, 0.0, 0.0, 0.0], [0.0, p_1, 0.0, 0.0], [0.0, 0.0, -p_1, 0.0], [0.0, 0.0, 0.0, -p_1]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    kraus_channel = cudaq.KrausChannel([k0, k1, k2, k3])\n",
-    "\n",
-    "    noise.add_all_qubit_channel(\"cz\", kraus_channel)\n",
-    "    noise.add_all_qubit_channel(\"cx\", kraus_channel)\n",
-    "\n",
-    "    if readout_error_prob is not None:\n",
-    "        # Readout error modeled with a Bit flip channel on identity before measurement\n",
-    "        bit_flip = cudaq.BitFlipChannel(readout_error_prob)\n",
-    "        noise.add_all_qubit_channel(\"meas_id\", bit_flip)\n",
-    "    return noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, with our example noise model defined above, we can synchronously & noisily sample all of our AIM circuits by passing `noise_model=cudaq_noise_model` to the workflow containing function `submit_aim_circuits()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example parameters that can model execution on hardware at the high, simulation, level:\n",
-    "# Take single-qubit gate depolarization rate: ~0.2% or better (fidelity ≥99.8%)\n",
-    "# Take two-qubit gate depolarization rate: ~1–2% (fidelity ~98–99%)\n",
-    "cudaq_noise_model = get_device_noise(0.002, 0.02, readout_error_prob=0.02)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running circuits associated with layer=('1:-9')\n",
-      "Running circuits associated with layer=('1:-1')\n",
-      "Running circuits associated with layer=('1:7')\n",
-      "Running circuits associated with layer=('5:-9')\n",
-      "Running circuits associated with layer=('5:-1')\n",
-      "Running circuits associated with layer=('5:7')\n",
-      "Running circuits associated with layer=('9:-9')\n",
-      "Running circuits associated with layer=('9:-1')\n",
-      "Running circuits associated with layer=('9:7')\n",
-      "\n",
-      "Completed all circuit sampling!\n"
-     ]
-    }
-   ],
-   "source": [
-    "aim_sim_data = submit_aim_circuits(sim_circuit_dict, noise_model=cudaq_noise_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_ordering = []\n",
-    "for key in circuit_layers:\n",
-    "    for basis in (\"z_basis\", \"x_basis\"):\n",
-    "        data_ordering.append((key, basis))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_physical_energies, sim_physical_uncertainties = aim_physical_energies(\n",
-    "    data_ordering, aim_sim_data[\"physical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_logical_energies, sim_logical_uncertainties = aim_logical_energies(\n",
-    "    data_ordering, aim_sim_data[\"logical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To analyze our simulated energy results in the above cells, we will compare them to the brute-force computed exact ground state energies for the AIM Hamiltonian. For simplicity, these are already stored in the dictionary `bf_energies` below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bf_energies = {\n",
-    "    \"1:-9\": -18.251736027394713,\n",
-    "    \"1:-1\": -2.265564437074638,\n",
-    "    \"1:7\": -14.252231964940428,\n",
-    "    \"5:-9\": -19.293350575766127,\n",
-    "    \"5:-1\": -3.608495283014149,\n",
-    "    \"5:7\": -15.305692796870582,\n",
-    "    \"9:-9\": -20.39007993367173,\n",
-    "    \"9:-1\": -5.260398644698076,\n",
-    "    \"9:7\": -16.429650912487233,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the above metric, we can assess the performance of the logical circuits against the physical circuits by considering how far away the respective energies are from the brute-force expected energies. The cell below computes these energy deviations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer=(1, -9) has brute-force energy of: -18.251736027394713\n",
-      "Physical circuit of layer=(1, -9) got an energy of: -15.929\n",
-      "Logical circuit of layer=(1, -9) got an energy of: -17.46016175277361\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, -1) has brute-force energy of: -2.265564437074638\n",
-      "Physical circuit of layer=(1, -1) got an energy of: -1.97\n",
-      "Logical circuit of layer=(1, -1) got an energy of: -2.176531948420889\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, 7) has brute-force energy of: -14.252231964940428\n",
-      "Physical circuit of layer=(1, 7) got an energy of: -12.268\n",
-      "Logical circuit of layer=(1, 7) got an energy of: -13.26321740664324\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -9) has brute-force energy of: -19.293350575766127\n",
-      "Physical circuit of layer=(5, -9) got an energy of: -16.8495\n",
-      "Logical circuit of layer=(5, -9) got an energy of: -18.46681284816878\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -1) has brute-force energy of: -3.608495283014149\n",
-      "Physical circuit of layer=(5, -1) got an energy of: -3.1965000000000003\n",
-      "Logical circuit of layer=(5, -1) got an energy of: -3.4531715120183297\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, 7) has brute-force energy of: -15.305692796870582\n",
-      "Physical circuit of layer=(5, 7) got an energy of: -13.336\n",
-      "Logical circuit of layer=(5, 7) got an energy of: -14.341784541550897\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -9) has brute-force energy of: -20.39007993367173\n",
-      "Physical circuit of layer=(9, -9) got an energy of: -17.802\n",
-      "Logical circuit of layer=(9, -9) got an energy of: -19.339249509416753\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -1) has brute-force energy of: -5.260398644698076\n",
-      "Physical circuit of layer=(9, -1) got an energy of: -4.8580000000000005\n",
-      "Logical circuit of layer=(9, -1) got an energy of: -5.1227150992242025\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, 7) has brute-force energy of: -16.429650912487233\n",
-      "Physical circuit of layer=(9, 7) got an energy of: -14.3635\n",
-      "Logical circuit of layer=(9, 7) got an energy of: -15.448422736181264\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "sim_physical_energy_diff, sim_logical_energy_diff = _get_energy_diff(\n",
-    "    bf_energies, sim_physical_energies, sim_logical_energies\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Both physical and logical circuits were subject to the same noise model, but the `[[4,2,2]]` provides additional information that can help overcome some errors. Visualizing the computed energy differences from the above the cell, our noisy simulation provides a preview of the benefits logical qubits can offer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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mMH7rrbcWOfeuu+4ybb/22mvKy8uzLDcAAAAEB5pBAAAAAAAAfKx+/fp68skntWnTJrVv397h8RUrVmjGjBl+yAxASbJv3z598803prE77rjDT9kEt9GjRzuMObuSh6fmzJmjEydOmMbatGmjli1bFjl32LBhio+Pt2/v2bNH3377rWW5AQAAIDjQDAIAAAAAAFBMEhMTNWfOHCUmJjo89sEHH/ghIwAlyTvvvKPc3Fz7drt27dS8eXM/ZhS8rrnmGpUrV8409ttvv2nnzp2WxHfWWOKsAcWZUqVK6YYbbjCNvfXWW5bkBQAAgOBBMwgAAAAAAEAxqly5sh555BGH8WXLlik9Pd0PGQEoCXJycvTpp5+axrhFlediYmJ04403OoxbcXWQo0ePav78+aaxUqVKOT1eQfK/tr/++qt2797tdW4AAAAIHhH+TgAAAAAAfC01NVXbtm3Tjh07dOrUKaWkpCg6Olrly5dXQkKC2rZt6/Sv9H0hPT1dK1eu1Pbt23XmzBlFREQoMTFR7du3V+PGjV2Oc+rUKa1atUq7du1SSkqK4uLiVLVqVXXr1k2VKlXy4TMITsePH9eaNWt0/PhxHT9+XOHh4UpISFCVKlXUsWNHxcXF+TyHvLw8rVu3Tps2bdLx48dls9lUqVIl1atXT506dVJUVJTPcyhKbm6u9uzZo23btunQoUNKTk5Wbm6uypcvr/Lly+uiiy5S8+bNFRZWPH9bsnPnTq1du1aHDh1SZmamKlasqGrVqqlz584qX758seTgK0OGDNFDDz1kGsvMzNTmzZvVrl27QucG4uu0YcMGHTx4UKmpqYqKilJiYqJGjhzp0vxDhw5p27Zt2rt3r5KSknTu3DnFxcWpQoUKqlWrltq1a6eYmBgfP4u/7d+/X2vWrNG+ffuUlpam2NhYNWjQQJ06dXLrPbd161atX79eR44cUVZWlhISElS/fn117txZERHW/zrOMAxt2rRJu3fv1okTJ3Tq1CmVKVNGlStXVp06ddSuXTufHNcXzp49q9WrV+vYsWM6ceKEMjMzValSJSUkJKhdu3aqWrWqz3M4/zPjr7/+UlJSkv18PXjw4CJ/xp47d06bN2/W1q1bdebMGaWkpCg8PFylS5dW+fLlVbt2bdWvX1/Vq1f3+fPIb8GCBTp27Jhp7Nprry32PELJ6NGj9d///tc0NnXqVI0fP17h4eEex506dapycnJMY0OGDDHd+qUo3bp1U+XKlU23mpk6daqeffZZj/MCAABAkDEAAAAAwAN//fWXIcn01a1bN0tiL1q0yCH2zTff7PL87OxsY8GCBcb9999vtGjRwrDZbA7x8n/Vr1/fePrpp40TJ054lPPkyZMdYv7111/2x7dt22YMHz7ciImJKTCH1q1bGz/88EOhx1m6dKnRt29fIzw83GmM8PBwo0+fPsaff/5p+XOwYl63bt2KfC2K+po8ebJLzyc9Pd145ZVXjDZt2hT6HoiIiDC6dOliTJo0ycjJyXH9G/b/nL1fFy1aZH88KSnJGDt2rFGlSpUCcyhTpowxatQoY//+/W4f39PX7bzt27cbL730knHFFVcYZcqUKfL7Hx8fb1x77bXGihUr3M71vPwxx40bZ38sNzfX+Pjjj41mzZoVmEN4eLjRq1cv4/fff/c4B09Zee5z9v0u6BwQaK9Tamqq8dJLLxn16tUrMIeCnDhxwvjwww+NYcOGFVoX57+ioqKMrl27Gl999ZWRm5vr0XPJf+7J/5pNnz7daNu2bYE5REdHGyNGjDAOHDhQ4DEyMjKMt956y2jQoEGBccqVK2c8/vjjRlpamkfPI7/Vq1cbI0aMKPL7GBsbawwZMsRYuXKlS3GdnVfc/XKnLtLT043XXnvNuPTSSwv8+Xb+q2nTpsaECROM1NRUt79fhb0P8vLyjM8++8zo2LFjgT8zLjy35zdz5kxjwIABRmRkpEvfn2rVqhlDhw41pk2bZiQlJbn9XDwxfPhwUw5NmjTxKE7t2rW9+rlTGHfOJYGiVatWLp/LXXXRRRc5xFy4cKHbcUaOHGmK0aBBA6/yAgAAQHAJ/NU0AAAAgIAUqM0g06dPNypVquTxh1elS5c23nnnHbdzLuwD+XfffdeIjo52OYcHHnjAyMvLM8XPzMw07rrrLpdjREREGFOnTrXsOVg1r7iaQb788kujevXqbsdu2rSpsWTJEre+b4U1gyxdutStPEqVKmXMmjXLreN7+rqdPHnSuOSSS7x6La6++mrjzJkzbuVrGAU3GRw8eNC49NJL3crhiSeecPv43rDy3FetWjWHWJ999plpn0B8nVasWGHUqlWryOM6c8MNNxgREREeP5eLL77Yo2a3gpoAkpKSjP79+7t8/Pj4eOOXX35xiL9ly5ZCG5jyfzVo0MCj5q/z9u7dawwZMsSj7+GQIUOKfD8UZzPIRx99ZFStWtXt+FWqVDFmzJjh1vetoPfB0aNHja5duxZ5TGfNIPv27XNpbmFfjz76qFvPwxO5ubkO66O7777bo1g0g5j95z//ccj52muv9Tjeb7/95hCvXr16DmtDVzir5e3bt3ucGwAAAIJL8VwvFAAAAACKyZYtW3Ty5EmP56enp+vee+/VXXfdZUk+L774ou655x5lZma6POett97Sk08+ad/OysrSoEGD9N5777kcIycnR6NGjdKsWbPcyjcUPP/887r++ut16NAht+du3rxZvXv31hdffOF1HnPmzNHll1/uVh7nzp3TNddco/nz53t9/KKkpKRo/fr1XsWYNWuW2rdvr4MHD3qdz549e9ShQwf9/vvvbs178cUX9dRTT3l9fH9ISkpyGCtXrpxpO9Bep6VLl6p79+7av3+/R/OXL1/ucOsDd2zdulUdO3bUzz//7HGM81JSUtSjRw/98MMPLs9JSkrSwIED9ccff9jH/vjjD3Xp0kV//vmny3F27dql7t27O30PFGXFihVq3769Zs6c6fZcSZo5c6Y6duyoXbt2eTTfKtnZ2frHP/6h2267TUeOHHF7/rFjxzRs2DA9//zzXuVx9OhRderUSUuXLnV77t69e9W5c2eP5ha31atXO6yPunfv7p9kQsxNN93kcCur2bNn69SpUx7Fmzx5ssPYrbfeKpvN5nasHj16OIzNmzfPo7wAAAAQfILjZqEAAAAA4KHatWvrkksuUZMmTVSjRg3FxsaqVKlSSk1N1eHDh/XHH39owYIFDh/Ivf/++2revLnuvvtuj4/93XffmZo6qlSpogEDBqh169aqVKmSUlJStGHDBn311Vc6duyYae6ECRM0aNAgtW/fXvfcc4/pF/cXXXSRBgwYoIYNG6pcuXI6ffq0li1bpq+//trUdJKXl6e77rpL3bt3d+se877UoEEDnT17VtLfH8Dlf94tW7YsMkaFChUKfOz555/X2LFjHcYjIiLUo0cPXX755apevbpycnJ04MABzZ07VytWrJBhGPZ9s7KydNNNNyk8PFzDhg1z8ZmZ/fHHH3r88ceVlZUlSSpVqpR69eqlrl27KjExURERETpw4IB+/PFH/fLLL6a5OTk5+sc//qHNmzcX6+tWtmxZtWvXThdffLEaNmyo+Ph4xcbGKisrS2fOnNGWLVu0aNEibd261TRv586duu6667RkyRJFRHj2a4aUlBT169fP3jhjs9nUqVMnXX755apVq5bKli2rEydO6LffftO3336rjIwM0/wJEyZo4MCB6tChg2dP3g/27duntLQ0h/HKlSsXOs+fr9PRo0c1ZMgQ0/e/ffv2uuKKK1S7dm3FxsbqyJEj2rJli2bMmFFkvPDwcLVu3VpNmzbVRRddpIoVKyouLk6GYSg5OVk7d+7UihUr9NtvvykvL88+LzU1Vddff73Wr1+vmjVrevRcJGnkyJFat26dfbtNmzbq16+f6tatq7Jly+ro0aNauHChvv/+e9Px09PTdfPNN2vdunU6efKkBgwYYP/QNzIyUj169FDPnj1VrVo1RUREaO/evZo1a5ZWrlxpOv6ePXv0+OOP67///a/LOS9evFj9+vVzqIGwsDB16dJFnTp1Ut26dVWuXDmdO3dOBw8e1JIlS/TLL78oNzfXvv/27dt15ZVXas2aNYqLi3M4ToUKFezn46ysLIf3U82aNQs9F0t/n+8LkpeXp0GDBmnu3LkOj1WrVk29evXSJZdcokqVKikmJkanT5/W+vXrNW/ePFMjkmEYGjt2rCpVquRRE2deXp6GDRumPXv22Mfq1aun/v3766KLLlKlSpV06tQp/fXXX/rmm28c5t966606cOCAw3irVq3UvXt3NWrUSOXKlVNkZKRSUlJ05swZbdu2TRs3btSaNWtMr4mvLVmyxGGsbdu2xXb8UFa+fHkNHjzY1EialZWlzz77TPfff79bsdLT0zV9+nTTWHh4uEaNGuVRbrVr11blypV14sQJ+9jixYv1wAMPeBQPAAAAQcbPVyYBAAAAEKQC9TYx48aNM5o3b268+eabxo4dO1yak5GRYbz99ttGXFyc6ZjR0dHGwYMHXYrh7DLc528NEx4ebjz//PPGuXPnnM5NSkpyern/K664wvjmm2/s25UrVza++uqrAnPYuXOn0ahRI4c4L774osfPwerbxFxo3Lhxll4KftmyZUZ4eLhDzM6dOxd6SfTly5cbF110kcO8cuXKGfv27SvyuM7erzExMfZ/jxgxwjh8+HCh8ytUqOAQ46WXXnLpeXv6/f/rr7+McuXKGffee6+xePFiIysry6Xj/fbbb0bbtm0djvnqq6+6NN8wHG8DcOH3q0OHDsbatWsLzbt169YOMfr06ePy8b1h1bnv9ddfd4gTFRVlpKamOhwvUF6nC+urRYsWxvLlywucW9D5rmHDhsaQIUOMmTNnGmfPnnUpj7179xo33HCDQz79+/d3+bnkvz3Ihbfuqlu3rvHTTz8VOHfNmjVGlSpVHI7/+eefGwMHDrRvX3755YX+3Pn4448dzlFhYWHGgQMHXHoOR44ccZrHLbfcUuS5ateuXUafPn0c5rpyKwtn73lXbtdVmLFjxzrErFGjhvHVV18ZOTk5Bc7Lzs42PvroI6Ns2bIOtVPYeeO8/O+DC1+PihUrGlOnTi3wVhx5eXlGRkaGffvXX391eA716tUzli1b5tL34PTp08Znn31mdO3a1XjsscdcmuONa6+91pRrbGysR7cdMQxuE+PMzz//7JB3q1at3I4zZcoUhzj9+vXzKrfLL7/cFK969epexQMAAEDwCI7VNAAAAICAE6jNIK5+uOjMhg0bHBpCHn/8cZfmOvtA/vwHfTNnzixyflZWltGsWTPTXJvNZlSqVMmQZFStWtWl5pZdu3aZPuSUZDRq1Mjj5xAszSB5eXlG48aNnX5YnJmZWeT8U6dOOXz/JRkDBgwocq6z9+v5r+eff96l/H/99VfDZrOZ5jZo0MCluZ5+/zMzM4309HSXjpHfuXPnjL59+5qOWbNmTSM7O9ul+QV9vwYMGFBgE8GFTp065fCheFhYmEvNO96y4tx34sQJIzEx0SFOz549HfYNxNfpsssuM5KSkjzKyZtz9DPPPONwjty2bZtLc/M3AZz/uvjii40jR44UOf+3335zqNGEhAT7v2+44QaXvq/jx493yOGFF15w6Tn069fPNC88PNz47LPPXJprGH+fJ2+55RaH469cubLQeVY3gyxfvtwICwszxbv00kvdem/88ccfDj+vXfnAvKD3QZUqVYzNmze79TwefvhhU4zIyEhj586dbsU4Ly0tzaN57qhTp44p344dO3oci2YQR3l5eUbdunUdcl+3bp1bcZy9R7/++muvchszZoxDzMKaVAEAABA6wgQAAAAAFlmzZo1atWrl9dc//vEPj3Pw5rYaLVq00IsvvmgamzRpksfxJOmxxx7T4MGDi9wvMjLS4fYmhmHo5MmTkqRPP/1UDRs2LDJO/fr1dcstt5jGduzYod27d7uRdfD54YcftH37dtNYrVq1NH36dEVFRRU5v0KFCpo9e7ZKlSpVZFxXDRkyRE899ZRL+3bu3FlDhw41je3atcunr1tUVJTD83VVTEyMPvnkE5UuXdo+dv62N56qU6eOpk2bppiYmCL3rVChgsaNG2cay8vL008//eTx8YvLsWPHdNVVV+no0aMOj912220OY4H2OsXHx2v69OlOby3i6nxPjR07Vu3atbNvG4bh1Tk6Ojpa06dPV2JiYpH7durUSf369TONHT9+XJLUuHFjTZw40aXb7/zzn/9UuXLlTGMX3gasIKtXr3bY76WXXtKNN95Y5NzzbDabPvjgA1188cWm8QkTJrgcwwrjx4833XanWrVqmjt3rlvvjZYtWzrcXmfevHnasGGDRzlNnDhRTZo0cWvOhbeWkaTu3bsXemucwlxYo76QlZWlffv2mcZq167t02OWNDabzWH9JUkff/yxyzF2796tpUuXmsYqV66sq666yqvcnL3WO3bs8ComAAAAggPNIAAAAAAsk5aWpg0bNnj95c/GheHDh8tms9m3jx8/7vEvzMuVK6cnnnjC5f0HDBig6Ohoh/HevXurV69eLse59tprHcbWrVvn8vxg9M477ziM/fvf/1aZMmVcjlG3bl09+uijpjHDMPTuu++6nU9YWJheeeUVt+YMHz7cYWzt2rVuH7u4JCQkqG/fvqaxZcuWeRxv3Lhxbn0YfP311ys8PNw0Fsjfrz179mjChAlq0aKFfv/9d4fH27Vrp+uuu87y41r9Oj300EOqXr26t2l5xGazacSIEaYxb57LiBEj1Lx5c5f3v+aaa5yOjx071uUP82NiYjRgwADT2IYNG2QYRqHzXn75ZdN2gwYN9NBDD7l0zAtFRkY6/FyaN2+eMjMz3Y7liT///FNz5841jb344osODTKuuPHGGx2aJL/77ju34/To0cPhNXFFSkqKabtixYpuxygu+/btc3iP+auOQ9moUaMUFmb+dfvnn3/ucn1NnjzZ4XUaMWKEIiMjvcqrRo0aDmN79+71KiYAAACCA80gAAAAAHCB+Ph4JSQkmMZWrFjhUazrrrvOrWaEUqVKqXHjxg7jo0ePduu4l1xyicOYp1e3CAZZWVlasmSJaSwxMdGlK7Lkd/vttzs0GHhytYmePXuqfv36bs1p3769w1igv275P4j1tFbKlCnj1hUOJKl8+fIOx/fX96uwqyI1btxYFStWVP369fX444/bryZxoerVq2vGjBmmRjQrWfU62Ww23XrrrVak5LH8z2XdunXKzs72KJYV59bY2FiHq/q4GyclJUWHDh0qcP+MjAzNmTPHNDZq1CiHc5WrrrzySof4nr4n3PX111+btmNjYz1ugrLZbA5Xa1m8eLHbcdx9H5yXv/lj5cqVysnJ8SiWrx08eNBhzJUr4sA9NWvW1BVXXGEaO336tGbNmlXk3Ly8PE2dOtVh3IpzbtWqVR3GDhw44HVcAAAABL6ir18JAAAAAEHMMAytXbtWa9eu1aZNm3Tw4EGlpKQoOTm5wA8QT58+bdrev3+/R8fu2rWr23Nq166tjRs3msa6dOniVowKFSooNjbW9FfLZ8+edTuXYLFu3TplZGSYxgYNGuTSLRvyq1q1qrp06WL6QHH79u06deqUW3/13a1bN7ePXaVKFZUpU0ZpaWn2saSkJLfjeOPQoUNavny5Nm7cqB07digpKUnJyck6d+6c0ysX5L/Viae10rFjR5du55Nf/fr1tW3bNvt2cX+/zjt/VSRPtGrVSl9++aVbt2zw1+vUoEEDp39h7o3U1FQtXbpUGzdu1JYtW3Tq1CklJycrLS3NdCuRC/e/UGZmpo4dO+Z2XqVLl1bbtm3dmuPsNerYsaPbf7Vfp04dh7GzZ88W+BxWrlzpcGWByy67zK1jXqhChQqKj4831cv69es9Om+5K3/jXuvWrV26NVRB6tata9pev3692zF69Ojh0bE7dOigL7/80r79119/6bbbbtO7777r89u+uCs5OdlhzJ1mVbhu9OjRmj9/vmls8uTJGjZsWKHzfvrpJ4cGjQ4dOqhp06Ze5+Ts/Zj/yjYAAAAITTSDAAAAALBMt27dPPqr3PwWL17s8Ycz5yUlJenf//63Pv30U+3bt8+rWJ42UjRo0MDtObGxsabtUqVKqVq1ah7FufAX/f76kLw4OLsFjrsf8l6oXbt2pvexYRhav369Lr/8cpdj5L96gavi4+P90gzy9ddf67///a+WLFni9AN4V3laK958vy4UTO/zWrVq6Z577tGYMWNcbibw9+vUunVrj4+Z39q1a/Xqq69q9uzZOnfunFexCmukKEjt2rXdbhjLf36WrDnPS4W/d3/77TeHsbvvvtujBqrz0tPTTdsnT570OJarcnNzHa5AsnHjRrVq1crjmPmbN5OSkpSdne1yTVWpUsWjn7HS31f/euKJJ0zv3ylTpmju3LkaNWqUhgwZonbt2jncNsQf8r/e0t/rC1jvqquuUqVKlUw19eOPP+rQoUOF3ppn8uTJDmOeXrUmP2ev9YVrDQAAAIQumkEAAAAAhJxZs2bpjjvu0LFjxyyJ5+kHzOXLl3d7Tv4PsDyJ4SyOp7dRCAbOPsS8+OKLPY7XpEkTl45RmAoVKnh07OJ+3Q4fPqwRI0Zo4cKFlsTztFaC5fvliejoaMXFxalcuXJq1KiR2rRpo65du6pHjx4uf0gcKK9T/ltoeSI7O1tjxozRe++951VDy4U8eT5WnJ+tjFPYe9fZLT62bt3q9nELc+rUKUvjFXSM/FdxOnPmjM6cOWPpcU6fPq0qVaq4tK837+mqVavqxRdf1JgxY0zjx48f1yuvvKJXXnlF5cqVU6dOndShQwd17NhRnTp1UtmyZT0+pqdyc3Mdxjy9zRAKFxUVpREjRuiNN96wj+Xl5emTTz7RE0884XTOmTNn9N1335nGypQpo+uvv96SnJw1vgXqLY0AAABgLZpBAAAAAISUzz//XCNHjnT6wYenPP2A2d1bB/gqRqhz9kFiuXLlPI7n7MPd/H99XpRgeN0OHTqk7t27a9euXZbF9PTDpWD4fhXGqqsiORNIr1NcXJxXx83OztbQoUM1a9Ysr+I4i+suq95zxfHeLY5GDW+vzuKK4ngeknvPxdv39IMPPqicnBw9/vjjTuvq7Nmzmjt3rubOnSvp7w/lO3bsqOuuu07XX3+9KlWq5NXxXeXsyhD5G3NgndGjR5uaQaS/rxpTUDPIZ5995nArqKFDhzq9ipAnnNVEoN3KCAAAAL7h/+sUAgAAAIBFdu/erVtvvdWhESQyMlKDBw/WG2+8oZ9//lnbt2/X6dOnlZaWpry8PBmGYfqqXbu2n54BPOHsvvdlypTxOJ6zuc6OEexGjRrltMGgVatWevzxx/Xtt99q3bp1Onr0qJKTk5WVleVQK+PGjfND5iVLIL1O7t5WJb+XX37ZaSNI9erVdffdd2vatGn6/fffdeDAAZ09e1YZGRkOz2XRokVe5RCMrL5yhr8E4vPw9j0tSQ8//LD+/PNP3XTTTYqJiSl035ycHC1btkz33XefateurX/961/FcrsOZz/XvGkAcvZ9s6K5xFlOwXgFk6ZNm6pDhw6msZ07d+rXX391ur+zW8TceuutluXj7PvqzToJAAAAwYMrgwAAAAAIGY899pjDX1b27dtXH3/8sapWrepynOL4C2lYx9lfznrz4ZqzuVb9dW6g+OGHH/Tzzz+bxhISEvTpp5/qiiuucDkOteJbofQ6HT9+XC+99JJpLCIiQq+++qruvfdelz+UD4TnUtycXdVh69atuuiii/yQjeecPY/rrrtOX375pR+ysVbjxo01bdo0vfvuu/rhhx+0aNEiLVu2TNu3b5dhGE7npKen69///rdmz56tH3/80aeNqM5uh+PuFa8u5OzqW6mpqR7HKyyGp7fL87fRo0dr5cqVprGPP/5YXbp0MY1t3LhR69atM401atTIYT9vOHutrbjtFwAAAAIfVwYBAAAAEBLS0tL0/fffm8Zat26t2bNnu9UIIgXmXy+jYM4+KDp79qzH8ZzNrVChgsfxAtEXX3xh2g4PD9f333/vVoOB5N2HiShaKL1Os2fPVnp6umns5Zdf1oMPPujW1RkC4bkUN2e3EgnG70OoPI/CxMfH68Ybb9RHH32krVu36tSpU5ozZ44effRRNW/e3OmcHTt2qH///srKyvJZXs4aTQ4ePOhxPKt/7hYWI1ibQa6//nqHq2/MmDHDoeFl0qRJDnOtvCqI5Py15ip4AAAAJQPNIAAAAABCwtKlSx2uCvL4448rMjLSrTgHDhxQdna2lanBxypXruwwtnXrVo/jbdmyxWHM2YeYweynn34ybfft21ft27d3O86ePXusSglOhNLrlP+5lC9fXvfdd5/bcQLhuRS3KlWqOIzt27fPD5l4p3LlyrLZbKaxYHwe7ihfvrz69++vCRMmaOPGjdq+fbvuuusuh1ufbN682WlTgFUqVqyouLg405g3zSDOfiZu27bN43jnOfvZHaw/f2NjYzV06FDTWFpamr766iv7dlZWlj777DPTPhEREbr55pstzeXQoUMOY3Xr1rX0GAAAAAhMNIMAAAAACAkHDhxwGPPkEtu///67FemgGLVu3dphbM2aNR7HW716tWnbZrM5PUawyszM1PHjx01jntRKbm6uVq1aZVVayCfUXqf85+gOHTq43awnlcxzdIcOHRzGli5d6odMvBMTE6OWLVuaxnbs2KFjx475KaPi16hRI/33v//V1KlTHR775ptvfHrsFi1amLa3b9/ucSxnPxM3btzocbzz/vzzT4exNm3aeB3XX0aPHu0wNnnyZPu/Z8+erVOnTpke79evnxITEy3NI3+jTnR0dNDdZgoAAACeoRkEAAAAQEg4efKkw5gnt/aYPn26FenABc5uDZGbm+t2nNatWysmJsY09t1333kU69ixY/r1119NY40bNw6p28Tk/+BJ8qxW5s6d63C5e1gn1F6n/OdoT57LyZMntWjRIqtSCho9evRwOF/OmTOn2K5iZdW5WpJ69+7tMDZz5kyPYgWzG2+8Ua1atTKNWdFMUZh27dqZtvft26fk5GSPYl122WUOY/PmzZNhGB7FO2/OnDkuHStYdO7cWY0bNzaNLVu2TDt37pQkffzxxw5znDWQeGvDhg2m7ZYtW3rUjAcAAIDgQzMIAAAAgJCQ/77skvMGkcLs3r1bs2bNsiolFCE2NtZhzJMPrSMjI9WjRw/T2NGjR/Xdd9+5HevDDz9UTk6OaeyKK65wO04gs6JWJOn111+3Ih0UINRep/zPx5Pn8u677yojI8OqlIJGXFycunfvbho7ePCgPv3002I5vlXnakm6+uqrHcb+/e9/O5x3S4L8V2ZISkry6fE6duzoMOZpA0qbNm2c3nbGm2atXbt2OVz5Jzw8XF27dvU4ZiC49dZbHcY+/vhjHT58WD/++KNpvEqVKurfv7+lx8/IyNCOHTtMY86uNgQAAIDQRDMIAAAAgJBQtWpVh7H8v2QvTF5enm699VaP/9oZ7itfvrzD2J49ezyKdc899ziMPfzww0pPT3c5xr59+zRhwgTTmM1m07333utRToEqPj5epUuXNo25UyuSNHHiRC1evNjCrJBfqL1O+c/Ry5cvV1pamsvzN2/erJdeesnqtILGU0895TD28MMPe3zOdEdsbKzD1UE8Pe5ll13m0NiyZ88e/fOf//Q0vaB15MgR03blypV9erxevXopLMz8q+D8V8JyVXR0tG677TaH8X/9618er6Meeughh7FrrrlG1apV8yheoBg5cqRD/UydOlUff/yxw/fq5ptvdnolHm/8/vvvDs1Wffr0sfQYAAAACFw0gwAAAAAICV26dHEYGz9+vEuXQM/Ly9Mdd9yhpUuX+iI1FKB58+YOY3PnzvUo1pVXXunwV9Z79+7VjTfe6NJfnJ85c0ZXX321Q/PIwIED1bBhQ49yCmSdO3c2bS9evNjl7/38+fN1//33+yIt5BNKr1P+c3RqaqqeffZZl+bu3btXV111lTIzM32RWlDo1q2bwy1Wzpw5o759+2rr1q0exczIyNAHH3xQ5NVjwsLC1KRJE9PYggULlJeX59Fxx48fL5vNZhp7++23NW7cOI9vM/Lnn39q5MiROnPmjEfzPfHPf/5TW7Zs8WjuunXrHBoxWrZsaUVaBapYsaLDFSG8uZLHAw884HCrkXXr1umuu+5y+73x3HPP6fvvv3cYf/jhh92K0717d9lsNtPXlClT3IphtcTERIerfRw+fFgvvviiw77OriLirfyvcUxMjHr27Gn5cQAAABCYaAYBAAAAEBKqVq3q8MHprl271KdPH+3bt6/Aedu3b1ffvn01ceJESVJERITDX+PDN5o1a+ZwmfmXXnpJU6ZM0blz59yKZbPZNGnSJIWHh5vGZ82apSuuuEK7du0qcO7KlSvVuXNnbdiwwTRerlw5/ec//3Erj2AxbNgwh7HrrrtOX3/9dYFzzp07p+eee05XX321/fXJ//rBWqH0Ol1zzTUOVyV49dVX9fTTTxfasPXFF1/o0ksvtV+JIhCei79MmTLF4SoJO3fuVPv27fXSSy+5dJsRwzC0fPlyjRkzRnXq1NGdd97p0lU+OnXqZNrevn27/vGPfxT687Ugl112mcaNG+cw/txzz6lnz54uX63i1KlTmjhxonr37q0WLVro008/Ldare02aNElNmzZV79699dFHH+n48eMuzZszZ4769evn0DAxfPhwX6RpMmjQINP2b7/95vbP2/Nq1qyp559/3mH8o48+Uu/evbV27doiY+zcuVPDhg1z+n6477771K5dO49yCzSjR492GMv/fb/sssvUuHFjy4/9888/m7Z79+6tUqVKWX4cAAAABCZrrzsHAAAAAH707LPPqlevXqaxFStWqFGjRrr66qvVuXNnJSYmKiMjQ4cOHdJPP/2kX3/91fRB5NixYzVp0iSPPuCCeyIjIzV8+HD997//tY+lpaXplltu0T/+8Q/VrFlTsbGxDh8gP/fcc7rqqqsc4nXq1Enjxo3T2LFjTeOLFi1SkyZN1KtXL/Xs2VPVq1dXbm6uDhw4oLlz52r58uUOf41us9n0wQcfqFatWhY+48AxcuRIvfTSS9q9e7d9LDU1VUOHDlXr1q01cOBANWjQQJGRkTp+/LjWrl2rOXPm6NSpU/b9mzZtqgEDBujll1/2x1MoEULpdWrUqJGGDx+uqVOnmsbHjx+vKVOm6Nprr1WLFi1UtmxZnT59Wtu3b9fs2bNNz7106dJ6+eWXdddddxV3+gGhWrVqmjVrlrp37266xU5qaqqeeOIJvfDCC+rcubM6deqkqlWrqnz58jp37pzOnj2rw4cPa926dVq7dq3p/eGqW2+9Ve+//75pbPLkyZo8ebIqV66sypUrO1wlom3btvZGy/zGjh2rbdu26csvvzSNL168WF27dlWjRo3UvXt3NW3aVBUqVFB0dLTOnj2rM2fOaMuWLVq7dq22bt0aELd2+/nnn/Xzzz/rzjvvVNOmTXXJJZeoSZMmqlixosqVK6fc3FydPn1aW7du1U8//aRt27Y5xOjSpYuuu+46n+d644036vHHH7c3oqSnp2v+/PkaPHiwR/EeeeQRLV++XLNnzzaNL1y4UG3btlWLFi3Uo0cPNWjQQBUqVFB4eLhOnz6tffv2afHixVqzZo3T17Bjx4567bXXPMopEF155ZWqWrWqw62BLuSsYcRbhw8f1ooVK0xjI0eOtPw4AAAACFw0gwAAAAAIGT179tRjjz2mCRMmmMazsrI0Y8YMzZgxo9D5w4cP11NPPaVJkyb5Mk1c4Omnn9bMmTN19OhR03hubq727t3rdM7p06cLjWcYhsNfGWdnZ2v+/PmaP39+kTlFRkZq8uTJTq/KECoiIyM1Y8YMde7c2eHWOOvWrdO6desKnV+9enXNmTPH75ffD3Wh9jq9/fbbWrVqlcOH4QcPHtSbb75Z6Nzz34uSfuWmtm3basWKFbrmmmu0Y8cO02NpaWlasGCBFixYYPlx27Vrp1GjRjl9L504cUInTpxwGC9XrlyB8Ww2mz777DPVr19fL774okND3o4dOxyeX6DLy8vTpk2btGnTJrfmNWvWTF9++aVD46Mv1KhRQz169NAvv/xiH/vmm288bgax2WyaPn267r77bk2ePNnh8Y0bN2rjxo1uxRw4cKA+/fRTh+aiYBYeHq6bb77ZYX16XmxsrE/WHDNnzjTVVvny5TVw4EDLjwMAAIDAxW1iAAAAAISUF198UU899ZRsNpvLc8LDw/XEE0/ok08+cWsevJeYmKiFCxeqTZs2lsUcO3asvvjiC4fbKbiiSZMm+umnn3TTTTdZlk+guuSSS7RgwQJVrVrVrXkdO3bUihUrVKdOHd8kBpNQep3i4+P1888/q2PHjm7Nq1atmn7++WddeeWVPsosuDRr1kyrV6/Wvffeq5iYGK9itWvXTv3793dp3/fff18PPPCAZU0LYWFhGj9+vObOnauWLVv+X3v3F1p1+ccB/L2mzT9tplkobpXLGFMSpgldmCJRKBaIYFk3ZRSCNyqIIBgUI4R1kay6qTC7qYggKgi8KEkWgdtE+zMMGm0hhc458e9FOX8XwSH7/TJ/Z2NHv3u9rs7nOef7PJ/DgXMuzvv7PCOaa9q0aXnuuedyyy23jEpv12LWrFkjur6qqipPP/10Ojo6yvq9Ktffd9b59NNPc+7cubLnmzRpUvbs2ZM9e/akoaGh7HlmzpyZV155JZ988kmmTZtW1hz/K5S0YMGCsnsaTc8+++w/PvfEE09k6tSpo77me++9d0X9zDPPpKamZtTXAQDg+iUMAgAAFEpVVVVaW1vT0dGRVatWXfVPqylTpuSpp55Kd3d3Xn755TG5K5f/1tzcnM7Oznz11VfZsmVLVqxYkfr6+tTV1aW6urqsOdevX5+ffvopbW1taWlpuWrIZ8KECVm6dGnefvvtfPvtt1m+fHm5b+WGs3Tp0hw5ciTbt2+/6l38yZ87Erz77rv5+uuvU19fPzYNkqRYn9OcOXNy4MCBvP7662lsbLzqa++66660trbm6NGjWbZs2Rh1eGOoq6vLa6+9lr6+vuzcuTMtLS3X9Bs2efLkPPTQQ9m1a1d6enpy8ODBrFq16prWrKmpye7du9PX15e2trasXbs2TU1Nue2223LzzTeX/V5WrlyZw4cP57PPPsvatWszY8aMa7qusbExzz//fD766KP89ttveeutt0Ycjvl/HD16NN3d3Wltbc3DDz+curq6a7rujjvuyKZNm3Lo0KHs3bu37OBDudasWZO5c+eW6rNnz+b9998f8bwbNmxIb29v9u7dm0ceeSS1tbX/es2kSZOybNmytLe3p7+/P9u2bSs7lHv8+PH09PRcMbZ69eosWbKkrPlG27333vuP32NXC4qU6/vvv88333xTqqurq7N58+ZRXwcAgOtb1eW/78MIAABQIKdPn05HR0d++eWXDA0NZcKECZk5c2aampqyZMkSd0iOE8ePH09nZ2dOnDiRgYGBVFdX5/bbb8+sWbPywAMPjPmfcdejS5cupaurKz/88ENOnjyZP/74I7W1tZk7d27uv//+Ed8Fz+go2uf0448/prOzMwMDAzl//nymTp2a+vr6LFy4ME1NTZVu74YyNDSUrq6unDhxIoODgzlz5kymTJmS2trazJ49O01NTWlsbCw7ZDdWLl++nO+++y69vb0ZHBzM4OBghoeHU1tbm1tvvTX33HNPmpub/zUYNdaGh4fT19eX3t7e9Pf358yZM7lw4UJqampSV1eX2bNnZ+HChdfFbj3t7e1XBAMWL16crq6uUV3j0qVLOXLkSH7++eecOnUqQ0NDGR4ezvTp0zN9+vQ0NDRk8eLFIwoS/dUHH3yQJ5988oqx7u7uLFq0aFTmv9Fs3rw57e3tpXrdunX58MMPK9gRAACVIAwCAAAAADBOXLx4MfPmzcuvv/5aGjtw4EAefPDBCnY1Mhs3bsybb75ZqtesWZOPP/64gh1VzunTp3PnnXfm7NmzSf48kunw4cO57777KtwZAABjzR7IAAAAAADjxOTJk7Nz584rxnbt2lWhbkbHl19+WXpcVVWVl156qYLdVNYbb7xRCoIkfx6dJwgCADA+2RkEAAAAAGAc+f3339Pc3Jze3t7S2KFDh9LS0lLBrspz7NixNDQ0lOrxfCTKhQsXcvfdd2dgYCBJMnHixPT09GTevHkV7gwAgEqwMwgAAAAAwDgyceLE7N69+4qxHTt2VKaZEfrrriA33XRTXnzxxco1U2GvvvpqKQiSJFu3bhUEAQAYx4RBAAAAAADGmUcffTSPPfZYqd63b1+++OKLCnZUnv3795cer1+/PvPnz69gN5Vz8uTJtLW1leo5c+bkhRdeqGBHAABUmmNiAAAAAADGof7+/rzzzjulev78+Xn88ccr2BHlOnjwYD7//PNSvWLFiixfvryCHQEAUGnCIAAAAAAAAAAABeKYGAAAAAAAAACAAhEGAQAAAAAAAAAoEGEQAAAAAAAAAIACEQYBAAAAAAAAACgQYRAAAAAAAAAAgAIRBgEAAAAAAAAAKBBhEAAAAAAAAACAAhEGAQAAAAAAAAAoEGEQAAAAAAAAAIACEQYBAAAAAAAAACgQYRAAAAAAAAAAgAIRBgEAAAAAAAAAKBBhEAAAAAAAAACAAhEGAQAAAAAAAAAoEGEQAAAAAAAAAIACEQYBAAAAAAAAACgQYRAAAAAAAAAAgAIRBgEAAAAAAAAAKBBhEAAAAAAAAACAAhEGAQAAAAAAAAAoEGEQAAAAAAAAAIACEQYBAAAAAAAAACiQ/wB364Bp0qss6QAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 2200x1400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(11, 7), dpi=200)\n",
-    "\n",
-    "layer_labels = [(int(key.split(\":\")[0]), int(key.split(\":\")[1])) for key in bf_energies.keys()]\n",
-    "plot_labels = [str(item) for item in layer_labels]\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    sim_physical_energy_diff,\n",
-    "    yerr=sim_physical_uncertainties.values(),\n",
-    "    ecolor=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    color=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Physical\",\n",
-    ")\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    sim_logical_energy_diff,\n",
-    "    yerr=sim_logical_uncertainties.values(),\n",
-    "    color=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    ecolor=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Logical\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Hamiltonian Parameters (U, V)\", fontsize=18)\n",
-    "ax.set_ylabel(\"Energy above true ground state (in eV)\", fontsize=18)\n",
-    "ax.set_title(\"CUDA-Q AIM Circuits Simulation (lower is better)\", fontsize=20)\n",
-    "ax.legend(loc=\"upper right\", fontsize=18.5)\n",
-    "plt.xticks(fontsize=16)\n",
-    "plt.yticks(fontsize=16)\n",
-    "\n",
-    "ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=2)\n",
-    "plt.ylim(\n",
-    "    top=max(sim_physical_energy_diff) + max(sim_physical_uncertainties.values()) + 0.2, bottom=-0.2\n",
-    ")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running logical AIM on Infleqtion's hardware "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The entire workflow we've seen thus far can be seamlessly executed on real quantum hardware as well. CUDA-Q has integration with Infleqtion's gate-based neutral atom quantum computer, [Sqale](https://arxiv.org/html/2408.08288v2), allowing execution of CUDA-Q kernels on neutral-atom hardware via Infleqtion’s cross-platform Superstaq compiler API that performs low-level compilation and optimization under the hood. Indeed, the AIM research results seen in [our paper](https://arxiv.org/abs/2412.07670) were obtained via this complete end-to-end workflow.\n",
-    "\n",
-    "To do so, users can obtain a Superstaq API key from [superstaq.infleqtion.com](https://superstaq.infleqtion.com/) to gain access to Infleqtion's neutral-atom simulator, with [pre-registration](https://www.infleqtion.com/sqale-preregistration) open for access to Infleqtion’s neutral atom QPU."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a tutorial, let us reproduce the workflow we've run so far but on Infleqtion's QPU. We begin with the same GPU-enhanced VQE to generate the AIM circuits:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "\n",
-    "if cudaq.num_available_gpus() == 0:\n",
-    "    cudaq.set_target(\"qpp-cpu\", option=\"fp64\")\n",
-    "else:\n",
-    "    cudaq.set_target(\"nvidia\", option=\"fp64\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9\n",
-      "Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1\n",
-      "Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7\n",
-      "Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9\n",
-      "Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1\n",
-      "Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7\n",
-      "Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9\n",
-      "Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1\n",
-      "Computed optimal angles=[-1.7301462945564499, 1.570796044872433] for U=9, V=7\n",
-      "\n",
-      "Finished building optimized circuits!\n"
-     ]
-    }
-   ],
-   "source": [
-    "device_circuit_dict = generate_circuit_set(\n",
-    "    ignore_meas_id=True\n",
-    ")  # Setting `ignore_meas_id=True` drops the noisy-identity gate from earlier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, we change backends! Before selecting an Infleqtion machine in CUDA-Q, we must first set our Superstaq API key, like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ['SUPERSTAQ_API_KEY'] = \"api_key\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we declare the type of execution we would like on Infleqtion's machine based on the keyword options specified:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "\n",
-    "# Set the following to run on Infleqtion's Sqale QPU:\n",
-    "cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\")\n",
-    "\n",
-    "# Set the following to run an ideal dry-run on Infleqtion's Sqale QPU:\n",
-    "# cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\", method=\"dry-run\")\n",
-    "\n",
-    "# Set the following to run a device-realistic noisy simulation of Infleqtion's Sqale QPU:\n",
-    "# cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\", method=\"noise-sim\")\n",
-    "\n",
-    "# Set the following to run a local, ideal emulation:\n",
-    "# cudaq.set_target(\"infleqtion\", emulate=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With that, we're all set! That simple change instructs our AIM circuits to execute on Infleqtion's QPU (or simulator). Due to the general queue wait time of running on hardware, we optionally recommend enabling the `run_async=True` flag to asynchronously sample the circuits. This will allow the cell to be executed and not wait synchronously until all the jobs are complete, allowing other classical code to be run in the meantime. When using `run_async`, an optional directory to store the job information can be specified with `folder_path` (this will be important to later retrieve the job results from the same directory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Posting circuits associated with layer=('1:-9')\n",
-      "Posting circuits associated with layer=('1:-1')\n",
-      "Posting circuits associated with layer=('1:7')\n",
-      "Posting circuits associated with layer=('5:-9')\n",
-      "Posting circuits associated with layer=('5:-1')\n",
-      "Posting circuits associated with layer=('5:7')\n",
-      "Posting circuits associated with layer=('9:-9')\n",
-      "Posting circuits associated with layer=('9:-1')\n",
-      "Posting circuits associated with layer=('9:7')\n",
-      "\n",
-      "All circuits submitted for async sampling!\n"
-     ]
-    }
-   ],
-   "source": [
-    "submit_aim_circuits(\n",
-    "    device_circuit_dict, folder_path=\"hardware_aim_future_results\", shots_count=1000, run_async=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the above cell execution, all the circuits will post to execute on QPU. We can then return at a later time to retrieve the job results with the cell below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Retrieving all circuits counts associated with layer=('1:-9')\n",
-      "Retrieving all circuits counts associated with layer=('1:-1')\n",
-      "Retrieving all circuits counts associated with layer=('1:7')\n",
-      "Retrieving all circuits counts associated with layer=('5:-9')\n",
-      "Retrieving all circuits counts associated with layer=('5:-1')\n",
-      "Retrieving all circuits counts associated with layer=('5:7')\n",
-      "Retrieving all circuits counts associated with layer=('9:-9')\n",
-      "Retrieving all circuits counts associated with layer=('9:-1')\n",
-      "Retrieving all circuits counts associated with layer=('9:7')\n",
-      "\n",
-      "Obtained all circuit samples!\n"
-     ]
-    }
-   ],
-   "source": [
-    "aim_device_data = _get_async_results(circuit_layers, folder_path=\"hardware_aim_future_results\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "physical_energies, physical_uncertainties = aim_physical_energies(\n",
-    "    data_ordering, aim_device_data[\"physical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logical_energies, logical_uncertainties = aim_logical_energies(\n",
-    "    data_ordering, aim_device_data[\"logical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer=(1, -9) has brute-force energy of: -18.251736027394713\n",
-      "Physical circuit of layer=(1, -9) got an energy of: -17.626499999999997\n",
-      "Logical circuit of layer=(1, -9) got an energy of: -17.69666562801761\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, -1) has brute-force energy of: -2.265564437074638\n",
-      "Physical circuit of layer=(1, -1) got an energy of: -2.1415\n",
-      "Logical circuit of layer=(1, -1) got an energy of: -2.2032104443266585\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, 7) has brute-force energy of: -14.252231964940428\n",
-      "Physical circuit of layer=(1, 7) got an energy of: -12.9955\n",
-      "Logical circuit of layer=(1, 7) got an energy of: -13.76919450035401\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -9) has brute-force energy of: -19.293350575766127\n",
-      "Physical circuit of layer=(5, -9) got an energy of: -18.331\n",
-      "Logical circuit of layer=(5, -9) got an energy of: -18.85730052910377\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -1) has brute-force energy of: -3.608495283014149\n",
-      "Physical circuit of layer=(5, -1) got an energy of: -3.476\n",
-      "Logical circuit of layer=(5, -1) got an energy of: -3.5425689231532203\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, 7) has brute-force energy of: -15.305692796870582\n",
-      "Physical circuit of layer=(5, 7) got an energy of: -14.043500000000002\n",
-      "Logical circuit of layer=(5, 7) got an energy of: -14.795918428433312\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -9) has brute-force energy of: -20.39007993367173\n",
-      "Physical circuit of layer=(9, -9) got an energy of: -19.4715\n",
-      "Logical circuit of layer=(9, -9) got an energy of: -19.96524696701215\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -1) has brute-force energy of: -5.260398644698076\n",
-      "Physical circuit of layer=(9, -1) got an energy of: -4.973\n",
-      "Logical circuit of layer=(9, -1) got an energy of: -5.207315773582224\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, 7) has brute-force energy of: -16.429650912487233\n",
-      "Physical circuit of layer=(9, 7) got an energy of: -15.182\n",
-      "Logical circuit of layer=(9, 7) got an energy of: -16.241375689575516\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "physical_energy_diff, logical_energy_diff = _get_energy_diff(\n",
-    "    bf_energies, physical_energies, logical_energies\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As before, we use the same metric of comparing against the true ground state energies; however, this time, both the physical and logical circuits are fully exposed to real hardware noise. Yet, we expect the use of logical qubits afforded to us by the `[[4,2,2]]` code to achieve energies closer to the true ground state than the bare physical circuits (up to a certain error threshold). And indeed they do! Visually, we can plot the energy deviations of both the physical and logical circuits from the cell above and observe that the logical circuits are able to outperform the physical circuits by obtaining much lower energies, demonstrating the power of error detection and the beginning possibilities of fault-tolerant quantum computation: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2200x1400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(11, 7), dpi=200)\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    physical_energy_diff,\n",
-    "    yerr=physical_uncertainties.values(),\n",
-    "    ecolor=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    color=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Physical\",\n",
-    ")\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    logical_energy_diff,\n",
-    "    yerr=logical_uncertainties.values(),\n",
-    "    color=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    ecolor=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Logical\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Hamiltonian Parameters (U, V)\", fontsize=18)\n",
-    "ax.set_ylabel(\"Energy above true ground state (in eV)\", fontsize=18)\n",
-    "ax.set_title(\"CUDA-Q AIM Infleqtion Hardware Execution (lower is better)\", fontsize=20)\n",
-    "ax.legend(loc=\"upper left\", fontsize=18.5)\n",
-    "plt.xticks(fontsize=16)\n",
-    "plt.yticks(fontsize=16)\n",
-    "\n",
-    "ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=2)\n",
-    "plt.ylim(top=max(physical_energy_diff) + max(physical_uncertainties.values()) + 0.2, bottom=-0.2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/qaoa.ipynb.txt b/pr-2308/_sources/applications/python/qaoa.ipynb.txt
deleted file mode 100644
index b064182dd6..0000000000
--- a/pr-2308/_sources/applications/python/qaoa.ipynb.txt
+++ /dev/null
@@ -1,376 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Max-Cut with QAOA\n",
-    "\n",
-    "[Farhi et al.](https://arxiv.org/abs/1411.4028) introduced the quantum approximation optimization algorithm (QAOA) to solve optimization problems like the max cut problem. Before diving into the details of QAOA, we'll first define the max cut problem. \n",
-    "\n",
-    "[Max Cut](https://en.wikipedia.org/wiki/Maximum_cut) is the problem of finding a partition of a graph's nodes into two sets which maximizes the edges between the two sets. Although this problem is relatively easy to solve for graphs with few vertices, this problem is [NP-hard](https://en.wikipedia.org/wiki/NP-hardness#:~:text=7%20References-,Definition,complete%20problem%20G%20to%20H.). The max cut problem has a wide range of applications including [machine learning](https://jmlr.org/papers/volume14/wang13a/wang13a.pdf), [circuit design](https://math.mit.edu/research/highschool/primes/materials/2020/Hong-Lee-Wei.pdf) and [statistical physics](https://www.researchgate.net/publication/262162554_An_Application_of_Combinatorial_Optimization_to_Statistical_Physics_and_Circuit_Layout_Design), among others. Furthermore, the QAOA algorithm presented in this tutorial can be adapted to other related optimization problems with an even wider application field including [portfolio optimization](https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.043204) and [job shop scheduling](https://www.sciencedirect.com/science/article/pii/S0377221723002072), just to name a few.\n",
-    "\n",
-    "We take the convention that $G=(V,E)$ represents a graph with vertex set $V\\subseteq \\mathbb{N}$ and edge set $E\\subseteq V\\times V$. We use the terms vertex and node interchangeably.  For this tutorial we assume that the graphs are undirected (that is, $(u,v)$ and $(v,u)$ represent the same edge). Our graphs contain no self loops (i.e., for every vertex $v$, there is no edge $(v,v)$). A *cut* of the graph $G$ is a partition, $(V_0,V_1)$, of the vertex set such that every vertex of $V$ is a member of exactly one of $V_0$ or $V_1$ (i.e., $V_0\\bigcup V_1 = V$ and $V_0\\bigcap V_0=\\emptyset$). The *cut value* for a partition is the sum of the edges with one node in $V_0$ and one node in $V_1$.\n",
-    "\n",
-    "In the images below, we illustrate two cuts of a graph with the dotted lines. Each of these cuts partitions the graph into two disjoint sets. The cut on the left is not optimal, and the cut on the right is the max cut. The cut on the left divides the graph into disjoint sets $\\{1,2\\}$ and $\\{0,3,4\\}$, and that cut contains 3 edges. To more easily visualize the cut, we have colored the vertices in one set of the partition green and the vertices in the other set of the partition gray.\n",
-    "The cut depicted in the diagram on the right divides the graph vertices into two disjoint sets $V_0=\\{0,2\\}$, colored gray, and $V_1=\\{1,3,4\\}$, colored green. The number of edges connecting vertices in the distinct sets is computed by $$\\sum_{\\substack{u \\in V_0; v\\in V_1\\\\ (u,v) \\in E}}1.$$ For the graph on the right, the number of edges in the cut (in this case there are $5$ edges) is maximal, and this value is referred to as the *max cut value*. The partitioning $(V_0,V_1)$  &mdash; and sometimes the set of edges connecting vertices in $V_0$ and $V_1$  &mdash; is referred to as a *max cut of a graph*. Note that the max cut of a graph need not be unique; that is, two distinct partitions may produce the same cut value.\n",
-    " \n",
-    "![](images/max-cut-illustration.png)\n",
-    " \n",
-    "We will use bitstrings to identify vertices in each of the two partitions. For example using the ordering of the vertices, the bitstring `01100` captures the partition in the image above on the left with vertices $1$ and $2$ in $V_1$, and the bitstring `01011` codes the partition in the image on the right with vertices $1$, $3$, and $4$ in $V_1$.\n",
-    "\n",
-    "Let's code our graph data as lists of integers so that we can call these variables when we create the `cudaq.kernel` for QAOA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "from typing import List\n",
-    "\n",
-    "# We'll use the graph below to illustrate how QAOA can be used to\n",
-    "# solve a max cut problem\n",
-    "\n",
-    "#       v1  0--------------0 v2\n",
-    "#           |              | \\\n",
-    "#           |              |  \\\n",
-    "#           |              |   \\\n",
-    "#           |              |    \\\n",
-    "#       v0  0--------------0 v3-- 0 v4\n",
-    "# The max cut solutions are 01011, 10100, 01010, 10101 .\n",
-    "\n",
-    "# First we define the graph nodes (i.e., vertices) and edges as lists of integers so that they can be broadcast into\n",
-    "# a cudaq.kernel.\n",
-    "nodes: List[int] = [0, 1, 2, 3, 4]\n",
-    "edges = [[0, 1], [1, 2], [2, 3], [3, 0], [2, 4], [3, 4]]\n",
-    "edges_src: List[int] = [edges[i][0] for i in range(len(edges))]\n",
-    "edges_tgt: List[int] = [edges[i][1] for i in range(len(edges))]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "QAOA is a variational algortihm with a particular ansatz. QAOA is made up of a variational quantum circuit (i.e., a kernel that depends on a set of parameter values) and a classical optimizer. The aim of QAOA is to use the classical optimizer to identify parameter values that generate a quantum circuit whose expectation value for a given cost Hamilitonian is minimized. \n",
-    "\n",
-    "What distinguishes QAOA from other variational algorithms is the structure of the quantum circuit. For each vertex in the graph, there is an associated qubit in the circuit. The circuit is initialized in a superposition state. The remainder of the QAOA circuit is made up of blocks (referred to as layers). The more layers there are, the better the approximation the algorithm achieves.\n",
-    "\n",
-    "![diagram of QAOA circuit layers](images/qaoa-circuit-layers.png)\n",
-    "\n",
-    "\n",
-    "\n",
-    "Each layer contains a problem kernel and a mixer kernel. The mixer kernel is composed of parameterized rotation gates applied to each qubit, depicted in green in the image above. The problem kernel encodes the graph edges. The image below shows an example of an graph edge encoded with controlled-X gates and a parameterized rotation gate.\n",
-    "\n",
-    "![diagram of a QAOA problem kernel for a max cut problem](images/qaoa-problem-kernel.png)\n",
-    "\n",
-    "Let's implement the QAOA circuit for our max cut problem in CUDA-Q."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Problem parameters\n",
-    "# The number of qubits we'll need is the same as the number of vertices in our graph\n",
-    "qubit_count: int = len(nodes)\n",
-    "\n",
-    "# We can set the layer count to be any positive integer.  Larger values will create deeper circuits\n",
-    "layer_count: int = 2\n",
-    "\n",
-    "# Each layer of the QAOA kernel contains 2 parameters\n",
-    "parameter_count: int = 2 * layer_count\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qaoaProblem(qubit_0: cudaq.qubit, qubit_1: cudaq.qubit, alpha: float):\n",
-    "    \"\"\"Build the QAOA gate sequence between two qubits that represent an edge of the graph\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    qubit_0: cudaq.qubit \n",
-    "        Qubit representing the first vertex of an edge\n",
-    "    qubit_1: cudaq.qubit \n",
-    "        Qubit representing the second vertex of an edge\n",
-    "    thetas: List[float]\n",
-    "        Free variable   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.Kernel\n",
-    "        Subcircuit of the problem kernel for Max-Cut of the graph with a given edge\n",
-    "    \"\"\"\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "    rz(2.0 * alpha, qubit_1)\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "\n",
-    "# We now define the kernel_qaoa function which will be the QAOA circuit for our graph\n",
-    "# Since the QAOA circuit for max cut depends on the structure of the graph,\n",
-    "# we'll feed in global concrete variable values into the kernel_qaoa function for the qubit_count, layer_count, edges_src, edges_tgt.\n",
-    "# The types for these variables are restricted to Quake Values (e.g. qubit, int, List[int], ...)\n",
-    "# The thetas plaeholder will be our free parameters\n",
-    "@cudaq.kernel\n",
-    "def kernel_qaoa(qubit_count: int, layer_count: int, edges_src: List[int],\n",
-    "                edges_tgt: List[int], thetas: List[float]):\n",
-    "    \"\"\"Build the QAOA circuit for max cut of the graph with given edges and nodes\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    qubit_count: int \n",
-    "        Number of qubits in the circuit, which is the same as the number of nodes in our graph\n",
-    "    layer_count : int \n",
-    "        Number of layers in the QAOA kernel\n",
-    "    edges_src: List[int]\n",
-    "        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    edges_tgt: List[int]\n",
-    "        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    thetas: List[float]\n",
-    "        Free variables to be optimized   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.Kernel\n",
-    "        QAOA circuit for Max-Cut for max cut of the graph with given edges and nodes\n",
-    "    \"\"\"\n",
-    "    # Let's allocate the qubits\n",
-    "    qreg = cudaq.qvector(qubit_count)\n",
-    "    # And then place the qubits in superposition\n",
-    "    h(qreg)\n",
-    "\n",
-    "    # Each layer has two components: the problem kernel and the mixer\n",
-    "    for i in range(layer_count):\n",
-    "        # Add the problem kernel to each layer\n",
-    "        for edge in range(len(edges_src)):\n",
-    "            qubitu = edges_src[edge]\n",
-    "            qubitv = edges_tgt[edge]\n",
-    "            qaoaProblem(qreg[qubitu], qreg[qubitv], thetas[i])\n",
-    "        # Add the mixer kernel to each layer\n",
-    "        for j in range(qubit_count):\n",
-    "            rx(2.0 * thetas[i + layer_count], qreg[j])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As mentioned earlier, QAOA is a variational algorithm that aims to minimize a cost function. To apply QAOA to the max cut problem, we assign a binary variable $z_v$ to each vertex $v$. These variables will take on the vales $-1$ or $1$. Notice that an assignment of variable values corresponds to a partition of the graph.  For example, $\\bar z = [-1, 1, -1, 1, 1]$ would partition the graph into two sets: $\\{0,2\\}$ and $\\{1,3,4\\}.$ We can compute the cut value of the graph for an arbirtrary assignment of variable values by the formula: $$C(\\bar{z})= \\frac{1}{2} \\sum_{(u,v)\\in E} (1-z_uz_v),$$ where $E$ is the set of edges of our graph.  Our goal is to find a variable assignment that maximizes $C(\\bar z)$.  Equivalently, we want to find a variable assignment that minimizes $-C(\\bar z)$. Since QAOA identifies minimum values, we have reframed the max cut optimization problem from one of maximization to minimization. Furthermore, we can promote this equation to a matrix equation by replacing $z_uz_v-1$ with Pauli-Z operators acting on qubits associated with nodes $u$ and $v$, respectively, and replacing 1 with the identity matrix. This leads to the reformulation of the problem from one of maximizing $C(\\bar{z})$ to one of minimizing the eigenvalues of $$H= \\frac{1}{2}\\sum_{(u,v)\\in E} (Z_uZ_v-II).$$  \n",
-    "\n",
-    "We can code this Hamiltonian using the `cudaq.spin` operators."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The problem Hamiltonian\n",
-    "# Define a function to generate the Hamiltonian for a max cut problem using the graph\n",
-    "# with the given edges\n",
-    "\n",
-    "\n",
-    "def hamiltonian_max_cut(edges_src, edges_tgt):\n",
-    "    \"\"\"Hamiltonian for finding the max cut for the graph with given edges and nodes\n",
-    "        \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    edges_src: List[int]\n",
-    "        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    edges_tgt: List[int]\n",
-    "        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.SpinOperator\n",
-    "        Hamiltonian for finding the max cut of the graph with given edges \n",
-    "    \"\"\"\n",
-    "\n",
-    "    hamiltonian = 0\n",
-    "\n",
-    "    for edge in range(len(edges_src)):\n",
-    "\n",
-    "        qubitu = edges_src[edge]\n",
-    "        qubitv = edges_tgt[edge]\n",
-    "        # Add a term to the Hamiltonian for the edge (u,v)\n",
-    "        hamiltonian += 0.5 * (spin.z(qubitu) * spin.z(qubitv) -\n",
-    "                              spin.i(qubitu) * spin.i(qubitv))\n",
-    "\n",
-    "    return hamiltonian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To inititiate the QAOA algorithm, we need to identify initial parameters and specify the classical optimization routine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Specify the optimizer and its initial parameters.\n",
-    "cudaq.set_random_seed(13)\n",
-    "optimizer = cudaq.optimizers.NelderMead()\n",
-    "np.random.seed(13)\n",
-    "optimizer.initial_parameters = np.random.uniform(-np.pi / 8, np.pi / 8,\n",
-    "                                                 parameter_count)\n",
-    "print(\"Initial parameters = \", optimizer.initial_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can either use the `observe` primitive or the `vqe` primitive to code up the optimization loop of QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.5+0j] IIZIZ\n",
-      "[0.5+0j] IZZII\n",
-      "[-3+0j] IIIII\n",
-      "[0.5+0j] ZZIII\n",
-      "[0.5+0j] IIIZZ\n",
-      "[0.5+0j] IIZZI\n",
-      "[0.5+0j] ZIIZI\n",
-      "\n",
-      "optimal_expectation = -4.495973826282007\n",
-      "Therefore, the max cut value is at least  4.495973826282007\n",
-      "optimal_parameters = [0.51349181993727, -0.21299416361632417, 0.3250526425808945, 0.886630847343767]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#cudaq.set_target('nvidia')\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "\n",
-    "# Generate the Hamiltonian for our graph\n",
-    "hamiltonian = hamiltonian_max_cut(edges_src, edges_tgt)\n",
-    "print(hamiltonian)\n",
-    "\n",
-    "# Define the objective, return `<state(params) | H | state(params)>`\n",
-    "# Note that in the `observe` call we list the kernel, the hamiltonian, and then the concrete global variable values of our kernel\n",
-    "# followed by the parameters to be optimized.\n",
-    "\n",
-    "\n",
-    "def objective(parameters):\n",
-    "    return cudaq.observe(kernel_qaoa, hamiltonian, qubit_count, layer_count,\n",
-    "                         edges_src, edges_tgt, parameters).expectation()\n",
-    "\n",
-    "\n",
-    "# Optimize!\n",
-    "optimal_expectation, optimal_parameters = optimizer.optimize(\n",
-    "    dimensions=parameter_count, function=objective)\n",
-    "\n",
-    "# Alternatively we can use the vqe call (just comment out the above code and uncomment the code below)\n",
-    "# optimal_expectation, optimal_parameters = cudaq.vqe(\n",
-    "#    kernel=kernel_qaoa,\n",
-    "#    spin_operator=hamiltonian,\n",
-    "#    argument_mapper=lambda parameter_vector: (qubit_count, layer_count, edges_src, edges_tgt, parameter_vector),\n",
-    "#    optimizer=optimizer,\n",
-    "#    parameter_count=parameter_count)\n",
-    "\n",
-    "print('optimal_expectation =', optimal_expectation)\n",
-    "print('Therefore, the max cut value is at least ', -1 * optimal_expectation)\n",
-    "print('optimal_parameters =', optimal_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have identified the optimal parameters, we can read out the partitioning(s) that gives us a max cut of the graph using the `sample` primitive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11111:4 11110:1 01101:2 01010:148 01110:47 01000:3 00110:54 10101:165 00101:2 01011:154 00100:3 01001:41 00000:1 00011:3 01100:8 10011:7 00010:2 01111:2 11011:3 00111:10 11100:7 10001:65 10010:7 10100:144 10110:40 10000:5 10111:3 11000:7 11101:1 11001:59 11010:2 }\n",
-      "\n",
-      "The max cut is given by the partition:  10101\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sample the circuit using the optimized parameters\n",
-    "# Since our kernel has more than one argument, we need to list the values for each of these variables in order in the `sample` call.\n",
-    "counts = cudaq.sample(kernel_qaoa, qubit_count, layer_count, edges_src,\n",
-    "                      edges_tgt, optimal_parameters)\n",
-    "print(counts)\n",
-    "\n",
-    "# Identify the most likely outcome from the sample\n",
-    "max_cut = max(counts, key=lambda x: counts[x])\n",
-    "print('The max cut is given by the partition: ',\n",
-    "      max(counts, key=lambda x: counts[x]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/quantum_fourier_transform.ipynb.txt b/pr-2308/_sources/applications/python/quantum_fourier_transform.ipynb.txt
deleted file mode 100644
index 52cd4e8e40..0000000000
--- a/pr-2308/_sources/applications/python/quantum_fourier_transform.ipynb.txt
+++ /dev/null
@@ -1,413 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quantum Fourier Transform "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classical Fourier transforms aid many areas such as signal processing and data compression. Below, we quantize it to enable Quantum Fourier Transforms (QFT) in CUDA-Q.\n",
-    "\n",
-    "QFT is a key ingredient in Shor's algorithm which is exponentially faster at factoring prime numbers and also plays a vital role in many other interesting quantum algorithms. \n",
-    "\n",
-    "The idea with classical Fourier transforms is to take the problem from the frequency domain, to the time domain where it is easier to solve. This is the same analogy we draw with QFT, where we take the basis state from the computational basis to the Fourier basis. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Quantum Fourier Transform is defined to be the linear operation which takes a basis state, $\\ket{x}$, \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\text{QFT}\\ket{x}  =   \\frac{1}{\\sqrt{N}}\\sum_{y=0}^{N-1} e^{\\frac{2\\pi ixy}{N}} \\ket{y}\n",
-    "\\tag{1}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "where $n$ is the number of qubits and $N= 2^n$. \n",
-    "\n",
-    "It is convenient to represent states like $\\ket{x}  = \\ket{101}$ in their binary representation which in this case would be $2^2 \\times 1 + 2^1 \\times 0 + 2^0 \\times 1 = \\ket{5}$. \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the $n=1$ qubit case, we have the following basis states: $\\ket{0}, \\ket{1}$. The QFT on these is as follows: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|0\\rangle &= \\frac{1}{\\sqrt{2}}\\sum_{y=0}^{2-1} e^{\\frac{2\\pi i(0)y}{2}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}\\left(e^0|0\\rangle + e^0|1\\rangle\\right) \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}(|0\\rangle + |1\\rangle) \\\\\n",
-    "&= \\ket{+} \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{2}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|1\\rangle &= \\frac{1}{\\sqrt{2}}\\sum_{y=0}^{2-1} e^{\\frac{2\\pi i(1)y}{2}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}\\left(e^0|0\\rangle + e^{\\pi i}|1\\rangle\\right) \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}(|0\\rangle - |1\\rangle) \\\\\n",
-    "&= \\ket{-} \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{3}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "             \n",
-    "\n",
-    "To summarize, $\\text{QFT}|0\\rangle = \\ket{+}$ and $\\text{QFT}|1\\rangle = \\ket{-}$ which can be achieved by applying Hadamard gates. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Let us now extend this and see how we can apply this to $n=3$ qubits. \n",
-    "\n",
-    "For $n=3, N = 2^3 = 8$ and we would like to perform $\\text{QFT}|101\\rangle$ which in binary is $2^2 \\times 1 + 2^1 \\times 0 + 2^0 \\times 1 = 5$: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|101\\rangle = \\text{QFT}|5\\rangle &= \\frac{1}{\\sqrt{8}}\\sum_{y=0}^{8-1} e^{\\frac{2\\pi i(5)y}{8}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{8}} [ e^{0}\\ket{0} + e^{\\frac{5i\\pi (1)}{4}}\\ket{1}  + e^{\\frac{5i\\pi (2)}{4}}\\ket{2} +...++ e^{\\frac{5i\\pi (7)}{4}}\\ket{7} ]   \\\\\n",
-    "&= \\frac{1}{\\sqrt{8}} [ \\ket{000} + e^{\\frac{5i\\pi (1)}{4}}\\ket{001}  + e^{\\frac{5i\\pi (2)}{4}}\\ket{010} +...++ e^{\\frac{5i\\pi (7)}{4}}\\ket{111} ]   \\\\\n",
-    "&=  (0.35+0i) \\ket{000} + (-0.25-0.25i)\\ket{001}  + (0+0.35i)\\ket{010} +...+ (-0.25+0.25i)\\ket{111}    \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{4}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "where we have used the fact that $e^{i\\theta} = \\cos(\\theta) + i\\sin(\\theta).$\n",
-    "\n",
-    "Here, we see how in addition to the Hadamard gates, we need some phase gates to apply the appropriate phases to the basis states. It turns out that the gate we need is the controlled $R_k$ rotation where the $R_k$ gate is denoted by:\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "R_k = \\begin{bmatrix}\n",
-    "1 & 0 \\\\\n",
-    "0 & e^{2\\pi i/ 2^{k}}\n",
-    "\\end{bmatrix}\n",
-    "\\tag{5}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "Here is a generalized ciruit for a $n$-qubit quantum Fourier transform:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "<img src=\"images/qft.png\" alt=\"qft\" width=\"600\">\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we show the code for a QFT implementation in CUDA-Q based on the circuit diagram above: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "from typing import List\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform(input_state: List[int]):\n",
-    "    '''Args: \n",
-    "    input_state (list[int]): specifies the input state to be Fourier transformed.  '''\n",
-    "\n",
-    "    qubit_count = len(input_state)\n",
-    "\n",
-    "    # Initialize qubits.\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # Initialize the quantum circuit to the initial state.\n",
-    "    for i in range(qubit_count):\n",
-    "        if input_state[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮╭───────────╮╭────────────╮                       \n",
-      "q0 : ┤ x ├┤ h ├┤ r1(1.571) ├┤ r1(0.7854) ├───────────────────────\n",
-      "     ╰───╯╰───╯╰─────┬─────╯╰─────┬──────╯╭───╮╭───────────╮     \n",
-      "q1 : ────────────────●────────────┼───────┤ h ├┤ r1(1.571) ├─────\n",
-      "     ╭───╮                        │       ╰───╯╰─────┬─────╯╭───╮\n",
-      "q2 : ┤ x ├────────────────────────●──────────────────●──────┤ h ├\n",
-      "     ╰───╯                                                  ╰───╯\n",
-      "\n",
-      "[ 0.35+0.j   -0.25-0.25j  0.  +0.35j  0.25-0.25j -0.35+0.j    0.25+0.25j\n",
-      " -0.  -0.35j -0.25+0.25j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Can be changed to 'nvidia' for single gpu, 'nvidia-mgpu' for multi-GPU or quantum hardware.\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "# The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.\n",
-    "input_state = [1, 0, 1]\n",
-    "\n",
-    "# Number of decimal points to round up the statevector to.\n",
-    "precision = 2\n",
-    "\n",
-    "# Draw the quantum circuit.\n",
-    "print(cudaq.draw(quantum_fourier_transform, input_state))\n",
-    "\n",
-    "# Print the statevector to the specified precision\n",
-    "statevector = np.array(cudaq.get_state(quantum_fourier_transform, input_state))\n",
-    "print(np.round(statevector, precision))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can verify that this is the correct answer as above we worked out that: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|101\\rangle = \\text{QFT}|5\\rangle &= \\frac{1}{\\sqrt{8}}\\sum_{y=0}^{8-1} e^{\\frac{2\\pi i(5)y}{8}}|y\\rangle \\\\\n",
-    "&=  (0.35+0i) \\ket{000} + (-0.25-0.25i)\\ket{001}  + (0+0.35i)\\ket{010} +...+ (-0.25+0.25i)\\ket{111}.   \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{6}\n",
-    "\\end{equation}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Barring the gates used in the state preparation step, the circuit starts off by implementing a Hadamard gate and then $n-1$ rotations on the zeroth qubit - a total of $n$ gates. This is followed by a Hadamard and $n-2$ conditional rotations on the first qubit which totals to $n -1$ gates. If we continue the series, we get $n + (n-1) + (n-2)... = n(n+1)/2$ gates. Hence, this circuit provides an algorithm with $O(n^2)$ gates for computing the QFT. \n",
-    "\n",
-    "The best classical algorithms for computing fast Fourier transforms on $2^{n}$ elements require $O(n2^{n})$ gates. This means that it requires exponentially more operations to compute the fast Fourier transform on a classical computer than it does to implement the QFT on a quantum computer.\n",
-    "\n",
-    "Why are we not using QFT for real life applications then? The problem lies in quantum measurement. The quantum-Fourier-transformed amplitudes cannot be accessed by quantum measurement. However there are other quantum algorithms that do make use of QFT such as Shor's algorithm. \n",
-    "\n",
-    "One can also invert the QFT where the circuit is executed in reverse with the inverse of each gate. The figure below shows the general $n$-qubit inverse QFT. Why don't you try implementing this in CUDA-Q?\n",
-    "\n",
-    "<img src=\"images/inverse_qft.png\" alt=\"qft\" width=\"600\">\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Fourier Transform revisited\n",
-    "We provided one implementation of the Quantum Fourier Transform above.  To demonstrate some of the other features of CUDA-Q, let's define a new kernel for the Quantum Fourier Transform, which we'll call `quantum_fourier_transform2`.  Pay attention to how the kernel definition differs from the example above.\n",
-    "\n",
-    " Since the Quantum Fourier Transform is a unitary operator, its inverse is its adjoint.  Therefore, once we have implemented the Quantum Fourier Transform as a kernel in CUDA-Q, we can use the built-in `adjoint` operation to create the Inverse Quantum Fourier Transform."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define kernels for the Quantum Fourier Transform and the Inverse Quantum Fourier Transform\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform2(qubits: cudaq.qview):\n",
-    "    '''Args: \n",
-    "    qubits (cudaq.qview): specifies the quantum register to which apply the QFT.'''\n",
-    "    qubit_count = len(qubits)\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def inverse_qft(qubits: cudaq.qview):\n",
-    "    '''Args: \n",
-    "    qubits (cudaq.qview): specifies the quantum register to which apply the inverse QFT.'''\n",
-    "    cudaq.adjoint(quantum_fourier_transform2, qubits)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see that this approach gives us the same results as the section above. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮╭─────────╮╭──────────╮                                      »\n",
-      "q0 : ┤ x ├┤ h ├┤ r1(1.5) ├┤ r1(0.75) ├──────────────────────────────────────»\n",
-      "     ╰───╯╰───╯╰────┬────╯╰────┬─────╯╭───╮╭─────────╮          ╭──────────╮»\n",
-      "q1 : ───────────────●──────────┼──────┤ h ├┤ r1(1.5) ├──────────┤ r1(-1.5) ├»\n",
-      "     ╭───╮                     │      ╰───╯╰────┬────╯╭───╮╭───╮╰────┬─────╯»\n",
-      "q2 : ┤ x ├─────────────────────●────────────────●─────┤ h ├┤ h ├─────●──────»\n",
-      "     ╰───╯                                            ╰───╯╰───╯            »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "     ╭───────────╮╭──────────╮╭───╮\n",
-      "─────┤ r1(-0.75) ├┤ r1(-1.5) ├┤ h ├\n",
-      "╭───╮╰─────┬─────╯╰────┬─────╯╰───╯\n",
-      "┤ h ├──────┼───────────●───────────\n",
-      "╰───╯      │                       \n",
-      "───────────●───────────────────────\n",
-      "                                   \n",
-      "\n",
-      "[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.-0.j 0.-0.j 0.+0.j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def verification_example(input_state: List[int]):\n",
-    "    '''Args: \n",
-    "    input_state (list[int]): specifies the input state to be transformed with QFT and the inverse QFT.  '''\n",
-    "    qubit_count = len(input_state)\n",
-    "    # Initialize qubits.\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # Initialize the quantum circuit to the initial state.\n",
-    "    for i in range(qubit_count):\n",
-    "        if input_state[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "\n",
-    "    # Apply the quantum Fourier Transform\n",
-    "    quantum_fourier_transform2(qubits)\n",
-    "\n",
-    "    # Apply the inverse quantum Fourier Transform\n",
-    "    inverse_qft(qubits)\n",
-    "\n",
-    "\n",
-    "# The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.\n",
-    "input_state = [1, 0, 1]\n",
-    "\n",
-    "# Number of decimal points to round up the statevector to.\n",
-    "precision = 2\n",
-    "\n",
-    "print(cudaq.draw(verification_example, input_state))\n",
-    "\n",
-    "# Print the statevector to the specified precision\n",
-    "statevector = np.array(cudaq.get_state(verification_example, input_state))\n",
-    "print(np.round(statevector, precision))  # The result should be the input state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/quantum_teleportation.ipynb.txt b/pr-2308/_sources/applications/python/quantum_teleportation.ipynb.txt
deleted file mode 100644
index ddf7b98ac5..0000000000
--- a/pr-2308/_sources/applications/python/quantum_teleportation.ipynb.txt
+++ /dev/null
@@ -1,396 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Teleporation\n",
-    "\n",
-    "\n",
-    "Below we shall study an interesting quantum phenomena - teleportation.\n",
-    "\n",
-    "Let us start by implementing the circuit shown below and discuss its implications later. We normally assume that the input qubits are initialised to the $\\ket{0}$ state unless specified otherwise. In this case however, the circuit diagram depicts the 0<sup>th</sup> qubit to be initialised in an arbitary quantum state, $\\ket{\\psi} = \\alpha\\ket{0} + \\beta\\ket{1}$, and the 1<sup>st</sup> and 2<sup>nd</sup> qubit to be in the state $\\ket{\\beta_{00}} = \\tfrac{1}{\\sqrt{2}} [\\ket{00} + \\ket{11}]$.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![tene.png](./images/teleportation.png)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "cudaq.set_random_seed(23)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We now replicate the circuit shown in the diagram above and step through each intermediate stage from psi0 to psi4.\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def teleportation():\n",
-    "\n",
-    "    # Initialize a 3 qubit quantum circuit\n",
-    "    qubits = cudaq.qvector(3)\n",
-    "\n",
-    "    # Psi0 - random quantum state, psi, on qubit 0.\n",
-    "    rx(3.14, qubits[0])\n",
-    "    ry(2.71, qubits[0])\n",
-    "    rz(6.62, qubits[0])\n",
-    "\n",
-    "    # Psi0 - create a maximally entangled state on qubits 1 and 2.\n",
-    "    h(qubits[1])\n",
-    "    cx(qubits[1], qubits[2])\n",
-    "\n",
-    "\n",
-    "# Let us save the statevector of the circuit so far for later use.\n",
-    "psi_0 = cudaq.get_state(teleportation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now we continue with the rest of the circuit\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def teleportation():\n",
-    "\n",
-    "    # Initialize a 3 qubit quantum circuit\n",
-    "    qubits = cudaq.qvector(3)\n",
-    "\n",
-    "    # Psi0 - random quantum state, psi, on qubit 0.\n",
-    "    rx(3.14, qubits[0])\n",
-    "    ry(2.71, qubits[0])\n",
-    "    rz(6.62, qubits[0])\n",
-    "\n",
-    "    # Psi0 - create a maximally entangled state on qubits 1 and 2.\n",
-    "    h(qubits[1])\n",
-    "    cx(qubits[1], qubits[2])\n",
-    "\n",
-    "    # Psi1\n",
-    "    cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    # Psi2\n",
-    "    h(qubits[0])\n",
-    "\n",
-    "    # Psi3 - measure qubits 0 and 1 and store it in variables m1 and m2\n",
-    "    m1 = mz(qubits[0])\n",
-    "    m2 = mz(qubits[1])\n",
-    "\n",
-    "    # Psi4 - apply conditioned pauli operators dependent on the measurement result of qubits 0 and 1.\n",
-    "    if m1 == 1:\n",
-    "        z(qubits[2])\n",
-    "\n",
-    "    if m2 == 1:\n",
-    "        x(qubits[2])\n",
-    "\n",
-    "\n",
-    "psi_4 = cudaq.get_state(teleportation)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The state of this 3 qubit system is described by a $2^3 = 8$ dimensional vector. Sometimes we would like to extract the state of a single qubit from a multipartite state. We can use the partial trace operation defined in the function below which allows us to translate our statevectors to the density matrix representation to trace out subsystems. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to be used below to calculate the partial trace yielding a density matrix.\n",
-    "\n",
-    "\n",
-    "def partial_trace(state_vector, trace_systems):\n",
-    "    \"\"\"\n",
-    "    Partial trace of multi-particle quantum state. \n",
-    "\n",
-    "    Arguments:\n",
-    "        state_vector: complex vector  of size 2**n\n",
-    "        trace_systems (list(int)): a list of subsystems (starting from 0) to trace over. \n",
-    "        dimensions (list(int)): a list of the dimensions of the subsystems.\n",
-    "  \n",
-    "    Returns:\n",
-    "        ndarray: A density matrix with the appropriate subsystems traced over.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    n_qubits = int(np.log2(state_vector.shape[0]))\n",
-    "\n",
-    "    dimensions = [2 for i in range(n_qubits)]\n",
-    "\n",
-    "    trace_systems = len(dimensions) - 1 - np.array(trace_systems)\n",
-    "\n",
-    "    rho = state_vector.reshape(dimensions)\n",
-    "    rho = np.tensordot(rho, rho.conj(), axes=(trace_systems, trace_systems))\n",
-    "    d = int(np.sqrt(np.prod(rho.shape)))\n",
-    "\n",
-    "    return rho.reshape(d, d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|qubit_0> == |qubit_2>?  True\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Trace out qubits 1 and 2 leaving us with qubit 0.\n",
-    "state_of_q0 = partial_trace(state_vector=np.array(psi_0), trace_systems=[1, 2])\n",
-    "\n",
-    "# Trace out qubits 0 and 1 leaving us with qubit 2.\n",
-    "state_of_q2 = partial_trace(state_vector=np.array(psi_4), trace_systems=[0, 1])\n",
-    "\n",
-    "print(f\"|qubit_0> == |qubit_2>?  {np.allclose(state_of_q0, state_of_q2)}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us discuss what has happened. \n",
-    "\n",
-    "We started with a 3 qubit quantum circuit with the 0<sup>th</sup> qubit in $\\ket{\\psi}$ and the 2<sup>nd</sup> qubit being a portion of the maximally entangled bell state $\\ket{\\beta_{00}}$.\n",
-    "\n",
-    "If we look closely at the code cell above, the output state on the 2<sup>nd</sup> qubit, `state_of_q2`, is the same as the random input state, $\\ket{\\psi}$, on the 0<sup>th</sup> qubit, `state_of_q0`. \n",
-    "\n",
-    "We have **teleported** a quantum state from one qubit to another. There is nothing to restrict how close qubits 0 and 2 have to be since they have no entangling operations between them. They can be placed in different labs or infinitely far apart. \n",
-    "\n",
-    "Moreover, we still obey the no-cloning theorem since the initial state $\\ket{\\psi}$ no longer exists on the 0<sup>th</sup> qubit and hence we only have 1 copy of it as shown below \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Using the final statevector, psi4, we show that psi has been teleported and only 1 copy of psi is preserved.\n",
-    "\n",
-    "# Trace out qubits 1 and 2 leaving us with qubit 0\n",
-    "state_of_q0 = partial_trace(state_vector=np.array(psi_4), trace_systems=[1, 2])\n",
-    "\n",
-    "# Trace out qubits 0 and 1 leaving us with qubit 2\n",
-    "state_of_q2 = partial_trace(state_vector=np.array(psi_4), trace_systems=[0, 1])\n",
-    "\n",
-    "np.allclose(state_of_q0, state_of_q2)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "None of this violates the universal limit of information travelling faster than the speed of light. Notice how we have conditioned operations meaning we have to communicate classical information via classical channels which adhere to this limit."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Teleportation explained\n",
-    "\n",
-    "We have stepped through the circuit above using code. We will now do the same using mathematics to enhance our understanding. \n",
-    "\n",
-    "\n",
-    "We start by gathering experimentalists in a lab and performing the following\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "CX_{01}H_{0}\\ket{00} &= CX_{01}\\tfrac{1}{\\sqrt{2}}(\\ket{0}+\\ket{1})\\ket{0} \\\\\n",
-    "                        &= CX_{01}\\tfrac{1}{\\sqrt{2}}(\\ket{00}+\\ket{10}) \\\\\n",
-    "                        &= \\tfrac{1}{\\sqrt{2}}(\\ket{00}+\\ket{11}) \\\\\n",
-    "                        &= \\ket{\\beta_{00}}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "to create an entangled bell pair. We then hand each qubit in $\\ket{\\beta_{00}}$ to two parties, namely Alice and Bob for safekeeping. Many moons later, Alice comes in posession of an arbitary quantum state $\\ket{\\psi} = \\alpha\\ket{0} + \\beta\\ket{1}$ which she would like to send to Bob who is many miles away. This entails communicating values of $\\alpha$ and $\\beta$. However, measurement would lead to state collapse and only yield one bit of information, 0 or a 1.\n",
-    "\n",
-    "Referring to circuit in the figure above, the 0<sup>th</sup> and the 1<sup>st</sup> qubits are in posession of Alice and the 2<sup>nd</sup> qubit is with Bob. The 1<sup>st</sup> and 2<sup>nd</sup> qubits have been entangled but are now seperated in distance. Let us now step through the circuit to describe its evolution. Our input state is \n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{0}} &=  \\ket{\\psi} \\ket{\\beta_{00}}  = (\\alpha\\ket{0} + \\beta\\ket{1})(\\tfrac{1}{\\sqrt{2}} (\\ket{00} + \\ket{11}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}[\\alpha\\ket{0}(\\ket{00} + \\ket{11}) +\\beta\\ket{1}(\\ket{00} + \\ket{11})]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "Alice then performs a CX gate between the qubits she posses i.e. qubit 0 and 1 \n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{1}} &= CX_{01}\\ket{\\psi_{0}} \\\\\n",
-    "                &= \\tfrac{1}{\\sqrt{2}}[\\alpha\\ket{0}(\\ket{00} + \\ket{11}) +\\beta\\ket{1}(\\ket{10} + \\ket{01})]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "which is then followed by Alice performing a Hadamard on the 0<sup>th</sup> qubit\n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= H_{0}\\ket{\\psi_{1}} \\\\\n",
-    "                &= \\tfrac{1}{{2}}[\\alpha(\\ket{0}+\\ket{1})(\\ket{00} + \\ket{11}) +\\beta(\\ket{0}-\\ket{1})(\\ket{10} + \\ket{01})].\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "If we expand all the inner brackets\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= \\tfrac{1}{{2}}[(\\alpha\\ket{0} + \\alpha\\ket{1})(\\ket{00} + \\ket{11}) + (\\beta\\ket{0} - \\beta\\ket{1})(\\ket{10} + \\ket{01})] \\\\\n",
-    "                &= \\tfrac{1}{{2}}[\\alpha\\ket{000}+\\alpha\\ket{011}  +\\alpha\\ket{100}  +\\alpha\\ket{111} +\\beta\\ket{010}+\\beta\\ket{001}-\\beta\\ket{110}-\\beta\\ket{101}]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "and then collect like terms whilst maintaining qubit ordering, we yield \n",
-    "\n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= \\tfrac{1}{{2}}[  \\ket{00}(\\alpha\\ket{0}+\\beta\\ket{1}) + \\ket{01}(\\alpha\\ket{1}+\\beta\\ket{0}) +\n",
-    " \\ket{10}(\\alpha\\ket{0}-\\beta\\ket{1}) + \\ket{11}(\\alpha\\ket{1}+\\beta\\ket{0})                              ]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "where the qubit ordering notation states that the left most qubit is the 0<sup>th</sup> qubit. This expression naturally breaks down into 4 terms where each term has all 3 qubits and the 4 terms represent all the possibilities they can be in after they have been evolved.  \n",
-    "\n",
-    "We now ask our friend Alice to measure her qubits. Quantum mechanically we know that upon measurement the possibilities of the 3 qubit system will collapse regardless of the distance between them and the unmeasured outcomes will therefore be deterministic. If her measurement result yields a 00, the first term in $\\ket{\\psi_{2}}$ tells us that Bob's qubit will be in the state $\\alpha\\ket{0}+\\beta\\ket{1}$ which is the the original state, $\\ket{\\psi}$, we wanted to teleport. Alice has other potential measurement outcomes all of which are summarised in the figure below \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "| Alices' measurement        | Bobs' state           | \n",
-    "| ---------------------------|:---------------------:| \n",
-    "|  $$00$$ | $$  \\ket{\\psi_3(00)} = \\alpha\\ket{0} + \\beta\\ket{1} $$ | \n",
-    "|  $$01$$ | $$ \\ket{\\psi_3(01)} =  \\alpha\\ket{1} + \\beta\\ket{0} $$ | \n",
-    "|  $$10$$ | $$ \\ket{\\psi_3(10)} = \\alpha\\ket{0} - \\beta\\ket{1} $$ | \n",
-    "|  $$11$$ | $$ \\ket{\\psi_3(11)} = \\alpha\\ket{1} - \\beta\\ket{0} $$ | \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A quick glance at the figure above shows us that Bob's qubit is nearly in the state $\\ket{\\psi}$ which is what we are after pending some minor corrections. The circuit diagram depicts conditioned gates that are applied depending on Alice's measurement result fulfilling the minor corrections required to complete the teleportation protocol. The final gate operations are summarised below "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "M1M2 = 00, \\ket{\\psi_4} = Z^{0}X^{0}\\ket{\\psi_3(00)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 01, \\ket{\\psi_4} = Z^{0}X^{1}\\ket{\\psi_3(01)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 10, \\ket{\\psi_4} = Z^{1}X^{0}\\ket{\\psi_3(10)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 11, \\ket{\\psi_4} = Z^{1}X^{1}\\ket{\\psi_3(11)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is clear to see that in all instances, we recover $\\ket{\\psi}$ on Bob's qubit thus fulfilling our ambition of teleporting an unknown quantum state between 2 parties. \n",
-    "\n",
-    "It is important to note that quantum teleportation does not allow communication faster than the speed of light. The state $\\ket{\\psi}$ does not instantly appear with Bob. Alice has to use a classical communication channel which is bound by the speed of classical physics to communicate her measurement results to Bob so that he can make the minor corrections required. \n",
-    "\n",
-    "Moreover teleportation does not violate the no-cloning theorem. The protocol does not allow us to create a copy of $\\ket{\\psi}$ leaving us with $\\ket{\\psi\\psi}$ but rather transmits $\\ket{\\psi}$ from Alice to Bob. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The images used in this notebook are courtesey of the Quantum Computation and Quantum Information textbook by Nielsen & Chuang.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/quantum_volume.ipynb.txt b/pr-2308/_sources/applications/python/quantum_volume.ipynb.txt
deleted file mode 100644
index a567ba857f..0000000000
--- a/pr-2308/_sources/applications/python/quantum_volume.ipynb.txt
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0b2a1f13-07b1-4b72-9c81-4da1ba62aec6",
-   "metadata": {},
-   "source": [
-    "# Quantum Volume\n",
-    "\n",
-    "[Quantum volume](https://en.wikipedia.org/wiki/Quantum_volume) (QV) is a metric for determining the power of a noisy quantum device.  The QV test is performed for a specified qubit number $N$. If the test is passed, then the device can claim a quantum volume of $2^N$.  In practice, the procedure repeats, until the device reaches a qubit number for which the test fails and its greatest passing score is the device's quantum volume. Though imperfect, the test is a reasonable  approximation of the devices usable processing power. This tutorial will demonstrate how CUDA-Q can be used to perform the quantum volume test.\n",
-    "\n",
-    "\n",
-    "The test consists of the following steps (see figure below):\n",
-    "1. A special random circuit is constructed (details below)\n",
-    "2. A simulation determines the exact probability distribution of every bitstring and the median probability is determined.\n",
-    "3. Every bitstring which has an associated probability greater than the median, is considered a heavy bitstring for that particular circuit.\n",
-    "4. The circuit is sampled on the noisy device, and the percent of shots resulting in heavy bitstring are\n",
-    "5. The process is repeated many times and the resulted averaged. The test is passed if the average is greater than 2/3.\n",
-    "\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/QVprocedure.png\" alt=\"Drawing\" style=\"width: 700px;\"/>\n",
-    "\n",
-    "the circuits used are square, meaning they have the same number of layers as qubits. Each layer consists of a random permutation of qubits, followed by random SU4 operations applied to n/2 pairs of qubits.  (See the first step in the figure above).  For CUDA-Q implementation, the SU4 gates are decomposed using the KAK decomposition (figure from this [paper](https://arxiv.org/pdf/1811.12926)).\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/kakdecomp.png\" alt=\"Drawing\" style=\"width: 500px;\"/>\n",
-    "\n",
-    "The cell below specifies a circuit size `n` and two CUDA-Q kernels, one performing an SU4 operation and another building the entire QV circuit. This example is constructed for an even number of qubits for simplicity.\n",
-    "\n",
-    "The QV kernel concludes with application of a bit flip operation on each qubit. This is not part of the QV circuit, but will be used later to introduce noise to the circuit. Otherwise the test would pass every time!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b748ca48-8c65-4158-8c8a-126bbbb39afe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "# Select an even number\n",
-    "n = 4\n",
-    "su4_per_circuit = int(n / 2 * n)\n",
-    "n_params_in_su4 = 21\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def su4_gate(q0: cudaq.qubit, q1: cudaq.qubit, params: list[float]):\n",
-    "    u3(params[0], params[1], params[2], q0)\n",
-    "    u3(params[3], params[4], params[5], q1)\n",
-    "    x.ctrl(q0, q1)\n",
-    "    u3(params[6], params[7], params[8], q0)\n",
-    "    u3(params[9], params[10], params[11], q1)\n",
-    "    x.ctrl(q1, q0)\n",
-    "    u3(params[12], params[13], params[14], q0)\n",
-    "    x.ctrl(q0, q1)\n",
-    "    u3(params[15], params[16], params[17], q0)\n",
-    "    u3(params[18], params[19], params[20], q0)\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qv(n: int, params: list[float], permutations: list[int]):\n",
-    "\n",
-    "    reg = cudaq.qvector(n)\n",
-    "    param_index = 0\n",
-    "\n",
-    "    for layer in range(n):\n",
-    "        for gate in range(n / 2):\n",
-    "            su4_gate(reg[permutations[layer * n + gate * 2]],\n",
-    "                     reg[permutations[layer * n + gate * 2 + 1]],\n",
-    "                     params[param_index:param_index + 21])\n",
-    "            param_index += 21\n",
-    "\n",
-    "    x(reg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3d3fb4f-ca18-4eea-9b65-609fbc31317c",
-   "metadata": {},
-   "source": [
-    "Each circuit must be random. These function randomly choose parameters and permutations for each circuit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b139613f-f6ca-48de-a4c2-59aa7d705764",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_random_params() -> list[float]:\n",
-    "\n",
-    "    params = np.random.uniform(0, 2 * np.pi, n_params_in_su4 * su4_per_circuit)\n",
-    "\n",
-    "    params_list = params.tolist()\n",
-    "\n",
-    "    return params_list\n",
-    "\n",
-    "\n",
-    "\n",
-    "def generate_random_permutations() -> list[int]:\n",
-    "\n",
-    "    circuit_permutations = []\n",
-    "\n",
-    "    for i in range(n):\n",
-    "        circuit_permutations.extend(\n",
-    "            np.random.permutation(n).astype(np.int64).tolist())\n",
-    "\n",
-    "    return circuit_permutations\n",
-    "\n",
-    "\n",
-    "parameters = generate_random_params()\n",
-    "permutations = generate_random_permutations()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d11fd5a-da31-4645-976d-7d944ba4e18e",
-   "metadata": {},
-   "source": [
-    "This function is an auxillary function used later to convert an integer into a \"big endian\" bitstring. This is used to help determine the heavy bitstrings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ded42cc6-3bf7-4b56-90ca-0e69cbe85dad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_bitstring(integer) -> str:\n",
-    "\n",
-    "    return bin(integer)[2:].zfill(n)[::-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d631da6-97e3-473c-b59f-beb2302fa75e",
-   "metadata": {},
-   "source": [
-    "The `percent_heavy_sampled` function takes the random circuit parameters and permutations and the error rate and returns the percent of heavy bitstrings produced by a noisy circuit sample.  \n",
-    "\n",
-    "The function first sets up the noise model.  It assumes that each $X$ gate applied at the end of the circuit will fail with some probability denoted by the `error_rate`.\n",
-    "\n",
-    "Next, the noiseless simulation is performed on a GPU simulated with the `nvidia` backend to obtain the state vector.  The `density-matrix-cpu` backend is used to sample the noisy circuit. \n",
-    "\n",
-    "The rest of function processes these results to determine the heavy bitstring sample probabilities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e852e3c5-29a0-4588-ac56-a102463aa100",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def percent_heavy_sampled(circuit_params,\n",
-    "                          layer_permutations,\n",
-    "                          error_rate,\n",
-    "                          print_output=False) -> float:\n",
-    "\n",
-    "    # Includes option to print results for a single circuit\n",
-    "\n",
-    "    # Define a bit flip error applied to all qubits\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "    bf = cudaq.BitFlipChannel(error_rate)\n",
-    "    for i in range(n):\n",
-    "        noise.add_channel('x', [i], bf)\n",
-    "\n",
-    "    # Gets noiseless probability distribution\n",
-    "    cudaq.set_target(\"nvidia\")\n",
-    "    clean_result = np.array(\n",
-    "        cudaq.get_state(qv, n, circuit_params, layer_permutations))\n",
-    "\n",
-    "    # Performs noisy sampling\n",
-    "    cudaq.set_target(\"density-matrix-cpu\")\n",
-    "    noisy_result = cudaq.sample(qv,\n",
-    "                                n,\n",
-    "                                circuit_params,\n",
-    "                                layer_permutations,\n",
-    "                                noise_model=noise,\n",
-    "                                shots_count=1000)\n",
-    "\n",
-    "    # Converts SV amplitudes to probabilities\n",
-    "    probs = clean_result * np.conjugate(clean_result)\n",
-    "\n",
-    "    # Determines the median value\n",
-    "    cutoff = np.median(probs).real\n",
-    "\n",
-    "    if print_output:\n",
-    "        print('The Median for this circuit is:')\n",
-    "        print(np.median(probs).real)\n",
-    "\n",
-    "    # Determines if a bitstring is heavy and saves the bitstring in a list if so.\n",
-    "    heavy = []\n",
-    "    index = 0\n",
-    "    circuit_prob = 0\n",
-    "\n",
-    "    for outcome_prob in probs:\n",
-    "        if outcome_prob.real > cutoff:\n",
-    "            heavy.append(make_bitstring(index))\n",
-    "            circuit_prob += outcome_prob.real\n",
-    "        index += 1\n",
-    "\n",
-    "    if print_output:\n",
-    "\n",
-    "        print('The heavy bitstrings for this circuit are')\n",
-    "        print(heavy)\n",
-    "\n",
-    "        print('This circuit has an ideal havy sampling probability of:')\n",
-    "        print(circuit_prob)\n",
-    "\n",
-    "    # Determines percent of noisy sample results that are heavy\n",
-    "    prob_heavy_in_noisy = 0\n",
-    "    for heavy_bitstring in heavy:\n",
-    "        prob_heavy_in_noisy += noisy_result.probability(heavy_bitstring)\n",
-    "\n",
-    "    if print_output:\n",
-    "        print('Percent of time noisy sample returned heavy bitstring')\n",
-    "        print(prob_heavy_in_noisy)\n",
-    "\n",
-    "    # Returns this probability\n",
-    "    return prob_heavy_in_noisy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8eb4fff-c031-40d1-b521-7aea8bc1014f",
-   "metadata": {},
-   "source": [
-    "You can test a single circuit below to see if it passes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9cd7d79d-f489-426e-97a7-8664e62799a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The Median for this circuit is:\n",
-      "0.04363711\n",
-      "The heavy bitstrings for this circuit are\n",
-      "['0000', '0100', '0010', '1010', '0101', '1101', '0011', '0111']\n",
-      "This circuit has an ideal havy sampling probability of:\n",
-      "0.8153219893574715\n",
-      "Percent of time noisy sample returned heavy bitstring\n",
-      "0.488\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.488"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "percent_heavy_sampled(parameters, permutations, 1, True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecd2b646-df3e-4b4c-b9be-851f6bdb6116",
-   "metadata": {},
-   "source": [
-    "The true quantum volume is detemined by repeating the process many times and averaging the results. This function repeatedly applies the `percent _heavy_sampled` function for `n_circuit`number of times and prints if the test is passed and returns the average."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f94562c0-7ceb-4609-9e55-ae8791cd8dc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_qv(n_circuits, circuit_size, prob_of_error) -> float:\n",
-    "\n",
-    "    n = circuit_size\n",
-    "    su4_per_circuit = int(n / 2 * n)\n",
-    "    number_of_circuits = n_circuits\n",
-    "\n",
-    "    counter = 0\n",
-    "    circuit_results = []\n",
-    "\n",
-    "    # Loop over n_circuits\n",
-    "    while counter < number_of_circuits:\n",
-    "        parameters = generate_random_params()\n",
-    "        permutations = generate_random_permutations()\n",
-    "        circuit_results.append(\n",
-    "            percent_heavy_sampled(parameters,\n",
-    "                                  permutations,\n",
-    "                                  prob_of_error,\n",
-    "                                  print_output=False))\n",
-    "\n",
-    "        counter += 1\n",
-    "\n",
-    "    # Average the results\n",
-    "    score = sum(circuit_results) / len(circuit_results)\n",
-    "\n",
-    "    print('The score is:')\n",
-    "    print(score)\n",
-    "\n",
-    "    # Determined if QV test is passed\n",
-    "    if score > 2 / 3:\n",
-    "        print('passed!')\n",
-    "        print('Quantum Volume')\n",
-    "        print(2**n)\n",
-    "\n",
-    "    else:\n",
-    "        print('failed QV Test')\n",
-    "\n",
-    "    return score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2279de84-bb73-48a9-a33b-3aa2efa66573",
-   "metadata": {},
-   "source": [
-    "Try running the QV procedure for 100 four qubit circuits with a 10% chance of error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3cab12da-7cbc-4ff3-80d8-2a81007942ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The score is:\n",
-      "0.7280300000000003\n",
-      "passed!\n",
-      "Quantum Volume\n",
-      "16\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.7280300000000003"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n = 4\n",
-    "calc_qv(100, n, .1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afbc7d0a-2507-42e4-9946-3aa93384c645",
-   "metadata": {},
-   "source": [
-    "an interesting benefit of simulation is the ability to explore how noise might affect the QV results.  In this case, the noise model is trivial, but it is still possible to see a relationship between the probability of error in the $X$ gates and the QV outcome. \n",
-    "\n",
-    "\n",
-    "<img src=\"./images/qvplot.png\" alt=\"Drawing\" style=\"width: 900px;\"/>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/readout_error_mitigation.ipynb.txt b/pr-2308/_sources/applications/python/readout_error_mitigation.ipynb.txt
deleted file mode 100644
index ec366366bd..0000000000
--- a/pr-2308/_sources/applications/python/readout_error_mitigation.ipynb.txt
+++ /dev/null
@@ -1,922 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5b2ac00e-c7fc-45cc-888e-8c26dabf80d2",
-   "metadata": {},
-   "source": [
-    "# Readout Error Mitigation\n",
-    "\n",
-    "Readout errors are caused by imperfect qubit measurement and are a common source of error in quantum computing. Properly modelling these errors in simulation can give the user tools to understand better what these errors are and how to mitigate them when running on actual quantum devices.\n",
-    "\n",
-    "Readout errors can be mitigated with a confusion matrix $A$. It is a square matrix of size $2^n \\times 2^n$ and tells about the probability of observing the state $|y\\rangle$ given the true state $|x\\rangle$. The confusion matrix characterizes the readout error of the device and is circuit-independent. Once $A$ is estimated, we can compute its pseudoinverse $A^+$ which can be applied to the noisy probability distribution $p_{\\text{noisy}}$ to obtain an adjusted quasi-probability distribution \n",
-    "\n",
-    "$$\n",
-    "p_{\\text{mitigated}} = A^+ p_{\\text{noisy}}\n",
-    "$$\n",
-    "\n",
-    "In this tutorial, we show how to build a confusion matrix with the following approaches.\n",
-    "\n",
-    "- Using a  single qubit model\n",
-    "- Using $k$ local confusion matrices\n",
-    "- A full confusion matrix for each $2^n$ combination\n",
-    "\n",
-    "The last method works well for correcting correlated errors (which affect multiple qubits together). However, it becomes impractical for large numbers of qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "04b58854-7dab-4f1b-8c59-d8b9506bce44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "\n",
-    "!pip install matplotlib==3.8.4 pandas==2.2.2 scipy==1.13.1 seaborn==0.13.2 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "710a2a46-4e55-4508-a0b7-21969aac6a87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import itertools\n",
-    "from functools import reduce\n",
-    "from scipy.optimize import minimize\n",
-    "from typing import Union\n",
-    "\n",
-    "sns.set_style(\"dark\")\n",
-    "\n",
-    "cudaq.set_target(\"density-matrix-cpu\")\n",
-    "seed = 42\n",
-    "\n",
-    "cudaq.set_random_seed(seed)\n",
-    "np.random.seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8eab761a-0a82-4e20-8cf8-54602cd05d6a",
-   "metadata": {},
-   "source": [
-    "To model the readout error, we apply a bitflip channel on every qubit at the end of the circuit using an Identity gate. The probability of bitflip `probs` can be the same or different for all the qubits.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-success\"><b>Note: </b>\n",
-    "In principle, readout error is applied to the measurement gate but we use the Identity gate because adding a quantum error on the measurement gate is not yet supported. Also, the Identity gate is not present in the <kbd>add_channel</kbd> method, so we model the Identity gate using a rotation gate with an angle of 0.0.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d84bb420-4fab-4c26-919a-7efa8d3a219b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_noise(n_qubits: int, probs: Union[float, list[float]]):\n",
-    "    \"\"\"\n",
-    "    Creates a noise model with specified probabilities of bit flip errors on each qubit.\n",
-    "\n",
-    "    Args:\n",
-    "        n_qubits (int): The number of qubits in the quantum circuit.\n",
-    "        probs (Union[float, list[float]]): Probabilities of a bit flip error occurring on each qubit.\n",
-    "        If a single probability value is provided, it will be applied to all qubits. If a list of\n",
-    "        probabilities is provided, each qubit will have its own probability.\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq noise model\n",
-    "    \"\"\"\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "\n",
-    "    if not isinstance(probs, list):\n",
-    "        probs = [probs] * n_qubits\n",
-    "\n",
-    "    for i, p in enumerate(probs):\n",
-    "        bit_flip = cudaq.BitFlipChannel(p)\n",
-    "        noise.add_channel(\"rx\", [i], bit_flip)\n",
-    "\n",
-    "    return noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb4c7a14-1c0c-4fab-a6aa-7174c23baa7f",
-   "metadata": {},
-   "source": [
-    "We define a cudaq kernel which will help create a set of quantum circuits to take measurements of the basis states for $n$ qubits. The kernel takes the number of qubits and the basis state as arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "53e3f450-000f-49b8-ad5c-57aa74461694",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(n_qubits: int, state_label: list[int]):\n",
-    "    qvector = cudaq.qvector(n_qubits)\n",
-    "    for i, val in enumerate(state_label):\n",
-    "        if val == 1:\n",
-    "            x(qvector[i])\n",
-    "        rx(0.0, qvector[i]\n",
-    "          )  # Identity gate at the end on each qubit to model readout error\n",
-    "    mz(qvector)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95411463-cd1e-499f-baef-d624acc0122c",
-   "metadata": {},
-   "source": [
-    "For the tutorial, we will use the GHZ state on three qubits to test readout error mitigation. The GHZ state on 3 qubits is described as:\n",
-    "\n",
-    "$$\n",
-    "\\frac{|000\\rangle + |111\\rangle}{\\sqrt{2}}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c1476028-923d-4f2b-b5fc-39ebcc8cbab9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def ghz_kernel(n_qubits: int):\n",
-    "    qvector = cudaq.qvector(n_qubits)\n",
-    "\n",
-    "    h(qvector[0])\n",
-    "    for i in range(n_qubits - 1):\n",
-    "        cx(qvector[i], qvector[i + 1])\n",
-    "\n",
-    "    # Apply identity gates for readout error mitigation\n",
-    "    for i in range(n_qubits):\n",
-    "        rx(0.0, qvector[i])\n",
-    "\n",
-    "    mz(qvector)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "900d2110-d613-426a-8aa9-6c12b1238536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to draw the confusion matrix\n",
-    "def plot_cmat(mat):\n",
-    "    fig, ax = plt.subplots()\n",
-    "    n = len(mat)\n",
-    "    im2 = ax.matshow(mat, cmap=plt.cm.Reds, vmin=0, vmax=1.0)\n",
-    "    ax.set_yticks(np.arange(n))\n",
-    "    ax.set_xticks(np.arange(n))\n",
-    "    ax.set_yticklabels(n * [\"\"])\n",
-    "    ax.set_xticklabels(n * [\"\"])\n",
-    "    ax.set_title(r\"Confusion Matrix\", fontsize=16)\n",
-    "    ax.set_xlabel(\"Prepared State\")\n",
-    "    ax.xaxis.set_label_position(\"top\")\n",
-    "    ax.set_ylabel(\"Measured State\")\n",
-    "    fig.colorbar(im2, ax=ax)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "032f6898-c953-4d30-a2bf-e8b46ee77c62",
-   "metadata": {},
-   "source": [
-    "It is possible that the adjusted quasi-probability distribution $p_{\\text{mitigated}}$ obtained by application of $A^+$ to $p_{\\text{noisy}}$ may be non-positive. We need to find the closest positive probability distribution in such a case.\n",
-    "\n",
-    " $$ p'' = \\min_{p_{\\rm positive}} \\|p_{\\rm noisy} - p_{\\rm positive}\\|_1$$\n",
-    "\n",
-    "Then, we can draw samples from $p''$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9b355dcb-bf80-42a1-ae11-dcc9f11ba1e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_closest_distribution(empirical_dist):\n",
-    "    \"\"\"\n",
-    "    Find the closest distribution to an empirical distribution by minimizing the L1 norm.\n",
-    "\n",
-    "    Args:\n",
-    "        empirical_dist: Empirical distribution that you want to find the closest distribution to.\n",
-    "\n",
-    "    Returns:\n",
-    "        Closest distribution to `empirical_dist`\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def objective(x):\n",
-    "        return np.linalg.norm(empirical_dist - x, ord=1)\n",
-    "\n",
-    "    # Constraint: all elements of p must be positive, and the distribution must sum to 1\n",
-    "    cons = (\n",
-    "        {\n",
-    "            \"type\": \"ineq\",\n",
-    "            \"fun\": lambda p: p\n",
-    "        },\n",
-    "        {\n",
-    "            \"type\": \"eq\",\n",
-    "            \"fun\": lambda p: np.sum(p) - 1\n",
-    "        },\n",
-    "    )\n",
-    "    bnds = [(0, 1) for _ in range(len(empirical_dist))]\n",
-    "    initial_value = np.random.uniform(size=len(empirical_dist))\n",
-    "\n",
-    "    res = minimize(\n",
-    "        objective,\n",
-    "        initial_value,\n",
-    "        method=\"SLSQP\",\n",
-    "        options={\"maxiter\": 1000},\n",
-    "        bounds=bnds,\n",
-    "        constraints=cons,\n",
-    "    )\n",
-    "\n",
-    "    return res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0ceb5a15-cecf-47ed-b1cc-cea79fa936f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_counts_from_distribution(n_qubits, size, dist):\n",
-    "    \"\"\"\n",
-    "    Generates samples based on a given distribution and returns the counts of each sample value.\n",
-    "\n",
-    "    Args:\n",
-    "        n_qubits: The number of qubits in the quantum circuit.\n",
-    "        dist: The probability distribution from which samples are drawn.\n",
-    "\n",
-    "    Returns:\n",
-    "        An array of counts for each possible value in the distribution. The array has a length of 2^n_qubits.\n",
-    "    \"\"\"\n",
-    "    samples = np.random.choice(np.arange(2**n_qubits), size=size, p=dist)\n",
-    "    values, counts = np.unique(samples, return_counts=True)\n",
-    "    res = np.zeros(2**n_qubits, dtype=int)\n",
-    "    res[values] = counts\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1225d4de-fcc8-4a56-846b-44d6a52a272f",
-   "metadata": {},
-   "source": [
-    "Let's run the `ghz_kernel` with the noise model. We will perform two experiments. First, we keep the bitflip probability the same for all the qubits. In the second experiment, we use different bitflip probabilities for qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "53453484-a8d5-416c-8aad-7c982cad7191",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_qubits = 3\n",
-    "shots = 1024\n",
-    "\n",
-    "labels = list(map(list, itertools.product([0, 1], repeat=n_qubits)))\n",
-    "states = list(map(lambda label: \"\".join(map(str, label)), labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2d147fe7-ce5e-475c-9014-59d4ff63a644",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bitflip probability: 0.1\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'000': 352,\n",
-       " '010': 43,\n",
-       " '111': 367,\n",
-       " '001': 47,\n",
-       " '011': 47,\n",
-       " '100': 54,\n",
-       " '101': 61,\n",
-       " '110': 53}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = 0.1\n",
-    "print(f\"bitflip probability: {p}\\n\")\n",
-    "\n",
-    "noise_1 = get_noise(n_qubits, p)\n",
-    "\n",
-    "ghz_result = cudaq.sample(ghz_kernel,\n",
-    "                          n_qubits,\n",
-    "                          shots_count=shots,\n",
-    "                          noise_model=noise_1)\n",
-    "noisy_dict_1 = dict(list(ghz_result.items()))\n",
-    "\n",
-    "noisy_res_1 = np.array(\n",
-    "    [noisy_dict_1.get(state, 0) for i, state in enumerate(states)])\n",
-    "\n",
-    "noisy_dict_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "976f13da-277b-4754-9a58-277a5988f739",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bitflip probability: [0.149816047538945, 0.3802857225639665, 0.292797576724562]\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'000': 212,\n",
-       " '010': 122,\n",
-       " '111': 189,\n",
-       " '001': 111,\n",
-       " '011': 87,\n",
-       " '100': 78,\n",
-       " '101': 131,\n",
-       " '110': 94}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = list(np.random.uniform(low=0.0, high=0.4, size=n_qubits))\n",
-    "print(f\"bitflip probability: {p}\\n\")\n",
-    "\n",
-    "noise_2 = get_noise(n_qubits, p)\n",
-    "\n",
-    "ghz_result = cudaq.sample(ghz_kernel,\n",
-    "                          n_qubits,\n",
-    "                          shots_count=shots,\n",
-    "                          noise_model=noise_2)\n",
-    "noisy_dict_2 = dict(list(ghz_result.items()))\n",
-    "\n",
-    "noisy_res_2 = np.array(\n",
-    "    [noisy_dict_2.get(state, 0) for i, state in enumerate(states)])\n",
-    "\n",
-    "noisy_dict_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1889f50a-f051-4f0e-bace-18477122eafe",
-   "metadata": {},
-   "source": [
-    "## Inverse confusion matrix from single-qubit noise model\n",
-    "\n",
-    "Here we assume that the readout error of each qubit is independent with the same confusion probabilities. Then, the confusion matrix $A_1$ for a single qubit can be defined as\n",
-    "\n",
-    "$$\n",
-    "A_1 = \\begin{bmatrix}\n",
-    "P(0|0) & P(0|1)\\\\\n",
-    "P(1|0) & P(1|1)\n",
-    "\\end{bmatrix}\n",
-    "$$\n",
-    "\n",
-    "where $P(y|x)$ is the probability of observing state $|y\\rangle$ when measuring the true state $|x\\rangle$. Using $A_1$, the full $2^n \\times 2^n$ confusion matrix $A$ can be written as \n",
-    "\n",
-    "$$\n",
-    "A = A_1 \\otimes \\dots \\otimes A_1\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e6ceb0a9-c2ec-4fcd-bb23-457890a8b57e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 becomes {'0': 924, '1': 100}\n",
-      "1 becomes {'0': 104, '1': 920}\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_qubit_labels = [[0], [1]]\n",
-    "single_qubit_states = [\"0\", \"1\"]  # Used for dictionary keys\n",
-    "\n",
-    "# we use noise_1 which has same bitflip probability for all qubits\n",
-    "results = [\n",
-    "    cudaq.sample(kernel, 1, label, shots_count=shots, noise_model=noise_1)\n",
-    "    for label in single_qubit_labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(single_qubit_states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9f030f99-0ad9-4f89-8d99-a570b3c5e745",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.90234375 0.1015625 ]\n",
-      " [0.09765625 0.8984375 ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Construct confusion matrix for single qubit\n",
-    "\n",
-    "A_1 = np.zeros((2, 2))\n",
-    "for i, state in enumerate(single_qubit_states):\n",
-    "    true_state = int(state, 2)\n",
-    "    for key, val in results[i].items():\n",
-    "        observed_state = int(key, 2)\n",
-    "        A_1[observed_state][true_state] = val / shots\n",
-    "\n",
-    "print(A_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cbc4a228-935f-413c-9ad3-b6e26f22e802",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[476   3   0   1  12  19   9 501]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = reduce(np.kron, [A_1] * n_qubits)  # joint confusion matrix\n",
-    "A_pinv = np.linalg.pinv(A)  # Generate pseudoinverse confusion matrix.\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_1),\n",
-    "                     dtype=int)  # Obtain mitigated counts\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "b65c4657-87f7-44df-b205-7fc6195b95a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    \"states\": states,\n",
-    "    \"noisy\": np.around(noisy_res_1 / sum(noisy_res_1), 3),\n",
-    "    \"mitigated_sg\": np.around(mitigated / sum(mitigated), 3),\n",
-    "})\n",
-    "\n",
-    "ax = df.plot(x=\"states\",\n",
-    "             y=[\"noisy\", \"mitigated_sg\"],\n",
-    "             kind=\"bar\",\n",
-    "             figsize=(8, 5))\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_sg\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2927e72-98bb-41cf-8e77-35ea9572474a",
-   "metadata": {},
-   "source": [
-    "## Inverse confusion matrix from k local confusion matrices\n",
-    "\n",
-    "This method works under the assumption that the readout errors for different qubits are independent of each other and the confusion matrix $A$ can be factorized into the product of $k$ small local confusion matrices. We consider the special case when $k = n$ for $n$ qubits and different $2 \\times 2$ confusion matrices for each qubit\n",
-    "\n",
-    "$$\n",
-    "A = A_1 \\otimes \\dots \\otimes A_n\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "1e8fd13a-e0cf-4139-84ca-d81f7a71baa6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "000 becomes {'000': 388, '010': 235, '111': 18, '001': 174, '011': 79, '100': 58, '101': 28, '110': 44}\n",
-      "111 becomes {'000': 23, '010': 38, '111': 370, '001': 40, '011': 60, '100': 107, '101': 224, '110': 162}\n"
-     ]
-    }
-   ],
-   "source": [
-    "local_labels = [[0] * n_qubits, [1] * n_qubits]\n",
-    "local_states = [\"0\" * n_qubits, \"1\" * n_qubits]\n",
-    "\n",
-    "results = [\n",
-    "    cudaq.sample(kernel,\n",
-    "                 n_qubits,\n",
-    "                 label,\n",
-    "                 shots_count=shots,\n",
-    "                 noise_model=noise_2) for label in local_labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(local_states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f592fb8-2ded-498e-b4fe-eff6eac74750",
-   "metadata": {},
-   "source": [
-    "The local confusion matrices are generated using the marginal probability distribution for each qubit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "7fabcc1f-8d9c-42c2-b02a-3ce42c1e34c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counts = [\n",
-    "    dict(list(results[i].items())) for i, state in enumerate(local_states)\n",
-    "]\n",
-    "matrices = []\n",
-    "\n",
-    "for k in range(n_qubits):\n",
-    "    matrix = np.zeros([2, 2], dtype=float)\n",
-    "    marginalized_counts = []\n",
-    "    total_shots = []\n",
-    "    for i in range(2):\n",
-    "        marginal_cts = dict(results[i].get_marginal_counts([k]).items())\n",
-    "        marginalized_counts.append(marginal_cts)\n",
-    "        total_shots.append(sum(marginal_cts.values()))\n",
-    "\n",
-    "    # matrix[i][j] is the probability of counting i for expected j\n",
-    "    for i in range(2):\n",
-    "        for j in range(2):\n",
-    "            matrix[i][j] = marginalized_counts[j].get(str(i),\n",
-    "                                                      0) / total_shots[j]\n",
-    "    matrices.append(matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7f016f9b-3243-42b3-ab59-4d03f7765b82",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.85546875 0.15722656]\n",
-      " [0.14453125 0.84277344]]\n",
-      "\n",
-      "[[0.6328125  0.38476562]\n",
-      " [0.3671875  0.61523438]]\n",
-      "\n",
-      "[[0.70800781 0.32226562]\n",
-      " [0.29199219 0.67773438]]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for m in matrices:\n",
-    "    print(m)\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "51734299-307f-4081-b8cc-1968ebfc7d52",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[559  -1 -58  32 -28  26 -16 510]\n",
-      "\n",
-      "Corrected for negative counts:\n",
-      "[521   0   0  35   0  22   0 446]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# To find the joint pseudoinverse confusion matrix, we can first perform pseudoinverse of the\n",
-    "# local confusion matrices and then do the tensor product\n",
-    "\n",
-    "pinv_confusion_matrices = [np.linalg.pinv(m) for m in matrices]\n",
-    "A_pinv = reduce(np.kron, pinv_confusion_matrices)\n",
-    "\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_2), dtype=int)\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")\n",
-    "\n",
-    "A_joint = reduce(np.kron, matrices)\n",
-    "plot_cmat(A_joint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "4261d8a3-b701-4939-8219-c0d823d42747",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    \"states\": states,\n",
-    "    \"noisy\": np.around(noisy_res_2 / sum(noisy_res_2), 3),\n",
-    "    \"mitigated_k_local\": np.around(mitigated / sum(mitigated), 3),\n",
-    "})\n",
-    "\n",
-    "ax = df.plot(x=\"states\",\n",
-    "             y=[\"noisy\", \"mitigated_k_local\"],\n",
-    "             kind=\"bar\",\n",
-    "             figsize=(8, 5))\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_k_local\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9329a30d-9224-42b1-9a66-1e87ba7c2674",
-   "metadata": {},
-   "source": [
-    "## Inverse of full confusion matrix\n",
-    "\n",
-    "Here we generate a quantum circuit for each of the basis states of $n$ qubits (i.e., $2^n$ combinations). This gives more accurate readout error mitigation. Since it scales exponentially with the number of qubits, it is only practical for small devices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "cf6025e8-1755-4c8f-96fa-1b67830d448d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "000 becomes {'000': 367, '010': 248, '111': 17, '001': 159, '011': 83, '100': 68, '101': 35, '110': 47}\n",
-      "001 becomes {'000': 161, '010': 93, '111': 46, '001': 363, '011': 253, '100': 17, '101': 76, '110': 15}\n",
-      "010 becomes {'000': 234, '010': 378, '111': 27, '001': 100, '011': 150, '100': 36, '101': 25, '110': 74}\n",
-      "011 becomes {'000': 106, '010': 145, '111': 65, '001': 251, '011': 386, '100': 11, '101': 30, '110': 30}\n",
-      "100 becomes {'000': 83, '010': 34, '111': 99, '001': 29, '011': 18, '100': 388, '101': 144, '110': 229}\n",
-      "101 becomes {'000': 22, '010': 13, '111': 247, '001': 70, '011': 43, '100': 163, '101': 371, '110': 95}\n",
-      "110 becomes {'000': 32, '010': 77, '111': 157, '001': 14, '011': 30, '100': 233, '101': 85, '110': 396}\n",
-      "111 becomes {'000': 14, '010': 31, '111': 393, '001': 38, '011': 60, '100': 95, '101': 233, '110': 160}\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = [\n",
-    "    cudaq.sample(kernel,\n",
-    "                 n_qubits,\n",
-    "                 label,\n",
-    "                 shots_count=shots,\n",
-    "                 noise_model=noise_2) for label in labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "28f2b2cb-13d3-4806-852d-06ea91e58c1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# generate full confusion matrix\n",
-    "A_full = np.zeros((2**n_qubits, 2**n_qubits))\n",
-    "for i, state in enumerate(states):\n",
-    "    true_state = int(state, 2)\n",
-    "    for key, val in results[i].items():\n",
-    "        observed_state = int(key, 2)\n",
-    "        A_full[observed_state][true_state] = val / shots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "c3b0b80b-63ac-438a-9f3d-a6a3687da167",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_cmat(A_full)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "507852a8-6715-4802-b6a0-10967d09ed94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.09734490298929"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Distance between full confusion matrix and tensored confusion matrix\n",
-    "np.linalg.norm(A_joint - A_full)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "9e6c3874-2905-4c25-9bf0-22ab27756fed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[ 714 -100 -211  152  -49   63   32  422]\n",
-      "\n",
-      "Corrected for negative counts:\n",
-      "[683   0   0  39   0   0   0 302]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A_pinv = np.linalg.pinv(A_full)\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_2), dtype=int)\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "cb109820-5478-4179-84b8-66e6af1008fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[\"mitigated_full\"] = np.around(mitigated / sum(mitigated), 3)\n",
-    "\n",
-    "ax = df.plot(\n",
-    "    x=\"states\",\n",
-    "    y=[\"noisy\", \"mitigated_k_local\", \"mitigated_full\"],\n",
-    "    kind=\"bar\",\n",
-    "    figsize=(12, 5),\n",
-    ")\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_k_local\"])\n",
-    "ax.bar_label(ax.containers[2], labels=df[\"mitigated_full\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/shors.ipynb.txt b/pr-2308/_sources/applications/python/shors.ipynb.txt
deleted file mode 100644
index 8063162ee4..0000000000
--- a/pr-2308/_sources/applications/python/shors.ipynb.txt
+++ /dev/null
@@ -1,986 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "628c747b-084c-4fb6-843e-7c7f4cbf07a1",
-   "metadata": {},
-   "source": [
-    "# Factoring Integers With Shor's Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d0627c4-95bd-47b9-9ab5-e8374d25508e",
-   "metadata": {},
-   "source": [
-    "The most famous application of quantum computers is factoring integers using Shor's algorithm. This algorithm is particularly significant because an efficient factorization algorithm could potentially break modern asymmetric encryption schemes, such as RSA.\n",
-    "\n",
-    "For small integers, this quantum algorithm can be simulated on classical computers. The main challenge in classical implementation lies in the order-finding algorithm. We will first introduce the classical implementation of this algorithm as a preliminary step, and then proceed to explain the quantum order-finding algorithm.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "148568d8",
-   "metadata": {},
-   "source": [
-    "First let's install some libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "486fb3d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install contfrac==1.0.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "88619b35-624e-4853-8d2e-6d9952390e5e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from math import gcd, log2, ceil\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import cudaq\n",
-    "from cudaq import *\n",
-    "import fractions\n",
-    "import matplotlib.pyplot as plt\n",
-    "import contfrac"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8523c6f-d729-4d5e-8df9-1780c343a58e",
-   "metadata": {},
-   "source": [
-    "## Shor's algorithm\n",
-    "The factoring problem for an integer $N$ aims to find integers $a_1$, $a_2$ which are factors of $N$.  This problem is interesting when $N$ is not prime and the solutions $a_1$ and $a_2$ are non-trivial.  In other words, we will attempt to find integers $a_1$ and $a_2$ satisfying $N=a_1\\cdot a_2$ with $a_1\\neq 1$ and $a_2\\neq 1$.\n",
-    "\n",
-    "The algorithm consists of two ideas:\n",
-    "\n",
-    "* Reduce the problem of factoring the integer $N$ to the order-finding problem.\n",
-    "* Solve the order-finding problem: \n",
-    "Given integers $a$ and $N$ so that $a$ and $N$ share no common factors (i.e., the greatest common divisor of $a$ and $N$ is 1), find the smallest positive integer which satisfies $a^r \\equiv 1 \\mod N$.  This value $r$ is refered to as the *order of* $a \\mod N$.\n",
-    "\n",
-    "These two ideas are combined in the following steps in Shor's algorithm:\n",
-    "\n",
-    "0. Rule out the easy case that $N$ is even\n",
-    "1. Select a random integer $a$ between $2$ and $N-1$\n",
-    "2. Check to see if $a$ is a factor of $N$ (if so we're done!)\n",
-    "3. Find the order of $a \\mod N$, (i.e., find $r$ so that $a^r\\equiv 1 \\mod N$)\n",
-    "4. Check to see if $a^{\\frac{r}{2}}-1$ or $a^{\\frac{r}{2}}+1$ are integers and share common, non-trivial, divisors with $N$ (if so we're done!)\n",
-    "5. If no factor is found, repeat steps 1 through 4 above.\n",
-    "\n",
-    "The function `shors_algorithm` below outlines these steps for both the classical and the quantum implementation of Shor's algorithm. For the purposes of demonstration, we will also control the initial random integer selected in step 1 so that we can investigate cases in which the selected integer produces common divisors of $N$ in step 4 and others in which step 4 produces no common factors.\n",
-    "\n",
-    "\n",
-    "**Note about terminology:** Some literature refers to the \"period-finding problem\" in Shor's algorithm.  The order-finding problem, as we have described above, can be recast as finding the period of the function $f(x) = a^x\\mod N$, by noticing that the period of $f(x)$ is one more than the order of $a\\mod N$. The period finding problem is more general than the order-finding problem since it aims to find the period of any periodic function, not just modular exponentiation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7dd05729",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shors_algorithm(N, initial, quantum):\n",
-    "    \"\"\" Factor N using Shor's algorithm with initial starting value and choice of \n",
-    "    using either classical or quantum approach for the period finding step\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    N: int \n",
-    "        Integer to be factored (assumed to be non-prime and >1)\n",
-    "    initial: int \n",
-    "        Initial choice of the random integer between 2 and N-1\n",
-    "    quantum: boolean\n",
-    "        True if we will call the quantum order-finding algorithm, and false if we call the classical one for step 3.   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    a1, a2: int\n",
-    "        Non-trivial factors of N\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # 0. Check to see if N is even.\n",
-    "    if N % 2 == 0:\n",
-    "        divisor1 = 2\n",
-    "        divisor2 = N // 2\n",
-    "        print(\"Found factors:\", divisor1, divisor2)\n",
-    "        return (divisor1, divisor2)\n",
-    "\n",
-    "    attempts = [initial]\n",
-    "    while (True):\n",
-    "        # 1. Select a random integer between 2 and N-1\n",
-    "        if len(attempts) == 1:\n",
-    "            a = initial\n",
-    "        else:\n",
-    "            a = random.choice(\n",
-    "                [n for n in range(N - 1) if n not in attempts and n != 1])\n",
-    "\n",
-    "        # 2. See if the integer selected in step 1 happens to factor N\n",
-    "        print(\"Trying a =\", a)\n",
-    "        divisor1 = gcd(a, N)\n",
-    "        if divisor1 != 1:\n",
-    "            divisor2 = N // divisor1\n",
-    "            print(\"Found factors of N={} by chance: {} and {}\".format(\n",
-    "                N, divisor1, divisor2))\n",
-    "            return (divisor1, divisor2)\n",
-    "\n",
-    "        # 3. Find the order of a mod N (i.e., r, where a^r = 1 (mod N))\n",
-    "        if quantum == True:\n",
-    "            r = find_order_quantum(a, N)\n",
-    "        else:\n",
-    "            r = find_order_classical(a, N)\n",
-    "        print(\"The order of a = {} is {}\".format(a, r))\n",
-    "\n",
-    "        # 4. If the order of a is found and it is\n",
-    "        # * even and\n",
-    "        # * not a^(r/2) = -1 (mod N),\n",
-    "        # then test a^(r/2)-1 and a^(r/2)+1 to see if they share factors with N.\n",
-    "        # We also want to rule out the case of finding the trivial factors: 1 and N.\n",
-    "        divisor1, divisor2 = test_order(a, r, N)\n",
-    "        if (divisor1 != 0):  # test_order will return a 0 if no factor is found\n",
-    "            print(\"Found factors of N = {}: {} and {}\".format(\n",
-    "                N, divisor1, divisor2))\n",
-    "            return divisor1, divisor2\n",
-    "\n",
-    "        # 5. Repeat\n",
-    "        print(\"retrying...\")\n",
-    "        attempts.append(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed682e81",
-   "metadata": {},
-   "source": [
-    "Let's first explore the idea of reducing the factoring problem into the order-finding problem. This gets captured in step 4 described above.  In this step, we have already established that $a$ and $N$ share no factors other than $1$ (i.e., $\\gcd(a,N)=1$) and we have found $r$, the order of $a\\mod N.$  With this information we know that $a^r \\equiv 1 \\mod N.$  Rewritten in another way:\n",
-    "$$\n",
-    "a^r -1 \\equiv 0\\mod N\n",
-    "\\tag{1}.\n",
-    "$$ \n",
-    " \n",
-    " If $r$ is even, we can rewrite $a^r$ as $x^2$ where $x=a^\\frac{r}{2}$. Next, we can factor equation $(1)$ using the identity $x^2-1 = (x-1)(x+1)$:\n",
-    " $$ \n",
-    " (a^{\\frac{r}{2}} - 1)(a^{\\frac{r}{2}} + 1) \\equiv 0\\mod N\n",
-    "\\tag{2}.\n",
-    "$$  \n",
-    "\n",
-    "If, in addition, the equation\n",
-    "$$\n",
-    "a^{\\frac{r}{2}}  \\not\\equiv -1\\mod N\n",
-    "\\tag{3}\n",
-    "$$\n",
-    "is satisfied, then  at least one of the terms $a^{\\frac{r}{2}} - 1$ or $a^{\\frac{r}{2}} + 1$ must share a common factor with $N$. [Peter Shor](https://arxiv.org/abs/quant-ph/9508027) demonstrated that there is greater than a $50\\%$ chance of randomly selecting a value for $a$ satisfying these properties.\n",
-    "\n",
-    "The code block below defines a function that tests whether $r$ is even and whether equation $(3)$ is satisfied and searches for a non-trivial factor of $N$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "6c8eed12",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_order(a, r, N):\n",
-    "    \"\"\"Checks whether or not a^(r/2)+1 or a^(r/2)-1 share a non-trivial common factor with N\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "    r: int\n",
-    "    N: int\n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    int, int factors of N, if found; 0,0 otherwise  \n",
-    "    \"\"\"\n",
-    "\n",
-    "    if r != None and (r % 2 == 0) and a**r % N == 1:\n",
-    "        if (a**(int(r / 2))) % N != -1:\n",
-    "            possible_factors = [gcd(r - 1, N), gcd(r + 1, N)]\n",
-    "            for test_factor in possible_factors:\n",
-    "                if test_factor != 1 and test_factor != N:\n",
-    "                    return test_factor, N // test_factor\n",
-    "    # period did not produce a factor\n",
-    "    else:\n",
-    "        print('No non-trivial factor found')\n",
-    "        return 0, 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a43af433-905c-4070-94b3-0f6be6f67f6b",
-   "metadata": {},
-   "source": [
-    "### Solving the order-finding problem classically\n",
-    "\n",
-    "The key component of Shor's algorithm is an efficient quantum algorithm to find the order $r$ of $a \\mod N$. While a straightforward classical algorithm can solve this problem, it is notably inefficient:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "14f4b0d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_order_classical(a, N):\n",
-    "    \"\"\"A naive classical method to find the order r of a (mod N).\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "        an integer in the interval [2,N-1]\n",
-    "    N: int\n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    r: int \n",
-    "        Period of a^x (mod N)\n",
-    "    \"\"\"\n",
-    "    assert 1 < a and a < N\n",
-    "    r = 1\n",
-    "    y = a\n",
-    "    while y != 1:\n",
-    "        y = y * a % N\n",
-    "        r += 1\n",
-    "    return r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aae6b36d",
-   "metadata": {},
-   "source": [
-    "Let's see how Shor's algorithm works on a few examples using the classical order-finding problem.  Notice how often our choice of value for the initial guess for $a$ produces factors of $N$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "3f75b314-9f0c-40c5-a7bc-58ae235fc183",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trying a = 42\n",
-      "Found factors of N=123 by chance: 3 and 41\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(3, 41)"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_integer = 123  #edit this value to try out a few examples\n",
-    "# Edit the value in the line below to try out different initial guesses for a.\n",
-    "# What happens when you choose a = 42 for the integer 123?\n",
-    "# What happens when you choose a = 100 for the integer 123?\n",
-    "initial_value_to_start = 42  # edit this value; it should be less than my_integer\n",
-    "\n",
-    "shors_algorithm(my_integer, initial_value_to_start, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "275efced-b10e-4438-9b5e-f3534079304b",
-   "metadata": {},
-   "source": [
-    "### Solving the order-finding problem with a quantum algorithm\n",
-    "\n",
-    "\n",
-    "The Fourier transform is a classical computation that provides a more efficient algorithm than the one encoded in `find_order_classical` for identifying the period of $f(x) = a^x\\mod N$. The quantum version of the Fourier transform is the central idea of Shor's Algorithm. This efficient quantum solution derives the period from a measurement of $n = \\lceil log2(N) \\rceil$ qubits. \n",
-    "\n",
-    "The image below outlines the quantum kernel used to find the order of $a$. Notice the last step involves applying the Inverse Quantum Fourier Transform."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aba9fbd7",
-   "metadata": {},
-   "source": [
-    "![](images/shor_circuit.png)\n",
-    "\n",
-    "Figure. Circuit diagram for finding the phase of the modular multiplication gate $U|x\\rangle = |a^x\\mod N\\rangle $, which will be used to compute the order of $a$ modulo $N$.  The number of qubits in the control register determines the accuracy of estimating the phase of $U$. The size of the work register depends on $N$. The goal of this section is to code the  diagram as a kernel named `phase_kernel`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7eff3b6",
-   "metadata": {},
-   "source": [
-    "We will need to create a quantum kernel for the Inverse Quantum Fourier Transform.  Additionally we'll need to create a kernel for modular multiplication: $g(y) = ay \\mod N$, which can be repeatedly applied $x$-times to $y=1$ to carry our modular exponentation $f(x)=a^x\\mod N$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "567e104e",
-   "metadata": {},
-   "source": [
-    "#### Inverse quantum Fourier transform\n",
-    "In the code block below we define a kernel for the quantum Fourier transform and then use `cudaq.adjoint` to create a kernel for the inverse quantum Fourier transform used in the quantum order-finding algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "0e04a0a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define kernels for the quantum Fourier transform and the inverse quantum Fourier transform\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform(qubits: cudaq.qview):\n",
-    "    qubit_count = len(qubits)\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def inverse_qft(qubits: cudaq.qview):\n",
-    "    cudaq.adjoint(quantum_fourier_transform, qubits)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39db9e6a",
-   "metadata": {},
-   "source": [
-    "#### Quantum kernels for modular exponentiation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b8b8884",
-   "metadata": {},
-   "source": [
-    "\n",
-    "While Shor's algorithm provably can factor numbers faster than known classical techniques, the resources required for implementing Shor's algorithm are hefty. A full-scale implementation to factor an $L$-bit number could require a quantum kernel with $5L+1$ qubits to achieve accuracy for the continued fractions part of the algoirthm and as many as $72L^3$ quantum gates for the modular exponentiaion [(Beckman, Chari, Devabhaktuni, & Preskill, 1996)](https://arxiv.org/pdf/quant-ph/9602016).  Both of these are well beyond the capabilities of modern quantum hardware for numbers of interest. Advancements to reduce the number of gates and qubits required focus on optimizing the kernel for $f(x) = a\\cdot x \\mod N$ for properties of the number to be factored. The difficulty is to create efficient quantum kernels (in terms of qubit and gate count) that compute $a\\cdot x \\mod{N}$.  \n",
-    "\n",
-    "For the purposes of this demonstration, we will consider only the order-finding problem for the values of $a$ to be either $4$ or $5$ with $N=21$. We'll be using the quantum circuits depicted in [this paper](https://arxiv.org/abs/2103.13855) and [this report](https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf), respectively. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a48d6fa1",
-   "metadata": {},
-   "source": [
-    "##### The case N = 21 and a = 5:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "c4b8e2e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def modular_mult_5_21(work: cudaq.qview):\n",
-    "    \"\"\"\"Kernel for multiplying by 5 mod 21\n",
-    "    based off of the circuit diagram in \n",
-    "    https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf\n",
-    "    Modifications were made to change the ordering of the qubits\"\"\"\n",
-    "    x(work[0])\n",
-    "    x(work[2])\n",
-    "    x(work[4])\n",
-    "\n",
-    "    swap(work[0], work[4])\n",
-    "    swap(work[0], work[2])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def modular_exp_5_21(exponent: cudaq.qview, work: cudaq.qview,\n",
-    "                     control_size: int):\n",
-    "    \"\"\" Controlled modular exponentiation kernel used in Shor's algorithm\n",
-    "    |x> U^x |y> = |x> |(5^x)y mod 21>\n",
-    "    \"\"\"\n",
-    "    x(work[0])\n",
-    "    for exp in range(control_size):\n",
-    "        ctrl_qubit = exponent[exp]\n",
-    "        for _ in range(2**(exp)):\n",
-    "            cudaq.control(modular_mult_5_21, ctrl_qubit, work)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c644786",
-   "metadata": {},
-   "source": [
-    "Verify in the code block below that the kernels defined above do in fact carry out multiplication and exponentiation with $a=5$ and $N=21$ by changing the `iterations` variable below. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "e93fcf39",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮      \n",
-      "q0 : ┤ x ├┤ x ├─╳──╳─\n",
-      "     ╰───╯╰───╯ │  │ \n",
-      "q1 : ───────────┼──┼─\n",
-      "     ╭───╮      │  │ \n",
-      "q2 : ┤ x ├──────┼──╳─\n",
-      "     ╰───╯      │    \n",
-      "q3 : ───────────┼────\n",
-      "     ╭───╮      │    \n",
-      "q4 : ┤ x ├──────╳────\n",
-      "     ╰───╯           \n",
-      "\n",
-      "Measurement results from sampling: { 10100:200 }\n",
-      "\n",
-      "For x = 1, 5^x mod 21 = 5\n",
-      "For x = 1, the computed result of the circuit is 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Demonstrate iterated application of 5y mod 21 where y = 1\n",
-    "@cudaq.kernel\n",
-    "def demonstrate_mod_exponentiation(iterations: int):\n",
-    "    qubits = cudaq.qvector(5)\n",
-    "    x(qubits[0]\n",
-    "     )  # initalizes the qubits in the state for y = 1 which is |10000>\n",
-    "    for _ in range(iterations):\n",
-    "        modular_mult_5_21(qubits)\n",
-    "\n",
-    "\n",
-    "shots = 200\n",
-    "\n",
-    "# The iterations variable determines the exponent in 5^x mod 21.\n",
-    "# Change this value to verify that the demonstrate_mod_exponentiation\n",
-    "# kernel carries out the desired calculation.\n",
-    "iterations = 1\n",
-    "\n",
-    "print(cudaq.draw(demonstrate_mod_exponentiation, iterations))\n",
-    "\n",
-    "results = cudaq.sample(demonstrate_mod_exponentiation,\n",
-    "                       iterations,\n",
-    "                       shots_count=shots)\n",
-    "\n",
-    "print(\"Measurement results from sampling:\", results)\n",
-    "\n",
-    "# Reverse the order of the most probable measured bit string\n",
-    "# and convert the binary string to an integer\n",
-    "integer_result = int(results.most_probable()[::-1], 2)\n",
-    "\n",
-    "print(\"For x = {}, 5^x mod 21 = {}\".format(iterations, (5**iterations) % 21))\n",
-    "print(\"For x = {}, the computed result of the circuit is {}\".format(\n",
-    "    iterations, integer_result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff07579c",
-   "metadata": {},
-   "source": [
-    "##### The case N = 21 and a = 4:\n",
-    "\n",
-    "The example below is one where the work register has been optimized to use fewer gates and qubits than would have been necessary through a straightforward implementation of modular multiplication as seen in the previous case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "69963075",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def modular_exp_4_21(exponent: cudaq.qview, work: cudaq.qview):\n",
-    "    \"\"\" Controlled modular exponentiation kernel used in Shor's algorithm\n",
-    "     |x> U^x |y> = |x> |(4^x)y mod 21>\n",
-    "     based off of the circuit diagram in https://arxiv.org/abs/2103.13855\n",
-    "     Modifications were made to account for qubit ordering differences\"\"\"\n",
-    "    swap(exponent[0], exponent[2])\n",
-    "    # x = 1\n",
-    "    x.ctrl(exponent[2], work[1])\n",
-    "\n",
-    "    # x = 2\n",
-    "    x.ctrl(exponent[1], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    x.ctrl([exponent[1], work[0]], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "\n",
-    "    # x = 4\n",
-    "    x(work[1])\n",
-    "    x.ctrl([exponent[0], work[1]], work[0])\n",
-    "    x(work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    x.ctrl([exponent[0], work[0]], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    swap(exponent[0], exponent[2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84a197e2",
-   "metadata": {},
-   "source": [
-    "Now we are ready to define the `phase_kernel` to carry out the instructions in the circuit diagram drawn at the beginning of this section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "206bcac3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def phase_kernel(control_register_size: int, work_register_size: int, a: int,\n",
-    "                 N: int):\n",
-    "    \"\"\" \n",
-    "    Kernel to estimate the phase of the modular multiplication gate |x> U |y> = |x> |a*y mod 21> for a = 4 or 5\n",
-    "    \"\"\"\n",
-    "\n",
-    "    qubits = cudaq.qvector(control_register_size + work_register_size)\n",
-    "    control_register = qubits[0:control_register_size]\n",
-    "    work_register = qubits[control_register_size:control_register_size +\n",
-    "                           work_register_size]\n",
-    "\n",
-    "    h(control_register)\n",
-    "\n",
-    "    if a == 4 and N == 21:\n",
-    "        modular_exp_4_21(control_register, work_register)\n",
-    "    if a == 5 and N == 21:\n",
-    "        modular_exp_5_21(control_register, work_register, control_register_size)\n",
-    "\n",
-    "    inverse_qft(control_register)\n",
-    "\n",
-    "    # Measure only the control_register and not the work_register\n",
-    "    mz(control_register)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "id": "ca9b570a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮                                                                    »\n",
-      "q0 : ┤ h ├──●────●────●───●──●────────────────────────────────────────────────»\n",
-      "     ├───┤  │    │    │   │  │                                                »\n",
-      "q1 : ┤ h ├──┼────┼────┼───┼──┼───●────●────●───●──●───●────●────●───●──●──────»\n",
-      "     ├───┤  │    │    │   │  │   │    │    │   │  │   │    │    │   │  │      »\n",
-      "q2 : ┤ h ├──┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───●──»\n",
-      "     ├───┤╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮»\n",
-      "q3 : ┤ x ├┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├»\n",
-      "     ╰───╯╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯»\n",
-      "q4 : ────────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼──────»\n",
-      "               ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      »\n",
-      "q5 : ──────────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────»\n",
-      "               ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         »\n",
-      "q6 : ─────────────────┼───┼────────────────┼───┼────────────────┼───┼─────────»\n",
-      "                    ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │         »\n",
-      "q7 : ───────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─────────»\n",
-      "                    ╰───╯                ╰───╯                ╰───╯           »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                                                                            »\n",
-      "────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                            »\n",
-      "────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                            »\n",
-      "──●────●───●──●───●────●────●───●──●───●────●────●───●──●───●────●────●───●─»\n",
-      "  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │ »\n",
-      "──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳─»\n",
-      "  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │ »\n",
-      "──┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼─»\n",
-      "╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │ »\n",
-      "┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼─»\n",
-      "╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │ »\n",
-      "───────┼───┼────────────────┼───┼────────────────┼───┼────────────────┼───┼─»\n",
-      "     ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │ »\n",
-      "─────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─»\n",
-      "     ╰───╯                ╰───╯                ╰───╯                ╰───╯   »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                           ╭─────────────╮╭────────────╮╭───╮\n",
-      "───────────────────────────┤ r1(-0.7854) ├┤ r1(-1.571) ├┤ h ├\n",
-      "        ╭────────────╮╭───╮╰──────┬──────╯╰─────┬──────╯╰───╯\n",
-      "────────┤ r1(-1.571) ├┤ h ├───────┼─────────────●────────────\n",
-      "   ╭───╮╰─────┬──────╯╰───╯       │                          \n",
-      "─●─┤ h ├──────●───────────────────●──────────────────────────\n",
-      " │ ╰───╯                                                     \n",
-      "─╳───────────────────────────────────────────────────────────\n",
-      " │                                                           \n",
-      "─┼───────────────────────────────────────────────────────────\n",
-      " │                                                           \n",
-      "─╳───────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "─────────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "─────────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "\n",
-      "Measurement results for a=5 and N=21 with 3 qubits in the control register \n",
-      "{ 000:2843 010:913 111:1850 001:1935 011:1830 100:2846 101:1861 110:922 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "control_register_size = 3\n",
-    "work_register_size = 5\n",
-    "values_for_a = [4, 5]\n",
-    "idx = 1  # change to 1 to select 5\n",
-    "N = 21\n",
-    "shots = 15000\n",
-    "\n",
-    "print(\n",
-    "    cudaq.draw(phase_kernel, control_register_size, work_register_size,\n",
-    "               values_for_a[idx], N))\n",
-    "\n",
-    "results = cudaq.sample(phase_kernel,\n",
-    "                       control_register_size,\n",
-    "                       work_register_size,\n",
-    "                       values_for_a[idx],\n",
-    "                       N,\n",
-    "                       shots_count=shots)\n",
-    "print(\n",
-    "    \"Measurement results for a={} and N={} with {} qubits in the control register \"\n",
-    "    .format(values_for_a[idx], N, control_register_size))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4718d04f",
-   "metadata": {},
-   "source": [
-    "### Determining the order from the measurement results of the phase kernel\n",
-    "\n",
-    "The measurement results from sampling the `phase_kernel` can be used to estimate the phase of the operator $U|x\\rangle = ax\\mod N$. We will only be interested in the most probable non-zero outcomes of the sampling.  Therefore, we'll create a function `top_results` to extract those values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "id": "9e96846f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def top_results(sample_results, zeros, threshold):\n",
-    "    \"\"\"Function to output the non-zero results whose counts are above the given threshold\n",
-    "    Returns\n",
-    "    -------\n",
-    "        dict[str, int]: keys are bit-strings and values are the respective counts  \n",
-    "    \"\"\"\n",
-    "    results_dictionary = {k: v for k, v in sample_results.items()}\n",
-    "    if zeros in results_dictionary.keys():\n",
-    "        results_dictionary.pop(zeros)\n",
-    "    sorted_results = {\n",
-    "        k: v for k, v in sorted(\n",
-    "            results_dictionary.items(), key=lambda item: item[1], reverse=True)\n",
-    "    }\n",
-    "    top_key = next(iter(sorted_results))\n",
-    "    max_value = sorted_results[top_key]\n",
-    "    top_results_dictionary = {top_key: max_value}\n",
-    "\n",
-    "    for key in sorted_results:\n",
-    "        if results_dictionary[key] > min(threshold, max_value):\n",
-    "            top_results_dictionary[key] = results_dictionary[key]\n",
-    "    return top_results_dictionary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b55ab5a",
-   "metadata": {},
-   "source": [
-    "Let's see how the `top_results` extracts the top results from the measurement results for a=4 and N=21 with 3 qubits in the control register that we computed above.  One of these top result is likely to be an estimate for the phase of $U$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "id": "cb717231",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'100': 2846,\n",
-       " '001': 1935,\n",
-       " '101': 1861,\n",
-       " '111': 1850,\n",
-       " '011': 1830,\n",
-       " '110': 922,\n",
-       " '010': 913}"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Example\n",
-    "top_results(results, '000', 750)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cda8137a",
-   "metadata": {},
-   "source": [
-    "The algorithm for translating the phase estimate of the operator $U|x\\rangle = ax\\mod N$ into the order of $a \\mod N$ involves continued fractions. The function `find_order_quantum` below carries out this computation.  To learn more about how the phase of $U$ relates to the period of $a \\mod N$, check out these three lectures by Scott Aaronson: lecture [19](https://www.scottaaronson.com/qclec/19.pdf), [20](https://www.scottaaronson.com/qclec/20.pdf), [21](https://www.scottaaronson.com/qclec/21.pdf)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "id": "a562fde9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_order_from_phase(phase, phase_nbits, a, N):\n",
-    "    \"\"\"Uses continued fractions to find the order of a mod N  \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    phase: int\n",
-    "        Integer result from the phase estimate of U|x> = ax mod N\n",
-    "    phase_nbits: int\n",
-    "        Number of qubits used to estimate the phase\n",
-    "    a: int\n",
-    "        For this demonstration a is either 4 or 5\n",
-    "    N: int\n",
-    "        For this demonstration N = 21\n",
-    "    Returns\n",
-    "    -------\n",
-    "    int: period of a mod N if found, otherwise returns None\n",
-    "    \"\"\"\n",
-    "\n",
-    "    assert phase_nbits > 0\n",
-    "    assert a > 0\n",
-    "    assert N > 0\n",
-    "\n",
-    "    eigenphase = float(phase) / 2**(phase_nbits)\n",
-    "\n",
-    "    f = fractions.Fraction.from_float(eigenphase).limit_denominator(N)\n",
-    "\n",
-    "    if f.numerator == 1:\n",
-    "        return None\n",
-    "    eigenphase = float(f.numerator / f.denominator)\n",
-    "    print('eigenphase is ', eigenphase)\n",
-    "    coefficients_continued_fraction = list(\n",
-    "        contfrac.continued_fraction(eigenphase))\n",
-    "\n",
-    "    convergents_continued_fraction = list(contfrac.convergents(eigenphase))\n",
-    "    print('convergent sequence of fractions for this eigenphase is',\n",
-    "          convergents_continued_fraction)\n",
-    "    for r in convergents_continued_fraction:\n",
-    "        print(\n",
-    "            'using the denominators of the fractions in the convergent sequence, testing order =',\n",
-    "            r[1])\n",
-    "        if a**r[1] % N == 1:\n",
-    "            print('Found order:', r[1])\n",
-    "            return (r[1])\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47511fbe",
-   "metadata": {},
-   "source": [
-    "We are now ready to combine all the elements above into a function to find the order of $a$ $\\mod N$ and test it in the factoring algoithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "7ad0abcf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_order_quantum(a, N):\n",
-    "    \"\"\"The quantum algorithm to find the order of a mod N, when x = 4 or x =5 and N = 21\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "        For this demonstration a will be either 4 or 5\n",
-    "    N: int\n",
-    "        For this demonstration N will be 21\n",
-    "    Returns\n",
-    "    r: int the period if it is found, or None if no period is found\n",
-    "    -------\n",
-    "    \n",
-    "    \"\"\"\n",
-    "\n",
-    "    if (a == 4 and N == 21) or (a == 5 and N == 21):\n",
-    "        shots = 15000\n",
-    "        if a == 4 and N == 21:\n",
-    "            control_register_size = 3\n",
-    "            work_register_size = 2\n",
-    "        if a == 5 and N == 21:\n",
-    "            control_register_size = 5\n",
-    "            work_register_size = 5\n",
-    "\n",
-    "        #cudaq.set_random_seed(123)\n",
-    "        results = cudaq.sample(phase_kernel,\n",
-    "                               control_register_size,\n",
-    "                               work_register_size,\n",
-    "                               a,\n",
-    "                               N,\n",
-    "                               shots_count=shots)\n",
-    "        print(\"Measurement results:\")\n",
-    "        print(results)\n",
-    "\n",
-    "        # We will want to ignore the all zero result\n",
-    "        zero_result = ''.join(\n",
-    "            [str(elem) for elem in [0] * control_register_size])\n",
-    "        # We'll only consider the top results from the sampling\n",
-    "        threshold = shots * (.1)\n",
-    "        most_probable_bitpatterns = top_results(results, zero_result, threshold)\n",
-    "\n",
-    "        for key in most_probable_bitpatterns:\n",
-    "            # Convert the key bit string into an integer\n",
-    "            # This integer divided by 8 is an estimate for the phase\n",
-    "            reverse_result = key[::-1]\n",
-    "            phase = int(reverse_result, 2)\n",
-    "\n",
-    "            print(\"Trying nonzero bitpattern from the phase estimation:\", key,\n",
-    "                  \"=\", phase)\n",
-    "            r = get_order_from_phase(phase, control_register_size, a, N)\n",
-    "            if r == None:\n",
-    "                print('No period found.')\n",
-    "\n",
-    "                continue\n",
-    "\n",
-    "            return r\n",
-    "            break\n",
-    "    else:\n",
-    "        print(\n",
-    "            \"A different quantum kernel is required for this choice of a and N.\"\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "id": "79927898",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trying a = 5\n",
-      "Measurement results:\n",
-      "{ 11010:452 11001:92 11101:47 00000:2485 01001:96 00100:131 01011:1747 00101:1654 10101:1736 00110:475 11111:23 01000:50 01101:38 01110:35 01010:420 11110:22 01111:26 00011:50 01100:120 10011:35 00010:25 00001:22 11011:1710 00111:79 11100:113 10001:14 10010:29 10100:129 10110:467 10000:2536 10111:81 11000:61 }\n",
-      "\n",
-      "Trying nonzero bitpattern from the phase estimation: 10000 = 1\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 01011 = 26\n",
-      "eigenphase is  0.8125\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (4, 5), (13, 16)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 5\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 16\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 10101 = 21\n",
-      "eigenphase is  0.65\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (1, 2), (2, 3), (13, 20)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 2\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 3\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 20\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 11011 = 27\n",
-      "eigenphase is  0.8421052631578947\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (5, 6), (16, 19)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 6\n",
-      "Found order: 6\n",
-      "The order of a =5 is 6\n",
-      "Found factors of N = 21: 7 and 3\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(7, 3)"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_integer = 21\n",
-    "initial_value_to_start = 5  # Try replacing 5 with 4\n",
-    "quantum = True\n",
-    "shors_algorithm(my_integer, initial_value_to_start, quantum)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c243a5ee",
-   "metadata": {},
-   "source": [
-    "### Postscript\n",
-    "Recent [work of Oded Regev](https://arxiv.org/abs/2308.06572) improves Shor's Algorithm by reducing the number of gates needed.  You can read more about it [here](https://www.quantamagazine.org/thirty-years-later-a-speed-boost-for-quantum-factoring-20231017/)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/trotter.ipynb.txt b/pr-2308/_sources/applications/python/trotter.ipynb.txt
deleted file mode 100644
index b508dd155c..0000000000
--- a/pr-2308/_sources/applications/python/trotter.ipynb.txt
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "231113c7-9432-497b-91f1-c4d068dd61b8",
-   "metadata": {},
-   "source": [
-    "# Computing Magnetization With The Suzuki-Trotter Approximation\n",
-    "\n",
-    "A key application for quantum computers is the simulation of quantum systems. This tutorial will demonstrate a CUDA-Q implementation of a Suzuki-Trotter simulation of a Heisenberg Model Hamiltonian to compute the magnetization of a spin chain.\n",
-    "\n",
-    "This example takes advantage of CUDA-Q's state handling abilities to perform the recursive Trotter simulations. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ccd64e3-9e36-470b-9b15-2a7de6bf6ee3",
-   "metadata": {},
-   "source": [
-    "### Problem Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5c2633a0-979b-46b8-a7a6-44fa61bc62c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import time\n",
-    "import numpy as np\n",
-    "from typing import List"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbbf2909-2607-48d1-aa72-6594f09bbd88",
-   "metadata": {},
-   "source": [
-    "The goal of this problem is to estimate the time evolution of an initial quantum state, governed by a Hamiltonian, and then compute the average magnetization fo the final state.  The time evolution can be approximated using the Trotter method:\n",
-    "\n",
-    "$$  e^{-iHt} \\approx \\prod_{n=0}^N e^{\\frac{-iHt}{n}}$$\n",
-    "\n",
-    "The Heisenberg Hamiltonian is defined below, parameterized by these predefined constants: $g$, $Jx$, $Jy$, and $\\omega = 2\\pi$.   The time step $dt$ is selected for this problem as well as `n_steps` and `n_spins` ($N$), determining the size of the simulation.  The default setup considers 11 spins and 10 steps, which can easily be simulated on a CPU.  If you have access to at least one GPU, the problem can be increased to around 25 steps and 100 time steps.\n",
-    "\n",
-    "\n",
-    "$$ H = \\sum_{j=1}^{N}(J_x\\sigma^x_j\\sigma^x_{j+1} + J_x\\sigma^y_j\\sigma^y_{j+1} + J_x\\sigma^z_j\\sigma^z_{j+1} + \\cos(\\omega * t)\\sigma^x_j   )$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e409539e-e97b-4815-8021-477308726155",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 1.0\n",
-    "Jx = 1.0\n",
-    "Jy = 1.0\n",
-    "Jz = g\n",
-    "dt = 0.05\n",
-    "n_steps = 10\n",
-    "n_spins = 11\n",
-    "omega = 2 * np.pi\n",
-    "\n",
-    "\n",
-    "def heisenbergModelHam(t: float) -> cudaq.SpinOperator:\n",
-    "    tdOp = cudaq.SpinOperator(num_qubits=n_spins)\n",
-    "    for i in range(0, n_spins - 1):\n",
-    "        tdOp += (Jx * cudaq.spin.x(i) * cudaq.spin.x(i + 1))\n",
-    "        tdOp += (Jy * cudaq.spin.y(i) * cudaq.spin.y(i + 1))\n",
-    "        tdOp += (Jz * cudaq.spin.z(i) * cudaq.spin.z(i + 1))\n",
-    "    for i in range(0, n_spins):\n",
-    "        tdOp += (np.cos(omega * t) * cudaq.spin.x(i))\n",
-    "    return tdOp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "067ae183-8e36-4171-adaf-c8506ac2ac48",
-   "metadata": {},
-   "source": [
-    "Next, two CUDA-Q kernels are defined. The first, `getInitState`, prepares an initial state where an $X$ gate is applied to the first and then every other qubit to initialize a chain of alternating spins. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1b64301b-f967-4e69-95d7-64a0091054cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def getInitState(numSpins: int):\n",
-    "    q = cudaq.qvector(numSpins)\n",
-    "    for qId in range(0, numSpins, 2):\n",
-    "        x(q[qId])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0077726b-7c07-4699-8f05-b685d8a4d3a7",
-   "metadata": {},
-   "source": [
-    "The second, `trotter`, performs a single Trotter step given some provided initial state and then returns the resulting state.  Two notes should be made about this kernel. \n",
-    "1. It takes advantage of CUDA-Q's state handling abilities, allowing each step to proceed from the previous, still stored in GPU memory during the simulation.  This provides a significant speedup which is demonstrated in the plot at the end of this tutorial.\n",
-    "2. This kernel takes advantage of the `pauli_word` object which allows a list of Pauli words and their coefficients to be passed into the kernel and applied as an exponentiated matrix operation using `exp_pauli`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4bd5de1b-76c5-4dd7-844a-dfc598a78e6a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def trotter(state: cudaq.State, coefficients: List[complex],\n",
-    "            words: List[cudaq.pauli_word], dt: float):\n",
-    "    q = cudaq.qvector(state)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(coefficients[i].real * dt, q, words[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74593ace-c2d5-4b4b-931f-54fc4f6ee855",
-   "metadata": {},
-   "source": [
-    "The functions below are used to strip the Hamiltonian spin operator into a list of coefficients and Pauli words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0aeba077-3a12-4c1d-919e-efc521f531fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def termCoefficients(op: cudaq.SpinOperator) -> List[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "def termWords(op: cudaq.SpinOperator) -> List[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02aa14dc-bf7a-40e2-b55c-77e4e1d13d33",
-   "metadata": {},
-   "source": [
-    "Finally, a second spin operator is defined which will be used to compute the expectation value corresponding to the average magnetization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e0be9949-0d1a-44f5-bdeb-6be751dfe136",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "average_magnetization = cudaq.SpinOperator(num_qubits=n_spins)\n",
-    "for i in range(0, n_spins):\n",
-    "    average_magnetization += ((1.0 / n_spins) * cudaq.spin.z(i))\n",
-    "average_magnetization -= 1.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5527c9b-86e0-4f3d-b5cf-db887d07b375",
-   "metadata": {},
-   "source": [
-    "### Running the Simulation\n",
-    "\n",
-    "Before looping through the Trotter steps, the initial state is constructed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "78348fa1-6f14-4238-a6ec-b0c8449892d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = cudaq.get_state(getInitState, n_spins)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "812b8f44-0a96-4d43-a622-8a4b4a59241d",
-   "metadata": {},
-   "source": [
-    "Next, the time steps are looped through. At each step, the time dependent Hamiltonian is defined. This Hamiltonian is used to construct the Trotter kernel for that time step, which is then used to compute the average magnetization expectation value.  The state is saved, and used as the initial state for the next time step. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "57e4929d-9b79-4d21-b48a-27d47d7e65a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 0: time [s]: 0.03444695472717285, result: -0.09042024163828166\n",
-      "Step 1: time [s]: 0.0026793479919433594, result: -0.08898564687193886\n",
-      "Step 2: time [s]: 0.002758026123046875, result: -0.08698024360923415\n",
-      "Step 3: time [s]: 0.002524852752685547, result: -0.08507694741170907\n",
-      "Step 4: time [s]: 0.0026259422302246094, result: -0.08394118068746997\n",
-      "Step 5: time [s]: 0.002542734146118164, result: -0.08394076573115139\n",
-      "Step 6: time [s]: 0.0027430057525634766, result: -0.08502222139504187\n",
-      "Step 7: time [s]: 0.0025305747985839844, result: -0.08677832064885871\n",
-      "Step 8: time [s]: 0.003045797348022461, result: -0.08863390649349775\n",
-      "Step 9: time [s]: 0.0025949478149414062, result: -0.09005513983609514\n",
-      "Step times: [0.03444695472717285, 0.0026793479919433594, 0.002758026123046875, 0.002524852752685547, 0.0026259422302246094, 0.002542734146118164, 0.0027430057525634766, 0.0025305747985839844, 0.003045797348022461, 0.0025949478149414062]\n",
-      "Results: [-0.09042024163828166, -0.08898564687193886, -0.08698024360923415, -0.08507694741170907, -0.08394118068746997, -0.08394076573115139, -0.08502222139504187, -0.08677832064885871, -0.08863390649349775, -0.09005513983609514]\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "times = []\n",
-    "for i in range(0, n_steps):\n",
-    "    start_time = time.time()\n",
-    "    ham = heisenbergModelHam(i * dt)\n",
-    "    coefficients = termCoefficients(ham)\n",
-    "    words = termWords(ham)\n",
-    "    magnetization_exp_val = cudaq.observe(trotter, average_magnetization, state,\n",
-    "                                          coefficients, words, dt)\n",
-    "    result = magnetization_exp_val.expectation()\n",
-    "    results.append(result)\n",
-    "    state = cudaq.get_state(trotter, state, coefficients, words, dt)\n",
-    "    stepTime = time.time() - start_time\n",
-    "    times.append(stepTime)\n",
-    "    print(f\"Step {i}: time [s]: {stepTime}, result: {result}\")\n",
-    "\n",
-    "print(f\"Step times: {times}\")\n",
-    "print(f\"Results: {results}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b46a3b14-31c0-4711-9064-51ca1f48e7d4",
-   "metadata": {},
-   "source": [
-    "CUDA-Q's state handling capabilities provide a massive performance boost for this algorithm. Rather than resimulate all previous operation at any given time step, saving the previous state in GPU memory allows completion of the simulation with fewer operations. The figure below demonstrates the 24X speedup realized by a 100 step Trotter simulation.\n",
-    "\n",
-    "![Htest](./images/statehandle.png)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f59cf33e-e939-4abe-ae48-2a77f25be5a0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/unitary_compilation_diffusion_models.ipynb.txt b/pr-2308/_sources/applications/python/unitary_compilation_diffusion_models.ipynb.txt
deleted file mode 100644
index 0e5d788ac2..0000000000
--- a/pr-2308/_sources/applications/python/unitary_compilation_diffusion_models.ipynb.txt
+++ /dev/null
@@ -1,1062 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d60f1e23-2541-4b01-84c8-0aff758061b4",
-   "metadata": {},
-   "source": [
-    "# Compiling Unitaries Using Diffusion Models\n",
-    "\n",
-    "This notebook is based on the work presented in [Quantum circuit synthesis with diffusion models, Florian Fürrutter, Gorka Muñoz-Gil & Hans J. Briegel , Nat. Mach. Intell. **6**, 515–524 (2024)](https://doi.org/10.1038/s42256-024-00831-9)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b6a7722-f302-458b-9f90-8493e46d786d",
-   "metadata": {},
-   "source": [
-    "Quantum computing relies on efficiently translating quantum operations into viable physical realizations on existing quantum hardware. Recently, diffusion models — a powerful class of generative models in machine learning — have demonstrated exceptional performance on this task. \n",
-    "\n",
-    "In this tutorial notebook, we will demonstrate how to use this method to synthesize arbitrary unitaries into a `cudaq.kernel`, effectively decomposing them into sequences of quantum gates, a process commonly known as unitary compilation.\n",
-    "\n",
-    "Our focus will be on generating circuits using a pre-trained model. Specifically, we will illustrate how to compile 3-qubit unitaries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fef3bf4a-1008-4df8-b6e8-d5357db1f6d5",
-   "metadata": {},
-   "source": [
-    "### Diffusion model pipeline\n",
-    "\n",
-    "Generative diffusion models (DMs) have recently delivered remarkable results across a wide range of applications, from image generation to protein folding. In this work, we leverage DMs to generate quantum circuits based on a user specified unitary matrix and a text prompt that defines the allowed gate set, effectively using DMs as unitary compilers. The method is thoroughly explained in Ref. [(Fürrutter et al., 2024)](https://doi.org/10.1038/s42256-024-00831-9). The following figure  provides an overview of the proposed circuit generation pipeline:\n",
-    "\n",
-    "<figure>\n",
-    "  <img src=\"images/pipeline_simplified.png\" alt=\"Quantum circuit generation pipeline overview\" width=\"70%\"/>\n",
-    "  <figcaption><b>Quantum circuit generation pipeline</b>. Figure adapted from <a href=\"https://doi.org/10.1038/s42256-024-00831-9\">(Fürrutter et al., 2024)</a></figcaption>\n",
-    "</figure>\n",
-    "\n",
-    "The pipeline consists on 3 main components:\n",
-    "\n",
-    "**1) Circuit encoding:** Like any neural network, diffusion models operate with continuous inputs and outputs. However, since the circuits we consider are composed of discrete gates (i.e., with no continuous parameters), we develop a mapping that transforms each gate into a continuous vector. This allows us to represent a given circuit as a three-dimensional tensor, as illustrated. Crucially, this mapping is invertible: when the DM generates continuous tensors, we can apply the inverse map to convert them back into the circuit form. An overview of these steps is provided in the figure below:\n",
-    "\n",
-    "<figure>\n",
-    "  <img src=\"images/circuit_encoding.png\" alt=\"Quantum circuit encoding\" width=\"60%\"/>\n",
-    "  <figcaption><b>Quantum circuit encoding</b>. Figure adapted from <a href=\"https://doi.org/10.1038/s42256-024-00831-9\">(Fürrutter et al., 2024)</a></figcaption>\n",
-    "</figure>\n",
-    "\n",
-    "**2) Conditioning:** The user's input (the set of available gates and the unitary to compile) is also transformed into a continuous tensor by two neural networks. For the gate set description, where the input is a text prompt (e.g., \"Compile using ['x', 'h']\"), we utilize a pre-trained language model. For the unitary, we employ a neural network that is training jointly with the diffusion model.\n",
-    "\n",
-    "**3) Unitary compilation:** The generation procedure follows the typical DM process: the model is given a fully noisy tensor which is iteratively de-noised until reaching a clean sample based on the given conditioning (the desired unitary and gate set). The tensors generated by the DM are then mapped to circuits via the inverse encoding procedure. To learn more about the practical implementation of diffusion models we recommend [this tutorial](https://course.fast.ai/Lessons/lesson9.html). \n",
-    "\n",
-    "In the following we will use `CUDA-Q` and `genQC` to perform all these steps and go from a desired unitary matrix, $U$, to a quantum circuit that we can execute using CUDA-Q. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1453ad1-e4d4-4e38-9684-fd967bc1a8de",
-   "metadata": {},
-   "source": [
-    "### Setup and compilation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f1b7d96a-92d4-46f3-95a6-1d1e46d63240",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.037362Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.036980Z",
-     "iopub.status.idle": "2024-08-27T18:11:38.867336Z",
-     "shell.execute_reply": "2024-08-27T18:11:38.866445Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.037346Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install genQC==0.1.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "07d177c6-60fd-497d-ae2b-41e225b56a2d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.868456Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.868206Z",
-     "iopub.status.idle": "2024-08-27T18:11:38.872654Z",
-     "shell.execute_reply": "2024-08-27T18:11:38.871931Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.868440Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import genQC\n",
-    "from genQC.imports import *\n",
-    "from genQC.pipeline.diffusion_pipeline import DiffusionPipeline\n",
-    "from genQC.inference.export_cudaq import genqc_to_cudaq\n",
-    "import genQC.inference.infer_compilation as infer_comp\n",
-    "import genQC.util as util\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "\n",
-    "# Fixed seed for reproducibility\n",
-    "torch.manual_seed(0)\n",
-    "np.random.seed(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ccb97524-0f13-49ff-893d-983c572e66c6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.873619Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.873403Z",
-     "iopub.status.idle": "2024-08-27T18:11:39.025224Z",
-     "shell.execute_reply": "2024-08-27T18:11:39.024476Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.873603Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\n",
-      "cuda\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = util.infer_torch_device()  # Use CUDA if we can\n",
-    "util.MemoryCleaner.purge_mem()  # Clean existing memory allocation\n",
-    "print(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acf7656c-be92-4b36-8c38-d3ff6c1912e3",
-   "metadata": {},
-   "source": [
-    "### Load model"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "0047cbe9-a21c-4c72-a144-7a2929752329",
-   "metadata": {},
-   "source": [
-    "First, we load the pre-trained model weights and setup the DM pipeline.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a6b18d03-6976-4eec-9ab0-7b763d73ad17",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:39.026541Z",
-     "iopub.status.busy": "2024-08-27T18:11:39.025956Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.879166Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.878426Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:39.026521Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = DiffusionPipeline.from_pretrained(\n",
-    "    \"Floki00/qc_unitary_3qubit\", device)  # Download from Hugging Face\n",
-    "pipeline.scheduler.set_timesteps(40)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc112d57-71a8-403c-a0a9-d2f7d0eb7492",
-   "metadata": {},
-   "source": [
-    "Set parameters the model was trained on. Note that these are fixed and depend on the pre-trained model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3037d98c-16fb-4a7f-bf37-7e9991b6603e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.880376Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.879966Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.883994Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.883336Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.880352Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "vocab = {\n",
-    "    i + 1: gate for i, gate in enumerate(pipeline.gate_pool)\n",
-    "}  # Gateset used during training, used for decoding\n",
-    "num_of_qubits = 3  # Number of qubits\n",
-    "max_gates = 12  # Maximum number of gates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d7cc1c9-7ddd-4464-98b8-8dbcaa02a53e",
-   "metadata": {},
-   "source": [
-    "### Unitary compilation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b962aea8-9c46-4795-8b74-bca35900f1ed",
-   "metadata": {},
-   "source": [
-    "Let's start by defining the unitary we want to compile. Note that this model has been trained to compile unitaries that arise from circuits composed of the gates contain in `vocab`. While this is a universal gate set (i.e. can perform universal computation), it can only do so with an infinite number of gates. Because the number of gates in the model is restricted to 12, we can only expect the model to generate unitaries under this constraints. We will consider here the compilation of one of such unitary. Nonetheless, stay tuned for bigger and better models!\n",
-    "\n",
-    "We start by defining our unitary as a `numpy.matrix`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5107c719-4220-4209-a657-4a9c0766b80a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.884880Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.884694Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.896385Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.895667Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.884863Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "U = np.matrix([[0.70710678, 0., 0., 0., 0.70710678, 0., 0., 0.],\n",
-    "               [0., -0.70710678, 0., 0., 0., -0.70710678, 0., 0.],\n",
-    "               [-0.70710678, 0., 0., 0., 0.70710678, 0., 0., 0.],\n",
-    "               [0., 0.70710678, 0., 0., 0., -0.70710678, 0., 0.],\n",
-    "               [0., 0., 0.70710678, 0., 0., 0., 0., 0.70710678],\n",
-    "               [0., 0., 0., 0.70710678, 0., 0., 0.70710678, 0.],\n",
-    "               [0., 0., -0.70710678, 0., 0., 0., 0., 0.70710678],\n",
-    "               [0., 0., 0., -0.70710678, 0., 0., 0.70710678, 0.]],\n",
-    "              dtype=np.complex128)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14954138-f16b-48f6-b35d-5b1ac00cff1e",
-   "metadata": {},
-   "source": [
-    "and check that the matrix is indeed unitary, i.e. $U^{\\dagger} U = U U^{\\dagger} = I$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "57164622-8359-4c58-b4d9-1b64ae603b43",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.897692Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.897368Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.904008Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.903243Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.897677Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "assert np.allclose(U.H @ U, np.identity(2**num_of_qubits)) and np.allclose(\n",
-    "    U @ U.H, np.identity(2**num_of_qubits))  #check if unitary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0ba07c5-075a-46ca-9a22-cc758ce33cb9",
-   "metadata": {},
-   "source": [
-    "The loaded model was trained on the gate set `['h', 'cx', 'z', 'x', 'ccx', 'swap']`. Specifically, it was trained to generate circuits using any arbitrary subset of this gate set. Therefore, during inference, we can instruct the model to compile the unitary using any of these subsets. However, it is crucial to follow the prompt structure `Compile using [...]`, as the model was trained with this specific format. For example, let's consider a scenario where we compile the unitary without using the `x` gate:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0f0d3e85-a22e-4a1b-ad17-b02417e56734",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.905214Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.904884Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.912812Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.911995Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.905195Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Notice how the x gate missing from the prompt since this is a restriction we set\n",
-    "prompt = \"Compile using: ['h', 'cx', 'z', 'ccx', 'swap']\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cab3a1e8-a404-4ebb-9a58-251d47eebb4a",
-   "metadata": {},
-   "source": [
-    "Now, we call the diffusion model pipeline to generate encoded circuits based on the specified conditions: `prompt` and `U`. One of the key advantages of this method is that, once the model is trained, sampling new circuits is very fast. Therefore, under the same conditions defined above, we will generate 128 circuits and analyze them in the following sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "56edb114-3ef7-455b-b4b8-0e262e7b6316",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.913747Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.913584Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.824624Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.823799Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.913732Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Number of circuits to sample from the trained DM.\n",
-    "samples = 128\n",
-    "\n",
-    "# As the neural network works only with real numbers, we first separate\n",
-    "# the two components and create a 2 dimensional tensor for the magnitude\n",
-    "# of each component:\n",
-    "U_r, U_i = torch.Tensor(np.real(U)), torch.Tensor(np.imag(U))\n",
-    "U_tensor = torch.stack([U_r, U_i], dim=0)\n",
-    "\n",
-    "# Now we generate a tensor representation of the desired quantum circuit using the DM based on the prompt and U. This is also known as inference.\n",
-    "out_tensors = infer_comp.generate_comp_tensors(\n",
-    "    pipeline=pipeline,\n",
-    "    prompt=prompt,\n",
-    "    U=U_tensor,\n",
-    "    samples=samples,\n",
-    "    system_size=\n",
-    "    num_of_qubits,  # Max qubit number allowed by the model (this model is only trained with 3 qubits)\n",
-    "    num_of_qubits=num_of_qubits,\n",
-    "    max_gates=max_gates,\n",
-    "    g=10  # classifier-free-guidance (CFG) scale\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81f09d75-a960-4456-bdb9-57ef7668864e",
-   "metadata": {},
-   "source": [
-    "Below is an example of a generated tensor which represents a quantum circuit: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "13c1af73-11bc-46ca-99dd-bccea29f95c1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:41.825914Z",
-     "iopub.status.busy": "2024-08-27T18:11:41.825524Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.831202Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.830421Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:41.825898Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],\n",
-       "        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],\n",
-       "        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out_tensors[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5605d0a1-626d-4aa5-8721-0416470215dc",
-   "metadata": {},
-   "source": [
-    "### Convert tensors to CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b43d006-1e6a-4911-9283-b5a96eb5cf06",
-   "metadata": {},
-   "source": [
-    "Next, we convert the generated tensors into `cudaq.kernels`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "01efc6f7-8f0f-425f-be58-7fcaf880d0fd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:41.832305Z",
-     "iopub.status.busy": "2024-08-27T18:11:41.831956Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.840723Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.839940Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:41.832289Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')  # Note that cpu is faster for 3 qubit kernels\n",
-    "\n",
-    "# cudaq.set_target('nvidia') # Set to GPU for larger circuits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9d5b9ae5-c11e-413b-af00-4412dc86b48d",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply.started": "2024-08-27T18:11:41.841726Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7cdb66833df4aee8a75c3c7a3558ebc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model generated 0 invalid tensors that does not correspond to circuits.\n"
-     ]
-    }
-   ],
-   "source": [
-    "kernel_list = []\n",
-    "valid_tensors = []\n",
-    "\n",
-    "invalid_tensors = 0\n",
-    "for out_tensors_i in tqdm(out_tensors):\n",
-    "\n",
-    "    # Use a try-except to catch invalid tensors (if any)\n",
-    "    try:\n",
-    "        kernel = genqc_to_cudaq(out_tensors_i,\n",
-    "                                vocab)  # Convert out_tensors to CUDA-Q kernels\n",
-    "    except:\n",
-    "        kernel = None\n",
-    "\n",
-    "    if kernel:\n",
-    "        kernel_list.append(kernel)\n",
-    "        valid_tensors.append(out_tensors_i)\n",
-    "    else:\n",
-    "        invalid_tensors += 1\n",
-    "\n",
-    "print(\n",
-    "    f\"The model generated {invalid_tensors} invalid tensors that does not correspond to circuits.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a5823e6-9189-46b7-974d-43a2a0cad531",
-   "metadata": {},
-   "source": [
-    "For example, the following tensor encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dc7d35c7-a7cb-4663-8220-b1030e534deb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:43.439383Z",
-     "iopub.status.busy": "2024-08-27T18:11:43.438787Z",
-     "iopub.status.idle": "2024-08-27T18:11:43.443973Z",
-     "shell.execute_reply": "2024-08-27T18:11:43.443101Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:43.439367Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],\n",
-       "        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],\n",
-       "        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "valid_tensors[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47116105-b1e1-454e-80fc-6a03061045f5",
-   "metadata": {},
-   "source": [
-    "corresponds to the following `cudaq.kernel`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4835c288-b018-49d7-b307-3ff05c2c8c56",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:43.444990Z",
-     "iopub.status.busy": "2024-08-27T18:11:43.444651Z",
-     "iopub.status.idle": "2024-08-27T18:11:43.569067Z",
-     "shell.execute_reply": "2024-08-27T18:11:43.568320Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:43.444975Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮        ╭───╮╭───╮     \n",
-      "q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──\n",
-      "     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮\n",
-      "q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├\n",
-      "       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯\n",
-      "q2 : ──●──┤ h ├─╳───●──────────────●──\n",
-      "          ╰───╯                       \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Arbitrary input state to the circuit for plotting\n",
-    "\n",
-    "input_state = [0] * (2**num_of_qubits)\n",
-    "\n",
-    "print(cudaq.draw(kernel_list[0], input_state))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59809685-5d70-4612-8642-c4e924a1ab6c",
-   "metadata": {},
-   "source": [
-    "### Evaluate generated circuits\n",
-    "\n",
-    "As mentioned earlier, one of the key advantages of using diffusion models (DMs) as a unitary compiler is the ability to rapidly sample many circuits. However, as is common in machine learning, the model has a certain accuracy, meaning not all generated circuits are expected to exactly compile the specified unitary. In this section, we will evaluate how many of the generated circuits are indeed correct and then perform post-selection to identify (at least) one circuit that successfully performs the desired unitary operation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2685d88b-aceb-4a8c-8404-f744196d34c5",
-   "metadata": {},
-   "source": [
-    "### Simulate kernels"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f5d1e8ca-3abf-47b4-bbca-932bdff86f94",
-   "metadata": {},
-   "source": [
-    "First, we calculate the unitary matrix $U$ implemented by each of the kernels. The elements of this matrix are defined by the transition amplitudes between the basis states, which can be expressed as:\n",
-    "$$\n",
-    "\\begin{equation}\n",
-    "   \\langle i|kernel|j\\rangle = U_{ij},\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "where $|i\\rangle$ and $|j\\rangle$ are computational basis states (typically in the $z$-basis), with $|n\\rangle$ representing the standard basis vector that has a 1 in the $n$-th position and 0 elsewhere."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "11143924-c33f-4fd5-8f04-422eba9ed5a0",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply.started": "2024-08-27T18:11:43.570011Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f3961ae5e254402a4dcd5124de3a5d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "N = 2**num_of_qubits\n",
-    "\n",
-    "got_unitaries = np.zeros((len(kernel_list), N, N), dtype=np.complex128)\n",
-    "\n",
-    "for i, kernel in tqdm(enumerate(kernel_list), total=got_unitaries.shape[0]):\n",
-    "    for j in range(N):\n",
-    "        basis_state_j = np.zeros((N), dtype=np.complex128)\n",
-    "        basis_state_j[j] = 1\n",
-    "\n",
-    "        got_unitaries[i, :,\n",
-    "                      j] = np.array(cudaq.get_state(kernel, basis_state_j),\n",
-    "                                    copy=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5eb62ba-40a6-486c-b307-47b11b9b3226",
-   "metadata": {},
-   "source": [
-    "For example, the circuit printed above corresponds to the following unitary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4a471612-a3e4-4bea-9f61-dc823283af95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.875219Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.874792Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.879735Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.878882Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.875200Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.set_printoptions(linewidth=1000)\n",
-    "print(np.round(got_unitaries[0], 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "203def54-bb97-4344-8544-6b1374b3f9a3",
-   "metadata": {},
-   "source": [
-    "### Compare unitaries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5241775-2692-4b10-b9ef-a75a3ddb0eb8",
-   "metadata": {},
-   "source": [
-    "Now that we have the unitaries for each of the kernels, we compare them to the user provided unitary matrix, `U`. To do so, we compute the infidelity between the exact unitary and the generated ones. The infidelity is a value between 0 and 1, where 0 indicates that the unitaries are identical (up to a global phase)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "bb4915e8-5a92-4038-ab49-fa67a0a5b668",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.880925Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.880574Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.891293Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.890501Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.880909Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def infidelity(want_unitary, got_unitary):\n",
-    "    return 1 - np.abs(\n",
-    "        np.trace(np.conj(want_unitary).T @ got_unitary) / 2**num_of_qubits)**2\n",
-    "\n",
-    "\n",
-    "infidelities = np.array(\n",
-    "    [infidelity(U, got_unitary) for got_unitary in got_unitaries])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2ae9d89-3aab-43db-85cb-2aa6fc494d87",
-   "metadata": {},
-   "source": [
-    "Let's now plot an histogram of the infidelities for our generated circuits:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c8313728-3a69-475b-bf3c-f0a8b8e8324a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.892419Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.892234Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.982604Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.981724Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.892404Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "plt.title(\n",
-    "    f\"Distribution of infidelities for {len(got_unitaries)} generated circuits\",\n",
-    "    fontsize=12)\n",
-    "plt.ylabel(\"Number of circuits\", fontsize=14)\n",
-    "plt.xlabel(\"Unitary infidelity\", fontsize=14)\n",
-    "plt.hist(infidelities, bins=30)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1912f6c-e685-4994-bf3a-fe9530dbd513",
-   "metadata": {},
-   "source": [
-    "As we can see, the model generated almost 30 circuits that compile the unitary with an infidelity close or equal to zero! The kernel with the lowest infidelity is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "656baf92-5a1e-4227-ac1f-67b5f9394310",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.983493Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.983309Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.988444Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.987556Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.983478Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The best kernel has an infidelity of 3.4e-09,\n",
-      "     ╭───╮╭───╮        ╭───╮╭───╮     \n",
-      "q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──\n",
-      "     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮\n",
-      "q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├\n",
-      "       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯\n",
-      "q2 : ──●──┤ h ├─╳───●──────────────●──\n",
-      "          ╰───╯                       \n",
-      "\n",
-      "with the unitary:\n",
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "min_index = np.argmin(infidelities)\n",
-    "\n",
-    "print(f\"The best kernel has an infidelity of {infidelities[min_index]:0.2},\")\n",
-    "\n",
-    "input_state = [0] * (2**num_of_qubits)\n",
-    "input_state[0] = 1\n",
-    "print(cudaq.draw(kernel_list[min_index], input_state))\n",
-    "\n",
-    "print(f\"with the unitary:\")\n",
-    "print(np.round(got_unitaries[min_index], 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e724b39-cc63-40c8-8ad0-9d90d94e23bd",
-   "metadata": {},
-   "source": [
-    "which, as we can see, exactly compiled our wanted unitary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "46d190d6-7bed-4490-a343-bf856190a101",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.989293Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.989114Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.996387Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.995546Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.989278Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.round(U, 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8bf2fe6-07ca-4bfa-a46b-96a91daeb185",
-   "metadata": {},
-   "source": [
-    "### Choosing the circuit you need"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "983dc8c1-f3ae-4d06-ad7a-e57addfc6e74",
-   "metadata": {},
-   "source": [
-    "As we have seen above, we now have almost 30 kernels that compile the desired unitary! This is particularly valuable when dealing with hardware constraints, where, for instance, we might want to avoid using certain qubits or specific gates. One practical example is finding the circuit with the fewest Toffoli gates (also known as `ccx`). In our `vocab` definition above, we identified that `ccx` corresponds to the label 5 in our tokenized tensors. Let's use this information to search for the circuit that minimizes the number of `ccx` gates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "acb0f4d8-2e8a-4b32-9e37-006571ca2aa5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.997443Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.997033Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.005395Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.004630Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.997427Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model generated 16 distinct correct circuits.\n",
-      "These circuits have this number of ccx gates: tensor([1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 1, 1, 1, 1, 1])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, we remove possible duplicates and only pick distinct circuits\n",
-    "_, idx_unique = np.unique(np.array(valid_tensors), axis=0, return_index=True)\n",
-    "unique_tensors = torch.stack(valid_tensors)[idx_unique]\n",
-    "unique_infidelities = infidelities[idx_unique]\n",
-    "unique_kernels = [kernel_list[idx] for idx in idx_unique]\n",
-    "\n",
-    "# Then, find the correct circuits\n",
-    "idx_correct = torch.argwhere(torch.tensor(unique_infidelities) < 0.01).flatten()\n",
-    "correct_tensors = unique_tensors[idx_correct]\n",
-    "print(\n",
-    "    f\"The model generated {correct_tensors.shape[0]} distinct correct circuits.\"\n",
-    ")\n",
-    "\n",
-    "# Now let's flatten the last two dimensions (related to the actual circuit) and find out how many 5's (i.e. ccx) gates each circuit has:\n",
-    "num_ccx = (correct_tensors.flatten(1, 2) == 5).sum(1)\n",
-    "print(\"These circuits have this number of ccx gates:\", num_ccx)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7625aac1-78d8-4ce3-9765-88ae48c92dd3",
-   "metadata": {},
-   "source": [
-    "As we can see, it appears that the diffusion model (DM) requires at least one Toffoli gate to compile the unitary. We can now print a few of these circuits to select the one that best suits our needs, or to study whether there are any interesting patterns the model employs for this specific unitary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "535501a6-cafb-48e6-b53c-636d7ac5a815",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:56.006584Z",
-     "iopub.status.busy": "2024-08-27T18:11:56.006136Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.013882Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.013117Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:56.006569Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        ╭───╮╭───╮               \n",
-      "q0 : ───┤ x ├┤ z ├───────●───────\n",
-      "        ╰─┬─╯├───┤╭───╮  │       \n",
-      "q1 : ─╳───●──┤ h ├┤ z ├──┼───────\n",
-      "      │   │  ├───┤╰───╯╭─┴─╮╭───╮\n",
-      "q2 : ─╳───●──┤ h ├─────┤ x ├┤ h ├\n",
-      "             ╰───╯     ╰───╯╰───╯\n",
-      "\n",
-      "     ╭───╮╭───╮             \n",
-      "q0 : ┤ x ├┤ z ├──●──────────\n",
-      "     ╰─┬─╯├───┤╭─┴─╮        \n",
-      "q1 : ──●──┤ h ├┤ x ├─╳──────\n",
-      "       │  ├───┤├───┤ │ ╭───╮\n",
-      "q2 : ──●──┤ h ├┤ z ├─╳─┤ h ├\n",
-      "          ╰───╯╰───╯   ╰───╯\n",
-      "\n",
-      "     ╭───╮╭───╮             \n",
-      "q0 : ┤ x ├┤ z ├─────●───────\n",
-      "     ╰─┬─╯├───┤     │  ╭───╮\n",
-      "q1 : ──●──┤ h ├─╳───┼──┤ z ├\n",
-      "       │  ├───┤ │ ╭─┴─╮├───┤\n",
-      "q2 : ──●──┤ h ├─╳─┤ x ├┤ h ├\n",
-      "          ╰───╯   ╰───╯╰───╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the correct kernels\n",
-    "correct_kernels = [unique_kernels[idx] for idx in idx_correct]\n",
-    "\n",
-    "# Get the ones with only one ccx\n",
-    "correct_kernels_ccx1 = [\n",
-    "    correct_kernels[idx] for idx in torch.argwhere(num_ccx == 1).flatten()\n",
-    "]\n",
-    "\n",
-    "# Draw a few of this circuits\n",
-    "for kernel in correct_kernels_ccx1[:3]:\n",
-    "    print(cudaq.draw(kernel, input_state))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "dd32819f-8bb6-4af8-bf69-84b600ba7ad5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:56.020863Z",
-     "iopub.status.busy": "2024-08-27T18:11:56.020639Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.027045Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.026361Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:56.020849Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n",
-      "genQC Version 0.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)\n",
-    "print(\"genQC Version\", genQC.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/applications/python/vqe.ipynb.txt b/pr-2308/_sources/applications/python/vqe.ipynb.txt
deleted file mode 100644
index 207e195938..0000000000
--- a/pr-2308/_sources/applications/python/vqe.ipynb.txt
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Variational Quantum Eigensolver"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A common application of the Variational Quantum Eigensolver (VQE) algorithm is to compute the ground state energy of a molecular system. The code below demonstrates how to perform classical preprocessing for a $H_2$ molecule (i.e. obtain the integrals from a Hartree-Fock computation to build the molecular Hamiltonian), prepare the initial Hartree-Fock state on the quantum register, add the parameterized UCCSD ansatz to the kernel, and select the COBYLA optimizer.  We are then ready to call `cudaq:vqe` to estimate the minimum energy of the system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install openfermionpyscf==0.5 matplotlib==3.8.4 scipy==1.13.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import numpy as np\n",
-    "\n",
-    "# Single precision\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "# Double precision\n",
-    "#cudaq.set_target(\"nvidia-fp64\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The problem of interest here is a chain of hydrogen atoms seperated along the z-axis at a fixed interval called the bond distance. \n",
-    "\n",
-    "The interatomic electrostatic forces due to the electrons and protons and the shielding by the neutrons creates a chemical system whose energy can be minimised to find a stable configuration. \n",
-    "\n",
-    "Let us first begin by defining the molecule and other metadata about the problem.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of hydrogen atoms.\n",
-    "hydrogen_count = 2\n",
-    "\n",
-    "# Distance between the atoms in Angstroms.\n",
-    "bond_distance = 0.7474\n",
-    "\n",
-    "# Define a linear chain of Hydrogen atoms\n",
-    "geometry = [('H', (0, 0, i * bond_distance)) for i in range(hydrogen_count)]\n",
-    "\n",
-    "hamiltonian, data = cudaq.chemistry.create_molecular_hamiltonian(\n",
-    "    geometry, 'sto-3g', 1, 0)\n",
-    "\n",
-    "electron_count = data.n_electrons\n",
-    "qubit_count = 2 * data.n_orbitals"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now generate a Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz from the template provided by CUDA-Q.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(thetas: list[float]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    for i in range(electron_count):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_count, qubit_count)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using CUDA-Q Optimizers\n",
-    "\n",
-    "We use the builtin optimizers within CUDA-Q for the minimization procedure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-1.1371744305855906\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "energy, parameters = cudaq.vqe(kernel,\n",
-    "                               hamiltonian,\n",
-    "                               optimizer,\n",
-    "                               parameter_count=parameter_count)\n",
-    "\n",
-    "print(energy)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Integration with Third-Party Optimizers\n",
-    "\n",
-    "We can also integrate popular libraries like scipy with CUDA-Q. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Define a function to minimize\n",
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, np.pi, parameter_count)\n",
-    "\n",
-    "# Use the scipy optimizer to minimize the function of interest\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 40})\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/applications/python/vqe_advanced.ipynb.txt b/pr-2308/_sources/applications/python/vqe_advanced.ipynb.txt
deleted file mode 100644
index 3135b93829..0000000000
--- a/pr-2308/_sources/applications/python/vqe_advanced.ipynb.txt
+++ /dev/null
@@ -1,729 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# VQE with gradients, active spaces, and gate fusion\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial will explore the Variational Quantum Eigensolver, a hybrid quantum classical algorithm for determining the ground state energy of molecules. The first part of this tutorial will walk through the key aspects of the VQE algorithm and how to implement it with CUDA-Q.  The following sections explore advanced topics: parallel gradients, active spaces, and gate fusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The Basics of VQE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE algorithm is hybrid quantum-classical algorithm, meaning some subroutines run on a quantum computer or quantum simulator and others run on a traditional (super)computer.  \n",
-    "\n",
-    "The goal is to take a parameterized quantum circuit and a qubit form of the molecular Hamiltonian, measure an expectation value that corresponds to the ground state energy of the molecule, and then repeat the process to variationally minimize the energy with respect to the parameters in the quantum circuit. The optimization is performed on a classical device while the expectation values are determined on a quantum device.  See the figure below.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![VQE.png](./images/VQE.png)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next few cells will elaborate on each part of the VQE procedure and show you how to build a VQE simulation to compute the ground state energy of the water molecule."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installing/Loading Relevant Packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "!pip install pyscf==2.6.2 openfermionpyscf==0.5 matplotlib==3.8.4 openfermion==1.6.1 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openfermion\n",
-    "import openfermionpyscf\n",
-    "from openfermion.transforms import jordan_wigner, get_fermion_operator\n",
-    "\n",
-    "import os\n",
-    "import timeit\n",
-    "\n",
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementing VQE in CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Like most quantum chemistry programs, the first step is to specify a molecular geometry, basis set, charge, and multiplicity. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = [('O', (0.1173, 0.0, 0.0)), ('H', (-0.4691, 0.7570, 0.0)),\n",
-    "            ('H', (-0.4691, -0.7570, 0.0))]\n",
-    "basis = 'sto3g'\n",
-    "multiplicity = 1\n",
-    "charge = 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE procedure requires some classical preprocessing.  The code below uses the PySCF package and OpenFermion to compute the Hartree Fock reference state and compute the integrals required for the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecule = openfermionpyscf.run_pyscf(\n",
-    "    openfermion.MolecularData(geometry, basis, multiplicity, charge))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the Hamiltonian is built using `get_molecular_hamiltonian`. The Hamiltonian must then be converted to a qubit Hamiltonian consisting of qubit operators. The standard Jordan-Wigner transformation is used to perform this mapping. \n",
-    "\n",
-    "Finally, the Jordan-Wigner qubit Hamiltonian is converted into a CUDA-Q spin operator which can be used to evaluate an expectation value given a quantum circuit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecular_hamiltonian = molecule.get_molecular_hamiltonian()\n",
-    "\n",
-    "fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)\n",
-    "\n",
-    "qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)\n",
-    "\n",
-    "spin_ham = cudaq.SpinOperator(qubit_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the quantum circuit needs to be defined, which models the wavefunction. This is done in CUDA-Q by specifying a CUDA-Q kernel. The kernel takes as an input the number of qubits, the number of electrons, and the parameters of the circuit ansatz (form of the wavefunction) yet to be defined. \n",
-    "\n",
-    "The number of qubits corresponds to the potential positions of electrons and is therefore twice the number of spatial orbitals constructed with the chosen basis set, as each can be occupied by two electrons. \n",
-    "\n",
-    "The Hartree-Fock reference is constructed by applying $X$ bitflip operations to each of the first $N$ qubits where $N$ is the number of electrons. Next, a parameterized ansatz is chosen. Theoretically, any set of operations can work as an ansatz, however, it is good practice to use an ansatz that captures the underlying physics of the problem. The most common choice for chemistry is the Unitary Coupled Cluster Ansatz with Single and Double excitations (UCCSD).  This UCCSD hate operations are automatically added to the kernel with the `cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)` function. \n",
-    "\n",
-    "The STO-3G water molecuule UCCSD ansatz requires optimization of 140 parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "140\n"
-     ]
-    }
-   ],
-   "source": [
-    "electron_count = 10\n",
-    "qubit_count = 2 * 7\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_num: int, electron_num: int, thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    for i in range(electron_num):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)\n",
-    "\n",
-    "print(parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The classical optimizer requires a custom cost function which is defined below. The `cudaq.observe()` function computes an expectation given the Hamiltonian and the kernel defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, spin_ham, qubit_count, electron_count,\n",
-    "                            theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final step is to run the optimization using the scipy minimize function and a selected optimizer, in this case COBYLA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "UCCSD-VQE energy =   -70.21455023422772\n",
-      "Total number of qubits =  14\n",
-      "Total number of parameters =  140\n",
-      "Total number of terms in the spin hamiltonian =  1086\n",
-      "Total elapsed time (s) =  3.9171073289999185\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.set_target('nvidia')\n",
-    "start_time = timeit.default_timer()\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 50})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('UCCSD-VQE energy =  ', result.fun)\n",
-    "print('Total number of qubits = ', qubit_count)\n",
-    "print('Total number of parameters = ', parameter_count)\n",
-    "print('Total number of terms in the spin hamiltonian = ',\n",
-    "      spin_ham.get_term_count())\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result of this procedure is an estimate of the ground state energy of water. However, the convergence behavior is not perfect, more iterations would greatly improve the result, but would take a few minutes to run."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parallel Parameter Shift Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One way to accelerate VQE is to use an optimizer that accepts a gradient. This can drastically lower the number of VQE iterations required at the cost of computing the gradient on the quantum side of the algorithm.\n",
-    "\n",
-    "The parameter shift rule is a common technique to compute the gradient for parameterized circuits. It is obtained by computing two expectation values for each parameter corresponding to a small forward and backward shift in the ith parameter. These results are used to estimate  finite difference contribution to the gradient.\n",
-    "\n",
-    "![parametershift.png](./images/parametershift.png)\n",
-    "\n",
-    "This procedure can become cost prohibitive as the number of parameters becomes large."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each of the expectation values needed to evaluate a parameter shift gradient can be computed independently.  The CUDA-Q `nvidia-mqpu` backend is designed for parallel computations across multiple simulated QPUs. The function below uses `cudaq.observe_asynch` to distribute all of the expectation values evaluations across as many GPUs that are available. First, try it with `num_qpus` set to 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\u001b[38;2;255;000;000mwarning\u001b[0m] Target \u001b[38;2;000;000;255mnvidia-mqpu\u001b[0m: \u001b[38;2;000;000;255mThis target is deprecating. Please use the 'nvidia' target with option 'mqpu,fp32' or 'mqpu' (fp32 is the default precision option) by adding the command line option '--target-option mqpu,fp32' or passing it as cudaq.set_target('nvidia', option='mqpu,fp32') in Python. Please refer to CUDA-Q \u001b]8;;https://nvidia.github.io/cuda-quantum/latest/using/backends/platform\u001b\\documentation\u001b]8;;\u001b\\ for more information.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)\n",
-    "\n",
-    "cudaq.set_target(\"nvidia-mqpu\")\n",
-    "\n",
-    "num_qpus = cudaq.get_target().num_qpus()\n",
-    "\n",
-    "epsilon = np.pi / 4\n",
-    "\n",
-    "\n",
-    "def batched_gradient_function(kernel, parameters, hamiltonian, epsilon):\n",
-    "\n",
-    "    x = np.tile(parameters, (len(parameters), 1))\n",
-    "\n",
-    "    xplus = x + (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    xminus = x - (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    g_plus = []\n",
-    "    g_minus = []\n",
-    "    gradients = []\n",
-    "\n",
-    "    qpu_counter = 0  # Iterate over the number of GPU resources available\n",
-    "    for i in range(x.shape[0]):\n",
-    "\n",
-    "        g_plus.append(\n",
-    "            cudaq.observe_async(kernel,\n",
-    "                                hamiltonian,\n",
-    "                                qubit_count,\n",
-    "                                electron_count,\n",
-    "                                xplus[i],\n",
-    "                                qpu_id=qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1\n",
-    "\n",
-    "        g_minus.append(\n",
-    "            cudaq.observe_async(kernel,\n",
-    "                                hamiltonian,\n",
-    "                                qubit_count,\n",
-    "                                electron_count,\n",
-    "                                xminus[i],\n",
-    "                                qpu_id=qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1\n",
-    "\n",
-    "    gradients = [\n",
-    "        (g_plus[i].get().expectation() - g_minus[i].get().expectation()) /\n",
-    "        (2 * epsilon) for i in range(len(g_minus))\n",
-    "    ]\n",
-    "\n",
-    "    assert len(gradients) == len(\n",
-    "        parameters) == x.shape[0] == xplus.shape[0] == xminus.shape[0]\n",
-    "\n",
-    "    return gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The cost function needs to be slightly updated to make use of the gradient in the optimization procedure and allow for a gradient based optimizer like L-BFGS-B to be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gradient = batched_gradient_function(kernel, x0, spin_ham, epsilon)\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector: list[float], \\\n",
-    "                       gradient=gradient, hamiltonian=spin_ham, kernel=kernel):\n",
-    "\n",
-    "    get_result = lambda parameter_vector: cudaq.observe\\\n",
-    "        (kernel, hamiltonian, qubit_count, electron_count, parameter_vector).expectation()\n",
-    "\n",
-    "    cost = get_result(parameter_vector)\n",
-    "    exp_vals.append(cost)\n",
-    "    gradient_vector = batched_gradient_function(kernel, parameter_vector,\n",
-    "                                                spin_ham, epsilon)\n",
-    "\n",
-    "    return cost, gradient_vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the code below. Notice how the result is converged to a lower energy using only 10% of the steps as optimization above without a gradient. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "VQE-UCCSD energy=  -73.19471262288755\n",
-      "Total elapsed time (s) =  57.27010986900132\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "init_params = np.random.normal(0, 1, parameter_count)\n",
-    "\n",
-    "start_time = timeit.default_timer()\n",
-    "result_vqe = minimize(objective_function,\n",
-    "                      init_params,\n",
-    "                      method='L-BFGS-B',\n",
-    "                      jac=True,\n",
-    "                      tol=1e-8,\n",
-    "                      options={'maxiter': 5})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('VQE-UCCSD energy= ', result_vqe.fun)\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, run the code again (the three previous cells) and specify `num_qpus` to be more than one if you have access to multiple GPUs and notice resulting speedup.  Thanks to CUDA-Q, this code could be used without modification in a setting where multiple physical QPUs were availible."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using an Active Space"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Performing electronic structure computations with all electrons and orbitals is often prohibitively expensive and unnecessary.  Most of the interesting chemistry can be modeled by restricting simulations to the highest energy occupied molecular orbitals and lowest energy unoccupied molecular orbitals. This is known as the active space approximation. \n",
-    "\n",
-    "Below is an example of STO-3G water modeled with a 4 electron 3 orbital active space simulated with UCCSD-VQE. Using an active space means you can run VQE for the same molecule using fewer qubits and a more shallow circuit.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![cas.png](./images/cas.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The molecule is defined the same way, expect for you now include variables `nele_cas` and `norb_cas` to define the active space. The `ncore` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = [('O', (0.1173, 0.0, 0.0)), ('H', (-0.4691, 0.7570, 0.0)),\n",
-    "            ('H', (-0.4691, -0.7570, 0.0))]\n",
-    "basis = 'sto3g'\n",
-    "multiplicity = 1\n",
-    "charge = 0\n",
-    "ncore = 3\n",
-    "nele_cas, norb_cas = (4, 3)\n",
-    "\n",
-    "molecule = openfermionpyscf.run_pyscf(\n",
-    "    openfermion.MolecularData(geometry, basis, multiplicity, charge))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian is now constrcuted with the same steps, but only models the active space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecular_hamiltonian = molecule.get_molecular_hamiltonian(\n",
-    "    occupied_indices=range(ncore),\n",
-    "    active_indices=range(ncore, ncore + norb_cas))\n",
-    "\n",
-    "fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)\n",
-    "\n",
-    "qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)\n",
-    "\n",
-    "spin_ham = cudaq.SpinOperator(qubit_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, the kernel is defined only by the orbitals and electrons in the active space. Notice how this means you only need to optimize 8 parameters now. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8\n"
-     ]
-    }
-   ],
-   "source": [
-    "electron_count = nele_cas\n",
-    "qubit_count = 2 * norb_cas\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_num: int, electron_num: int, thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    for i in range(electron_num):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)\n",
-    "\n",
-    "print(parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, spin_ham, qubit_count, electron_count,\n",
-    "                            theta).expectation()\n",
-    "    thetas = theta\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE procedure below is much faster using an active space compared to inclusion of all orbitals and electrons."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "UCCSD-VQE energy =   -74.96341992791962\n",
-      "Total number of qubits =  6\n",
-      "Total number of parameters =  8\n",
-      "Total number of terms in the spin hamiltonian =  62\n",
-      "Total elapsed time (s) =  1.754178541001238\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.set_target(\"nvidia\")\n",
-    "\n",
-    "start_time = timeit.default_timer()\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 500})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('UCCSD-VQE energy =  ', result.fun)\n",
-    "print('Total number of qubits = ', qubit_count)\n",
-    "print('Total number of parameters = ', parameter_count)\n",
-    "print('Total number of terms in the spin hamiltonian = ',\n",
-    "      spin_ham.get_term_count())\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gate Fusion for Larger Circuits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CUDA-Q simulations take advantage of a technique called gate fusion. Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. \n",
-    "\n",
-    "![gate-fuse.png](./images/gate-fuse.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is particularly important for larger circuits and can have a significant impact on the performance of the simulation. Each system is different, so you should test different gate fusion levels to find out what is best for your system. You can find more information [here](https://developer.nvidia.com/blog/new-nvidia-cuda-q-features-boost-quantum-application-performance/)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/_sources/examples/python/executing_kernels.ipynb.txt b/pr-2308/_sources/examples/python/executing_kernels.ipynb.txt
deleted file mode 100644
index 0d3250c8c1..0000000000
--- a/pr-2308/_sources/examples/python/executing_kernels.ipynb.txt
+++ /dev/null
@@ -1,284 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Executing Quantum Circuits \n",
-    "\n",
-    "In CUDA-Q, there are 3 ways in which one can execute quantum kernels: \n",
-    "\n",
-    "1. `sample`: yields measurement counts \n",
-    "2. `observe`: yields expectation values \n",
-    "3. `get_state`: yields the quantum statevector of the computation \n",
-    "\n",
-    "## Sample\n",
-    "\n",
-    "Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q `sample` call enables this: \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮     \n",
-      "q0 : ┤ h ├──●──\n",
-      "     ╰───╯╭─┴─╮\n",
-      "q1 : ─────┤ x ├\n",
-      "          ╰───╯\n",
-      "\n",
-      "{ 11:506 00:494 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np \n",
-    "\n",
-    "qubit_count = 2\n",
-    "\n",
-    "# Define the simulation target.\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "# Define a quantum kernel function.\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_count: int):\n",
-    "    qvector = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # 2-qubit GHZ state.\n",
-    "    h(qvector[0])\n",
-    "    for i in range(1, qubit_count):\n",
-    "        x.ctrl(qvector[0], qvector[i])\n",
-    "\n",
-    "    # If we dont specify measurements, all qubits are measured in\n",
-    "    # the Z-basis by default or we can manually specify it also \n",
-    "    # mz(qvector)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, qubit_count))\n",
-    "\n",
-    "result = cudaq.sample(kernel, qubit_count, shots_count=1000)\n",
-    "\n",
-    "print(result)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that there is a subtle difference between how `sample` is executed with the target device set to a simulator or with the target device set to a QPU. In simulation mode, the quantum state is built once and then sampled $s$ times where $s$ equals the `shots_count`. In hardware execution mode, the quantum state collapses upon measurement and hence needs to be rebuilt over and over again.\n",
-    "\n",
-    "There are a number of helpful tools that can be found in the [API docs](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api) to process the `Sample_Result` object produced by `sample`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Observe\n",
-    "\n",
-    "The `observe` function allows us to calculate expectation values. We must supply a spin operator in the form of a Hamiltonian, $H$,  from which we would like to calculate $\\bra{\\psi}H\\ket{\\psi}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<H> = 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from cudaq import spin\n",
-    "\n",
-    "# Define a Hamiltonian in terms of Pauli Spin operators.\n",
-    "hamiltonian = spin.z(0) + spin.y(1) + spin.x(0) * spin.z(0)\n",
-    "\n",
-    "# Compute the expectation value given the state prepared by the kernel.\n",
-    "result = cudaq.observe(kernel, hamiltonian, qubit_count).expectation()\n",
-    "\n",
-    "print('<H> =', result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Get state\n",
-    "\n",
-    "The `get_state` function gives us access to the quantum statevector of the computation. Remember, that this is only feasible in simulation mode. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.70710678+0.j 0.        +0.j 0.        +0.j 0.70710678+0.j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute the statevector of the kernel\n",
-    "result = cudaq.get_state(kernel, qubit_count)\n",
-    "\n",
-    "print(np.array(result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statevector generated by the `get_state` command follows Big-endian convention for associating numbers with their binary representations, which places the least significant bit on the left.  That is, for the example of a 2-bit system, we have the following translation between integers and bits:\n",
-    "$$\\begin{matrix} \\text{Integer} & \\text{Binary representation}\\\\\n",
-    "& \\text{least signinificant bit on left}\\\\\n",
-    "0 =\\textcolor{red}{0}*2^0+\\textcolor{blue}{0}*2^1 & \\textcolor{red}{0}\\textcolor{blue}{0} \\\\\n",
-    "1 = \\textcolor{red}{1}*2^0 + \\textcolor{blue}{0} *2^1 & \\textcolor{red}{1}\\textcolor{blue}{0}\\\\\n",
-    "2 = \\textcolor{red}{0}*2^0 + \\textcolor{blue}{1}*2^1 & \\textcolor{red}{0}\\textcolor{blue}{1} \\\\\n",
-    "3 = \\textcolor{red}{1}*2^0 + \\textcolor{blue}{1}*2^1 & \\textcolor{red}{1}\\textcolor{blue}{1} \\end{matrix}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Parallelization Techniques\n",
-    "\n",
-    "The most intensive task in the computation is the execution of the quantum kernel hence each execution function: `sample`, `observe` and `get_state` can be parallelized given access to multiple quantum processing units (multi-QPU). \n",
-    "\n",
-    "Since multi-QPU platforms are not yet feasible, we emulate each QPU with a GPU.\n",
-    "\n",
-    "\n",
-    "### Observe Async\n",
-    "\n",
-    "Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result. \n",
-    "\n",
-    "`observe` can be a time intensive task. We can parallelize the execution of `observe` via the arguments it accepts. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.220446049250313e-16\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set the simulation target to a multi-QPU platform \n",
-    "# cudaq.set_target(\"nvidia\", option = 'mqpu')\n",
-    "\n",
-    "# Measuring the expectation value of 2 different hamiltonians in parallel\n",
-    "hamiltonian_1 = spin.x(0) + spin.y(1) + spin.z(0)*spin.y(1)\n",
-    "# hamiltonian_2 = spin.z(1) + spin.y(0) + spin.x(1)*spin.x(0)\n",
-    "\n",
-    "# Asynchronous execution on multiple qpus via nvidia gpus.\n",
-    "result_1 = cudaq.observe_async(kernel, hamiltonian_1, qubit_count, qpu_id=0)\n",
-    "# result_2 = cudaq.observe_async(kernel, hamiltonian_2, qubit_count, qpu_id=1)\n",
-    "\n",
-    "# Retrieve results \n",
-    "print(result_1.get().expectation())\n",
-    "# print(result_2.get().expectation())"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Above we parallelized the `observe` call over the `hamiltonian` parameter however we can parallelize over any of the argument it accepts by just iterating obver the `qpu_id`."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sample Async\n",
-    "\n",
-    "Similar to `observe_async` above, `sample` also supports asynchronous execution for the [arguments it accepts](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.sample_async(kernel%3A%20object%2C%20%5C*args%2C%20shots_count%3A%20int%20%3D%201000%2C%20qpu_id%3A%20int%20%3D%200)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult). One can parallelize over various kernels, variational parameters or even distribute shots counts over multiple QPUs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get State Async\n",
-    "\n",
-    "Similar to `sample_async` above, `get_state` also supports asynchronous execution for the [arguments it accepts](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.get_state(arg0%3A%20object%2C%20%5C*args)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version  (https://github.com/NVIDIA/cuda-quantum 0eb6b444eb5b3a687e6fd64529ee9223aaa2870e)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/_sources/examples/python/measuring_kernels.ipynb.txt b/pr-2308/_sources/examples/python/measuring_kernels.ipynb.txt
deleted file mode 100644
index 40fa54bff8..0000000000
--- a/pr-2308/_sources/examples/python/measuring_kernels.ipynb.txt
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13858fc6-edc7-49cb-8b56-152ebec2f729",
-   "metadata": {},
-   "source": [
-    "# Measurements\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ec9af8fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa410efc-fcb1-43e1-ab99-25a7fcbab868",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Kernel measurement can be specified in the Z, X, or Y basis using `mz`, `mx`, and `my`. If a measurement is specified with no argument, the entire kernel is measured in that basis. Measurement occurs in the Z basis by default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7f45a62e-48cb-4705-8081-e59782c91b5f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    mz()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39efaac5-5400-4df6-ac5d-80637d9d3082",
-   "metadata": {},
-   "source": [
-    "Specific qubits or registers can be measured rather than the entire kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "16a14177-fa79-4eb3-8fc8-f3e937bc21b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits_a = cudaq.qvector(2)\n",
-    "    qubit_b = cudaq.qubit()\n",
-    "    mz(qubits_a)\n",
-    "    mx(qubit_b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb5dd767-5db7-4847-b04e-ae5695066800",
-   "metadata": {},
-   "source": [
-    "### Midcircuit Measurement and Conditional Logic\n",
-    "\n",
-    "In certain cases, it it is helpful for some operations in a quantum kernel to depend on measurement results following previous operations. This is accomplished in the following example by performing a Hadamard on qubit 0, then measuring qubit 0 and savig the result as `b0`. Then, an if statement performs a Hadamard on qubit 1 only if `b0` is 1. Measuring this qubit 1 verifies this process as a 1 is the result 25% of the time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "44001a51-3733-472c-8bc1-ee694e957708",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    q = cudaq.qvector(2)\n",
-    "    h(q[0])\n",
-    "    b0 = mz(q[0])\n",
-    "    if b0:\n",
-    "        h(q[1])\n",
-    "        mz(q[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/examples/python/noisy_simulations.ipynb.txt b/pr-2308/_sources/examples/python/noisy_simulations.ipynb.txt
deleted file mode 100644
index 38187dc845..0000000000
--- a/pr-2308/_sources/examples/python/noisy_simulations.ipynb.txt
+++ /dev/null
@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Noisy Simulation \n",
-    "\n",
-    "Quantum noise can be characterized into coherent and incoherent sources of errors that arise during a computation. Coherent noise is commonly due to systematic errors originating from device miscalibrations, for example, gates implementing a rotation $\\theta + \\epsilon$ instead of $\\theta$.\n",
-    "\n",
-    "Incoherent noise has its origins in quantum states being entangled with the environment due to decoherence. This leads to mixed states which are probability distributions over pure states and are described by employing the density matrix formalism. \n",
-    "\n",
-    "We can model incoherent noise via quantum channels which are linear, completely positive, and trace preserving maps. These maps are called Kraus operators, $\\{ K_i \\}$, which satisfy the condition $\\sum_{i} K_i^\\dagger K_i = \\mathbb{I}$. \n",
-    "\n",
-    "The bit-flip channel flips the qubit with probability $p$ and leaves it unchanged with probability $1-p$. This can be represented by employing Kraus operators: \n",
-    "\n",
-    "$$K_0 = \\sqrt{1-p} \\begin{pmatrix}\n",
-    "    1 & 0 \\\\\n",
-    "    0 & 1\n",
-    "\\end{pmatrix}$$\n",
-    "\n",
-    "$$K_1 = \\sqrt{p} \\begin{pmatrix}\n",
-    "  0 & 1 \\\\\n",
-    "  1 & 0\n",
-    "\\end{pmatrix}$$\n",
-    "\n",
-    "Let's implement the bit-flip channel using CUDA-Q:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "# To model quantum noise, we need to utilize the density matrix simulator target.\n",
-    "cudaq.set_target(\"density-matrix-cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮\n",
-      "q0 : ┤ x ├\n",
-      "     ├───┤\n",
-      "q1 : ┤ x ├\n",
-      "     ╰───╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's define a simple kernel that we will add noise to.\n",
-    "qubit_count = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_count: int):\n",
-    "    qvector = cudaq.qvector(qubit_count)\n",
-    "    x(qvector)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, qubit_count))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11:1000 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "# In the ideal noiseless case, we get |11> 100% of the time.\n",
-    "\n",
-    "ideal_counts = cudaq.sample(kernel, qubit_count, shots_count=1000)\n",
-    "ideal_counts.dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11:844 10:88 01:65 00:3 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, we will define an out of the box noise channel. In this case,\n",
-    "# we choose depolarization noise. This depolarization will result in\n",
-    "# the qubit state decaying into a mix of the basis states, |0> and |1>,\n",
-    "# with our provided probability.\n",
-    "error_probability = 0.1\n",
-    "depolarization_channel = cudaq.DepolarizationChannel(error_probability)\n",
-    "\n",
-    "# Other built in noise models \n",
-    "bit_flip = cudaq.BitFlipChannel(error_probability) \n",
-    "phase_flip = cudaq.PhaseFlipChannel(error_probability)\n",
-    "amplitude_damping = cudaq.AmplitudeDampingChannel(error_probability)\n",
-    "\n",
-    "# We can also define our own, custom noise channels through\n",
-    "# Kraus operators. Here we will define two operators representing\n",
-    "# bit flip errors.\n",
-    "\n",
-    "# Define the Kraus Error Operator as a complex ndarray.\n",
-    "kraus_0 = np.sqrt(1 - error_probability) * np.array([[1.0, 0.0], \n",
-    "                                                     [0.0, 1.0]],\n",
-    "                                                    dtype=np.complex128)\n",
-    "\n",
-    "kraus_1 = np.sqrt(error_probability) * np.array([[0.0, 1.0], \n",
-    "                                                 [1.0, 0.0]],\n",
-    "                                                dtype=np.complex128)\n",
-    "\n",
-    "# Add the Kraus Operator to create a quantum channel.\n",
-    "bitflip_channel = cudaq.KrausChannel([kraus_0, kraus_1])\n",
-    "\n",
-    "# Add noise channels to our noise model.\n",
-    "noise_model = cudaq.NoiseModel()\n",
-    "\n",
-    "# Apply the depolarization channel to any X-gate on the 0th qubit.\n",
-    "noise_model.add_channel(\"x\", [0], depolarization_channel)\n",
-    "# Apply the bitflip channel to any X-gate on the 1st qubit.\n",
-    "noise_model.add_channel(\"x\", [1], bitflip_channel)\n",
-    "\n",
-    "# Due to the impact of noise, our measurements will no longer be uniformly\n",
-    "# in the |11> state.\n",
-    "noisy_counts = cudaq.sample(kernel,\n",
-    "                            qubit_count,\n",
-    "                            noise_model=noise_model,\n",
-    "                            shots_count=1000)\n",
-    "noisy_counts.dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.8666666666666666"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can also use noise models with the observe function\n",
-    "\n",
-    "hamiltonian = spin.z(0)\n",
-    "\n",
-    "noisy_result = cudaq.observe(kernel,\n",
-    "                             hamiltonian,\n",
-    "                             qubit_count,\n",
-    "                             noise_model=noise_model)\n",
-    "\n",
-    "noisy_result.expectation()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/_sources/examples/python/operators.ipynb.txt b/pr-2308/_sources/examples/python/operators.ipynb.txt
deleted file mode 100644
index 801b2357d2..0000000000
--- a/pr-2308/_sources/examples/python/operators.ipynb.txt
+++ /dev/null
@@ -1,99 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9a37411d-09c5-42f4-9d62-132715cead3e",
-   "metadata": {},
-   "source": [
-    "# Operators\n",
-    "\n",
-    "Operators are important constructs for many quantum applications.  This section covers how to define and use spin operators as well as additional tools for defining more sophisticated operators."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3695762-03b5-46d6-9aac-853d1ebca9d1",
-   "metadata": {},
-   "source": [
-    "### Constructing Spin Operators\n",
-    "\n",
-    "The `spin_op`  type provides an abstraction for a general tensor product of Pauli spin operators, and their sums.\n",
-    "\n",
-    "Spin operators are constructed using the `spin.z()`, `spin.y()`, `spin.x()`, and `spin.i()` functions, corresponding to the $Z$, $Y$, $X$, and $I$ Pauli operators. For example,  `spin.z(0)` corresponds to a Pauli $Z$ operation acting on qubit 0.  The example below demonstrates how to construct the following operator $2XYX - 3ZZY$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "29ca747b-ee90-4a83-a41b-6480e0c7b6c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "operator = 2 * spin.x(0) * spin.y(1) * spin.x(2) - 3 * spin.z(0) * spin.z( 1) * spin.y(2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "222ea088-ea18-46cf-872d-5e76d25e5e93",
-   "metadata": {},
-   "source": [
-    "There are a number of convenient methods for combining, comparing, iterating through, and extracting information from spin operators and can be referenced [here](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.SpinOperator) in the API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "970e6f09-f0db-4aa3-b611-c33e8436aa55",
-   "metadata": {},
-   "source": [
-    "### Pauli Words and Exponentiating Pauli Words\n",
-    "\n",
-    "The `pauli_word` type specifies a string of Pauli operations (e.g. ‘XYXZ’) and is convenient for applying operations based on exponentiated Pauli words.  The code below demonstrates how a list of Pauli words, along with their coefficients, are provided as kernel inputs and converted into operators by the `exp_pauli` function.\n",
-    "\n",
-    "The cell below applies the following operation: $e^{i(0.432XZY +0.324IXX)}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3fe49bcb-0fe1-48e3-aee2-bff882f03fe6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words = ['XYZ', 'IXX']\n",
-    "coefficients = [0.432, 0.324]\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(coefficients: list[float], words: list[cudaq.pauli_word]):\n",
-    "    q = cudaq.qvector(3)\n",
-    "\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(coefficients[i], q, words[i])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/examples/python/optimizers_gradients.ipynb.txt b/pr-2308/_sources/examples/python/optimizers_gradients.ipynb.txt
deleted file mode 100644
index 35b7442af7..0000000000
--- a/pr-2308/_sources/examples/python/optimizers_gradients.ipynb.txt
+++ /dev/null
@@ -1,303 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e466cb3d-c036-45f7-af9f-b18b9aa22e8a",
-   "metadata": {},
-   "source": [
-    "# Optimizers and Gradients\n",
-    "\n",
-    "Many quantum algorithms require the optimization of quantum circuit parameters with respect to an expectation value.  CUDA-Q has a number of tools available for optimization techniques.  This example will demonstrate how to optimize the variational parameters of a circuit using:\n",
-    "\n",
-    "1. Built in CUDA-Q optimizers and gradients\n",
-    "2. A Third-Party Optimizer\n",
-    "3. A Parallel parameter shift gradient.\n",
-    "\n",
-    "First, the kernel and Hamiltonian and specified below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a77a963a-6f5c-4751-93c8-b3ccbb5921f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "import numpy as np\n",
-    "\n",
-    "hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(\n",
-    "    0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: list[float]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    x(qubits[0])\n",
-    "    ry(angles[0], qubits[1])\n",
-    "    x.ctrl(qubits[1], qubits[0])  \n",
-    "\n",
-    "initial_params = np.random.normal(0, np.pi, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c3de68e-754a-4412-8063-78ee9646b4b0",
-   "metadata": {},
-   "source": [
-    "### Built in CUDA-Q Optimizers and Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eea5d9f2-ade2-4411-88a7-b8c5505c29c3",
-   "metadata": {},
-   "source": [
-    "The optimizer and gradient are specified first from a built in CUDA-Q optimizer and gradient technique. An objective function is defined next which uses a lambda expression to evaluate the cost (a CUDA-Q `observe` expectation value). The gradient is calculated using the `compute` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "87387c0a-86cf-4dfb-9130-b1c3054e43d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = cudaq.optimizers.Adam()\n",
-    "gradient = cudaq.gradients.CentralDifference()\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector: list[float],\n",
-    "                       hamiltonian=hamiltonian,\n",
-    "                       gradient_strategy=gradient,\n",
-    "                       kernel=kernel) -> tuple[float, list[float]]:\n",
-    "\n",
-    "        get_result = lambda parameter_vector: cudaq.observe(kernel, hamiltonian, parameter_vector).expectation()\n",
-    "\n",
-    "        cost = get_result(parameter_vector)\n",
-    "    \n",
-    "        gradient_vector = gradient_strategy.compute(parameter_vector, get_result, cost)\n",
-    "\n",
-    "        return cost, gradient_vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b98f7129-d360-4e9c-9ea5-d4173ecb5ef1",
-   "metadata": {},
-   "source": [
-    "Finally, the optimizer is called to return the optimal cost and parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "713d7619-7b62-42b7-bb46-601ed9883e6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "minimized <H> = -1.748382901613712\n",
-      "optimal theta 0 = 0.58409164053813\n"
-     ]
-    }
-   ],
-   "source": [
-    "energy, parameter = optimizer.optimize(dimensions=1,function=objective_function)\n",
-    "\n",
-    "print(f\"\\nminimized <H> = {round(energy,16)}\")\n",
-    "print(f\"optimal theta 0 = {round(parameter[0],16)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a36a46fc-b04b-43e3-ac10-3b2ee59c3418",
-   "metadata": {},
-   "source": [
-    "### Third-Party Optimizers\n",
-    "\n",
-    "The same procedure can be accomplised using any third-party such as SciPy. In this case, a simple cost fucntion is defined and provided as an input for the standard SciPy `minimize` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0541fa5c-3c4e-44e7-bd13-52d3b07af50e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " message: Optimization terminated successfully.\n",
-      " success: True\n",
-      "  status: 1\n",
-      "     fun: -1.7488646919931474\n",
-      "       x: [ 5.944e-01  1.288e+00]\n",
-      "    nfev: 33\n",
-      "   maxcv: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.optimize import minimize\n",
-    "\n",
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "result = minimize(cost, initial_params ,method='COBYLA', options={'maxiter': 40})\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d73755a8-2c45-41c8-8d9b-d88a0b411869",
-   "metadata": {},
-   "source": [
-    "### Parallel Parameter Shift Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b01d93b-0953-4139-8a90-44414327f726",
-   "metadata": {},
-   "source": [
-    "CUDA-Q's `mqpu` backend allows for parallel computation of parameter shift gradients using multiple simulated QPUs. Gradients computed this way can be used in any of the previously discussed optimization procedures.  Below is an example demonstrating how parallel gradient evaluation can be used for a VQE procedure. \n",
-    "\n",
-    "The parameter shift procedure computes two expectations values for each parameter shifted forwards and backwards. These are used to estimate the gradient contribution for that parameter.\n",
-    "\n",
-    "The following code defines a function that takes a kernel, a Hamiltonian (spin operator), and the circuit parameters and produces a parameter shift gradient with shift `epsilon`. The first step of the function builds `xplus` and `xminus` , arrays consisting of the shifted parameters. \n",
-    "\n",
-    "Next, a for loop iterates over all of the parameters and uses the `cudaq.observe_async` to compute the expectation value.  This command also takes `qpu_id` as an in out which specifies the GPU that will be used to simulate the ith QPU.  In the example below, four GPUs (simulated QPUs) are available so the gradient is batched over four devices. \n",
-    "\n",
-    "The results are saved in the `g_plus` and `g_minus` lists, the elements of which are accessed with commands like  `g_plus[1].expectation()` to compute the finite differences and construct the final gradient. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7508c92a-ff80-4a92-9396-691825b1cabb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import  numpy as np\n",
-    "# cudaq.set_target('nvidia', option = 'mqpu')\n",
-    "\n",
-    "num_qpus = 1\n",
-    "epsilon =np.pi/4\n",
-    "\n",
-    "\n",
-    "def batched_gradient_function(kernel, parameters, hamiltonian, epsilon): \n",
-    "\n",
-    "    #Prepare an array of parameters corresponding to the plus and minus shifts\n",
-    "    x = np.tile(parameters, (len(parameters),1))\n",
-    "    xplus = x + (np.eye(x.shape[0]) * epsilon)\n",
-    "    xminus = x - (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    g_plus = []\n",
-    "    g_minus = []\n",
-    "    gradient = []\n",
-    "\n",
-    "    qpu_counter = 0 # Iterate over the number of GPU resources available\n",
-    "    \n",
-    "    \n",
-    "    for i in range(x.shape[0]): \n",
-    "\n",
-    "        g_plus.append(cudaq.observe_async(kernel,hamiltonian, xplus[i], qpu_id = qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1 \n",
-    "\n",
-    "        g_minus.append(cudaq.observe_async(kernel, hamiltonian, xminus[i], qpu_id = qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1 \n",
-    "        \n",
-    "    #use the expectation values to compute the gradient    \n",
-    "    gradient = [(g_plus[i].get().expectation() - g_minus[i].get().expectation()) / (2*epsilon) for i in range(len(g_minus))]\n",
-    "\n",
-    "    return gradient\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a317073-0e29-40d1-bf09-d168e7227f14",
-   "metadata": {},
-   "source": [
-    "This function can be used in a VQE procedure as presented below. First, the gradient is computed using the initial parameters, then the standard VQE construction is used, but the `batched_gradient_function` is used to evaluate the gradient at each step. This objective function will return the cost and gradient at each step and can be used with any SciPy optimizer that uses a gradient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6313f4ed-a620-4982-b587-42e19267ef84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gradient = batched_gradient_function(kernel, initial_params, hamiltonian, epsilon)\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector,\n",
-    "                       hamiltonian=hamiltonian,\n",
-    "                       gradient=gradient,\n",
-    "                       kernel=kernel):\n",
-    "\n",
-    "    cost=cudaq.observe(kernel,hamiltonian,parameter_vector).expectation()\n",
-    "   \n",
-    " \n",
-    "    gradient_vector = batched_gradient_function(kernel, initial_params, hamiltonian, epsilon)\n",
-    "\n",
-    "    return cost, gradient_vector\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "edd702a6-213d-469e-861b-9beee6207284",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " message: Optimization terminated successfully.\n",
-      " success: True\n",
-      "  status: 1\n",
-      "     fun: -1.7488646919931474\n",
-      "       x: [ 5.944e-01  1.288e+00]\n",
-      "    nfev: 33\n",
-      "   maxcv: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "result_vqe=minimize(objective_function,initial_params, method='L-BFGS-B', jac=True, tol=1e-8, options={'maxiter': 5})\n",
-    "print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/examples/python/performance_optimizations.ipynb.txt b/pr-2308/_sources/examples/python/performance_optimizations.ipynb.txt
deleted file mode 100644
index db8a563f80..0000000000
--- a/pr-2308/_sources/examples/python/performance_optimizations.ipynb.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf94a9f4-6be0-4eda-b441-ecc85f89e0c7",
-   "metadata": {},
-   "source": [
-    "# Optimizing Performance\n",
-    "\n",
-    "Performance is a key focus for the CUDA-Q design.  This section highlights some features that advanced users can take advantage of to increase performance in certain situations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dcc9c85-780b-495e-aebe-8cd9c0012792",
-   "metadata": {},
-   "source": [
-    "### Gate Fusion\n",
-    "\n",
-    "Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. A full command line example would look like `CUDAQ_MGPU_FUSE=4 python c2h2VQE.py --target nvidia --target-option fp64,mgpu`\n",
-    "\n",
-    "<img src=\"../images/gate-fuse.png\" style=\"width:400px;\">\n",
-    "\n",
-    "The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation.  See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.\n",
-    "\n",
-    "<img src=\"../images/gatefusion.png\" style=\"width:300px;\">\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/_sources/examples/python/visualization.ipynb.txt b/pr-2308/_sources/examples/python/visualization.ipynb.txt
deleted file mode 100644
index 76347fe487..0000000000
--- a/pr-2308/_sources/examples/python/visualization.ipynb.txt
+++ /dev/null
@@ -1,394 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization\n",
-    "\n",
-    "## Qubit Visualization\n",
-    "\n",
-    "What are the possible states a qubit can be in and how can we build up a visual cue to help us make sense of quantum states and their evolution?\n",
-    "\n",
-    "We know our qubit can have two distinct states: $\\ket{0}$ and $\\ket{1}$. If these were the only two states, we could represent them as two vectors on a one-dimensional line (i.e., the z-axis in the image below). We also know that qubits can be in an equal superposition of states: $\\ket{+}$ and $\\ket{-}$. In order to capture all of the states in equal superposition, we will need a 2D plane (i.e., the $xy$-plane in the image below). If you dive deeper you will learn about the existence of other states that will call for a 3D extension. \n",
-    "\n",
-    "In general, a quantum state can be written in the form $\\ket{\\psi} = \\cos(\\frac{\\theta}{2})\\ket{0}+e^{i\\varphi}\\sin(\\frac{\\theta}{2})\\ket{1}$ where $\\theta$ is a real number between $0$ and $\\pi$ and $\\varphi$ is a real value between $0$ and $2\\pi$.  For example, the minus state, $\\ket{-} = \\frac{1}{\\sqrt{2}}\\ket{0}- \\frac{1}{\\sqrt{2}}\\ket{1}$, can be rewritten as\n",
-    "$$\\ket{-}  = \\cos(\\frac{\\theta}{2})\\ket{0}+e^{i\\varphi}\\sin(\\frac{\\theta}{2})\\ket{1}\\text{ with }\\theta = \\frac{\\pi}{2}\\text{ and }\\varphi = \\pi.$$ \n",
-    "This can be visualized in the image below as a unit vector pointing in the direction of the negative $x$-axis.\n",
-    "\n",
-    "Using spherical coordinates, it is possible to depict all the possible states of a single qubit on a sphere. This is called a Bloch sphere. \n",
-    "\n",
-    "<img src=\"../images/Bloch_sphere.png\" alt=\"Bloch Sphere\" width=\"300\" height=\"300\">\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us try to showcase the functionality to render such a 3D representation with CUDA-Q. \n",
-    "First, let us define a single-qubit kernel that returns a different state each time. This kernel uses random rotations.\n",
-    "\n",
-    "Note: CUDA-Q uses the [QuTiP](https://qutip.org) library to render Bloch spheres. The following code will throw an error if QuTiP is not installed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# install `qutip` in the current Python kernel. Skip this if `qutip` is already installed.\n",
-    "# `matplotlib` is required for all visualization tasks.\n",
-    "# Make sure to restart your kernel if you execute this!\n",
-    "# In a Jupyter notebook, go to the menu bar > Kernel > Restart Kernel.\n",
-    "# In VSCode, click on the Restart button in the Jupyter toolbar.\n",
-    "\n",
-    "# The '\\' before the '>' operator is so that the shell does not misunderstand\n",
-    "# the '>' qualifier for the bash pipe operation.\n",
-    "\n",
-    "import sys\n",
-    "\n",
-    "try:\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    import qutip\n",
-    "\n",
-    "except ImportError:\n",
-    "    print(\"Tools not found, installing. Please restart your kernel after this is done.\")\n",
-    "    !{sys.executable} -m pip install qutip\\>=5 matplotlib\\>=3.5\n",
-    "    print(\"\\nNew libraries have been installed. Please restart your kernel!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "## Retry the subsequent cells by setting the target to density matrix simulator.\n",
-    "# cudaq.set_target(\"density-matrix-cpu\")\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: np.ndarray):\n",
-    "    qubit = cudaq.qubit()\n",
-    "    rz(angles[0], qubit)\n",
-    "    rx(angles[1], qubit)\n",
-    "    rz(angles[2], qubit)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we instantiate a random number generator, so we can get random outputs. We then create 4 random single-qubit states by using `cudaq.add_to_bloch_sphere()` on the output state obtained from the random kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.default_rng(seed=11)\n",
-    "blochSphereList = []\n",
-    "for _ in range(4):\n",
-    "    angleList = rng.random(3) * 2 * np.pi\n",
-    "    sph = cudaq.add_to_bloch_sphere(cudaq.get_state(kernel, angleList))\n",
-    "    blochSphereList.append(sph)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can display the spheres with `cudaq.show()`. Show the first sphere:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also show multiple Bloch spheres side by side - simply set the `nrows` and `ncols` in the call to `cudaq.show()` accordingly. Make sure to have more spaces than spheres in your list, else it will throw an error! Let us show two spheres in a row:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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rK3j7Jz9BNpPBY48/jiCKYDsOFFVFKp1GOpNBYWAAQ0NDSKfTn9pn86Cj7wzcZbiui3q9DqteR61aRa1UguU4oFEEg6fbPNcFIQSGYciavtbKDeA1dU1VZdRNOR8g3gYkCIU0iuCFIXzPY6/hzgPltX3bdeF6HgilKJbL2NrYQBAEOLh/Pw4cPAiDL3jDMBCGIRqWBVVVkeNTxkgsE6FyUiONOQcCIkJpB0opLpw/j/mFBTx+9iwOT0/Dtm3UGg1YlgXNMJDJZpHKZDAiyhR9p6CPhwBRFLHsYb0Ou9FAZWsLtWoVQRgyW6Hr8HwfURhCIQSpVAqmaUpugIi+BRcn3lUkNnVf2A5uPyiloPz/Pd+H53nSzgBsg/bDEBYnM7ueh7X1dUYMTCRw/PhxFPJ56JoGwzBg6Dpq9TrCMEQ6lWJaJTESdNx2tAY04jx3bEeUAoqCcrmMt954A0RV8eVXXoGiqqzryrLghyGSqRQrI+TzGB8fR4bzl/roHX1n4C7BdV1UKhUUi0UUNzZQr1ah8rYdwZ5VFAVBEMDQNORyOWRzuR2pMeE9B8JTjv2q1aOWHIAw3F7kUQQ/COB5HhzPY61Fvg/f92E7DjY2NrC6uQkQgpHBQYyNjSGXySCbTjOiIU8t1up1ADzd16EuR3gGIL7IVU4oAtD+7yjFBx9+iLm5ObzyyisYKBRAwUiQ9Xod1XodIAQDg4PI5HIYGxtDNpvtEw77eCARRREajQZKpRK2NjdR3tqC53kwdR2mYTSl1ikAQ9cxMjS0Yx6A0BChUcQ4SLGfAdt8HmB7XQqtkTBmO4Td8D0Pjusi8H24noeGZWHx1i04rotkIoHJ/fuRzWSQzWSQTCTYORICy7Lg+T5Mw0Cyg+6IKBEIcSItVuqkwA6CtIBlWXj1tdcwUCjISYmipbFaq8GybaQyGeQKBQwODmJ4eBjJZPJOP5qHDn1n4BPCdV0Ui0VsbW2hVCzCtSyYqgrTNKFpmmzzSyQScDlTV1EU5GLp99ZHX2oJtIACrF+Yk2xEfbC1jCCMgIgMXN+H73lYXlnB4uIi0skkaraNA5OTSPP6PY0imalIJ5NMGATMGVB5diKMImk8pJBRm8eHcmdGkCB1TWM1PeG8RBFee+01hGEovXyFOxagFJV6nbU0ZrPIFwrIZrMo8H/7mYI+HgREUYR6vS5tR6VcBjiLX+f1e2E3DE1DtV4HpZTxfmKlAQHRHRCGYVt9EdGOHOcVBGHYbHtajheGIRzPg21ZuHrtGlzHQRhFOLB/PxLJJAxNQ8RT+slEAulkEiAEjutC58Jl4r3l+wuyMbAjqwhsC6JpmgZd16GrKrNP/LWrq6t484038IXHHsPhw4cBQJIlvSBAqVSC6/soDA8jwzMF+Xy+7xT0gL4zcIcIwxCbm5tYXV1FtVpF5PvQACQMA/l8HulUSvbrAoBl27AtCxRALp+H1mVTE1mBpnQdj/bjwh0AZJ1NJQQKZxELYlDg+3C5nsCNmzdx+/ZtTB04gJHRUbzzzjt44uxZqKqKIIpYtoEQFlWEIdwggKnrGB0eRjab3bFwQ+6wCLIjjSJZnoh4aYK2ODSapkHVNBiaJr38/QcO4IkWqdJ433IYRcjk88jmcshy0lCfaNjH/QzHcXD79m0Ui0XYtg2FZwvTqRSy2SzSqZTMhNEoQrlaRRRFrH03nd6ZTeQQTrhAFEXwfR8BLx8Kol4chJOIxb8iKvd4AOG6Lj744AMAwJEjRzBz8SLOPf00NE2D7TiMo0AIojBEBEgbkEmnMTk+zsqJsQynCCoCLpseDzBEEBS22DjCdRJUTYOh65i5eBGzN27glVdeQT6f33EfbNtGqVZDkvMJ8vk8MpkMCoVCP8PYBX0K5h1ga2sLi4uLqFarQBDA1DSWnspmkctktltnhBhQGMKxbVBCmH5AF0dAEIji0T+wTdgTi1nXdRh8gcR7byml8H0fjm1LgZGrH3+MjfV1nP3CFzA0PIwK7yQQUUa8Bci1bZYmDAI0bBu3l5cZ4z+f3ybn8Dqgqiigmta2/idqkB43QhF3FoIggMtfd+LECVy+cgUjQ0M4cPDg9t+DlR4GBwZg2TZq5TJrqcrnJdM5n8/3GcR93FfwfR+Li4tYW1tD4HlQoggpXUd2cBD5XK5JzltsnnXbBo0i1tefSrV1BChvLxQpfbHOolgULta4qiiSIKhzHROxWUecmOd6HgiAeq2GmZkZpNJpPHn2rOwUyIpuoEQClHcnBL4Py3HgEYJ6o4FiqQTP85DPZpHLZpvIhIIfFWjajjIoKIXlOAi4DQr4zATP8wDPgw0262BtdRU/eecdvPzyyzB1vSkbkkwmoRsGavU6iuvrqJbLGBkbg+d5zNlKp/stiW3Qt6Z7QKPRwPXr11GpVECCABohyOdyGCwUkBTRKl90JLbBNnj93dB1mKa547gUzKMWm6doKRRQFGWbZChS7uJvY153GARoWJZMxYVhiJmLF1GtVPDcc89haGgIFicGEkJgmCZSqRQajYZc1KNjY6BRhM1iEdVqlUUbvo/V9XXomoZCPo9si8OD2LUCLNrQVBVKrM0xDAJ4sdJGFEUYGxvDxsYGzn/4IRLJJNKZDOuTVlWAECiA7HOu1GoobWygUixi34ED8DwPuVyuzx7u477A0tIS5ubmEPk+SBQhqWkoDA+jEHdqhe3gHQGu68LjQmK5lomAwHYGTTjefhDsiKpVvuEb3HYoLZGxOKLjurBsWzL5i8UiZj76CCPDw3j2uefQsCz5N4qiIJvNMsl0TjrMZDIYGh6GbVlY29iA47pQCUGtXkelWkUmnUaBOzzCsimc4Bw/FwrA4NlD4cQEQYCACxYFQQAC4JHTp/Heu+/igw8+wMlHHmEcCz5cDZRCU1XkcznUGg3YloWF2VnkBwYwvm8fbNvutyO2Qf9u9Ijl5WXMzs4i8jwolGKgUMDw4CCSyaRkzzdxAPj3juvK36dTKenFE7BygMdTcfHNX+U1dF3ToInNsaWlUJQHhO6A4zhyMYsa2nsXLsD3fbz80ksoFApyQYtFQABJbqzX6wijCLV6HVmeWhMM5ZCT+8IowkaxiK1SCflcDvlsFpqmgfJUID8xANhBVlI1DQlVBXj0EwYB/CDAo1/4An7wwx9icXER00eOwHFdOWfB4I6EaRgYLBRQrlQQBAEW5+aQzWQwum8fBoeGkM/n+55+H/ckPM/Dxx9/jNLWFojvI2EYGB4dxUA+zwhzwpmPtRYL2I4DAEhyETLw11FK4fPAwY/r/YPJiau6zurtPPIOW4jIcYScwCi6FnRVxdr6Oj766CMcPHAATz75pOxKioPwzKLFZYUt20ZEKVKpFAYKBQRhCJ2XA8Xv6/U6krxdOZ1KQQFraRRdSfHSqLge0Umh6zqSYNlRPwiQSCRw5PBhXL9xAwenphAYBizHgWEYMHUdmq5DAZBLp6EqCizLQq1SgVWtYnBkBL7nYXhkZGf740OMvjOwCzzPw+zsLNbX1qAEAdLJJA7s34+kacrWHCW+kFsWtOM4oAASiQQUQhBGERsgImp4EH9G5IOs8B7fphRaSypeRA/xxQyxcAwDP3r9dQRhiJdeeonNGQdkyUFVlG1NAjDnI5vLoVavIwoC1Op1eb4EwMDAAAq5nGT7B2GIUrWKcrWKHHccZC2OEFmbbO2GiBs74WiIRT138yaOHD0q645BEMCybXZPuEOUz2ZRqdWgKAoaloWlhQXUazVMTE5iaGioTy7s457C5uYmrl+/Dt+yoAIYGR7G2NjYNvk2ijo+syJDGFGKBFcP9XkGQAiJCQiyXcIwZMeRROsajH3vcL0TkQ1IpVLY2NjAhx98gKPHj+OxL3wBFIDveYyUzIXG5NH534j1aLsua0FWFKaoahjIZrNwHQeVWg22bcNzXaysrcHg3KpUMsl4Ctw2BJwA2eq8iP8nfEaLYRg4deoU5ubmsLK8jOkjRxBxWXbXdaFyEreh64xjFAtSSsUirEYDjm1jfN++PgeJo+8MdMHW1hbmbt6EVa1CAzA4NITJiQlZ22pl9LbC5Up+QqmvVq83efIAq/0L5a/4Zkm4GqHoCIiwc4HYPBsgkOYKg2+88QZs28ZLX/qS7LcNYkxeTZCThOgIP79MKsUyAFwwSbQxEgBUUZDN5ZDJ5dCo11Gt1eDzGQilSgXpdBoDhQIMXUfIz1tcT9QhMhG/P3H8OG7OzmJ9dRVHjx2Dy2ufrYvbMAyk02nUGw1onKtQ2tyEVa/D2r8f+w8c6BOE+vjcEQQBFhYWsLK8jMh1kdB1HOCteEIwSKytdqC8bk7BMndiDcQ3eYUQZjdE5pBDtBfSKAIUZTuijwv+RBHrz/d9ACwYyKTTKG5t4d133sHBqSk89uijTdcjyoji/OLdRCYPjCzbhtVoyDJpxN9PNwyMDA+zmSm1Guo8eNnY2ICiKMjncsjlctJOSP4Tv5Z25RGAlV2PHT2K6zdu4PSZM6y04nnw+Gh3y7JgE8KyJLqOJGWjlyOeWbm9sIB6vY6p6em2RMSHDX1noA08z8PS0hK2NjZgV6tI6jrGx8dZOhrb6at27X9x2LYNx3F21NS1mEhHp8hATB0LeTkg7pFHYcjU+/hi1jQNmXQaCiF49733sFUs4sUXXkCOP+Ci5QhgWYF2KT+AGYV0JoNarSZnC6R4S450fChFJpNBJpOBZduoVKtSMa1WrSKRSmEwn4fOST1yYQseRRskEwlMHzqE2dlZHD12TLZj+twpEIvbtm3WdqQo8HhGBoTA9zwszM7Cqtdx/JFH+g5BH58bSqUSlpaWUN3aAgkCDORymJyYgGEYiCiFSohUAmyFKP0FYQjHcWA7DlLJJPyYjRDlM13XtxVBW8S/FEIQxmxGvI3P55oBYjNPJZNIJBKoVqt48623MDw8LEsDABs1HMRsxw7w1yW4sqnrOHBsG7qub2/uvByiaRoGBwaQz+WYU1CvIwgCbJRK2CiVMJjLIc35SNJaxrlJbXD02DFcn53FzdlZPHLypAxGvCCAy0u0goslSrSKaF9UVZS3tmDVajhy4gRGHvLpqn1noAWVSgW3b9+GW6/DrdeRz+UwygVwENtEWx/N+KhPSimqtRqK5TKiKEI2k2n25LlAR7eeTpGW04CmckK7xWyaJgghmJmZwe1bt/DMM89geHi46XiiRKBpGvyWNGMcGo8SqrUaG0/MjZcUAokZnlQyiVQyCcu2USyVYPGMgmVZGCgUkMtmd3AJwDdwyssg4p4dO34cN+fmMD8/jyNHjgCAdJgESUp0WYjWS8/zoHPDGIQhNtbWYFsWHjl1Csm+AlkfnyGiKMLi4iIqpRLsWg0qgMGREYwMD0PXNERRBLGVtpb8mnhEQYDNzU1YjgOdBwtCd0DT9WZBnta1JcDbBCmw3dfPFQ49zwM4wTedTkNVVVi2jR+/8QYymQyee/bZbZGjWGszgLZOdjxmTyWTUqnQsiymQNjy2ogfZ6BQQCGfR7laRalUghcEWC8WkWg0dkgZx4MKSbLkv0uYJqanp3FzdhbHjx6FyjuskqqKhGmy7iXuDMRF2VRCEBAmB+95Hq5cvAhrehoHpqYe2nLjw3nVHVAsFrEwNwe3WkXouhgZHsbw4CAyXJI3Dpkm4/8v0vm2baNUqaDCe4OTySRyfEpfMpmUTHlgZ9pfHFcek3+J2n2j0UCdi48ItqyoJ964fh3Xr1/HFx57DJP79zcdT0QbADoyaOPmRNd1mWnwPI+NSI2dk9jQxfENw8Dw8DBGx8aQ4ITErXIZy6urLDMSew/BswBYtkCIjKTTaRzYvx/Xrl3b4UCofGHncznk+EAnMau9wZ2PhGEAhKBeq+Hihx9iY3W17XX20cfdRhRFuHnzJipbW/BqNSQNA6MjIxgeHGQZslb+j/yGfSfaAmu1GnOq+eTAHBfbymSzTGhoF5KsyAKQ2JfKOTyVahUen2SaSCSkqqfn+3jzxz+GQgief/55aC3KhvGZAbtl3ET7o8haNOr1ZiJxC0kyohSpdJplXXM56IoC3/extLqKrVKpacKiKE0IGyJkkymA40ePwvd9zM3PN99XQM5+EcJDwrlyfR+2bTNFWF0HKMXi/Dw+vngRbsxmPUzoOwMcW1tbuLWwgMjzoKsqRoaGkE4mkeNyvK2pdRp74CilsB0HpUoFDduWSlsJ02SRga63VeprBQWk1xtf9kKy1Pd9EEVBMraYAeD27dv46KOPcPz4cRw9erTpmHHJYqCzMyDaegRM00QimQQBYPMWotjFS/EQn9cTCViqcGxsDIVcDjrvPV5ZW8Pm1haCdiWVmEMVUYrDR4/Ctiysr69v3w/+OnFPDO4UpFIpGIYBVVXhuC6K5bIc+Ww7DpYWF3F7fn7HyNY++ribCIIAs7OzaJTLoL6PHNfkyGUyrJbe8vw1kfs4L6jGybheEMD3fVhhA7998Xfwy9/9Ffzu+f9Brq+OYkNAUwCB2Pd+EMjWZlVV2eyPZJIJBUUR3n7zTTiOgxdeeKFJ56A1K6B1cQTidoMQwjKhvMRZbzR26JBElMLn2iMErFVxoFDA2Pg4kqYJXVFQq9WwtLLS1NLYBJ5ZjSiFmUhg3759mBfOQOtLwUocKW7PTdOUWYNqvS5VYWkUoVQqYfbqVdRrtY7X+6Ci7wyAsX6XFhcBz0OabzaqqiLDH+omBS1AerhNToBlIYoiaKoKwzSRzWSQSqWY/jY6L2RxzLhHH0cYRajVavB9HwpfaCK9RwjB1uYm3nvvPUxNTeHRM2eajys27Zh33y0F1vreiUQChmGAEiYk4vk+Uw8LQ0SCGMkZxiJToCgK8vk8JsfHWZSgqmjYNpaXl+W8g07vLWaULy0tyeFLO1woXrJImKY0vGLmQ50TNMMwZJ/L1hYWbtzYodrYRx93A0EQYG52Fk6lAiWKMMDZ8bquI5lMbov9cIh0t8jU1Wo1GbGLFlozkcC/uvL/wcf1j1H1a/jm7Lfwv3v1N1Byym3PoTWLGIftOKjzMemGriOfy8HUddmJ9O6776JUKuGLzz/PVEbjEOcpOpC69eTT5umIqqoyhwOshbheq0m10pCLCCnifGP8CdMwMDY2hqHBQSYkRCnWNzexur4Or8MaFqXGyclJVCoV1hHFywFNtiOW3UilUijERNSEQ+BxLQPXcTB3/Tq2NjY6X/MDiIfeGdjY2MDKrVugnicHbxBA1qJp7GEVi1kwZ8stToDQwhaLo3WYSDvQLiWDIAhQrVbhhyEUVUW2RcnL9zy8+957GBwcxBNPPimPFW/9aVrQ3IHoeC7YdiCEN29wSeWIM4HDFj1zQRBqPa6qaRgZGcHw8DAbpUwINkslLK+uNmcZWo61b/9+LC8vb9/z2BCVpuwKd0QSySRG+Ht4nFBp2TZq9Tob+VqrYfbq1Yc29dfHp4MgCDB7/TrcahU0ijA2MiLr+SmuJ0L5WokHEEEYMiGeSgUuJ7WZPAARkfmWtYWQbK+zi1uX8LVv/1O8vfqOfP9uTgAANGybiYlxJ0MENsIZuXnzJpaXlnDu3DkMDQ01rf24zZNcoy6ZAeFciPMS52aaJiMVcoJxu/bodsFJOpPB+NgYctksdEWB57pYXllBqVLpmOkbHx+HqqpYWVravkftbAcP7DRVxWChgGwmA01V4fPupUajwYalAVhaWMDK4mLH637Q8FA7A+vr61i9fRsRl80cGRqSDP1UMimFfpoWs++jXKnA4nK/aswJSJgmIsoU+0QtvRNkNoC2b7vzfJ+R+MIQqqIgl8k0p/gpxfvnzyMMQ5w7d2570A+2Sxc0YqNKfd9negI8yxGJ64pt/OL/RYlDLKCIUsYD4MOMrDZpu26Zj1QqhYmxMRSyWVk6aFcTFJicmIDH7/GOe8ZrhULeWCzspGmyDod0WrKWfd/HVqkkZzrcvH69K3Gyjz56hXAE/HodFMDk+LjMjIk2Nrm5xvg1lm2jyrtvKBj5rcB181X+zAKAaRqgLTmxklvCf/P6/wm/e+H/BTfs/BxTAPV6HY5tA5SyAUIt8ruVSgUzMzM4cvQo9k1OAtiOsEmM2CxnjFAqbUcYsxtiPYo1KbJywm4CTGiNECLr860QKfxWR0HlnQdjY2NMdIkQVCqVHTwkAU3TMDY2htXVValx0nRfKJVD3gQUzrtKpVLQdF0ORKo3GqhUqwCAjfV1rMUcjAcZD60zsLa2hpWlJUSeh4FcDqMjI1IDQEj+CuUu0RrTsCyUecpLVRQmscmdACmaEdt423nTwkB020Ad10WtVkMElt7LZrM7ZERnZ2exvLyMJ598su1ELrGwCSDnmctUH93WF5BRAP9q0jqIdRCkuRyq5/twXHfH+8UZvq1QFAX5QgETY2NIplKyJri6vr7DQAzxKH9lZWX7nsV+L0lE/LpEOlAMdzF0HSlOMgx8XxrGwPOwePNmn0PQxyeCECHz+KjtyYkJJJNJuZEnTFP28os1EQYBSjyAoGARs3QCxLrm3TGUUuSSubbvTSnFn1z7E/yvv/e/xVxlfsfvI8q6mIQoUTqd3mEbwiDAOz/5CTLZLB6NaQnE0cozEsPPALZhNGUQYn/TTnhN13UYpslUTrl2yY73E0JFbWCaJsbGxjA4OMimJEYRVtbXUW1T09+3bx+2ikX4ntf2eBG3b2IgUhSG0FQVKdNk5ykEisDawmv1OqIwxNrKCoqbm23P70HCQ+kMFItFrC4tAZ6HwXxetuGJBa3zKFjUtHzPQ7lalfKgpmliYGAAZiIhiSzi8Repar1NVoACgKJ07LcHWPTQaDSY0TAMScaJQ3r2R45gYmKi7XHiCzWuMbAb2joDvHshydXCLMtq8rCB7QeJdHEKdF3H2MgIBgcHoWkaAt/H8uqqvK/i7/ft24eVlRVpYBT2CwBoa0zEBm/yex4FAVLJJFSu5Gg7Dur1Ouq1Gm51IBn10cduiKIICwsLcPigr/3j4ywbyDcWAFB5f70oGTRsGxVeXlMIQZYP7tG49n782IKMq6jtNrIIBDzFX7mJ/9V3/zf4j9e/KY8RRBEq1aq0Ya0lRYEPPvwQlmXJbGIr4t0+0hnoUbdDZibj3UZgWVZB0GuXWWzqyOpw7Ewmg4nxcdaRpSjYKpWwvrnZ5NyPj48DYGOOxfHiAkbtAoEwimTLpmh7FC2SYRiixrlSS4uLDzyp8KFzBhzHwdKtW0AQYCCfx9DQkPxdEASgADROmqOUNi1mlRDksllk+RhRBZCpavYtS8lHlLJ2lRjkA9/BEaAA6o2GbONLJZPItBnCIzz7bC7X0bOPv18Yq5ftWYwn1j4IMEKhpusgXH60o0vDHYJOHRSZTAZjo6PSWK2ur6MUKwtMTEygWq2i0Wg0HS9+Lu0g5iwAkGlYMd/A5zLLW+vrWL51q5er76OPJqyursKuVKBSisnxcdlHL8qCiqKwNmDKhuuUq1UplGUaBgqFAkxhF2iz2p4UENN1aGozWY8phCpNqUQv8vC7F/6f+Oc/+jo26puoVavMRnECr97ibACs62h+fh6PPf74TsJgDOKcpNhQr3Yjlp6Pa64QRemaWYwHD006Ci1QNQ1jIyOsW0lRYNt2EwcpkUhgcHCQcY5a0JrJiENRFMbX4A6AoihSFZZSCsuy0Gg0sHDjRlOb9YOGh8oZiKIItxYWQHwfScPA4MBA8+/5AlV5v2trNiDP5XYlh6CFOBfwWrZCSPNkQf5vt9ager3eNb0nIDz7p59+umNqLd79EMUWdK/DfJo8fDSn6dO8Q0KoIDb9Xfz4nBzUaQGahoHxsTHWcaAoKFerrGwQRRgdHYWiKFhbW2v6m6jF0Ow4b8LmO4DrIwCMKJTl3RdRFKFuWbi1sMC6R/roo0dUq1UU19aAKGKEVe4IyIhTUaBxwrHVmg3gxGQxVIwQAtKywQpnwNB1aAqzHYKz023dvrfyPv6r7/5zzGxchK5pyPFOqFZYjQbOnz+PAwcOYCo2LrwbemkrjENooojvgW3bsVtmsfU43ZAvFDAyOsrKBmGI5bU11Dnpb2JiAutrazs5A13mQAAsGyxmzEjeWDqNJCd1ep6HcqWC61euwO7U7nif46FyBlaXlxE1GgClGOMbTjx9JB5kQSBpzQaIxQzEBIFEax0gx4dqfFrYboxf8Nc0eCoKlCLL+2DbQXr2Z8929ezj6KYe1gnC+LRTTFMUBSlOCnIdR5ZF4n8XRzeHQFEUjAwPo1AoQCcErutiZWUFQRAgn8+jXC7L14r0pRRU6WAgTcNgBCZOfiLsxNjcBi4u4rouFubmsDg31/M96ePhRRAEWLl1CyQMkU2nm9ce3VYP9H1/mxsQzwYYhuS6SLsB7AgkBPlVV3RElE0j6RYpa9CQRx4Vr4L/xwf/Azajrfa18ijCT955B7qu4+zjjzfV9ONobYWM7sB2tAqqxXUWmjKLvBTa6Ri7OQTJRALjnFyoKgo2ikVsbm2hkM8jjCLpHACxCapA81C5prck8nMSthhg5d4MJ2CGYYhyqYSLH33Uttxxv+OhcQYqpRLqm5vwggAjQ0PMi+e/Ey03URiiWq9LoYumbEDLYhYQBEOgOa22u8QQQ6PRYBkBSpHNZHa0I4rFaTUaeP/995lnf+BA0++jKELARwKLqWbiy7JteL6PwPflYCObs5ldz4PjOHBcV75e9NsGnBkcCvnf2KLWdV0qH1qWtT2joVPE3sUhAIBcLodR7ulTSrG6sYFkMokKLx3EZyvIY2JbmbHp52JRg0dbMSJlMpGQGRc/CHBzdhaz1693PK8++gCA23NzgG0z55WXFePpcEIIHMeRAUS7bEC7DU4O8uJsfFFqUIkChSidlhMAQIWKPPKgEUWIEMWoiItbM21fe+XyZZTKZTxz7lyTwqDoBAi42JHveXIeiO04cLg98LlanyPshusyu+E4bIhSi+3wfR+h6Ezi1yWQEZnFKJKZxbaWoQeHQLQv5zIZ6IqCeqMBlxO4he3Y0ZKMzsGEKFtG/LxlKUdRkEmlpKiZVa/jg/feQ+MB4xA8FLMJPMfB5tIS/CBAJpVClmvmiweC8Ki0zjUDACCXzcq6f6fFLCDIJ2HLdK/dHuaGbcPl9bNMJtNEOhSbvGAY/+Tdd6HqOo4fP876YEVbT5zRC6b9jdjPGpYltQHEgBTR3+y4LgzOcBZ/H182Pp81oKgqcwYIi1RE9BEEARMhiiLkMhl5j0hLdBLvyOiEBPf0N0slOLYNRdNQqlR2Fwxqw08Qym9iQcuXKgo0TUM6nYbtOAiDgGUHoghHTpzo/j59PJTYWFqC32jAoxT7Rkflcyzb8ChFo9GQdXCTT9YUEX23EqHILIq0tMJZ9braPjMoEHcEIhKhiip0VcfjI48D4M5FELBR46USrl69imMnTkA3DFSr1e22Yr6m4zoq4rz8IIDjOFAIgc3toJic6HgedL6RC5sRtx2izh7wTVVolSjYtg1iEqNowRbti00e0C7cI3HPBgYGYJomisUigiiCbhhY39zEvn37OnYPCYcgLv+uiA4wLmIm34MQUFVFIpmE4rrMSXIcfHD+PL7wxBM9Z2nvdTzwzkAUBFhbWEDgulAJwcjICAA0OQK2baNhWexBoGx+eJMjgN03dtGyQqMIKi8TdIPNp3tFUYREMomILyDZ58/nekdRhLW1NWxubODs2bMIogiE/y4Okd1oTegRsLJFIpFgvAEABudBiDno8QVBgSbBETkxUfQVA1BEWYUPOXH4xqrpOmgYArHZAWrs+90GgAiCUKVcZqpllGJ2bg7TXYaHyPtMyLZxwzajOQhDSNPKWd5EUZBOJtnglijCwvw8IgDH+g5BHzHUi0VUNjbgBQEGBwbY/AtsZwNCriIY8TG7QuZWEUS6HqJbAkjNDE3XmeCZ0tksK1CQQw6EEoAANmw8VvgCvnbm11BAAcVSSfb5R2GICx98gHQmg3379sHjRMfW9xfl0ni9P+BCapquy4hZ8KV0TdvmTsVth3AqROkkZsPAbQeJbc6u58EvFpHi+iCgVNoJaT9UVa7ZbkjxmQibW1vIZjJY39hApVpFdreBZZzwKe28ICDzrImwOwQAOLFQURSWcXVdzLz/Ph49exbZB2AE8gPtDERBgNLyMhzbhk8p9o2PNzkBlFLWhx6Lzl3Pk6I2TaM0d4HoRFA1jU0lbAEFyxwEQYCGbaPOe1hFD64XY9jGU5BRFOHatWsYn5jAxMSEVO0S/8qHVXi5sYxHGIZyQ8/FvFch2pFMJJBq07EgkEgk5HvkslkWUfBshPgeALu/vs/GlrI3ZvKjQQC/JR0nz1tVofHF3lqTzBcKgKLg4qVLqHI9gtHh4Y5zFYDt7EME1t5pGIYsd8TviTgHEIJUKgWvVgPCELcXFkCjCMdOnuyZaNnHgwu/Xsfmygr8METSNFHI5ZpsR8AVOaMoAlFVFAoFOSFP44N6erYdnMEunm+txRlQoEDn/xVQgEZ1KCBIJ1P4W8d/EY+NfEF2zACQTu/yygpq1Sqee+45JGNrGYSNR2/SBkALN8iyoBDSXFrzPDmDpJvdEPfI930kk0k2vpkHShHYRqvrOsBF1UKusBpFERAEiAAE4rxi/xJA2gtN03aQokXr8mI2i8qtWyhyYbN8rr1uAwDJKRLOXcQ7MjTDgOd5TfMaCKWAokA3DCQB1PjeMXPhAk4//jizW/cxHlhnIAoC+NUqk/0MAgxxIo9YAFEUoVavyxRdOp2GaRgolssI+aJOxh6EbqBgG6/QviaKIhW5Av4lamee67J2I7DWN3FOmqpuR9L8gSeE4PyFC4iiCE+cPduxw0CixQCJWv6eWwrl4bZTnYQQqIRszxfnSJgmKqrK0oG6DiOTYTU3nt0QwkBRrHYYhCGo70O4PyI9p2kaFH7/0qkUUskkXMdBGIZY39zc1SFgJ8vaOzWeBaG8zauViyGch2wmg1q9DhqGuM07DI4/8sgd3a8+HgwEjQaqxaLsSBkdHW1yBDzPY88MLwlmMxn43DlwPU/21fcCESSAEOhctluHhgQS0gFQOLUrgwwUqgKgqKOOx4cew5MTT0ibocRsh+04uDE7i+npaezbt2/382jJGNwJ8TiO+CYtRYtajqVpGmtRjiJkeATfajPE9+DiYpI71MY5ECqs+yYmMD83hygMUeZlkYHdIndKWZkXrNSj8ImOidY9gPMIdF1HMpWCZVmwuUPw6Nmz97VD8EA6A1EUIWg0UKvVEPo+NN57C6ApvReEIUAIsum09F4TpolGEMB2HPZQ7LKoxRJyXRee67LolD/IojYnUmlhGML3fZimyXQE4gpkbVAqlzF74wZOnzmzuyMANNXHgRgbuEfD1ApZD+xSsyOEIJVMsgyL40iPXVVVoGUDFqnLkJOWAl7XpDwT4/CZ48KpyhcKsHn5JghDrG1sYGRoqKvMs+u6rI1IVaHrOnxOrOw0J0LhJYOGbSMKQyzduoVEIoGD09N3csv6uM8Rui4Cx0GtWkUQRXIQltjcHMdBnetf6LrOZpmI7hsurGPbthyC0w0ULEp2PQ+e57EghVKkwhTSSCPu2ieRBKEENiyUUIIHD/9p+Vv46qO/iH3pnZv9zMwMCCE40zK8rNN5tEJ2WN2p7RDOQBfbYRoGPNeFD3ZfU8kkFE3bsSlRbDsJItspsrdRFCHwfVjc1qqKItd6FIbQdV2WcoZaWsmb3oOyoXMErH3a4bMK4qWCOBRCoGsaUtwh8DwPlz78EE8++2zHbrB7HQ9kN0FoWaBBgFq1Ci+KkBcLGiydX6lWZWqukMvBNE258ZlcmpLynvROEGz1RqOBUrnM5hUI0gxPTWuahmQigUw2i3QyyaQvUynkslnk8/ldve4PLlxANpfbMZa42zk13Ye7sKB7SZkboq7IhUA6QegvmIaBdCqFbC6HQj6PVDotPXuAGcggCJBMJlGr15nKWxDAc12sbWx0HHQUhqEs+aQTCSQMQ37mHa8RLLoQcsZhFGH2xg1sPQTyo300IwoCBFxgxg8CEEDaDoC1HAtHwORTMxXu/IN3EABMTtzv8sxFUQSbS45vbm2hwYdqRdwxPjZ4DJESwSY2KrQCTdXwU4e/gv/quf8DKkoFDhxEiBBGIX7/0h/sOP7m5iYWFhbw6JkzXR1nidYggpOTgd7VB9sftnXCwk4kufyv53kd75lYo4auM3uaySCXyyGbzTLtAs4rEF1HKuc6WCKQ4APfNra2OhIKLc7fUhUFyWSSdQ60EAlboRACQ9OQSiZZMOM4mPnggx2dT/cLHrjMQGBZiHwftXodQRAwXfxcDgSA43mocZ0BjU8BFBuyeEQU3pNeqVbhex5c15Wenhj64/EWHGEEALbxqpqGdDLJ+ml5uhtgi7/CFfsMXe8papibn8dmsYgvvfhiz5t567b9SVN98phdvHuBZDLJCEFhyGr2PUxsFEQnVVGgxtJxwvvP8PRrBCCXz6NULqNh25i7dQtjIyPIpFKybCBmR4BSpjPOMz2EH6+Thw9AzpJIJZOy++LSRx/hiXPnZPqyjwcbIpuIiI8MD0MM5POyDa4WEwVLJZNSwz7ecqxrGkzTZJ1J9TpzJLh9CIIAXhCw9r3YBhNwbpLBo0xN0/Di4POoKVX8ePlNHM8cxSvjryCtpZDJZPBzR38Gf3ztG/Lvv7/4Gv7+iV/CkcJheT4fXLiAgYEBHJya6u3iYx0FwHZGUSgq3glkP/8utkNTVSRMExZve85lsz1xLSj/EnNkAF4S5MFYIpGAbdtI8M+pVq9jc2sLtm1jZHi4KesrbDrAsgIKL9uKrGInh0qUVnQ+D8WyLFTKZVy9cgWnTp++77hHD5QzEPo+IscBKEWtVoMXhhgaGICqKHAcBzUuRCHSezISjQnaANtqWbZlMZIIf1ACzi+IE/x0w5CtMTQMkeIRpgQnKQqJYkHka20BjMP1PFycmcGhgweb5JK7oV37zSdN9QlHp5fRPqqiIJVIoGFZsG2bRfptFoMQNpH3EDszGqLMkM9mQaIIhqZJqeGtYhGe72NlZYUxvLnjJcSPRNlCXLfQfAg6LOp4F4Wqqsik06g3GvBcFxc/+ghPnTu3O0+hj/seYaMBykfY+r4PAqBQKCCilAUGfAPPptNsJgnQtmMglUrJQKFUKsEwDARBINeiUBQUka6QvVVVtSm9/DOH/wZ+ev9X5MCyTCYD0zDwDx/5+/jzm38OK2AkYAqKf33x9/Hfv/B/AwDcuHEDlVoNL7/0Uk+bUdyZERDneqflRQDMdvS4GSYSCbieh4C3M3biasWnrAoiclz/hPDUvUjfh1GEdCrFeFmKggofOR+sriKXy21LlfMSjWmacq3rus7OqVuUH+u+MHQdSCZh2TZWl5aQTqdx6D4rNT4wZYIoihBaFkCIJAaqqop8Pg/XddGwLMaEjaX3gM49rCpPEVWrVWxubkoJSqFjnc/lMFAoML5BTHGwdQNs2DZLORLCoky+SBT+RdosmksXLwIAznSZPbDr/bgLqb4407Zbr6+AmUgwTQKuYSBAY+fTKkJCuhzfTCRAOWFLzHw/sH8/cuk0NF3HVqmEeqOBcq2GGheLapVOFWqQQZt2zFYIomQ6lYKqaahXKvj4ypWerr2P+xeh4yDiqnNVnhXIFwoghEhuEeEZxrgjQACgTdpZ1zT2XFYqKJZKMtWsc42LQj6PQi7HauQ8em512ENepqRgm6UYwlVIDOCXH/llgFBEYHykt1bexszmRdi2jctXruDY0aN7IrJ1yih+khKBOG4vK0chBMlkkmVvHWdbxAzbDkBIt8fJk9gm3MnhSCYS8FwXOi/Vjo2OYmJiAgnDgB8EKFcqcGwbpVKJDSPyPBY48LUeJyCHbdZ/u+vSDUMOOZqfnW1SUL0f8MA4AyFP8dEoQpWPGS7k8wiCQNb5Erze1K7NDGAOheU4KJXLqNfrUDkJTVVVRJQimUwin88jzdN5soWPlwtISz+s67pyrniGj9VtAv8bIeRDCEGxWMTcwgJOnznziYgodyPVF/d8e4HCo3JCCGs3jJF+aMtCbn4b0jZ7kUgkmIGIORa6pmFiYgKFXA7pVAqlahWO57ESDF/olWqVRfe8qwBgmYHW+yAIXK3XLEoGRFGwuryM2/3BRg8sQt9HaFkghLCsgOdBIQSFXI45Avy5ycfUQaUjQLblyT1emiyVSnB5S5pY24J7kM1mkTTNpohbbHBNzyalaNTriCI2US/dQh7+u8e/isHEEDRFBVEVgFD8jzP/X3z44YfQVBWPnDrVU2kP/Bp23JO7mBno1ZE2dB2aYTCRIz7qOIy1MXe6nk62zUwkpKCbQJZPPkwnkwCl2CyV2HpXVeiGAcuyUK3VYAv+gKpCAXtGWu/Tjuvin7Wh60iYJoIgwOWPPtpxDvcyHghnILBt5tkDsoNAaOiL0oDBNaYlYgIZruOgWq+jVC6zB5Fv7olEAmPj42yWNiekWJa140GgXFQj7ggEQQCL628nk8mObPY4CCG4fOUKCrkcpg8d2j7+HdyTT1wiaD3eLos6AmSrlcpTbSKb0ku6sl2GRNd1KIrCHKqW1xYKBcboJQSOZaGQz7OMj6oiwja50+IyqqKfmRACGmM6d5JCjY8ynb1+/b7z8vvYHWEYsiCCMMGqSq2GgFLk8nnYXDsDhCCXy0kZX1EaAO9Katg2SuUyK0vycqKqqshls5jct49xCwhBNTZeOA4qZG9j67Qpm5hO71gXSS2Jf/TIPwAAEBAoRMWV4hW8ufw2Hn30UUZ+6zUA6FZe/ISZgZ7eHtsKrwnTREiZQmq7e9UJ8ZkxAolEArbj7Pi5mUggl83KsnAQhhgeHGSBFyEIeVdTtVaD67qMT8DLRnE71o6IKIIdUW6wLOu+yize985A6PsIbVuSVaxGAyFlA3/qgiyo680ZAb4JOK6LUrnMUoNiyh1P5Q0UCmy4Df9/8bA4rsuMRqyWFKG5RECjCHV+HoZh9NYWCGBjYwMbGxs4ffr09jhUQpqGmsRTY3HFrx335S6QByViXQVi8YraXdxzF2eRME2IcaV7Yda2evmEECSSyR0jTz3Pg9VoIJPJIGGaSKdSqFSrktGdy2TYVDneqiXUHaV2PL+mTlwIcR26rsM0DAS+j6sXL+4ujdzHfYWIZ+0AZgvCIAAohanr2zLh3AYQsj1jIApDlgVoCR5M00Qul0M+n2cqpoaBXC7HHFRKUa3X5RAjgZA7Fyp/9nfNJnL8/JGfw1h6TP4/BcVP8A72H9gvS2+tX9uvFd+036Sk7bgLgUTTBkq3lUyl/eCzAwST3+QKjkIYrcc32XGuCdNExInIAkI6OqIUBT5zRiEEtUYDSe4kCM5XBIASAtt1UalUmGPBnTbZMt4GwiFI8szQ+soKFhcW9nLLPjfc/84AT/GBM0mF0A+lFOCpnmwmsz1FizJt7VK5jEa9LlNiggeQE62GsQ2XgBmFDD9OGIaoVquwucEQKXnh3dc5I10Q0nrFpUuXUCgUMD4x0fwL4QjwiER4qa365wJxcQ7CpUbFBt70Orqtg9D6Rdt8Lxbubi1DmqpKwt+eFjV2evmJRAIuP0YURWhYFjOoYKnF/fv3M8IRpdjkrUOqqjYtbp1Lp7qeJwdRhUGws0QQg3imEqYJVVVRr9cxe+PGfePl99EdoeeBxqJPIQ2ua5p0BIQQGQApSlPnToDQ6W8NHppGl1PWtZTP5WR3jc2zkCKYiGcVm7KJ/LntBF3R8bVTv7p9PTTCerSB127/sOPfCKdABBk0tlGLbIcQByPYlimmbTa/drZDBiex14DX3IWdpbEvcU5xJLgTLwav7QXxYwmxIGF/fN+XZR9CCPL5PCbGx6Hx8lClWgXhaf5MOo18NosU39AjsOejWqvB5VoEHTMv/OciswhCMD872zRF8V7Ffe0MBI4jCTyE15qEYpV4mLPZrNxgHNdFtVpl7H4ubpNOpTAwMMA8whbWeJzxDgCmriOfz0PTNFAAlmWhXK2yYUL8/WzbZgIigBx92QvW1tawWSzi1KlTO2aBd0SHmr4gvhBCoHMiTGuaK764aZuv1uPthR0MbNf7hSRwz2jx8hOGAdtxYPNuEMH0TiYSSKfTUFUVw0ND0Pls8/XNze2RpXxxZzOZbbJWFMH3fVRrNVi23TFzIZniMR7E6tISSqVS79fSxz2LUJSe+MYnpvKJZzyZTLKNCcyxrvO1LhwFQ9fbBg8CklcAyCmGad7mJrRO6lyXRKzFOo9aDcPoSf30p6d+CodyU6CgbFYBgN+/+G/gh72ttyYxsdjGTQhTDCSxwENkRlqDilabEXcQhN1oHdfc9lzEJnqn2QG0cQYIQcOyUOcBQMTLmJlMBoauI8U5YJqioFKtymm1ALPlyWQSWd7FIfYVy7ZR5m3nHQMDfh467xbxfR9zs7P3fGbxvnUGwjBEGH9Y+AffaDRYipjr6SuKAtd1US6X0Wg0WJqYE8QGCgUk2yzkOFp/oyoKcnxzkVmCWo0Jh7gubM4pSLVECbvh8pUrGBocxNjY2O4vjqGTwxDGNsRWiEW+FwgHY/cTYq9RFQWGaULlpZW9QnQY6KYJ27LkhDONL+Y4uVJVVQwNDkIhTA+91FLfFy2GCdNElqs+hmHIlMvqdVZaanUKxD3i/IFkIgHf93FrcXHPRqqPewuB624HEYoiR3g7rsskwrlCaBhFqNfrKPJMgFCdy3PBG71DCa7TOknwjhgxCte2bdRrNTiuK4eUKXvIJipEwd89+FWEUST99KXGMv7z3F/u+redsnu0C9fojrvm9xBEANsbechHK+/trdh7iaFP1UqFdYNwTkImk2kKNrLZLAsqeNuy0yJopvIsZzqVQjKRYCWNMITNlSjdNk5B/GpFZnFzbQ3r6+v3dGbxvnUGqOcxz5Z7slEYol6rwQsC5vFlMqCUolKpoFavM2NP2HCaQZ4JiLOBdxw/5tm3QtSECoWC3JR838dmsYg6l+Q1e1H+4lhZWUFxawunY1mBT4K4935XOAMcu00OYy+Keeci5bfH7IAY3Sz030XqP81LNe2uyTAMDA0OQgVY2o/PMwe2a59CeCgZcwoiMBJRvVaDw+uC7a7F4HoSxfV1bGxs3LcqYw87oihiXAFsG+1GowHbspgjkEgwiVnHQalUgu26EP3rgg8gOlTabqYt2cRWaJxcmM1koClsHLIsPXgeMqlUz9lEAEitp3BAn2z62R9e+Xdwgu4Oa6f3+KSqpR3ebE8vVzj/gtxBdiCKItiOA4u3mYdhCNMwkMtmZbayFYV8nm3ahGBra6t5fHGsBV2UEMRxojCEw4fOtXIT5KUDSPOptKvLy6hWq3u6ns8S96UzEM8KiId6q1hkk/M0DYV8Hq7nsXQO/5CSqRTLBPCUL9B5wXZzBOJQCEEmlUI2m2Xtc2HIJvZxToHruj21+Fy+fBnDw8NyvHLsRLr+XbtaHrDNYQB6Y/L3ir0eKZ7ys3tY1GEYNrX3gJdyACa6ou+SaUkmk8gXCtBUFZVaTab9SCzKD/nAEyFBnE2nmXEnhCnHNRpyYTfdWUqR5M7Nxtpak7PRx/2DqGVN0jBEqVSCH4bIZDJIJhKMD8TbkYVQWI5H9HEy3g6uzi6OQByGriOXz7N2WN6CSwjTSGnEnsFuWF9fx+bWFv7xI7/e9PMtZwvfvPmtrn/bybLc7S4k4M4yCqI1U8xz6QZKqQweapwMLs7f5ATurtlfQjA4MCBl6OOlRnGcMIqYWiQhMA0DWf6siKF3jUaDDaCLlRf5wVlmMZlEaWsLZZ5luhdxXzoDlNfkhYH3fR+1ahU+H1dpWRbbfLg3NzAwIOu+AoKM13ZB7zGVE4YhNE1DKp1mYiVg0WbDslCqVGDZdsfFvby8jFKlglOnTu3pPeV1tPmZVBD7pFmBlgXU06Ju+RvZstMh5SeyAHW+kD3PAygTYUny1FzP7w3WS9yU9nMcCB2DiNIdn4NwCtK87BNxFTq5sMWzQJiGRMo0USkW0eBCJX3cP4iiSDoDwnZUajX5XGa4DLkQqMqk08wJaJ14yccAx7EXRyD+N2EUIcm7EFKJxDbRtVaTUxA7pZYvXb6MoYEBfPnkl/H02JNNv/sPV/8INb/W+b07kGfvivpgG+x2X1qvUOFpfdKFhBzydH2tVmNlFj790eCbdStPqhtUVcXQ0BA0TUMYhnKOgXAGAj6rQl4PdwpyXH8ins30BYE9dm2mrkPXNGyur6NW6/y5fJ6473RWoyhiksMclNf1XM9DGARyfDBRlO2WwA4QEaPw5joxXLueD29bo7wVKJfNQijwua4rB1g4jgNFUVi6WddlqvHypUsYGx3F8PDwjmPv5pJEUQTXdeE6jpTOjMIQruvCdhw5GEho84uxwiJivnbtGpKpFDRFYcM++L+qokDl31uOw8Yrx4R49nJ/RHZA3AOxcALuHPi8nUtA13UYXKIVgBR82gsK+TwCbig2i0WMjowwDgInAbWDruvI8tGvYoqcz0tOcVVDVdeh+j421taQTCbbfm593JuIxERLbuA910W9VkPg+9D4hEsRQKTT6a599m25OHs8H9txEIQhVEWR0xGDIGCDjrg8bxAEsMBannVuOxRFwerqKra2tvDiCy+AAPinZ/4J3l17Xx67HtTxRx//L/inZ/7xjvellMIPAmY3eC+9sA913jYtuh/CKEIUBAj5NEYAuPbxxyzajo1cV4TN4APHojBEEIZM619RoPOhYR3vJ3baO5PLFIdhCM/zpLSz7/vbs2E4pG2NzRwQ3KBeoWsahgcHsc6HoZUqFRSy2aZM647zJgTpZBKGpqFh26BRBKvRgK5pSJimfIYo70yqFItojIywtugeSKKfJe47Z4AKz55H9jYnDTqOA4XX6k3TRIo/hL1AjB0G9r6gG7yDIa4tLjgFSdOEzxd3wB9esSkSQrC5uYlytYqXz55tvkawVpZKtcpYzo4jN3zbtlkvsuMwUR/uxMQ7EMSiUgmBqmlskYpZ54rCnAZKUSyVgFJJjgelXC1wx4YpyircaUokEuzLNJFIJmHy700um5rJZJrIk6ZpwrJtuI7TdnEqfOyoaZo7PjOFkK4jlNuBECIXtet52NjcRIYzuTs5A+I8UqkUNF1nmQGeJdD5tDSxASQSCZS2tjB0jy7qPnZCZAVEO65wiuuNBhzfxyDXIcmkUjB6+DwppVAIkT3ne7UbQRjCsSwgimAmk/K51zQNGU1DlEyysehcq0MMzRGOwczMDIaGhjDMS4snBo/j5f0v4Qex1sL/eP1P8Sg9g0RowOE2w3VdlqlsydLF7YYCbG/w3GYofMYHjSI5pVUw7MWo37bZT0Kks6/rOpNWNk0kk0mY3GYI0mYylWrK4CqEzX7x63WUKxWmHRBfv5zLoes6dF3f4aApigLsMcNhGAYGBwdR5NMkRbAoeFidAiFd15HjwYQIJIIw3J6AyDs0dF3H2toakqlU2y6UzxP3lTMQRRFCx5FENt/z0Gg0UKtUAO6hZbNZ5tXu4SYT8NRfl42iHTzfh+e6rNWNR83NBybyQQX3xj3Pg+f7oJTixvXrKBQK2NjawuLt23AsS7bChJx7QMFIR6Zpyg13cGAAJhc1ERuzYRhQNQ2aqjL1tCBAKpWS7VFxFItFfOe738Uz586h0EbDXJD2giBAuVyGHwTQdR2+68JyHDi8Dcu2bdQ3N2UWRPwdwCa7pdNppNJpqcCoqioCw5DCHuLedCtnCJ0E0jKUZDcQQjA0OIjV9XV4vo9qo4FUItHVGQDYs2DqOlTeheJ5HnzPQxRFrD2RO1a6rmN9dZXd474zcM+DxrkCnLRnNRpwbBuqqm4rWO5x81AIF6+Koj3ZHMuyEIFlmtpN+BStbUne1SAmpQZBgLXVVZQrFRw/fhyXL1+GY9uwbRvHa0fxOn6EiMtpedTDH83+Mf5G+q8jwTskBgcGYHCnXWzM8Wi63miAEIKBNnahXC7jr77zHZzrYjfEGGHbcWDxVkkFrK27YVkyG1Gv1bDJbUfANT8owCa/ptPIpFKsTMidA40PFRI2Q+fTCrt9Xoqw6TGHpBekkkkEuRxK5TIq1SqSySTLUPKgr9v7pbitE5LGVqPBAiZOKE8kEigXi2iMjiKTycjpl/cC7itngPJ6MggBDUOUSyWWuuLp8MGBgd5G57YeFzxNJRZzDw+OULOiAGuhU5Su0YEfBNja2kKxWESlUmGiR40GaL2OYrEITVWRzmSQTKUwODiIdCaDDN9IDcOQnrpkt/LF0+167nQmAeGkF4W3BxqmKcexCuUwcQ8oTy8GQQDHtlGLDQ1qNBrY2NjYrr8DIKqKXCaDfD6PQqGAwcFBptLWYVELgo5CCCLSu9Y5BZNTHSgUsLm5ycRCFAXpHjduVVGQTCSkrGjIswTCkUmYJsqlEjyerelVZbKPzweRsB2KApuTVH2eelY0re3m1hUxgR1CCKii9LzhODw1D0p70hOwGg1scvJZjQ9OA6X4+No1JoyVTCKdSuHI8BF8Rf8y3i2/h5D/dz26gf/y3G9gMjspo9BODrHQ8LjTaFWKGvFSbZhIwDBNOVshitksYTeiMITr+7AbDVRrNdQbDViNBrMdm5vynCLwIXN8yNPA4CCGhoa6l4F5t4bI4PQKSinS6TQs20bkuqjV68jncrs6AwK6pkFNp2HzYMJxHERRJLOLCdNEcXMTqZizcy/g/nIGfB9CGW6zWIRl21B4H6iK7Va2vR+4mQFKgV09/YZtI+SbVIILS8RR55v81uYmNre2UKvVGJGE9xoL4Zwnn3qqufedp5fYKUSybiggNmoxFKl10JESK3l8Um1xIRcaUQqfZzNoFMELAtAwRNjazaAoyGSzSPPx0BofRUz4fIFN7ghZloVSqYSF+XlQSqFqGoaGhuTXAGf2iuuNX3vUY0o24q2kCS5OFFQqqFWrSPToLIr31TUN2UwGdS5aUm802KAqVYWp66iUyzL9ea8s6j6aEfk+60xRFHiui62tLakQapomdNOUNmAviG8vUq54l00njCKZFUgmk1KwTP6edzdsbW3JL2Fbsrkckjy7deToURw6dIj1vgPSTgxaQ7j45iX4dNse/fHMN/C107/eLHEesxeE//xuEY/FpD+h5eGqalPQEHHbEYduGBgYGMDAwIDMcKpcSKxWq2FjYwM1Pi9gfmEBVz/+GADrMhoeHsYgdw6y2eyObjFhK3vJLEqHhXcYrK2vw+HKkJkeonjxHBBCpIKhyDCK7KJpGKhVKrJ7Kr0HldpPE/eNMxDx1j0KsIxAo8FmjufzKJVKiHyfiQ3tAVJ4o5U1Twioqm5LbbYg4OQbUIp0JoOGZaFSqWB1dRWVchlFzmIH2AIeHhrC8ePHMTg0hEw6jXq9jm9/+9t44sknMTY62va8Au4Y+L7PJv9xhirljPhO3j0Fc0TEAlA4t0J6/IRIIpBl21JNEaIuJr7nTkCDt1hpMQZ1XJubALKmKEaAaqq6wxExDAOpdBq1QkFOlIyiCOVyWRq9Gzdu4PLlyxByoUNDQ5IYKep1rTPM2927qMWw5/N5dh2eh0qthnwu1/Hv20FRFKRTKRYp8DYikbGpVSooDAzcU4u6j2ZEfOCQ57pSI8JMJKDrOiqVSk9DxFrRLkMlO5Qo7RhMWHyWgaZpSCQSqNfrKJfLuLWwwKL/SgWU/35wYABHjx7F4NAQhgYGoGkaLly4AMM08eijjzYz/rnty2Yy+NLhF/Bns38Blf/37vp7+OmDX8H+zP6dDnwMDi+JGYYhFVrFdREwewH+r6br2/LDcQEjbjc8z4PrujA4YVi8Lh6hK7yOLmyL4Cg0OdXc0U6nUnA8D4auI5lMwrZtaTeKxSIWFhYASqEbBgaHhjA8NCTLFfKzQRdSNt05qljlbeoN20aDc7V6GjjH3lCSBhWejQrAOGapRAKqosBqNGQ56K7qOtwh7htngPKadLVSkTrPA0NDrDeUt6Ld8ajeNpAfKNDcl8yjQz8IUKtWcXN2FkvLy3BdF6qqYnBwEFNTUxgaGsLg0BCMNr3xszdvwjAMHNi/v/17c2KMqKsLRJywEwQBwiCQm3J84IcYFwzwVFyb4/s889CT/jfPPGiati1Ryr9UHlXEF2839q6macyI+D5c10UikZDZAIF6vY7NzU0Ui0Wsra0x6WhK8b3vfQ/79+/HxMQEMtnsjuxAq6GJQ1EUDPAN2+W1y3SPXn78GOlUCrZtIwhDWLbNmN08o1Ov15Hao2BMH58+KKWgfJMrFosIggCGYWB4eBilYpFxcvbIMRKtiZ02Flk2AJqcApdzjBqWhUathtXVVWxsbiLiaenhoSEc5LYjx2XU43B9H/MLCzh+4kTH1j9VVfHLp34Jf77wn1ELtlvY/mjxG/hvz/0LhEHASMLC4eeRsPyeb+ztGPSCdCiClF3vUYw0JwITOdmU247tl9OOAQ7ASrFezG4kk0ns378f+7kNDcOQZWK5g3D16lUEQYC5mzfhOg727duHsbGxHaqw4to7fZapdJpNU4wilCqVnonpQkGVgGlKKOm07OKqNRpImiaqlQpS6TQajQay2eyux/y0cd84AxFP1dT5Dc3m88im06jxzUI3jD0v6F5qfDL9B8CxLCzcuoWlpSUUSyXQKEI+n8fBgweRz+cxOjq6aw3QDwIszM/j8OHDe07HCc6AqqqgHRQOXU5oVBQFmUwGAHZ48EIeOJFIIJVOb2s2YLvuh1hUAKDpYe228e4Gk7cHeb7flniXyWSQyWQwMTGBt956ixkUsAzLtevXcfnKFaSSSYyPjWFichKDg4O9vS/vMLEsC+VKBaZh9CQXHU//EsIULB3e2eG4LpKJBOq1GvKFAhp8kmIf9w5oECDgQ2oc14VpGKyfXFXheR7CKEJiD2qhEruQ0mQkyjNZm5ubmF9YwNraGousVRVjY2N45NQpDAwMYLxNhrAVC/PziChtGm/eDnkzj79z9G/jD67+W/mzn6z/BNcqH+PUYHs9E0opCB+znOJZL5kl5HZDOAAJrtIoM478fojso9AGcF1XSjsLdJv41w0aD44iykYctxKjVVXFyMgIRkZGcOvWLWxsbAAABgYG0LAsvPvee1AIwcDQEPaNj2NiYgKJZJJlGXfZN/L5PFxe969UKhgYGOjpnMWEQ1A20CqTycjMkO26UHig12g0WCvr55wduC+cgSgImIa8ZSHwPGS4IhgIExyKKEVyLwt6Dw9jrVbDyvIyVlZWsLG5iTAMkS8UcOrkSew/cACZTAa1ep2VDXpwRhYWFuD7PqYPH+79fFtPf5ffq4oCTXQxtIFgthq6DrNL2qvjhk9Yz3674Sy7QedMfRFdtDvHer2ON954A1EU4eDBg1hbX8e5c+cQRRE2NzawsrqK24uL+Pj6dei6jomJCUxMTLT1/ONIpdPwuEErlssY3YNGQLytKMHrtI7jwHZd2PU68oUCLMvqOwP3GHzbZiJktg2dDxozDENm0UDp3soEMeJgN0RhiLX1dayurGBlZQUNywJRFIyNjeHxxx9n5UFCUCqXe1o3EaWYvXkTB/bv76l75avHvopv3vwWKt62Uua/vvT7+O0XfqttBlWUAsTE0Xb3xInbjR7tbes7dcuo7AbTMBBwzYF2XVIAcPXqVVy5cgVTU1NYXFzEgQMHcPjIEdi2jZWVFSwvLeHChx/i/IULyOfz2LdvH/ZNTCCXz3fM6qmKglwuh7ploW7bTLK6B8KwCMpElkXl2cVqvY4gimDzbpZEMgnHcT73zoL7whmwuLSv53lIp9NIp1IyTeYFAWP072EokEQH7z4MQyzfvo2bc3PY2tqCoqoYHx/Ho2fOIJvLIcUliHeoju2yqCmAGzduYHL//p4eps4H6vw+YgO/GyWTTqpqolOh9Sx6eUdCmEJYyAWTWo3O5uYm3nrrLaRSKXzxi1/E9evXpeiPoigYHR3F8MgIzpw+ja1iESvc2C4sLkLTNEwdPIjpQ4eQb8MOVwhBLpuVvcC1Wq2n9BwBI2PGuQpiGlkYhihubWGID5jq5OD08dkjDALUSyUp/yrY2wBrC47AoreeP68eNu1arYb5uTksLC7Cc11kMhlM7t+PbC6HdEzRkICz+rkNimuFtMPq6irq9Tqefuqpnk41pSXwD0/8Mv7fM/9K/mxmcwbvrr2LZ8bPtb88/m9PM0g+B+iGAZULNbWuM0opzp8/j8XFRZw6dQpHjx7F/MKCDA6SiQQOTU9j6tAh+J6H5ZUVrCwvy2xjIZ/H9OHDOHDgwA7ZcwLOeYoieL6PcqXChON62XN4ECFsqeAIWPU6PEqxtbmJyQMHYNt23xnYDY5lwa5UEHieVOGKe4UBZ7nf0YJuWdy1ahXzc3OYX1yE73kYGR3FuWeewfj4OCiAarmMiNIdtWGZEtzFWIgF/cSTT3Z9XezA7S8BnQ3Hp6Et3u484g94/Lx6gcHHEguSpFhUi4uLOH/+PEZHR3Hu3Dlomgbf9xnJMZauBAAoCgaHhlAYGMAjp07BajSwsLCA+fl53Lx5E4MDA5iensb+/fuhapq8X7qmwcjlUK5UUKnVZJ/1blAIQdjiPKaSSSY6RSmWbt/G1KFDMgLt4/MFpRSVzU2EERtZneLS1mKtCrtxJ63IrYjCEMsrK5ibm8Pm+jo0w8ChqSlMTU0hm8ux6XaOAy0WUYtME+Elt26CNgBwY3YWg4ODPZfFKICfnf4Z/MmNP8WavcbOk1D8T5f+DZ4eewoKabYPERcdA+5OICHReqxO5ZUeS7aGaSJsIfMFQYC33noLxWIRTz/9NPbv3y9lzYXtiGLlAF3XJd+ARhHW1tcxPzeHDy5cwMzMDMsmTE/vaDfNZbNy3k25WsVwj59FaxeUxh0C23FQ3NxEjr+P6HD5vHBPOwNBEKBWLMoatWGacrwlwG5exFN93fpNJVofOEIQBQGWlpcxPzeHjY0NGKaJQ4cO4dChQ00p31qthggsIhSbl1TK4iS63R7nmzdvopDPY6jXh6jNAtlN7Uws6E/bu6e71Ey7QVEUmAZTRfP4Zi/Se4cOHcLjjz8u763neVA6SBKLeiUFKwE8cuoUTp48idWVFdycn8d758/jo5kZTB08iANTU/K12UyGCSd5niwX7Oo80eZ+ZcG2TiWTqDcaqJbLKJVK0Ph0uz4+X1TLZYSOgzAIYCYSUFQVpkivU8rKRVHUewdS6/PHO23m5uawMD8P13UxPDyMp55+GvsmJ7c17cMQHp+GGc8Gxom40rHusAlXqlWsr63h6XPtI/p2IAAM1cCvPfIr+K3zv8PPGbhZvYkf3H4dXz7wctPrxXNNYlomdwPtsoqfBPFAQsgUv/HGG3AcBy+88IIkI4suLFXTdnQXgZOfQ95uOj4+jvHxcTi2jfn5eczPz2Nubg6DhQKmDx+WDhhRFAwODGBjYwOO4/RERBY2R3RBiadI13UpJrV8+zYOHz0Kx3E+146ke9oZqJRKAK/xCmEfWatqiRT36lE5rotr167h5twcPNfF8MgInj53Dvv27duxGALfh889zXbiMpJ01/rQxQxIrV7HysoKnnzyyZ4Z5+222l1rlXczM9ChTAAwR+VOygQChmHIuQqXL13CrVu3cPr0aRw7dow5aZx40zXt3qZ/mCgKJiYnMTE5CavRwNz8PBbm53Fjdhb5QgEHDhxAjouWrK2tIQwCNCwL2V1q/cJgCx0Hca1CdazeaGBtZQWZTEbqqPfx+cD3fTi1GkgYyj56Kf3K16iwHT3ZDUE25g7/2vo6rl69ivW1Nei6joMHD2J6ehrZNk6gY9uIwOxXu7SyGKAFoKPtmJ2dhZlIYN++fb3fBH6tXz7wZfzR9W9gobYgf/VvLv8BXtz3PHQ1lma/Qzn2TuiWJW3LG+gxuFAUBaauw/E8bGxs4MKFC1BVFS+99BIymYzMsoghYt1S+a3nkUgmcfKRR3DyxAmsrq1h7uZNvH/+PAxdx9jYGI4ePYpCoYBMNotarcbUCROJngIJQghoTMsBgJRXtup1lEolVgLvOwM74bouAsdhYxVjxC25YOKs1F42V/5az/dx7fp13LhxAzSKMDU1henpaWS61I4t2wbl79/OeIiNojVqp7HzujE7C9M0ZSvMnaLdJhyH5Ax8BmWC1l5q2qOTA0DqG3zw0UeolEp46qmnMDk5idapkb7vd1X36/aOqXQap0+fxqlHHsHS0hKu37iBmZkZLC4s4NTp08jxckGtXpcDVTqBdvheXEvCNFG3LFSrVQwMDPSdgc8R1XIZKqVSK0ThzgAA+WyJlrqeW0EJwcbWFi5fuoTNzU3kcjk8+eSTmNy/v6NDEQQBPC6DnOpA+ouXGNvZDtfzMD8/j5MnT97RJEFVUfG1U7+O/8tP/q8AJQABVqwV/NXCt/Hzh39Ovu5uCZW1ou1ApzYlxr3AME0sLS9jZmYG2VwOX/ziF2WLOcBsghBq0/YQSEgoCsYnJjA+MQG70cD1GzewcOsWbi8t4fD0NI4dOwZN0+DxLpV8Pt/1fIUSohiWFr8jQrBua32dBSkDA59bqeCedQaq1SoUnsahUQSVT6WSiKVse6lx+UGAGzdu4Pr16wijCEePHMFR/qF2/TuuCQ6gY9ugTPW1/pz/G1KK27du4eDUVNMUq10NUZvfd1tCQiEQuLsEwo7OVsvPexkoJI5pNRp47513YDkOHj97FpOTk21fHwRB5wXNzyGuutj2JXxxZ3M5VMpl3Jybw5tvvIGBwUFM7tuHRCq1+6KOs8jbXKdhGFAcB5VSCaVcDrlcrq858DnAdV14jgM99hk1qUO2yAbv+hlRilKphEuXL2NtbQ35QgFffO45DPfQCmhbFgsiTBNqBzvTSQxHnNXy0hKiKMIh3k4oN7w9PFtfnHgWJwrHcaV4Vf7s31399/jpqZ9GQjXv+Lhd0c0WtMsC9OIc8EBobm4OH83MYGhwEGefeKIt70PY7FYyYPNpkF27G5LpNI4ePYoD+/djeWUFN2/exNzcHKYOHkRhcBA13hbYbR8RxxfSyHFHSJRMa7aNRqMBy7I+N82Be9IZsCwLvusiQbdVoZqyAkDT990e4DAMMXvzJj7++GMEnofpw4dx4sQJJEwTFEwetOu5OI7MCnTymncjEK6trsJ1XUwdPNjUzw9skwHbkQI7cgY6XK/cEPkG+Umx2/Js9fDbljXotkCLeG25VMJbb70FRVVx7qmnoJtmE5EwDr/Dz1vPY9eWJf7eAwMDeGn/fqytr+Py5cu4ePEiCgMDGJ+Y6Lqo49kf/oMmAybLBfU6G6nNxVH6+GxRrVah8jUi5tEbsdIiAFkmANB1ql21UsGlK1ewvLSEbCaDc+fOYXJyEgToquQHsM1IDCTrOrdiF9uxuLiIsfFx+SztaOfl19XNBhJC8E9OfQ3/xx//N/JnRbeIP73xTfzyiV8CsG0H7yT70A7yajqRoHsJhmIOuMgYfjQzg9nZWRw+fBgHp6Y6jhf2fZ8JSu1mO3qQKY4ohaJpOHrsGI4dO4brPLN86/ZtjI6NwTCM7m3K/NwjEdy2nLOY6lqr1VAsFvvOgAClFLVaDVoYgqgqaBA0ZwXidfkuNW1KKebm53H1yhXYto1Dhw7h5MmTe2rpcz0PAZ+H0E1MaDdn4NatW8jn822JZaTlXyDmGHCj1ZRK7LKAPi3vvtPR4s6ATO/HyhjCUMXvysrKCt59910UCgU8++yzTNqZT3Js6wx0+PmOc9llUUdRxIymqgKEYGxsDGOjo1heWcHFS5dw9coVrK2t4ezjjyPXYTG2Zl5aSY2maaJWr6NSqWBra6tjtqOPTwe2bcN3HJixZ64pKxAvaYmsYpvjWJaFS5cuYWFxEelUCk8++SQOHDiwp2ybkO5NJJNdU+9ybbd5dhuWhc3NTTzVpZ1Q2IkmxNakOP5jo4/jyZEn8f7G+/Jlf3z9f8HPTf8MskasTfpuOQNdsopEvI94DSeISwGgNqXQMAzx7rvvYnV1FY8//jgOHTqESqUCn49fb3VigiBgUum7pNybCJydL0ZG9Zqm4dSpUzhy5AiuXL6MxVu3sLa2hsOHD+PkiRNtgzBCCCMr8mO1BhKEEKTSaWxtbCCbzX5unKN7zhmwbRuh78PEdqTblBVoWdC05WcAE615//33sbm5iQP79+ORU6eQ2Ssxg1I2bQ/YlSSidFnQXhBgaWkJp061V/5qh7iDEN/Yd2yuMadIRELA3eMLiPeRwzdiP2NvyxZwvLOgmzLh7OwsPvroI0xOTuKpp56SEtJiPCttGfYjhq/0EmHvlh0Qz0mTo0QI9u3bh4HBQVy/dg23l5bw6quv4pGTJ3Hs+PEm40+x8/PdYUQoRTqVQnFzE4VCARMTE5+7qtjDhFqtBi2KoKgqfN+XKVgATVkBgVbOAAUwd/MmZmZmoGkazj72GKamp6Huse7t8SACuwQRAKCK57bNsW7dugVVUTAxMdH1GG1OrjkdzTfbr53+dbzz2ru8C4eg5tXxR9e+gX965msywr7rz2ssg9K0PoXtED8Xdq3NfXBdF2+99RZq1Sqee+45jHFND03XEXFyt9pyn23HYd0jPThwCiE75hLEITstYscyTROPnz2LkfFxzM/N4eq1a1hZXsZTTz21o9woZJ4F2mYHDAO1ahUVPqRqz5/5XcA95ww4jgOVL+igXVYAaEqtNUXUlOLG7CwuXbyIRCKBL734Ioa7pG+aPLXW8/A8hEHARCJ2WdDC84/4ootvIstLSwjDEPsPHNj12tui9fy48Wi+ELbwhaKaFASK/a24Vsp/J1i3bZcA/zuRFhfM/nZ1TYrtckbH5UQpPvzoI9y8eRPHjx/H6dOn5a/ETPKQb/zxzgExUKnX8cAKbxdqewqCLd3ms04mEpicnERhYAArq6u4dOkSlvjCFlmCtvrligLEFjUhBAnDQKVaRaPRuCdUxR4WBEGAwPNgAMzQUtq1tNgauVqWhffPn8f62hqmp6dx5syZrvXmjqBUEo57YZqLTFXrc0sBLC4sYGLfvt7EbbqdEtgaPTFwDK9MvoTXl38kf/ens3+Kv3Xkb0KN1M62I7ZR09hXU4a25Xsx7XRHMCOuW5yX+F2HzbhWq+HNN99EFIZ48Utfaur9N3RdlmNaAwbbtnvPAnfLDsSdmTaf5ejwMEAphkZGsDA3h1dfew0nT5zACZ4liJdH5dtxO9yaHUgmk6hx2/F54J4KW0RLiBLzGs3WrEA7L11V0ajX8aPXX8dHH36IQ4cO4Stf+UpXR2D7j9t4jpTCsW1W7+thNG18JGgrkW3x1i0Mj4z0NLd81/Pr4r1KAqM4F2xHy2T7RfL/SXzxtn7Ff47uZMROi10gDEO89fbbmJubw9mzZ5scAXFOuq5DURQpFSwgUq09b6g8ImoH4Th2+n0ul4OqqpiYmMBzzz2HwPfx6quv4uOPP5ZO3o63a+NIKpoGQ9Ng85nsfXw2cF0XJIqggDm6pF0Q0QaEENycm8P3vvc91Go1vPDCCzj7+ON35giAlRbDIJCjzXeDzAy02I1KuYxqrYYDdxpExBBf779+6lehYDt17kUe/uer/347c9bOdpDteSVNT3sHe7FbeRH8mLthc2MDP/jBD6BpGl5+5ZUdIkBxJcfWAWm2ZfUcRACdMyJxnlC7M9Z1HalkEulUCo9+4Qs4fuwYrly9itdeew3lcrkjsXmHYxFFMHSdyZvz6aifNe4pZ8D3fURBIFW5QOn2oow/bBwidbu6soLvfu97aFgWXnzxRTz22GNSwvZO4HoewjBkQiU9ipKIhyn+IVqOg/W1NRw8ePCOz6WVS9ANd71M0MOiblJibPmd4zh4/fXXsbm5iS9+8YuSEd0KsagDPmdCwOZDqfbiSHUkV3YoKcXPIZVMMiOoqnjly1/G4cOHMXPxIl79/vdRq1Z7fj/dMGBZVt8Z+AzheV5TGlrX9R0loThoFMF1HLz/3ns4f/489k9O4qe+8hWM9tAl0A22IBz3OJZWZBVbMwO3bt2CrusY5SnxT4K43TiQPYD/4tBfa/r9X85/GxsWG+xzV9UH0d12dLNni4uL+PEbb2BoaAgvvfRSR30XwzBYIMF1BQQalrXnrFy7tRzPIHWyLblcDgohCIIAU4cO4eWXXkIYhvje97+Pq1eutM1Wkjb8L03TQKII9XodDh+9/FninnIGXNeFwkkkBGxoRhP5puXmVatVvHf+PG7evImJ8XF85ad+CiMiG9DjQ93uA3ZcV7YE9UrGU1R1W2+c4/atW4CifDIimSiJYBdPG58CgbDlHNr+qs37Ayy998Mf/hCO4+BLX/pSVyMrJpKBkKbRqJZts/TfXpybDouW9pDlEIva56NSz5w+jRdefBGu7+P73/8+bs7N9XQKhq7Dtu3PzcN/2EApZSPEucNHge12sw5ZgStXrmBmZgalSgXPnDuHs088wUpU3YhkMbR7igLfRxgEIIT0PMinXRARUYrFW7ewf4+kxU7n15qm/pUT/wCGsn1+FBR/PvcXkkx3V7CLLYpnK1rv+JUrV/D+++9jamoKzz33XNcyiQgkvCBous69ZgaA9rZBlDq6WSBVVZFJp6EQgmq9jlw+j5dfeQXHjh3D5StX8IMf/EBmOZvfMHZUntHVNO1zCyTuKWdAlgj4l9EiFBJHo9HAhfPnWZ1LUbC8vIzvfPvbeOfdd7G4uAin3c3vAYHvsw4CoOesALD9IMUX9eKtW9g3Pn7HKcc4eurhF4Qc/pBJpj//XvAEBHM3zvoXX4j/rCUz0K4k0G6xb/D0nq7reOXll3cV5QC2I7m4M2DfgXff6ZykKFQXY9e6qP0gwNDgIL7y5S9jamoKH3zwAS5dvNhMBkJLJoYyPXQC5qy2Rix93H34vo+IywsDkKUn8X1rqe3G9esolUqsVu/7eO/dd/Hqq6/i0qVL2NzauuPx3CKIMHm02gsURZH8HPG+G5ubsGz7E2UU24I7SyOpEfzC4Z/fPgcoeG/9fdyqLzXbBGErxPecCNdqK9Du/7vwiKTtaPk3iiK8//77uHr1Kk6fPo2zZ8/u6pzomiY7iYTQkMtHU+/ZdrTjY/UYYGUyGSZxHIaoNxoghODUqVN4+eWX4ToOfvDaa6iUyzuP3ZK9MgwDNp/Q+1njniIQ+r4PlT9wRFGaxSRiN61cKuGNN9+Eqqo48+ij0A0DuUwGq6urWFtbwzvvvgsFwMDgIMbHxzE2NoaBgYH2kXULkeNOFjTAMwMxzkC1VkOpVMKJ48f3dhNaIBZVFHOSdtMZ2NHyx9G0+Dp45fH/j1p+3hphRLFzE+zlhYUFXLhwoWnYUC8w+KwCMZJaIQQNy0LyTpwBtDCXEbs3u3ym2VwODcuC57qSAKiqKh4/exapTAYXZ2Zg2TaefPLJ5ucj9hypqgpFUdDo6w18JgiCQPKMFEVhznd8Xcf4MTMffYTrN25g//79GBweRobPqVhbW8PNuTlcvXoVhmGw1lP+1bH2HzPmURRJtcFeuAIChPNcIkIQhSEUTcOtW7eQTqUwODBwx/ckvlYjSrf5LXwz/3vH/h7+Yv6vYAUNKFBAQfGnN7+JM5OndhD9Om2IXW1HLJBo6ygIJ43bNN/38bYYNvTUU70TrgmBoetsTgEnIMvy4h3YjlYSspAe381uqKqKbDaLIlc0FeTRQqGAV155BW+8+SZ++PrreObZZ9n4amzb4fhnpes66pxv1JMWw13EPeUMUD7FTtU0NmmuRS0MYJP/fvKTnyCfy+Gpp57CVrEIPwjkRK9TjzwC27axur6OtdVVXL9+HVeuXIFpmnJxj46NweSORvxhbVrQezTgrZmBxcVF6JqGsfHx3q5dnEuLtyh+1ksJIL4A7wpajWkLxLmIqVyXLl3Cx1ev4tD0NBs2hG1io3RQOkRdiqJAU1VEUQTf86QQR7tRxLuCNEuNxoVJdku7EkLYeOIggNUyVvTo0aNIJhJ47/334TgOnn32WRmBxq9PGHgvpl7Zx6cHsTZ8bjuasgIcYRjivffew9Lt23j88ceRLxRQrlSgEIIDBw7gwIEDiMIQpVIJK6urWF9bw/u3bwNgQlVjY2MYHx+XQQVpOb7DuQK6rndUG+wEhXelRJQiCEPcvn0bR48c6f362U1oOh/xM1lebHnu82YOf/foV/EHV/8QCk8Qf7j1IS5uXcSZoTN7Ov8d59PCkm9FnLxMwbK8P/rxj+G6Ll548UU2yK0NWbFTvkbnswoC3p4sujk6SUDvBUK2erfAUPJEKhWEYQjX8yTXyTRNfOnFF/GTn/wEb77xBp548klMdcj6CL2Ehz4zoFAKly9oo82Cnp+bw/kLF7BvYgJPPf00FEXBVrEIytmkqqrKD+XQ1BQOHTyIiFIUt7awuraG1bU1LC4uggAYHBpiC3xsjM0lIAQuX9Bar7OqYxA1b/HwLN66xcbntnmIRJTd1tNu+Vm7ntR2iJcn7jqBcJcNVKT35ufncebRR3H86NHtzEObDIUEae6x1nUdfhDA832Ypgm70diTSFTTodmbys9ElJM6XYv4TCilSKfTLDvA25biGarJ/fuRSCTw1ttv44c//CGef/55JDnxUKRPCVimyHUcmbrs49NFxNX+CGJ69Pzz9zwPb731FkqlEp597jnsm5hAuVyGEIMRryWEYHBoCINDQzh9+jQcEVSsreHGjRsyayCyjSMjI+y9KIXnuqCUbk9G3AMUVQXxfURhiPX1dfi+3zYybrfpS7TaDbL7FNW/ffQX8Z9ufguexxzWECH+p0v/Br/z4m9/sog0vta7HYcQFDc38eabb0LVNLz88svbk2Lbbf6xjIKYjULBOUeKgiCK4AcBbMtiLel7yNDEzykuKCaVGbsQ0im2p5gmUykW2VtWE/FZ1TQ899xzOH/+PN577z04to3jx4/LLg1hA8X7NOr1hzwzEEUIufHUWwg4H1+9ikuXLuHw4cP4wmOPyQhP1NxCrknQBE78GB4exvDwMM6cPg3LcbC2soK1tTVcu3YNly5fhsFZu9lsFrl8/o4mRyk85RVRinK5jEajgcnHH++4IHv+kHsoD7CXbW/cn4m+OIfveXjjzTextbmJc+fO7W0Qk1jwvCxkivGkPCqPKL3zPn2RHaCUqX8R0lkJTtxj/r+6riNhmrD5mFKjhfMwNDyMl770Jbzx5pv4wQ9+gBdfeEE6lILboRICx/Pguu6dnX8fe4LI/miGwZ5//jk4joMfvf46XM/DiyLqBDO6hNLmlrSWdZNIJnHo4MHtoKJYxOrqqgwqoijCwOAghoeHkclmkc1k2urk7wZhy0JKsby8jHwuh2wm037z38PaJuguApbUkvgHJ/4+/u3M/wyAEQkvFi/hnfV38cxY7+OSu55Dl/NdWV7G22+/jXw+j3PPPLM7RyueUWjJPohSge/7sER58U7tYOz5CcMQlNKOMs3CERBIp1JoNBps0B6XUpccCUXBk08+iVQ6jYuXLsGxbXzhscea9EpEICcCic9SifCecgbC2NhJNTba89biIi5euoRTjzyCkydPNj1gKo/2gjBE29vWQh5KJRKYnp7G9PQ0QkqxtbWF5aUlLC8v4+bNmyCKgpHhYTnjOpvN9ra5ijIBpVhdWYGiqnK29idCLBXfDXG+wN3Cbgz8Rr2ON998E47j4MUXX0ThTmucwjMmBJqqIghD1KpV7KrrvguEspjYKDpladoZzFQ6Dcdx4DgOomx2R7Ylm8vh5Zdfxo9efx1vvv02Xnn5ZaaIhmZSYaeWxD7uHgilCDwPmqYx4xlzzN9++234vo+X+YhbAVGGFMHHbm6vQgiGh4YwPDQkg4rVlRUsr6zg+o0b8HhaeGJiAuPj4xgdHe08erv12DyjGQQBVlZWcGh6eldt/55AdpHZBfAzh/4G/ur6d1B1qog4S+j3L/0Bnh59Cgq5swzjbi3JlFLc4BNExycm9jTWvdPxNM4TCYIAtXqdZRQ7ZVF2gSjzhWHISo08q9j0nmgu3wroug5D1+F6HhqWhXyrBD0heOSRR5BMJHDhgw+QSqdx7NgxaTcA5qh63Jl4aJ2BgDsDce+6XCrh/fPnMTU1tcMRAGJ9utyD28uHrxKC0eFhDA4N4eDUFCrVKurVKoqlEq7wITapZJKlBcfHWVowVj6Qnjv/Uni0sbq6irHR0bs2DrQXbrPUGLibzoD4ps0xS1tbePOtt6AbBr78yitIJJO7Dn3qBZqmQQ0ClMtlgBBkuQEXZ7AnnjcvQ4QRM3NNzgCliNChfAGmbaBqmuQOtJOzTiQSeO6LX8QPXnsN77z7Lp579tltkibPWH0etb+HDZ7jyOdf1zTp3F24cAHlUglfanEEAJ4Z4EFE1wxYLBqNQwQV+/btQ6lcRqVSQaPRwPraGhYWFqAQgqHhYclTik+xlHaKbmveA0CpWITreRi/C9oC8tx3cQYM1cAvTv8C/vDKv5XOwM3qTfxw6XW8sv/lO3rbbnMJaBTho48+wuzNmzhx/DhOnjoFGkWfuAVXU1XW608pqtUqkyzm9/lObYfIKLZmW1uzAa1Ip9PwPA+WbSObybQlHx6anka90cDFixeRy+UwOjbW5ES5rvuZtyXfM86A2MxpzAtzbBtvvfUWCvl8xzYTlRtdoQXeFS1Zgvh7B76PZCKBfePjUFQVYRBgc3NTpgVvzs2x6GB4GGO8ZphJp7ePSQhUVYVlWSgWi3ji7NlPfE+AZjZvL7ib2uKdPPyl27fx/nvvoTAwgGeffRYGnzp4N6DrOmzHQaVaZW1+fCZ9E8OZ7JRM7QSFt/sIZ02SRXu4p6lkEjXe5tOpdJTJZHDu6afx5ttv48rly3jk1CnIgUaUwv0cxEMeNkScpCmUN8Ejz/n5eTz91FNtWfnCMYx4GWnXddNhY3Vcl43IHhtDmjscjUaDdTatrzcFFaNjYyyoGB5uCipEnXhjcxO6rmOAlzI+KQQfZ7eo+5mxp/Hdhe+hbJXlz/7gyh/ixX0vQFP2vkXItHjL+wZBIIcNnT17FtPT06CUItiDfesIQqBrGnzfR73RwLFcrql7QrymXXdD+8MxZ4CimS8gurq6QUy4DcIQdhdJ8tOnT6NWqeCdd97Byy+/jHQ6LR0ASiksy9rhxH6auHecAc9jKWJKpbzkW2+/DUopnnnmmY41G1XTmEe4Vy8q5vG7joMIjPUponmVdwKMjY/jC5SiVq9jlXMNZi5exEcffoh0JiNZxiMjI1AVBeVSCVEUYbzHLoLdQIEdOtbtEH4KZQKJWERz4/p1zFy8iAP797P2On6/7tb7qqoKlbfldRrl2a5dstPdaSJWEtJ+xkAHpNNp1Op1uag7KSGOjo/jzJkzuDgzg2wuh8nJSZmN8Ps6A586FEB2roSUYn1tDTMzMzh+7FjHXn3x3AqektJLOrYlSxDvPooTB9PpNI4cOYIjR44gDAJsbG5iZWUFa6urmLt5E0RVMcKzBuPj48hkMlAUBRsbGxgdHb2rBODe1iXBzx/+OVy6eFn+ZLmxgr9a+DZ+bvpn7+h9gWab4Ng23nzrLdTrdXzxueeauqzugisAgGUVG40GCNBe22SvjkGM/xPnHu0GRVGQSqVQq9VgddFKIYTgqXPn8IPXXsNbb7+Nl770JWavOL/JqteBT6iIuRfcM86AqPlFUQTP93Hp0iVUymV8qYMUpYCIBu40MqWcCYwo2tFOKEllUYRMOo2jR4/i6NGjCIIAGxsbWF1dxcrKCm7OzjKOwPAwrEYDmUwGiU9Q646D8A1st4dQDuL5NDID/Bw+/PBD3Jybw4njx3Hq9Onmc7qLToiqaag3GjjYA+eiXRtTfIGLPmFKdo4c3vU8VBVJ3qpk23ZXWeSjR46gXC7j/PnzMBMJSWTriw59+ojCkAUFioJyuYy33nkHo2NjOH2mc4ucQogMMMIowp5of/w5cz2vbfeRbOvjxNjR0VGmwPnYY6jX61IP5eKlS5j56COkMxlkcznUazUcnp7e8/Xvdp7dIJzlLww9ihOFY7hSvip/9+8+/v/hpw7+FBLqHln5LWWCarmMN996CxTAS1/6UlO78F4zn92g67pU7ts1oo45Bp2UEKMwZN1h3HbsJeDJpNOoc+GybiOJdV2XpcZ3330Xjz72GMAJi5+1CuG94wy4LosKNQ1rq6u4OT+Pcx1SfHFomsZSTXfoDHi+D0qI1DYQM7WbUkEtD4GmaZiYmGBjJilFtVbD2toaVpaXUa7VEAYBvvvd7zIS4tgYhoaG7hp/ANgeiRnxWhulbNyy5/uIYkpcTQSX2MNfrdUAsFHPsiYmrjNWY7MsS7bKzczMYGNzE48/9ljbGQN3Mx8RhSHCMNxzJ0GT88J+gICXn+402spkMrA4kVCwg9uCEDxx9qwcn33u3DmoXDeh0WjcUYdKH7sjDENEvB0ZhODC+fNIGAaefvrpXfkzQkLcDwLcSUe653kAYZMq4/aiWxo5k8k0BxWbm1hbXcWtxUVQQnDx4kWsb2xgnJcU7uZzE1cRFP8GQSCng/7K4X+I3zr/fwcAEBBQJ8I3L30Lf2Pqr4Fi227UhN0AZGsc+yP2M9/34TgOdF1HtVzG++fPI51K4Zlnn0W6ZU3frawA+LnYto1kKrVrKr/pHFoCChEECjvara2wE1RVRSKRYB1Jtt2VCChKjW+88QYKAwMYGh6GpqqwHlZnIPR9EM4mv3HjBsZGR3uS4zQMgwm8+D6LoPdo9D3PAyiFYRhNA5J6jnQJQS6XQy6Xw9DQEFa//30cOXwYYRDg1q1buH79OjRNw+jIiOxP3k0ZS4wjDrkIk8flNSkfC9oODh+upGlaz45R0zjSNvCDAL7rYuajj+A4Dh577DEMDg5KoyBY8yQ2qlNTFChcge9OYVkWCNjo4ogPjOoZfCHLPmGuFX8nCxpgz5ehaXB9H7ZtdyxdAGxzeeLsWbz66qtYWV7GKE+F1qrVvjPwKSHgrZu6rmNrcxOlchnPP/98Ty1+pmHAchy4riuJqr0iDEP2bPH3jrfJAujJfmiahonxcUyMj8NqNEDKZUxyQuJHH32E6MMPkc1m9xRUCLshMqw+DxA6rfMwDOHzKYtHc0fx2MCjuFzazg58b+F7eGHii0jrseeXxlpx2xzT5+PI1zc2MDc7i4HBQZw+fRpBEKDCu2sUwlp/FV6bVxQFiqJIYuedol6vI5NOI/D9O2Li0yhCyK9JED3v1JalUynYjgPHttt2JMUxOj6O/VNTuDk3h8GhISiKIpUUPyvcM86AaP/a3NyE1Wjgscce6+nvdF1nD1AQwImpPvUCyhdMRCl0Xd9RY9ur17q2ugpN03BwagqZdBqGYaBSqTAS4uoqLnzwASilyOdyGOOti7lcbnvz5/XL+IYfj/7jEBG9wheUp+vQVBWpVIrpdcdaEuPfA5DtVOlUSvYzxzsjKL83pVIJV69cAQjBuXPnkE6nmxwSCs5ViCJ27rGebQJG0lJ4+nYvTkK1WpV1tyAIYPSykYvzFs4cR+D7TBnOMO641SiTycArlXZ1BgAwHsnEBG7Oz0tnoM8b+PQQcvKgrmm4OTeHgYGBnmV8dcOACtzRhDjP8xCBlQjk+gJYVnGPx4qiCOsbG5icnMT+Awdw5tFH4fs+1rmK6u2WoEJ0NhmGITOBEV+DcTshnIJWyI2Yf5mGAU3TkEom8dVTX8Xbb/xzMNUBinJYxnfWvotfe+QfyVKksBvAdosd+5/t7oj5+XncvnULBw4cwMmTJ+V1xuXVEYYIYtk7eX7CMeDOwV6chFqthonxcRYQ9breRQAhugewvWY1VW2rhNsLEokENFVl2gcdOpLiOHb0KJaWlrCyvIxD09OfuXrpPeEMiE6CiFLMzs5idGICiUSie1pWgFLopgnH9+G4bs/OAKUUju8z5UJFgW4YO5T+mshpPTxYK6urGBkdlQMrCCEoFAooFAo4efIkXMfBysqK7E64+vHH0DUNg4ODGBoawuDgoFTNUjiJROHCOTIK5w5AO6ZuRKl8ALtBbMgKL8u0w+rqKmZmZpBMJvHC88/vyGaIdqCIf++LKCQMEXCSXhBFQMtGKKJ0TdOgqSo715ZrqVarklTlB8HuSmKUts2YRGGIkBsoTVVZluEOuANCDMXnmZpuz6Trujg8PY3l5WUsLS1hamqqP7nwU4TPHdu1tTXU63U8+dRTMkXdFZTCNAwQRYG/x6wipRQOb/1qZ29I7HW9bEZbW1tsKNbwMCOeRhF0Xcfk5CQmJycRRRFKxSJWeIfC+QsXQClFNpPB0OAgBoeH2cRNsa65tDe44I3YXEmbKFe0sGmaBtM08cjISTy77xm8vvy6fM2f3vwmfuHoz0NVmF3pZjeiMMTly5exvLKCEydO4NSpU80BFu+0EcPSQkrheZ50aOJfrflNlU/1E/aj3bV4rotMNstKIDxT2haxzEa77ocgDFkQwUtJvWg2tIIQggTvtPI8D+jiDIRhCEVRsG/fPiwsLGBy//67MuBuL7gnnAHhAS3fvg3XdXHuxAlQQmDZNnK7RGIgBKauwyJMThi7TMiLk3s81wUI2TWd1PQwxJn9cbas46BULDICCCAj5SAI4Ps+Au6l5woF5AoFHDt+HLVqFcViEcViEVeuXAEAFAoFjPPUYY4LVojF0Q13cy7B/Pw8Lly4gKGhIZw6daptWYMIz53/v6qq2waYUin2I6IT6STwOqUoZRAwwqBwDDRNQ6VSQS6f3yaGthrVeHTRBQFP44qOkyjmCJA9LG6FMIUzlzucmQ6LNAgCBGEI0zQxdfAgW9T79vWdgU8RAc/sXbt2DZOTkyhks1LSulupgIKXGHnU53rervNIxPMinl8CVmoAJ9i2YkeWsUMJYW1tDQnTRD6fRxCGCMIQhNsO8UwRVcW+yUnsm5yE73koFYsolstYWV3FwuIiDF3HCC9Fjk9MwDRNWTLoiQEfe82vn/pV/Hj5DURgwZEbufj3V/8D/tGhf9j1GJ7r4u2330axVMKZ06dxYGpqx3sTQkBUVY7L1QA5k4TdIipthsiWCvsXRpEUpgO2nR6N872q1SooWCeBoihtHXeROdyNECjsk3A6RMAqgrNekUwmmSKh5yHq0sIqlEqP8uzAwsICTp482fVv7jbuGWcgCALMzc9jenoaQ4ODqFSrCHwfrud1ng3OPxQzkdiVuS02crFoKTh5UPAFsHtZQD488Y2E/7u2toaIUgwODaHRaEhpzFYIr1bXNAwUCpKM5zoOVtfWsL62hrmbN/Hxxx/DMAyMjIxgdHQUw8PDHeU6dxsM0isopbh86RI+vnYNhw4dwvT0dM8PYtO7EgKVs7WbIjTK2nOCIJCOUtTiHCCKUK3VMD4xIQlJQRhC4xoBe1mIwrsXmZL48KJ4SWjXYxKCRCIBz/fhum7bdJ+IFkEpTF3HqVOnsHT7NhYWF/HI6dM9n3Mfe0MYhli+fRuO4+D06dPQ+Tx4y7Kgx4R+OsEwDLj8c+3kDLTO1/B46UkTw9TQg+0Atlt0tw8MEILVlRWMjo1JGe527HNCWB+9pmnQslmMjo5Kh7ZUKmGNdyicv3ABuHABhXweI6OjGBkZQaFQ6LiOozYOyv7MJP6LqZ/Gf174K/mz/zz/l/jpsa90vD6hRuq5Ls49/TRS6XRvgUnLpkwIaRvNU0oRxpwjEVx5vNQLMBusEIJUKiWzDfG/F+Rw8T4dT0mUXNBMHpTkQl4K2c2hoNgezU4phef7bSdaCvsXUYpcJoPD09OYm5vDIZ5VfKicgSAIsLG2Bs/3cfLECRBFQTKRQN2yYNs2jFhdLg7Rf2/wNi6fk+hayWKSPBMz+sIRUHjqCdhbtBhvR/E8D7eXlpDJZBBxJ4DyKFjntXyNL+ROH6yZSGBqakqmlUvFIlbX1qSXSCllI5l5b3I+n5fncDeGFEVhiPfffx+3bt/Go2fO4ND0NBqNRm+Khvye7ZqC5yWC+OcjeBJikdfqdURhCMM04TgOW9RRhGwms2cSYBjz7sX5xQtBcZErUS/cecrsmTBNE6hWWa24zQIVJRJQCsM0QQjBvslJLC0tNU1N7OPuQUSQt5eXceDAAcl/cVyXTY7rYXy0aRhoWFbbrGKrEyDgeZ4MIu4E4ikLogjVSgVbpRImeMQvInmFb4rCAeiUlieEyImtj5w6BddxsLa+jtXlZdycncXly5eh67rUNBgdHW0KKmRLcsuz/w9P/gN879ar8CIWYIUI8cdz38BxHNtxDsWtLbzF1UhffuUVycS/m2qohBDWwhnLPgYie8LJnLV6Hel0Gr7rosE7oQQnQiiC9gKZUeTlFfH+rd1KhH91tHjcWTATCUS2zZ7HFmdABBEUgMll+KenpzE/P4/l5eXPNKt4TzgDvuuiWC5joFCQ/flGIgHVcRBGERzHaa81wD8cEYH6QQDHcSRzu9NiFu8JQI4yBtBRZawVEffyfD6iloJJiQ5xZbFUIgEQwkiEprnn1L2iKBjiMsknTpyA4zhYXlnB6soKrl27hsuXLyORSDCp0/FxOQPhThefSO+VSyU8c+4cJvfv3ya99XLMWMZkr0QbVVGgGoY0rOtra2wATKHA7i2lsHl3gaIo0HRdOljdIPgCwLZ33+5KxCIXxKEdZ8+PYRgGU6YMQ3gtKWXK08wUXLiKH3NoeBi3bt3CVrG4p3vSR2/wfR+ObcNqNHCGZ19EF0q9Xofjuowk2MVBNvjn5bZk8TrZDpHZIsAdDSUKeAlDdAitb2xA4dwiQ9dBFQWGprFe/F0iz3YwEwkcPHgQBw4cQBAEKG5tMduxtoZbt26BEIKBgQE5e0Vsrq22YyQ5gr95+OfxjRt/In/2xvqbGESz9kc7NdJavQ6gxywl50PtGdxZEo5+RCmzwSMjjGtFCHzfl0ODhGOl6/qu3JAgCGTmR0DMOWk9h6hLMCGeHdMwYNs23DaZ69YgAmCZzEKhgGKx+JkGEveEMyAi4ampKfkzuagbDTiOwz7MLovP4G1Cjusy1nsXR4AC8IKARe4x7363sZ8iTSwcAAE/CGA7DsbHx1HI59HQNBmdkNg5xDeensBfm0gkMHXwIA5NTSGMIhS3trC2tobV1VUsLCyAKApyuRxGRkYwdfBgkw76bmjU63jjzTfh86luA9yxaKci1vk074yvIO6FiNAJISiWSsgXCsjncpIvIDZaofjmuS4UQqAbxrbX3wKfG2ylhYXcLXshFnY8nRj3yxOmCUt4+NwZEBoPQtxIbBAhpcjn81BVFctLS3u8M330Akop1tfXAQAjMaU20zDgcDlYIQLWqUvI5FkcL5ZV7FaO8oXQkKY1tfl1yypGlMJ1Xbiu2zS/gwBo1GpIplIY4+dfKpdlG3HcXuxlPYp7I4IK0dpnOw7W1tawtrqK69ev4/LlyzAMA4NDQ5jYtw+T+/Y1OTi/dOzv4T/P/xWsoMHvG8V7yvv4u/g76KZG2okb0ek8d7t/bf4IlLAJoaL86zoOLMvCqZEROVyu0WjIz1qk4m2+lxi8g6Ld/ZT6Am34Bu04EO2Cifj1JBIJoFKBz0sXwskIwxCO44BSigR3SgFmcwYGBjB74wbjstwlAbvdcE84A1ulEjzPw8jISNPPddOEwb3oRqOBTDYrI8LWx8bgbUIuT7l0e7BE763SWtMGdkS3EScaOi0LWVVVGLoOwzCwuroKAjCVMfB56q7LhqCgeQG3PvQE2+WOHZ5l7Ofid6qiYGRkBCMjIzhz5gwsy8LtpSWsrK7i5uwsbly/Lo2LSAt2YtRWymVcvHgRhmHg5ZdfltrqwN6cgZ4gOBstn0vr+2xubkopZ03TWOSmKEim00DE5pX7nDQmDKzOP4f4dbaWCARIl+yF/GziCzv2WpOrEcbHEotRpTSKkEylto/PnYNsLoel5eU7uWN97AJKKTY3NpDNZpt4RRRs6mS9VoMfBGhYVjPPI/aZCoa6HwSwXXd72l0HuL7POpja1PRbn+0gCOC4LisriNcB8nnVdR21eh3Dw8MyHS24Ma0T61qHHMWDjPjv4/egFclEAoemppqCisXFRWxyueQPzp/H0NCQLCnkcjn83aNfxR9c/UN5jNvkNvzAx4cffNBRjbRT6aEVO+xg5xdKWyg3ZMq7APh7bG1tgQIyS2qYJgsIFAWpREKSSqMYMVMhBIZpsjK0mFURhnL+gNbi7O1GHmwXTADb5GrqeXBjSruWbTNeQus+FLHR2GEU4dbiIvKPPtr1Pt4t3BPOwMrSEgghO0b+EgCpVEr20ta5Xr3apu/TNE0QRYHLPfxuKXNBNGyX5hOLOuA1R+FciPMxTROmaTY9KKViEclkUrYZiQ0o4NmHdt5k63W2bpTxLEVrdBBHKpXC1MGDcjaCbdtySMr8/DwURWHDlWI66AIXLlzA4OAgnnvuuR3GLe7h7gq+MOPnK68nfv27eP42T/nGnwOND42KOEtf52pvge/D9X2E3DnwfR+KqsLkRjYIAtYHfoftORHlg41iaTqx4YgWwzCKJIcgyVs6RRuoIBcNDQ1hYWGhtzbZPvaEKIqwsbGBR44ebfq5yAil0mk06nV4ngdbUVipsc0zaBgGbMeBbdusxNfp/fhzR9FSXuSIEwwd7iQKaKrKbAfnNwGs5FAqlXBmcnL7dZoma+HtOAmtgYV4X7le6bZOSLe1K4IKwzBw+MgRqIqCzc1NrK2v4+rVq7h06RKSqRSODE9jQC+g5JcBABmkcXFmBuVyBU888URbNdJeM4WEEDm6t8l2oE0UvktwsrW1xWwwj6I13rEQcIdA2O0oDGWZJqJUjik3dB2GaTYFEa3v1bZU0HI90laHYZNDYBoGfJ5ZTiWTTN2VBwzJZLIpSKGUIp1KwTAMzM/P48xD5QysrGCcp1RbQXjtvVarIeTSrtlMZkcEr+s6q91wec1W2cs44l0ErfB9H5Zlyf5lgC2chGnK+mIrtjhfIP56RVFAOeu1137RHU4C7+MHIYxIwjdZALLWRsFS0oL5Ojo2JjMUDctijsHqKi5dvoyZmRmkUinpBY8MD+O5L35xh6qZZN6Keh4/fjunBZQP8OC/j3dZxBnUvUB494OxqW0q1yEIgwDgNTXCSwS6YbAavutKr9+2bTQaDQS+D61NvVh4+LsRcwghTalacS6ixbDOyZURZeRC4dmLqxV/Ozw0hNnZWdy+fbut4ezjzlEsFuE6DgbbzLAQEXgqlULDsmA7DhT++bUikUigVqsxxbcugkUiwtdUte2acVwXjuM0tfkavJTVrsRZLpcRhWHT8y6zinuQV2+yG8Iu8mBCkqfjv49BRMGpVArT09OYnp5GFEXMMVhbw+raGv66+9N4W3kHiqrgjHsGtUp1x7Ch2I1gjjDQVL7bEcjQbVJ3qyOw45p6wNbWVlMQoSgKiKoCvD1RBG+KqiLBHTPf8+Byu+H5vvwihCDdjhzaY5mXUoqIkCZnSLQYOq6LeqMhCeZpkU2MQWSgh4eHMTc3t6f78EnwuTsDlFKUy2Ucm5xs60lSyoZ9ZDIZNkGOZwjSmUyTKBClFMlkEl4QMC34Ds5AEIbSI4tHakEQwLKs7b5ewnrLTZ5G6oQwilAsFnGmZSiKSAUFQXDH4hHx5SNr3fEShnwhj8Djiw9sBO/hw4dx+PBhhGGI9fV1XLp4EUU+WXFtfR1vvPkmJnhvcjrWDhTxY3TslRZoXRh3QCIU2NraQjqVahJyUbnoR9iBSKOqKpKpFBK8xchzXXg8JRhQioTrbveT7wE7sjQ89WcmEnBcF7VaDblsFoamyYxB3LsXmYFCoQACYGNjo+8M3GVsbm6CKAryXI8jDuHEGaYpSchWowGSyTStxwh85CzPKnZrZRYbdHxjF07A/7+9Pw2S40zPQ9En98zae9837AABkNgGnCFBDqkZSbZ1JHkkb0eyrAifa8c9vnLElSzZYUcch30lXXl8fKWwdaTwOYq48pXtYzmORtKMLVkac4bkkABJrCQWYutuAA2g1+ru2rJyz/vjWzqrOqu6GgOSAJEPgwGgu6pyqfze712e93nr9ToX0xEFgUei7ciLxWIRoig2TNjjWUXa1/5IZbqYDKMQ8zt2/kDjxi2IIm9LPHToECrVKo7PHsetW7dgBw6cwMP5CxcwODSEoYEB9NFJi3ydoDHSj70OWvpkmbfvpxjpeR7W1tcxOjbW8HNJkuCLInzP20Q4FliJgIkCMbtBMwZcGC3yrHAHZyv7FuEzsNezFkPHtmHV61AUBelUitulhowi/YyuQgH3PsUS4xPhDADUkMakz9lDJEoS0qwO6LqoUYcAoL2yggAjlUKtWoVZr/NF2YwoU1SgEadpWQ2SsYZhkFJABxvIeqmEwPfR1RRRyLK8qdd1u2Dp9qj3DEScBPr7KOO0VRQQBgFmZ2bw3eqbuKBegu7p+FvDP4PQCvHR5cv48KOPkMlkeOuibhg8/dhMuGuI+JtqeG1rf1ugWCxuivIkKgfqUw++lTY7SwWqqgqPOmGyKMJyHDiOA03XeYtqRyIskexBGIYArQXKksRbgQRq9DdOIuIM0D5lURCAT6lP+FnFVo6eYRgIgwC246BWqSCVyTTMExBppOh5Hqqm2d4ZCEM+HM2xbZiWxTdASZKQYjXoDp6x1dVVdHV3N5w/yyoG28wqRsGddnYO0RR05HdRoaTYdUsdCbNWw+zsLDLZLJzVVRw+fBjVahWL8/OYnZmBREuRg4OD6Ovv37Bb2LBHbDSvED1WxK6E3wc3aY0GN81lZlmS4DRlFePANn3FceA6DtctqJomZDpwiGcW0EgqjkVkTwuDgHQX0V+xsmY+l2vUMGBvZc+SIDzWCbSd4IlwBtjCiXscomlaWZaJQ1CtwnFd+JUKUobBNwhd03idt1WpgDNFRZG3nTCoqoqUYTSI02yFVRqdFCJjOQHwRex34Aw0k+hY+iwIQwiRlOOmM2pacK2Wk1Wv4/Tp03hYm8c54SKCMEBZqOB71jv41Zd/heg8LC1hYXER9x88wO3paUiShK6uLgwNDWFkeLhBhXDTQm4qG8Rd31bGkXn3zcOpWFpfEATCBdmipZCl53WaYXBsG34Yom5ZcGybOwXbRkjEkVzH4cdQIvVfgBg8kb+cfHePjYCZYBP4c9fiHkefO4Nyj2zHQbVSgW4Y0CIZKCOVQr1eR71WA5rWMkAcYpadCqk+AEvnMpKaqml87XZy7sWVFYzGDGNTZJm3MG7lDLCIkt2DhqFEzC40BRPNdiP6s2bMzs7i0qVLGBwYwO49e/DGG2+gp6cHO3fuBA4fRqVa5R0KH12+zEsOPT09GB8bI+TIpo6eMPJnQ2ATd33YOmtQLBYhSxJXbGXYKqvYDM/zoKgq9FQKkkDaTT3fR7VWIxlAKm7X9vsNG9UNBZrRNU2zoczQkHEQRYA+SyzwEjvJQDxmfObOAEOUGdoOiqoik82iWq3Cs21UfB+ZdBoSVQPbqlTgui7qlkVqyvQLkRUFKcNo+II6nV+9uraGrq6uTdEJG6zBBDGibSpsAceRZppf0wnakf1KdJY4AIwcHAE+2vjdfZO0vMmyjKHhYQwNDwNhiHK5jDv37qG4soIrly/j8uXLyGWzfLhST3d3a681zpGK1DHZ+TafaXF1Faw3vxmyJMGj93KrbdyhLYUyTd+rikJaQi0LfhgShTfb5l0KcWius4YhIRqxXnTWzhj4PsC4AjQ1yMAzA0lW4BNDO2Z3NKsI+vd0JgOBCpmZprkxJlsQkE6lsL621rJUwCbxsaiRfigMXYfONokOzovBrNdRr9fRE+ELMMiyDKFdVjEk7PqGlHwr5xzxnQ7R18XamTDE1atXcfPmTUxNTeH555/nEwcjH4xsNotsNstHMs/Pz+Phw4dYWl7G3Nwc4TH19xMCc/PE1phyxiYEQUNmLS6wWFlZQXdPz6br6DSryD6XtYzrtDOJCZ+5ngfH8+BWq1Cp8FGrvUEQRQgR++f7PkzTJIGCQKTvN9nO6HcX7cRo4h180vjMnYFOFk7zAyBLEtKZDMxajShPVSpIpVJQNa1tqcChIzQ9z4OWzZK0XioVS+7pRIM6DEOsrKxgdHR00++Y98fkdiU6NIQRZ7a86qaHjXv6MQ9hqzbAxYUFfPDBB8hkMvjil76Ey+uXtzoqIAjI5fOYHB/H+NgYdF1HsVjE4sIC7t29y6enDfT3Y2BwEP0DAw2Gs9UiaU4DNjsEq8UiFEWJnQrISzodePgunSanR1QlNVWFQss2NlU0dE0TCh3Osok13FQiME0Tru8j9H3ouk6evXq90Vg3lQi2uh8JHh9aGuaYTdCgmb96rUayRjSY2KpUUK3VUK3VuK3QNY1/1qZjdnDOqzFkWYZoN1LzJr9VJL0dtAoimBrp/fv3cfDQIezetaujQE2WZQwODCBLW8CDICAE5sVFfHjpEi4BDUHFJpnkuKhbFNvez4BytnY2dZSw6+o0q8jKxyI2RMokUUQ6lYJH9QA83+fS1UYqFUt4jz5vjIzObF2+UICzvMyVM+MCBWY7Posg4olxBlou6KbfMZKOJIrIZrOo1WpwHIc4Br7fslRgWRZK5TJ8Oskqm8221PqnJ7TlgmPtSHELGiCLw7Zt2K67qXVvK8R59+3Sofw1FDy9NziIEydOxLe1tbk+tp2pkelpCAnZc4GmBS9cuIAwCJDP5zEwOIiBgYFN3Im21xj5+9LSErq7uxtY/Ay8o2ALUhUbbMLqulGIoghd16GqKpE5pnMvPM9DKkLkaf480zT5aFjmONqOA5FmKjYuJmx4H0CMCRMjSfD4ERV76fANCEMyrVAWRVRpMFGuVpFJpzdKBabJSwV+EBBHoFIBABiaRuq932ebaHFtDelMZpMNCsOQb1qB78OJIb9teZlN/26V2o4jDzq2jTPvvYf19XV8gaqRbgdB5DNz2Sxy+Tz27N0L13VJKZIGFTdv3tyUNVA0raMgKfqa9fV12I6Dvt7e2Ixjp1lFNkdGiYn6ZUlCJp3eyCqDOIfNraJRsqRlWWQ4EbVFhmGQ0dGCwInlTEY/+t0wmyKyaYmfIj5zZwAgF+5GavfNYB5+XESdTqchSxKRe6Tem6qqvFSg6zpqpsknB0qyjEIu194RABp6xVthtVhEGIbcGYhG/sDGLGyPtjJuJ0rcVOdDa/W8htppGOLK1au4dfMmduzYgcOHD/PFvikCaLH0GuqN0fcIAgpdXSh0dWHfvn1wbBsLCwtYWFjAzMwMbly/DllROJmov7+/oS7bCq7jYKVYxPOHD5PDiBsjmgM62lOiaXnmzMV+TmRBt/KsRVFEKpWC5LqoVqvE2Fer3FEIw5AoHToOUQcDeRbYSGWAOHkhGvkgQRhyvgBb0IxECmDL5y3B9qHS1lLbsmIzSsCGwxCGZJIme5olGhDUajV4NLuoqiqRJqYOPMKQaNzTCXq6rqNQKGzpCHTCOVpdWUFvVO2TkXAj/KiApq636wx0mjVoDiKq1SrOnD4Nh6qRtgpytvOZDIqi8KmLLKh4OD+PhYUFXLxwAQCQzWbRPzCAgYEBEhh0EB0vLi5CURQeSEQ7zFhQsFVWMVoiaBe0KYoCie41luMQwTHX5cEEm5TIM4lBAE1VG7KPsiwjpHtR85ECansAsnfYjrP1+PbHiM/cGWC98YsrKx1tmHGPuabrECUJJu0vD4IAnuui5Dg8qgQIMYf1/W4JYWvFqfVSCZquQ9e02L51WZYJgYWSj7YjOrOdJGC0t//s2bMt03vNMVSra+OGbAtGq6ppGBsbw+jYGMKQTE9bWFzEg/v3ce7cOQBo0EEvFAqx2Y2lpSWEYdjQtxx1cARRhCTLGzrerZwBuvFuOcseRDRGzGZJO6nvE4+fCgPZNH3c7NUzyLIMhCHcyHjYKF+Ajz+VJDyYn4ckyw1S2wkeD0ZHR1GzLKysrKC3Sb0UIE4cc6DjODhiU3bRsW14ARmXu7K8vCEDSzkFUpuBQdtBEIZYL5UwPDKyiezHoNJZK67rNrTadoJY0l1MiTHqsKwWizh95gw0TcOrX/5ygzjZto69RaaX/hKFri7k8nns27cPtuNgaXGRqKjSia2bhiu1uAcLCwtkgiNr0Wv6fVT6txU8GkQIgrCl48WCCUEUYdHZOSyYCIKAdBqFIVckbSYry9SOxZ0PCy6YY1EsFrF7//625/M48Zk7A6IoYnR0FB+dPYtypRLbMwyg5aJhUBQF2VwO9XodCEPY9TpqlkVGBXd1IZ1KoVSpIAiCzjdlUQRivjTmyZdLJWSz2baT+hRF4YONvl8Futh0H/236zh459IllEolnDx5knjgce+PvrVVZoC9fovzaVA+Ezamp+3ZvRs2yxosLuLW7dv4+OOPoWka+mlKsL+/n3u9CwsLyGYyhMwVc85hSGQ+IYot77VHN28AHbdjSbRd1bZtmPU6KtUqfM+DqmlEv8AwYjsP+PfIWhijMsi0pMA+f2VlBf10ymSCxwtd15HN57FcLGJfi9eEgsCZ2q2QTqehqCrqtJVsbW0NlVoNo8PDZOiZIMAMw+2Jh7Ug7YFxUDwP2Wy2pUOuKApAeSlhEHTcZtaSeBzHNaJ/Li4u4vLly+ju6opVI90Owk5r3pHr1lSVBxXeCy/woGJpcbEhqGDOQVdXFyCQeQTF1VUcO3o0/hjMUads/VZ1epYViFOVbAUmf87kyavF4gY/SVGgp9Oxx2rggzR9Jw0ZRddFqVwmXRufEj5zZwAgHv7pt9/G4uJivIAIOiMaiqKIdDrNDbQAoFKtcp1n0MxDp949a+fjXxlj8dK/lyuV2IgkCsZm91wX2MbAibirjf0ZNS4ffvghfM9rm97rtEzB21selfwmEHGeiclJTExOIvB9ssBpSWHu3j3uPAwMDGB+YQFjTYIhzRBFkciB+j7v34+yqHlWoMXwkfanS17v0TKE4LpIp9MtWxBFGkE4dFx13EwE1k2ysryMky+/vK3zSdAZJEnC0MgIHs7Pt94wOyHrgqxTMZ2GSSdkMjXBTCbD1Ui35cw3Oe7sb0EYolQqAUDL0gYAXhoLwxBuC2ni7SBORyAMQ9y9exfT09MYGxvDsaNHt2zd3QodBxItAhtBFNHd04Punh4coCOZGQnx9vQ0rl+/DlVVMTA4yL+PgYGBlsdhJEIIAlFKpXaEl3TpvJMQ2BiPvAWiWiWCICDwPN7SqanqhrxwDBqcAXoODF7EdiwtL0OUZezYsaOjc3oceCKcAcMwkMnlsLS8jD27N8/LDtC6PaYZtWqVt6gJoog6JRfWajUEYYjMNidAsXG0ITuPCGO8Uq1iaosvi6WsvTYM0ljEXGvc47VSLOL8+fNQFAWvvfZaw7Chze/vLDPAI6ktzrXVd9LMbRAlCT29vejp7cVzBw+ibppc6vT69evwPA/37t6F67okLdjXt2lhclIVkwjeOAkAG9PkOikR8MukPcQsZZeOCC1Z9TqXtI0DG27DMhIiNW5R775qmqjbNqampjo+pwSdQ5IkjIyM4N7t21hbW9skWBWisYTWDj7lGBmGge7ublRNEyb9v2aapKVsG6lz1l8uNK0FQRBQqVYhs1kJbaAqCjzqcHbqDLS6ymZnIAwCXLl8GXP372Pnzp2Er/MYOl9YuXRLhzzu+4h5TzSoCGnnAAsqSqUSwjDE+++/TzoUBgaQy+c3HVuSJAh0w2a2I6S22GVk3w5KBBunHhLSOu0UkGQZGVXlwkqmacJIpWKDKYWWGD060ZY5jUEkoyhLEhYXFtDb17dJv+aTxBPhDMiyjMHhYTyYn4/lDfDaVpsFHYYhJwMBQKFQIN5euQzbtiFKEmzbJj3oisK7DtoiDHm9EWjcjGtUX7qddw9s1KEC6oF2xFdAfN2vub3w/v37OHvuHAr5PA4dOtTWESDn32GZoMPMQEvnbItODCOVwuTUFCanpvDxtWu4ceMGxsbGsLS0hLt37kAQRfRGpqdlsllI9Fz4eNeIo+K6Ls/gxLX7NMP1PD5tkMlTM/VCgAxMcumMixDxA60YMYkvano/mGMhSxLmHz6EoqqJDPEnBFmW0dfXB8t1STdKszMQsSUB+UHs57C59wAx7INDQ1hcWEDNNOHQIVgO1R4w6ACZtqBrtMEZiaBaqSDTohwahaIoEGybRK4dEpDjiMcAiGNP7ajnuvjggw+wuLSE/fv3E2f1MbXAxnUoxL6uabIfsJEBbtfDz4KKAwcO4L9861vo6umBJIq4ceMGrl29Cl3XeesiCypEUSRZ3kjJV6AtizZrRe4giAgikueMUySJItK5HOku8jzSckxHZ6fS6U02lOnhMNKiJMsNnUkso7i6uoqjJ09ueU6PE0+EMyBJEsbHx/H+7Cwezs9jZHiY/45lBQDEC9qAPECsTUgQBNJhQNnClmURz5pOsgtBWgKtep2T/+LKBowE0mpSVYUKcGQ7iBbYsV3X7cgZaFX34+2FYYibN2/i6tWrGB0dxc6dOztKYW5yssL4WipvD3pMBqIdFhcXMTAwgMPPPw+ASJ8uUjLRxx9/jCtXriCVSmFgcBD5XA7pTIYMHmlqhxIAzgaPc0ZYutWho6WZoVaouAhz2ACSqQJ9bhgHpXkDYB0FzPlkXQi83U0UMXv3LsYnJ7eMABM8GpiWR09/P2bv3sWevXs3tN7RfmNhcFwXJnUEFFnmDrVuGCTrQ7uTWDdSpVIhfBKqOLiZhwPy7FGeSxyxuFwuI7dFEAFsEJAD2s3QyRpvmRmg64GpkdZME8eOHUMul3usLWwdEQjJC1r8uLOzWV1dheN52L9vH7q6uxHS4Uqs7ZkFFT09Pejr7UWG2o4ofM8jZT2a3m+V6WTKlWxuAcIQEEXoETJ6SMnGaToYiw3VSze1LbMSIxOxai4byJKElWIRVdPErhjthE8ST4wz0N/fj67eXly9cgXDQ0MbvZvRnvMwjI2Yq9UqqfUKAjKZDI8ODV2HIsvwPA91Wv9LGQap4dNZ47ZlQdE0MoKWpnBYOilaF2p+SCqVCskwdMD0lWUZEARCVOnEw9/i9xcvXsSdO3ewb98+7Nq1CzXT7IhgtDkzEI+OF/T3Cdu2UVxdxdEjR/jPUuk0pnbswBQdrrSysoJFmha8MzsLPwzR092NoaEhMkOBzioPQZwB7ghEnh/uzUekWlkmgPX9Rr9fQRCQMgzUQTaLumUBgtCQIZAkiZRDIsY+6t3PLyxgdXUVf+Nv/s1P9B4+65AkCUePHsWl06dx5+5d7KAlmRBoWL9xEbMbdQQUhZAFKbLZLCw6ATOfzxOSoaIQJUtaXhItC7quQ6eqcpsIzk2ZPPIjovDZtwXXiIERkJu5KS3RKisnCCitrXE10ldfeQWiJPERv48DUeXOR+UbxXEb4rC4uAhVUXganQ9X6u/HoUOHYJomtxs3b96E7/tQVBWDQ0MYHBhAb18fXMchQYSi8O+PkZXDIIAfhrCpJgkAngnQ6L7CprVGy0AS1SSo1moImEOQyTTcDzEyTZF9LtcXkGVcuXoVff392Lt37yPdw0fFE+MMAMDhF17AxTNncOfOHTJKE5v73EU0pt6YrKggCCSd3PRg57JZOLaNdapSaOg6DMOAR408mzHtWBYkRSEef1PKKO4BrVQqHWUFgA0PHyFRI9yyrt2KMe95eO+997C4uIijx45hYmKCE+ce57bdiQoWy5zEOS6dnsvS0hKA1gQgSZIwQPuODx0+jGKxiIcPH6K4uoorly/jo48+QjqVQldPD/r7+hq+j5COJbVpqylzKjVVbRhFzQx43B1nRCDbcWDV65BEcaMMIQibHFNGNBMlCVevXMHg0NCnSgB6FiFJEnp6ejAyOopr165hfGyMZG2ahKua17AfBEQvHnQmSVMni6HrJE1fr6NSq6E/lULKMGDQqZUWdQpqtRrqpglVVaHreoP9YeS16Gbh2DZcx9mko98KiqLAcRzSYthJhqmF7VhYXMR7Z85wNVI2tpndm8eBTluS235Gh69bmJ/HwMBAy+OkUqmNoMLzcPfePT5DYXZmBqIgoKurC109PRgdHQX79gUatDFBMmbnJFmGoWmbOkriiJCiKCKTTnOemlWvNzxfIkBsDuOfscmNgoClxUWsrKzgr/71v/6pZGajeKKcgXyhgLGxMVy7dg1jY2MQYlSYoouapW4A0h4UNy40lU5DWFuDAFILZpuXrCjIUoJO3TRh2zYCWh+URBGqqkKlMrZxkUW5UkG2wwUNEO/TBolGtnIG4haEaZo4feYMzFoNL730Evr6+xtf20k9seluBi3KBJ1494IgtJ401uFDvLiwgHwut9HP3QaCICCby2FckjA5OQlV07C8uIh79+9jeXkZc/fu4dKlS6Sm2NODQj5PPpel9Gib4CYeBjvfFkaU9Q+7noeaaRLxIUHYuDf0fT7rRBBFPHzwAJVqFV9+/fWO7kOCRwezHfsPHMD33nwTt2/fxt59+zYb0qbv2KzVEIYhJFmObWkFSCBRrlR4hwH5GDKPwNA0PrrYpzoVFm1lVunkQlarjjqN5UoFYRhuyTViUBUFNVpT7oSAHPcUMzXSvv7+RjXSx5wBZMfeyg1ot+G3ElaLwqrXsVYqYWcM2TwOkiyjr7cXhUIBqXQaVr2O+w8eYGlxEdO3b+PWzZtIpdPo6+1Fd3c3cvk815hRZBlqOh07wZZlFWOvg+oR1Go1zlHiwmNNEssOzRiLkoSr166hr78fuz/lrADwhDgD0VnY+/fvxxtvvIHbt29jT5sb4tNNHMBGij8GgkAEQ8qlEmr09VFIoohMJgPDMLiEZHRxi5JEekdVlUsjh2GISqVCBvt0CEVVYbsujx7boTmKKZVKOH36NARRxJe//OWG2lcnHRYMotD8QLcnED4uUlEcfDrUZDuRM+MD+FQrorunB6l0msi2ui4WFxexUizi+rVr8AFk0mn09/VhaHgYmXQ6Vqehky4V3TDgV6sNXj77LGYKmPohwhDXP/4YIyMjGN7G85Hg0cCcAc0wsHPHDty4eRMTk5ObFR/DDSXCaDYxbpgZQ4oFGEGAejS6o4GBpmnQNI1kjujwM4eu8RrAA4qobapUKkTEqMOsIiMgs3KXvpUiXeRZDsONYUM7du7EoUOHml76eNc57yTYhsbApl91cJwHDx5AFAQMtmkpbAZLzQe+j3Qmg5HhYQz09wOUrLe8vIyFxUXcuXsXoiiiu7sbg4ODGB4aIo5AC9vRDpIkQTcMolho25AkiTyvjGBKCerMdiwtLqJSqeD4sWNbf8+fAJ4IZ0AQBJIOC0NoioIdU1P4+Pr1+EVNb2K1WgVASD9bSd4ahgFJkuDStF46nd6UGmYCNKlUitSYaWrO9zyYvg+hXocoy1BlGT5t9+nUuwfAo1Lf9zcJ1bTDwuIiPnj/fWSzWXzpS1+CqqoNnvN26vudcgY+jWEZi4uLcBwHo1voC0QhUzXJgI4SLpfLcKhkJ1NDHJ+YgACiWb64tISHDx9iZmYGsixzHfSBwcFtqbqJgrDJy4/emzAMeV1x7t491G0bBw8e7EiKOcH3B0bs9DwPe/ftw507d3D944/x/AsvNL6QZvcs227IJrZ7xgVBgEGNebVWQw/tVmjQHgERzdGoNDIbhuV7Hp+AKIDYKUXTUCmXN9WQt7xGSkB2HGfLTYJlIXzfx7lz5/DgwQMcPnyYiNc0RbKdhxGdIezQGWjnfHfinM/NzaGvv39b4kgsS8MmT7KW4nQqhe6uLhQKBRzYvx+2bWOlWMTS4iKZ2PrRR8hksxiiHQpd3d1E/yFyve0QJY+b9TpRdoxkFZkjEIYhrl+/jpHhYXR1dX0mtuOJcAYA4mU7tE6zb/9+zN69iw/efx8vvfTSRgsg9c7qpsmJHqkI6aclBIGMzqzXUSqXkTKMtmxWtriZRr3junAdB57rwnMcrK6uIggCqKq6rY2dlQocx2n7HrYYWHpvaGgIx48f35TeazjvTk6g6UVxi65TElDbBY2tmdz35uaQz+c7dqhCqvbn2jZMmsFxHIeweCWJjxZlvcKZTIZPk1wvlQiZaHERFy9eBADk8nnOR8jl81sa5wYv33G4WlkYWdDlchk3btzAzl27kEmnk3kEnwLYHAovCCBJEvbu3YvLly+jv79/U+bODwJY9TqA9tnEKHQaSHi0TGQ0jSuOgqlWGobBnQGHZhqZY7C6tkbGrFMb0InDraoqTCqXvWWpIAxhuy7eO3MG6+vrOPnii7w7q3nFxrVMfz/opEzw/R7LrNVQXF3F8WPHOn4PC8Asy4JH+UOsO0OSJCiKwgcUpdNprqLqeR6WlpawsLiIubk53Lp1CxINKgYHBtDX39+R/oOh62SuCuWpsOfHD0Ouj3Lt449h2TaeO3gQkix/32q1j4InZtg6Y+U7tJXniydPYml5GR9+9FGDcAjzsgDi2XcSEYe+jxRd1PV6HTVqELYCm3SXy2ZRKBSQoS2LJu0/D0OiQri2vo5KtcpJRa3AHhxWI2p5vmGIK1eu4OLFi9ixYwdOnjzZ8HA0bFzbyAw0b3ixtLnoebX7zC2893Zn47kuFubn26oOhkHAW/sqlQpK5TIZOEV1AWyadstls8jlctB1vaVoSCGfx969e/HqK6/gL/3Ij+DEiRPI53K4c+cO3n77bfzJn/wJPjh7Fvfu3YPdZmCWSo0GwpBHlwDtOKjXcenDD9HV3Y2DBw4goNoFUYyOjkIQBLzeAZfgypUrXMvgX/2rf7Xl659VCILA77Pruti1ezeGR0bw/gcfYH19veG1dbru5Q6yiQBxHlhWSJJlrK6udlyWY1yEQqHAn08IRHBIVVVUazWUSiWUymUiT9zGJoiiSLhLotjw3DWDaa289eabqFSrOHXqVEObdqs1+bg4A52UCba6f1udy9z9+5BEsW2JlgVxpmmiXC6jWqmQTC/dO8IwJMONurqI0mhk8mAUsixjeHgYR48cwV/4C38Br73+Ovbu2QPbtnHh4kX86Z/+Kb7zne/g6tWrWFlZacl1EOgzxAYm+b4PIQy5VsGdO3cw/+ABTpw4ERtEfFp244nJDDAPPwDZLPr6+nDkyBFcvHAB2WyW91yyBa11IhpE4QcBRElCJpOBaZpYXV2FoevbSoOzeo+maRBFkdcLmXY4EydBvc7LHrIsQ6HeJ7tGgZ5Pq64C3/fxwQcf4P79+zh8+HBsr2m09v39lAniNvSoxkC7z+ST1lodS2g91/3hw4fwg4BH7uzzPM/jXrxHNSNYpiIEKRMoqsqZuLKqNrSDdQJVUTA6OorR0VEiDbu+jrkHD7C4sIBz588DALoKhcaZ65HrNHQdHh00Evg+AhDp2o8++giyJOHFF1/kUxe1JmLkyZMn8Y1vfIOMft4ic/LzP//z8H0fO3fuxM/93M9t6xqfNWiahjoVHFNVFcePHcPbb7+NM6dP48uvvcYjddd1AUHoLJsIwvIOQbJM5UoFruOgUq22nJ/SCizyZM9OOp2GJEmkz51uDhbVymBRoUL/ZM+Iqihc/KhVO/NKsYjTp09D0zSiRtqGDxFd+4+NQNiJ+mAbsm4nuH/vHgaHhhqCIzYS2KO6AUwLgAtNUXusqSpC20Y6lYL8CNF3IZ8ngQWb2Lq4iIcPH+LOnTu4cfMmVEVpmL0S/Z5EUYSmqrBoNwlA9rnV1VXMTE9j/4EDGB4eRhiGmyYVflp244lxBgBwMo7reZAkCVOTk6hUKvjoo4+QyWTQ09PDNwnDMDruSfVpNNlVKPCaXrlSQeFRhscIAhzbhpFKIZvJgKnOsYyFS50DliIUQDxlTh4BSCrPcTY5A7Zt47333kOxWMSLL77YEQFteySgpm6CmLvX8VyCTn7fYtHfm5tDd1cXRElC3TTJpDjfb+BxhCHRKWdGUaaRkWSaWGMiQzGzx7cD1l6Uy+dxYN8+WLZN2o8WFzFDddA1VeXlBLbAFUXhHQZBGGL69m1YloVXXnkFqqpyTflmo/3iiy/iG9/4BkqlEm7evNmyj/hb3/oWvv3tbwMAvv71r3/fuvSfd2iaBogiPJrVkWUZL37xi/jud7+LM2fO4NVXX+VBhKqqkNsMvIoioA6frCjI5/NYW1vD+vo6MjRTsF0EYYjA95HLZpHP5fgmxiLWIOIIWyCrVaR2g0nnMj5As9Lm/fv3cfbsWXR1deHkyZMtnxlmM5t1NR4HWAX9UcuL9AUtf1WmmZRdu3fziYGB522IwkXahKMOlSxJnNfj1+vwgwCp72dNhUSEbGxsDENDQwjDEGvr61ikMxTigoquQoGQyGlZwPU8mJaFGx9/jOHhYezZswdAY6aL4dOyG0+eM1CpkKE+9IYcPHgQ1WoV77//Pk6cOMHnzoui2NCr2Qo+22hAHpBcLoe11VWUSiVk0+ltL2oBJC2s0xvNVNBkWYau6yTC9X141DHwPA8hrRcJVMWsVqsBdEKeRDMHddPE2bNn4XseXnnlFTKZqwPEXn2LqF1o6iYIEeNlUs/zcZAHmaPk07Yo3/dhWRaWl5exa9cuIuYT5SjQzV+iCzgu88PGU0u0y+NxQBAEIkmq6xgfH8f4+DiCMMQa1UFfXFrCvbk5CAB6mKZBLgfP8zC/uIi1tTWc/MIXOP8hDEMo7BmN4MUXX+R/P3fuXOyidl0X/+Af/AMAwCuvvIKvfe1rj+UaP89gdd+QEvdkWUbKMPClL30Jb731Fs6dPYvdu3cDgsDT9S1leyPgQjCiSJTlajVYto31UomTCbcD27ZJFwJ1EsVICzOwUdt2PY+PYmc/E2gQ4tKhZ0YqRdaIKGJmZgbXr1/H6OgoXjhyJLbFmoFn27Z99hSR4ENo+HHIo1a2bjuVUI5D1Gawv09PT0OUJKQzGViWxc8nxEZGhW3+zfaP6QeAcow6kS2PRSTIYYGeCKC7qwvdXV3YT0mIi0tLWFxYwMz0ND7++GMYdGJrd08PmVtSrWL2zh1kMhkcO3aM21wB2FTC+rTsxhPnDASiCN91+ZAPURDwhRMn8N3vfhfnz5/HsaNHNwQ7tljUQbihFS/Q9pBsJoMqre+vra+jt7d32+fpWFZLVq8gCIQ5LMswAO4csJSg7PuwqH61ZdtQggBra2u4cvkyVE3DETo5rFqtkgeN/c8eFrZRR9pTYoexdJiKa3amopK6cdcGbKQDA+poBfRnQbghyevSlF3zWSzOzyPwffT39/MNn4n5dFL2cT0PItCYaXkMaGYxi4KAnp4e9PT04LnnnoNZr2OJZg1u3r6NgDpyAYA9u3djYHCQvI8a4jhRGUYC9TwPZ8+exU/91E9tes1v/uZv4ubNmxAEAb/+67/+2K7v8w5N02DW63AihN6uQgEnTpzAe++9B0EUsX//fv7MbMVa56qUYchbWguFAlaWl1Eql5HN5VpOtWwF27YhCEJL2xEtRQIb2hX8f02D57qwHAcyLRvcuHEDD+fnMTU5iakdO2DWag12gk3pE6liJr/isEnNtRN7EQ0yYuxu6PsNtqP5zxAg8xHCkARp1GZEpbxZp1ZDxxR938OFBfTReQMStRuiJMVu/s1g9hfAo2fa4toLseH0MOVaTdMwPjaG8bExBGGI9fV1LMzPcyKiQM8bgoATx483BLYCfc6i+LTsxhPlDEiSBEXTENCWPlYflmUZR44exZn33sO5c+fwpZde4p55q0XdsEkikgqLLOpypYJsNrtt1rdl28h3OE0q6hzw65RlIiEsCFhbXcVHH32EfD6Pg4cOEeay68JrJglG/02dJEEQUDdN+LSzgs80iHjtvPYvCAi9ADJkBPARQIQUEo1sll4DANuyiEynIMCyrI1Rn5FFy4YFRe86WxQAWbweXexswbINf2llBT19feh5BCeM1wQFYduGeOPWbX5e2slOM6RTKUxOTmJychK+7/NyjijLGJ2YIC+KLuiYMdKGYeDw4cO4cOECzp49u+n3xWIR//yf/3MAwM/8zM/gaKs57Qk2QdM0VEWRZBV1nW90g4OD2L17N27evIkgCHD8+HHiENA1Evd9s7Qzi6DZ82EYBjRdh1+vY3V1dVt97sCGM9CpvWl2eFkrpEOliT+8eBErxSIO7N+Pwf5++J4Hj9mKVmQ2UYQQhlwzQxBF1CnPidmYqA1hP2flCWAjg8FsA+jn2Y7DybXRtcSkfRmLP1raZNwiphgZBAE83yf2TRR5wFApl2HWajj6wgukPW+bsG0bIuUOPHLWcwuHSYrMN2EQsJE1OHDgAJaWl/H+++8jCEP09fVx7QqRtitKsoxsU/n607IbT5QzAFAP3zThUmeALWpZlvGF48dx9do1vP3WWzh+4gRhowtEorjZk2TjJKMpHQbDMIiQTK2GtbU1DNKorlPUqSb5I1+jqsKyLMzMzmJ2Zgbj4+M4dvQoBIFMvrIp74B5y0E06maeNcgCsimZTaJM1Xaw6w7yYQ5+SKN/iHzkMwMTT2GCGHELwKc/Z5kblr1g0YgoEHVClkZDECAUBNTrdawUizgWmUWwHViWBYE6Ao+afgzC+CFQDDyKicmYsFTjB++/j6XlZTI8qVCAwqLNiJGJcwYAkvK7cOECLl26tKn2+0//6T/F+vo60uk0fvVXf/WRru9ZRXTOhE+dUIA4kGNjY9B1HVevXsXbb7+NL37xi9B1PZZzFH3m40x/d1cXbMuCWa/DNM2W6oVxYM/vo9oO1vZctyy8f+YMLNvGyy+/jL6+PlITdxyuVBjQzTdqQ8LIbA6Ptj6KokjKdY0Hil33VSqva5om4cVEicwgyoAQhE3jxznCEH7Ebgh0wxcFASLbpAUiPsccNiZ5fmthAZqmdTzTIQrG5wrDkLeMfxLgI5KjGUY2swJEcfX9Dz6AommcnMzA3tEqyPw07MYT6QxURZEwLilJkHmTqqbhlVOncOHiRXzw/vuoVirYv3//ps+Iaua3ivS6CgVY9TpMy0K1VkOmQ4Yxi0512m+8HQVABkEUcf3GDdy/fx979uzBc889R84VZFCFugUBh0fongefjUbWNKLJHjaS8OhfENJ0ZwiyGRKetN/AD2B/l2SZEK3opssihOhrmefeCowsxbIagiDg/v37EARhW8qNDJ7rIqDqjc3CS9tB3J2N+yQhMiGTOQhmrYZ3z5xB3TTx3MGDRGNCVfnAEfY8pFKplgb/xRdfxG/91m/BNE1cvXoVhw8fBgBcu3YN//bf/lsAwC/90i8l6oXbBOvecBwHruNAotkB5iCPDA+jp6cHZ06fxpvf/S6+9NJLyOVyDYFEM7Eu8uH8r7KiIJPNolQuY3VtDfo2upKYJG2raYadwKzXcf7cOUAQ8MqrryKXzfJzlmW5bamNOQi+5/EggrHco9cejfj5z5uzk3QTZ1mEwPf5OF7Ws8/WDbMVUTvTzpnn2YNIKfTe3BxGR0bIKOZtwrYsUovXNHKuj2g74hDdA+L2A/bv6elpfPjhh+jt68PIyAgs24ZKicgKnYAKtA8iPmm78cQ5A2wTCinrX1NVTuSRJQmSLOP48ePIZjK4eu0aqrUajh49CokyhIO4WhY2M2bZoi6Xy1hbW0PKMDpa1Iy48ijOQAjAdRy89/77WFpawoEDBzA8MtK4QW3xeXxTphuzSutnhmG09sgpDKyhiFUE9D9gc22blQDSmUzL1ptODFnzUg+DADMzMxgbHSVRHCLGt4Mo37JtBCAL2nVdeI/Ruxdi7nkY8fLDMMTq6irOnDkDUZJw4gtfAECmZUqSRIxq5FnItyF/RslAZ8+e5Yv6F37hF+B5HkZHRzkRKMH2YBgG7HIZtm3zyJ+PhlUUdKfTeO3113GaOgQnX3yRDLuhNqP5uW7VtpvP51Gr1eBssyvJtizeNhZ1NjtBGIZYWFzEmTNnkEqncfDgwU0O51aWSBQEgJIOAcDVNEiiSATZOgDbRDOZzKb2SiYAxpRcW11DJ/ayobwL4OH8PGzLwuTUFBfGCdFZJ5lHJ9SGYciF7TrlU22cROuyS2zJke5DzOn76KOPMD09jakdOzA5MYG19XVCMJYkeL7fUEJuRRz/NOzGEyM6xCAIAtLZLEJR5AIwTM8/Sv7Zt38/Tp48ifv37+Ptt9+GRZm629mc8/k8J2aUyuWO3mPbNqJtY1ulq0OAOynVahVvvvUW1tbW8NJLL2FocJDU+bZI78ciSsqJ/LvtW+LOr9mT7aRX+BGwtLQEs1bjswjYUQVa5ml3NJeyxAHS58+udVvRVbvrifkdW9RhGGJubg5vv/020uk0Xn75ZdI1EgRQqTENw5BnBwCSdWqF3bt3o5t6/+fOnQMA/Mmf/An+23/7bwCAX/3VX91W6jnBBnRdh6CqCGn9OkogZo5tKpXCq6++iu6eHrz7zju4PT3N1+gmtLAloigin8tBkiSUSiX+bG4FizopQPvnnR+enpcfBLg9PY13330XAwMDePHFF6GpaluBrHaIru3HpjFA79X31VbY4pxmpqfR3d2NfD7f4AQIWxwPIPccIKVZ1jm2rcxAG0eg3XsEQYDjODh9+jSmp6fxwvPPY+euXfx3ClWfDCLcq3Q63VIM69OwG0+cMwCQBStIEgLf56x0gHj30QdqdHQUp06dQq1axZ/9+Z/jzuwsYat2CFEUkcvlIEsS1tbWOlpc7DWMBNRqMUXT+WEYYm1tDW+++SY82jrYT6UshchnAmg9CTAG213IcQsnmkeJbq6Pey7BzMwMcrkcf6Abz4FAoP83d0WwBa3TFCtLT3a8qB9lQQOo2zY++OADnD17FkPDw3jp5Zc3VNZohMVKKdE2tHyLVB/DyZMnARAP3/M8/MIv/AIAwhr+6Z/+6W2fZwICQRCQzecRCgJs2yaOAI3Qos+zoij44he/iB07d+LDS5fwzjvvkHbfVp8b87NsNguVOh7LxWJH51e3rIaZGHHrl5X5ONs+CHD58mVcunQJO3fswMkvfIGLCUUZ8ttCNEJ/TM4AFyt7HHYjck7VahVLy8vYMTUV+1JGmo47qkeJ6CwrwLpCHidnIO7uBUGAhw8f4tvf/jaKxSK+9KUvYWR0lHdXSVRMShRFImzVQUYR+OTtxhPpDAiCAJ32bNuWxaPVuFaynp4efOWrX8Xg4CDOnz+Pd959lw8xAlqn+hiy2SwfZLS8srLlg8JJbJH2lOgmG80EsAWysLCAt996C4Zh4MuvvspT8yxl6NAohp5w2+NHEd04O3MM4oxPZHAJ2+jafN6jLCSzXsfCwgKmOpxQyLIFAL03rCWIlkHYou7oXj2CIxCGIWZnZvDtP/9zLC8v4/jx4zhx/HiDNrxEe4JlSeLENQB8/Gk7sJTf5cuX8Ru/8Ru4fv06AODXf/3XH3tG5llDNJCwLQsQhFi7IYoinn/+eXzxi19EqVTCf//2t3Hrxo3G1H2750YgracyZeN3klm0LauhkyD6XTMngBGFgQ010lu3buHw4cM4/PzzvPauqiogCITB/wTgkxpuNjM7C1VVMRJRK40DI5qzLGMYBBsl3UgQQV68DXuwxWubgzfTNPHee+/hvfffR6GrC1/5ylfQ19fHz0WWJKI0SVs9fdbCCqBriyDik7YbTxxngCGTy8FaWyOKXIw9GYaxBBJVVXHs6FGMjY7iwsWL+Pa3v40D+/djz549HdWVuru6iDKh62J1bQ29bQRFXNflBBkGdowg3FwTm5mZwYcffojhoSEcP3GiwTAxhSyPTuF7lME229rmYjMDG2Bm8LF49xHMzs5CkKS2swhiz4sypENsLGhygu3Jod8PyuUyLly4gGKxiMmJCRw8dIgPpLIjfBHLshAIAiRK2mTYakEDG4vacRz843/8jwEAP/mTP4mXX375sV/PswZBEKCn07BKJdiOsyHA04KbMjAwgK9+9au4du0aLl+9irn793H0yBFO5GpnYlVVRb5QwPr6OpE4N4y2La9OjOqoEN0QIs8RUyMtra/HqpGy+rfjONtSY6UH7fSVneMxlhfZtfi+j7t372J8YqJjJ4PdA8/zuJw0D9xoRtF/jHaDnWsQBJidncWVK1cgShJOfuELGBoaAgTSoh2EISTaKgkQZUkhYs9EUdyyXf2TthtPrDMgSRLUVApOrQbXdclNpNFYM9hX29/fj6985Sv4+No1XL1yBXNzczhEjXm7h1SSZXR3dxPtgXIZhmG01PUOm6aGRbXzWWsJ+/nly5dx+/Zt7Nq1C4cOHozdZDVN4wJE23YGhNb6/3GIu3vRjSx8nN49q+uHZBDH+NjYtrXALdsmi0iSSK11u6nNbWQFfN/HjRs3cOPGDRipFF4+dYq3MYX0XEJQmVNFIcNGaAtU9B52d6CfcPLkSU5OdF0Xmqbh61//emfXlGBLpPJ5WOUyZ6XLkRblKELKrpdlGYcPH8bY2BjOnzuH77z5Jnbv3InRsbFY8nEUuVyOdyUtLy9jaHBwy7HI7NhAfKmrUqngzOnT8DwPp06dinUwFUWBJMvw6WhjjWqydIrtcI06AZcxb3Ht2yVaA8D9Bw/gOA52TE5u61zCMOTy04ZhQJRlIGKbHydCAKVSiQcQUzt24OBzz/HxyqHvw2FlTl0nBEZB2KQS2dXdvaXd/aTtxhNZJmAwKEvXc10i54sYwhtd0AyyJOHQoUN4/StfgShJePedd3Djxo22074A8tCkMxnIkoSVYrElKYgpI0b5AAAxNOwLZuk9Rhw5fPhwy2ibRRJM6/5RfdaOigQxiyGIlAmCLbz77Zwb+4T5hw9hWVbLml8rsEg8CAIYbOQ0VVTjvc1bpHE7Yi4DWFpcxBtvvIEbN25gz549+IHXX2/oZ47yVhh50Pd9PpCIXW9Xdzc51y2Qz+exb98+/u+///f/Pqa2eX8StIaiKFDo9+C6LieabbIdQMNm2NXVhdd/4Adw8OBBTM/O4vTp01haXt7yOerp6YFEJwquNU1KjMKngQQPIGi9O7raVlZW8NZbb0EQRXz5tdfaZpp4qeARiITR5/ZxgJcJ4mxHh10EDOwTZmZm0N/fj/Q2RYaYIyDK8oZkObW/HZ1Fh06D47q4evky3njjDbiuiy+/+iqOHDnCHQEAqNEJt7IsQ2ZBRBg2tIAKAAZHRrY83idtN57YzABAZolXdR2gE8MQdq513VUo4LXXXsON69dx7do1zM/PY8eOHdi1e3dLg93d1QXbtmHZNpaLxViFMVa/jhWvEQTYloUzZ86gXCrhxRdfJKmiNhDoBETbsmDbNlKp1CM5BJ3cl61ewRd0i17l7bZCAcD0zAx6uruR28ZQqBCEbMUEhqIZhRBorP+1eL8Q9/00vWZhYQE3rl/HysoKcoUCXv/yl5FtStUFvg/LNImmvKpCkiQ+gCoEcT4dKgQ1vI0ySH9/Pz7++GP09fXhn/yTf9Lx+xJ0hlShgNr6OpG2bfHcxj3Poihi7549GB4exrmzZ/HRhx/i3t272Ld/P+lzj3nuJFlGT3c3isUiSqUSDMNAKsbGMLJYXDuaAODevXu4cOECenp6cPLkydipplHoqgqrXieKgI9CJHxMiF5TbNCzXd6OIPDSS7SlrhO4NFMCNHYekY8lLdlticcdnKvtOLh9+zamp6fhOA720ZJ0c2TPRtpLosiJo57nAVSRlSGTyXQ8i+aTtBtPtDMAAKlcDuvLy3xRy4rSsBG38zhFAFM7dqDQ1YUHDx5g9s4dTE9PY2JiAnv37t08ypSSghYXFjgpKNpPy1rI4lKOAFCtVPDOO+/A832c2sawIU1ViQxwJAPSKbZTKxRiE0ExZYIW3v12IAgCypUKJ+BtB45tcwXJlmptjEQY96s2nx2EIR4+eIDr16+TgTPd3fjSl76Enr6+Ru12kO/brNd5zzA7F8dxyERFReGvz2SzHX/fZ8+exVtvvQUA+Gf/7J8h/yjTMxO0hW4YEDUNsG3Yto1cNtugI88j81bE4kwGR48dw/LSEubu38cH77+Pa9ks9u7di/Hx8U3vS6XTqFsWgmoVKysrGBkaahiCxtoc41LBYRjixvXruHrtGsbHx3H0yJGOeDsCJRLa9BpVTevYdkQzmt8vWIAkIL5M8CjBzczsLHRd35Y6bAiigsgI3rFD6Oh3HqsRs4UjULcs3Lp1CzMzMwgB7JicxI5du2LL0K7n8YyNrus8wLIdB0EQkJHUVAp+pMMg4pO2G0++M5DNQlAUBK6LWq2GQqHARUK2FP0RBMh0Et6OqSkcfO45TM/M4PatW5i9cwfj4+PYu3cvnzYHREhBa2sNpKCopGfcZrm8vIz33nsPuq7j1KlTHQt5AODTtjzPg7MN7sB2CXRCzNKPThhgxJrHxQieZUzgDlJg/HwoCzgMQxip1JaRxibxlhYL2g8CzN27h5s3b6JcqaCvrw+nTp1Cb28vbw1sfhfz7AVBQJqeSxiGfPqcGpE27STNx/BLv/RLAMhEzr/zd/5Ox+9LsD2kcjnUSiUyGIwOlopiq2yaKJA5JsPDw6jVarhx8ybOnT2Lj69dw569ezExMdFACO7q6oJj27AcByurqxjo7weX9Wb95E3H9IMAFy9exL27d/HcgQPYvWfPtmrbqqbBtm1OTuyU/MvP5THU0Xlb4WMaHOY6Dubm5rB79+5t3QuLlhVbST7zGS3kH7y1j6OFPa3Warh16xbuzM5CkiTs3rULO6kTwKSfo2DZRIDYCIW2oLrUERAEMunW9Txouo6+DmdcfNJ244l3BgAg29WF0tIS6vU6Uuk06e2O1t3aPDCsFScIQ8iKgr1792LXrl24MzuLmzdv4u7duxgdHcXePXtQoJFdLptF3TQRWBaWlpYwODDAjxFgcyrs3r17OH/+PHp7e3HyxRf5vPvtQNd1VKtV4uFvQXhkiD6CHZUJ4gL+aGaA9cpv0XXQCWzbxuzsLHbv3r0t54LV+2QqiRyH5rNrIGU1LUzP93H3zh3cvHULtVoNg4ODOHbsWEMtNu67cl13I91oGIT9S1/L1NZURSEqmZq2ZesTw+/8zu/gzTffBEAmjT3OyYsJGpHv7sba4iJ810W1WkUun+cOdCeOtChJCGlauau7Gy+++CLKdKb8xUuXcO3aNezdswdTU1OQqbZ+d3c3lpaWUDNNlCsVTkQOyEEb1pbjunj//fexsryMY8eOYWJiYsN56BDRjiTbtjvirNAbsImv8Khoyxegv9/OcaZv30YYBJjaBnHQ9304dLBaKpXqyMkJae0+8P3YIKJcqeDmjRu4e+8eVFXF/gMHMDU11VC+af6motlETnymsCl5UI3sD0Ojox3Z7k/Dbjw1zkCNthmapolsNrtBgNnKu492IND2IkmSsHPXLkzt2IF7NFr8zne+g+6eHkyMj2NkdBS9vb14+PAhbNvGysrKBrM80k0QhiGuX7+Ojz/+GOMTEzgSmSW+ZW2qCZwd7DhcTrVjdOg9x5UJOKs5ooQVF11s1xmYnp4GAOzcubPj97BZ7QGAzFZGLZoVon+PKnqtrq5i7t49zN2/D8dxMDIyghdffHFTao3JDkcNQeD7qFPPXtO0hsXPZqKLdAojAPQNDrbslDBNEw8fPkSlUsE3v/lN/Mqv/AoA4O/+3b+LV199teN7k2D7EEUR6Xwe5ZUV2I5D+vyZTHEnzgCt5UedxVw+j+MnTmD/gQO4eeMGLl+5go+vX8fo6CjGx8bQ29uLbC6H9bU1LC8vQxoYgK7rCHy/IY1eq9Vw+vRpWJbFhw0xbFfm3NB1VKpV4pjq+paqfI8b7QSHWpVUW8H1PNyansbk5GRLNb64Y9TpkCSFEvXavRZoLBdFZ0W4nocH9+/j3twclpeWoOs6Dh8+jMnJydgNuPl7imYTU4bRsE+xrgKVSuxLktQya/pZ2I2nwhnQNA1qOo16qQTP82DV6zAo0W7LzABjoTPBmMgXKooiJicnMTE+jofz87h79y4uXLqES5cuYXBoCH20jlwzTcilErryebJ5iGJDem///v3Yt29fw3k0bzBbQRDInHOHOgOapnWcIut0sbVSPAMaBYeaI/m4iLsdXNfF7elpTE1NdTw7PAxDPj0tWmOLRcx1iIKAaq2Ge/fu4d6dO6hUq9B1HRPj45icmoode9oQGUR+VqOEQTkyWx4g0R1rMdQUhaSe2yxoAPgP/+E/bErpfeELX3jkmeMJtodUPg+rUiF2w7YhqypkOklzKyeaTdQDe/4jr0+n0zhy9Cj27d+P2dlZ3Lt3D7Ozs0inUhgZGUE6k4EoilheWWkkIgsC1tbWcPrMGUiiiFcjImTk1zQDuY1WOKZzD9rGtp1A4nFodbD109wuB2yP0wQAd2Zn4bkudu/Z0/F7HNvm6n7tMiOMqNkMPwiwuLCAu3fv4uH8PHzfR19vL44dP47R0dHW7ZK0m4x9T3HZxOhrHTo5kWUUBwYHW85x+CzsxlPhDIiiCCWVQmBZpK5O62OyoiCMtJq1giSK8LcoJYyMjGBkZASObWNubg537tzB3NwcFEVBoasLlUoFsiTxdN/pd9/FSrGI48ePY3x8PP68t5kdUClbnV3jo4gQtUfr9H+7ut92F/TMzAx838fu3bs7fo9FlSZFSYK+xXVHr8J1HNy/fx93793DarEIkW7Oz7/wAuEDtClR8Dav6HnU68RpFASiZhdpwWJ64wI2vPu+/v62muAXLlwAQBzayclJ/NW/+lfxi7/4i52ncxN8X9A0DUoqBatcBsIQddMkjmEHDoHERuoCvNTYDMMwcODAARzYvx/F1VXcvXMHt6en4bouZ4l7nofBgQHSk76+jgvnzyOXy/FRys0QQHRWtlNqNHQdrutyvZJOZqYArTuHtoOtVF47RRAEuHXrFsbHxzteH4xjBEGAoesdcSZYeXl9bQ335uYwNzcH27aRz2RwYP9+jI6Odsb5EjamL7bLJooALBqMsnZkURQxMDTUMqP4WdiNp8IZAEi06Ok6fNq3adbryHYoYsOJI8yLCzdPNmRQVBVTU1OYmppCpVrF3bt3cffOHSwtLmJmZgaqLMO0LEiShJdfeqntfG0mq9upQyAIAjRdh1erkRGXHXIHgK0zJAAgtuEMMMMjxWUPtpHl8H0ft27fxsTEBAw6RnYruK67wbxlmgJt4HkelpaXsTg/j6WlJfhBgP7+fhw/fhxDw8NEoEpor0Eel4p1bBuO6wLAJvJiCOrdU0VERVXhOA56+/vbOm2//du/jd/+7d/e4g4k+KSgKAoEWYaq65y9bds26QTZwiHgXJSmEdUtXoyu7m50FQo4ePgwjzTn5uZw59495HM5hGGImzdvYmhoCCdOnNhahGsb606hnAXf97cXSDyGkgIb0hXnWGwnGLp75w4sy8KevXs7en0Ikk5nx1a3uOYwDGGaJuYfPsT8wgJqtRo0TcPY2Bgmxsc32p/b7A9hGHKtk2hHG9MTkJqyiew8nQhfwPd9dHV3x2YrGT4Lu/HUOAOpVArVchkiTc/KkoR6vd5RXYmlh/wggEJ+EL+oaccA+5KzmQwOPvcc9u/fj5npadx/+BCLS0sIfB+GruPOnTuomSb6+/tj+4qjx+40HaerKur1OgLPg+u6HafZ2cYVUnZr4Psb8sj04bYtB1lkESKEDx8hQtTNOhRfJu1JjgNBEOB6HkRQycxtljtmZ2fh2Db2dpjmC4IApmmSaFvXYw1k6PtYpTXY5cVFFFdX4fo+spkM9h84gPHx8c3ZhEhNcNO9avp5COKQ1CNa5uw8WE0R4cbAJEmS4LsuhoaHkc1mH7see4LHB0EQYKTTMH0fQb2OAKS+LDE52GiQEPNehCHvshHID2OfqZB2DACALIoYGR7GyPAwKtUqrt+4gQcPHxLRIUFAvV7H9evX0T8wgO7ubi5R23BsgGvXd0YOJgx60zS3HUjQC+DMeM6QZ7aD2g9G7rUsi2zC1LZZlgUBgO55vMzIU/Id2o4wCHD95k2MjIwgk8l0NIDJsiwu5dzK/tpUGXJpaQmLi4tcGnh4aAjPP/88+vr7N3EsWgU/0WASAH8WzFoNAR1jno5kE4GNLKZtWUAYQpFlhACGRkaeuOmkT40zIMsyUpkMzCCAQPvQHcuCrCh8bkErsgr3WCNfsMgix8gXFyskBJIu3LlrF9LZLHzfx3qphPGJCSwvL+Pu3buAICCbzaK/vx8D/f3o7etr0CjfDiGIcQfqNP0V5wywNkfXdWHRQT5CpE2mue2S17Q8Dxo0hAjg00kEnufAgY26ZcGni5nzCEAlKinpkv/PlACbEAQBbt68ibGxMaRSKSKE0ua6Gzx7Wd7Y0MMQlXIZi8vLWF5awvLyMjzPg6Io6Ovtxb59+5DJZlEoFLaOgGIWdvN34ToOPw9VURocTClyH5nRE0URKm0JiralJngykc1mUa/XIXoeAs+DQHlAmUyGb2hxYJtatDbMo8HoC5uCiIZj09RzT3c33j97FgODg1AUBTMzM7h+/TokSUJfXx/6+vvR39+PQj7fUJraTrlAU1WideD7cFx3Q30vgiAMuYRx3bLgeh5Js4cb7drkkhodpDAM+Sh5NkuFZd9cWiev0/XB7DBLiTNNfpFO+oxzUubu30fdNLH3xRc74kC5ntfQPcBsvOd5KK6sYGlpCUuLi2SAVBgim89jeHgYmXQamVwOXYVCS2cpzuljQUHzz+qmCY+SQ1u1QgdhyAdKhQBGx8eRyWQ6DvQ+LTw1zgCwsahD34fo+/CCALVqFblslpAEY4g+wIaD4AcBLNvGL/z8z6NUKqGntxf/8l/+S8hs2Ezki7ZdF//vX/1V3Lp9G4os4x/+w3+I3bt24V46jXK5jL6+Phw8eBCe63LPc35hAdO3b0MQBHT19GCALvJ8Pg9VVTnLdCtoqko8WLqoFVmG7/uEbU8HN4G2IDHCChvKAYDXpKIeuiAI0KDCRA0+AgQIIECArCjQVA0udQRURYEUYdcGAIQwJAaApllDEGeK6SPIdDb33bt3YVkW9naY5mPM2yAMIVAdgMXlZSxRD14QRfR2d2PPnj3EUNIFXKvVtpSXZt+7AKKfwO970/Phui5q1BFQFIWUKSLvj3IqzHodPgBVkjA2OQnDMLZUiUvw2UOSJKTTaVR9HyLVCnFtG7YsE8evRSTIyosA2eh+8R/8A5RKJfT39+PrX/862YDoOuQRYIzd2L9vH3p6eqBIEmRZxoEDB3Di+HGUymUsLS1heWkJV69cweUwhK5p3DHo7urinVMdBRMskKjXYVsW0UcBkXN3PY8P7wFI66/recTRl2WSSYvUs5laHyL2g7PhFYXIclO7oMgyYfJLEslkUBsR0s4en9oxdg0SvQ8y/RMArt+4gcHBQeQj7Z+twDZhCAIkQUCpXMYKDRyKxSKCIIBuGBgYGMDuPXvQ39cHTdcRBAEqlEy6lR1udgjiHAGzVuN2M5VOb2QTGVGdvp5P0RUE5AsFFOj3+qThqXIGJElCKpVCjW6GkiDAdRyUazXkqJcPbPZqZTo/2vd9pFMp/A8/+qP497/3eyiurOCdd97Bq1/+coNnH4Qhfut/+99w6/ZtiIKA//v//D9jP9WEzuVyuP/gAWzHwcrKCvr7+jj5ECAtQ0tLS1haXsbt27fx8fXrQBgilUohl88jn8uRP/N5ZNLpWE9SEEWoioKybcMqFvnCY2Cet0QFlSRRRIY+XFILzxsAHNGBiTp1Bchmr2oKDN2A7ThQZBmZTIY/1Cxd6Pk+At+Hz/6ni5w5COx+36ALul0tzHVdlEslrK6vo1gsolapoFqtcgcnXyhgbGwM/f39RPO9KYUa7RPvNA3asJCjjoDnbTgCskymv0WjIWw4kmatRhY5gJHRURiG8UQu6ATxyGQyME0TgapCpnX1Wq1GyMltIjRZkmDTgVTMbiwtLeF73/sesRsAN/rt7EY+lyM8hTDEysoK5IEBFPJ5FPJ57Nm9G34QYLVYxNLyMpaWl3H//HlOXMzncsjmcshTu8GCizjomoZqrcb5L5Io8ueYkwZppk8NQ6R0nTDaqRPQDmwMr6ZpnPho2zZ834dMbQe5HaS0wOyG5/sI6DyPEBtaHTa9b8ViEdVyGUdeeIG8v8Xxa9Uq1tfXsVIsolKpoFypEKcgJBoy/b29OHT4MPr6+pDJZDbZB6bhsJ3ySfNgKfb3er0Oh2ZKjIgjAICXFUGvtWqaCMMQumFglJIjn8Qg4qlyBgCSHTBNE4EsQwfgex5c20ZNEMhAC8bwjHx5kiRBoOmxMAzxlR/4Afzpn/wJisUivvXNb+Lll15qWAj//t//e5w7fx4A8FM/9VM4eeIE/52RSnG2uWmaWF1bQ3dEhjadTnMCYhiGqFSrKK2vo1Quo1QqYfbOHZ5Ok0QRuXwehUIB+XweuWwWqqbxiN+2LASgaWnaPSHLMhRZJi1xEfUrpWmUbjziuwmiWZHofWAaDaIoAlGhjZBIq3o02nA9D/MPH8Ks1/Hcc8+hXK2SOe+WhVq1ilKphPVSCaVSCWatxj4cmXQauVwOY2NjyBUKKLQxcvycIsTATtXW4tJ+nudx9q8iyyTFF1n40TqiAKBMzzudzWJweBipVGrbUxgTfHYQRRHpdBoVmvLXNA11y0K1VkNWFDe+y6Y1JMsyHNeF7/sNduOb3/wmXj51qmFFtbMbAMn4CYIAQRSxuLyMoYEB/rxLokjKBX19eA5EkKi0vo5yuYx1un7m7t7l/AXDMJCntiOXyyGdTkOi4kOe68JxXbiui1Q6zYWJWDQuiCLq9Tos2yZtiTQz8CiI0xhgGRVZEABZRnRFB8xu+D4814UXhpiZnkZXTw9kWUa1UkEIoFKroVouE7u5vo5SqcQDBlVVkc5kMDw0tGE7c7ktN3ke8G3DbnD7SMEcAZd2B6RSKZIZibwnciNI9pPuO5O01fpJDSKeOmsmiiIymQwqlQoc1yWZAtPkaeU4UoYoinzcJ/Ni//Jf/sv4nd/5HSwtLeGtt97Ca6+/jjAM8Sd/+qf4sz//cwDAX/qLfxE//EM/1PBZjAuQyWRQq1ZRLpcRhiF6YqaLCYKAXDaLXDYLpj4dgnjY5XIZ6/QhXy0WMXvnDklfeh5EykjVVJWnrrsKBaQMA7phwDCM+Pa7Lch+8cqCYQNZp2E8M+KJj77vo16vE4NiWahWq7h16xZ0VcWNGzdgWxYsx+F1RVVVkc/lMDIyglwux+vykiyT7EiHnjrjM3SiPBl37cyQMvEqxv7Vdb2lIwBaXw48D34QYPfevRAEoW32I8GTCZYd8IMAou9DoZKwlXIZuVxuQ644UkpiWUXP85AyDG43lpeX8eabb+K1114DwhD/dQu7AQCarhPtCtqJMr+42OAQRKEqCncOGIIwRJlukOX1dayXSpidnSXE15DMTZFlmbS2qSo0TUM2k0E2m0WK2Q3DIJt0FI+wnvg5tdAYiGtHDsMQjutu2I56HfPz86hUq8jncnj/gw9gWxZxvjyPBAyZDLryeQwODfGspaqqMFKpBl5WOzTMo0DnLZD8dRHeiEUdgZBme6MRfpQvwYKPaq2GMAyRy+fR3dv7RAcRT+ZZbYFMJkP0uGm6Sdd18oCZ5sZAmebsgCxDoJGsLMt45ZVX8F/+63/FgwcP8M1vfQuvvPoqzp47h//0f/6fAIAvffGL+Bt/429sOraqqhBAe0llGevr66hWqwjDEL09PVueuwCSylN7e1Ho6oJlWVzetlaroVqtwqVevVWvo1qroVQu497cHJcL5hwAXYeqaVCpARAliaQAZZmT/cQI6c/G5jr73NwcimGRiOkEAeQHD0gdPwh45M9SfGwRe3Qx8HsrSfA9D4VCAalUihsjCAKymQxZMIIAjWp5u56HMAg2MW+3QrOU7JbvjDDFmRFwXZc7AjJtR4qegyRsHlZUrVbh0fbFVCqFQqGQyAg/hRAEMmugWCzC9X2oqsrLXZVKBfl8fiPCpQ5Bc1aR2Y35hw/xzW9+E6+88grOnTuH//gf/gMgCC3tBkDmCHieh/7+fiwtLcGmDsFAf/+W2hoAcWhzuRwymQz6ens5m95xXVTKZcIDcF04to1avU4GhS0tEdXMCPmROQxMN19TFEAUIVFVzShRWKJZSFmSYNFugsWlJZTKZQRBgJppwnNdvh6i9sKnGQA34gDwzTLcaN9TFAXpdBq6rkOlAkqqpiFNy3aSokBRFE50lBWlY0eAfe+sM4LxiNqBkwXZfQfgg2iQsNJAqklUiB0j6gg4dICU5/vYtWcPZFl+YrMCwFPqDAiCgK6uLqysrJABM54HXdNg2TapA9JoNOoQSFRb3qNfpiiK+ImvfQ3/+t/8G6ysrOB3f/d38e677yIMAhw4cAB/9+/8ndiHhklduo6D7u5uiKKI1bU11KgH2NPTs6UcqO/7MOt12FTaNqSpL9amFu3NtW0bdVZz0jTYjsMXVr1eR6lU4iNMfduG43kIaV2f1et8urE7ggNf8hH4AUKQ43740YfIClnuOCiSBIEufjFiHBRFQSaTgUEjDEPXOdnujTfewO49e3Dw4MGG62SKXLZtww+Jsl/dNKGoKgr5fMctec0dDh1FMmxxRl7j+36DI5BKpXiPNMsINDgCINoDLOu0Y9cufg8SPJ3QNA25XA7lchmWacJQVYRBAD8IUK5UyHRD2nIIuilIsgyflgpkWcZP/MRP4F//63/dYDcEQcD+/ftb2g2ARPtlKl7W39+P5eVlWLaNxaUl9Pf18TG3rRCGRCPBpMJYwEb2sbe7G0Hji1GmGzaLRK16HXXLQr1eR6VSgUkHglmOQ8qtkY2c1/ppYMA28SAIcPnyZX4YZiNEyl8SqRMhRpyKdDqN3t5ebjt0XYdhGJiensbNW7fw+uuvN2R0A+rgsAFMvu+jWi4jABFX2uo+MUTtBrt/vFW41T0GKXk024F6kyMgyXLDdxBGHQHapVapVuF6HoZHR5HJZPh+8aTiqXQGALK5d3V1oVgswnMcUlen6TdWB2QsV4QhWRAC6aFnD/YXTp7E+B//Me7du8eHQIxPTOD/+fM/Hz/+EuDtOkycJpvNAoKA1dVVskhXVtDX2xvrEAQ0zWTZNu/jVVWVyO/Sh6S5jUijI0oZ+YaRjtjnlUolgP6cRcCxfdAAanYV/78/+z1AFBH4pJvgh37oh5ATslwCObrRdcJgPnf+PCRJiu8gEASSuVBVWLaNtVqN1OtC0rMvCEJHRJpNCxpUv6FVa5AgbCIKOY7DOQLMEeCvR1NKMHK8SrUK23WxY+dO5HK5J9qzT9AZMpkMPM9DlTrlacOAaZrkZ9UqUun0Rto7JANnolnFF0+exB+NjzfajfFx/PzP/zwJFlqsG0VRSJQO8gz29fVtOASLi231Slh6nWXtRFEkmUFF4c9uQLsDAACCACOVQq1ahes4yFHCMgPjDGiahpRhtLQbALhuydraGr773e/i5VOnyMYmCNz+5CMtkcxhbwezXset27exe9euzaVdmpFJpVLQNY0EPBFCXt2yiM3cIuhqvp52MxTY68UmRyAIApINpd9bStehUOEgcqobGQF+3ZSsXqlWISsKxicm0NXV9cSWBxieXDelA6iqikKhAFHXYVsWJ9fx1C71dAGy+Fh6in2RYRiSmh9FPp/HL/7iLzYMmGgGS3mzRQ2QXuKenh5IlDCyvLKySXnLdhyUSiVYdGNXFQX5XA7pVKq9tyjQgRegzN0teo7b9c5KUtzDGPLPjKa+O6ETrZdKuHfvHvbt2xe/qUcWsOM4SKXTyKbT3ODVTRO1Wi22j3qTwEfTZwLx1xotCbDPYSUkgOpV0PJEtFsAMY6U53lYL5WQSqUwMjrKWxsTPP3I5/MksyWKMOt1/kzwTZelsWlmIJpVBBBrN7gj3ZRhYlBVlffkA8QmDfT3I0WDgaWlJZg0Fc8Q0Loz67gJAaRTKUK2jTgC7LhRMMl2Rhhsh3ZPNWsjZmucdTBFI+PtrourV69CaRVERNagQzlUKcNALpvlExor5TLv3e/0WraSTRaavjff91Gr1TYcAcPgnSc8E0A3/+bve319HZ7rYnBoCAMDA5+AtPzjx1PtDACEVZvN5yGqKk9BM6GOSrVKald0YSus75Yu6oWFBXzjG9/gn2XbdqP3FvPQsJ56Jp/LkEmnuUNQr9extLzMFQBrtRpqtRpxQgQiUJROp+PrznHHZIuafjZ/acz9aBfNx74eiGXnd6Jtfvmjj5DJZDA1NdXigCRlZtIyh0TbpHK5HKmRUg+6Wqs1OFcsPRvGOAlBiwUd5Qbw1wYBTNpmBRACVzpCWGSkIPb+hs8LAiwtLSEMQwwMDbUdWJLg6QMrNSrpNI822WZu2zZRlaPdOswmeDSruKXdwOaNBSD2h/GDGETaRWAYBkTaZcDaXT3PQ7lc5qlyRVGQy2b5xtLJ88iuyaEaJZETBNDo9G93Q+eji5vPY4uswNr6Oubm5rD/wIG20bLrOGRmCUiXFsvMsY4ImyohNgdeXD2xGRHeQCwi1+/SsddM0C2TyXCeAp902iITa9s21tbXoafTmJyaajmM6EnD58K6ZbNZGIUCV7STI55rpVKBTwUvmJiG57pYL5XwL7/+dVQqFc4MtywL3/rWtxo/nH7p/J8A0oaBGhOSiCCdTqO3rw8yfVDnFxZQoh6sT4UwctlsIwNVaFRAE1osJL6oHadxUVOwd7VbzoIQN8I42JhL0NRJ0A6LCwtYXl7GwYMHY40Sa1es1evw6OJJMYUugUin5ujCRkgEPBhBSGjhCAB01DJN3fKfxaT3fM9DtVYj94qmTHU6vpbcC2qsY4x2GAQorq3BrNWQyWaxb9++Jz7Fl2D7kCQJPb29kDQNPlXiYxut4zioVauEtMsMfxhifX0dv/Zrv7a13cBmhyCdyXDHIwpRFNHX20tq0YKApZUVFNfWUK5UeNYul89vyiIKaOoQirEdkiQRUp4gNGQdhDbv6RRxMwlCtLcdIYDLly8jm81icnKy5es8z0ONnq9OOyMAcj2ZTAYpShL3fB9VGvSx62llA5k+SoO9itgBBsuySAt0GJKOp0yGlI2ZfgCTfo+5d45tY3FhAQKA0bGxthNNnzR8LpwBQRDQ3d2NdD4PhCFsy4JE+2pZewfz3kNKZPsXX/86lpaXoes6/tE/+kc4duwYAOC///f/jrW1tcYDRMlrAPKFAsrlcuy5pAwDvbQdqFQuY2FpCa7nIZvJkKlaLVLbzQzbZvBFTdOa7H2t7kfsz2N+5gU+T3U1LJI25Qi2oHv7+jA0NNTydaZpconjdDq9aZCJSBe2pqoQJAlOEzkqDqyVic9iADY5Aow3EjKvPp3m7VucCNXCCPq+j3KlgnKpBFGW8dyhQ+jt7W15PgmebiiKgr6REUiKQspZtg2ddpi4nodKtQoEARRZhmXb+I3f+I3O7QYoiY2uxyx1HioxtkMURfT29iKdSiFwXTycnydkQ8pAj5tfwI9BP7/VumdEX6YE2BJt7E8c4jIDzcFNM+bn57GysoJDBw+2PF+PZgsFAKosN6iCMqiahkw6zZ2haq3W9tpYBpQFDuxco0TkIAhQq9XIHAEAiqYRuxXhj7Dx9XH3ybIsrK6vw3Zd5Lu6cPjw4aeq6+hz4QwANO03MECEh0A8NAGkTheGZFqV67oQBAH/8T/+R9ybm0MQBPi5n/s5TExM4Cd+4id469kf/eEfNn84ac2hXmQ2m0WlUml5LqqqEjU72pJUM80tB28wMZJ2izG6qK2mMkVHiziO1OjTbotoVmCLdOHt27dRrlZx6NChlq+pWxbpbACpcbZcFDRqT+k6IIpwPY+3IMWBLUQ2Xrb5TC3L2lAli3r12OgYCMIwNvPgeh5q1SppFQVRGtyxY0fbe5Hg6YeqqugZHCTBAgi/R6OONyOCQRDwn37/97G0tIQgDPH/+Ht/b2u7AWw8o4LAhYHKLWyHKIow6FoRaFCzVa0fwJbtcqIoQqc8KLNeJ45wizLBdgoFzKY1rO02mQbP8/DRRx+hr68PA4ODsa8JgoA7AlIT0bcZsiyT1mWqBVFnmcAY8PkS2BjAFuUWNfMDdMNo4I6xLEBcCYLtL/VaDbVqFZph4PALLzw15QGGz1XuUxRFFAYHISwuEhat5/GI2nEcWLaNP/qjP8LtW7dgaBr+2t/6Wzh48CDCMMTY2BiOHz+Os2fP4s233sJf+pEfQX9//6ZjCIKAfD7P9QGaBUNY2kqWZfT395Po2PexuLiIru5uMkehBVjaz2+xoJixqJsm6vV6I2kvssDZw/ru/Gm8O38aju8gCAME4eYN8N9e/t+RQQae6MKTPRzqOYSf2PmX0cosVKtVXL12Dbt27kShUIh9jW3bvE6f0nXejtnyugWBZz1Yar9er8cyq32qLc+7PSJefZT1q1LJ1IbBK9jICDQPqXIdByYVFHFsG909PThy7FhSHnhGoGcy6OrrQ2l5GY7rcqa9R4XK/vN//s+4MzsLAPif/vbfxuHnn0cQBBjt0G4AZP3mstnYzABAGPaObSOXyyGVTsM0TZimCcd10d/b27bzhm3krbZiXdPgUAIym9oIYNPG1vEcBGBTeXGrLoJrH3+MumXhpZdeavl5NZqeF0UR6VQqNoCJQpQkpFMpVE0THs36ptPpTZkUPwjI50YcAQamPcIifyYMFDKb2tTNFEUYBDCp9kq1VoOqqti5ezfGxsbwtOFzkxlgkBQFmZ4eZDIZsrFS2UtVUfDd73wHFy9cQCqVwg9+9as4depUg2f44z/+4zyd9Af/1//V8hi5XA4QhE0efhAEvItBEkUUCgUMDg7CSKUgCgJW19awQgdptIIoCG1bZjQ6XlcQBNTp6N9mCIKA88sX8MtnfwXfvf9dvDv/Ls4snMH7i+9veu3t9WlcX7+OD1c/wrml8/j/fvy7+MbMH8UeOwRw4eJF6JqG5w4ciH0NY2OHoOJKW7BoufERyNCkNI0EPNpn3Hz8MKbux8g+HhVXMVIpzrFgvcXs/ZEDk58FAeqmCZOes1mrIZPLYd+BA8iz+eYJnglo2SyyXV18aqVN21+/+53v4Pz581BkGT/w+us4TmWG2bP7Yz/+4yQFvYXdAEjnQVyJ0bZtWLREZhgGent70dfXB1mS4LkuFhYXN3UaNGMr3Y1UOs27klpF0EBnWcYwkl3ja7HN8Yurq7h9+zaeO3AgVr2TRddMMjjdYgJg5A0bbcCCgEwqxTsn4kiFnu8jFIRGrhElN2/iB0jSRudBMzkxYrtd2orq0QAiFAQMjIxg//79TyXZ+Ok74w6gGgaMQgGZbJYTCc+89x7eeOMNhGGI5w4exMkvfpEragGkBj06Oorjx4/D932cPnMGc3NzsZ+fzWYhCsLGNCoK1gcs0sFBAn34+np7kS8UIAlk4t7i0hLcNmWDrQR1jFQKCMlIUcbCb/ZYr6/e2PI+AYAMsjh8bJzPdGk69rWzs7NYWVnB0aNHY3UYHMfhswcUOtmsJaKLOQJFVUlGQBB4GyYAztoNgwCCKPIUbq1W40OEBCpwwgbCxJKJwo1WSuZE2K4LUSBDrxRNw/jkJMbHx9vctQSfR4iiCKNQQDqTIWRXAO+//z6+893vAgAOPPccTpw4gWqE2CcAGB8dxRe+8AV4vo93T59uaTcAEkiwEiN79pkYVhCSYTbMGTEMA4ODg9BoqXN5aYn39cdiizS/LMtQNQ0ibTWMywBsVXJgYOuSRdr0guJfGwS4cP48ugoF7Nq1a9PvWceVR6eiGpGRxLFgpO7o8ZhDQDfhhvKKsCEnzLIYjuMQUSDansj4AZu6lGL+zgOIWo07HY7rorurC3v27Glb2niS8bl0BgCS9lMNA7lcDtMzM/jjP/5jCKKIXbt24atf/SqCIMDa2hppXaGbhiAI+ImvfQ0CdSD+0+//fuyCEUUR2UyGePj0wWRKeyEdXtH8UOXzefT19UESRbiui4WFBT4FrBmsv75VhkCSJOj0GHUmYNSEQz2t6/kNnxXjDLzQ+/ym15n1Oq5cuYLJyckGvXQG27ZJqk0gQkKtxFMYYjUEKFRNg8K8/HqdD2vi3j1t7eTZAPqebCYTzxRuPDBJ7dVq3ADLsgxZUeC5LoaGhzE5NZWUB55RiJIEvVCApmmYvXMH3/iDPwDCELt37cL/8CM/AkmS4DgO1lZXN3hAgoCvfe1rRMEyDPGffv/3CS8F2PSM53I5eJ5HRLcoO52RZiVJ2iRLLMsyBgcHeSfBWqmE5eXlltlFPnq4BQxdJ/YNZM3GrcFOWgybyYPRYWfNuH79OqrVKo4eO7bps1lpwGNE48jU1Fi0sRssKwiQbiKHzkbh2jL0fbVajfCKaGCRpuRudh2trj8MQx5AOK4LEST76dO5FaOTkxgYGGh97k84PtcWT8vlUF9bw/Hjx3HkyBH+0IVhCMuyeOuh4zi87390dBT//vd+r6H/PKAPDfeaBQG5QmGj9hcSYZvA96EZRsuHmXn6KysrcF2X8Ai6ukjZoQnsgWSEt2bomgaXKi9aloV8Exfh+d6DONh9EFdWr7S8PwL9DwB8KmbaZ/ThtZHXGl4XArh48SJkWd4kOQxspDgFkIyAYRhb1h23SkWmDIO0hQYBuU5N42OUPc8jWvEgMyd0OgAGQGOUErOoLctCjeoeAICu65BEESvFInr6+zE0PNySC5Hg2YCsKNByObzwwgv4rd/6LVj0+Q4BLtlrWRYqlQp0XYemaRgdHcXv/d7vQRDoVE1angpAIi72XLK1Xi6VoOs6mUPiOAgBElG26Dbq7e3lw81My4KzuLiJRxDlDDBnu3mdCbQW79g26q4Lo025oB04eTBaIohZ8+vr67hx4wb279+/yc6x2QasQ4g5Aq1KGJ3wGSRJgm4YsCwLlm1DprNk/CCA7Tjw6MRZxlNiXKWtjsGIjQ7NJEiyDEPXYVIxqNHJSYyNjT2V5QGGp/fMO4AkSTAKBQhUPzuXzSJFPex0Ok1qxJT8UalUeJYAoIScaOsJXdw+/ZNnBkDSzQEln2ylW68oSiOPYH0dy8vLDfKWzQ9jrKdPa4AAMVDNalyCIOCn9/10+/tDfUGSFSDH/Ku7/goUsdGZmZubw+LiIo4cObKJxMQYzwFIqi2q7rf5lDvnKguMAS2KsB0HAY2gTDoDAiAbeaaJLBRVFIwioO1KzBGQZBm5TAayJKFYLKLQ3Y3B4WGMjo52fI4JPr9QDQNqNku4L7qObC4HWZJg6DoUSkhmc0LKlQqJbFmLX5MWAAso2MhbWZI438imjoCu61u2oeVyOcIjEEV4rov5hYVNpcrocVutN1lRoGoaJNpd0GxvWpUKeKkuUmprl84PwhDnL15ENpvFnj17Gn9HMwIBjdizkYxAqw2/I2JjSGa4MCfFc13SJWSasB2HDHKj9j8qA9/u8xzHQZkGjQB9HtJpomxqWRgeG8PY2NhTP8n0c+0MAMSDS3V1EaEIgYrd5HIwDIPMLqCbjO04ME0TFbqwGZjSFP83yEOZy+dRM03UqlVS2w4CaBFRm3YQBKGBR2DW63g4P88XdtxnxDkELK0oUjIhUzwMwxBBGOJw76G25QK5wRkAevVefHXiKw3pdcu28eGlSxgdHcVgUzuQZVm81KHreuvSQCTLsiUidX5VUSCAMP3X1tZ4Z4aqqshElNgAtNRpCGg9tlKpwKPiUwZdzACwUiwiWyhgcHgYU0l5IEEEejoNNZPhIldZyvJXFQWyLMOyLELGc11UKhVeh48KWzUjDEPkCwUsLS3BdRw49JnkglhbOMwsu6hrGhCGWFldxeLiYqOCZwSt2gVZ25xPsxzAxmYfhCEPD4KII8PZ9YLApZFFNtQp5txv3ryJ0vo6jh8/3rDpsiibdfQ0t/82fM42AggAXBBIVVWEApmdsL6+Dp+SunVdJ1yyrfr/qRNQqVaJbQ0CiJKEHC0p1Go1VGs1DI2NYXRsrGUHydOEz70zAAAidQjYRiFJEnJUFpd5tkyhq25ZKJfLGwubLqbmhTrQ3w9JFPHg4UM4to2AyoU29+22Qz6fR39/PycIsYXdKk0W5xAw75ZpmPs0e8Ecg5/e2zo7wMiDHsjxfnLXT0IRlIZzv3DhAgRRxOHDhxveW6dpOBbVbJokxjb+JmeqHaISn4wg6bgu6lSMCACMdJpPd4w6DiE7FkXUCWAkIVEUiRSyriPwfawUi0hnsxgaGcHOnTtj58oneLahZzJQ02nuyGqahv6+PjKwjGYDHdqWapomypUK1zMB4iP0wcFBLC0toU5LlRJtYwuxmb0eB1mWMTAwgHw+T9ROHQcP5+djtU+Y7Wo+B0GSOFHRtCxYrgs/CLjtYJlQ8uKmNRzpJIgy7/lrQcoDH1+/jt27dzeU3Xzaeh1QZccM1V7YBPaZnQQQ0eujjosXBKiWy7BsG7bjEJufzzfOCIizS81OAOUp6aqKXDYLSZZRq9VQrlYxPDaGkdHRtsJrTxOeCWcA2MgQMO9XEMhsc5aeEgQyECigDPVypcIXNgDO1GUpcFmW0dffj4WFBQAbMwt4ZN7sTbeArusYHBxEPpdrWNitFA7FaG2OLlpVUchcc6rlHcXBnufwQu8LsZ/FMgMePHRr3fih8a82/P76jRtYXFjAiRMneM9+yDIpVDrY0PWWXQPtSIINr6PXwhi/bDEy8REGnY4/bZgw2PRZvufBrNUanABZUZDOZIjhoeTQYrEILZXCyNgYduzYkTgCCVpCz2Yh6zrn7siKgu7ubmQyGYRBAE3ToCoK6vU6qtUqSqUSqYVHtCxYMBGGIQYGBuB7HorFItj4cmAjKg8p56Dd2mH2i3UbCIKAtbU1zC8sbM4SRAMaciAIIenlZxF7rVrdJMTVympFSwRxAY/runj/gw9QyOdx4MABzrdic0iYCmAmZj4Ld+o7CSAi9yeko5dZMOfYNiRFgSiKkCUJmqqS2QKRAKLh/oYhHNtucAIEmknIZjI862uaJkrlMoaoE/A0yQ1vhWcqJyqrKlJdXaitrpK+UiqHG4I8+JqmQZJlxL3xGAAAKBdJREFUOLbNhXPq9TqZw63rfNGyCH1wYAAfffQRPN+H3lRLjy4Srn7VtHkx54It7HQ6jWKxCN/3sb62hppporenZ7PYSKRmx0h0Km21s+j0xmi6+3/c8zdwaeXSpvsR7ST4K7t/Eqq0sSEuLS3h42vXsH//fs6QZb3AbGBLyjA2TeOKqvzFgt0HVjqghCum4hZtJwTAJZyZcpraIo3Pen2jhlCm7Y2yLMP3PAQg38VKsQhF1zE8OorJycn2LZAJEgBIFQqora7CcxwIIGQ/23EgShJRwKO8JMdxeH26rqpIpVLQKElVAOETFPJ5GIaBpeVlpGIEcqJObkj77sOmdD+zL4qiYGBgAJVKBevr63BpMMEGgkXT8yFIxiyqoqdpGiRJIm26pknGHEfT/i3IdJIkNYx55u8JQ5w9dw6e6+LUqVNksw9DMmuA8nWYIxBXr+8kgGABFsuksKmoNs1UAiQLmDYM2I5DMjXABich+llBAJdqmkQHtmmqCk3XN7RNKOl8fX0dg7Rk+nnjFz0zmQEGWVWRLhR45K7TNjYW0eeyWeRyOWQyGTJlzLZRWl/HysoKiqur5IGjD+vQ4CACAKurqw1yvnGIm7LHzoGl9JlqYVdXF0RJ4gt7fX2dZxnY65uJcgplxgoC0TKIevkHe57D0b6jDceXIEGAgBAhsloOPzT2g/x39XodZz/4AP0DA9i3bx8AcFlWPnkxk4ndRBscnqZFzVKhQWQxs3JApVLhss0CSM0vl82S2fKSRGqXdJ48/7wggE1Z3bVqlTsCiqIgk8lwARHmOAU0GpNlGUOjo5iamnpqe4ITfPpId3dD1jSyFkHmbQggkbCu68gXCsjmcsjQskLNNLG6uoqVlRXeGQOQzWZwcBDF5WUiMtbGdnDOEl1LAavpR20HiPbJ0NAQ5xCVy2UsLC5yJdAgCEjKG43lRpYRBcAja4ZWHJ9NMsSRz2PZxOMnTvC1ZVkWqhH9kTzVf4keh3VC8GNGeEZMX4SXUhihm6opVioVXrKURBEpwyD2yTAQBAH8IOCjhxk8z0OdlnXqtLWTZQIYp4y1IQdhiDr9LvuHhjA4NITx8fGnunMgDs9UZoBB1nVkentRW11FEARIp1IoUe9Q0zSS9lNVGKkU0bqv1+E6DqrVKsxaDZqmIZPJkNRzKoVisYjxiQmeYWiHhk2cPmxAo7eayWahqirWVldhOw5JO9IJemn6kG58DPl7GBKxDp9u2FEvn3AHfgoXli/w90WzAj+58yegySTCD4IA7733HiRZxokTJwAasddMk/QwUyZuVII0DBsnDUZHAzc4BJHoIYh45FEBF1XTuC48uz4/2JhUKAgCJ1550XSoQPQNNFUlSmSMTCgIQEAmxa2triKVzaJ/aAhTU1NPPfs3wacPI58nHS7VKmHlqyrJApgm0pkM0nQ6pk7JtdH/S+Uy0qkUstksBgYGMDM7u0HeC9sLjUU5CIiseb5pgg476u9HrVrF2vo6PBpMpNJpUu+OpPVZSZOv6VRqY6CbJPHNM9quyMCyfpIkNfy+OZvYPBNGU1XSIRR5DxdeotfCAqSGDAGzL/Q1vu/DchzekgkQJ0DTdT5mmP3M8zwEngdFURDQQU2u4zToNIhUsp6VW9j9ZM7W+vo66vU6BkZG0D8wgImJic+dIwA8o84AQDIE2f5+mGtrCC0LqqrCosI52WyWlA1onSmTTpP+dLpY6paFumVBkWUUCgUsLC0RAQvEL544bHIKIpsm69fvHxhAtVrF6urqhkFhETPzXOnxWDoxZRik1cl1YVJ9/xDA3q69ONZ/DGcXz8ELfGjQECCALCv4i5M/zI99+cMPsV4q4dVXXoGiKEQv3bIAQeCDQ9hApSjBMprab0jDhZHxwnQh244Dj7ZjsutlDljUCWDGwHMceL4Pn853j94rSZahKgoUWh9kmQk2bCnwPJQqFVSrVfQODKC3vx8TExNJRiDBI0EURaTyeciqinqpBD2SivZcF4qikNa1VAopw4BL5blrtRpcz0O5UkGFTiMURRErKysYHBxs2ITaBRQNDkPUKaD/FgFkKON9dW0NlWoVFXrMDCXfKrJMeATU7gRhSLQVNI2IeZkmsrTsIYgimWDKovGQDF/j5T56KnXTxPvvvYe+/n7s2buXlwUYUTBlGFAoUZp9ligICNA4QTSMXiN1Alh5z3UcOJ7XQLBmHVW8lErvQUDPk2mTWLZNzjtyHxVVhaIokCVp0+TTEKQtcWVlBZKiYHxqCgODgxgeHv5cOgLAM+wMAGRhZ3p6YFWrCMOQ8AQoCY+lwAVBIOTDdBqpVIorUJmmCdfzoOk6HNfFvbk5DA8Nkfpg1GPegkAYt7ijYPK6pXIZVZoKL66uoqKqyOdyRPWMlRDo4kkZBh/FKUkSVNrl8FO7/0ecXTgHEQKkkHz1Lw99EYpAFunc3Bxuz8zgyAsvIJ/Po8JYvwA0XeejXTedO3UUor3WLB0ZgqqBuS4c1230yEEyAdwJiCzkkNYZHddFuVqF7TgwNI07Fwp11Fg3SJzIiksXcygIGJ2YQF9/P0ZHR5P2wQTfN1TDgKwoqK2twaCBQq1WQzaXgxSJuhWaPchmsySlXa0SO+O6yKTTePjwIcbGxkirs6Jw53o7AQUfwRvh40iyjO7ubui6jhIl1FUpKTeTTiOXy/HBXSwS1+lgJs/zyLVkMiRrJwgk0xb9n2YUQurgn3nvPciKguPHj5PBP1SEjGUSY+v19NyjWUUWAAiUd+TSDIBLeUoMsixDo5t5gwMBMofA8zysl0rkPASBOwKyLEOl72PEyk3nFYao1GpYX11FvrsbfQMDGBsb+9yLkSVWEaR9SFZVeEGAUqmEaq2GENhol2M1elGEqqro7u5GoVAgNXTPgyJJePDgAVRVhUQ3KoNqjCuyzBd3px6/EPGYfUrW6e7qQi6bRblcJkpYto2llRVIosgJg+w4iqpCp2RC0zQh0sW4O78LI+kRzFXnIEOGCAl/Ze9f40zkCxcuYJz2zUbblFLpdEdse1mW4bFZAkEAmzoAXiRrIABQZBmKqpJzFkWI9Lx9z4NNjRHz/m2aFRBAOi90OqiJRTZNN5Efo1avEw2BbBZ9g4MYGRlBb2/vlteQIEGnEGUZ6Z4eSKoKd34etm2jUqkQ4h57FiNp+VQqhXQ6DdtxUCmXkc/ncffePSwsLiJlGCTK1XUyn0BRNlrutggoGARBIH32vs83bcMwYBgGYcGXSiSYoT3yLLUeBgFANVfSqRTK1SpXB0wzafUm/oDEsnBhiEuXLqFUKuGVU6cQBAEf8CTLMhd5azhH8peN+ygI8KgjE2AjePAcB9H+BlGgqoEsCGCOUxCQjIHrkrIA5XTU6nX4QYA07UJSmoKHOE2DwPextrqKumVhcHQUvX19mJiYeCa6jRJngEJWVfSOjhKhitVVVKtViKJIhG8iaSsGURSRz+WQMgysFIuYmZ3F5OQkAso6tSyLOAZU7Yux2iW6aW8Frn4IkvIOgwAy9fZzuRzK1GlxXBeVahWmZZGyBn1odV3nHnK1VkOGtlD+m1d+A//rB/8frMwX8dcP/DXk1DTK5TLeffdd5PN57Nu/H9VajROb2DjPLc8X4BMi65YFp6m9SZZlqHQGAEvnB0EAh05QYzLRzfeApV7TlNTJfh7HOBZA2p7K5TIqlQr6BgfR29eH8fHxpCyQ4BMBKxsMyjLm792D6zhYX19HgXILGtY6tSOqqqK3txeSJGF+YQErS0uYmJwka7VaRa1W4+UvnXYxyYrCM22t7Ec06PBotM/IxqlUCqlUCmathlK5TDhQlkWyf0GA3u5uCJT5n06l+NwPs15HOpXatDaZTbh65Qru3r1L1ElVFY5t87Jfp106zL5a9TpMy2ooG4ggtpkJPbF7GHge3KbAIQ6apqGnpweqpm3wLSLZB0R+Zts2JxiP7diBwcFBDA4Ofm7LAs1InIEIRFHEwNgYIAgor66iUqkgS7sKWAtgMxRFweTEBO7euYNqpYLde/agXq/DojLHlm3Dsm3UqlVSn6IPtSzLpFZF61XND5xAiW8AeEsSgyTL6O7pQS6fx+rqKhzLgmvbWFhchK5pZOqaYSCTTpNFTaU/s5kMdFnH/23X/4S3Hr6NofQgzHod77zzDnRdx6FDh8jURYGME04ZRvuFEIbwfB+u68L3PK5Kxlj9Es2ksHqe3/za5s0f4EODZFnmUwklSUKKjqRuEEKJvM8PyPjocrUKURAwQoeGjI6OPjOLOcFnBy2dxtDUFB7OzPBSopFKcdIe4xMB4JF+Kp3GxMQEbt++jaPHj3N2vO04ZJIh5RtIskyyaTRbIEsSae2jn90QeaMxCxntHAjCkJQ702k+PdWiffm24yBtGEil04QgnU6jWqvBdRyYkY4DBkmScOPGDdy8dQv79+9HV1cX11VIpVLcZrZCEARwPQ8+i+bDEC5te4xmD3knURCQ9mLKAWiGKIqQFQUKta3lcpmUPlS10RFo/pM6AZVKBWa1ikJvL/oHBzE2NhY7M+bzjMQZiEH/yAhCUUR5ZQW1ahUZWgdkbT7NSoDpTAYjY2OYuXMH+/btQz6fRy6XIzMDaJbA9TzYtI9eEIg+thRxBhgBTpJlcqwmQksUzLgoioKenh4IoohqpcJnlduOgzVRhKHrZAYDNS6VWg25TAYB9aT9IMD3vvc9iKKIw4cOcc/boMNXmhEEAd/IPc+LXZSCIECkPcgC5RHYltXyWiRJgswMXZOISbVahet5UGiUxLsDNg5GZg5Uq6jUagh9n4wgnprCyMgIurq6Wn7HCRI8bmi6jqEdO/Dwzh3S0kej6gCARG1HNKhQZBmjo6OYmZ7GzPQ0Djz3HFKpFHzKerdoF5PnebyrKWojJFGEKEmQKWlRpCXDVi2BUbuVTqcx0N+PUqlEeuypIFClWoWqqkin09BUleutRNPqIci8kqtXr2LHzp0YHh4m10MDCKHJ+Wa8Ah4MxAUCgkD0FkQRoiwjoKS/IMbG8NezoEqWG8SLPM8jGYYggEFHyTe9ucEJsEwToSCgb2gIo+PjGB4efibKAs1InIEYCIKAgaEhCIKA8vIyqpVKgwSuRx0Ctinruo7J8XHM3b2LmTt3sHvXLgiCAFVRSDdCJsOnkznME/Z9+GEIl4pdMNESmS5wUNKLQBc5iwBEutmKggCBbrqKLKPQ1YW0YcCkzGXf82CaJkzThCzLCMIQhqaRGd60pnbt6lX4vo9jx4+TthxNQ4oqrXmeR1r6mLyn7xPBEtpuw9qA2LlJ9JyZsInv+xAipQIBpMbKnKBoZNN875n2gElV3Jq1xAWBaKPXmBNAnZtsoYCDzz+P3t7eJBuQ4DOBrusYnpzEw3v34JgmREGAYRh8zUSdAVEUkclkMDY+jpnZWezbtw+yJPHI39B17hgwIp0fhghoOc5mLbd0A5UkCZIgwIvoGbByH7ch1HYwpyKVTiOfz0MAUDVN7oCsuy7hH1F1VgC8DXJ1dRUz09MYHR3F1MQEIBBZYVmWecDANvxoCZDppLC/N9gOULsRBEBT2p85ChLtwGC2gyE6DwIgw+ds2ybt4ZGMhgAgAJl1UimXyRhjGrTs3LMHO3bseKbLiYkz0AKCIKCP9spWV1dRLpeRymSgyvIGs5a+ThAE5AsFDA0N4fbt29i5cyeJBLDBmGXjMtMgEbZHI2xWZ/fpZuuHIVzLghcEpI8+Ek2IlHkrRBwRQZJQr9UIo59mGAqFAmzL4ovCcRwEvo/VtTWoioJKtYoQgOU4OHjgACHuUUelQlv3ogIgTPQDIJG8JMukN5cSAKOLkTGTo6lMFsFsEl5CTP2THrNG+RCMhAQamTC5aJYJEOg5Te3cif2HDiVOQILPHLquY3B0FEsPH8KqVuEHAVLpNCHKRmyHKJDBaRPj47h39y6mp6exl073Y+si6hjwCJtutqxmzgSFmIPg0uFHzCawFLkYsR0s2HAdh8h1p9PIUI0EyzRh1utkNghrA/Z9OHRTn56eRl9fH4aHh2HZNkRZxuraGo/iG1oFI5LsMs0CsrbKZjCZ4miwINHMaRwaUv8ULEgIgoALx7GuLot2VDAnACBtmM8fO5aQi5E4A20hiiL6BgYgSBKqq6uo0vnlBhXOADaEP1RVxfj4OB7Mz2N6ehp7du0CsHmzY+IgmqpCjbBhWeQddRLqlsUVsNiMdJ9G56yVEGFIVMNo/Z6JAfHam6KgTqeruY6DtdVVrCwtQQDQ090N1/Mg07ad6OJibUEi9ZxFuphZVCHQNKUQ+TdvNWrSOGcSxRwtCIAMjuuiTBXbcrkccQCqVUJMdBwumymJIvKFAvYeOID+pomKCRJ8lkilUugbGkJxaQl2tYpKqdTQYidgw9nO5fMYHBrCzVu3MDk5SYYgRcB4AGwzV2SZl91YP70XdRJcl4xGborEHdclg3yoM+L7Pmk/lmU4VLKXnZPEUvV0VLNjWSiurQEg5ZB8oUBU/8IQIh1DTE52I4MpiCIk2scvRH7G7IoYcVBEUYQTaU1uvv6NfzTZjqasABs1LEsSUroO0zTJqGHbJhlE6gxpqoqhkRHsO3AAaiJHDiBxBraEJEno7++HrutYLxbhUIW/VCrVIKkpCgIGBgcxMjSEK5cvo7enh9esow9rw98jrFZZliHIMiG7UBawYdukv7ZJipg7CNQxYLr+jGQUrccxmWND01CqVlGZnyd66iEZcBSGIWyamdA1DbqmcYGQqHMQLU9EHYaG9pww3FjQkXPYtMDZdUd/RP8MggAry8uwLQt+GBKnwHV5BkIEIRmm02kMjYxgcseOZDEneCKRTqehjo6iuLICc30dlWqV8HGaRp1rmoZdO3dicWEB58+fx8mTJzfEt+hrBKBh42N/SoIAkYpusZ95tN6+SfI88veAZhZY6UKnHKEwCIhgVxgSfX7XheM4eFirwbEsqIqCQj4PEeDlTlXXYdDugZRhQJQkHlwIwEZZk/EmsJEV5OueEpfJxW0QhVtlDht+RM+3VquhtL6OumVBNwwsLi8T+8junUjkhguFAiZ37ED/52Ta4OOCELZimyTYBMuysFosol4qIXBdPtsc2Hi4y5UK3n77bYiyjFdOnSKtiUBDX3wnrYUA8fqbW/Q2IQy5jCjLWrBjeNTz96jy2eXLlyEKAvbs3UsEk1QVnudxT52pGYI6ECqdxsZSe0qL9F7D+dJJgZ0gpO/xXJdwKjwPK8UiqlS/IZ/Pc4eL1f/YPR8ZG0Nvkg1I8BQgoPol5bU1ONUqiVrT6YZWwRDA9Y8/xuWrV3Ho4EHs2LGjgazMyoKdwm3Bum8+rzUa7efz+Y2x4KJIiMh0aNj09DTu37+PwcFBdHV1EdlzyimSRJGk3CMlRY1KijN2vyRJm4etNcGhjkUn4O2TlE/hU7n05ZUVWLYNTVX5fAim3ZCiJZBsLoexHTuS4WQxSJyBbcL3faytraFaLsOlC5uJCzHcv38fp8+cwcTEBJ47eJAz4VkkzVJjQHvHIAR4yaAdbNtG3TSh0YfedV0Ui0XcuXMHDx48wMzMDO7evYtqtYpsJoMXXngBP/U3/yYs6iiEVNiIaSQ4joMwCPDn3/42rly9iqmpKfz4j/0YAOLls9of6wSQKR/Ao+ShuOtgTobreaRWSeudfkRZzKSlAM/3kc/lkM1moRsGF2QBgGw+j/HJySQbkOCpg0mH3diVCkLX5eqbzOH1PA9nzpzB4tISvvilL6G3pyeWYNtczouDT8l7bc27IKBUKiH0feTyeYiCANtxUCwWMTc3h7m5Ody4cQMPHjyA67pIGQYOHDyIn/3Zn0W1WiWfEYYQ6bRG27K4rfpvf/ZnuHL1Knbs2EFsRxNnIGpDwjCE7Xm8lZohjPzJpgt6rguHERTp630qOe65LkRJQk93N4xUCoZh8IyHJEkYHB1NyoltkJQJtglJkoiIhaqirKpwTBPVapV7oIqiYGR0FHv27MHNmzfR1d2NwYGBBnlj9nDzVjmaqhPZv7GRYmP1tOaJXQDwjT/4A/zhH/0Rvv4v/gUUVUWtVuMtf/+vX/5liIIAx3FQr9d5uSDa1iRnMjApUS8IQ+iahkKhQPqdLQsH9u/HtatXsfDwIVn0VDbUDUM+fCQqUerR7oM4AxQlC0bTg+xnjm3D830oioLBwUH09vbycw3DEKlMBoMjI8h/ziVBE3x+wfrv1zUN9WoVrmnConNRmCjZsePH8cYbb+DKlSs4cuQI72LiBF1WKghDBAC3GcxWADQ1Dzqoh6XJm8Bsxy//8i8jZRioVCqcCPxrv/ZrCKgNcNnI5nQaAHiHQTaTQa1WIwqBYYiuQgGSLJMx5PU6Dhw4gBvXr2N5cZFE6JSwyBQKGcIwhE95D+zf7BqiVqRZP4GVJ4MggE05ArqmYWR0lDsAoPekZ2AAA4ODUGPapRNsIHEGHgGCICCXyyGVShFme6UCxzRRM02IIDXAQ4cOoVgs4vrHHyNlGJxnEN3M2aIG/TOI/JtnEoKAD/RAGPKhIWzRGIZBFi2t1TO9As9xSJ9uECCfy2F8YgK3bt2ih9o4fjqdhhiZrOb5PlKGgVQqhePHj+N//z/+D6Lzvb6OQ4cO8VYh9r/LZg6EIQTaIsk3/RgyEBNQYRECBAG2ZaFKWyRT2SxvdYIgIJ1OY3BkBLnECUjwOYCiKOjt7YVFN1OT2o5SuQxVlqHpOr5w4gS+9847mKWqpulIOTK6onjBrqn7h/8Y4A482zyZTQEIk96s13m5gon8mKZJdBIEAd1dXRgZGcHc/fvkQ+l7ZVlGNpslAYjvo1KpEAl2TYOSy+ELJ07gd5jtWFvDocOHSXuk68KnnVKu68J2XYhhiLCZI9GcEaH2QaITFWVJIp0DtRoJFlIpHqSBXndPfz8GhoY2jS9OEI/EGfg+IMsy8vk8XxTVahV1Oh/bsiwcOnQI77/3Hi5cuICDhw7xjXar+hnQqCDmUyIQ24BZ77FPa2JsKAprw5ufn8fE1BS6u7rw6quvYv+BA1heWsI//V/+Fz7vgCEEcSgkUeSZhVKpBCOVgqaqOPjcc7h46RLOnT+P5194AQpV+mIZjoAyiRk7uVOEYUhYvrSNKQgCGHTUqgAi5DQwPIx8IhyU4HMGgWoPGIYBJ5cjA4RKJZJlpB1Lu3fvxq1bt+C6Lnbv2gVHVYnOf4yD3Q5hEJD2Zdpt4FAFUJ9mJgUAoButqqq4cuUKJicnMTU1hR/8oR9Cf38/Fufn8cu/8iu8e4lfBx1CxMYU1+t1OI4DwzCgqioOPPccLn34Ic5duIAXjhyBqGlkFgI9L57mb5FNbAWmQVKnQkyqqiKbyfCBZ4kT8GhInIHHAFEUkc1mkclkUM/nUS6XYVUqgG3j+IkTuHjpEi5dvIidO3eiUChAo0Q/SRRb1v5YC6HP+nsjU73YVD8BZMymQevqNdPEmTNnsLK8jB94/XU8f/gwH0TiBwEEyvINmhTAwjCEoijI5nKomyacIIBpmrBtGy8cOYJLH36IixcvbugJ0PRcdJ56dBrhVnAihqNWq5GMQCaDfD6PQk8Penp7kc3nH/n7SJDgaQEbfJbL5VCr1VBeX4djmhgbH4coirhx/TrqtRp27NyJSrmMDN30GDMfaOrKYVoEEbXQ6NjgkHYfCYIAx7bJuGPDwPzCAm7fvg1BFPEzP/MzGBsbA5NEd5lcsONw28NBs3c2LUcysrKmaTh69Cg+/PBDXKK2g2U0WaYijJxzJ2ABhG1ZqNs2XNeFruvIZDIodHWhi9qOxAl4NCTOwGOEIAh8IIjX20vq9bUa0vk8zr73Hm7euIHxyUl09/SgUqlAVZSNNqMteu8BcPEeiUXnIGpboiTh7r17mJmZgaZpeOnllzEwMLBxXgCvybUb6iFRRTTHdYn6n+9j9549ZJJZuYyZmRlM7diBwPcbxEtY37IgCAgZwSlsHCcMEOUvRlpkgiYpOk51fHIS3X19yXjhBM8kWJYxl8sRMp5tI9fTg1Q2i0vnz+Pj69exa9cuUg4EKUXKkU6lVraDZQ2ZeE+U2W/bNizLwvXr11EqlTA+Po5Dhw836BwElB8EILZTiDkibJxwvV6HQzsR9uzZA1VVsb62hpnbtzG5Ywd904Zomk+7FyInTP6gn82GtFlUKyUMAph0CFwmm8XQyAiGR0eRyWYf+d4nIEgs7ycEVhNPpVLo6evD2I4d+NP/+l9x++ZNjJgmBgcHAUWBaZp82iDrzwXAtcYlKtjB0nlReL4PURTx9ptvQpIkjE1MYM+ePchHBmwwkp9LnQC3TesfMxyqokDO5VCv15EDMD45ibl793D27FmMDA9DbipzNJOX6IH5313HQd2ySKRC1Q4VRcHg8DAGh4e59HOCBM86BEEgrXmahlwuh6HRUQxPTOCbf/iHuPbxxxgbGUFPTw8vF2pspDd7P7Ah20vFwJpV+kAzfwKA02fOoLenB0deeAHjExONst8gNgY0A+i2CSQAUtJMp1JQFQVmvY5MJoMdO3fi/twczp4/j7Gmz2flgqYbwI8dBgGsep1oJlCioO/7yBUKKHR3Y2JiAik6yTTB94/EGfiUoKoqfvTHfxwXLlzAO9/7Hu7ev48ROh0rnUohoO19zMNuXsBMcTD0fcwvLGB2dha3bt1CEAQYGBzE4UOHADQScLhqIX0fsDFRsBWiKTvdMCDLMvbv24cH9+/j+vXrqH3lKxCogqKiqhBEkfAGms438H04jkPkTGm60rYsGIaB0clJFGhqNJt49AkStMX4xAT+1t/+2/j2n/0ZLl27hnwmg/HRUYyOjhJnPwi43LkY2WwZGEeobpq4e+cOZu/cwd179xAKAg4eOICdu3bx+SPMsWDiQywb0M5uRCeJhmEIkYqCWbaNA/v348GDB7h+/TrK5TLhJmgaacWOCQDCiO6I7TikW8CyEIYhCt3d6BsYQDqXQ1dXV0fcqwSdI3EGPkUIgoBjx47hhRdewNWrV3H69GnMnT2LnkKBiHkwFa9Uivf91ut1MpWvWkVpfR2ra2uwbRtdXV0YHhrC9evX8dyBAygUCiiVy1xxLDoEyK7XyWhhurjimMdRRLXFZVnG8ePH8Y1vfAO2ZWG9VEKhq4tMUaOzx9kURuaA2NQJ4IpnQYB0NoupPXuQ7+pCKpVCOp1uiBISJEjQGtlsFl/7yZ/E+vo63nvvPXx48SJuz8xgZHCQsPip+l8mk0E6k4Fj26jRtudKuYxSqYTVtTWSQRwdxY6pKdy4fh3j4+PQVJXoe7guwJQIQRwCh8oaR+WNyQsay4DNUb4gkOmnx44dwzf/+I/x8OFDlNbWUOjpgWua3DawCYwA+HRG3/M4aVoSRXT39aFvcBDpTAaZTAYaVWlN8HiROAOfASRJwuHDh3Ho0CHMzs7i3AcfYH5lBZVyGaAEH9YnzBZKKpVCNpPBxPg4RsfHUcjnsba6yj9ToONLXSofynQNArqgAcB2nFhvvBl8XCnF6Ogoent7sby0hBs3buAHXn+db/h+pM3Qp4tbUVVoug6dzk7PZLOc6GMYRjJMKEGCR0ShUMAP//AP49VXX8W5c+dw88YNPFhZgVWv83KdJIpEphikXTBD1+H4xATGRkchKwpmZ2f5Z8qqClAuD9vsBdryG1JRIY/JjDfZBgbGXWj+3djYGApdXcR23LqFV0dGyJwU14Xr++TvtPwgqyqZpJhKIZ3NIpVOw6AcLEacTPDJIXEGPkMIgoAdO3YQ6VGQaL1arWJpaQnLy8ukf9YwoCsKfDrGlM0IsCxr09Q/VVVJeo1KEwOEJBRd0B2dFzm5hoV99OhR/Nmf/RkuffghXn31VR4lyIqCVC4HVVWhaBpESYJhGMhms0Rdrc244gQJEjwaDMPAqVOncOrUKQAkqi4Wi1haWkKpVIKmaUgbBoQwhOc4ZMKnIBDZ36aonk1G9OnmrOs6z/AB6HgT5nYDaCA0Hj16FN/+8z/HpQ8/xKlTp4hdEUWkNA2qrkPVdUiKApW2T6ZSKc65aje1MMHjReIMPEEQBAHZbBbZbBY7d+4kxD+6uTM2rWvbRFDINBEEAbK5HMqVChlwJIqwbJuPNlaogAgoKZCl8VgyjzH/G1p9IkqCfDZ5EGD//v04c+YM5u7dg+W6yHd1oYfqfTPCk6ZpSeo/QYLPALIsY2BggHcRsY2d/e/QqaWuZaEWsR3VWg2arhNOD43YBVqe9H2fOAqyzAf+MBvSUCIIN6YjsrIga230fR/79u3DmdOnMT09DR9Az8AA9FSqwW7wdskEnxkSZ+AJhiAInBiUzWZ5yt+lBJu67+P23bswcjno2SwC34cehqhVKlhfX0dIe4xVOsY4n8/z2Qhr6+sbkX+EACRJEkRZJrU8TYOWSgGShOfzecz+838Os17H8toaDr7wAhQ2LS1ZxAkSPFGQJIlH2QC4zWDiQMx2qKkUtHQaEhUkslwX9aUlvvmnUilUymUUqO2QZBmrdLhR1G4INGMp0cFmGnUiRFHECz09mP5n/wylSgWLxSKee/55njVM8OQgcQaeIoh0BCcrAfhhiNuzs+ju68PwyAgXKSqXy6hUKrz1KJ1OY35+Hldu3oQoipjYswd9o6NkWiH5YJ6KY+2Mzf+/9dZbmJ6dhaqqePXVV5GmWuUJEiR48sEcd4BMKPTDELN376K7vx9DIyPwfR+9g4NYXV2F4zg8SylJEjA/jyu3b0MURYzv3o3BsTFiMyiHgA1eY7ai2Ya89dZbmL17F6qq4pVXXuGTVRM8WUicgaccAW0rim7OhUJhk8Kg4zh4uLBAiEGOg5GRES4cFP2/Ff7Lf/kvAIDXXnstaQdMkOBzAM/zoGkaMpFe/b6+Pq43wgaQOY6Dhw8fEs6B62Jom7bjW9/6FoDEdjzpSJyBzymii5NNPmSzA5j88HbAFvSP/uiPPr6TTJAgwRMHLiDG0v+J7XgmkNA0E2yJixcv4t69ewCSBZ0gQYLOkdiOpweJM5BgS/zxH/8xAODIkSMYHR39jM8mQYIETwsS2/H0ICkTfE7xzjvv4Pbt2/zfKysr/O+3b9/G7/7u7za8/md/9mdbftY3v/lNAMCP/diPPdZzTJAgwZOHxHY8m0icgc8pfud3fgf/7t/9u9jfvfvuu3j33XcbftZqQc/NzeHixYsAkjRfggTPAhLb8WwicQaeYnz5y18GQLoHPikwz35sbAxHjhz5xI6TIEGCTw+J7UjQDCFsN7EmwTOPH/zBH8S3v/1t/L2/9/fwm7/5m5/16SRIkOApQWI7ni4kBMIELVEul/Hmm28CSNJ8CRIk6ByJ7Xj6kDgDCVriT//0T+G6LnK5HE8rJkiQIMFWSGzH04eEM5CgJb7zne8gn8/jx37sx5LxoQkSJOgYie14+pBwBhIkSJAgQYJnHEmZIEGCBAkSJHjGkTgDCRIkSJAgwTOOxBlIkCBBggQJnnEkzkCCBAkSJEjwjCNxBhIkSJAgQYJnHIkzkCBBggQJEjzjSJyBBAkSJEiQ4BlH4gwkSJAgQYIEzzgSZyBBggQJEiR4xpE4AwkSJEiQIMEzjsQZSJAgQYIECZ5xJM5AggQJEiRI8IwjcQYSJEiQIEGCZxyJM5AgQYIECRI840icgQQJEiRIkOAZR+IMJEiQIEGCBM84EmcgQYIECRIkeMaROAMJEiRIkCDBM47EGUiQIEGCBAmecSTOQIIECRIkSPCMI3EGEiRIkCBBgmcc/3/cnOCF980SvQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:2], nrows=1, ncols=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can show them in a column too, if we want! Simply set the `nrows = 2` and `ncols = 1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:2], nrows=2, ncols=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Can we show the entire list of 4 Bloch spheres we created? Absolutely!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:], nrows=2, ncols=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if we had to add multiple vectors to a single Bloch sphere? CUDA-Q uses the [QuTiP](https://www.qutip.org) toolbox to construct Bloch spheres. We can then add multiple states to the same Bloch sphere by passing the sphere object as an argument to `cudaq.add_to_bloch_sphere()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qutip\n",
-    "\n",
-    "rng = np.random.default_rng(seed=47)\n",
-    "blochSphere = qutip.Bloch()\n",
-    "for _ in range(10):\n",
-    "    angleList = rng.random(3) * 2 * np.pi\n",
-    "    sph = cudaq.add_to_bloch_sphere(cudaq.get_state(kernel, angleList), blochSphere)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This created a single Bloch sphere with 10 random vectors. Let us see how it looks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "blochSphere.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unfortunately, there is no such handy visualization for multi-qubit states. In particular, a multi-qubit state cannot be visualized as multiple Bloch spheres due to the nature of entanglement that makes quantum computing so powerful. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Kernel Visualization\n",
-    "\n",
-    "A CUDA-Q kernel can be visualized using the `cudaq.draw` API which returns a string representing the drawing of the execution path, in the specified format. ASCII (default) and LaTeX formats are supported."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel_to_draw():\n",
-    "    q = cudaq.qvector(4)\n",
-    "    h(q)\n",
-    "    x.ctrl(q[0], q[1])\n",
-    "    y.ctrl([q[0], q[1]], q[2])\n",
-    "    z(q[2])\n",
-    "    \n",
-    "    swap(q[0], q[1])\n",
-    "    swap(q[0], q[3])\n",
-    "    swap(q[1], q[2])\n",
-    "\n",
-    "    r1(3.14159, q[0])\n",
-    "    tdg(q[1])\n",
-    "    s(q[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮                  ╭───────────╮       \n",
-      "q0 : ┤ h ├──●────●────╳───╳─┤ r1(3.142) ├───────\n",
-      "     ├───┤╭─┴─╮  │    │   │ ╰───────────╯╭─────╮\n",
-      "q1 : ┤ h ├┤ x ├──●────╳───┼───────╳──────┤ tdg ├\n",
-      "     ├───┤╰───╯╭─┴─╮╭───╮ │       │      ╰┬───┬╯\n",
-      "q2 : ┤ h ├─────┤ y ├┤ z ├─┼───────╳───────┤ s ├─\n",
-      "     ├───┤     ╰───╯╰───╯ │               ╰───╯ \n",
-      "q3 : ┤ h ├────────────────╳─────────────────────\n",
-      "     ╰───╯                                      \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.draw(kernel_to_draw))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\documentclass{minimal}\n",
-      "\\usepackage{quantikz}\n",
-      "\\begin{document}\n",
-      "\\begin{quantikz}\n",
-      "  \\lstick{$q_0$} & \\gate{H} & \\ctrl{1} & \\ctrl{2} & \\swap{1} & \\swap{3} & \\gate{R_1(3.142)} & \\qw & \\qw \\\\\n",
-      "  \\lstick{$q_1$} & \\gate{H} & \\gate{X} & \\ctrl{1} & \\targX{} & \\qw & \\swap{1} & \\gate{T^\\dag} & \\qw \\\\\n",
-      "  \\lstick{$q_2$} & \\gate{H} & \\qw & \\gate{Y} & \\gate{Z} & \\qw & \\targX{} & \\gate{S} & \\qw \\\\\n",
-      "  \\lstick{$q_3$} & \\gate{H} & \\qw & \\qw & \\qw & \\targX{} & \\qw & \\qw & \\qw \\\\\n",
-      "\\end{quantikz}\n",
-      "\\end{document}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.draw('latex', kernel_to_draw))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copy this output string into any LaTeX editor and export it to PDF.\n",
-    "\n",
-    "<img src=\"../images/circuit_pdf.png\" alt=\"Circuit in PDF format\" width=\"500\" \n",
-    "height=\"250\">\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/_sources/index.rst.txt b/pr-2308/_sources/index.rst.txt
deleted file mode 100644
index 2b6837424c..0000000000
--- a/pr-2308/_sources/index.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-************
-CUDA-Q
-************
-
-Welcome to the CUDA-Q documentation page!
-
-CUDA-Q streamlines hybrid application development and promotes productivity and scalability
-in quantum computing. It offers a unified programming model designed for a hybrid
-setting |---| that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for 
-programming in Python and in C++.
-
-You are browsing the documentation for |version| version of CUDA-Q. You can find documentation for all released versions :doc:`here <versions>`.
-
-**CUDA-Q** is a programming model and toolchain for using quantum acceleration in heterogeneous computing architectures available in C++ and Python.
-
-.. toctree::
-   :caption: Contents
-   :maxdepth: 2
-
-      Quick Start <using/quick_start.rst>
-      Basics <using/basics/basics.rst>
-      Examples <using/examples/examples.rst>
-      Applications <using/applications.rst>
-      Backends <using/backends/backends.rst>
-      Dynamics <using/backends/dynamics.rst>
-      Installation <using/install/install.rst>
-      Integration <using/integration/integration.rst>
-      Extending <using/extending/extending.rst>
-      Specifications <specification/index.rst>
-      API Reference <api/api.rst>
-      Other Versions <versions.rst>
-
-.. |---|   unicode:: U+2014 .. EM DASH
-   :trim:
\ No newline at end of file
diff --git a/pr-2308/_sources/releases.rst.txt b/pr-2308/_sources/releases.rst.txt
deleted file mode 100644
index de3e12d78b..0000000000
--- a/pr-2308/_sources/releases.rst.txt
+++ /dev/null
@@ -1,153 +0,0 @@
-************************
-CUDA-Q Releases
-************************
-
-**latest**
-
-The latest version of CUDA-Q is on the main branch of our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__ 
-and is also available as a Docker image. More information about installing the nightly builds can be found 
-:doc:`here <using/install/install>`
-
-- `Docker image (nightly builds) <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/latest>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/main/docs/sphinx/examples>`__
-
-**0.9.0**
-
-We are very excited to share a new toolset added for modeling and manipulating the dynamics of physical systems. 
-The new API allows to define and execute a time evolution under arbitrary operators. For more information, take 
-a look at the `docs <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/dynamics.html>`__.
-The 0.9.0 release furthermore includes a range of contribution to add new backends to CUDA-Q, including backends 
-from `Anyon Technologies <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#anyon-technologies-anyon-computing>`__, 
-`Ferimioniq <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/simulators.html#fermioniq>`__, and 
-`QuEra Computing <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#quera-computing>`__, 
-as well as updates to existing backends from `ORCA <https://nvidia.github.io/cuda-quantum/latest/using/backends/hardware.html#orca-computing>`__ 
-and `OQC <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#oqc>`__.
-We hope you enjoy the new features - also check out our new notebooks and examples to dive into CUDA-Q.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.9.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.9.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.9.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.9.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.9.0>`__.
-
-**0.8.0**
-
-The 0.8.0 release adds a range of changes to improve the ease of use and performance with CUDA-Q. 
-The changes listed below highlight some of what we think will be the most useful features and changes 
-to know about. While the listed changes do not capture all of the great contributions, we would like 
-to extend many thanks for every contribution, in particular those from external contributors.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.8.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.8.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.8.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.8.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.8.0>`__.
-
-**0.7.1**
-
-The 0.7.1 release adds simulator optimizations with significant performance improvements and 
-extends their functionalities. The `nvidia-mgpu` backend now supports user customization of the 
-gate fusion level as controlled by the `CUDAQ_MGPU_FUSE` environment variable documented 
-`here <https://nvidia.github.io/cuda-quantum/0.7.1/using/backends/simulators.html>`__.
-It furthermore adds a range of bug fixes and changes the Python wheel installation instructions.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.7.1>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.7.1>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.7.1>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.7.1/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.7.1>`__.
-
-**0.7.0**
-
-The 0.7.0 release adds support for using :doc:`NVIDIA Quantum Cloud <using/backends/nvqc>`,
-giving you access to our most powerful GPU-accelerated simulators even if you don't have an NVIDIA GPU.
-With 0.7.0, we have furthermore greatly increased expressiveness of the Python and C++ language frontends. 
-Check out our `documentation <https://nvidia.github.io/cuda-quantum/0.7.0/using/quick_start.html>`__ 
-to get started with the new Python syntax support we have added, and `follow our blog <https://developer.nvidia.com/cuda-q>`__
-to learn more about the new setup and its performance benefits.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.7.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.7.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.7.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.7.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.7.0>`__.
-
-**0.6.0**
-
-The 0.6.0 release contains improved support for various HPC scenarios. We have added a
-:ref:`plugin infrastructure <distributed-computing-with-mpi>` for connecting CUDA-Q 
-with an existing MPI installation, and we've added a :ref:`new platform target <remote-mqpu-platform>` that distributes workloads across multiple virtual QPUs, 
-each simulated by one or more GPUs.
-
-Starting with 0.6.0, we are now also distributing 
-:ref:`pre-built binaries <install-prebuilt-binaries>` for using CUDA-Q with C++.
-The binaries are built against the `GNU C library <https://www.gnu.org/software/libc/>`__ 
-version 2.28.
-We've added a detailed :doc:`Building from Source <using/install/data_center_install>` guide to build these binaries for older `glibc` versions.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.6.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.6.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.6.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.6.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.6.0>`__.
-
-**0.5.0**
-
-With 0.5.0 we have added support for quantum kernel execution on OQC and IQM backends. For more information, see :doc:`using/backends/hardware`.
-CUDA-Q now allows to executing adaptive quantum kernels on quantum hardware backends that support it.
-The 0.5.0 release furthermore improves the tensor network simulation tools and adds a matrix product state simulator, see :doc:`using/backends/simulators`.
-
-Additionally, we are now publishing images for experimental features, which currently includes improved Python language support.
-Please take a look at :doc:`using/install/install` for more information about how to obtain them.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.5.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.5.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.5.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.5.0>`__.
-
-**0.4.1**
-
-The 0.4.1 release adds support for ARM processors in the form of multi-platform Docker images and `aarch64` Python wheels. Additionally, all GPU-based backends are now included in the Python wheels as well as in the Docker image.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.4.1>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.4.1>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.4.1/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.4.1>`__.
-
-**0.4.0**
-
-CUDA-Q is now available on PyPI!
-The 0.4.0 release adds support for quantum kernel execution on Quantinuum and IonQ backends. For more information, see :doc:`using/backends/hardware`.
-
-The 0.4.0 PyPI release does not yet include all of the GPU-based backends.
-The fully featured version is available as a Docker image for `linux/amd64` platforms.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.4.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.4.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/0.4.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/tag/0.4.0>`__.
-
-**0.3.0**
-
-The 0.3.0 release of CUDA-Q is available as a Docker image for `linux/amd64` platforms.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.3.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/0.3.0/docs/sphinx/examples>`__
diff --git a/pr-2308/_sources/specification/cudaq.rst.txt b/pr-2308/_sources/specification/cudaq.rst.txt
deleted file mode 100644
index e0e87a44e4..0000000000
--- a/pr-2308/_sources/specification/cudaq.rst.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Language Specification 
-***********************************
-
-NVIDIA CUDA-Q is a programming model in C++ for heterogeneous 
-quantum-classical computing. As such, the model provides primitive types, concepts, syntax, and
-semantics that enable the integration of quantum processing units (QPUs) with
-existing NVIDIA multi-node, multi-GPU parallel programming models and architectures. 
-The language is designed for C++. Extensions to other languages is fully supported 
-via appropriate bindings. 
-
-CUDA-Q implementations are intended to be enabled via library-based language 
-extensions that define quantum code as callables in the classical language being extended. 
-These callables are meant to be compiled with standard compilers and runtimes 
-and run via quantum computer simulators or available physical quantum architectures. The 
-language callables intended for quantum coprocessing must be annotated for 
-compilers and runtimes to process. These annotations should directly enable quantum-specific 
-optimization and transformation for optimal execution on simulators and QPUs. 
-
-The rest of this document will detail all aspects of the language specification - its machine model, 
-primitive types and operations, core concepts and abstractions, and algorithmic primitives. All code 
-specification and demonstrative examples are provided in C++ (the native CUDA-Q classical language). 
-Where possible, bindings for higher-level languages (e.g. Python) will be displayed to aid in understanding.
-
-.. toctree::
-  :numbered:
-  :caption: CUDA-Q
-  :maxdepth: 1
-
-  Machine Model <cudaq/machine_model.rst>
-  Namespace and Standard <cudaq/namespace.rst>
-  Quantum Types <cudaq/types.rst>
-  Quantum Operators <cudaq/operators.rst>
-  Quantum Operations <cudaq/operations.rst>
-  Quantum Kernels <cudaq/kernels.rst>
-  Sub-circuit Synthesis <cudaq/synthesis.rst>
-  Control Flow <cudaq/control_flow.rst>
-  Just-in-Time Kernel Creation <cudaq/dynamic_kernels.rst>
-  Quantum Patterns <cudaq/patterns.rst>
-  Platform <cudaq/platform.rst>
-  Algorithmic Primitives <cudaq/algorithmic_primitives.rst>
-  Example Programs <cudaq/examples.rst>
diff --git a/pr-2308/_sources/specification/cudaq/algorithmic_primitives.rst.txt b/pr-2308/_sources/specification/cudaq/algorithmic_primitives.rst.txt
deleted file mode 100644
index 7e8b8240f6..0000000000
--- a/pr-2308/_sources/specification/cudaq/algorithmic_primitives.rst.txt
+++ /dev/null
@@ -1,637 +0,0 @@
-Quantum Algorithmic Primitives
-******************************
-**[1]** The general philosophy of the CUDA-Q specification is that quantum 
-device code should be encapsulated as stand-alone callable instances of generic 
-signature, and that operations or primitive algorithms targeting a quantum 
-coprocessor be implemented as adaptors on those callable instances. Adaptors, by 
-definition, are generic functions that take any quantum kernel as input along with 
-the runtime arguments for that kernel. Runtime arguments passed to adaptor functions 
-flow through the adaptor to the provided kernel. This pattern allows general pre- 
-and post-processing around concrete kernel execution. 
-
-.. _cudaq-sample-spec:
-
-:code:`cudaq::sample`
--------------------------
-**[1]** A common task for near-term quantum execution is to sample the state
-of a given quantum circuit for a specified number of shots (circuit
-executions). The result of this task is typically a mapping of observed
-measurement bit strings to the number of times each was observed. This
-is typically termed the counts dictionary in the community. 
-
-**[2]** The CUDA-Q model enables this functionality via template functions within the
-:code:`cudaq` namespace with the following structure:
-
-.. code-block:: cpp
-
-    template <typename ReturnType>
-    concept HasVoidReturnType = std::is_void_v<ReturnType>;
-
-    // Kernel only
-    template<typename QuantumKernel, typename... Args>
-      requires HasVoidReturnType<QuantumKernel, Args...>
-    sample_result sample(QuantumKernel&& kernel, Args&&... args);
-
-    // Specify shots
-    template<typename QuantumKernel, typename... Args>
-      requires HasVoidReturnType<QuantumKernel, Args...>
-    sample_result sample(std::size_t shots, QuantumKernel&& kernel, Args&&... args);
-
-    // Specify sample options (including shots and noise model)
-    template<typename QuantumKernel, typename... Args>
-      requires HasVoidReturnType<QuantumKernel, Args...>
-    sample_result sample(const sample_options &options,
-                         QuantumKernel&& kernel, Args&&... args);
-
-**[3]** This function takes as input a quantum kernel instance followed by the
-concrete arguments at which the kernel should be invoked. CUDA-Q kernels 
-passed to this function must be entry-point kernels and return :code:`void`. 
-
-**[4]** Overloaded functions exist for specifying the number of shots to sample and the
-noise model to apply.
-
-**[5]** The function returns an instance of the :code:`cudaq::sample_result` type which encapsulates
-the counts dictionary produced by the sampling task. Programmers can
-extract the result information in the following manner: 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      auto bell = []() __qpu__ { ... };
-      
-      // Sample the state generated by bel
-      auto counts = cudaq::sample(bell)
-  
-      // Print to standard out
-      counts.dump();
-  
-      // Fine-grained access to the bits and counts
-      for (auto& [bits, count] : counts) {
-        printf("Observed: %s, %lu\n", bits, count);
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def bell(): 
-       ... 
-    
-    # Sample the state generated by bell
-    counts = cudaq.sample(bell) 
-
-    # Print to standard out
-    counts.dump()
-
-    # Fine-grained access to the bits and counts 
-    for bits, count in counts:
-        print('Observed: {}, {}'.format(bits, count))
-
-
-**[6]** CUDA-Q specifies the following structure for :code:`cudaq::sample_result`:
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      using CountsDictionary = std::unordered_map<std::string, std::size_t>;
-      inline static const std::string GlobalRegisterName = "__global__";
-      class sample_result {
-        public:
-          sample_result() = default;
-          sample_result(const sample_result &);
-          ~sample_result();
-          
-          std::vector<std::string> register_names();
-          
-          std::size_t count(std::string_view bitString,
-                      const std::string_view registerName = GlobalRegisterName);
-          
-          std::vector<std::string>
-          sequential_data(const std::string_view registerName = GlobalRegisterName);
-          
-          CountsDictionary
-          to_map(const std::string_view registerName = GlobalRegisterName);
-          
-          sample_result
-          get_marginal(const std::vector<std::size_t> &&marginalIndices,
-                 const std::string_view registerName = GlobalRegisterName);
-
-          double expectation(const std::string_view registerName == GlobalRegisterName);
-          double probability(std::string_view bitString, const std::string_view registerName == GlobalRegisterName);
-          std::size_t size(const std::string_view registerName == GlobalRegisterName);
-          
-          void dump();
-          void clear();
-
-          CountsDictionary::iterator begin();
-          CountsDictionary::iterator end();
-      };
-    }
-
-**[7]** The :code:`sample_result` type enables one to encode measurement results from a 
-quantum circuit sampling task. It keeps track of a list of sample results, each 
-one corresponding to a measurement action during the sampling process and represented 
-by a unique register name. It also tracks a unique global register, the implicit sampling 
-of the state at the end of circuit execution. The API gives fine-grain access 
-to the measurement results for each register. To illustrate this, observe 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-    auto kernel = []() __qpu__ {
-      cudaq::qubit q;
-      h(q);
-      auto reg1 = mz(q);
-      reset (q);
-      x(q);
-    };
-    cudaq::sample(kernel).dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def kernel():
-       q = cudaq.qubit()
-       h(q)
-       reg1 = mz(q)
-       reset(q)
-       x(q)
-    
-    cudaq.sample(kernel).dump()
-
-should produce 
-
-.. code-block:: bash 
-
-    { 
-      __global__ : { 1:1000 }
-      reg1 : { 0:501 1:499 }
-    }
-
-Here we see that we have measured a qubit in a uniform superposition to a 
-register named :code:`reg1`, and followed it with a reset and the application 
-of an NOT operation. The :code:`sample_result` returned for this sampling 
-tasks contains the default :code:`__global__` register as well as the user 
-specified :code:`reg1` register. 
-
-The contents of the :code:`__global__` register will depend on how your kernel
-is written:
-
-1. If no measurements appear in the kernel, then the :code:`__global__`
-   register is formed with implicit measurements being added for *all* the
-   qubits defined in the kernel, and the measurements all occur at the end of
-   the kernel. The order of the bits in the bitstring corresponds to the qubit
-   allocation order specified in the kernel.  That is - the :code:`[0]` element
-   in the :code:`__global__` bitstring corresponds with the first declared qubit
-   in the kernel. For example,
-
-.. tab:: C++ 
-
-   .. code-block:: cpp
-
-       auto kernel = []() __qpu__ {
-         cudaq::qubit a, b;
-         x(a);
-       };
-       cudaq::sample(kernel).dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel 
-    def kernel():
-        a, b = cudaq.qubit(), cudaq.qubit() 
-        x(a) 
-    
-    cudaq.sample(kernel).dump() 
-
-should produce 
-
-   .. code-block:: bash 
-
-       { 
-         __global__ : { 10:1000 }
-       }
-
-2. Conversely, if any measurements appear in the kernel, then only the measured
-   qubits will appear in the :code:`__global__` register. Similar to #1, the 
-   bitstring corresponds to the qubit allocation order specified in the kernel.
-   Also (again, similar to #1), the values of the sampled qubits always
-   correspond to the values *at the end of the kernel execution*. That is - if a
-   qubit is measured in the middle of a kernel and subsequent operations change
-   the state of the qubit, the qubit will be implicitly re-measured at the end
-   of the kernel, and that re-measured value is the value that will appear in
-   the :code:`__global__` register. For example,
-
-.. tab:: C++ 
-
-   .. code-block:: cpp
-
-       auto kernel = []() __qpu__ {
-         cudaq::qubit a, b;
-         x(a);
-         mz(b);
-         mz(a);
-       };
-       cudaq::sample(kernel).dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel
-    def kernel():
-        a, b = cudaq.qubit(), cudaq.qubit()
-        x(a)
-        mz(b)
-        mz(a)
-
-    cudaq.sample(kernel).dump() 
-  
-should produce 
-
-   .. code-block:: bash 
-
-       { 
-         __global__ : { 10:1000 }
-       }
-
-.. note::
-
-  If you don't specify any measurements in your kernel and allow the :code:`nvq++`
-  compiler to perform passes that introduce ancilla qubits into your kernel, it
-  may be difficult to discern which qubits are the ancilla qubits vs which ones
-  are your qubits. In this case, it is recommended that you provide explicit
-  measurements in your kernel in order to only receive measurements from your
-  qubits and silently discard the measurements from the ancillary qubits.
-
-**[8]** The API exposed by the :code:`sample_result` data type allows one to extract
-the information contained at a variety of levels and for each available 
-register name. One can get the number of times a bit string was observed via 
-:code:`sample_result::count`, extract a `std::unordered_map` representation via 
-:code:`sample_result::to_map`, get a new :code:`sample_result` instance over a subset of 
-measured qubits via :code:`sample_result::get_marginal`, and extract the 
-measurement data as it was produced sequentially (a vector of bit string observations 
-for each shot in the sampling process). One can also compute probabilities and expectation 
-values. 
-
-**[9]** There are specific requirements on input quantum kernels for the use of the
-sample function which must be enforced by compiler implementations. The kernel 
-must be an entry-point kernel that returns :code:`void`.
-
-**[10]** CUDA-Q also provides an asynchronous version of this function 
-(:code:`cudaq::sample_async`) which returns a :code:`sample_async_result`. 
-
-.. code-block:: cpp 
-
-    template<typename QuantumKernel, typename... Args>
-    async_sample_result sample_async(const std::size_t qpu_id, QuantumKernel&& kernel, Args&&... args);
-
-Programmers can asynchronously launch sampling tasks on any :code:`qpu_id`. 
-
-**[11]** The :code:`async_sample_result` wraps a :code:`std::future<sample_result>` and exposes the same 
-:code:`get()` functionality to extract the results after asynchronous execution. 
-
-**[12]** For remote QPU systems with long queue times, the :code:`async_sample_result` type encodes job ID 
-information and can be persisted to file and loaded from file at a later time. After loading from file, 
-and when remote queue jobs are completed, one can invoke :code:`get()` and the results will 
-be retrieved and returned. 
-
-:code:`cudaq::observe`
--------------------------
-**[1]** A common task in variational algorithms is the computation of the expected
-value of a given observable with respect to a parameterized quantum circuit
-(:math:`\langle H \rangle(𝚹) = \langle \psi(𝚹)|H|\psi(𝚹) \rangle`). 
-
-**[2]** The :code:`cudaq::observe` function is provided to enable one to quickly compute
-this expectation value via execution of the parameterized quantum circuit
-with repeated measurements in the bases of the provided :code:`spin_op` terms. The
-function has the following signature:
-
-.. code-block:: cpp
-  
-    // Kernel only
-    template<typename QuantumKernel, typename... Args>
-    observe_result observe(QuantumKernel&&, cudaq::spin_op&, Args&&... args);
-    
-    // Specify shots
-    template<typename QuantumKernel, typename... Args>
-    observe_result observe(std::size_t shots, QuantumKernel&&, cudaq::spin_op&, Args&&... args);
-
-    // Specify sample options (including shots and noise model)
-    template<typename QuantumKernel, typename... Args>
-    observe_result observe(const cudaq::observe_options &options,
-                          QuantumKernel&&, cudaq::spin_op&, Args&&... args);
-
-**[3]** :code:`cudaq::observe` takes as input an instantiated quantum kernel, the
-:code:`cudaq::spin_op` whose expectation is requested, and the concrete
-arguments used as input to the parameterized quantum kernel. 
-
-**[4]** :code:`cudaq::observe` returns an instance of the :code:`observe_result` type which can be implicitly 
-converted to a :code:`double` expectation value, but also retains all data directly
-generated and used as part of that expectation value computation. The 
-:code:`observe_result` takes on the following form:
-
-.. code-block:: cpp
-
-    class observe_result {
-      public:
-        observe_result(double &e, spin_op &H);
-        observe_result(double &e, spin_op &H, MeasureCounts counts);
-
-        sample_results raw_data() { return data; };
-        operator double();
-        double expectation();
-        
-        template <typename SpinOpType>
-        double expectation(SpinOpType term);
-
-        template <typename SpinOpType>
-        sample_result counts(SpinOpType term);
-        double id_coefficient() 
-        void dump();
-    };
-
-**[5]** The public API for :code:`observe_result` enables one to extract the 
-:code:`sample_result` data for each term in the provided :code:`spin_op`. 
-This return type can be used in the following way.
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-      // I only care about the expected value, discard 
-      // the fine-grain data produced
-      double expVal = cudaq::observe(kernel, spinOp, args...);
-
-      // I require the result with all generated data 
-      auto result = cudaq::observe(kernel, spinOp, args...);
-      auto expVal = result.expectation();
-      auto X0X1Exp = result.expectation(x(0)*x(1));
-      auto X0X1Data = result.counts(x(0)*x(1));
-      result.dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    # I require the result with all generated data 
-    result = cudaq::observe(kernel, spinOp, *args)
-    expVal = result.expectation()
-    X0X1Exp = result.expectation(x(0)*x(1))
-    X0X1Data = result.counts(x(0)*x(1))
-    result.dump()
-
-
-Here is an example of the utility of the :code:`cudaq::observe` function:
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      struct ansatz {
-        auto operator()(double theta) __qpu__ {
-          cudaq::qarray<2> q;
-          x(q[0]);
-          ry(theta, q[1]);
-          x<cudaq::ctrl>(q[1], q[0]);
-        }
-      };
-    
-      int main() {
-        using namespace cudaq::spin; // make it easier to use pauli X,Y,Z below
-    
-        spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                    .21829 * z(0) - 6.125 * z(1);
-    
-        double energy = cudaq::observe(ansatz{}, h, .59);
-        printf("Energy is %lf\n", energy); 
-        return 0;
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def ansatz(theta : float): 
-       q = cudaq.qvector(2)
-       x(q[0])
-       ry(theta, q[1])
-       x.ctrl(q[1], q[0])
-    
-    h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                    .21829 * z(0) - 6.125 * z(1)
-    energy = cudaq.observe(ansatz, h, .59).expectation() 
-    print('Energy is {}'.format(energy))
-
-
-**[5]** There are specific requirements on input quantum kernels for the use of the
-observe function which must be enforced by compiler implementations. The
-kernel must be an entry-point kernel that does not contain any conditional
-or measurement statements.
-
-**[6]** By default on simulation backends, :code:`cudaq::observe` computes the true
-analytic expectation value (i.e. without stochastic noise due to shots-based sampling). 
-If a specific shot count is provided then the returned expectation value will contain some 
-level of statistical noise. Overloaded :code:`observe` functions are provided to 
-specify the number of shots and/or specify the noise model to apply.
-
-**[7]** CUDA-Q also provides an asynchronous version of this function 
-(:code:`cudaq::observe_async`) which returns a :code:`async_observe_result`. 
-
-.. code-block:: cpp 
-
-    template<typename QuantumKernel, typename... Args>
-    async_observe_result observe_async(const std::size_t qpu_id, QuantumKernel&& kernel, cudaq::spin_op&, Args&&... args);
-
-Programmers can asynchronously launch sampling tasks on any :code:`qpu_id`. 
-
-**[8]** For remote QPU systems with long queue times, the :code:`async_observe_result` type encodes job ID 
-information for each execution and can be persisted to file and loaded from file at a later time. After loading from file, 
-and when remote queue jobs are completed, one can invoke :code:`get()` and the results will 
-be retrieved and returned. 
-
-:code:`cudaq::optimizer` (deprecated, functionality moved to CUDA-Q libraries)
-------------------------------------------------------------------------------------
-The primary use case for :code:`cudaq::observe` is to leverage it as
-the core of a broader objective function optimization workflow. 
-:code:`cudaq::observe` produces the expected value of a specified 
-:code:`spin_op` with respect to a given parameterized ansatz at a concrete
-set of parameters, and often programmers will require an extremal value of that expected value 
-at a specific set of concrete parameters. This will directly require
-abstractions for gradient-based and gradient-free optimization strategies. 
-
-The CUDA-Q model provides a :code:`cudaq::optimizer` data type that exposes
-an :code:`optimize()` method that takes as input an 
-:code:`optimizable_function` to optimize and the number of independent
-function dimensions. Implementations are free to implement this abstraction
-in any way that is pertinent, but it is expected that most approaches will
-enable optimization strategy extensibility. For example, programmers should
-be able to instantiate a specific :code:`cudaq::optimizer` sub-type, thereby 
-dictating the underlying optimization algorithm in a type-safe manner. 
-Moreover, the optimizer should expose a public API of pertinent optimizer-specific 
-options that the programmer can customize.
-
-CUDA-Q models the :code:`cudaq::optimizer` as follows:
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      // Encode the optimal value and optimal parameters
-      using optimization_result = std::tuple<double, std::vector<double>>;
-      // Initialized with user specified callable of a specific signature
-      // Clients can query if the function computes gradients or not
-      class optimizable_function {
-        public:
-          template<typename Callable>
-          optimizable_function(Callable&&);
-          bool providesGradients() { return _providesGradients; }
-          double operator()(const std::vector<double> &x, std::vector<double> &dx);
-      };
-      class optimizer {
-        public:
-          virtual bool requiresGradients() = 0;
-          virtual optimization_result optimize(const int dimensions,
-                                              optimizable_function&& opt_function) = 0;
-      }; 
-    }
-
-Here, :code:`optimization_result` should encode the optimal value and optimal
-parameters achieved during the optimization workflow
-(i.e. a :code:`tuple<double, std::vector<double>>`). The optimize method takes
-as input the number of parameters (or dimensions of the objective function),
-and a function-like object (i.e. :code:`std::function` or a lambda, something 
-:code:`optimizable_function` can be constructed from) that takes a 
-:code:`const std::vector<double>&` and :code:`std::vector<double>&` for the
-function input parameters and gradient vector, respectively. The objective
-function must return a double representing the scalar cost for the
-objective function (e.g. the expected value from :code:`cudaq::observe()`).  
-
-Here is an example of how the :code:`cudaq::optimizer` is intended to be used: 
-
-.. code-block:: cpp 
-
-    auto ansatz = [](double theta, double phi) __qpu__ {...};
-    cudaq::spin_op H = ... ;
-  
-    cudaq::optimizers::cobyla optimizer;
-    optimizer.max_eval = 200;
-  
-    auto [opt_energy, opt_params] = optimizer.optimize(
-          2, [&](const std::vector<double> &x, std::vector<double> &grad_vec) {
-            return cudaq::observe(ansatz, H, x[0], x[1]);
-          });
-
-:code:`cudaq::gradient` (deprecated, functionality moved to CUDA-Q libraries)
------------------------------------------------------------------------------------
-Typical optimization use cases will require the computation of gradients for the specified
-objective function. The gradient is a vector over all ansatz circuit
-parameters :math:`∂H(𝚹) / ∂𝚹_i`. There are a number of potential strategies for
-computing this gradient vector, but most require additional evaluations
-of the ansatz circuit on the quantum processor. 
-
-To enable true extensibility in gradient strategies, CUDA-Q programmers can
-instantiate custom sub-types of the :code:`cudaq::gradient` type. The :code:`cudaq::gradient`
-type defines a :code:`compute(...)` method that takes a mutable reference to the 
-current gradient vector and is free to update that vector in a strategy-specific way. 
-The method also takes the current evaluation parameter vector, the :code:`cudaq::spin_op` used 
-in the current variational task, and the computed expected value at the given parameters. 
-The gradient strategy type takes the following form:
-
-.. code-block:: cpp
-
-    namespace cudaq {
-      class gradient {
-        public:
-          gradient(std::function<void(std::vector<double>)> &&kernel);
-  
-          template <typename QuantumKernel, typename ArgsMapper>
-          gradient(QuantumKernel &&kernel, ArgsMapper &&argsMapper);
-  
-          virtual void compute(std::vector<double>& x, std::vector<double> &dx,
-                            spin_op& h, double exp_h) = 0;
-          
-          virtual std::vector<double>
-          compute(const std::vector<double> &x,
-                  std::function<double(std::vector<double>)> &func) = 0;
-
-      };
-  
-      // gradient is intended for subclassing
-      class central_difference : public gradient {
-        public:
-          void compute(std::vector<double>& x, std::vector<double> &dx, spin_op& h,
-                  double exp_h) override { ... }
-      };
-    }
-
-The :code:`compute` function can make use of the quantum kernel parameterized ansatz, the 
-:code:`spin_op` for which the expected value is being computed, the
-pre-computed expected value at the current iteration's parameter, and the
-concrete arguments for the given quantum kernel at this iteration.
-
-A non-trivial aspect of the computation of gradients (in an extensible manner)
-is that we model the gradient as a derivative over concrete parameters for the
-circuit ansatz represented as a :code:`std::vector<double>` when the actual
-quantum kernel may be defined with general variadic :code:`Args...` types.
-To address this issue, programmers can provide a default translation
-mechanism for mapping common quantum kernel ansatz functional expressions to a :code:`vector<double>` representation - the 
-:code:`ArgsMapper` callable template type. This type must implement the 
-:code:`std::tuple<Args...>(std::vector<double>&)` callable concept. 
-
-The overall CUDA-Q workflow for leveraging the :code:`cudaq::optimizer`
-will work as follows (here we demonstrate with an ansatz without the
-default :code:`std::vector<double>` signature):
-
-.. code-block:: cpp
-
-    auto deuteron_n3_ansatz = [](double x0, double x1) __qpu__ {
-      cudaq::qarray<3> q;
-      x(q[0]);
-      ry(x0, q[1]);
-      ry(x1, q[2]);
-      x<cudaq::ctrl>(q[2], q[0]);
-      x<vctrl>(q[0], q[1]);
-      ry(-x0, q[1]);
-      x<cudaq::ctrl>(q[0], q[1]);
-      x<cudaq::ctrl>(q[1], q[0]);
-    };
-
-    cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-              .21829 * z(0) - 6.125 * z(1);
-    cudaq::spin_op h3 = h + 9.625 - 9.625 * z(2) - 3.913119 * x(1) * x(2) -
-                3.913119 * y(1) * y(2);
-
-    // The above ansatz takes 2 doubles, not a single std::vector<double>, which 
-    // the gradient type is expecting. So we must provide an ArgsMapper callable type
-    auto argsMapper = [](std::vector<double> x) {return std::make_tuple(x[0],x[1]);};
-
-    // Create the gradient strategy
-    cudaq::gradients::central_difference gradient(deuteron_n3_ansatz, argsMapper);
-
-    // Create the L-BFGS optimizer, requires gradients
-    cudaq::optimizers::lbfgs optimizer;
-
-    // Run the optimization routine. 
-    auto [min_val, opt_params] = optimizer.optimize(
-        2, [&](const std::vector<double>& x, std::vector<double>& grad_vec) {
-          // Compute the cost, here its an energy
-          auto cost = cudaq::observe(deuteron_n3_ansatz, h3, x);
-          
-          // Compute the gradient, results written to the grad_vec reference
-          gradient.compute(x, grad_vec, h3, cost);
-
-          // Return the cost to the optimizer
-          return cost;
-        });
-
-    // Print the results
-    printf("Optimizer found %lf at [%lf,%lf]\n", min_val, opt_params[0], opt_params[1]);
-
diff --git a/pr-2308/_sources/specification/cudaq/control_flow.rst.txt b/pr-2308/_sources/specification/cudaq/control_flow.rst.txt
deleted file mode 100644
index da3120dd74..0000000000
--- a/pr-2308/_sources/specification/cudaq/control_flow.rst.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-Control Flow
-************
-**[1]** All classical control flow expressions are inherited from the host language
-(e.g. C++, Python). 
-
-**[2]** Programmers can define quantum kernels using standard loop
-constructs and conditional statements. 
-
-**[3]** Conditional statements on quantum memory and measured :code:`cudaq::qudit` 
-results are permitted. 
diff --git a/pr-2308/_sources/specification/cudaq/dynamic_kernels.rst.txt b/pr-2308/_sources/specification/cudaq/dynamic_kernels.rst.txt
deleted file mode 100644
index 8681ad6a62..0000000000
--- a/pr-2308/_sources/specification/cudaq/dynamic_kernels.rst.txt
+++ /dev/null
@@ -1,163 +0,0 @@
-Just-in-Time Kernel Creation
-****************************
-
-**[1]** CUDA-Q provides a set of programming abstractions for dynamically constructing
-quantum kernel code at runtime. 
-
-**[2]** The callable :code:`cudaq::kernel_builder` abstraction
-facilitates the dynamic definition of quantum kernels that can optionally
-be parameterized by user-defined input arguments. 
-
-The :code:`kernel_builder` takes the following structure
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      // Type wrapping a value in the kernel IR
-      struct Value;
-      template <typename... Args>
-      class kernel_builder {
-        private:
-          std::vector<Value> arguments;
-
-        public:
-          std::vector<Value>& getArguments();
-          std::string name() const;
-          std::size_t getNumParams() const;
-
-          Value qalloc(std::size_t nQubits = 1);
-          Value qalloc(Value size);
-
-          void h(Value& qubit);
-          //... all other quantum operations ... 
-
-          // if (mz(q)) thenFunctor()
-          void c_if(Value& result, std::function<void()>&& thenFunctor);
-          
-          // Invoke a predefined kernel
-          template<typename OtherKernelBuilder, typename... Values>
-          void call(OtherKernelBuilder&& kernelToCall, Values&... args);
-          
-          // General multi-control on a predefined kernel
-          // models cudaq::control(...)
-          template<typename OtherKernelBuilder, typename... Values>
-          void control(OtherKernelBuilder&& kernelToControl, Value& ctrl, Values&... values);
-          
-          // General adjoint on a predefined kernel
-          // models cudaq::adjoint(...)
-          template<typename OtherKernelBuilder, typename... Values>
-          void adjoint(OtherKernelBuilder&& kernelToAdjoint, Values&... values);
-       
-          // The constructed kernel is callable
-          void operator()(Args... args);
-          void operator()(void** argsArray);
-
-          // Enable structured bindings
-          template <std::size_t N>
-          decltype(auto) get() {
-            if constexpr (N == 0)
-              return *this;
-            else
-              return arguments[N - 1];
-          }
-      };
-    }
-
-    /// Enable structured bindings on the kernel_builder type.
-    /// auto [kernel, theta, phi] = std::make_kernel<double,double>();
-    namespace std {
-      template <typename... Args> 
-      struct tuple_size<cudaq::kernel_builder<Args...>>
-        : std::integral_constant<std::size_t, sizeof...(Args) + 1> {};
-
-      template <std::size_t N, typename... Args>
-      struct tuple_element<N, cudaq::kernel_builder<Args...>> {
-        using type = std::conditional_t<N == 0, cudaq::kernel_builder<Args...>,
-                                  cudaq::QuakeValue>;
-      };
-    } // namespace std
-
-    namespace cudaq {
-      kernel_builder<> make_kernel();
-      template<typename... Args>
-      kernel_builder<Args...> make_kernel();
-    }
-
-**[3]** The structure above allows one to leverage the provided factory functions (:code:`make_kernel`)
-to construct an empty CUDA-Q kernel with defined argument signature. 
-
-**[4]** For each type in the signature, the programmer is returned a new :code:`Value` instance which 
-can be leveraged in the construction of the kernel. The intended mechanism for 
-kernel creation and argument value extraction is via standard C++ 
-`structured bindings <https://en.cppreference.com/w/cpp/language/structured_binding>`_.
-
-**[5]** Once the kernel is created, the programmer is free to build up the kernel expression 
-using the exposed API. There are methods for qubit allocation, quantum operation 
-invocation, control and adjoint synthesis, and conditional branching based on 
-`boolean` values. 
-
-Here is a simple example how one might build a CUDA-Q kernel dynamically. 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    auto kernel = cudaq::make_kernel();
-    auto qubits = kernel.qalloc(2);
-    kernel.h(qubits[0]);
-    kernel.x<cudaq::ctrl>(qubits[0], qubits[1]);
-    kernel.mz(qubits);
-    
-    // See algorithmic primitives section for more on sample
-    auto counts = cudaq::sample(kernel);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    kernel = cudaq.make_kernel()
-    qubits = kernel.qalloc(2)
-    kernel.h(qubits[0])
-    kernel.cx(qubits[0], qubits[1])
-    kernel.mz(qubits)
-
-Here is an example demonstrating how one may build a dynamic set of 
-CUDA-Q kernels for executing the standard Hadamard test. 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      auto [xPrep, qubitIn] = cudaq::make_kernel<cudaq::qubit>();
-      xPrep.x(qubitIn);
-
-      // Compute <1|X|1> = 0
-      auto hadamardTest = cudaq::make_kernel();
-      auto q = hadamardTest.qalloc();
-      auto ancilla = hadamardTest.qalloc();
-      hadamardTest.call(xPrep, q);
-      hadamardTest.h(ancilla);
-      hadamardTest.control(xPrep, ancilla, q);
-      hadamardTest.h(ancilla);
-      hadamardTest.mz(ancilla);
-
-      // See algorithmic primitives section for more on sample
-      auto counts = cudaq::sample(hadamardTest);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    xPrep, qubitIn = cudaq.make_kernel(cudaq.qubit)
-
-    # Compute <1|X|1> = 0
-    hadamardTest = cudaq.make_kernel()
-    q = hadamardTest.qalloc()
-    hadamardTest.call(xPrep, q)
-    hadamardTest.h(ancilla)
-    hadamardTest.control(xPrep, ancilla, q)
-    hadamardTest.h(ancilla)
-    hadamardTest.mz(ancilla)
-
-    # See algorithmic primitives section for more on sample
-    counts = cudaq.sample(hadamardTest)
diff --git a/pr-2308/_sources/specification/cudaq/examples.rst.txt b/pr-2308/_sources/specification/cudaq/examples.rst.txt
deleted file mode 100644
index c67874b4c1..0000000000
--- a/pr-2308/_sources/specification/cudaq/examples.rst.txt
+++ /dev/null
@@ -1,270 +0,0 @@
-
-Example Programs
-****************
-Hello World - Simple Bell State
--------------------------------
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      #include <cudaq.h>
-
-      struct bell {
-        int operator()(int num_iters) __qpu__ {
-          cudaq::qarray<2> q;
-          int nCorrect = 0;
-          for (int i = 0; i < num_iters; i++) {
-            h(q[0]);
-            x<cudaq::ctrl>(q[0], q[1]);
-            auto results = mz(q);
-            if (results[0] == results[1]) 
-              nCorrect++;
-            
-            reset(q[0]);
-            reset(q[1]);
-          }
-          return nCorrect;
-        }
-      };
-
-      int main() { printf("N Correct = %d\n", bell{}(100)); }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq 
-
-    @cudaq.kernel()
-    def bell(num_iters : int) -> int:
-        q = cudaq.qvector(2)
-        nCorrect = 0
-        for i in range(num_iters):
-            h(q[0])
-            x.ctrl(q[0], q[1])
-            results = mz(q)
-            if results[0] == results[1]:
-               nCorrect = nCorrect + 1
-            
-            reset(q)
-        return nCorrect 
-
-    counts = bell(100)
-    print(f'N Correct = {counts}')
-    assert counts == 100
-
-GHZ State Preparation and Sampling
-----------------------------------
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-  
-      #include <cudaq.h>
-
-      __qpu__ ghz(const int n_qubits) {
-        cudaq::qvector q(n_qubits);
-        h(q[0]);
-        for (int i = 0; i < n_qubits - 1; ++i) 
-          // note use of ctrl modifier
-          x<cudaq::ctrl>(q[i], q[i+1]); 
-        
-        mz(q);
-      }
-
-      int main() {
-        // Sample the state produced by the ghz kernel
-        auto counts = cudaq::sample(ghz, 10);
-        for (auto [bits, count] : counts) {
-          printf("Observed %s %lu times.\n", bits.c_str(), count);
-        }
-        return 0;
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq 
-    
-    @cudaq.kernel
-    def ghz(numQubits:int):
-        qubits = cudaq.qvector(numQubits)
-        h(qubits.front())
-        for i, qubit in enumerate(qubits.front(numQubits - 1)):
-            x.ctrl(qubit, qubits[i + 1])
-    
-    counts = cudaq.sample(ghz, 10)
-    for bits, count : counts:
-        print('Observed {} {} times.'.format(bits, count))
-
-Quantum Phase Estimation
-------------------------
-
-.. tab:: C++ 
-
-  .. literalinclude:: ../../applications/cpp/phase_estimation.cpp
-      :language: cpp
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq, numpy as np
-
-    # Compute phase for U |psi> = exp(-2 pi phase) |psi> 
-    # This example will consider U = T, and |psi> = |1>
-    # Define a Inverse Quantum Fourier Transform kernel
-    @cudaq.kernel
-    def iqft(qubits: cudaq.qview):
-        N = qubits.size()
-        for i in range(N // 2):
-            swap(qubits[i], qubits[N - i - 1])
-
-        for i in range(N - 1):
-            h(qubits[i])
-            j = i + 1
-            for y in range(i, -1, -1):
-                r1.ctrl(-np.pi / 2**(j - y), qubits[j], qubits[y])
-
-        h(qubits[N - 1])
-
-
-    # Define the U kernel
-    @cudaq.kernel
-    def tGate(qubit: cudaq.qubit):
-        t(qubit)
-
-
-    # Define the state preparation |psi> kernel
-    @cudaq.kernel
-    def xGate(qubit: cudaq.qubit):
-        x(qubit)
-
-    # General Phase Estimation kernel for single qubit 
-    # eigen states. 
-    @cudaq.kernel
-    def qpe(nC: int, nQ: int, statePrep: Callable[[cudaq.qubit], None],
-            oracle: Callable[[cudaq.qubit], None]):
-        q = cudaq.qvector(nC + nQ)
-        countingQubits = q.front(nC)
-        stateRegister = q.back()
-        statePrep(stateRegister)
-        h(countingQubits)
-        for i in range(nC):
-            for j in range(2**i):
-                cudaq.control(oracle, [countingQubits[i]], stateRegister)
-        iqft(countingQubits)
-        mz(countingQubits)
-
-    # Sample the state to get the phase. 
-    counts = cudaq.sample(qpe, 3, 1, xGate, tGate)
-    assert len(counts) == 1
-    assert '100' in counts
-
-
-Deuteron Binding Energy Parameter Sweep
----------------------------------------
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-      #include <cudaq.h>
-
-      struct deuteron_n2_ansatz {
-        void operator()(double theta) __qpu__ {
-          cudaq::qarray<2> q;
-          x(q[0]);
-          ry(theta, q[1]);
-          x<cudaq::ctrl>(q[1], q[0]);
-        }
-      };
-
-      int main() {
-        using namespace cudaq::spin;
-        cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                    .21829 * z(0) - 6.125 * z(1);
-    
-        // Perform parameter sweep for deuteron N=2 Hamiltonian
-        const auto param_space = cudaq::linspace(-M_PI, M_PI, 25);
-        for (const auto& param : param_space) {
-          // KERNEL::observe(...) <==> 
-          // E(params...) = <psi(params...) | H | psi(params...)>
-          double energy_at_param = cudaq::observe(deuteron_n2_ansatz{}, h, param);
-          printf("<H>(%lf) = %lf\n", param, energy_at_param);
-        }
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq 
-    from cudaq import spin 
-
-    @cudaq.kernel
-    def ansatz(angle:float):
-        q = cudaq.qvector(2)
-        x(q[0])
-        ry(angle, q[1])
-        x.ctrl(q[1], q[0])
-
-    hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(
-        0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-    # Perform parameter sweep for deuteron N=2 Hamiltonian
-    for angle in np.linspace(-np.pi, np.pi, 25):
-         # KERNEL::observe(...) <==> 
-         # E(params...) = <psi(params...) | H | psi(params...)>
-         energyAtParam = cudaq.observe(ansatz, hamiltonian, .59)
-         print('<H>({}) = {}'.format(angle, energyAtParam))
-
-
-Grover's Algorithm
-------------------
-
-.. tab:: C++ 
-
-  .. literalinclude:: ../../applications/cpp/grover.cpp
-      :language: cpp
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel 
-    def reflect(qubits: cudaq.qview):
-        ctrls = qubits.front(qubits.size() - 1)
-        last = qubits.back()
-        cudaq.compute_action(lambda: (h(qubits), x(qubits)),
-                              lambda: z.ctrl(ctrls, last))
-
-    @cudaq.kernel
-    def oracle(q: cudaq.qview):
-        z.ctrl(q[0], q[2])
-        z.ctrl(q[1], q[2])
-
-    @cudaq.kernel
-    def grover(N: int, M: int, oracle: Callable[[cudaq.qview], None]):
-        q = cudaq.qvector(N)
-        h(q)
-        for i in range(M):
-            oracle(q)
-            reflect(q)
-        mz(q)
-
-    counts = cudaq.sample(grover, 3, 1, oracle)
-    assert len(counts) == 2
-    assert '101' in counts
-    assert '011' in counts
-
-
-Iterative Phase Estimation
---------------------------
-
-.. literalinclude:: ../../applications/cpp/iterative_qpe.cpp
-    :language: cpp
-    :start-after: [Begin Documentation]
-    :end-before: [End Documentation]
diff --git a/pr-2308/_sources/specification/cudaq/kernels.rst.txt b/pr-2308/_sources/specification/cudaq/kernels.rst.txt
deleted file mode 100644
index a0c104bb3d..0000000000
--- a/pr-2308/_sources/specification/cudaq/kernels.rst.txt
+++ /dev/null
@@ -1,362 +0,0 @@
-Quantum Kernels
-***************
-**[1]** To differentiate between host and quantum device code, the CUDA-Q programming
-model defines the concept of a **quantum kernel**. A quantum kernel is any callable 
-in C++ annotated to indicate compilation and execution on an available quantum coprocessor. 
-
-**[2]** All quantum kernels must be annotated to indicate they are to be compiled for, and executed
-on, a specified quantum coprocessor. CUDA-Q requires the :code:`__qpu__` function
-attribute for quantum kernel declarations. Other language bindings may opt to use other language 
-features to enable function annotation or decoration (e.g. a :code:`@cudaq.kernel()` function 
-decorator in Python). 
-
-**[3]** CUDA-Q specifically differentiates between kernels invoked from host code and those invoked
-from within another quantum kernel. The former are denoted **entry-point**
-quantum kernels, the latter are **pure device** quantum kernels. Entry point kernels can 
-only take classical types as input and return classical output. Pure-device kernels 
-can take quantum types as input.
-
-.. tab:: C++ 
-  
-  .. code-block:: cpp
-  
-      // Entry point lambda kernel
-      auto my_first_entry_point_kernel = [](double x) __qpu__ { ... quantum code ... };
-      
-      // Entry point typed callable kernel
-      struct my_second_entry_point_kernel { 
-        void operator()(double x, std::vector<double> params) __qpu__ { 
-          ... quantum code ...
-        }
-      };
-
-      // Pure device free function kernel
-      __qpu__ void my_first_pure_device_kernel(cudaq::qview<> qubits) {
-         ... quantum code ... 
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def my_first_entry_point_kernel(x : float):
-       ... quantum code ... 
-    
-    @cudaq.kernel()
-    def my_second_entry_point_kernel(x : float, params : list[float]):
-       ... quantum code ... 
-
-    @cudaq.kernel()
-    def my_first_pure_device_kernel(qubits : cudaq.qview):
-       ... quantum code ... 
-    
-**[4]** Quantum kernel function bodies are programmed in a subset of the parent classical language. 
-Kernels can be composed of the following: 
-
-  * Quantum intrinsic operations and measurements
-  * In-scope kernel calls with or without any kernel modifiers (e.g. :code:`cudaq::control`)
-  * Classical control flow constructs from the classical language (:code:`if`, :code:`for`, :code:`while`, etc.)
-  * Stack variable declarations for supported types. 
-  * Arithmetic operations on integer and floating point stack variables
-  * Coherent conditional execution - :code:`if ( boolExprFromQubitMeasurement ) { x (another_qubit); }` 
-  * Syntax for common quantum programming patterns (e.g. compute-action-uncompute).
-
-
-**[5]** CUDA-Q defines a set of allowed types that can be leveraged in quantum kernel 
-function signatures (input and return types), in-function variable declaration or construction, 
-and in variable capture from parent scope. 
-
-The allowed types are as follows: 
-
-  * :code:`T` such that :code:`std::is_arithmetic_v<T> == true`
-  * :code:`std::vector<T>` such that :code:`std::is_arithmetic_v<T> == true`
-  * :code:`std::span<T>` such that :code:`std::is_arithmetic_v<T> == true`
-  * :code:`std::vector<V>` such that type :code:`V` is a valid :code:`std::vector<T>` (possibly recursively)
-  * :code:`struct` types composed of any valid CUDA-Q type.
-  * :code:`std::tuple<Ts...>` such that T is any allowed type.
-  * :code:`std::pair<T,U>` such that T is any allowed type.
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-    struct MyCustomSimpleStruct {
-       int i = 0;
-       int j = 0;
-       std::vector<double> angles;
-    }; 
-
-    // Valid CUDA-Q Types used in Kernels
-    auto kernel = [](int N, bool flag, float angle, std::vector<std::size_t> layers,
-             std::vector<double> parameters, std::vector<std::vector<float>> recursiveVec, 
-             MyCustomSimpleStruct var) __qpu__ { ... }
-    
-    __qpu__ double kernelThatReturns() { 
-       ... 
-       return M_PI_2;
-    }
-    
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @dataclass
-    class MySimpleStruct:
-        i : int
-        j : int
-
-    @cudaq.kernel
-    def kernel(N : int, flag : bool, angle : float, layers : list[int], 
-                parameters : list[float], recursiveList : list[list[float]], 
-                var : MySimpleStruct): ... 
-    
-    @cudaq.kernel 
-    def kernelThatReturns() -> float:
-        ... 
-        return np.pi / 2.0
-
-**[6]** Any variable with an allowed CUDA-Q type can be allocated on the stack within 
-CUDA-Q kernels. Variables of type :code:`std::vector<T>` for any allowed type 
-:code:`T` can only be constructed with known size. Vector-like variables cannot be 
-default constructed and later filled with type :code:`T` data (i.e. no dynamic memory allocation). 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-    // Valid CUDA-Q Types used in Kernels
-    auto kernel = []() __qpu__ {
-      
-      // Not Allowed. 
-      // std::vector<int> i; 
-      // i.push_back(1);
-
-      // Valid variable declarations
-
-      std::vector<int> i(5);
-      i[2] = 3; 
-
-      std::vector<float> f {1.0, 2.0, 3.0};
-
-      int k = 0;
-
-      double pi = 3.1415926
-    };
-    
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel
-    def kernel(): 
-       # Not Allowed 
-       # i = [] 
-       # i.append(1)
-
-       i = [0 for k in range(5)]
-       i[2] = 3 
-
-       f = [1., 2., 3.]
-
-       k = 0 
-
-       pi = 3.1415926
-
-**[7]** All entry-point kernel arguments adhere to pass-by-value semantics. 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    auto kernel = [](int i, std::vector<double> v) __qpu__ {
-       // i == 2, allocate 2 qubits 
-       cudaq::qvector q(i); 
-       // v[1] == 2.0, angle here is 2.0
-       ry(v[1], q[0]);
-
-       // Change the variables, caller does not see this
-       i = 5; 
-       v[0] = 3.0;
-    };
-
-    int k = 2;
-    std::vector<double> d {1.0, 2.0};
-    
-    kernel(k, d);
-
-    // k is still 2, pass by value 
-    // d is still {1.0, 2.0}, pass by value 
-  
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel 
-    def kernel(i : int, v : list[float]): 
-        # i = 2, allocate 2 qubits 
-        q = cudaq.qvector(i)
-        # v[1] == 2.0, angle here is 2.0 
-        ry(v[1], q[0])
-
-        # Change the variables, caller does not see this 
-        i = 5 
-        v[0] = 3.0 
-
-    k, d = 2, [1., 2.]
-    kernel(i, d)
-
-    # k is still 2, pass by value 
-    # d is still {1.0, 2.0}, pass by value 
-
-
-.. FIXME Pass by value vs reference, should we mandate pass by reference for inter-kernel calls
-
-**[8]** CUDA-Q kernel lambdas in C++ can capture variables of allowed type 
-by value. CUDA-Q kernels defined as custom callable types can define non-reference type 
-class members of any allowed type. These member variables can be set with 
-pass-by-value semantics at kernel construction. 
-
-.. tab:: C++ 
-
-  .. code-block:: C++ 
-
-    struct kernel {
-      int i; 
-      float f; 
-
-      void operator()() __qpu__ {
-         cudaq::qvector q(i); 
-         ry(f, q[0]);
-      }
-    };
-
-    kernel{2, 2.2}();
-
-    int i = 2; 
-    double f = 2.2;
-
-    auto kernelLambda = [=]() __qpu__ {
-      // Use captured variables 
-      cudaq::qvector q(i); 
-      ry(f, q[0]);
-      i = 5; 
-      return i; 
-    };
-
-    auto k = kernelLambda(); 
-
-    // Pass by value semantics 
-    assert(k != i);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    i = 2 
-    f = np.pi / 2. 
-
-    @cudaq.kernel
-    def kernel() -> int:
-       # Use captured variables 
-       q = cudaq.qvector(i) 
-       ry(f, q[0])
-
-       i = 5 
-       return i 
-    
-    k = kernel() 
-
-    # Pass by value semantics 
-    assert k != i 
-
-**[9]** All quantum kernel invocations are synchronous calls by default. 
-
-**[10]** CUDA-Q kernels can serve as input to other quantum kernels and invoked by kernel 
-function body code. To support CUDA-Q kernel parameterization on callable quantum kernel 
-code, programmers can leverage standard C++ template definitions or dynamic typing in language bindings such as Python:
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-      __qpu__ void MyStatePrep(cudaq::qview<> qubits) {
-          ... apply state prep operations on qubits ...
-      }
-
-      struct MyGenericAlgorithm {
-        template<typename StatePrep>
-        void operator()(const StatePrep& statePrep) __qpu__ {
-          cudaq::qarray<10> q;
-          statePrep(q);
-          ...
-        }
-      };
-
-      // -or- with placeholder type specifiers
-      struct MyGenericAlgorithm2 {
-        void operator()(const auto& statePrep) __qpu__ {
-          cudaq::qarray<10> q;
-          statePrep(q);
-          ...
-        }
-      };
-
-      void callKernels() {
-
-        MyGenericAlgorithm algorithm;
-        algorithm(MyStatePrep);
-
-        MyGenericAlgorithm2 anotherVersion;
-        anotherVersion(MyStatePrep);
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def MyStatePrep(qubits : cudaq.qview):
-        ... apply state prep operations on qubits ... 
-    
-    @cudaq.kernel()
-    def MyGenericAlgorithm(statePrep : typing.Callable[[cudaq.qview], None]):
-        q = cudaq.qvector(10)
-        statePrep(q)
-        ...
-    
-    MyGenericAlgorithm(MyStatePrep)
-
-CUDA-Q kernel inputs can also be `constrained <https://en.cppreference.com/w/cpp/language/constraints>`_. 
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-
-      // Generic constraint on Kernel Function Signatures
-      template <typename Kernel, typename Signature>
-      concept signature = std::is_convertible_v<Kernel, std::function<Signature>>; 
-
-      // Specialized for taking a single qubit
-      template<typename Kernel>
-      concept takes_qubit = signature<Kernel, void(qubit&)>;
-    }
-
-    struct MyGenericAlgorithmOnQarray {
-      void operator()(cudaq::signature<void(cudaq::qarray&)> auto&& statePrep) __qpu__ {
-        cudaq::qarray<10> q;
-        statePrep(q);
-        ...
-      }
-    };
-
-    struct MyGenericAlgorithmOnQubit {
-      void operator()(cudaq::takes_qubit auto&& statePrep) __qpu__ {
-        cudaq::qarray<10> q;
-        statePrep(q[0]);
-        ...
-      }
-    };
-
-This approach enables the development of generic libraries of quantum 
-algorithms that are parameterized on sub-units of the global circuit representation. 
diff --git a/pr-2308/_sources/specification/cudaq/machine_model.rst.txt b/pr-2308/_sources/specification/cudaq/machine_model.rst.txt
deleted file mode 100644
index e2389c6dfc..0000000000
--- a/pr-2308/_sources/specification/cudaq/machine_model.rst.txt
+++ /dev/null
@@ -1,59 +0,0 @@
-Machine Model
-*************
-
-.. role:: raw-html(raw)
-    :format: html 
-
-:raw-html:`<a name="mm1"></a>`
-
-**[1]** CUDA-Q presumes the existence of one or more classical host processors, 
-zero or more NVIDIA graphics processing units (GPUs), and zero or more quantum processing units (QPUs). 
-
-**[2]** Each QPU is composed of a classical quantum control system (distributed FPGAs, GPUs, etc.) 
-and a register of quantum bits (qubits) whose state evolves via signals from the classical control system.
-
-**[3]** The machine model allows for three modes of quantum process parallelism: concurrent execution
-of quantum circuits on independent QPUs, dependent quantum parallelism via
-quantum message passing and inter-QPU entanglement, and QPU thread-level
-parallelism or the ability for concurrent execution of independent quantum
-circuits on a single QPU qubit connectivity fabric. 
-
-**[4]** The model assumes a
-classical memory space for the host processor which inherits the native language memory
-model semantics (e.g. C++ or Python). 
-
-**[5]** The model assumes a classical memory space for each control
-system driving the evolution of the multi-qubit state. This control system
-memory space should enable primitive arithmetic variable declarations,
-stores, and loads, as well as qubit measurement persistence and loading
-for fast-feedback and conditional circuit execution. 
-
-**[6]** The quantum memory
-space on an individual QPU is modeled as an infinite register of 
-qubits and physical connectivity constraints are hidden from the
-programmer. Note that compiler implementations of the CUDA-Q model
-are free to enable developer access to the details of the QPU
-qubit connectivity for the purpose of developing novel placement strategies. 
-
-**[7]** CUDA-Q considers general :math:`D`-level quantum information systems, e.g. qudits. Qudits
-are non-copyable, and can be allocated in chunks, via instantiation of 
-user-level quantum container types. Quantum containers come in two flavors - 
-quantum memory owning and non-owning (views). Moreover, the size of a quantum container 
-can be specified at either compile-time or dynamically at runtime. 
-As all qudits are non-copyable, qudits and containers 
-thereof can only be passed by reference. 
-
-**[8]** Each allocated qudit is unique and if  
-deallocated, is available for subsequent allocation. Deallocation occurs implicitly
-when the qudit goes out of scope. Uncomputation of qudit state should occur 
-automatically via compiler implementations of the CUDA-Q model.
-
-**[9]** The CUDA-Q model considers both remotely hosted QPU execution models as well as
-tightly-coupled quantum-classical architectures. Remotely hosted models
-support batch circuit execution where each circuit may contain simple
-quantum-classical operation integration. Tightly-coupled execution models provide
-streaming instruction execution, measure-reset, and fast-feedback of
-qubit measurement results. This multi-modal execution model directly
-influences the syntax and semantics of quantum kernel expressions
-and their associated host-code context. 
-
diff --git a/pr-2308/_sources/specification/cudaq/namespace.rst.txt b/pr-2308/_sources/specification/cudaq/namespace.rst.txt
deleted file mode 100644
index f20c0c8b94..0000000000
--- a/pr-2308/_sources/specification/cudaq/namespace.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-Namespace and Standard
-**********************
-
-**[1]** The CUDA-Q language is a library-based extension for C++. As such, all
-pertinent data structures and library functions exist within the CUDA-Q
-namespace. This namespace is denoted :code:`::cudaq`. 
-
-**[2]** CUDA-Q is a C++ library-based language extension adherent to the `C++20 <https://en.cppreference.com/w/cpp/20>`_
-language specification. Any implementations that require backwards compatibility to previous 
-standards must retain the semantics of the user-facing API (e.g. `SFINAE <https://en.cppreference.com/w/cpp/language/sfinae>`_ 
-instead of `concepts and constraints <https://en.cppreference.com/w/cpp/language/constraints>`_).
\ No newline at end of file
diff --git a/pr-2308/_sources/specification/cudaq/operations.rst.txt b/pr-2308/_sources/specification/cudaq/operations.rst.txt
deleted file mode 100644
index 09497945f3..0000000000
--- a/pr-2308/_sources/specification/cudaq/operations.rst.txt
+++ /dev/null
@@ -1,127 +0,0 @@
-Quantum Intrinsic Operations
-****************************
-**[1]** To support low-level quantum programming and build foundational
-quantum kernels for large-scale applications, CUDA-Q specifies the *quantum
-intrinsic operation* abstraction for device-specific single-qudit unitary operations. 
-
-**[2]** A quantum intrinsic operation is modeled via a standard free function in the native classical language. It 
-has a unique instruction name and general specified function operands. 
-
-**[3]** These operands can model classical rotation parameters or units of quantum information (e.g. the :code:`cudaq::qudit`).
-The syntax for these operations is 
-
-.. code-block:: cpp 
-
-    void INST_NAME( PARAM...?, cudaq::qudit<N>&...);
-
-where :code:`INST_NAME` is the name of the instruction, :code:`qudit<N>&...` indicates one many
-:code:`cudaq::qudit` instances, and :code:`PARAM...?` indicates optional parameters of 
-floating point type (e.g. :code:`double`, :code:`float`). Quantum intrinsic operations return :code:`void`.
-
-**[4]** All intrinsic operations should start with a base declaration targeting a single :code:`cudaq::qudit`, and overloads
-should be provided that take more than one :code:`cudaq::qudit` instances to model the application
-of that instruction on all provided :code:`cudaq::qudits`, e.g. :code:`void x(cudaq::qubit&)` and
-:code:`x(cudaq::qubit&, cudaq::qubit&, cudaq::qubit&)`, modeling the NOT operation on a single 
-:code:`cudaq::qubit` or on multiple :code:`cudaq::qubit`. 
-
-**[5]** The specific set of quantum intrinsic operations available to the programmer can 
-be platform specific and provided via platform specific header files. 
-
-**[6]** Implementations should provide overloads to support broadcasting of single-qubit
-intrinsic operations across containers of :code:`cudaq::qudit`. For example,
-:code:`x(cudaq::qvector<>&)` should apply a NOT operation on all
-:code:`cudaq::qubit` in the provided :code:`cudaq::qvector`. 
-
-**[7]** Programmers can further modify quantum intrinsic operations via 
-an extra specified template parameter, and CUDA-Q leverages this syntax 
-for synthesizing control and adjoint variants of the operation. Specifically CUDA-Q 
-provides the `cudaq::ctrl`, and `cudaq::adj` type modifiers for synthesizing control and 
-adjoint forms of the operation. For language bindings that do not support template 
-parameterization, implementations may rely on static method calls (e.g. :code:`x.ctrl(q, r)`)
-
-Here is an example of how one might modify an intrinsic operation for multi-control and adjoint operations.
-
-.. tab:: C++ 
-  
-  .. code-block:: cpp 
-
-    cudaq::qubit q, r, s;
-    // Apply T operation
-    t(q);
-    // Apply Tdg operation
-    t<cudaq::adj>(q);
-    // Apply control Hadamard operation
-    h<cudaq::ctrl>(q,r,s);
-    // Error, ctrl requires > 1 qubit operands
-    // h<cudaq::ctrl>(r);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    q, r, s = cudaq.qubit(), cudaq.qubit(), cudaq.qubit()
-    # Apply T operation
-    t(q)
-    # Apply Tdg operation
-    t.adj(q)
-    # Apply control Hadamard operation
-    h.ctrl(q,r,s)
-    # Error, ctrl requires > 1 qubit operands
-    # h.ctrl(r);
-
-Operations on :code:`cudaq::qubit`
-----------------------------------
-The default set of quantum intrinsic operations for the cudaq::qubit type is as follows: 
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      struct base;
-      struct ctrl;
-      struct adj;
-  
-      // Single qubit operations, ctrl / adj variants, and broadcasting
-      template<typename mod = base, typename... QubitArgs>
-      void NAME(QubitArgs&... args) noexcept { ... }
-  
-      template<typename mod = base>
-      void NAME(const qvector& qr) noexcept { ... }
-  
-      template<typename mod = ctrl>
-      void NAME(qvector& ctrls, qubit& target) noexcept { ... }
- 
-      // Single qubit rotation operations and ctrl / adj variants
-      template <typename mod = base, typename ScalarAngle, typename... QubitArgs> 
-      void ROTATION_NAME(ScalarAngle angle, QubitArgs &...args) noexcept { ... }
- 
-      bool MEASURE_OP(qubit &q) noexcept;
-      std::vector<bool> MEASURE_OP(qvector &q) noexcept;
-      double measure(cudaq::spin_op & term) noexcept { ... }
-  }
-
-**[1]** For the default implementation of the :code:`cudaq::qubit` intrinsic operations, we let 
-NAME be any operation name in the set :code:`{h, x, y, z, t, s}` and :code:`ROTATION_NAME` be any 
-operation in :code:`{rx, ry, rz, r1 (phase)}`. Implementations may provide appropriate 
-function implementations using the above foundational functions to enable other 
-common operations (e.g. :code:`cnot -> x<cudaq::ctrl>`).
-
-**[2]** Control qubits can be specified with positive or negative polarity. 
-By this we mean that a control qubit can specify that a target operation is 
-applied if the control qubit state is a :code:`|0>` (positive polarity) or :code:`|1>` (negative polarity). 
-By default all control qubits are assumed to convey positive polarity. The 
-syntax for negating the polarity is the not operator preceding the control 
-qubit 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    x<cudaq::ctrl>(!q, r);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    x.ctrl(~q, r)
-  
-The set of gates that the official CUDA-Q implementation supports can be found in the :doc:`API documentation </api/api>`.
diff --git a/pr-2308/_sources/specification/cudaq/operators.rst.txt b/pr-2308/_sources/specification/cudaq/operators.rst.txt
deleted file mode 100644
index 2645b861fe..0000000000
--- a/pr-2308/_sources/specification/cudaq/operators.rst.txt
+++ /dev/null
@@ -1,81 +0,0 @@
-Quantum Operators
-*****************
-
-:code:`cudaq::spin_op`
-----------------------
-**[1]** CUDA-Q provides a native :code:`spin_op` data type in the :code:`cudaq` namespace for the
-expression of quantum mechanical spin operators. 
-
-**[2]** The :code:`spin_op` provides an abstraction for a general tensor product of Pauli
-spin operators, and sums thereof:
-
-.. math:: 
-
-    H = \sum_{i=0}^M P_i, P_i = \prod_{j=0}^N \sigma_j^a
-
-for :math:`a = {x,y,z}`, :math:`j` the qubit index, and :math:`N` the number of qubits.
-
-**[3]** The :code:`spin_op` exposes common C++ operator overloads for algebraic expressions. 
-
-**[4]** CUDA-Q defines convenience functions in :code:`cudaq::spin` namespace that produce
-the primitive X, Y, and Z Pauli operators on specified qubit indices
-which can subsequently be used in algebraic expressions to build up
-more complicated Pauli tensor products and their sums.
-
-.. tab:: C++ 
-
-    .. code-block:: cpp
-
-        using namespace cudaq::spin;
-        auto h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) + \
-                 .21829 * z(0) - 6.125 * z(1);
-
-.. tab:: Python
-
-    .. code-block:: python 
-
-        from cudaq import spin 
-        h = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(0) * spin.y(1) + \
-                 .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-
-**[5]** The :code:`spin_op` also provides a mechanism for the expression of circuit
-synthesis tasks within quantum kernel code. Specifically, operations
-that encode :math:`N`\ :sup:`th`\ order trotterization of exponentiated :code:`spin_op`
-rotations, e.g. :math:`U = \exp(-i H t)`, where :math:`H` is the provided :code:`spin_op`.
-
-**[6]** The :code:`spin_op` can be created within classical host code and quantum kernel
-code, and can also be passed by value to quantum kernel code from host code. 
-
-The :code:`spin_op` should take on the following structure: 
-
-.. code-block:: cpp
-
-    namespace cudaq {
-    class spin_op {
-      public:
-        spin_op();
-        spin_op(const spin_op&);
-        bool empty() const;
-        std::size_t num_qubits() const;
-        std::size_t num_terms() const;
-        std::complex<double> get_coefficient();
-        bool is_identity() const;
-        void for_each_term(std::function<void(spin_op &)> &&) const;
-        void for_each_pauli(std::function<void(pauli, std::size_t)> &&) const;
-        spin_op& operator=(const spin_op&);
-        spin_op& operator+=(const spin_op&);
-        spin_op& operator-=(const spin_op&);
-        spin_op& operator*=(const spin_op&);
-        bool operator==(const spin_op&);
-        spin_op& operator*=(const double);
-        spin_op& operator*=(const std::complex<double>)
-    };
-
-    namespace spin {
-      spin_op x(const std::size_t);
-      spin_op y(const std::size_t);
-      spin_op z(const std::size_t);
-    }
-  }
-
diff --git a/pr-2308/_sources/specification/cudaq/patterns.rst.txt b/pr-2308/_sources/specification/cudaq/patterns.rst.txt
deleted file mode 100644
index 0f96c164b6..0000000000
--- a/pr-2308/_sources/specification/cudaq/patterns.rst.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-
-Common Quantum Programming Patterns
-***********************************
-
-Compute-Action-Uncompute
-------------------------
-**[1]** CUDA-Q specifies syntax and semantics to support the compute-action-uncompute
-pattern, :math:`W = U V U \dagger` (here :math:`U` is the compute block, and :math:`V` is the action block).
-
-**[2]** Via special syntax for this pattern CUDA-Q enables compiler implementations to 
-key on circuit optimizations via programmer intent. Specifically, controlled versions of :math:`W` do not produce controlled
-operations of all instructions in :math:`U`, :math:`V`, and :math:`U \dagger`, but instead we are free to only
-control :math:`V`. 
-
-**[3]** The CUDA-Q specification requires the following syntax for this pattern:
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      // Will invoke U V U^dag
-      cudaq::compute_action (
-          [&](){ 
-            /*U_code*/ 
-          }, 
-          [&]() { 
-            /*V_code*/ 
-          } 
-      ); 
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    def computeF():
-       ... 
-    
-    def actionF():
-       ...
-
-    # Can take user-defined functions
-    cudaq.compute_action(computeF, actionF)
-
-    # Can take Pythonic CUDA-Q lambda kernels
-    computeL = lambda : (h(q), x(q), ry(-np.pi, q[0]))
-    cudaq.compute_action(computeL, actionF)
-
-
-**[4]** Compiler implementations must add the uncompute segment
-and optimize on any controlled versions of this block of code. 
\ No newline at end of file
diff --git a/pr-2308/_sources/specification/cudaq/platform.rst.txt b/pr-2308/_sources/specification/cudaq/platform.rst.txt
deleted file mode 100644
index fc44e7f12f..0000000000
--- a/pr-2308/_sources/specification/cudaq/platform.rst.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-
-Quantum Platform
-****************
-
-**[1]** CUDA-Q provides an abstraction describing the underlying quantum computing resource(s). 
-The underlying quantum platform can contain one or many quantum processing units (QPUs) each with 
-its own qubit connectivity and noise configuration.
-
-**[2]** CUDA-Q defines a :code:`cudaq::quantum_platform` in an effort to expose relevant system 
-information and enable asynchronous quantum kernel invocations.
-
-**[3]** The :code:`cudaq::quantum_platform` provides an API for querying the number of available 
-quantum processing units (QPUs), with each QPU assigned a logical integer index (:code:`{0,1,2,...}`). 
-
-**[4]** Programmers can specify the ID of the desired QPU and all subsequent CUDA-Q 
-kernel executions will target that QPU.
-
-The :code:`cudaq::quantum_platform`  should take the following structure
-
-.. code-block:: cpp
-
-  namespace cudaq {
-    class quantum_platform {
-    public:
-      quantum_platform();
-      ~quantum_platform();
- 
-      using QubitEdge = std::pair<std::size_t, std::size_t>;
-      using QubitConnectivity = std::vector<QubitEdge>;
-      std::optional<QubitConnectivity> connectivity();
-
-      std::size_t num_qpus() const;
-      std::size_t get_num_qubits(std::size_t qpu_id = 0) const;
- 
-      bool is_simulator(std::size_t qpu_id = 0) const;
-      bool is_remote(std::size_t qpuId = 0);
-      bool is_emulated(std::size_t qpuId = 0) const;
-      std::string name() const;
- 
-      std::size_t get_current_qpu() const ;
-      void set_current_qpu(const std::size_t device_id);
-
-    };
-  }
-
-CUDA-Q provides the following public functions to interact with the current
-:code:`cudaq::quantum_platform`
-
-.. code-block:: cpp
-
-  namespace cudaq {
-    quantum_platform &get_platform() ;
-  }
-
diff --git a/pr-2308/_sources/specification/cudaq/synthesis.rst.txt b/pr-2308/_sources/specification/cudaq/synthesis.rst.txt
deleted file mode 100644
index 038151ab8d..0000000000
--- a/pr-2308/_sources/specification/cudaq/synthesis.rst.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-Sub-circuit Synthesis
-*********************
-**[1]** Execution of pure device quantum kernels can be modified via explicit intrinsic
-library calls that provide multi-qubit controlled application and adjoint,
-or circuit reversal, semantics. 
-
-**[2]** CUDA-Q defines the following library functions
-for enabling multi-controlled and adjoint operations on a general pure
-device kernel:
-
-.. code-block:: cpp
-
-    template <typename QuantumKernel, typename... Args>
-    void control(QuantumKernel &&kernel,
-                 cudaq::qubit& ctrl_qubit, Args &... args);
- 
-    template <typename QuantumKernel, typename QuantumRegister, typename... Args>
-      requires(std::ranges::range<QuantumRegister>)
-    void control(QuantumKernel &&kernel,
-                 QuantumRegister& ctrl_qubits, Args &... args);
- 
-    template <typename QuantumKernel, typename... Args>
-    void adjoint(QuantumKernel &&kernel, Args &... args);
-
-These functions can be leveraged in quantum kernel code in the following way
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      __qpu__ void x_gate(cudaq::qubit& q) { x(q); }
-      
-      struct kernel {
-        void operator() () __qpu__ {
-          cudaq::qarray<3> q;
-          ...
-          // Create Toffoli gate
-          auto ctrl_bits = q.front(2);
-          cudaq::control(x_gate, ctrl_bits, q[2]);
-          ...
-        }
-      };
-      
-      void rx_and_h_gate(double x, cudaq::qubit& q) __qpu__ { rx(x,q); h(q); }
-      
-      __qpu__ kernel(int N) {
-        cudaq::qvector q(N);
-        ...
-        // apply h(q[2]); rx(-pi, q[2]);
-        cudaq::adjoint(rx_and_h_gate{}, M_PI, q[2]);
-        ...
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def x_gate(q : cudaq.qubit):
-        x(q)
-    
-    @cudaq.kernel()
-    def kernelTestControl():
-        q = cudaq.qvector(3)
-        ...
-        ctrl_bits = q.front(2)
-        cudaq.control(x_gate, ctrl_bits, q[2])
-        ...
-    
-    @cudaq.kernel()
-    def rx_and_h_gate(x : float, q : cudaq.qubit):
-        rx(x, q)
-        h(q)
-    
-    @cudaq.kernel()
-    def kernelTestAdjoint(N : int):
-        q = cudaq.qvector(N)
-        ...
-        # apply h(q[2]); rx(-pi, q[2])
-        cudaq.adjoint(rx_and_h_gate, np.pi, q[2])
-        ...
-
-
-**[3]** The :code:`cudaq::control(...)` function takes as input an instantiated pure
-device quantum kernel, a std::range of control qubits (:code:`cudaq::qvector`
-or :code:`cudaq::qview`), and the remaining arguments for the kernel itself.
-
-**[4]** Compiler implementations are free to synthesize multi-controlled operations
-using any pertinent synthesis strategy available. Qubits may be aggregated into
-a range of control qubits with or without the use of the :code:`operator!`
-:doc:`negated polarity operator <operations>`.
-
-.. tab:: C++ 
-  
-  .. code-block:: cpp
-
-      cudaq::control(kernel{}, {qubit0, !qubit1}, kernel_arg);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    cudaq.control(kernel, [qubit0, ~qubit1], kernel_arg)
-  
-**[5]** The :code:`cudaq::adjoint(...)` function takes as input an
-pure device quantum kernel and the remaining arguments for the kernel.
diff --git a/pr-2308/_sources/specification/cudaq/types.rst.txt b/pr-2308/_sources/specification/cudaq/types.rst.txt
deleted file mode 100644
index 2a61b8576c..0000000000
--- a/pr-2308/_sources/specification/cudaq/types.rst.txt
+++ /dev/null
@@ -1,451 +0,0 @@
-Quantum Types
-*************
-The CUDA-Q language extension provides certain fundamental types that are pertinent
-to quantum-classical computing. These types are provided via defined classical library
-types in C++ in the :code:`cudaq` namespace. 
-
-:code:`cudaq::qudit<Levels>`
-----------------------------
-**[1]** The :code:`cudaq::qudit` models a :math:`D`-level unit of quantum information. The state of
-this system (for :math:`N` qudits) can be described by a :math:`D`\ :sup:`N`\-dimensional vector in
-Hilbert space with the absolute square of all elements summing to 1. 
-
-**[2]** The :code:`cudaq::qudit` encapsulates a unique :code:`std::size_t` modeling the index of the
-qudit in the underlying quantum memory space (assuming an infinite register
-of available qudits). 
-
-**[4]** To adhere to the no-cloning theorem of quantum mechanics,
-the :code:`cudaq::qudit` is non-copyable and non-movable. Therefore, all :code:`cudaq::qudit` 
-instances must be passed by reference, and the no-cloning theorem is satisfied
-at compile-time. 
-
-**[5]** :code:`cudaq::qudit` instances can only be allocated within CUDA-Q quantum
-kernel code and can never be allocated from classical host code.
-
-**[6]** All instantiated :code:`cudaq::qudit` instances start in the :code:`|0>` computational basis 
-state and serve as the primary input argument for quantum intrinsic operations 
-modeling typical logical quantum gate operations.
-
-**[7]** :code:`cudaq::qudit` instances can be negated when leveraged as controls in 
-quantum operation invocations. The mechanism for negation should be via overloading of :code:`qudit<N>::operator!()`.
-
-The :code:`cudaq::qudit` takes on the following structure
-
-.. code-block:: cpp
-
-    template <std::size_t Levels>
-    class qudit {
-      protected: 
-        const std::size_t idx = 0;
-      public:
-        qudit();
-        qudit(const qudit&) = delete;
-        qudit(qudit &&) = delete;
-        std::size_t id() const;
-        static constexpr std::size_t n_levels();
-        qudit<Levels> &operator!(); 
-    };
-
-:code:`cudaq::qubit`
---------------------
-**[1]** The specification provides a primitive :code:`cudaq::qubit` type which models a
-single quantum bit (:math:`2`-level) in the discrete quantum memory space.
-:code:`cudaq::qubit` is an alias for :code:`cudaq::qudit<2>` 
-
-.. code-block:: cpp
-    
-    namespace cudaq {
-      using qubit = qudit<2>;
-    }
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      {
-        // Allocate a qubit in the |0> state
-        cudaq::qubit q;
-        // Put the qubit in a superposition of |0> and |1>
-        h(q); // cudaq::h == hadamard, ADL leveraged
-        printf("ID = %lu\n", q.id()); // prints 0
-        cudaq::qubit r;
-        printf("ID = %lu\n", r.id()); // prints 1
-        // qubit out of scope, implicit deallocation
-      }
-      cudaq::qubit q;
-      printf("ID = %lu\n", q.id()); // prints 0 (previous deallocated)
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    # Allocate a qubit in the |0> state
-    q = cudaq.qubit()
-    # Put the qubit in a superposition of |0> and |1>
-    h(q)
-    print("ID = {}".format(q.id())) # prints 0
-    
-    r = cudaq.qubit()
-    print("ID = {}", r.id()) # prints 1
-    # qubits go out of scope, implicit deallocation
-    
-Quantum Containers
-------------------
-**[1]** CUDA-Q specifies abstractions for dealing with groups of :code:`cudaq::qudit` instances in the
-form of familiar C++ containers. 
-
-**[2]** The underlying connectivity of the :code:`cudaq::qudit` instances stored in these containers is opaque to
-the programmer and any logical-to-physical program connectivity mapping should be done by compiler implementations. 
-
-:code:`cudaq::qview<Levels = 2>`
-++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qview<N>` is a non-owning reference to a subset of the discrete quantum memory space, 
-and as such, it is a :code:`std::span`-like C++ range of :code:`cudaq::qudit`.
-
-**[2]** The :code:`cudaq::qview` does not own its elements and can therefore be passed by value or reference. 
-
-**[3]** The :code:`cudaq::qview` is templated on the dimensionality of the contained quantum information unit, 
-and defaults to :math:`2` for qubit systems.
-
-**[4]** The :code:`cudaq::qview` provides an API for individual qubit extraction and sub-register slicing. 
-Programmers can extract the front :math:`N` :code:`qudits`, the back :math:`N` :code:`qudits`, and the 
-inner slice starting at a given index and including user-specified :code:`count` :code:`qudits`.
-
-The :code:`cudaq::qview` should take on the following structure:
-
-.. code-block:: cpp
-    
-    namespace cudaq { 
-      template <std::size_t Levels = 2>
-      class qview {
-        private:
-          std::span<qudit<Levels>> qudits;
-        public:
-          // Construct a span that refers to the qudits in `other`.
-          template <typename R>
-          requires(std::ranges::range<R>)
-          qview(R&& other);
-          qview(const qview& other);
-
-          // Iterator interface.
-          auto begin();
-          auto end();
-
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
-
-          // Returns the `[0, count)` qudits.
-          qview<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qview<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
-
-
-          // Returns the `[start, start+count)` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-      };
-    }
-
-:code:`cudaq::qvector<Levels = 2>`
-++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qvector<Levels>` is a container of elements from the discrete quantum memory space - a C++ container of :code:`cuda::qudit`.  
-
-**[2]** The :code:`cudaq::qvector` is a dynamically constructed owning container for :code:`cuda::qudit` (:code:`std::vector`-like), 
-and since it owns the quantum memory, it cannot be copied or moved. 
-
-**[3]** The :code:`cudaq::qvector` is templated on the dimensionality of the contained 
-quantum information unit, and defaults to :math:`2` for qubit systems.
-
-**[4]** The :code:`cudaq::qvector` can only be instantiated within CUDA-Q kernels
-
-**[5]** All qudits in the :code:`cudaq::qvector` start in the :code:`|0>` computational basis state. 
-
-**[6]** The :code:`cudaq::qvector` provides an API for individual qubit extraction and sub-register slicing. 
-Programmers can extract the front :math:`N` :code:`qudits`, the back :math:`N` :code:`qudits`, and the 
-inner slice starting at a given index and including user-specified :code:`count` :code:`qudits`.
-
-The :code:`cudaq::qview` should take on the following structure:
-
-.. code-block:: cpp
-    
-    namespace cudaq { 
-      template <std::size_t Levels = 2>
-      class qvector {
-        private:
-          std::vector<qudit<Levels>> qudits;
-
-        public:
-          // Construct a qreg with `size` qudits in the |0> state.
-          qvector(std::size_t size);
-          qvector(const qvector&) = delete;
-
-          // Iterator interface.
-          auto begin();
-          auto end();
-
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
-
-          // Returns the `[0, count)` qudits.
-          qview<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qview<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
- 
-          // Returns the `[start, start+count)` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the `{start, start + stride, ...}` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t stride, std::size_t end);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-
-          // Destroys all contained qudits. Postcondition: `size() == 0`.
-          void clear();
-      };
-    }
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    // Allocate 20 qubits, std::vector-like semantics
-    cudaq::qvector q(20);
-    // Get first qubit
-    auto first = q.front();
-    // Get first 5 qubits
-    auto first_5 = q.front(5);
-    // Get last qubit 
-    auto last = q.back();
-    // Can loop over qubits with size() method
-    for (int i = 0; i < q.size(); i++) {
-      ... do something with q[i] ...
-    }
-    // Range based for loop supported 
-    for (auto & qb : q) {
-      ... do something with qb ...
-    }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    # Allocate 20 qubits, vector-like semantics
-    q = cudaq.qvector(20)
-    # Get the first qubit 
-    first = q.front()
-    # Get the first 5 qubits 
-    first_5 = q.front(5)
-    # Get the last qubit 
-    last = q.back()
-    # Can loop over qubits with size or len function 
-    for i in range(len(q)):
-      .. do something with q[i] ..
-    # Range based for loop 
-    for qb in q:
-      .. do something with qb .. 
-
-
-:code:`cudaq::qarray<N, Levels = 2>`
-++++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qarray<N, Levels>` (where :code:`N` is an integral constant) is a statically 
-allocated container (:code:`std::array`-like). The utility of this type is in the compile-time 
-knowledge of allocated containers of qudits that may directly enable ahead-of-time quantum 
-optimization and synthesis. 
-
-**[2]** The second template parameter defaults to :math:`2`-level :code:`cudaq::qudit`.
-
-**[3]** The :code:`cudaq::qarray` owns the quantum memory it contains, and therefore cannot be copied or moved.
-
-**[4]** The :code:`cudaq::qarray` can only be instantiated within CUDA-Q kernels
-
-**[5]** All qudits in the :code:`cudaq::qarray` start in the :code:`|0>` computational basis state. 
-
-**[6]** The :code:`cudaq::qarray` provides an API for individual qubit extraction and sub-register slicing. 
-Programmers can extract the front :math:`N` :code:`qudits`, the back :math:`N` :code:`qudits`, and the 
-inner slice starting at a given index and including user-specified :code:`count` :code:`qudits`.
-
-The :code:`cudaq::qarray` should take on the following structure:
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      template <std::size_t N, std::size_t Levels = 2>
-      class qarray {
-        private:
-          std::array<qudit<Levels>, N> qudits;
-
-        public:
-          // Construct a qreg with `size` qudits in the |0> state.
-          qarray();
-          qarray(const qvector&) = delete;
-          qarray(qarray &&) = delete;
-
-          qarray& operator=(const qarray &) = delete;
-
-          // Iterator interface.
-          auto begin();
-          auto end();
-
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
-
-          // Returns the `[0, count)` qudits.
-          qview<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qview<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
-
-          // Returns the `[start, start+count)` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the `{start, start + stride, ...}` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t stride, std::size_t end);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-
-          // Destroys all contained qudits. Postcondition: `size() == 0`.
-          void clear();
-      };
-    }
-
-:code:`cudaq::qspan<N, Levels>` (Deprecated. Use :code:`cudaq::qview<Levels>` instead.)
-++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qspan` is a non-owning reference to a part of the discrete quantum
-memory space, a :code:`std::span`-like C++ range of :code:`cudaq::qudit` 
-(see C++ `span <https://en.cppreference.com/w/cpp/container/span>`_). It does not
-own its elements. It takes a single template parameter indicating the levels for 
-the underlying qudits that it stores. This parameter defaults to 2 for qubits. 
-It takes on the following structure:
-
-.. code-block:: cpp
-    
-    namespace cudaq {
-      template <std::size_t Levels = 2>
-      class qspan {
-        private:
-          std::span<qudit<Levels>> qubits;
-        public:
-          // Construct a span that refers to the qudits in `other`.
-          qspan(std::ranges::range<qudit<Levels>> auto& other);
-          qspan(qspan const& other);
- 
-          // Iterator interface.
-          auto begin();
-          auto end();
- 
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
- 
-          // Returns the `[0, count)` qudits.
-          qspan<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qspan<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
- 
-          // Returns the `[start, start+count)` qudits.
-          qspan<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-      };
-    }
-
-:code:`cudaq::qreg<N, Levels>` (Deprecated. Use :code:`cudaq::qvector<Levels>` instead.)
-++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qreg<N, Levels>` models a register of the discrete quantum memory space - a
-C++ container of :code:`cudaq::qudit`.  As a container, it owns its elements and
-their storage. :code:`qreg<dyn, Levels>` is a dynamically allocated container
-(:code:`std::vector`-like, see C++ `vector <https://en.cppreference.com/w/cpp/container/vector>`_).
-:code:`cudaq::qreg<N, Levels>` (where N is an integral
-constant) is a statically allocated container (:code:`std::array`-like, 
-see `array <https://en.cppreference.com/w/cpp/container/array>`_). 
-Its template parameters default to dynamic allocation and :code:`cudaq::qudit<2>`.
-
-.. code-block:: cpp
-
-    namespace cudaq {
-      template <std::size_t N = dyn, std::size_t Levels = 2>
-      class qreg {
-        private:
-          std::conditional_t<
-            N == dyn,
-            std::vector<qudit<Levels>>,
-            std::array<qudit<Levels>, N>
-          > qudits;
-        public:
-          // Construct a qreg with `size` qudits in the |0> state.
-          qreg(std::size_t size) requires (N == dyn);
-          qreg(qreg const&) = delete;
- 
-          // Iterator interface.
-          auto begin();
-          auto end();
- 
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
- 
-          // Returns the `[0, count)` qudits.
-          qspan<dyn, Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qspan<dyn, Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
- 
-          // Returns the `[start, start+count)` qudits.
-          qspan<dyn, Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
- 
-          // Destroys all contained qudits. Postcondition: `size() == 0`.
-          void clear();
-      };
-    } 
-
-:code:`qreg` instances can only be instantiated from within quantum kernels,
-they cannot be instantiated in host code. All qubits in the :code:`qreg` 
-start in the :code:`|0>` computational basis state. 
-
-.. code-block:: cpp
-
-    // Allocate 20 qubits, std::vector-like semantics
-    cudaq::qreg q(20);
-    auto first = q.front();
-    auto first_5 = q.front(5);
-    auto last = q.back();
-    for (int i = 0; i < q.size(); i++) {
-      ... do something with q[i] ...
-    }
-    for (auto & qb : q) {
-      ... do something with qb ...
-    }
- 
-    // std::array-like semantics
-    cudaq::qreg<5> fiveCompileTimeQubits;
\ No newline at end of file
diff --git a/pr-2308/_sources/specification/index.rst.txt b/pr-2308/_sources/specification/index.rst.txt
deleted file mode 100644
index 6895a6de94..0000000000
--- a/pr-2308/_sources/specification/index.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-*****************
-Specifications
-*****************
-
-The following specifications for the CUDA-Q programming model and toolchain are available:
-
-.. toctree::
-   :maxdepth: 2
-
-      Language Specification <cudaq.rst>
-      Quake Specification <quake-dialect>
diff --git a/pr-2308/_sources/specification/quake-dialect.md.txt b/pr-2308/_sources/specification/quake-dialect.md.txt
deleted file mode 100644
index 0650f71206..0000000000
--- a/pr-2308/_sources/specification/quake-dialect.md.txt
+++ /dev/null
@@ -1,150 +0,0 @@
-# Quake Dialect
-
-## General Introduction
-
-The quantum circuit model is the most widely used model of quantum
-computation.  It provides a convenient tool for formulating quantum
-algorithms and an architecture for the physical construction of quantum
-computers.
-
-A _quantum circuit_ represents a computation as a sequence of quantum
-operators applied to quantum data.  In our case, the quantum data is a set
-of quantum bits, or qubits for short.  Physically, a qubit is an object with
-only two distinguishable states, i.e., it is a two-state quantum mechanical
-system such as a spin-1/2 particle.
-
-Conceptually, a _quantum operator_ is an effect that might modify the state
-of a subset of qubits. Most often, this effect is unitary evolution---in
-this case, we say that the operator is a _unitary_.  The number of target
-qubits an operator acts upon is an intrinsic property.
-
-A _quantum instruction_ is the embodiment of a quantum operator when applied
-to a specific subset of qubits.  The number of qubits must be equal to (or
-greater than) the number of target qubits intrinsic to the operator.  If
-greater, the extra qubits are considered controls.
-
-## Motivation
-
-The main motivation behind Quake's value model is to directly expose
-quantum and classical data dependencies for optimization purposes,
-i.e., to represent the dataflow in quantum computations.  In contrast
-to Quake's memory model, which uses memory semantics (quantum
-operators act as side-effects on qubit references), the value model
-uses value semantics, that is quantum operators consume and produce
-values. These values are not truly SSA values, however, as operations
-still have side-effects on the value itself and the value cannot be
-copied.
-
-Let's see an example to clarify the distinction between the models.  Take the
-following Quake implementation of some toy quantum computation:
-
-```cpp
-func.func foo(%veq : !quake.veq<2>) {
-    // Boilerplate to extract each qubit from the vector
-    %c0 = arith.constant 0 : index
-    %c1 = arith.constant 1 : index
-    %q0 = quake.extract_ref %veq[%c0] : (!quake.veq<2>, index) -> !quake.ref
-    %q1 = quake.extract_ref %veq[%c1] : (!quake.veq<2>, index) -> !quake.ref
-
-    // We apply some operators to those extracted qubits
-    // ... bunch of operators using %q0 and %q1 ...
-    quake.h %q0 : (!quake.ref) -> ()
-
-    // We decide to measure the vector
-    %result = quake.mz %veq : (!quake.veq<2>) -> cc.stdvec<i1>
-
-    // And then apply another Hadamard to %q0
-    quake.h %q0 : (!quake.ref) -> ()
-    // ...
-}
-```
-
-Now imagine we want to optimize this code by removing pair of adjacent
-adjoint operators, e.g., if we have a pair Hadamard operations next to each
-other on the same qubit---visually:
-
-```text
-    ┌───┐ ┌───┐         ┌───┐
-   ─┤ H ├─┤ H ├─  =  ───┤ I ├───  =  ─────────────
-    └───┘ └───┘         └───┘
-```
-
-Where `I` is the identity operator. Now note that a naive implementation of
-this optimization for Quake would optimize away both `quake.h` operators
-being applied to `%q0`.  Such an implementation would have missed the fact
-that a measurement is being applied to the vector, `%veq`, which contains
-`%q0`.
-
-Of course it is possible to correctly implement this optimization for Quake.
-However such an implementation would be quite error-prone and require
-complex analyses.  For this reason, Quake has overloaded gates.
-
-In the value model operators consume values and return new values:
-
-```text
-  %q0_1 = quake.op %q0_0 : (!quake.wire) -> !quake.wire
-```
-
-We can visualize the difference between memory and value representation as:
-
-```text
-            Memory                                   Value
-
-        ┌──┐ ┌──┐     ┌──┐                  ┌──┐ %q0_1 ┌──┐     ┌──┐
-   %q0 ─┤  ├─┤  ├─···─┤  ├─ %q0  vs  %q0_0 ─┤  ├───────┤  ├─···─┤  ├─ %q0_Z
-        └──┘ └──┘     └──┘                  └──┘       └──┘     └──┘
-```
-
-If we look at the implementation again, we notice that the problem
-with the naive optimization happens because the Hadamard operators are
-implicitly connected by the same value `%q0`. In value form, all the gates
-are explicitly connected by distinct values, which eliminates the need
-to do further analysis via implicit side-effects.
-The following is the implementation in value form.
-
-```text
-func.func @foo(%array : !quake.qvec<2>) {
-    // Boilerplate to extract each qubit
-    %c0 = arith.constant 0 : index
-    %c1 = arith.constant 1 : index
-    %r0 = quake.extract_ref %array[%c0] : (!quake.qvec<2>, index) -> !quake.qref
-    %r1 = quake.extract_ref %array[%c1] : (!quake.qvec<2>, index) -> !quake.qref
-
-    // Unwrap the quantum references to expose the wires.
-    %q0 = quake.unwrap %r0 : (!quake.qref) -> !quake.wire
-    %q1 = quake.unwrap %r1 : (!quake.qref) -> !quake.wire
-
-    // Misc. operators applied
-    %q0_M = quake.h %q0_L : (!quake.wire) -> !quake.wire
-
-    // Re-wrap the wire to its original source
-    quake.wrap %q0_M to %r0 : !quake.wire, !quake.qref
-    quake.wrap %q1_X to %r1 : !quake.wire, !quake.qref
-
-    // Measure the entire vector of quantum references
-    %result = quake.mz %array : (!quake.qvec<2>) -> !cc.stdvec<i1>
-
-    // Unwrap the wire for qubit 0 again
-    %q0_P = quake.unwrap %r0 : (!quake.qref) -> !quake.wire
-    ...
-    %q0_Z = quake.h %q0_Y : (!quake.wire) -> !quake.wire
-    // Re-wrap the wire back to the original reference
-    quake.wrap %q0_Z to %r0 : !quake.wire, !quake.qref
-    return
-}
-```
-
-In this code we can more straightforwardly see that the Hadamard
-operators cannot cancel each other.  One way of reasoning about this
-is as follows: In value form we need to follow a chain of values to
-know the qubit operators are being applied to, in this example:
-
-```text
-Mmeory                          Value
-    %q0         [%q0_0, %q0_1 ... %q0_L, %q0_M; %q0_P ... %q0_Y, %q0_Z]
-
-```
-
-We know that one Hadamard is applied to `%q0_L` and generates `%q0_M`, and
-the other is applied `%q0_Y` and generates `%q0_Z`.  Hence, there is no
-connection between them---which means they cannot cancel each other out.
diff --git a/pr-2308/_sources/using/applications.rst.txt b/pr-2308/_sources/using/applications.rst.txt
deleted file mode 100644
index b3119decf4..0000000000
--- a/pr-2308/_sources/using/applications.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-*************************
-CUDA-Q Applications
-*************************
-
-Applications that give an in depth view of CUDA-Q and its applications in Python.
-
-.. nbgallery::
-
-    /applications/python/bernstein_vazirani.ipynb
-    /applications/python/unitary_compilation_diffusion_models.ipynb
-    /applications/python/trotter.ipynb
-    /applications/python/cost_minimization.ipynb
-    /applications/python/deutschs_algorithm.ipynb
-    /applications/python/divisive_clustering_coresets.ipynb
-    /applications/python/shors.ipynb
-    /applications/python/hybrid_qnns.ipynb
-    /applications/python/qaoa.ipynb
-    /applications/python/digitized_counterdiabatic_qaoa.ipynb
-    /applications/python/krylov.ipynb
-    /applications/python/afqmc.ipynb
-    /applications/python/quantum_fourier_transform.ipynb
-    /applications/python/quantum_teleportation.ipynb
-    /applications/python/quantum_volume.ipynb
-    /applications/python/readout_error_mitigation.ipynb
-    /applications/python/vqe.ipynb
-    /applications/python/vqe_advanced.ipynb
-    /applications/python/hadamard_test.ipynb
-    /applications/python/logical_aim_sqale.ipynb
-
diff --git a/pr-2308/_sources/using/backends/backends.rst.txt b/pr-2308/_sources/using/backends/backends.rst.txt
deleted file mode 100644
index 9ee2bbbbbf..0000000000
--- a/pr-2308/_sources/using/backends/backends.rst.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-CUDA-Q Backends
-**********************
-
-.. toctree::
-   :caption: Backend Targets
-   :maxdepth: 1
-
-      Simulation <simulators.rst>
-      Quantum Hardware <hardware.rst>
-      NVIDIA Quantum Cloud <nvqc.rst>
-      Multi-Processor Platforms <platform.rst>
-
-**The following is a comprehensive list of the available targets in CUDA-Q:**
-
-* :ref:`anyon <anyon-backend>`
-* :ref:`braket <braket-backend>`
-* :ref:`density-matrix-cpu <default-simulator>`
-* :ref:`fermioniq <fermioniq-backend>`
-* :ref:`infleqtion <infleqtion-backend>`
-* :ref:`ionq <ionq-backend>`
-* :ref:`iqm <iqm-backend>`
-* :ref:`nvidia <nvidia-backend>`
-* :ref:`nvidia-fp64 <nvidia-fp64-backend>`
-* :ref:`nvidia-mgpu <nvidia-mgpu-backend>`
-* :ref:`nvidia-mqpu <mqpu-platform>`
-* :ref:`nvidia-mqpu-fp64 <mqpu-platform>`
-* :doc:`nvqc <nvqc>`
-* :ref:`oqc <oqc-backend>`
-* :ref:`orca <orca-backend>`
-* :ref:`qpp-cpu <qpp-cpu-backend>`
-* :ref:`quantinuum <quantinuum-backend>`
-* :ref:`quera <quera-backend>`
-* :ref:`remote-mqpu <mqpu-platform>`
-* :ref:`stim <stim-backend>`
-* :ref:`tensornet <tensor-backends>`
-* :ref:`tensornet-mps <tensor-backends>`
-
-.. deprecated:: 0.8
-   The `nvidia-fp64`, `nvidia-mgpu`, `nvidia-mqpu`, and `nvidia-mqpu-fp64` targets can be
-   enabled as extensions of the unified `nvidia` target (see `nvidia` :ref:`target documentation <nvidia-backend>`).
-   These target names might be removed in a future release.
\ No newline at end of file
diff --git a/pr-2308/_sources/using/backends/dynamics.rst.txt b/pr-2308/_sources/using/backends/dynamics.rst.txt
deleted file mode 100644
index a5b8c056b8..0000000000
--- a/pr-2308/_sources/using/backends/dynamics.rst.txt
+++ /dev/null
@@ -1,275 +0,0 @@
-CUDA-Q Dynamics 
-*********************************
-
-CUDA-Q enables the design, simulation and execution of quantum dynamics via 
-the ``evolve`` API. Specifically, this API allows us to solve the time evolution 
-of quantum systems or models. In the simulation mode, CUDA-Q provides the ``dynamics``
-backend target, which is based on the cuQuantum library, optimized for performance and scale
-on NVIDIA GPU.
-
-Quick Start
-+++++++++++
-
-In the example below, we demonstrate a simple time evolution simulation workflow comprising of the 
-following steps:
-
-1. Define a quantum system model
-
-A quantum system model is defined by a Hamiltonian. 
-For example, a superconducting `transmon <https://en.wikipedia.org/wiki/Transmon>`_ qubit can be modeled by the following Hamiltonian
-
-.. math:: 
-    
-    H = \frac{\omega_z}{2} \sigma_z + \omega_x \cos(\omega_d t)\sigma_x,
-
-where :math:`\sigma_z` and :math:`\sigma_x` are Pauli Z and X operators, respectively.
-
-Using CUDA-Q `operator`, the above time-dependent Hamiltonian can be set up as follows.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Transmon]
-        :end-before: [End Transmon]
-
-In particular, `ScalarOperator` provides an easy way to model arbitrary time-dependent control signals.   
-Details about CUDA-Q `operator`, including builtin operators that it supports can be found :ref:`here <operators>`.
-
-2. Setup the evolution simulation
-
-The below code snippet shows how to simulate the time-evolution of the above system
-with `cudaq.evolve`.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Evolve]
-        :end-before: [End Evolve]
-
-Specifically, we need to set up the simulation by providing:
-
-- The system model in terms of a Hamiltonian as well as any decoherence terms, so-called `collapse_operators`.
-
-- The dimensionality of component systems in the model. CUDA-Q `evolve` allows users to model arbitrary multi-level systems, such as photonic Fock space.
-
-- The initial quantum state.
-
-- The time schedule, aka time steps, of the evolution.
-
-- Any 'observable' operator that we want to measure the expectation value with respect to the evolving state.
-
-
-.. note::
-
-    By default, `evolve` will only return the final state and expectation values.
-    To save intermediate results (at each time step specified in the schedule),
-    the `store_intermediate_results` flag must be set to `True`.
-
-3. Retrieve and plot the results
-
-Once the simulation is complete, we can retrieve the final state and the expectation values
-as well as intermediate values at each time step (with `store_intermediate_results=True`).
-
-For example, we can plot the Pauli expectation value for the above simulation as follows.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Plot]
-        :end-before: [End Plot]
-
-In particular, for each time step, `evolve` captures an array of expectation values, one for each  
-observable. Hence, we convert them into sequences for plotting purposes.
-
-
-Operator
-+++++++++++
-
-.. _operators:
-
-CUDA-Q provides builtin definitions for commonly-used operators, 
-such as the ladder operators (:math:`a` and :math:`a^\dagger`) of a harmonic oscillator, 
-the Pauli spin operators for a two-level system, etc.
-
-Here is a list of those operators.
-
-.. list-table:: Builtin Operators
-        :widths: 20 50 
-        :header-rows: 1
-
-        *   - Name
-            - Description
-        *   - `identity`
-            - Identity operator
-        *   - `zero`
-            - Zero or null operator
-        *   - `annihilate`
-            - Bosonic annihilation operator (:math:`a`)
-        *   - `create`
-            - Bosonic creation operator (:math:`a^\dagger`)
-        *   - `number`
-            - Number operator of a bosonic mode (equivalent to :math:`a^\dagger a`)
-        *   - `parity`
-            - Parity operator of a bosonic mode (defined as :math:`e^{i\pi a^\dagger a}`)
-        *   - `displace`
-            - Displacement operator of complex amplitude :math:`\alpha` (`displacement`). It is defined as :math:`e^{\alpha a^\dagger - \alpha^* a}`.  
-        *   - `squeeze`
-            - Squeezing operator of complex squeezing amplitude :math:`z` (`squeezing`). It is defined as :math:`\exp(\frac{1}{2}(z^*a^2 - z a^{\dagger 2}))`.
-        *   - `position`
-            - Position operator (equivalent to :math:`(a^\dagger + a)/2`)
-        *   - `momentum`
-            - Momentum operator (equivalent to :math:`i(a^\dagger - a)/2`)
-        *   - `spin.x`
-            - Pauli :math:`\sigma_x` operator
-        *   - `spin.y`
-            - Pauli :math:`\sigma_y` operator
-        *   - `spin.z`
-            - Pauli :math:`\sigma_z` operator
-        *   - `spin.plus`
-            - Pauli raising (:math:`\sigma_+`) operator
-        *   - `spin.minus`
-            - Pauli lowering (:math:`\sigma_-`) operator
-
-As an example, let's look at the Jaynes-Cummings model, which describes 
-the interaction between a two-level atom and a light (Boson) field.
-
-Mathematically, the Hamiltonian can be expressed as
-
-.. math:: 
-    
-    H = \omega_c a^\dagger a + \omega_a \frac{\sigma_z}{2} + \frac{\Omega}{2}(a\sigma_+ + a^\dagger \sigma_-).
-
-This Hamiltonian can be converted to CUDA-Q `Operator` representation with
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Jaynes-Cummings]
-        :end-before: [End Jaynes-Cummings]
-
-In the above code snippet, we map the cavity light field to degree index 1 and the two-level atom to degree index 0. 
-The description of composite quantum system dynamics is independent from the Hilbert space of the system components.
-The latter is specified by the dimension map that is provided to the `cudaq.evolve` call. 
-
-
-Time-Dependent Dynamics
-++++++++++++++++++++++++
-
-.. _time_dependent:
-
-In the previous examples of operator construction, we assumed that the systems under consideration were described by time-independent Hamiltonian. 
-However, we may want to simulate systems whose Hamiltonian operators have explicit time dependence.
-
-CUDA-Q provides multiple ways to construct time-dependent operators.
-
-1. Time-dependent coefficient
-
-CUDA-Q `ScalarOperator` can be used to wrap a Python function that returns the coefficient value at a specific time.
-
-As an example, we will look at a time-dependent Hamiltonian of the form :math:`H = H_0 + f(t)H_1`, 
-where :math:`f(t)` is the time-dependent driving strength given as :math:`cos(\omega t)`.
-
-The following code sets up the problem
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Hamiltonian]
-        :end-before: [End Hamiltonian]
-
-2. Time-dependent operator
-
-We can also construct a time-dependent operator from a function that returns a complex matrix representing the time dynamics of 
-that operator.
-
-As an example, let's looks at the `displacement operator <https://en.wikipedia.org/wiki/Displacement_operator>`__. It can be defined as follows:
-
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin DefineOp]
-        :end-before: [End DefineOp]
-
-The defined operator is parameterized by the `displacement` amplitude. To create simulate the evolution of an 
-operator under a time dependent displacement amplitude, we can define how the amplitude changes in time:
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Schedule1]
-        :end-before: [End Schedule1]
-
-Let's say we want to add a squeezing term to the system operator. We can independently vary the squeezing 
-amplitude and the displacement amplitude by instantiating a schedule with a custom function that returns 
-the desired value for each parameter: 
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Schedule2]
-        :end-before: [End Schedule2]
-
-Numerical Integrators
-++++++++++++++++++++++
-
-.. _integrators:
-
-CUDA-Q provides a set of numerical integrators, to be used with the ``dynamics``
-backend target.
-
-.. list-table:: Numerical Integrators
-        :widths: 20 50 
-        :header-rows: 1
-
-        *   - Name
-            - Description
-        *   - `RungeKuttaIntegrator`
-            - Explicit 4th-order Runge-Kutta method (default integrator)
-        *   - `ScipyZvodeIntegrator`
-            - Complex-valued variable-coefficient ordinary differential equation solver (provided by SciPy)
-        *   - `CUDATorchDiffEqDopri5Integrator`
-            - Runge-Kutta of order 5 of Dormand-Prince-Shampine (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqAdaptiveHeunIntegrator`
-            - Runge-Kutta of order 2 (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqBosh3Integrator`
-            - Runge-Kutta of order 3 of Bogacki-Shampine (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqDopri8Integrator`
-            - Runge-Kutta of order 8 of Dormand-Prince-Shampine (provided by `torchdiffeq`)  
-        *   - `CUDATorchDiffEqEulerIntegrator`
-            - Euler method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqExplicitAdamsIntegrator`
-            - Explicit Adams-Bashforth method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqImplicitAdamsIntegrator`
-            - Implicit Adams-Bashforth-Moulton method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqMidpointIntegrator`
-            - Midpoint method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqRK4Integrator`
-            - Fourth-order Runge-Kutta with 3/8 rule (provided by `torchdiffeq`) 
-     
-.. note::
-    To use Torch-based integrators, users need to install `torchdiffeq` (e.g., with `pip install torchdiffeq`).
-    This is an optional dependency of CUDA-Q, thus will not be installed by default.
-
-.. warning:: 
-    Torch-based integrators require a CUDA-enabled Torch installation. Depending on your platform (e.g., `aarch64`),
-    the default Torch pip package may not have CUDA support. 
-
-    The below command can be used to verify your installation:
-
-    .. code:: bash
-
-        python3 -c "import torch; print(torch.version.cuda)"
-
-    If the output is a '`None`' string, it indicates that your Torch installation does not support CUDA.
-    In this case, you need to install a CUDA-enabled Torch package via other mechanisms, e.g., building Torch from source or
-    using their Docker images.
- 
\ No newline at end of file
diff --git a/pr-2308/_sources/using/backends/hardware.rst.txt b/pr-2308/_sources/using/backends/hardware.rst.txt
deleted file mode 100644
index 33f4907cec..0000000000
--- a/pr-2308/_sources/using/backends/hardware.rst.txt
+++ /dev/null
@@ -1,903 +0,0 @@
-CUDA-Q Hardware Backends
-*********************************
-
-CUDA-Q supports submission to a set of hardware providers. 
-To submit to a hardware backend, you need an account with the respective provider.
-
-
-Amazon Braket
-==================================
-
-.. _braket-backend:
-
-`Amazon Braket <https://aws.amazon.com/braket/>`__ is a fully managed AWS 
-service which provides Jupyter notebook environments, high-performance quantum 
-circuit simulators, and secure, on-demand access to various quantum computers.
-To get started users must enable Amazon Braket in their AWS account by following 
-`these instructions <https://docs.aws.amazon.com/braket/latest/developerguide/braket-enable-overview.html>`__.
-To learn more about Amazon Braket, you can view the `Amazon Braket Documentation <https://docs.aws.amazon.com/braket/>`__ 
-and `Amazon Braket Examples <https://github.com/amazon-braket/amazon-braket-examples>`__.
-A list of available devices and regions can be found `here <https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html>`__. 
-
-Users can run CUDA-Q programs on Amazon Braket with `Hybrid Job <https://docs.aws.amazon.com/braket/latest/developerguide/braket-what-is-hybrid-job.html>`__.
-See `this guide <https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-first.html>`__ to get started.
-
-Setting Credentials
-```````````````````
-
-After enabling Amazon Braket in AWS, set credentials using any of the documented `methods <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`__.
-One of the simplest ways is to use `AWS CLI <https://aws.amazon.com/cli/>`__.
-
-.. code:: bash
-
-    aws configure
-
-Alternatively, users can set the following environment variables.
-
-.. code:: bash
-
-  export AWS_DEFAULT_REGION="<region>"
-  export AWS_ACCESS_KEY_ID="<key_id>"
-  export AWS_SECRET_ACCESS_KEY="<access_key>"
-  export AWS_SESSION_TOKEN="<token>"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in Amazon Braket,
-pass the flag ``--target braket`` to the ``nvq++`` compiler.
-By default jobs are submitted to the state vector simulator, `SV1`.
-
-.. code:: bash
-
-    nvq++ --target braket src.cpp
-
-
-To execute your kernels on different device, pass the ``--braket-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target braket --braket-machine "arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet" src.cpp ...
-
-where ``arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet`` refers to IQM Garnet QPU.
-
-To emulate the device locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. 
-
-.. code:: bash
-
-    nvq++ --emulate --target braket src.cpp
-
-To see a complete example for using Amazon Braket backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target("braket")
-
-By default, jobs are submitted to the state vector simulator, `SV1`.
-
-To specify which Amazon Braket device to use, set the :code:`machine` parameter.
-
-.. code:: python
-
-    device_arn = "arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet"
-    cudaq.set_target("braket", machine=device_arn)
-
-where ``arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet`` refers to IQM Garnet QPU.
-
-To emulate the device locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``.
-
-.. code:: python
-
-    cudaq.set_target("braket", emulate=True)
-
-The number of shots for a kernel execution can be set through the ``shots_count``
-argument to ``cudaq.sample``. By default, the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=100)
-
-To see a complete example for using Amazon Braket backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-.. note:: 
-
-    The ``cudaq.observe`` API is not yet supported on the `braket` target.
-
-Infleqtion
-==================================
-
-.. _infleqtion-backend:
-
-Infleqtion is a quantum hardware provider of gate-based neutral atom quantum computers. Their backends may be
-accessed via `Superstaq <https://superstaq.infleqtion.com/>`__, Infleqtion’s cross-platform software API
-that performs low-level compilation and cross-layer optimization. To get started users can create a Superstaq
-account by following `these instructions <https://superstaq.readthedocs.io/en/latest/get_started/credentials.html>`__.
-
-For access to Infleqtion's neutral atom quantum computer, Sqale,
-`pre-registration <https://www.infleqtion.com/sqale-preregistration>`__ is now open.
-
-Setting Credentials
-`````````````````````````
-
-Programmers of CUDA-Q may access Infleqtion backends from either C++ or Python. Generate
-an API key from your `Superstaq account <https://superstaq.infleqtion.com/profile>`__ and export
-it as an environment variable:
-
-.. code:: bash
-
-  export SUPERSTAQ_API_KEY="superstaq_api_key"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution on Infleqtion's backends,
-pass the flag ``--target infleqtion`` to the ``nvq++`` compiler.
-
-.. code:: bash
-
-    nvq++ --target infleqtion src.cpp
-
-This will take the API key and handle all authentication with, and submission to, Infleqtion's QPU 
-(or simulator). By default, quantum kernel code will be submitted to Infleqtion's Sqale
-simulator.
-
-To execute your kernels on a QPU, pass the ``--infleqtion-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu src.cpp ...
-
-where ``cq_sqale_qpu`` is an example of a physical QPU.
-
-To run an ideal dry-run execution on the QPU, additionally pass ``dry-run`` with the ``--infleqtion-method`` 
-flag to the ``nvq++`` compiler:
-
-.. code:: bash
-
-    nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu --infleqtion-method dry-run src.cpp ...
-
-To noisily simulate the QPU instead, pass ``noise-sim`` to the ``--infleqtion-method`` flag like so:
-
-.. code:: bash
-
-    nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu --infleqtion-method noise-sim src.cpp ...
-
-Alternatively, to emulate the Infleqtion machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target infleqtion src.cpp
-
-To see a complete example for using Infleqtion's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion")
-
-By default, quantum kernel code will be submitted to Infleqtion's Sqale
-simulator.
-
-To specify which Infleqtion QPU to use, set the :code:`machine` parameter.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", machine="cq_sqale_qpu")
-
-where ``cq_sqale_qpu`` is an example of a physical QPU.
-
-To run an ideal dry-run execution of the QPU, additionally set the ``method`` flag to ``"dry-run"``.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", machine="cq_sqale_qpu", method="dry-run")
-
-To noisily simulate the QPU instead, set the ``method`` flag to ``"noise-sim"``.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", machine="cq_sqale_qpu", method="noise-sim")
-
-Alternatively, to emulate the Infleqtion machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=100)
-
-To see a complete example for using Infleqtion's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-Moreover, for an end-to-end application workflow example executed on the Infleqtion QPU, take a look at the 
-:doc:`Anderson Impurity Model ground state solver <../applications>` notebook.
-
-IonQ
-==================================
-
-.. _ionq-backend:
-
-Setting Credentials
-`````````````````````````
-
-Programmers of CUDA-Q may access the `IonQ Quantum Cloud
-<https://cloud.ionq.com/>`__ from either C++ or Python. Generate
-an API key from your `IonQ account <https://cloud.ionq.com/>`__ and export
-it as an environment variable:
-
-.. code:: bash
-
-  export IONQ_API_KEY="ionq_generated_api_key"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in the IonQ Cloud,
-pass the flag ``--target ionq`` to the ``nvq++`` compiler.
-
-.. code:: bash
-
-    nvq++ --target ionq src.cpp
-
-This will take the API key and handle all authentication with, and submission to,
-the IonQ QPU(s). By default, quantum kernel code will be submitted to the IonQ
-simulator.
-
-.. note:: 
-
-    A "target" in :code:`cudaq` refers to a quantum compute provider, such as :code:`ionq`.
-    However, IonQ's documentation uses the term "target" to refer to specific QPU's themselves.
-
-To execute your kernels on a QPU, pass the ``--ionq-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target ionq --ionq-machine qpu.aria-1 src.cpp ...
-
-where ``qpu.aria-1`` is an example of a physical QPU.
-
-A list of available QPUs can be found `in the API documentation
-<https://docs.ionq.com/#tag/jobs>`__. To see which backends are available 
-with your subscription login to your `IonQ account <https://cloud.ionq.com/jobs>`__.
-
-To emulate the IonQ machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target 
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target ionq src.cpp
-
-To see a complete example for using IonQ's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target('ionq')
-
-By default, quantum kernel code will be submitted to the IonQ
-simulator.
-
-.. note:: 
-
-    A "target" in :code:`cudaq` refers to a quantum compute provider, such as :code:`ionq`.
-    However, IonQ's documentation uses the term "target" to refer to specific QPU's themselves.
-
-To specify which IonQ QPU to use, set the :code:`qpu` parameter.
-
-.. code:: python
-
-    cudaq.set_target("ionq", qpu="qpu.aria-1")
-
-where ``qpu.aria-1`` is an example of a physical QPU.
-
-A list of available QPUs can be found `in the API documentation
-<https://docs.ionq.com/#tag/jobs>`__. To see which backends are available 
-with your subscription login to your `IonQ account <https://cloud.ionq.com/jobs>`__.
-
-To emulate the IonQ machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target 
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('ionq', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using IonQ's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-Anyon Technologies/Anyon Computing
-==================================
-
-.. _anyon-backend:
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access the Anyon API from either
-C++ or Python. Anyon requires a credential configuration file with username and password. 
-The configuration file can be generated as follows, replacing
-the ``<username>`` and ``<password>`` in the first line with your Anyon Technologies
-account details. The credential in the file will be used by CUDA-Q to login to Anyon quantum services 
-and will be updated by CUDA-Q with an obtained API token and refresh token. 
-Note, the credential line will be deleted in the updated configuration file. 
-
-.. code:: bash
-    
-    echo 'credentials: {"username":"<username>","password":"<password>"}' > $HOME/.anyon_config
-
-Users can also login and get the keys manually using the following commands:
-
-.. code:: bash
-
-    # You may need to run: `apt-get update && apt-get install curl jq`
-    curl -X POST --user "<username>:<password>"  -H "Content-Type: application/json" \
-    https://api.anyon.cloud:5000/login > credentials.json
-    id_token=`cat credentials.json | jq -r '."id_token"'`
-    refresh_token=`cat credentials.json | jq -r '."refresh_token"'`
-    echo "key: $id_token" > ~/.anyon_config
-    echo "refresh: $refresh_token" >> ~/.anyon_config
-
-The path to the configuration can be specified as an environment variable:
-
-.. code:: bash
-
-    export CUDAQ_ANYON_CREDENTIALS=$HOME/.anyon_config
-
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in the Anyon Technologies backends,
-pass the flag ``--target anyon`` to the ``nvq++`` compiler. CUDA-Q will 
-authenticate via the Anyon Technologies REST API using the credential in your configuration file.
-
-.. code:: bash
-
-    nvq++ --target anyon --<backend-type> <machine> src.cpp ...
-
-To execute your kernels using Anyon Technologies backends, pass the ``--anyon-machine`` flag to the ``nvq++`` compiler
-as the ``--<backend-type>`` to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target anyon --anyon-machine telegraph-8q src.cpp ...
-
-where ``telegraph-8q`` is an example of a physical QPU (Architecture: Telegraph, Qubit Count: 8).
-
-Currently, ``telegraph-8q`` and ``berkeley-25q`` are available for access over CUDA-Q.
-
-To emulate the Anyon Technologies machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag as the ``--<backend-type>`` to ``nvq++``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --target anyon --emulate src.cpp
-
-To see a complete example for using Anyon's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq.set_target()`` function.
-
-To execute your kernels using Anyon Technologies backends, specify which machine to submit quantum kernels to
-by setting the :code:`machine` parameter of the target. 
-If :code:`machine` is not specified, the default machine will be ``telegraph-8q``.
-
-.. code:: python
-
-    cudaq.set_target('anyon', machine='telegraph-8q')
-
-As shown above, ``telegraph-8q`` is an example of a physical QPU.
-
-To emulate the Anyon Technologies machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('anyon', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python 
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using Anyon's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-IQM
-==================================
-
-.. _iqm-backend:
-
-Support for submissions to IQM is currently under development. 
-In particular, two-qubit gates can only be performed on adjacent qubits. For more information, we refer to the respective hardware documentation.
-Support for automatically injecting the necessary operations during compilation to execute arbitrary multi-qubit gates will be added in future versions.
-
-Setting Credentials
-`````````````````````````
-
-Programmers of CUDA-Q may access the IQM Server from either C++ or Python. Following the `quick start guide <https://iqm-finland.github.io/cortex-cli/readme.html#using-cortex-cli>`__, install `iqm-cortex-cli` and login to initialize the tokens file.
-The path to the tokens file can either be passed explicitly via an environment variable or it will be loaded automatically if located in
-the default location :code:`~/.cache/iqm-cortex-cli/tokens.json`.
-
-.. code:: bash
-
-    export IQM_TOKENS_FILE="path/to/tokens.json"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution on an IQM Server,
-pass the ``--target iqm`` flag to the ``nvq++`` compiler, along with a specified ``--iqm-machine``.
-
-.. note::
-    The ``--iqm-machine`` is  a mandatory argument. This provided architecture must match
-    the device architecture that the program has been compiled against. The hardware architecture for a
-    specific IQM Server may be checked  via `https://<IQM server>/cocos/quantum-architecture`.
-
-.. code:: bash
-
-    nvq++ --target iqm --iqm-machine Adonis src.cpp
-
-Once the binary for a specific IQM QPU architecture is compiled, it can be executed against any IQM Server with the same QPU architecture:
-
-.. code:: bash
-
-    nvq++ --target iqm --iqm-machine Adonis src.cpp -o program
-    IQM_SERVER_URL="https://demo.qc.iqm.fi/cocos" ./program
-
-    # Executing the same program against an IQM Server with a different underlying QPU
-    # architecture will result in an error.
-    IQM_SERVER_URL="https://<Apollo IQM Server>/cocos" ./program
-
-To emulate the IQM machine locally, without submitting to the IQM Server,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target iqm --iqm-machine Adonis src.cpp
-
-To see a complete example for using IQM server backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target("iqm", url="https://<IQM Server>/cocos", **{"qpu-architecture": "Adonis"})
-
-To emulate the IQM Server locally, without submitting to the IQM Server,
-you can also set the ``emulate`` flag to ``True``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('iqm', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using IQM server backends, take a look at our :doc:`Python examples<../examples/examples>`.
-
-OQC
-==================================
-
-.. _oqc-backend:
-
-`Oxford Quantum Circuits <https://oxfordquantumcircuits.com/>`__ (OQC) is currently providing CUDA-Q integration for multiple Quantum Processing Unit types.
-The 8 qubit ring topology Lucy device and the 32 qubit Kagome lattice topology Toshiko device are both supported via machine options described below.
-
-Setting Credentials
-`````````````````````````
-
-In order to use the OQC devices you will need to register.
-Registration is achieved by contacting `oqc_qcaas_support@oxfordquantumcircuits.com`.
-
-Once registered you will be able to authenticate with your ``email`` and ``password``
-
-There are three environment variables that the OQC target will look for during configuration:
-
-1. ``OQC_URL``
-2. ``OQC_EMAIL``
-3. ``OQC_PASSWORD`` - is mandatory
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution on the OQC platform, provide the flag ``--target oqc`` to the ``nvq++`` compiler.
-
-Users may provide their :code:`email` and :code:`url` as extra arguments
-
-.. code:: bash
-
-    nvq++ --target oqc --oqc-email <email> --oqc-url <url> src.cpp -o executable
-
-Where both environment variables and extra arguments are supplied, precedent is given to the extra arguments.
-To run the output, provide the runtime loaded variables and invoke the pre-built executable
-
-.. code:: bash
-
-   OQC_PASSWORD=<password> ./executable
-
-To emulate the OQC device locally, without submitting through the OQC QCaaS services, you can pass the ``--emulate`` flag to ``nvq++``.
-This will emit any target specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target oqc src.cpp -o executable
-
-
-.. note::
-
-    The oqc target supports a ``--oqc-machine`` option.
-    The default is the 8 qubit Lucy device.
-    You can set this to be either ``toshiko`` or ``lucy`` via this flag.
-
-.. note::
-
-    The OQC quantum assembly toolchain (qat) which is used to compile and execute instructions can be found on github as `oqc-community/qat <https://github.com/oqc-community/qat>`__
-
-Submission from Python
-`````````````````````````
-
-To set which OQC URL, set the :code:`url` parameter.
-To set which OQC email, set the :code:`email` parameter.
-To set which OQC machine, set the :code:`machine` parameter.
-
-.. code:: python
-
-    import os
-    import cudaq
-    # ...
-    os.environ['OQC_PASSWORD'] = password
-    cudaq.set_target("oqc", url=url, machine="lucy")
-
-You can then execute a kernel against the platform using the OQC Lucy device
-
-.. code:: python
-
-    kernel = cudaq.make_kernel()
-    qvec = kernel.qalloc(2)
-    kernel.h(qvec[0])
-    kernel.x(qvec[1])
-    kernel.cx(qvec[0], qvec[1])
-    kernel.mz(qvec)
-    str(cudaq.sample(kernel=kernel, shots_count=1000))
-
-
-ORCA Computing
-==================================
-
-.. _orca-backend:
-
-ORCA Computing's PT Series implement the boson sampling model of quantum computation, in which 
-multiple single photons are interfered with each other within a network of beam splitters, and 
-photon detectors measure where the photons leave this network. This process is implemented within 
-a time-bin interferometer (TBI) architecture where photons are created in different time-bins 
-and interfered within a network of delay lines. This can be represented by a circuit diagram, 
-like the one below, where this illustration example corresponds to 4 photons in 8 modes sent into 
-alternating time-bins in a circuit composed of two delay lines in series.
-
-.. image:: ../examples/images/orca_tbi.png
-   :width: 400px
-   :align: center
-
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access the ORCA API from either C++ or Python. There is an environment 
-variable ``ORCA_ACCESS_URL`` that can be set so that the ORCA target can look for it during 
-configuration.
-
-.. code:: bash
-
-  export ORCA_ACCESS_URL="https://<ORCA API Server>"
-
-
-Sometimes the requests to the PT-1 require an authentication token. This token can be set as an
-environment variable named ``ORCA_AUTH_TOKEN``. For example, if the token is :code:`AbCdEf123456`,
-you can set the environment variable as follows:
-
-.. code:: bash
-
-  export ORCA_AUTH_TOKEN="AbCdEf123456"
-
-
-Submission from C++
-`````````````````````````
-
-To execute a boson sampling experiment on the ORCA platform, provide the flag 
-``--target orca`` to the ``nvq++`` compiler. You should then pass the ``--orca-url`` flag set with 
-the previously set environment variable ``$ORCA_ACCESS_URL`` or an :code:`url`.
-
-.. code:: bash
-
-    nvq++ --target orca --orca-url $ORCA_ACCESS_URL src.cpp -o executable
-
-or
-
-.. code:: bash
-
-    nvq++ --target orca --orca-url <url> src.cpp -o executable
-
-To run the output, invoke the executable
-
-.. code:: bash
-
-   ./executable
-
-
-To see a complete example for using ORCA server backends, take a look at our :doc:`C++ examples <../examples/hardware_providers>`.
-
-Submission from Python
-`````````````````````````
-
-To set which ORCA URL to be used, set the :code:`url` parameter.
-
-.. code:: python
-
-    import os
-    import cudaq
-    # ...
-    orca_url = os.getenv("ORCA_ACCESS_URL", "http://localhost/sample")
-
-    cudaq.set_target("orca", url=orca_url)
-
-
-You can then execute a time-bin boson sampling experiment against the platform using an ORCA device.
-
-.. code:: python
-
-    bs_angles = [np.pi / 3, np.pi / 6]
-    input_state = [1, 1, 1]
-    loop_lengths = [1]
-    counts = cudaq.orca.sample(input_state, loop_lengths, bs_angles)
-
-To see a complete example for using ORCA's backends, take a look at our :doc:`Python examples <../examples/hardware_providers>`.
-
-Quantinuum
-==================================
-
-.. _quantinuum-backend:
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access the Quantinuum API from either
-C++ or Python. Quantinuum requires a credential configuration file. 
-The configuration file can be generated as follows, replacing
-the ``email`` and ``credentials`` in the first line with your Quantinuum
-account details.
-
-.. code:: bash
-
-    # You may need to run: `apt-get update && apt-get install curl jq`
-    curl -X POST -H "Content Type: application/json" \
-        -d '{ "email":"<your_alias>@email.com","password":"<your_password>" }' \
-        https://qapi.quantinuum.com/v1/login > $HOME/credentials.json
-    id_token=`cat $HOME/credentials.json | jq -r '."id-token"'`
-    refresh_token=`cat $HOME/credentials.json | jq -r '."refresh-token"'`
-    echo "key: $id_token" >> $HOME/.quantinuum_config
-    echo "refresh: $refresh_token" >> $HOME/.quantinuum_config
-
-The path to the configuration can be specified as an environment variable:
-
-.. code:: bash
-
-    export CUDAQ_QUANTINUUM_CREDENTIALS=$HOME/.quantinuum_config
-
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in the Quantinuum backends,
-pass the flag ``--target quantinuum`` to the ``nvq++`` compiler. CUDA-Q will 
-authenticate via the Quantinuum REST API using the credential in your configuration file.
-By default, quantum kernel code will be submitted to the Quantinuum syntax checker.
-Submission to the syntax checker merely validates the program; the kernels are not executed.
-
-.. code:: bash
-
-    nvq++ --target quantinuum src.cpp ...
-
-To execute your kernels, pass the ``--quantinuum-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target quantinuum --quantinuum-machine H1-2 src.cpp ...
-
-where ``H1-2`` is an example of a physical QPU. Hardware specific
-emulators may be accessed by appending an ``E`` to the end (e.g, ``H1-2E``). For 
-access to the syntax checker for the provided machine, you may append an ``SC`` 
-to the end (e.g, ``H1-1SC``).
-
-For a comprehensive list of available machines, login to your `Quantinuum user account <https://um.qapi.quantinuum.com/user>`__ 
-and navigate to the "Account" tab, where you should find a table titled "Machines".
-
-To emulate the Quantinuum machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target quantinuum src.cpp
-
-To see a complete example for using Quantinuum's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target('quantinuum')
-
-By default, quantum kernel code will be submitted to the Quantinuum syntax checker.
-Submission to the syntax checker merely validates the program; the kernels are not executed.
-
-To execute your kernels, specify which machine to submit quantum kernels to
-by setting the :code:`machine` parameter of the target.
-
-.. code:: python
-
-    cudaq.set_target('quantinuum', machine='H1-2')
-
-where ``H1-2`` is an example of a physical QPU. Hardware specific
-emulators may be accessed by appending an ``E`` to the end (e.g, ``H1-2E``). For 
-access to the syntax checker for the provided machine, you may append an ``SC`` 
-to the end (e.g, ``H1-1SC``).
-
-For a comprehensive list of available machines, login to your `Quantinuum user account <https://um.qapi.quantinuum.com/user>`__ 
-and navigate to the "Account" tab, where you should find a table titled "Machines".
-
-To emulate the Quantinuum machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('quantinuum', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python 
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using Quantinuum's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-QuEra Computing
-==================================
-
-.. _quera-backend:
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access Aquila, QuEra's first generation of quantum
-processing unit (QPU) via Amazon Braket. Hence, users must first enable Braket by 
-following `these instructions <https://docs.aws.amazon.com/braket/latest/developerguide/braket-enable-overview.html>`__. 
-Then set credentials using any of the documented `methods <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`__.
-One of the simplest ways is to use `AWS CLI <https://aws.amazon.com/cli/>`__.
-
-.. code:: bash
-
-    aws configure
-
-Alternatively, users can set the following environment variables.
-
-.. code:: bash
-
-  export AWS_DEFAULT_REGION="us-east-1"
-  export AWS_ACCESS_KEY_ID="<key_id>"
-  export AWS_SECRET_ACCESS_KEY="<access_key>"
-  export AWS_SESSION_TOKEN="<token>"
-
-
-Submission from C++
-`````````````````````````
-
-Not yet supported.
-
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target('quera')
-
-By default, analog Hamiltonian will be submitted to the Aquila system.
-
-Aquila is a "field programmable qubit array" operated as an analog 
-Hamiltonian simulator on a user-configurable architecture, executing 
-programmable coherent quantum dynamics on up to 256 neutral-atom qubits.
-Refer to QuEra's `whitepaper <https://cdn.prod.website-files.com/643b94c382e84463a9e52264/648f5bf4d19795aaf36204f7_Whitepaper%20June%2023.pdf>`__ for details.
-
-Due to the nature of the underlying hardware, this target only supports the 
-``evolve`` and ``evolve_async`` APIs.
-The `hamiltonian` must be an `Operator` of the type `RydbergHamiltonian`. Only 
-other parameters supported are `schedule` (mandatory) and `shots_count` (optional).
-
-For example,
-
-.. code:: python
-
-    evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
-                                                 amplitude=omega,
-                                                 phase=phi,
-                                                 delta_global=delta),
-                               schedule=schedule)
-
-The number of shots for a kernel execution can be set through the ``shots_count``
-argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is 
-set to 100.
-
-.. code:: python 
-
-    cudaq.evolve(RydbergHamiltonian(...), schedule=s, shots_count=1000)
-
-To see a complete example for using QuEra's backend, take a look at our :doc:`Python examples <../examples/hardware_providers>`.
diff --git a/pr-2308/_sources/using/backends/nvqc.rst.txt b/pr-2308/_sources/using/backends/nvqc.rst.txt
deleted file mode 100644
index 7586d08719..0000000000
--- a/pr-2308/_sources/using/backends/nvqc.rst.txt
+++ /dev/null
@@ -1,256 +0,0 @@
-NVIDIA Quantum Cloud
----------------------
-NVIDIA Quantum Cloud (NVQC) offers universal access to the world’s most powerful computing platform, 
-for every quantum researcher to do their life’s work.
-To learn more about NVQC visit this `link <https://www.nvidia.com/en-us/solutions/quantum-computing/cloud>`__. 
-
-Apply for early access `here <https://developer.nvidia.com/quantum-cloud-early-access-join>`__. 
-Access to the Quantum Cloud early access program requires an NVIDIA Developer account.
-
-Quick Start
-+++++++++++
-Once you have been approved for an early access to NVQC, you will be able to follow these instructions to use it.
-
-1. Follow the instructions in your NVQC Early Access welcome email to obtain an API Key for NVQC. 
-You can also find the instructions `here <https://developer.nvidia.com/quantum-cloud-early-access-members>`__ (link available only for approved users)
-
-2. Set the environment variable `NVQC_API_KEY` to the API Key obtained above.
-
- .. code-block:: console
-
-    export NVQC_API_KEY=<your NVQC API key>
-
-You may wish to persist that environment variable between bash sessions, e.g., by adding it to your `$HOME/.bashrc` file.
-
-3. Run your first NVQC example
-
-The following is a typical CUDA-Q kernel example. 
-By selecting the `nvqc` target, the quantum circuit simulation will run on NVQC in the cloud, rather than running locally.
-
-
-.. tab:: Python
-    
-    .. literalinclude:: ../../snippets/python/using/cudaq/nvqc/nvqc_intro.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-    .. code-block:: console
-        
-        [2024-03-14 19:26:31.438] Submitting jobs to NVQC service with 1 GPU(s). Max execution time: 3600 seconds (excluding queue wait time).
-
-        ================ NVQC Device Info ================
-        GPU Device Name: "NVIDIA H100 80GB HBM3"
-        CUDA Driver Version / Runtime Version: 12.2 / 12.0
-        Total global memory (GB): 79.1
-        Memory Clock Rate (MHz): 2619.000
-        GPU Clock Rate (MHz): 1980.000
-        ==================================================
-        { 1111111111111111111111111:486 0000000000000000000000000:514 }
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/nvqc/nvqc_intro.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-
-    The code above is saved in `nvqc_intro.cpp` and compiled with the following command, targeting the :code:`nvqc` platform
-
-    .. code-block:: console
-
-        nvq++ nvqc_intro.cpp -o nvqc_intro.x --target nvqc 
-        ./nvqc_intro.x
-
-        [2024-03-14 19:25:05.545] Submitting jobs to NVQC service with 1 GPU(s). Max execution time: 3600 seconds (excluding queue wait time).
-
-        ================ NVQC Device Info ================
-        GPU Device Name: "NVIDIA H100 80GB HBM3"
-        CUDA Driver Version / Runtime Version: 12.2 / 12.0
-        Total global memory (GB): 79.1
-        Memory Clock Rate (MHz): 2619.000
-        GPU Clock Rate (MHz): 1980.000
-        ==================================================
-        { 
-        __global__ : { 1111111111111111111111111:487 0000000000000000000000000:513 }
-        result : { 1111111111111111111111111:487 0000000000000000000000000:513 }
-        }
-
-
-Simulator Backend Selection
-++++++++++++++++++++++++++++
-
-NVQC hosts all CUDA-Q simulator backends (see :doc:`simulators`). 
-You may use the NVQC `backend` (Python) or `--nvqc-backend` (C++) option to select the simulator to be used by the service.
-
-For example, to request the `tensornet` simulator backend, the user can do the following for C++ or Python.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        cudaq.set_target("nvqc", backend="tensornet")
-
-.. tab:: C++
-    
-    .. code-block:: console
-
-        nvq++ nvqc_sample.cpp -o nvqc_sample.x --target nvqc --nvqc-backend tensornet
-
-
-.. note::
-
-  By default, the single-GPU single-precision `custatevec-fp32` simulator backend will be selected if backend information is not specified.
-
-Multiple GPUs
-+++++++++++++
-
-Some CUDA-Q simulator backends are capable of multi-GPU distribution as detailed in :doc:`simulators`.
-For example, the `nvidia-mgpu` backend can partition and distribute state vector simulation to multiple GPUs to simulate 
-a larger number of qubits, whose state vector size grows beyond the memory size of a single GPU.
-
-To select a specific number of GPUs on the NVQC managed service, the following `ngpus` (Python) or `--nvqc-ngpus` (C++) option can be used.
-
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        cudaq.set_target("nvqc", backend="nvidia-mgpu", ngpus=4)
-
-.. tab:: C++
-
-    .. code-block:: console
-
-        nvq++ nvqc_sample.cpp -o nvqc_sample.x --target nvqc --nvqc-backend nvidia-mgpu --nvqc-ngpus 4
-
-
-.. note::
-
-    If your NVQC subscription does not contain service instances that have the specified number of GPUs, 
-    you may encounter the following error.
-
-    .. code-block:: console
-        
-        Unable to find NVQC deployment with 16 GPUs.
-        Available deployments have {1, 2, 4, 8} GPUs.
-        Please check your `ngpus` value (Python) or `--nvqc-ngpus` value (C++).
-
-.. note::
-
-    Not all simulator backends are capable of utilizing multiple GPUs. 
-    When requesting a multiple-GPU service with a single-GPU simulator backend, 
-    you might encounter the following log message:
-
-    .. code-block:: console
-        
-        The requested backend simulator (custatevec-fp32) is not capable of using all 4 GPUs requested.
-        Only one GPU will be used for simulation.
-        Please refer to CUDA-Q documentation for a list of multi-GPU capable simulator backends.
-
-    Consider removing the `ngpus` value (Python) or `--nvqc-ngpus` value (C++) to use the default of 1 GPU 
-    if you don't need to use a multi-GPU backend to better utilize NVQC resources.
-
-    Please refer to the table below for a list of backend simulator names along with its multi-GPU capability.
-
-    .. list-table:: Simulator Backends
-        :widths: 20 50 10 10
-        :header-rows: 1
-
-        *   - Name
-            - Description
-            - GPU Accelerated 
-            - Multi-GPU 
-        *   - `qpp`
-            - CPU-only state vector simulator
-            - no
-            - no
-        *   - `dm`
-            - CPU-only density matrix simulator
-            - no
-            - no
-        *   - `custatevec-fp32`
-            - Single-precision `cuStateVec` simulator
-            - yes
-            - no
-        *   - `custatevec-fp64`
-            - Double-precision `cuStateVec` simulator
-            - yes
-            - no
-        *   - `tensornet`
-            - Double-precision `cuTensorNet` full tensor network contraction simulator
-            - yes
-            - yes
-        *   - `tensornet-mps`
-            - Double-precision `cuTensorNet` matrix-product state simulator
-            - yes
-            - no
-        *   - `nvidia-mgpu`
-            - Double-precision `cuStateVec` multi-GPU simulator
-            - yes
-            - yes
-    
-
-Multiple QPUs Asynchronous Execution
-+++++++++++++++++++++++++++++++++++++
-
-NVQC provides scalable QPU virtualization services, whereby clients
-can submit asynchronous jobs simultaneously to NVQC. These jobs are
-handled by a pool of service worker instances.
-
-For example, in the following code snippet, using the `nqpus` (Python) or `--nvqc-nqpus` (C++) configuration option,
-the user instantiates 3 virtual QPU instances to submit simulation jobs to NVQC
-calculating the expectation value along with parameter-shift gradients simultaneously.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/nvqc/nvqc_mqpu.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/nvqc/nvqc_mqpu.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-
-    The code above is saved in `nvqc_vqe.cpp` and compiled with the following command, targeting the :code:`nvqc` platform with 3 virtual QPUs.
-
-    .. code-block:: console
-
-        nvq++ nvqc_vqe.cpp -o nvqc_vqe.x --target nvqc --nvqc-nqpus 3 
-        ./nvqc_vqe.x
-
-
-.. note::
-
-    The NVQC managed-service has a pool of worker instances processing incoming requests on a 
-    first-come-first-serve basis. Thus, the attainable speedup using multiple virtual QPUs vs. 
-    sequential execution on a single QPU depends on the NVQC service load. For example, 
-    if the number of free workers is greater than the number of requested virtual QPUs, a linear
-    (ideal) speedup could be achieved. On the other hand, if all the service workers are busy, 
-    multi-QPU distribution may not deliver any substantial speedup.  
-
-FAQ
-++++
-
-1. How do I get more information about my NVQC API submission?
-
-The environment variable `NVQC_LOG_LEVEL` can be used to turn on and off
-certain logs. There are three levels:
-
-- Info (`info`): basic information about NVQC is logged to the console. This is the default.
-
-- Off (`off` or `0`): disable all NVQC logging.
-
-- Trace: (`trace`): log additional information for each NVQC job execution (including timing)
-
-2. I want to persist my API key to a configuration file.
-
-You may persist your NVQC API Key to a credential configuration file in lieu of 
-using the `NVQC_API_KEY` environment variable. 
-The configuration file can be generated as follows, replacing
-the `api_key` value with your NVQC API Key.
-
-.. code:: bash
-
-    echo "key: <api_key>" >> $HOME/.nvqc_config
-
diff --git a/pr-2308/_sources/using/backends/platform.rst.txt b/pr-2308/_sources/using/backends/platform.rst.txt
deleted file mode 100644
index da019f3f65..0000000000
--- a/pr-2308/_sources/using/backends/platform.rst.txt
+++ /dev/null
@@ -1,341 +0,0 @@
-Multi-Processor Platforms
----------------------------------------------------
-The CUDA-Q machine model elucidates the various devices considered in the 
-broader quantum-classical compute node context. Programmers will have one or many 
-host CPUs, zero or many NVIDIA GPUs, a classical QPU control space, and the
-quantum register itself. Moreover, the :doc:`specification </specification/cudaq/platform>`
-notes that the underlying platform may expose multiple QPUs. In the near-term,
-this will be unlikely with physical QPU instantiations, but the availability of
-GPU-based circuit simulators on NVIDIA multi-GPU architectures does give one an
-opportunity to think about programming such a multi-QPU architecture in the near-term.
-CUDA-Q starts by enabling one to query information about the underlying quantum
-platform via the :code:`quantum_platform` abstraction. This type exposes a
-:code:`num_qpus()` method that can be used to query the number of available
-QPUs for asynchronous CUDA-Q kernel and :code:`cudaq::` function invocations.
-Each available QPU is assigned a logical index, and programmers can launch
-specific asynchronous function invocations targeting a desired QPU.
-
-.. _mqpu-platform:
-
-NVIDIA `MQPU` Platform
-++++++++++++++++++++++
-
-In the multi-QPU mode (:code:`mqpu` option), the NVIDIA target provides a simulated QPU for every available NVIDIA GPU on the underlying system. 
-Each QPU is simulated via a `cuStateVec` simulator backend as defined by the NVIDIA target. For more information about using multiple GPUs 
-to simulate each virtual QPU, or using a different backend for virtual QPUs, please see :ref:`remote MQPU platform <remote-mqpu-platform>`.
-This target enables asynchronous parallel execution of quantum kernel tasks.
-
-Here is a simple example demonstrating its usage.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/sample_async.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/sample_async.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-
-    One can specify the target multi-QPU architecture with the :code:`--target` flag:
-    
-    .. code-block:: console
-
-        nvq++ sample_async.cpp --target nvidia --target-option mqpu
-        ./a.out
-
-CUDA-Q exposes asynchronous versions of the default :code:`cudaq` algorithmic
-primitive functions like :code:`sample`, :code:`observe`, and :code:`get_state` 
-(e.g., :code:`sample_async` function in the above code snippets).
-
-Depending on the number of GPUs available on the system, the :code:`nvidia` multi-QPU platform will create the same number of virtual QPU instances.
-For example, on a system with 4 GPUs, the above code will distribute the four sampling tasks among those :code:`GPUEmulatedQPU` instances.
-
-The results might look like the following 4 different random samplings:
-
-.. code-block:: console
-  
-    Number of QPUs: 4
-    { 10011:28 01100:28 ... }
-    { 10011:37 01100:25 ... }
-    { 10011:29 01100:25 ... }
-    { 10011:33 01100:30 ... }
-
-.. note::
-
-  By default, the :code:`nvidia` multi-QPU platform will utilize all available GPUs (number of QPUs instances is equal to the number of GPUs).
-  To specify the number QPUs to be instantiated, one can set the :code:`CUDAQ_MQPU_NGPUS` environment variable.
-  For example, use :code:`export CUDAQ_MQPU_NGPUS=2` to specify that only 2 QPUs (GPUs) are needed.
-
-Since the underlying :code:`GPUEmulatedQPU` is a simulator backend, we can also retrieve the state vector from each
-QPU via the :code:`cudaq::get_state_async` (C++) or :code:`cudaq.get_state_async` (Python) as shown in the bellow code snippets.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/get_state_async.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/get_state_async.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-
-    One can specify the target multi-QPU architecture with the :code:`--target` flag:
-    
-    .. code-block:: console
-
-        nvq++ get_state_async.cpp --target nvidia --target-option mqpu
-        ./a.out
-
-.. deprecated:: 0.8
-    The :code:`nvidia-mqpu` and :code:`nvidia-mqpu-fp64` targets, which are equivalent to the multi-QPU options `mqpu,fp32` and `mqpu,fp64`, respectively, of the :code:`nvidia` target, are deprecated and will be removed in a future release.
-
-Parallel distribution mode
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The CUDA-Q :code:`nvidia` multi-QPU platform supports two modes of parallel distribution of expectation value computation:
-
-* MPI: distribute the expectation value computations across available MPI ranks and GPUs for each Hamiltonian term.
-* Thread: distribute the expectation value computations among available GPUs via standard C++ threads (each thread handles one GPU).
-
-For instance, if all GPUs are available on a single node, thread-based parallel distribution 
-(:code:`cudaq::parallel::thread` in C++ or :code:`cudaq.parallel.thread` in Python, as shown in the above example) is sufficient.
-On the other hand, if one wants to distribute the tasks across GPUs on multiple nodes, e.g., on a compute cluster, MPI distribution mode
-should be used.
-
-An example of MPI distribution mode usage in both C++ and Python is given below:
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/observe_mqpu_mpi.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-    .. code-block:: console
-
-        mpiexec -np <N> python3 file.py
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/observe_mqpu_mpi.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-    .. code-block:: console
-
-        nvq++ file.cpp --target nvidia --target-option mqpu
-        mpiexec -np <N> a.out
-
-In the above example, the parallel distribution mode was set to :code:`mpi` using :code:`cudaq::parallel::mpi` in C++ or :code:`cudaq.parallel.mpi` in Python.
-CUDA-Q provides MPI utility functions to initialize, finalize, or query (rank, size, etc.) the MPI runtime. 
-Last but not least, the compiled executable (C++) or Python script needs to be launched with an appropriate MPI command, 
-e.g., :code:`mpiexec`, :code:`mpirun`, :code:`srun`, etc.
-
-Remote `MQPU` Platform
-+++++++++++++++++++++++++++
-
-.. _remote-mqpu-platform:
-
-As shown in the above examples, the multi-QPU NVIDIA platform enables
-multi-QPU distribution whereby each QPU is simulated by a :ref:`single NVIDIA GPU <cuQuantum single-GPU>`.
-To run multi-QPU workloads on different simulator backends, one can use the :code:`remote-mqpu` platform,
-which encapsulates simulated QPUs as independent HTTP REST server instances. 
-The following code illustrates how to launch asynchronous sampling tasks on multiple virtual QPUs, 
-each simulated by a `tensornet` simulator backend.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/sample_async_remote.py
-        :language: python
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/sample_async_remote.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-    The code above is saved in `sample_async.cpp` and compiled with the following command, targeting the :code:`remote-mqpu` platform:
-
-    .. code-block:: console
-
-        nvq++ sample_async.cpp -o sample_async.x --target remote-mqpu --remote-mqpu-backend tensornet --remote-mqpu-auto-launch 2
-        ./sample_async.x
-
-In the above code snippets, the :code:`remote-mqpu` platform was used in the auto-launch mode,
-whereby a specific number of server instances, i.e., virtual QPUs, are launched on the local machine
-in the background. The remote QPU daemon service, :code:`cudaq-qpud`, will also be shut down automatically
-at the end of the session.
-
-.. note:: 
-    By default, auto launching daemon services do not support MPI parallelism.
-    Hence, using the `nvidia-mgpu` backend to simulate each virtual QPU requires 
-    manually launching each server instance. How to do that is explained in the rest of this section.
-
-.. _custom_remote_qpud_launch:
-
-To customize how many and which GPUs are used for simulating each virtual QPU, one can launch each server manually.
-For instance, on a machine with 8 NVIDIA GPUs, one may wish to partition those GPUs into
-4 virtual QPU instances, each manages 2 GPUs. To do so, first launch a :code:`cudaq-qpud` server for each virtual QPU:
-
-.. tab:: Python
-
-     .. See scripts/validate_wheel.sh for examples of how similar commands are run automatically during release validation.
-
-     .. code-block:: bash
-         
-         # Use cudaq-qpud.py wrapper script to automatically find dependencies for the Python wheel configuration.
-         cudaq_location=`python3 -m pip show cudaq | grep -e 'Location: .*$'`
-         qpud_py="${cudaq_location#Location: }/bin/cudaq-qpud.py"
-         CUDA_VISIBLE_DEVICES=0,1 mpiexec -np 2 python3 "$qpud_py" --port <QPU 1 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=2,3 mpiexec -np 2 python3 "$qpud_py" --port <QPU 2 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=4,5 mpiexec -np 2 python3 "$qpud_py" --port <QPU 3 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=6,7 mpiexec -np 2 python3 "$qpud_py" --port <QPU 4 TCP/IP port number>
-
-.. tab:: C++
-     
-     .. code-block:: bash
-         
-         # It is assumed that your $LD_LIBRARY_PATH is able to find all the necessary dependencies.
-         CUDA_VISIBLE_DEVICES=0,1 mpiexec -np 2 cudaq-qpud --port <QPU 1 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=2,3 mpiexec -np 2 cudaq-qpud --port <QPU 2 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=4,5 mpiexec -np 2 cudaq-qpud --port <QPU 3 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=6,7 mpiexec -np 2 cudaq-qpud --port <QPU 4 TCP/IP port number>
-
-
-In the above code snippet, four :code:`nvidia-mgpu` daemons are started in MPI context via the :code:`mpiexec` launcher.
-This activates MPI runtime environment required by the :code:`nvidia-mgpu` backend. Each QPU daemon is assigned a unique 
-TCP/IP port number via the :code:`--port` command-line option. The :code:`CUDA_VISIBLE_DEVICES` environment variable restricts the GPU devices 
-that each QPU daemon sees so that it targets specific GPUs. 
-
-With these invocations, each virtual QPU is locally addressable at the URL `localhost:<port>`. 
-
-.. warning:: 
-
-    There is no authentication required to communicate with this server app. 
-    Hence, please make sure to either (1) use a non-public TCP/IP port for internal use or 
-    (2) use firewalls or other security mechanisms to manage user access. 
-
-User code can then target these QPUs for multi-QPU workloads, such as asynchronous sample or observe shown above for the multi-QPU NVIDIA platform platform.
-
-.. tab:: Python
-
-     .. code:: python 
-
-        cudaq.set_target("remote-mqpu", url="localhost:<port1>,localhost:<port2>,localhost:<port3>,localhost:<port4>", backend="nvidia-mgpu")
-        
-.. tab:: C++
-
-    .. code-block:: console
-
-        nvq++ distributed.cpp --target remote-mqpu --remote-mqpu-url localhost:<port1>,localhost:<port2>,localhost:<port3>,localhost:<port4> --remote-mqpu-backend nvidia-mgpu
-    
-
-Each URL is treated as an independent QPU, hence the number of QPUs (:code:`num_qpus()`) is equal to the number of URLs provided. 
-The multi-node multi-GPU simulator backend (:code:`nvidia-mgpu`) is requested via the :code:`--remote-mqpu-backend` command-line option.
-
-.. note:: 
-
-    The requested backend (:code:`nvidia-mgpu`) will be executed inside the context of the QPU daemon service, thus 
-    inherits its GPU resource allocation (two GPUs per backend simulator instance). 
-
-Supported Kernel Arguments
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The platform serializes kernel invocation to QPU daemons via REST APIs. 
-Please refer to the `Open API Docs <../../openapi.html>`_  for the latest API information.
-Runtime arguments are serialized into a flat memory buffer (`args` field of the request JSON). 
-For more information about argument type serialization, please see :ref:`the table below <type_serialization_table>`.
-
-When using a remote backend to simulate each virtual QPU, 
-by default, we currently do not support passing complex data structures, 
-such as nested vectors or class objects, or other kernels as arguments to the entry point kernels.
-These type limitations only apply to the **entry-point** kernel and not when passing arguments
-to other quantum kernels.
-
-Support for the full range of argument types within CUDA-Q can be enabled by compiling the 
-code with the :code:`--enable-mlir` option. This flag forces quantum kernels to be compiled with 
-the CUDA-Q MLIR-based compiler. As a result, runtime arguments can be resolved by the CUDA 
-Quantum compiler infrastructure to support wider range of argument types. However, certain
-language constructs within quantum kernels may not yet be fully supported.
-
-.. _type_serialization_table:
-
-.. list-table:: Kernel argument serialization
-   :widths: 50 50 50
-   :header-rows: 1
-
-   * - Data type
-     - Example
-     - Serialization
-   * -  Trivial type (occupies a contiguous memory area)
-     -  `int`, `std::size_t`, `double`, etc.
-     - Byte data (via `memcpy`)
-   * - `std::vector` of trivial type
-     - `std::vector<int>`, `std::vector<double>`, etc. 
-     - Total vector size in bytes as a 64-bit integer followed by serialized data of all vector elements.
-   * - `cudaq::pauli_word`
-     - `cudaq::pauli_word("IXIZ")`
-     - Same as `std::vector<char>`: total vector size in bytes as a 64-bit integer followed by serialized data of all characters.
-   * - Single-level nested `std::vector` of supported `std::vector` types
-     - `std::vector<std::vector<int>>`, `std::vector<cudaq::pauli_word>`, etc. 
-     - Number of top-level elements (as a 64-bit integer) followed sizes in bytes of element vectors (as a contiguous array of 64-bit integers) then serialized data of the inner vectors.
-     
-For CUDA-Q kernels that return a value, the remote platform supports returning simple data types of 
-`bool`, integral (e.g., `int` or `std::size_t`), and floating-point types (`float` or `double`) 
-when MLIR-based compilation is enabled (:code:`--enable-mlir`).
-
-Accessing Simulated Quantum State
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The remote `MQPU` platform supports accessing simulator backend's state vector via the 
-`cudaq::get_state` (C++) or `cudaq.get_state` (Python) APIs, similar to local simulator backends.
-
-State data can be retrieved as a full state vector or as individual basis states' amplitudes.
-The later is designed for large quantum states, which incurred data transfer overheads.
-
-.. tab:: Python
-
-    .. code:: python 
-        
-        state = cudaq.get_state(kernel)
-        amplitudes = state.amplitudes(['0000', '1111'])
-        
-.. tab:: C++
-
-    .. code-block:: cpp
-        
-        auto state = cudaq::get_state(kernel)
-        auto amplitudes = state.amplitudes({{0, 0, 0, 0}, {1, 1, 1, 1}});
-
-In the above example, the amplitudes of the two requested states are returned.
-
-For C++ quantum kernels [*]_ compiled with the CUDA-Q MLIR-based compiler and Python kernels,
-state accessor is evaluated in a just-in-time/on-demand manner, and hence can be customize to
-users' need.
-
-For instance, in the above amplitude access example, if the state vector is very large, e.g.,
-multi-GPU distributed state vectors or tensor-network encoded quantum states, the full state vector
-will not be retrieved when `get_state` is called. Instead, when the `amplitudes` accessor is called,
-a specific amplitude calculation request will be sent to the server. 
-Thus, only the amplitudes of those basis states will be computed and returned. 
-
-Similarly, for state overlap calculation, if deferred state evaluation is available (Python/MLIR-based compiler)
-for both of the operand quantum states, a custom overlap calculation request will be constructed and sent to the server.
-Only the final overlap result will be returned, thereby eliminating back-and-forth state data transfers. 
-
-.. [*] Only C++ quantum kernels whose names are available via run-time type information (RTTI) are supported.
-    For example, quantum kernels expressed as named `struct` are supported but not standalone functions.
-    Kernels that do not have deferred state evaluation support will perform synchronous `get_state`, whereby the full state
-    vector is returned from the server immediately.   
diff --git a/pr-2308/_sources/using/backends/simulators.rst.txt b/pr-2308/_sources/using/backends/simulators.rst.txt
deleted file mode 100644
index a3dba35d92..0000000000
--- a/pr-2308/_sources/using/backends/simulators.rst.txt
+++ /dev/null
@@ -1,615 +0,0 @@
-CUDA-Q Simulation Backends
-*********************************
-
-.. _nvidia-backend:
-
-The simulation backends that are currently available in CUDA-Q are as follows.
-
-State Vector Simulators
-==================================
-
-The :code:`nvidia` target provides a state vector simulator accelerated with 
-the :code:`cuStateVec` library. 
-
-The :code:`nvidia` target supports multiple configurable options.
-
-Features 
-+++++++++
-
-* Floating-point precision configuration 
-
-The floating point precision of the state vector data can be configured to either 
-double (`fp64`) or single (`fp32`) precision. This option can be chosen for the optimal performance and accuracy.
-
-
-* Distributed simulation
-
-The :code:`nvidia` target supports distributing state vector simulations to multiple GPUs and multiple nodes (`mgpu` :ref:`distribution <nvidia-mgpu-backend>`)
-and multi-QPU (`mqpu` :ref:`platform <mqpu-platform>`) distribution whereby each QPU is simulated via a single-GPU simulator instance.
-
-
-* Host CPU memory utilization 
-
-Host CPU memory can be leveraged in addition to GPU memory to accommodate the state vector 
-(i.e., maximizing the number of qubits to be simulated).
-
-.. _cuQuantum single-GPU:
-
-
-Single-GPU 
-++++++++++++++++++++++++++++++++++
-
-To execute a program on the :code:`nvidia` target, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target nvidia
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('nvidia')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target nvidia program.cpp [...] -o program.x
-        ./program.x
-
-.. _nvidia-fp64-backend:
-
-By default, this will leverage :code:`FP32` floating point types for the simulation. To 
-switch to :code:`FP64`, specify the :code:`--target-option fp64` `nvq++` command line option for `C++` and `Python` or 
-use `cudaq.set_target('nvidia', option='fp64')` for Python in-source target modification instead. 
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target nvidia --target-option fp64
-
-    The precision of the :code:`nvidia` target can also be modified in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('nvidia', option='fp64')
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target nvidia --target-option fp64 program.cpp [...] -o program.x
-        ./program.x
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU and CUDA runtime libraries. If you do not have these dependencies installed, you may encounter an error stating `Invalid simulator requested`. See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-
-In the single-GPU mode, the :code:`nvidia` target provides the following
-environment variable options. Any environment variables must be set prior to
-setting the target.
-
-.. list-table:: **Environment variable options supported in single-GPU mode**
-  :widths: 20 30 50
-
-  * - Option
-    - Value
-    - Description
-  * - ``CUDAQ_FUSION_MAX_QUBITS``
-    - positive integer
-    - The max number of qubits used for gate fusion. The default value is `4`.
-  * - ``CUDAQ_FUSION_DIAGONAL_GATE_MAX_QUBITS``
-    - integer greater than or equal to -1
-    - The max number of qubits used for diagonal gate fusion. The default value is set to `-1` and the fusion size will be automatically adjusted for the better performance. If 0, the gate fusion for diagonal gates is disabled.
-  * - ``CUDAQ_FUSION_NUM_HOST_THREADS``
-    - positive integer
-    - Number of CPU threads used for circuit processing. The default value is `8`.
-  * - ``CUDAQ_MAX_CPU_MEMORY_GB``
-    - non-negative integer, or `NONE`
-    - CPU memory size (in GB) allowed for state-vector migration. `NONE` means unlimited (up to physical memory constraints). Default is 0GB (disabled, variable is not set to any value).
-  * - ``CUDAQ_MAX_GPU_MEMORY_GB``
-    - positive integer, or `NONE`
-    - GPU memory (in GB) allowed for on-device state-vector allocation. As the state-vector size exceeds this limit, host memory will be utilized for migration. `NONE` means unlimited (up to physical memory constraints). This is the default.
-
-.. deprecated:: 0.8
-    The :code:`nvidia-fp64` targets, which is equivalent setting the `fp64` option on the :code:`nvidia` target, 
-    is deprecated and will be removed in a future release.
-
-.. _nvidia-mgpu-backend:
-
-Multi-node multi-GPU
-++++++++++++++++++++++++++++++++++
-
-The NVIDIA target also provides a state vector simulator accelerated with 
-the :code:`cuStateVec` library with support for Multi-Node, Multi-GPU distribution of the 
-state vector, in addition to a single GPU.
-
-The multi-node multi-GPU simulator expects to run within an MPI context.
-To execute a program on the multi-node multi-GPU NVIDIA target, use the following commands 
-(adjust the value of the :code:`-np` flag as needed to reflect available GPU resources on your system):
-
-.. tab:: Python
-
-    Double precision simulation:
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 program.py [...] --target nvidia --target-option fp64,mgpu
-
-    Single precision simulation:
-    
-    .. code:: bash 
-
-        mpiexec -np 2 python3 program.py [...] --target nvidia --target-option fp32,mgpu
-
-    .. note::
-
-      If you installed CUDA-Q via :code:`pip`, you will need to install the necessary MPI dependencies separately;
-      please follow the instructions for installing dependencies in the `Project Description <https://pypi.org/project/cuda-quantum/#description>`__.
-
-    In addition to using MPI in the simulator, you can use it in your application code by installing `mpi4py <https://mpi4py.readthedocs.io/>`__, and 
-    invoking the program with the command
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 -m mpi4py program.py [...] --target nvidia --target-option fp64,mgpu
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('nvidia', option='mgpu,fp64')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-    .. note::
-        
-        * The order of the option settings are interchangeable.
-          For example, `cudaq.set_target('nvidia', option='mgpu,fp64')` is equivalent to `cudaq.set_target('nvidia', option='fp64,mgpu')`.
-
-        * The `nvidia` target has single-precision as the default setting. Thus, using `option='mgpu'` implies that `option='mgpu,fp32'`.  
-
-.. tab:: C++
-
-    Double precision simulation:
-
-    .. code:: bash 
-
-        nvq++ --target nvidia  --target-option mgpu,fp64 program.cpp [...] -o program.x
-        mpiexec -np 2 ./program.x
-
-    Single precision simulation:
-
-    .. code:: bash 
-
-        nvq++ --target nvidia  --target-option mgpu,fp32 program.cpp [...] -o program.x
-        mpiexec -np 2 ./program.x
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU, CUDA runtime libraries, as well as an MPI installation. If you do not have these dependencies installed, you may encounter either an error stating `invalid simulator requested` (missing CUDA libraries), or an error along the lines of `failed to launch kernel` (missing MPI installation). See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-  
-  The number of processes and nodes should be always power-of-2. 
-
-  Host-device state vector migration is also supported in the multi-node multi-GPU configuration. 
-
-
-In addition to those environment variable options supported in the single-GPU mode,
-the :code:`nvidia` target provides the following environment variable options particularly for 
-the multi-node multi-GPU configuration. Any environment variables must be set
-prior to setting the target.
-
-.. list-table:: **Additional environment variable options for multi-node multi-GPU mode**
-  :widths: 20 30 50
-
-  * - Option
-    - Value
-    - Description
-  * - ``CUDAQ_MGPU_LIB_MPI``
-    - string
-    - The shared library name for inter-process communication. The default value is `libmpi.so`.
-  * - ``CUDAQ_MGPU_COMM_PLUGIN_TYPE``
-    - `AUTO`, `EXTERNAL`, `OpenMPI`, or `MPICH` 
-    - Selecting :code:`cuStateVec` `CommPlugin` for inter-process communication. The default is `AUTO`. If `EXTERNAL` is selected, `CUDAQ_MGPU_LIB_MPI` should point to an implementation of :code:`cuStateVec` `CommPlugin` interface.
-  * - ``CUDAQ_MGPU_NQUBITS_THRESH``
-    - positive integer
-    - The qubit count threshold where state vector distribution is activated. Below this threshold, simulation is performed as independent (non-distributed) tasks across all MPI processes for optimal performance. Default is 25. 
-  * - ``CUDAQ_MGPU_FUSE``
-    - positive integer
-    - The max number of qubits used for gate fusion. The default value is `6` if there are more than one MPI processes or `4` otherwise.
-  * - ``CUDAQ_MGPU_P2P_DEVICE_BITS``
-    - positive integer
-    - Specify the number of GPUs that can communicate by using GPUDirect P2P. Default value is 0 (P2P communication is disabled).
-  * - ``CUDAQ_GPU_FABRIC``
-    - `MNNVL`, `NVL`, or `NONE`
-    - Automatically set the number of P2P device bits based on the total number of processes when multi-node NVLink (`MNNVL`) is selected; or the number of processes per node when NVLink (`NVL`) is selected; or disable P2P (with `NONE`). 
-  * - ``CUDAQ_GLOBAL_INDEX_BITS``
-    - comma-separated list of positive integers
-    - Specify the inter-node network structure (faster to slower). For example, assuming a 8 nodes, 4 GPUs/node simulation whereby network communication is faster, this `CUDAQ_GLOBAL_INDEX_BITS` environment variable can be set to `3,2`. The first `3` represents **8** nodes with fast communication and the second `2` represents **4** 8-node groups in those total 32 nodes. Default is an empty list (no customization based on network structure of the cluster).
-  * - ``CUDAQ_HOST_DEVICE_MIGRATION_LEVEL``
-    - positive integer
-    - Specify host-device memory migration w.r.t. the network structure. If provided, this setting determines the position to insert the number of migration index bits to the `CUDAQ_GLOBAL_INDEX_BITS` list. By default, if not set, the number of migration index bits (CPU-GPU data transfers) is appended to the end of the array of index bits (aka, state vector distribution scheme). This default behavior is optimized for systems with fast GPU-GPU interconnects (NVLink, InfiniBand, etc.) 
-
-.. deprecated:: 0.8
-    The :code:`nvidia-mgpu` target, which is equivalent to the multi-node multi-GPU double-precision option (`mgpu,fp64`) of the :code:`nvidia`
-    is deprecated and will be removed in a future release.
-
-The above configuration options of the :code:`nvidia` backend 
-can be tuned to reduce your simulation runtimes. One of the
-performance improvements is to fuse multiple gates together during runtime. For
-example, :code:`x(qubit0)` and :code:`x(qubit1)` can be fused together into a
-single 4x4 matrix operation on the state vector rather than 2 separate 2x2
-matrix operations on the state vector. This fusion reduces memory bandwidth on
-the GPU because the state vector is transferred into and out of memory fewer
-times. By default, up to 4 gates are fused together for single-GPU simulations,
-and up to 6 gates are fused together for multi-GPU simulations. The number of
-gates fused can **significantly** affect performance of some circuits, so users
-can override the default fusion level by setting the setting `CUDAQ_MGPU_FUSE`
-environment variable to another integer value as shown below.
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        CUDAQ_MGPU_FUSE=5 mpiexec -np 2 python3 program.py [...] --target nvidia --target-option mgpu,fp64
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target nvidia --target-option mgpu,fp64 program.cpp [...] -o program.x
-        CUDAQ_MGPU_FUSE=5 mpiexec -np 2 ./program.x
-
-.. _OpenMP CPU-only:
-
-OpenMP CPU-only
-++++++++++++++++++++++++++++++++++
-
-.. _qpp-cpu-backend:
-
-This target provides a state vector simulator based on the CPU-only, OpenMP threaded `Q++ <https://github.com/softwareqinc/qpp>`_ library.
-This is the default target when running on CPU-only systems.
-
-To execute a program on the :code:`qpp-cpu` target even if a GPU-accelerated backend is available, 
-use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target qpp-cpu
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('qpp-cpu')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target qpp-cpu program.cpp [...] -o program.x
-        ./program.x
-
-Tensor Network Simulators
-==================================
-
-.. _tensor-backends:
-
-CUDA-Q provides a couple of tensor-network simulator targets accelerated with 
-the :code:`cuTensorNet` library. 
-These backends are available for use from both C++ and Python.
-
-Tensor network simulators are suitable for large-scale simulation of certain classes of quantum circuits involving many qubits beyond the memory limit of state vector based simulators. For example, computing the expectation value of a Hamiltonian via :code:`cudaq::observe` can be performed efficiently, thanks to :code:`cuTensorNet` contraction optimization capability. On the other hand, conditional circuits, i.e., those with mid-circuit measurements or reset, despite being supported by both backends, may result in poor performance. 
-
-Multi-node multi-GPU
-+++++++++++++++++++++++++++++++++++
-
-The :code:`tensornet` backend represents quantum states and circuits as tensor networks in an exact form (no approximation). 
-Measurement samples and expectation values are computed via tensor network contractions. 
-This backend supports multi-node, multi-GPU distribution of tensor operations required to evaluate and simulate the circuit.
-
-To execute a program on the :code:`tensornet` target using a *single GPU*, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target tensornet
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('tensornet')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target tensornet program.cpp [...] -o program.x
-        ./program.x
-
-If you have *multiple GPUs* available on your system, you can use MPI to automatically distribute parallelization across the visible GPUs. 
-
-.. note::
-
-  If you installed the CUDA-Q Python wheels, distribution across multiple GPUs is currently not supported for this backend.
-  We will add support for it in future releases. For more information, see this `GitHub issue <https://github.com/NVIDIA/cuda-quantum/issues/920>`__.
-
-Use the following commands to enable distribution across multiple GPUs (adjust the value of the :code:`-np` flag as needed to reflect available GPU resources on your system):
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 program.py [...] --target tensornet
-
-    In addition to using MPI in the simulator, you can use it in your application code by installing `mpi4py <https://mpi4py.readthedocs.io/>`__, and 
-    invoking the program with the command
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 -m mpi4py program.py [...] --target tensornet
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target tensornet program.cpp [...] -o program.x
-        mpiexec -np 2 ./program.x
-
-.. note::
-
-  If the `CUTENSORNET_COMM_LIB` environment variable is not set, MPI parallelization on the :code:`tensornet` backend may fail.
-  If you are using a CUDA-Q container, this variable is pre-configured and no additional setup is needed. If you are customizing your installation or have built CUDA-Q from source, please follow the instructions for `activating the distributed interface <https://docs.nvidia.com/cuda/cuquantum/latest/getting-started/index.html#from-nvidia-devzone>`__ for the `cuTensorNet` library. This requires 
-  :ref:`installing CUDA development dependencies <additional-cuda-tools>`, and setting the `CUTENSORNET_COMM_LIB`
-  environment variable to the newly built `libcutensornet_distributed_interface_mpi.so` library.
-
-Specific aspects of the simulation can be configured by setting the following of environment variables:
-
-* **`CUDA_VISIBLE_DEVICES=X`**: Makes the process only see GPU X on multi-GPU nodes. Each MPI process must only see its own dedicated GPU. For example, if you run 8 MPI processes on a DGX system with 8 GPUs, each MPI process should be assigned its own dedicated GPU via `CUDA_VISIBLE_DEVICES` when invoking `mpiexec` (or `mpirun`) commands. 
-* **`OMP_PLACES=cores`**: Set this environment variable to improve CPU parallelization.
-* **`OMP_NUM_THREADS=X`**: To enable CPU parallelization, set X to `NUMBER_OF_CORES_PER_NODE/NUMBER_OF_GPUS_PER_NODE`.
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU and CUDA runtime libraries. 
-  If you do not have these dependencies installed, you may encounter an error stating `Invalid simulator requested`. 
-  See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-
-.. note::
-
-  Setting random seed, via :code:`cudaq::set_random_seed`, is not supported for this backend due to a limitation of the :code:`cuTensorNet` library. This will be fixed in future release once this feature becomes available.
-
-
-Matrix product state 
-+++++++++++++++++++++++++++++++++++
-
-The :code:`tensornet-mps` backend is based on the matrix product state (MPS) representation of the state vector/wave function, exploiting the sparsity in the tensor network via tensor decomposition techniques such as QR and SVD. As such, this backend is an approximate simulator, whereby the number of singular values may be truncated to keep the MPS size tractable. 
-The :code:`tensornet-mps` backend only supports single-GPU simulation. Its approximate nature allows the :code:`tensornet-mps` backend to handle a large number of qubits for certain classes of quantum circuits on a relatively small memory footprint.
-
-To execute a program on the :code:`tensornet-mps` target, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target tensornet-mps
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('tensornet-mps')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target tensornet-mps program.cpp [...] -o program.x
-        ./program.x
-
-Specific aspects of the simulation can be configured by defining the following environment variables:
-
-* **`CUDAQ_MPS_MAX_BOND=X`**: The maximum number of singular values to keep (fixed extent truncation). Default: 64.
-* **`CUDAQ_MPS_ABS_CUTOFF=X`**: The cutoff for the largest singular value during truncation. Eigenvalues that are smaller will be trimmed out. Default: 1e-5.
-* **`CUDAQ_MPS_RELATIVE_CUTOFF=X`**: The cutoff for the maximal singular value relative to the largest eigenvalue. Eigenvalues that are smaller than this fraction of the largest singular value will be trimmed out. Default: 1e-5
-* **`CUDAQ_MPS_SVD_ALGO=X`**: The SVD algorithm to use. Valid values are: `GESVD` (QR algorithm), `GESVDJ` (Jacobi method), `GESVDP` (`polar decomposition <https://epubs.siam.org/doi/10.1137/090774999>`__), `GESVDR` (`randomized methods <https://epubs.siam.org/doi/10.1137/090771806>`__). Default: `GESVDJ`.
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU and CUDA runtime libraries. 
-  If you do not have these dependencies installed, you may encounter an error stating `Invalid simulator requested`. 
-  See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-
-.. note::
-
-  Setting random seed, via :code:`cudaq::set_random_seed`, is not supported for this backend due to a limitation of the :code:`cuTensorNet` library. This will be fixed in future release once this feature becomes available.
-
-.. note::
-    The parallelism of Jacobi method (the default `CUDAQ_MPS_SVD_ALGO` setting) gives GPU better performance on small and medium size matrices.
-    If you expect a large number of singular values (e.g., increasing the `CUDAQ_MPS_MAX_BOND` setting), please adjust the `CUDAQ_MPS_SVD_ALGO` setting accordingly.  
-
-Clifford-Only Simulator
-==================================
-
-Stim (CPU)
-++++++++++++++++++++++++++++++++++
-
-.. _stim-backend:
-
-This target provides a fast simulator for circuits containing *only* Clifford
-gates. Any non-Clifford gates (such as T gates and Toffoli gates) are not
-supported. This simulator is based on the `Stim <https://github.com/quantumlib/Stim>`_
-library.
-
-To execute a program on the :code:`stim` target, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target stim
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('stim')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target stim program.cpp [...] -o program.x
-        ./program.x
-
-.. note::
-    CUDA-Q currently executes kernels using a "shot-by-shot" execution approach.
-    This allows for conditional gate execution (i.e. full control flow), but it
-    can be slower than executing Stim a single time and generating all the shots
-    from that single execution.
-
-Fermioniq
-==================================
-
-.. _fermioniq-backend:
-
-`Fermioniq <https://fermioniq.com/>`__ offers a cloud-based tensor-network emulation platform, `Ava <https://www.fermioniq.com/ava/>`__, 
-for the approximate simulation of large-scale quantum circuits beyond the memory limit of state vector and exact tensor network based methods. 
-
-The level of approximation can be controlled by setting the bond dimension: larger values yield more accurate simulations at the expense 
-of slower computation time. For a detailed description of Ava users are referred to the `online documentation <https://docs.fermioniq.com/>`__.
-
-Users of CUDA-Q can access a simplified version of the full Fermioniq emulator (`Ava <https://www.fermioniq.com/ava/>`__) from either
-C++ or Python. This version currently supports emulation of quantum circuits without noise, and can return measurement samples and/or 
-compute expectation values of observables.
-
-.. note::
-    In order to use the Fermioniq emulator, users must provide access credentials. These can be requested by contacting info@fermioniq.com 
-
-    The credentials must be set via two environment variables:
-    `FERMIONIQ_ACCESS_TOKEN_ID` and `FERMIONIQ_ACCESS_TOKEN_SECRET`.
-
-.. tab:: Python
-
-    The target to which quantum kernels are submitted 
-    can be controlled with the ``cudaq::set_target()`` function.
-
-    .. code:: python
-
-        cudaq.set_target('fermioniq')
-
-    You will have to specify a remote configuration id for the Fermioniq backend
-    during compilation.
-
-    .. code:: python
-
-        cudaq.set_target("fermioniq", **{
-            "remote_config": remote_config_id
-        })
-
-    For a comprehensive list of all remote configurations, please contact Fermioniq directly.
-
-    When your organization requires you to define a project id, you have to specify
-    the project id during compilation.
-
-    .. code:: python
-
-        cudaq.set_target("fermioniq", **{
-            "project_id": project_id
-        })
-
-    To specify the bond dimension, you can pass the ``bond_dim`` parameter.
-
-    .. code:: python 
-
-        cudaq.set_target("fermioniq", **{
-            "bond_dim": 5
-        })
-
-.. tab:: C++
-
-    To target quantum kernel code for execution in the Fermioniq backends,
-    pass the flag ``--target fermioniq`` to the ``nvq++`` compiler. CUDA-Q will 
-    authenticate via the Fermioniq REST API using the environment variables 
-    set earlier.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq src.cpp ...
-
-    You will have to specify a remote configuration id for the Fermioniq backend
-    during compilation.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq --fermioniq-remote-config <remote_config_id> src.cpp ...
-
-    For a comprehensive list of all remote configurations, please contact Fermioniq directly.
-
-    When your organization requires you to define a project id, you have to specify
-    the project id during compilation.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq --fermioniq-project-id <project_id> src.cpp ...
-
-    To specify the bond dimension, you can pass the ``fermioniq-bond-dim`` parameter.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq --fermioniq-bond-dim 10 src.cpp ...
-
-Default Simulator
-==================================
-
-.. _default-simulator:
-
-If no explicit target is set, i.e., if the code is compiled without any :code:`--target` flags, then CUDA-Q makes a default choice for the simulator.
-
-If an NVIDIA GPU and CUDA runtime libraries are available, the default target is set to `nvidia`. This will utilize the :ref:`cuQuantum single-GPU state vector simulator <cuQuantum single-GPU>`.  
-On CPU-only systems, the default target is set to `qpp-cpu` which uses the :ref:`OpenMP CPU-only simulator <OpenMP CPU-only>`.
-
-The default simulator can be overridden by the environment variable `CUDAQ_DEFAULT_SIMULATOR`. If no target is explicitly specified and the environment variable has a valid value, then it will take effect.
-This environment variable can be set to any non-hardware backend. Any invalid value is ignored.
-
-For CUDA-Q Python API, the environment variable at the time when `cudaq` module is imported is relevant, not the value of the environment variable at the time when the simulator is invoked.
-
-For example,
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        CUDAQ_DEFAULT_SIMULATOR=density-matrix-cpu python3 program.py [...]
-        
-.. tab:: C++
-
-    .. code:: bash 
-
-        CUDAQ_DEFAULT_SIMULATOR=density-matrix-cpu nvq++ program.cpp [...] -o program.x
-        ./program.x
-
-This will use the density matrix simulator target.
-
-
-.. note:: 
-
-    To use targets that require an NVIDIA GPU and CUDA runtime libraries, the dependencies must be installed, else you may encounter an error stating `Invalid simulator requested`. See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
\ No newline at end of file
diff --git a/pr-2308/_sources/using/basics/basics.rst.txt b/pr-2308/_sources/using/basics/basics.rst.txt
deleted file mode 100644
index 308ba7492c..0000000000
--- a/pr-2308/_sources/using/basics/basics.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-CUDA-Q Basics
-*******************
-
-.. toctree::
-   :caption: Contents
-   :maxdepth: 2
-
-      What is a CUDA-Q Kernel? <kernel_intro.rst>
-      Building your first CUDA-Q Program <build_kernel.rst>
-      Running your first CUDA-Q Program <run_kernel.rst>
-      Troubleshooting <troubleshooting.rst>
diff --git a/pr-2308/_sources/using/basics/build_kernel.rst.txt b/pr-2308/_sources/using/basics/build_kernel.rst.txt
deleted file mode 100644
index 08a8d9f9cc..0000000000
--- a/pr-2308/_sources/using/basics/build_kernel.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-Building your first CUDA-Q Program
------------------------------------------
-
-.. tab:: Python
-
-  We can define our quantum kernel as a typical Python function, with the additional use of the
-  `@cudaq.kernel` decorator. Let's begin with a simple GHZ-state example, producing a state of
-  maximal entanglement amongst an allocated set of qubits. 
-  
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-      :language: python
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-.. tab:: C++
-
-  We can define our quantum kernel as we do any other typed callable in C++, through the use of the
-  `__qpu__` annotation. For the following example, we will define a kernel for a simple GHZ-state as
-  a standard free function.
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-      :language: cpp
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-This kernel function can accept any number of arguments, allowing for flexibility in the construction
-of the quantum program. In this case, the `qubit_count` argument allows us to dynamically control the
-number of qubits allocated to the kernel. As we will see in further :doc:`examples <../examples/examples>`,
-we could also use these arguments to control various parameters of the gates themselves, such as rotation
-angles.
diff --git a/pr-2308/_sources/using/basics/kernel_intro.rst.txt b/pr-2308/_sources/using/basics/kernel_intro.rst.txt
deleted file mode 100644
index aaafcea8a5..0000000000
--- a/pr-2308/_sources/using/basics/kernel_intro.rst.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-What is a CUDA-Q kernel?
--------------------------------
-
-Quantum kernels are defined as functions that are executed on a quantum processing unit (QPU) or
-a simulated QPU. They generalize quantum circuits and provide a new abstraction for quantum programming.
-Quantum kernels can be combined with classical functions to create quantum-classical applications
-that can be executed on a heterogeneous system of QPUs, GPUs, and CPUs to solve real-world problems.
-
-**What’s the difference between a quantum kernel and a quantum circuit?**
-
-Every quantum circuit is a kernel, but not every quantum kernel is a circuit. For instance, a quantum
-kernel can be built up from other kernels, allowing us to interpret a large quantum program as a sequence
-of subroutines or subcircuits.  
-
-Moreover, since quantum kernels are functions, there is more expressibility available compared to a
-standard quantum circuit. We can not only parameterize the kernel, but can also contain classical control
-flow statements (`if`, `for`, `while`, etc.), and classical computations such as additions, multiplication, etc.
-Conditional statements on quantum memory and qubit measurements can be included in quantum kernels to enable 
-dynamic circuits and fast feedback, particularly useful for quantum error correction. To learn more about what
-language constructs are supported within quantum kernels, take a look at the CUDA-Q 
-:doc:`specification <../../specification/cudaq/kernels>`.
-
-**How do I build and run a quantum kernel?**
-
-Once a quantum kernel has been defined in a program, it may be called as a typical function, or can be executed
-using the `sample` or `observe` primitives.
-
-Let’s take a closer look at how to build and execute a quantum kernel with CUDA-Q.
\ No newline at end of file
diff --git a/pr-2308/_sources/using/basics/run_kernel.rst.txt b/pr-2308/_sources/using/basics/run_kernel.rst.txt
deleted file mode 100644
index 37a82cf3b4..0000000000
--- a/pr-2308/_sources/using/basics/run_kernel.rst.txt
+++ /dev/null
@@ -1,258 +0,0 @@
-Running your first CUDA-Q Program
-----------------------------------------
-
-Now that you have defined your first quantum kernel, let's look at different options for how to execute it.
-In CUDA-Q, quantum circuits are stored as quantum kernels. For estimating the probability distribution of 
-a measured quantum state in a circuit, we use the ``sample`` function call, and for computing the
-expectation value of a quantum state with a given observable, we use the ``observe`` function call.
-
-Sample
-++++++++
-
-Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q `sample` call enables this.
-
-.. tab:: Python
-
-  The :func:`cudaq.sample` method takes a kernel and its arguments as inputs, and returns a :class:`cudaq.SampleResult`.
-
-.. tab:: C++
-
-  The `cudaq::sample` method takes a kernel and its arguments as inputs, and returns a `cudaq::SampleResult`.
-
-This result dictionary contains the distribution of measured states for the system.
-
-Continuing with the GHZ kernel defined in :doc:`Building Your First CUDA-Q Program <build_kernel>`,
-we will set the concrete value of our `qubit_count` to be two.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin Sample1]
-        :end-before: [End Sample1]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-        :language: cpp
-        :start-after: [Begin Sample1]
-        :end-before: [End Sample1]
-
-The code above can be run like any other program:
-
-.. tab:: Python
-
-  Assuming the program is saved in the file `sample.py`, we can execute is with the command
-
-  .. code-block:: console
-
-      python3 sample.py
-
-.. tab:: C++
-
-  Assuming the program is saved in the file `sample.cpp`, we can now compile this file with the `nvq++` toolchain, and then run the compiled executable.
-
-  .. code-block:: console
-
-      nvq++ sample.cpp
-      ./a.out
-
-By default, `sample` produces an ensemble of 1000 shots. This can be changed by specifying an integer argument
-for the `shots_count`.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin Sample2]
-        :end-before: [End Sample2]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-        :language: cpp
-        :start-after: [Begin Sample2]
-        :end-before: [End Sample2]
-
-Note that there is a subtle difference between how sample is executed with the target device set to a simulator or with the target device set to a QPU. When run on a simulator, the quantum state is built once and then sampled repeatedly, where the number of samples is defined by `shots_count`. When executed on quantum hardware, the quantum state collapses upon measurement and hence needs to be rebuilt every time to collect a sample.
-
-A variety of methods can be used to extract useful information from a `SampleResult`. For example,
-to return the most probable measurement and its respective probability:
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin Sample3]
-        :end-before: [End Sample3]
-
-  See the :doc:`API specification <../../../api/languages/python_api>` for further information.
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-        :language: cpp
-        :start-after: [Begin Sample3]
-        :end-before: [End Sample3]
-
-  See the :doc:`API specification <../../../api/languages/cpp_api>` for further information.
-
-Sampling a distribution can be a time intensive task. An asynchronous version of sample exists and can be useful to parallelize your application. Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result.
-
-Asynchronous execution allows to easily parallelize execution of multiple kernels on a multi-processor platform. Such a platform
-is available, for example, by choosing the target `nvidia-mqpu`:
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin SampleAsync]
-        :end-before: [End SampleAsync]
-
-.. note::
-
-  This kind of parallelization is most effective
-  if you actually have multiple QPU or CPU available. Otherwise, the 
-  sampling will still have to execute sequentially due to resource constraints. 
-
-More information about parallelizing execution can be found at :doc:`../backends/platform` page.
-
-Observe
-+++++++++
-
-The observe function allows us to calculate expectation values for a defined quantum operator, that is the value of :math:`\bra{\psi}H\ket{\psi}`, where :math:`H` is the desired operator and :math:`\ket{\psi}` is the quantum state after executing a given kernel. 
-
-.. tab:: Python
-
-  The :func:`cudaq.observe` method takes a kernel and its arguments as inputs, along with a :class:`cudaq.SpinOperator`.
-
-  Using the `cudaq.spin` module, operators may be defined as a linear combination of Pauli strings. Functions, such
-  as :func:`cudaq.spin.i`, :func:`cudaq.spin.x`, :func:`cudaq.spin.y`, :func:`cudaq.spin.z` may be used to construct more
-  complex spin Hamiltonians on multiple qubits.
-
-.. tab:: C++
-
-  The `cudaq::observe` method takes a kernel and its arguments as inputs, along with a `cudaq::spin_op`.
-
-  Within the `cudaq::spin` namespace, operators may be defined as a linear combination of Pauli strings. Functions, such
-  as `cudaq::spin::i`, `cudaq::spin::x`, `cudaq::spin::y`, `cudaq::spin::z` may be used to construct more
-  complex spin Hamiltonians on multiple qubits.
-
-Below is an example of a spin operator object consisting of a `Z(0)` operator, or a Pauli Z-operator on the qubit zero. 
-This is followed by the construction of a kernel with a single qubit in an equal superposition. 
-The Hamiltonian is printed to confirm it has been constructed properly.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_observe.py
-        :language: python
-        :start-after: [Begin Observe1]
-        :end-before: [End Observe1]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_observe.cpp
-        :language: cpp
-        :start-after: [Begin Observe1]
-        :end-before: [End Observe1]
-
-The `observe` function takes a kernel, any kernel arguments, and a spin operator as inputs and produces an `ObserveResult` object.
-The expectation value can be printed using the `expectation` method. 
-
-.. note:: 
-  
-  It is important to exclude a measurement in the kernel, otherwise the expectation value will be determined from a collapsed 
-  classical state. For this example, the expected result of 0.0 is produced.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_observe.py
-        :language: python
-        :start-after: [Begin Observe2]
-        :end-before: [End Observe2]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_observe.cpp
-        :language: cpp
-        :start-after: [Begin Observe2]
-        :end-before: [End Observe2]
-
-Unlike `sample`, the default `shots_count` for `observe` is 1. This result is deterministic and equivalent to the
-expectation value in the limit of infinite shots.  To produce an approximate expectation value from sampling, `shots_count` can
-be specified to any integer.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_observe.py
-        :language: python
-        :start-after: [Begin Observe3]
-        :end-before: [End Observe3]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_observe.cpp
-        :language: cpp
-        :start-after: [Begin Observe3]
-        :end-before: [End Observe3]
-
-Similar to `sample_async` above, observe also supports asynchronous execution. 
-More information about parallelizing execution can be found at 
-:doc:`../backends/platform` page.
-
-Running on a GPU
-++++++++++++++++++
-
-.. tab:: Python
-
-  Using :func:`cudaq.set_target`, different targets can be specified for kernel execution.
-
-.. tab:: C++
-
-  Using the `--target` argument to `nvq++`, different targets can be specified for kernel execution.
-
-If a local GPU is detected, the target will default to `nvidia`. Otherwise, the CPU-based simulation
-target, `qpp-cpu`,  will be selected.
-  
-We will demonstrate the benefits of using a GPU by sampling our GHZ kernel with 25 qubits and a
-`shots_count` of 1 million. Using a GPU accelerates this task by more than 35x. To learn about
-all of the available targets and ways to accelerate kernel execution, visit the
-:doc:`Backends <../backends/backends>` page.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/time.py
-        :language: python
-        :start-after: [Begin Time]
-        :end-before: [End Time]
-
-.. tab:: C++
-
-  To compare the performance, we can create a simple timing script that isolates just the call
-  to `cudaq::sample`. We are still using the same GHZ kernel as earlier, but the following
-  modification made to the main function:
-
-  .. literalinclude:: ../../snippets/cpp/using/time.cpp
-    :language: cpp
-    :start-after: [Begin Time]
-    :end-before: [End Time]
-
-  First we execute on the CPU backend:
-
-  .. code:: console
-
-    nvq++ --target=qpp-cpu sample.cpp
-    ./a.out
-  
-  seeing an output of the order:
-  ``It took 22.8337 seconds.``
-
-  Now we can execute on the GPU enabled backend:
-
-  .. code:: console
-
-    nvq++ --target=nvidia sample.cpp
-    ./a.out
-
-  seeing an output of the order:
-  ``It took 3.18988 seconds.``
\ No newline at end of file
diff --git a/pr-2308/_sources/using/basics/troubleshooting.rst.txt b/pr-2308/_sources/using/basics/troubleshooting.rst.txt
deleted file mode 100644
index 608e7086f6..0000000000
--- a/pr-2308/_sources/using/basics/troubleshooting.rst.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Troubleshooting
------------------
-
-Debugging and Verbose Simulation Output
-+++++++++++++++++++++++++++++++++++++++++
-
-One helpful mechanism of debugging CUDA-Q simulation execution is
-the :code:`CUDAQ_LOG_LEVEL` environment variable. For any CUDA-Q
-executable, just prepend this and turn it on:
-
-.. tab:: Python
-
-  .. code-block:: bash
-
-      CUDAQ_LOG_LEVEL=info python3 file.py
-
-.. tab:: C++
-
-    .. code-block:: bash
-
-      CUDAQ_LOG_LEVEL=info ./a.out
-
-Similarly, one may write the IR to their console or to a file before remote
-submission. This may be done through the :code:`CUDAQ_DUMP_JIT_IR` environment
-variable. For any CUDA-Q executable, just prepend as follows:
-
-.. tab:: Python
-
-  .. code-block:: bash
-
-      CUDAQ_DUMP_JIT_IR=1 python3 file.py
-      # or
-      CUDAQ_DUMP_JIT_IR=<output_filename> python3 file.py
-
-.. tab:: C++
-
-  .. code-block:: bash
-
-      CUDAQ_DUMP_JIT_IR=1 ./a.out
-      # or
-      CUDAQ_DUMP_JIT_IR=<output_filename> ./a.out
\ No newline at end of file
diff --git a/pr-2308/_sources/using/examples/building_kernels.rst.txt b/pr-2308/_sources/using/examples/building_kernels.rst.txt
deleted file mode 100644
index 3cd3a4e324..0000000000
--- a/pr-2308/_sources/using/examples/building_kernels.rst.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Building Kernels
--------------------------
-This section will cover the most basic CUDA-Q construct, a quantum kernel.
-Topics include, building kernels, initializing states, and applying gate operations.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../examples/python/building_kernels.py
-      :language: python
diff --git a/pr-2308/_sources/using/examples/examples.rst.txt b/pr-2308/_sources/using/examples/examples.rst.txt
deleted file mode 100644
index f62c32afd9..0000000000
--- a/pr-2308/_sources/using/examples/examples.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-*************************
-CUDA-Q by Example
-*************************
-
-Examples that illustrate how to use CUDA-Q for application development are available in C++ and Python.
-
-.. toctree::
-   :maxdepth: 1
-
-      Introduction <introduction.rst>
-      Building Kernels <building_kernels.rst>
-      Quantum Operations <quantum_operations.rst>
-      Measuring Kernels <../../examples/python/measuring_kernels.ipynb>
-      Visualizing Kernels <../../examples/python/visualization.ipynb>
-      Executing Kernels <../../examples/python/executing_kernels.ipynb>
-      Computing Expectation Values <expectation_values.rst>
-      Multi-Control Synthesis <multi_control.rst>
-      Multi-GPU Workflows <multi_gpu_workflows.rst>
-      Optimizers & Gradients  <../../examples/python/optimizers_gradients.ipynb>
-      Noisy Simulations <../../examples/python/noisy_simulations.ipynb>
-      Constructing Operators <../../examples/python/operators.ipynb>
-      Performance Optimizations <../../examples/python/performance_optimizations.ipynb>
-      Using Quantum Hardware Providers <hardware_providers.rst>
diff --git a/pr-2308/_sources/using/examples/expectation_values.rst.txt b/pr-2308/_sources/using/examples/expectation_values.rst.txt
deleted file mode 100644
index 254302e5ed..0000000000
--- a/pr-2308/_sources/using/examples/expectation_values.rst.txt
+++ /dev/null
@@ -1,66 +0,0 @@
-Computing Expectation Values
------------------------------
-
-CUDA-Q provides generic library functions enabling one to compute expectation values 
-of quantum spin operators with respect to a parameterized CUDA-Q kernel. Let's take a look 
-at an example of this:
-
-.. tab:: C++
-
-   .. literalinclude:: ../../examples/cpp/basics/expectation_values.cpp
-      :language: cpp
-
-   Here we define a parameterized CUDA-Q kernel, a callable type named :code:`ansatz` that takes as 
-   input a single angle :code:`theta`. This angle becomes the argument of a single :code:`ry` rotation. 
-
-   In host code, we define a Hamiltonian operator via the CUDA-Q :code:`spin_op` type. 
-   CUDA-Q provides a generic function :code:`cudaq::observe`. This function takes as input three terms. 
-   The first two terms are a parameterized kernel and the :code:`spin_op` whose expectation value we wish to compute.
-   The last term contains the runtime parameters at which we evaluate the parameterized kernel. 
-
-   The return type of this function is an :code:`cudaq::observe_result` which contains all the data 
-   from the execution, but is trivially convertible to a double, resulting in the expectation value we are interested in. 
-
-   To compile and execute this code, we run the following:
-
-   .. code:: bash 
-
-      nvq++ expectation_values.cpp -o exp_vals.x 
-      ./exp_vals.x 
-
-
-Parallelizing across Multiple Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-:doc:`multi-processor platforms <../backends/platform>` page.
-
-
-One typical use case of :doc:`multi-processor platforms <../backends/platform>` is to distribute the
-expectation value computations of a multi-term Hamiltonian across multiple virtual QPUs.
-
-The following shows an example using the :code:`nvidia-mqpu` platform:
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/observe_mqpu.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/observe_mqpu.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-
-    One can then target the :code:`nvidia-mqpu` platform by executing the following commands:
-
-    .. code-block:: console
-
-        nvq++ observe_mqpu.cpp -target nvidia-mqpu
-        ./a.out
-
-In the above code snippets, since the Hamiltonian contains four non-identity terms, there are four quantum circuits that need to be executed
-in order to compute the expectation value of that Hamiltonian and given the quantum state prepared by the ansatz kernel. When the :code:`nvidia-mqpu` platform
-is selected, these circuits will be distributed across all available QPUs. The final expectation value result is computed from all QPU execution results.
diff --git a/pr-2308/_sources/using/examples/hardware_providers.rst.txt b/pr-2308/_sources/using/examples/hardware_providers.rst.txt
deleted file mode 100644
index 6abbb8dea7..0000000000
--- a/pr-2308/_sources/using/examples/hardware_providers.rst.txt
+++ /dev/null
@@ -1,142 +0,0 @@
-Using Quantum Hardware Providers
------------------------------------
-
-CUDA-Q contains support for using a set of hardware providers (Amazon Braket, 
-Infleqtion, IonQ, IQM, OQC, ORCA Computing, Quantinuum, and QuEra Computing). 
-For more information about executing quantum kernels on different hardware 
-backends, please take a look at :doc:`hardware <../backends/hardware>`.
-
-Amazon Braket
-==================================
-
-The following code illustrates how to run kernels on Amazon Braket's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/braket.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/braket.cpp
-      :language: cpp
-
-Infleqtion
-==================================
-
-The following code illustrates how to run kernels on Infleqtion's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/infleqtion.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/infleqtion.cpp
-      :language: cpp
-
-IonQ
-==================================
-
-The following code illustrates how to run kernels on IonQ's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/ionq.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/ionq.cpp
-      :language: cpp
-
-IQM
-==================================
-
-The following code illustrates how to run kernels on IQM's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/iqm.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/iqm.cpp
-      :language: cpp
-
-OQC
-==================================
-
-The following code illustrates how to run kernels on OQC's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/oqc.py
-      :language: python
-
-ORCA Computing
-==================================
-
-The following code illustrates how to run kernels on ORCA Computing's backends.
-
-ORCA Computing's PT Series implement the boson sampling model of quantum computation, in which 
-multiple photons are interfered with each other within a network of beam splitters, and photon 
-detectors measure where the photons leave this network.
-
-The following image shows the schematic of a Time Bin Interferometer (TBI) boson sampling experiment 
-that runs on ORCA Computing's backends. A TBI uses optical delay lines with reconfigurable coupling 
-parameters. A TBI can be represented by a circuit diagram, like the one below, where this 
-illustration example corresponds to 4 photons in 8 modes sent into alternating time-bins in a circuit 
-composed of two delay lines in series. 
-
-.. image:: ./images/orca_tbi.png
-   :width: 400px
-   :align: center
-
-The parameters needed to define the time bin interferometer are the the input state, the loop 
-lengths, beam splitter angles, and optionally the phase shifter angles, and the number of samples.
-The *input state* is the initial state of the photons in the time bin interferometer, 
-the left-most entry corresponds to the first mode entering the loop.
-The *loop lengths* are the lengths of the different loops in the time bin interferometer.
-The *beam splitter angles* and the phase shifter angles are controllable
-parameters of the time bin interferometer.
-
-This experiment is performed on ORCA's backends by the code below.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/orca.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/orca.cpp
-      :language: cpp
-      
-Quantinuum
-==================================
-
-The following code illustrates how to run kernels on Quantinuum's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/quantinuum.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/quantinuum.cpp
-      :language: cpp
-
-QuEra Computing
-==================================
-
-The following code illustrates how to run kernels on QuEra's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/quera_basic.py
-      :language: python
-
diff --git a/pr-2308/_sources/using/examples/introduction.rst.txt b/pr-2308/_sources/using/examples/introduction.rst.txt
deleted file mode 100644
index a12fb6cdad..0000000000
--- a/pr-2308/_sources/using/examples/introduction.rst.txt
+++ /dev/null
@@ -1,42 +0,0 @@
-Introduction
---------------------------------
-
-Welcome to CUDA-Q! On this page we will illustrate CUDA-Q with several examples. 
-
-.. tab:: Python
-
-   We're going to take a look at how to construct quantum programs through CUDA-Q's `Kernel` API.
-
-   When you create a `Kernel` and invoke its methods, a quantum program is constructed that can then be executed by calling, for example, `cudaq::sample`. Let's take a closer look!
-
-   .. literalinclude:: ../../examples/python/intro.py
-      :language: python
-
-.. tab:: C++
-
-   We're going to take a look at how to construct quantum programs using CUDA-Q kernels.
-
-   CUDA-Q kernels are any typed callable in the language that is annotated with the :code:`__qpu__` attribute. Let's take a look at a very 
-   simple "Hello World" example, specifically a CUDA-Q kernel that prepares a GHZ state on a programmer-specified number of qubits. 
-
-   .. literalinclude:: ../../examples/cpp/basics/static_kernel.cpp
-      :language: cpp
-
-   Here we see that we can define a custom :code:`struct` that is templated on a :code:`size_t` parameter. 
-   Our kernel expression is free to use this template parameter in the allocation of a 
-   compile-time-known register of qubits. Within the kernel, we are free to apply various quantum operations, 
-   like a Hadamard on qubit 0 :code:`h(q[0])`. Controlled operations are **modifications** of single-qubit 
-   operations, like the :code:`x<cudaq::ctrl>(q[0],q[1])` operation which implements a controlled-X gate. We 
-   can measure single qubits or entire registers. 
-
-   In this example we are interested in sampling the final state produced by this CUDA-Q kernel. 
-   To do so, we leverage the generic :code:`cudaq::sample` function, which returns a data type 
-   encoding the qubit measurement strings and the corresponding number of times that string 
-   was observed (here the default number of shots is used, :code:`1000`).
-
-   The following example illustrates how to compile and execute this code.
-
-   .. code:: bash 
-
-      nvq++ static_kernel.cpp -o ghz.x
-      ./ghz.x
\ No newline at end of file
diff --git a/pr-2308/_sources/using/examples/multi_control.rst.txt b/pr-2308/_sources/using/examples/multi_control.rst.txt
deleted file mode 100644
index 045e367488..0000000000
--- a/pr-2308/_sources/using/examples/multi_control.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-Multi-control Synthesis
--------------------------
-Now let's take a look at how CUDA-Q allows one to control a general unitary 
-on an arbitrary number of control qubits. 
-
-.. tab:: Python
-
-   Our first option is to describe our general unitary by another pre-defined
-   CUDA-Q kernel.
-
-   .. literalinclude:: ../../examples/python/multi_controlled_operations.py
-      :language: python
-      :start-after: [Begin OptionA]
-      :end-before: [End OptionA]
-
-   Alternatively, one may pass multiple arguments for control qubits or vectors
-   to any controlled operation.
-
-   .. literalinclude:: ../../examples/python/multi_controlled_operations.py
-      :language: python
-      :start-after: [Begin OptionB]
-      :end-before: [End OptionB]
-
-.. tab:: C++ 
-
-   For this scenario, our general unitary can be described by another pre-defined 
-   CUDA-Q kernel expression. 
-
-   .. literalinclude:: ../../examples/cpp/basics/multi_controlled_operations.cpp
-      :language: cpp
diff --git a/pr-2308/_sources/using/examples/multi_gpu_workflows.rst.txt b/pr-2308/_sources/using/examples/multi_gpu_workflows.rst.txt
deleted file mode 100644
index 4cdf2504aa..0000000000
--- a/pr-2308/_sources/using/examples/multi_gpu_workflows.rst.txt
+++ /dev/null
@@ -1,155 +0,0 @@
-Multi-GPU Workflows
-===================
-
-There are many backends available with CUDA-Q which enable seamless
-switching between GPUs, QPUs and CPUs and also allow for workflows
-involving multiple architectures working in tandem.
-
-Available Targets
--------------------
-
--  **`qpp-cpu`**: The QPP based CPU backend which is multithreaded to
-   maximize the usage of available cores on your system.
-
--  **`nvidia`**: GPU-accelerated state-vector based backend which accelerates quantum circuit
-   simulation on NVIDIA GPUs powered by cuQuantum.
-
--  **`nvidia-mgpu`**: Allows for scaling circuit simulation on multiple GPUs.
-
--  **`nvidia-mqpu`**: Enables users to program workflows utilizing
-   multiple virtual quantum processors in parallel, where each QPU is simulated by the `nvidia` backend.
-
--  **`remote-mqpu`**: Enables users to program workflows utilizing
-   multiple virtual quantum processors in parallel, where the backend used to simulate each QPU is configurable.
-
-Please see :doc:`../backends/backends` for a full list of all available backends. 
-Below we explore how to effectively utilize multiple CUDA-Q targets with the same GHZ state preparation code
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
-    :language: python
-    :start-after: [Begin state]
-    :end-before: [End state]
-
-You can execute the code by running a statevector simulator on your CPU:
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
-    :language: python
-    :start-after: [Begin CPU]
-    :end-before: [End CPU]
-
-.. parsed-literal::
-
-    { 00:475 11:525 }
-
-You will notice a speedup of up to **2500x** in executing the circuit below on
-NVIDIA GPUs vs CPUs:
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
-    :language: python
-    :start-after: [Begin GPU]
-    :end-before: [End GPU]
-
-.. parsed-literal::
-
-    { 0000000000000000000000000:510 1111111111111111111111111:490 }
-
-If one incrementally increases the qubit count, we
-reach a limit where the memory required is beyond the capabilities of a
-single GPU: A :math:`n` qubit quantum state has :math:`2^n` complex amplitudes, each
-of which require 8 bytes of memory to store. Hence the total memory
-required to store a :math:`n` qubit quantum state is :math:`8` bytes
-:math:`\times 2^n`. For :math:`n = 30` qubits, this is roughly :math:`8`
-GB but for :math:`n = 40`, this exponentially increases to 8700 GB.
-
-
-Parallelization across Multiple Processors
----------------------------------------------
-
-The ``nvidia-mgpu`` target allows for memory from additional
-GPUs to be pooled enabling qubit counts to be scaled.
-Execution on the ``nvidia-mgpu`` backend is enabled via ``mpirun``. Users
-need to create a ``.py`` file with their code and run the command below
-in terminal:
-
-``mpirun -np 4 python3 test.py``
-
-where 4 is the number of GPUs one has access to and ``test`` is the file
-name chosen.
-
-The ``nvidia-mqpu`` target uses a statevector simulator to simulate execution 
-on each virtual QPU.
-The ``remote-mqpu`` platform allows to freely configure what backend is used 
-for each platform QPU. 
-For more information about the different platform targets, please take a look at
-:doc:`../backends/platform`.
-
-Batching Hamiltonian Terms
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Expectation value computations of multi-term Hamiltonians can be
-asynchronously processed via the ``mqpu`` platform.
-
-.. image:: ../../applications/python/images/hsplit.png
-
-For workflows involving multiple GPUs, save the code below in a
-``filename.py`` file and execute via:
-``mpirun -np n python3 filename.py`` where ``n`` is an integer
-specifying the number of GPUs you have access to.
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/hamiltonian_batching.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    mpi is initialized?  True
-    rank 0 num_ranks 1
-
-
-Circuit Batching
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Execution of parameterized circuits with different parameters can be
-executed asynchronously via the ``mqpu`` platform.
-
-.. image:: ../../applications/python/images/circsplit.png
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin prepare]
-    :end-before: [End prepare]
-
-Let's time the execution on single GPU.
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin single]
-    :end-before: [End single]
-
-.. parsed-literal::
-
-    31.7 s ± 990 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
-
-Now let's try to time multi GPU run.
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin split]
-    :end-before: [End split]
-
-.. parsed-literal::
-
-    We have 10000 parameters which we would like to execute
-    We split this into 4 batches of 2500 , 2500 , 2500 , 2500
-
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin multiple]
-    :end-before: [End multiple]
-
-.. parsed-literal::
-
-    85.3 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
-
diff --git a/pr-2308/_sources/using/examples/quantum_operations.rst.txt b/pr-2308/_sources/using/examples/quantum_operations.rst.txt
deleted file mode 100644
index 3ce35eb098..0000000000
--- a/pr-2308/_sources/using/examples/quantum_operations.rst.txt
+++ /dev/null
@@ -1,175 +0,0 @@
-Quantum Computing 101
-======================
-
-The fundamental unit of classical information storage, processing and
-transmission is the bit. Analogously, we define its quantum counterpart,
-a quantum bit or simply the qubit.
-
-Classical bits are transistor elements whose states can be altered to
-perform computations. Similarly qubits too have physical realizations
-within superconducting materials, ion-traps and photonic systems. We
-shall not concern ourselves with specific qubit architectures but rather
-think of them as systems which obey the laws of quantum mechanics and
-the mathematical language physicists have developed to describe the
-theory: linear algebra.
-
-Quantum States
------------------------------
-
-Information storage scales linearly if bits have a single state. Access
-to multiple states, namely a 0 and a 1 allows for information encoding
-to scale logarithmically. Similarly we define a qubit to have the states
-:math:`\ket{0}` and :math:`\ket{1}` in Dirac notation where:
-
-.. math:: \ket{0} = \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix}
-
-.. math:: \ket{1} = \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}
-
-Rather than just the two states each classical bit can be in,
-quantum theory allows one to explore linear combinations of states,
-also called superpositions:
-
-.. math::   \ket{\psi} = \alpha\ket{0} + \beta\ket{1} 
-
-where :math:`\alpha` and :math:`\beta` :math:`\in \mathbb{C}`. It is
-important to note that this is still the state of one qubit; 
-although we have two kets, they both represent a
-superposition state of one qubit.
-
-If we have two classical bits, the possible states we could encode
-information in would be 00, 01, 10 and 11. Correspondingly, multiple
-qubits can be combined and the possible combinations of their states
-used to process information.
-
-A two qubit system has four computational basis states:
-:math:`\ket{00}, \ket{01}, \ket{10}, \ket{11}`.
-
-More generally, the quantum state of a :math:`n` qubit system is written
-as a sum of :math:`2^n` possible basis states where the coefficients
-track the probability of the system collapsing into that state if a
-measurement is applied.
-
-For :math:`n = 500`, :math:`2^n \approx 10^{150}` which is greater than
-the number of atoms in the universe. Storing the complex numbers
-associated with :math:`2^{500}` amplitudes would not be feasible using
-bits and classical computations but nature seems to only require 500
-qubits to do so. The art of quantum computation is thus to build quantum
-systems that we can manipulate with fine precision such that evolving a
-large statevector can be offloaded onto a quantum computer.
-
-
-Quantum Gates
------------------------------
-
-We can manipulate the state of a qubit via quantum gates. 
-For example, the Pauli X gate allows us to flip the state of the qubit:
-
-.. math::  X \ket{0} = \ket{1} 
-
-.. math::  \begin{bmatrix} 0 & 1 \\ 1 & 0 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix} 
-
-.. literalinclude:: ../../snippets/python/using/examples/pauli_x_gate.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    { 1:1000 }
-
-The Hadamard gate allows us to put the qubit in an equal superposition
-state:
-
-.. math::  H \ket{0} =  \tfrac{1}{\sqrt{2}} \ket{0} + \tfrac{1}{\sqrt{2}} \ket{1}  \equiv \ket{+}
-
-.. math:: \tfrac{1}{\sqrt{2}}\begin{bmatrix} 1 & 1 \\ 1 & -1 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} = \tfrac{1}{\sqrt{2}} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} + \tfrac{1}{\sqrt{2}} \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}. 
-
-The probability of finding the qubit in the :math:`\ket{0}` or :math:`\ket{1}` state is hence
-:math:`\lvert \tfrac{1}{\sqrt{2}} \rvert ^2 = \tfrac{1}{2}`. Lets verify
-this with some code:
-
-.. literalinclude:: ../../snippets/python/using/examples/hadamard_gate.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    { 0:502 1:498 }
-
-For a qubit in a superposition state, quantum gates
-act linearly:
-
-.. math::    X (\alpha\ket{0} + \beta\ket{1}) = \alpha\ket{1} + \beta\ket{0} 
-
-As we evolve quantum states via quantum gates, the normalization
-condition requires that the sum of modulus squared of amplitudes must
-equal 1 at all times:
-
-.. math::   \ket{\psi} = \alpha\ket{0} + \beta\ket{1},          |\alpha|^2 + |\beta|^2 = 1. 
-
-This is to adhere to the conservation of probabilities which translates
-to a constraint on types of quantum gates we can define.
-For a general quantum state :math:`\ket{\psi}`, upholding the
-normalization condition requires quantum gates to be unitary, that is
-:math:`U^{\dagger}U = U^{*^{T}}U = \mathbb{I}`.
-
-Just like the single-qubit gates above, we can define 
-multi-qubit gates to act on multiple-qubits.
-The controlled-NOT or CNOT gate, for example, acts on 2 qubits: the control qubit and
-the target qubit. Its effect is to flip the target if the control is in
-the excited :math:`\ket{1}` state.
-
-.. literalinclude:: ../../snippets/python/using/examples/cnot_gate.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    { 11:1000 }
-    
-
-The CNOT gate in matrix notation is represented as
-
-.. math::  CNOT \equiv \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 1 & 0 \end{bmatrix} 
-
-and one can check that :math:`CNOT^\dagger CNOT = \mathbb{I}`.
-Its effect on the computational basis states is:
-
-.. math::  CNOT\ket{00} = \ket{00} 
-
-.. math::  CNOT\ket{01} = \ket{01} 
-
-.. math::  CNOT\ket{10} = \ket{11} 
-
-.. math::  CNOT\ket{11} = \ket{10} 
-
-For a full list of gates supported in CUDA-Q see :doc:`../../api/default_ops`. 
-
-Measurements
------------------------------
-
-Quantum theory is probabilistic and hence requires statistical inference
-to derive observations. Prior to measurement, the state of a qubit is
-all possible combinations of :math:`\alpha` and :math:`\beta` and upon
-measurement, wavefunction collapse yields either a classical 0 or 1.
-
-The mathematical theory devised to explain quantum phenomena tells us
-that the probability of observing the qubit in the state
-:math:`\ket{0}` or :math:`\ket{1}`, yielding a classical 0 or 1, is
-:math:`\lvert \alpha \rvert ^2` or :math:`\lvert \beta \rvert ^2`, respectively. 
-
-As we see in the example of the Hadamard gate above,
-the result 0 or 1 each is yielded roughly 50% of the times as predicted 
-by the postulate stated above thus proving the theory.
-
-Classically, we cannot encode information within states such as 00 + 11
-but quantum mechanics allows us to write linear superpositions
-
-.. math::   \ket{\psi} = \alpha_{00}\ket{00} + \alpha_{01}\ket{01} + \alpha_{10}\ket{10} + \alpha_{11}\ket{11}
-
-where the probability of measuring :math:`x = 00, 01, 10, 11` occurs
-with probability :math:`\lvert \alpha_{x} \rvert ^2` with the
-normalization condition that
-:math:`\sum_{x \in \{ 0,1 \}^2} \lvert \alpha_{x} \rvert ^2 = 1`.
diff --git a/pr-2308/_sources/using/extending/cudaq_ir.rst.txt b/pr-2308/_sources/using/extending/cudaq_ir.rst.txt
deleted file mode 100644
index da32cb9233..0000000000
--- a/pr-2308/_sources/using/extending/cudaq_ir.rst.txt
+++ /dev/null
@@ -1,260 +0,0 @@
-Working with the CUDA-Q IR
-********************************
-Let's see the output of :code:`nvq++` in verbose mode. Consider a simple code like the one below, saved to file :code:`simple.cpp`.
-
-.. code-block:: console 
-
-    #include <cudaq.h>
-
-    struct ghz {
-      void operator()(int N) __qpu__ {
-        cudaq::qvector q(N);
-        h(q[0]);
-        for (int i = 0; i < N - 1; i++) {
-          x<cudaq::ctrl>(q[i], q[i + 1]);
-        }
-        mz(q);
-      }
-    };
-
-    int main() { ... }
-
- We see the following output from :code:`nvq++` verbose mode (up to some absolute paths).
-
-.. code-block:: console 
-
-    $ nvq++ simple.cpp -v -save-temps
-    
-    cudaq-quake --emit-llvm-file simple.cpp -o simple.qke
-    cudaq-opt --pass-pipeline=builtin.module(canonicalize,lambda-lifting,canonicalize,apply-op-specialization,kernel-execution,indirect-to-direct-calls,inline,func.func(quake-add-metadata),device-code-loader{use-quake=1},expand-measurements,func.func(lower-to-cfg),canonicalize,cse) simple.qke -o simple.qke.LpsXpu
-    cudaq-translate --convert-to=qir simple.qke.LpsXpu -o simple.ll.p3De4L
-    fixup-linkage.pl simple.qke simple.ll
-    llc --relocation-model=pic --filetype=obj -O2 simple.ll.p3De4L -o simple.qke.o
-    llc --relocation-model=pic --filetype=obj -O2 simple.ll -o simple.classic.o
-    clang++ -L/usr/lib/gcc/x86_64-linux-gnu/12 -L/usr/lib64 -L/lib/x86_64-linux-gnu -L/lib64 -L/usr/lib/x86_64-linux-gnu -L/lib -L/usr/lib -L/usr/local/cuda/lib64/stubs -r simple.qke.o simple.classic.o -o simple.o
-    clang++ -Wl,-rpath,lib -Llib -L/usr/lib/gcc/x86_64-linux-gnu/12 -L/usr/lib64 -L/lib/x86_64-linux-gnu -L/lib64 -L/usr/lib/x86_64-linux-gnu -L/lib -L/usr/lib -L/usr/local/cuda/lib64/stubs simple.o -lcudaq -lcudaq-common -lcudaq-mlir-runtime -lcudaq-builder -lcudaq-ensmallen -lcudaq-nlopt -lcudaq-spin -lcudaq-em-default -lcudaq-platform-default -lnvqir -lnvqir-qpp
-
-This workflow orchestration is represented in the figure below: 
-
-.. image:: ../../_static/nvqpp_workflow.png
-
-We start by mapping CUDA-Q C++ kernel representations (structs, lambdas, and free functions) 
-to the Quake dialect. Since we added :code:`-save-temps`, 
-we can look at the IR code that was produced. The base Quake file, :code:`simple.qke`, contains the following: 
-
-.. code-block:: console 
-
-    module attributes {qtx.mangled_name_map = {__nvqpp__mlirgen__ghz = "_ZN3ghzclEi"}} {
-        func.func @__nvqpp__mlirgen__ghz(%arg0: i32) attributes {"cudaq-entrypoint", "cudaq-kernel"} {
-           %alloca = memref.alloca() : memref<i32>
-           memref.store %arg0, %alloca[] : memref<i32>
-           %0 = memref.load %alloca[] : memref<i32>
-           %1 = arith.extsi %0 : i32 to i64
-           %2 = quake.alloca(%1 : i64) !quake.veq<?>
-           %c0_i32 = arith.constant 0 : i32
-           %3 = arith.extsi %c0_i32 : i32 to i64
-           %4 = quake.extract_ref %2[%3] : (!quake.veq<?>, i64) -> !quake.ref
-           quake.h %4 : (!quake.ref) -> ()
-           cc.scope {
-            %c0_i32_0 = arith.constant 0 : i32
-            %alloca_1 = memref.alloca() : memref<i32>
-            memref.store %c0_i32_0, %alloca_1[] : memref<i32>
-            cc.loop while {
-                %6 = memref.load %alloca_1[] : memref<i32>
-                %7 = memref.load %alloca[] : memref<i32>
-                %c1_i32 = arith.constant 1 : i32
-                %8 = arith.subi %7, %c1_i32 : i32
-                %9 = arith.cmpi slt, %6, %8 : i32
-                cc.condition %9
-            } do {
-              cc.scope {
-                %6 = memref.load %alloca_1[] : memref<i32>
-                %7 = arith.extsi %6 : i32 to i64
-                %8 = quake.extract_ref %2[%7] : (!quake.veq<?>, i64) -> !quake.ref
-                %9 = memref.load %alloca_1[] : memref<i32>
-                %c1_i32 = arith.constant 1 : i32
-                %10 = arith.addi %9, %c1_i32 : i32
-                %11 = arith.extsi %10 : i32 to i64
-                %12 = quake.extract_ref %2[%11] : (!quake.veq<?>, i64) -> !quake.ref
-                quake.x [%8] %12 : (!quake.ref, !quake.ref) -> ()
-              }
-              cc.continue
-            } step {
-                %6 = memref.load %alloca_1[] : memref<i32>
-                %c1_i32 = arith.constant 1 : i32
-                %7 = arith.addi %6, %c1_i32 : i32
-                memref.store %7, %alloca_1[] : memref<i32>
-            }
-            }
-            %5 = quake.mz %2 : (!quake.veq<?>) -> !cc.stdvec<i1>
-            return
-        }
-    }
-
-This base Quake file is unoptimized and unchanged. It is produced by the 
-:code:`cudaq-quake` tool, which also allows us to output the full LLVM IR representation 
-for the code. This LLVM IR is classical-only, and is directly produced by :code:`clang++` 
-code-generation. The LLVM IR file :code:`simple.ll` contains the CUDA-Q kernel 
-:code:`operator()(Args...)` LLVM function, with a mangled name. Ultimately, we 
-want to replace this function with our own MLIR-generated function. 
-
-Next, the :code:`cudaq-opt` tool is invoked on the :code:`simple.qke` file. This runs an
-MLIR pass pipeline that canonicalizes and optimizes the code. It will also process quantum 
-lambdas, lift those lambdas to functions, and synthesis adjoint and controlled versions of 
-CUDA-Q kernel functions if necessary. The most important pass that this step applies is the 
-:code:`kernel-execution` pass, which synthesizes a new entry point LLVM function with the 
-same name and signature as the original :code:`operator()(Args...)` call function in the 
-classical :code:`simple.ll` file. We also extract all Quake code representations as strings
-and register them with the CUDA-Q runtime for runtime IR introspection. 
-
-After :code:`cudaq-opt`, the :code:`cudaq-translate` tool is used to lower the transformed 
-Quake representation to an LLVM IR representation, specifically the QIR. We finish by lowering 
-this representation to object code via standard LLVM tools (e.g. :code:`llc`), and merge all 
-object files into a single object file, ensuring that our new mangled :code:`operator()(Args...)` 
-call is injected first, thereby overwriting the original. Finally, based on user compile flags, 
-we configure the link line with specific libraries that implement the :code:`quantum_platform` 
-(here the :code:`libcudaq-platform-default.so`) and NVQIR circuit simulator backend (the 
-:code:`libnvqir-qpp.so` Q++ CPU-only simulation backend). These latter libraries are controlled 
-via the :code:`--platform` and :code:`--target` compiler flags. 
-
-.. image:: ../../_static/dialects.png
-
-The above figure demonstrate the MLIR dialects involved and the overall workflow mapping 
-high-level language constructs to lower-level MLIR dialect code, and ultimately LLVM IR. 
-
-CUDA-Q also provides value-semantics form of Quake for static circuit
-representation. This dialect directly enables robust circuit 
-optimizations via data-flow analysis of the representative circuit. This dialect 
-is typically produced just-in-time when the structure of the circuit is fully known. 
-
-You will notice that there are a number of CUDA-Q executable tools installed as part 
-of this open beta release. These tools are directly related to the generation, 
-processing, optimization, and lowering of the core :code:`nvq++` compiler representations.
-The tools available are 
-
-1. :code:`cudaq-quake` - Lower C++ to Quake, can also output classical LLVM IR file
-2. :code:`cudaq-opt` - Process Quake with various MLIR Passes
-3. :code:`cudaq-translate` - Lower Quake to external representations like QIR
-
-CUDA-Q and :code:`nvq++` rely on Quake for the core quantum intermediate representation.
-Quake represents an IR closer to the CUDA-Q source language and models qubits and
-quantum instructions via memory semantics. Quake can be fully dynamic and in
-that sense represents a quantum circuit template or generator. With runtime 
-arguments fully specified, Quake code can be used to generate or synthesize
-a fully known quantum circuit. The value semantics form of Quake can thus be
-used as a representation for fully known
-or synthesized quantum circuits. Its utility, therefore, lies in its ability to 
-optimize quantum code. It departs from the memory semantics model of Quake and 
-expresses the flow of quantum information explicitly as MLIR values.
-This approach makes it easier for finding circuit patterns and leveraging it for common 
-optimization tasks. 
-
-To demonstrate how these tools work together, let's take the simple GHZ CUDA-Q 
-program and lower the kernel from C++ to Quake, synthesize that Quake code, 
-and produce QIR. Recall the code snippet for the kernel
-
-.. code-block:: cpp 
-
-    // Define a quantum kernel
-    struct ghz {
-      auto operator()() __qpu__ {
-        cudaq::qarray<5> q;
-        h(q[0]);
-        for (int i = 0; i < 4; i++) 
-          x<cudaq::ctrl>(q[i], q[i + 1]);
-        mz(q);
-      }
-    };
-
-Using the toolchain, we can lower this directly to QIR,
-
-.. code-block:: console
-
-    cudaq-quake simple.cpp | cudaq-opt --canonicalize | cudaq-translate --convert-to=qir 
-
-which prints: 
-
-.. code-block:: console 
-
-    ; ModuleID = 'LLVMDialectModule'
-    source_filename = "LLVMDialectModule"
-    target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128"
-    target triple = "x86_64-unknown-linux-gnu"
-
-    %Array = type opaque
-    %Qubit = type opaque
-    %Result = type opaque
-
-    declare void @invokeWithControlQubits(i64, void (%Array*, %Qubit*)*, ...) local_unnamed_addr
-
-    declare void @__quantum__qis__x__ctl(%Array*, %Qubit*)
-
-    declare %Result* @__quantum__qis__mz(%Qubit*) local_unnamed_addr
-
-    declare void @__quantum__rt__qubit_release_array(%Array*) local_unnamed_addr
-
-    declare i64 @__quantum__rt__array_get_size_1d(%Array*) local_unnamed_addr
-
-    declare void @__quantum__qis__h(%Qubit*) local_unnamed_addr
-
-    declare i8* @__quantum__rt__array_get_element_ptr_1d(%Array*, i64) local_unnamed_addr
-
-    declare %Array* @__quantum__rt__qubit_allocate_array(i64) local_unnamed_addr
-
-    define void @__nvqpp__mlirgen__ghz(i32 %0) local_unnamed_addr {
-      %2 = sext i32 %0 to i64
-      %3 = tail call %Array* @__quantum__rt__qubit_allocate_array(i64 %2)
-      %4 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 0)
-      %5 = bitcast i8* %4 to %Qubit**
-      %6 = load %Qubit*, %Qubit** %5, align 8
-      tail call void @__quantum__qis__h(%Qubit* %6)
-      %7 = add i32 %0, -1
-      %8 = icmp sgt i32 %7, 0
-      br i1 %8, label %.lr.ph.preheader, label %._crit_edge
-
-    .lr.ph.preheader:                                 ; preds = %1
-      %wide.trip.count = zext i32 %7 to i64
-      br label %.lr.ph
-
-    .lr.ph:                                           ; preds = %.lr.ph.preheader, %.lr.ph
-      %indvars.iv = phi i64 [ 0, %.lr.ph.preheader ], [ %indvars.iv.next, %.lr.ph ]
-      %9 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 %indvars.iv)
-      %10 = bitcast i8* %9 to %Qubit**
-      %11 = load %Qubit*, %Qubit** %10, align 8
-      %indvars.iv.next = add nuw nsw i64 %indvars.iv, 1
-      %12 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 %indvars.iv.next)
-      %13 = bitcast i8* %12 to %Qubit**
-      %14 = load %Qubit*, %Qubit** %13, align 8
-      tail call void (i64, void (%Array*, %Qubit*)*, ...) @invokeWithControlQubits(i64 1, void (%Array*, %Qubit*)* nonnull @__quantum__qis__x__ctl, %Qubit* %11, %Qubit* %14)
-      %exitcond.not = icmp eq i64 %indvars.iv.next, %wide.trip.count
-      br i1 %exitcond.not, label %._crit_edge, label %.lr.ph
-
-    ._crit_edge:                                      ; preds = %.lr.ph, %1
-      %15 = tail call i64 @__quantum__rt__array_get_size_1d(%Array* %3)
-      %16 = icmp sgt i64 %15, 0
-      br i1 %16, label %.lr.ph3, label %._crit_edge4
-
-    .lr.ph3:                                          ; preds = %._crit_edge, %.lr.ph3
-      %17 = phi i64 [ %22, %.lr.ph3 ], [ 0, %._crit_edge ]
-      %18 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 %17)
-      %19 = bitcast i8* %18 to %Qubit**
-      %20 = load %Qubit*, %Qubit** %19, align 8
-      %21 = tail call %Result* @__quantum__qis__mz(%Qubit* %20)
-      %22 = add nuw nsw i64 %17, 1
-      %exitcond5.not = icmp eq i64 %22, %15
-      br i1 %exitcond5.not, label %._crit_edge4, label %.lr.ph3
-
-    ._crit_edge4:                                     ; preds = %.lr.ph3, %._crit_edge
-      tail call void @__quantum__rt__qubit_release_array(%Array* %3)
-      ret void
-    }
-
-    !llvm.module.flags = !{!0}
-
-    !0 = !{i32 2, !"Debug Info Version", i32 3}
-
-
-Note that the results of each tool can be piped to further tools, creating a
-composable pipeline of compiler lowering tools. 
-
-
diff --git a/pr-2308/_sources/using/extending/extending.rst.txt b/pr-2308/_sources/using/extending/extending.rst.txt
deleted file mode 100644
index fd755f7969..0000000000
--- a/pr-2308/_sources/using/extending/extending.rst.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Extending CUDA-Q
-****************************
-
-.. toctree::
-  :maxdepth: 1
-
-  Create a new NVQIR Simulator <nvqir_simulator.rst>
-  Working with CUDA-Q IR <cudaq_ir.rst>
-  Create an MLIR Pass for CUDA-Q <mlir_pass.rst>
diff --git a/pr-2308/_sources/using/extending/mlir_pass.rst.txt b/pr-2308/_sources/using/extending/mlir_pass.rst.txt
deleted file mode 100644
index 8d61325ea1..0000000000
--- a/pr-2308/_sources/using/extending/mlir_pass.rst.txt
+++ /dev/null
@@ -1,97 +0,0 @@
-Create your own CUDA-Q Compiler Pass 
-******************************************
-
-The CUDA-Q IR can be transformed, analyzed, or optimized 
-using standard MLIR patterns and tools. CUDA-Q provides a registration 
-mechanism for the :code:`cudaq-opt` tool that allows one to create, load, and 
-use custom MLIR passes on Quake code. 
-
-CUDA-Q MLIR Passes can only be created within an existing CUDA-Q 
-development environment. Therefore, you must clone the repository and add your 
-Pass code as part of the existing CUDA-Q CMake system. 
-
-As an example, clone the repository and add the following directory structure 
-under :code:`lib`, :code:`lib/Plugins/MyCustomPlugin/`. Within this directory create a 
-:code:`CMakeLists.txt` file and a :code:`MyCustomPlugin.cpp` file. In the CMake file, 
-add the following: 
-
-.. code:: cmake 
-
-    add_llvm_pass_plugin(MyCustomPlugin MyCustomPlugin.cpp)
-
-Creating a CUDA-Q IR pass starts with the implementation of an 
-:code:`mlir::OperationPass`. A full discussion of the MLIR Pass infrastructure 
-is beyond the scope of this document; please see 
-`MLIR Passes <https://mlir.llvm.org/docs/PassManagement>`_. To create such 
-a pass, start with the following template in the :code:`MyCustomPlugin.cpp` file:
-
-.. code:: cpp 
-    
-    #include "cudaq/Optimizer/Dialect/Quake/QuakeDialect.h"
-    #include "cudaq/Optimizer/Dialect/Quake/QuakeOps.h"
-    #include "cudaq/Support/Plugin.h"
-    #include "mlir/Rewrite/FrozenRewritePatternSet.h"
-    #include "mlir/Transforms/DialectConversion.h"
-
-    // Here is an example MLIR Pass that one can write externally and 
-    // use via the cudaq-opt tool, with the --load-cudaq-plugin flag. 
-    // The pass here is simple, replace Hadamard operations with S operations. 
-
-    using namespace mlir;
-
-    namespace {
-
-    struct ReplaceH : public OpRewritePattern<quake::HOp> {
-      using OpRewritePattern::OpRewritePattern;
-      LogicalResult matchAndRewrite(quake::HOp hOp,
-                                    PatternRewriter &rewriter) const override {
-        rewriter.replaceOpWithNewOp<quake::SOp>(
-            hOp, hOp.isAdj(), hOp.getParameters(), hOp.getControls(),
-            hOp.getTargets());
-        return success();
-      }
-    };
-
-    class CustomPassPlugin
-        : public PassWrapper<CustomPassPlugin, OperationPass<func::FuncOp>> {
-    public:
-      MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(CustomPassPlugin)
-  
-      llvm::StringRef getArgument() const override { return "cudaq-custom-pass"; }
-
-      void runOnOperation() override {
-        auto circuit = getOperation();
-        auto ctx = circuit.getContext();
-
-        RewritePatternSet patterns(ctx);
-        patterns.insert<ReplaceH>(ctx);
-        ConversionTarget target(*ctx);
-        target.addLegalDialect<quake::QuakeDialect>();
-        target.addIllegalOp<quake::HOp>();
-        if (failed(applyPartialConversion(circuit, target, std::move(patterns)))) {
-          circuit.emitOpError("simple pass failed");
-          signalPassFailure();
-        }
-      }
-    };
-
-    } // namespace
-
-    CUDAQ_REGISTER_MLIR_PASS(CustomPassPlugin)
-
-This example serves as a very simple template for creating custom MLIR 
-Passes that analyze the CUDA-Q Quake representation and perform 
-some general transformation. In this example, we create a rewrite pattern 
-that replaces :code:`Hadamard` operations with :code:`S` operations. 
-
-Ensure that :code:`add_subdirectory(Plugins)` is in the :code:`lib/CMakeLists.txt` file, 
-and also that there is a :code:`lib/Plugins/CMakeLists.txt` file that adds your 
-plugin directory with :code:`add_subdirectory`.
-
-Then build CUDA-Q and you will have a :code:`MyCustomPlugin.so` plugin library 
-in the install. You can load the plugin and use it with :code:`cudaq-opt` as follows: 
-
-.. code:: bash 
-
-    cudaq-opt --load-cudaq-plugin MyCustomPlugin.so file.qke -cudaq-custom-pass
-
diff --git a/pr-2308/_sources/using/extending/nvqir_simulator.rst.txt b/pr-2308/_sources/using/extending/nvqir_simulator.rst.txt
deleted file mode 100644
index 899d2989c6..0000000000
--- a/pr-2308/_sources/using/extending/nvqir_simulator.rst.txt
+++ /dev/null
@@ -1,172 +0,0 @@
-Extending CUDA-Q with a new Simulator
-*******************************************
-
-Backend circuit simulation in CUDA-Q is enabled via the 
-NVQIR library (:code:`libnvqir`). CUDA-Q code is ultimately lowered 
-to the LLVM IR in a manner that is adherent to the `QIR specification <https://qir-alliance.org>`_.
-NVQIR provides function implementations for the various declared functions 
-in the specification, which in turn delegate to an extensible simulation 
-architecture. 
-
-The primary extension point for NVQIR is the :code:`CircuitSimulator` class. This class
-exposes an API that enables qubit allocation and deallocation, quantum operation 
-invocation, and measurement and sampling. Subtypes of this class are free to 
-override these methods to affect simulation of the quantum code in any 
-simulation-strategy-specific manner (e.g., state vector, tensor network, etc.). Moreover, 
-subtypes are free to implement simulators that leverage classical accelerated computing. 
-
-In this document, we'll detail this simulator interface and walk through how to extend it for new types of simulation. 
-
-:code:`CircuitSimulator`
-------------------------
-
-The :code:`CircuitSimulator` type is defined in :code:`runtime/nvqir/CircuitSimulator.h`. It
-exposes a public API to `libnvqir` that is immediately subclassed in the :code:`CircuitSimulatorBase` 
-type. This type is templated on the floating point type used in the simulator's computations (e.g. :code:`double,float`).
-This templated type handles a lot of the base functionality required for allocating and deallocated qubits, 
-as well as measurement, sampling, and observation under a number of execution contexts. 
-This is the type that downstream simulation developers should extend. 
-
-The actual definition of the quantum state data structure, and its overall evolution are 
-left as tasks for :code:`CircuitSimulatorBase` subclasses. Examples of simulation subtypes can be found 
-in :code:`runtime/nvqir/qpp/QppCircuitSimulator.cpp` or :code:`runtime/nvqir/custatevec/CuStateVecCircuitSimulator.cpp`.
-The :code:`QppCircuitSimulator` models the state vector using the `Q++ <https://github.com/softwareqinc/qpp>`_ library, which 
-leverages the :code:`Eigen::Matrix` type and OpenMP threading for matrix-vector operations. 
-The :code:`CuStateVecCircuitSimulator` type models the state vector on an NVIDIA GPU device 
-by leveraging the cuQuantum library. 
-
-The key methods that need to be overridden by subtypes of :code:`CircuitSimulatorBase` are as follows:
-
-.. list-table:: Required Circuit Simulator Subtype Method Overrides
-
-    * - Method Name 
-      - Method Arguments
-      - Method Description
-    * - :code:`addQubitToState`
-      - :code:`void` 
-      - Add a qubit to the underlying state representation.
-    * - :code:`addQubitsToState`
-      - :code:`nQubits : std::size_t` 
-      - Add the specified number of qubits to the underlying state representation.
-    * - :code:`resetQubit`
-      - :code:`qubitIdx : std::size_t`
-      - Reset the state of the qubit at the given index to :code:`|0>`
-    * - :code:`resetQubitStateImpl`
-      - :code:`void` 
-      - Clear the entire state representation (reset to 0 qubits).
-    * - :code:`applyGate`
-      - :code:`task : GateApplicationTask`
-      - Apply the specified gate described by the :code:`GateApplicationTask`. This type encodes the control and target qubit indices, optional rotational parameters, and the gate matrix data. 
-    * - :code:`measureQubit`
-      - :code:`qubitIdx : std::size_t -> bool` (returns bit result as bool)
-      - Measure the qubit, produce a bit result, collapse the state.
-    * - :code:`sample`
-      - :code`qubitIdxs : std::vector<std::size_t>, shots : int`
-      - Sample the current multi-qubit state on the provided qubit indices over a certain number of shots
-    * - :code:`name`
-      - :code:`void`
-      - Return the name of this CircuitSimulator, must be the same as the name used in :code:`nvq++ -qpu NAME ...`
-
-To extend a subtype class, you will need to create a new :code:`cpp` implementation file with the same name as your 
-subtype class name. In this file, you will subclass the :code:`CircuitSimulatorBase<FloatType>` and implement the methods in 
-the above table. Finally, the subclass must be registered with the NVQIR library so that it 
-can be picked up and used when a user specifies :code:`nvq++ --target mySimulator ...` from the command line (or :code:`cudaq.set_target('mySimulator')` in Python.)
-Type registration can be performed with a provided NVQIR macro, 
-
-.. code:: cpp 
-
-    NVQIR_REGISTER_SIMULATOR(MySimulatorClassName, mySimulator)
-
-where :code:`MySimulatorClassName` is the name of your subtype, and :code:`mySimulator` is the 
-same name as what :code:`MySimulatorClassName::name()` returns, and what you desire the 
-:code:`-qpu NAME` name to be. 
-
-A further requirement is that the code be compiled into its own standalone shared library 
-with name :code:`libnvqir-NAME.{so,dylib}`, where NAME is the 
-same name as what :code:`MySimulatorClassName::name()` returns, and what you desire the 
-:code:`-qpu NAME` name to be. You will also need to create a :code:`NAME.config` file that 
-contains the following contents 
-
-.. code:: bash 
-
-    NVQIR_SIMULATION_BACKEND="NAME"
-
-The library must be installed in :code:`$CUDA_QUANTUM_PATH/lib` and the configuration file 
-must be installed to :code:`$CUDA_QUANTUM_PATH/platforms`.
-
-Let's see this in action 
-------------------------
-
-CUDA-Q provides some CMake utilities to make the creation of your new simulation library 
-easier. Specifically, by using :code:`find_package(NVQIR)`, you'll get access to a :code:`nvqir_add_backend` function
-that will automate much of the boilerplate for creating your library and configuration file.
-
-Let's assume you want a simulation subtype named :code:`MySimulator`. You can create a folder or 
-repository for this code called :code:`my-simulator` and add :code:`MySimulator.cpp` and 
-:code:`CMakeLists.txt` files. Fill the CMake file with the following: 
-
-.. code:: cmake 
-
-    cmake_minimum_required(VERSION 3.24 FATAL_ERROR)
-    project(DemoCreateNVQIRBackend VERSION 1.0.0 LANGUAGES CXX)
-    find_package(NVQIR REQUIRED)
-    nvqir_add_backend(MySimulator MySimulator.cpp "")
-
-and then fill out your :code:`MySimulator.cpp` file with your subtype implementation. For example, 
-
-.. code:: cpp
-
-    #include "CircuitSimulator.h"
-
-    namespace {
-
-      class MySimulator : public nvqir::CircuitSimulatorBase<double> {
-
-      protected:
-        /// @brief Grow the state vector by one qubit.
-        void addQubitToState() override { ... }
-
-        /// @brief Grow the state vector by `count` qubit.
-        void addQubitsToState(std::size_t count) override { ... }
-
-        /// @brief Reset the qubit state.
-        void resetQubitStateImpl() override { ... }
-
-        /// @brief Apply the given gate
-        void applyGate(const GateApplicationTask &task) override { ... }
-
-      public:
-        MySimulator() = default;
-        virtual ~MySimulator() = default;
-        
-        bool measureQubit(std::size_t qubitIdx) override { ... }
-
-        void resetQubit(std::size_t &qubitIdx) override { ... }
-
-        cudaq::SampleResult sample(std::vector<std::size_t> &measuredBits,
-                              int shots) override { ... }
-
-        const std::string_view name() const override { return "MySimulator"; }
-      
-      };
-
-    } // namespace
-
-    /// Register this Simulator with NVQIR.
-    NVQIR_REGISTER_SIMULATOR(MySimulator)
-
-To build, install, and use this simulation backend, run the following from the top-level of :code:`my-simulator`:
-
-.. code:: bash 
-
-    export CUDA_QUANTUM_PATH=/path/to/cuda_quantum/install
-    mkdir build && cd build 
-    cmake .. -G Ninja -DNVQIR_DIR="$CUDA_QUANTUM_PATH/lib/cmake/nvqir"
-    ninja install 
-
-Then given any CUDA-Q source file, you can compile and target your backend simulator with the following: 
-
-.. code:: bash 
-
-    nvq++ file.cpp --target MySimulator 
-    ./a.out 
diff --git a/pr-2308/_sources/using/install/data_center_install.rst.txt b/pr-2308/_sources/using/install/data_center_install.rst.txt
deleted file mode 100644
index 8b0fb1a0d0..0000000000
--- a/pr-2308/_sources/using/install/data_center_install.rst.txt
+++ /dev/null
@@ -1,476 +0,0 @@
-Installation from Source
-=========================
-
-In most cases, you should not need to build CUDA-Q from source. For the
-best experience, we recommend using a container runtime to avoid conflicts with
-other software tools installed on the system. Note that `Singularity
-<https://docs.sylabs.io/guides/2.6/user-guide/faq.html#what-is-so-special-about-singularity>`__
-or `Docker rootless mode <https://docs.docker.com/engine/security/rootless/>`__
-address common issue or concerns that are often the motivation for
-avoiding the use of containers. Singularity, for example, can be installed 
-in a user folder and its installation does not require admin permissions; see
-:ref:`this section <install-singularity-image>` for more detailed instructions 
-on how to do that. Our installation guide also contains instructions for how to 
-:ref:`connect an IDE <local-development-with-vscode>` to a running container.
-
-If you do not want use a container runtime, we also provide pre-built binaries
-for using CUDA-Q with C++, and Python wheels for using CUDA-Q with Python. 
-These binaries and wheels are built following the instructions 
-in this guide and should work for you as long as your system meets the compatibility 
-requirements listed under :ref:`Prerequisites <compatibility-prebuilt-binaries>`. 
-To install the pre-built binaries, please follow the instructions 
-:ref:`here <install-prebuilt-binaries>`. To install the Python wheels, please 
-follow the instructions :ref:`here <install-python-wheels>`.
-
-If your system is not listed as supported by our official packages, e.g. because you would 
-like to use CUDA-Q on an operating system that uses an older C standard library, 
-please follow this guide carefully without skipping any steps to build and install
-CUDA-Q from source. The rest of this guide details system requirements 
-during the build and after installation, and walks through the installation steps.
-
-.. note::
-
-  CUDA-Q contains some components that are only included as
-  pre-built binaries and not part of our open source repository. We are working on
-  either open-sourcing these components or making them available as separate downloads
-  in the future. Even without these components, almost all features of CUDA-Q
-  will be enabled in a source build, though some pieces may be less performant. 
-  At this time, the :ref:`multi-GPU state vector simulator <nvidia-mgpu-backend>` 
-  backend will not be included if you build CUDA-Q from source.
-
-
-.. _compatibility-prebuilt-binaries:
-
-Prerequisites
-++++++++++++++++++++++++++++++++++++
-
-The following pre-requisites need to be satisfied both on the build system and
-on the host system, that is the system where the built CUDA-Q binaries
-will be installed and used.
-
-- Linux operating system. The instructions in this guide have been validated
-  with the `AlmaLinux 8 image <https://hub.docker.com/u/almalinux>`__ that
-  serves as the base image for the `manylinux_2_28 image
-  <https://github.com/pypa/manylinux>`__, and should work for the operating
-  systems CentOS 8, Debian 11 and 12, Fedora 38 and 39, OpenSUSE/SLED/SLES 15.5
-  and 15.6, RHEL 8 and 9, Rocky 8 and 9, and Ubuntu 24.04 and 22.04. Other 
-  operating systems may work, but have not been tested.
-- `Bash <https://www.gnu.org/software/bash/>`__ shell. The CUDA-Q 
-  build, install and run scripts expect to use `/bin/bash`.
-- `GNU C library <https://www.gnu.org/software/libc/>`__. 
-  Make sure that the version on the host system is the same one
-  or newer than the version on the build system. Our own builds
-  use version 2.28.
-- CPU with either x86-64 (x86-64-v3 architecture and newer) or ARM64
-  (ARM v8-A architecture and newer). Other architectures may work but are not
-  tested and may require adjustments to the build instructions.
-- Needed **only on the host** system: NVIDIA GPU with Volta, Turing, Ampere, Ada, or
-  Hopper architecture and `Compute Capability
-  <https://developer.nvidia.com/cuda-gpus>`__ 7+. Make sure you have the latest
-  `drivers <https://www.nvidia.com/download/index.aspx>`__ installed for your
-  GPU, and double check that the driver version listed by the `nvidia-smi`
-  command is 470.57.02 or newer. You do *not* need to have a GPU available on the
-  build system; the CUDA compiler needed for the build can be installed and used
-  without a GPU.
-
-We strongly recommend using a virtual environment for the build that includes
-*only* the tools and dependencies listed in this guide. If you have additional
-software installed, you will need to make sure that the build is linking against
-the correct libraries and versions.
-
-
-Build Dependencies
-++++++++++++++++++++++++++++++++++++
-
-In addition to the prerequisites listed above, you will need to install the
-following prerequisites in your build environment prior to proceeding with 
-the build as described in the subsequent sections:
-
-- Python version 3.10 or newer: If you intend to build CUDA-Q with Python
-  support, make sure the Python version on the build system matches the version
-  on the host system. If you intend to only build the C++ support for
-  CUDA-Q, the Python interpreter is required only for some of the 
-  LLVM build scripts and the Python version used for the build does not have 
-  to match the version on the host system.
-- Common tools: `wget`, `git`, `unzip`. The commands in the rest of this guide assume
-  that these tools are present on the build system, but they can be replaced by
-  other alternatives (such as, for example, manually going to a web page and
-  downloading a file/folder).
-
-The above prerequisites are no longer needed once CUDA-Q is built and 
-do not need to be present on the host system.
-
-.. note::
-
-  The CUDA-Q build scripts and the commands listed in the rest of this 
-  document assume you are using `bash` as the shell for your build.
-
-In addition to installing the needed build dependencies listed above, make sure
-to set the following environment variables prior to proceeding:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>InstallLocations]
-    :end-before: [<InstallLocations]
-
-These environment variables *must* be set during the build. We strongly
-recommend that their value is set to a path that does *not* already exist; 
-this will ensure that these components are built/installed as needed when
-building CUDA-Q.
-The configured paths can be chosen freely, but the paths specified during the
-build are also where the corresponding libraries will be installed on the 
-host system. We are working on making this more flexible in the future.
-
-.. note::
-
-  Please do **not** set `LLVM_INSTALL_PREFIX` to an existing directory;
-  To avoid compatibility issues, it is important to use the same compiler
-  to build the LLVM/MLIR dependencies from source as is later used to 
-  build CUDA-Q itself.
-
-.. note::
-
-  If you are setting the `CURL_INSTALL_PREFIX` variable to an existing 
-  CURL installation (not recommended), please make sure the command 
-  `curl --version` lists HTTP and HTTPS as supported protocols. If these 
-  protocols are not listed, please instead set the `CURL_INSTALL_PREFIX` 
-  variable to a path that does *not* exist. In that case, a suitable 
-  library will be automatically built from source as part of
-  building CUDA-Q.
-
-If you deviate from the instructions below for installing one of the
-dependencies and instead install it, for example, via package manager, you will
-need to make sure that the installation path matches the path you set for the
-corresponding environment variable(s).
-
-CUDA
-~~~~~~~~~~
-
-Building CUDA-Q requires a full installation of the CUDA toolkit.
-**You can install the CUDA toolkit and use the CUDA compiler without having a GPU.**
-The instructions are tested using version 11.8 and 12.0, but other CUDA 11 or 12 versions
-should work, as long as the CUDA runtime version on the host system matches the 
-CUDA version used for the build, and the installed driver on the host 
-system supports that CUDA version. We recommend using the latest CUDA version
-that is supported by the driver on the host system.
-
-Download a suitable `CUDA version <https://developer.nvidia.com/cuda-toolkit-archive>`__
-following the installation guide for your platform in the online documentation
-linked on that page.
-
-Within the tested AlmaLinux 8 environment, for example, the following commands
-install CUDA 12.0:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAInstall]
-    :end-before: [<CUDAInstall]
-
-Toolchain
-~~~~~~~~~~
-
-The compiler toolchain used for the build must be a supported 
-`CUDA host compiler <https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#supported-host-compilers>`__
-for the installed CUDA version.
-The following instructions have been tested with `GCC-11 <https://gcc.gnu.org/index.html>`__.
-Other toolchains may be supported but have not been tested.
-
-Within the tested AlmaLinux 8 environment, for example, the following commands
-install GCC 11:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>gccInstall]
-    :end-before: [<gccInstall]
-
-Independent on which compiler toolchain you installed, set the following
-environment variables to point to the respective compilers on your build system:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>ToolchainConfiguration]
-    :end-before: [<ToolchainConfiguration]
-
-- The variables `CC` and `CXX` *must* be set for the CUDA-Q build.
-- To use GPU-acceleration in CUDA-Q, make sure to set `CUDACXX` to 
-  your CUDA compiler, and `CUDAHOSTCXX` to the CUDA compatible host 
-  compiler you are using. If the CUDA compiler is not found when building
-  CUDA-Q, some components and backends will be omitted automatically 
-  during the build.
-
-
-Building CUDA-Q
-++++++++++++++++++++++++++++++++++++
-
-This installation guide has been written for a specific version/commit of CUDA-Q. 
-Make sure to obtain the source code for that version. 
-Clone the CUDA-Q `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__ and
-checkout the appropriate branch, tag, or commit. 
-Note that the build scripts assume that they are run from within a git repository, 
-and merely downloading the source code as ZIP archive hence will not work.
-
-Please follow the instructions in the respective subsection(s) to build the necessary 
-components for using CUDA-Q from C++ and/or Python.
-After the build, check that the GPU-accelerated components have been built by confirming
-that the file `nvidia.config` exists in the `$CUDAQ_INSTALL_PREFIX/targets` folder.
-We also recommend checking the build log printed to the console to confirm that all desired 
-components have been built. 
-
-.. note::
-
-  The CUDA-Q build will compile or omit optional components automatically depending
-  on whether the necessary pre-requisites are found in the build environment.
-  If you see a message that a component has been skipped, and/or the CUDA compiler
-  is not properly detected, make sure you followed the 
-  instructions for installing the necessary prerequisites and build dependencies, 
-  and have set the necessary environment variables as described in this document.
-
-.. _cudaq-python-from-source:
-
-Python Support
-+++++++++++++++++++++++++++++++
-
-The most convenient way to enable Python support within CUDA-Q is to build 
-a `wheel <https://pythonwheels.com/>`__ that can then easily be installed
-using `pip`. To ensure the wheel can be installed on the host system, make sure to
-use the same Python version for the build as the one that is installed on the host system.
-To build a CUDA-Q Python wheel, you will need to install the following additional 
-Python-specific tools:
-
-- Python development headers: The development headers for your Python version are installed
-  in the way as you installed Python itself. If you installed Python via the package manager
-  for your system, you may need to install an additional package to get the development headers.
-  The package name is usually your python version followed by either a `-dev` or `-devel` suffix.
-  If you are using a `Conda environment <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-python.html>`__,
-  the necessary headers should already be installed.
-- Pip package manager: Make sure the `pip` module is enable for your Python version, and that your `pip` version is 24 or newer.
-  We refer to the Python `documentation <https://pip.pypa.io/en/stable/installation/>`__ for
-  more information about installing/enabling `pip`.
-- Python modules: Install the additional modules `numpy`, `build`, `auditwheel`, and `patchelf` for your
-  Python version, e.g. `python3 -m pip install numpy build auditwheel patchelf`.
-
-.. note::
-
-  The wheel build by default is configured to depend on CUDA 12. To build a wheel for CUDA 11, 
-  you need to adjust the dependencies and project name in the `pyproject.toml` file. 
-
-From within the folder where you cloned the CUDA-Q repository, run the following
-command to build the CUDA-Q Python wheel:
-
-.. literalinclude:: ../../../../docker/build/assets.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumPythonBuild]
-    :end-before: [<CUDAQuantumPythonBuild]
-
-.. note::
-
-  A version identifier will be automatically assigned to the wheel based on the commit
-  history. You can manually override this detection to give a more descriptive identifier
-  by setting the environment variable `SETUPTOOLS_SCM_PRETEND_VERSION` to the desired
-  value before building the wheel.
-
-After the initial build, `auditwheel <https://github.com/pypa/auditwheel>`__ is used to 
-include dependencies in the wheel, if necessary, and correctly label the wheel.
-We recommend not including the CUDA runtime libraries and instead install them separately
-on the host system following the instructions in the next section. The following 
-command builds the final wheel, not including CUDA dependencies: 
-
-.. literalinclude:: ../../../../docker/build/assets.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumWheel]
-    :end-before: [<CUDAQuantumWheel]
-
-The command above will create a new wheel in the `wheelhouse` folder. This wheel can be
-installed on any `compatible platform <https://packaging.python.org/en/latest/specifications/platform-compatibility-tags/>`__.
-
-.. note::
-  You can confirm that the wheel is indeed compatible with your host platform by 
-  checking that the wheel tag (i.e. the file name ending of the `.whl` file) is listed under
-  "Compatible Tags" when running the command `python3 -m pip debug --verbose` on the host.
-
-.. _cudaq-cpp-from-source:
-
-C++ Support
-+++++++++++++++++++++++++++++++
-
-From within the folder where you cloned the CUDA-Q repository, run the following
-command to build CUDA-Q:
-
-.. literalinclude:: ../../../../docker/build/assets.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumCppBuild]
-    :end-before: [<CUDAQuantumCppBuild]
-
-Note that `lld` is primarily needed when the build or host system does not already
-have an existing default linker on its path; CUDA-Q supports the same linkers as
-`clang` does.
-
-To easily migrate the built binaries to the host system, we recommend creating a
-`self-extracting archive <https://makeself.io/>`__. To do so, download the 
-`makeself script(s) <https://github.com/megastep/makeself>`__ and move the necessary 
-files to install into a separate folder using the command
-
-.. literalinclude:: ../../../../docker/release/installer.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumAssets]
-    :end-before: [<CUDAQuantumAssets]
-
-You can then create a self-extracting archive with the command
-
-.. code-block:: bash
-
-    ./makeself.sh --gzip --sha256 --license cuda_quantum_assets/cudaq/LICENSE \
-        cuda_quantum_assets install_cuda_quantum.$(uname -m) \
-        "CUDA-Q toolkit for heterogeneous quantum-classical workflows" \
-        bash cudaq/migrate_assets.sh -t /opt/nvidia/cudaq
-
-Installation on the Host
-++++++++++++++++++++++++++++++++++++
-
-Make sure your host system satisfies the `Prerequisites`_ listed above.
-
-- To use CUDA-Q with Python, you should have a working
-  Python installation on the host system, including the `pip` package manager.
-- To use CUDA-Q with C++, you should make sure that you have the necessary development
-  headers of the C standard library installed. You can check this by searching for
-  `features.h`, commonly found in `/usr/include/`. You can install the necessary headers
-  via package manager (usually the package name is called something like `glibc-devel`
-  or `libc6-devel`). These headers are also included with any installation of GCC.
-
-If you followed the instructions for building the 
-:ref:`CUDA-Q Python wheel <cudaq-python-from-source>`,
-copy the built `.whl` file to the host system, and install it using `pip`; e.g.
-
-.. code-block:: bash
-
-    pip install cuda_quantum*.whl
-
-.. TODO: update pypi links throughout the docs
-
-To install the necessary CUDA and MPI dependencies for some of the components, 
-you can either follow the instructions on `PyPI.org <https://pypi.org/project/cuda-quantum/>`__, 
-replacing `pip install cudaq` with the command above, or you can follow the
-instructions in the remaining sections of this document to customize and better
-optimize them for your host system.
-
-If you followed the instructions for building the 
-:ref:`CUDA-Q C++ tools <cudaq-cpp-from-source>`,
-copy the `install_cuda_quantum` file that you created to the host system, 
-and install it by running the commands
-
-.. code-block:: bash
-
-    sudo bash install_cuda_quantum.$(uname -m) --accept
-    . /opt/nvidia/cudaq/set_env.sh
-
-This will extract the built assets and move them to the correct locations.
-The `set_env.sh` script in `/opt/nvidia/cudaq` defines the necessary environment
-variables to use CUDA-Q. To avoid having to set them manually every time a 
-new shell is opened, we highly recommend adding the following lines to
-the `/etc/profile` file:
-
-.. code-block:: bash
-
-    if [ -f /opt/nvidia/cudaq/set_env.sh ];
-      . /opt/nvidia/cudaq/set_env.sh
-    fi
-
-.. note::
-  CUDA-Q as built following the instructions above includes and uses the LLVM
-  C++ standard library. This will not interfere with any other C++ standard library
-  you may have on your system. Pre-built external libraries, you may want to use with 
-  CUDA-Q, such as specific optimizers for example, have a C API to ensure compatibility
-  across different versions of the C++ standard library and will work with CUDA-Q without 
-  issues. The same is true for all distributed CUDA libraries. To build you own CUDA 
-  libraries that can be used with CUDA-Q, please take a look at :doc:`../integration/cuda_gpu`.
-
-The remaining sections in this document list additional runtime dependencies 
-that are not included in the migrated assets and are needed to use some of the 
-CUDA-Q features and components.
-
-CUDA Runtime Libraries
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-To use GPU-acceleration in CUDA-Q you will need to install the necessary 
-CUDA runtime libraries. Their version (at least the version major) needs to match the version
-used for the build. While not necessary, we recommend installing 
-the complete CUDA toolkit like you did for the CUDA-Q build.
-If you prefer to only install the minimal set of runtime libraries, the following 
-commands, for example, install the necessary packages for the AlmaLinux 8 environment:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>CUDARTInstall]
-    :end-before: [<CUDARTInstall]
-
-MPI
-~~~~~~
-
-To work with all CUDA-Q backends, a CUDA-aware MPI installation is required. 
-If you do not have an existing CUDA-aware MPI installation, you can build one from 
-source. To do so, in addition to the CUDA runtime libraries listed above 
-you will need to install the CUDA runtime development package 
-(`cuda-cudart-devel-${version_suffix}` or `cuda-cudart-dev-${version_suffix}`, 
-depending on your distribution).
-
-The following commands build a sufficient CUDA-aware OpenMPI installation.
-To make best use of MPI, we recommend a more fully featured installation including
-additional configurations that fit your host system.
-The commands below assume you have the necessary prerequisites for the OpenMPI build
-installed on the build system. Within the tested AlmaLinux 8 environment, for example,
-the packages `autoconf`, `libtool`, `flex`, and `make` need to be installed.
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>OpenMPIBuild]
-    :end-before: [<OpenMPIBuild]
-
-Confirm that you have a suitable MPI implementation installed. For OpenMPI and MPICH,
-for example, this can be done by compiling and running the following program:
-
-.. literalinclude:: ../../../../docker/test/installer/mpi_cuda_check.cpp
-    :language: cpp
-
-.. note::
-
-  If you are encountering an error similar to "The value of the MCA parameter `plm_rsh_agent` 
-  was set to a path that could not be found", please make sure you have an SSH Client installed
-  or update the MCA parameter to another suitable agent.
-  MPI uses `SSH <https://en.wikipedia.org/wiki/Secure_Shell>`__ or
-  `RSH <https://en.wikipedia.org/wiki/Remote_Shell>`__ to communicate with each node
-  unless another resource manager, such as
-  `SLURM <https://slurm.schedmd.com/overview.html>`__, is used. 
-
-Different MPI implementations are supported via a plugin infrastructure in CUDA-Q.
-Once you have a CUDA-aware MPI installation on your host system, you can 
-configure CUDA-Q to use it by activating the necessary plugin.
-Plugins for OpenMPI and MPICH are included in CUDA-Q and can be activated by
-setting the environment variable `MPI_PATH` to the MPI installation folder 
-and then running the command
-
-.. code-block:: bash
-
-    bash "${CUDA_QUANTUM_PATH}/distributed_interfaces/activate_custom_mpi.sh"
-
-.. note::
-
-  To activate the MPI plugin for the Python support, replace `${CUDA_QUANTUM_PATH}`
-  with the path that is listed under "Location" when you run the command
-  `pip show cuda-quantum`.
-
-If you use a different MPI implementation than OpenMPI or MPICH, you will need to 
-implement the necessary plugin interface yourself prior to activating the plugin 
-with the command above.
-
-.. TODO:
-  For more information about building and activating a custom MPI plugin, see ...
diff --git a/pr-2308/_sources/using/install/install.rst.txt b/pr-2308/_sources/using/install/install.rst.txt
deleted file mode 100644
index ad3e59decb..0000000000
--- a/pr-2308/_sources/using/install/install.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-Installation Guide
-*******************************************
-
-.. _install-cuda-quantum:
-
-.. toctree::
-  :caption: Contents
-  :maxdepth: 1
-
-  Local Installation <local_installation.rst>
-  Data Center Installation <data_center_install.rst>
diff --git a/pr-2308/_sources/using/install/local_installation.rst.txt b/pr-2308/_sources/using/install/local_installation.rst.txt
deleted file mode 100644
index 737ca2a336..0000000000
--- a/pr-2308/_sources/using/install/local_installation.rst.txt
+++ /dev/null
@@ -1,933 +0,0 @@
-Local Installation
-===================
-
-Introduction
--------------------
-
-This guide walks through how to :ref:`install CUDA-Q <install-cuda-quantum>` on your system, and how to set up :ref:`VS Code for local development <local-development-with-vscode>`.
-The section on :ref:`connecting to a remote host <connect-to-remote>` contains some
-guidance for application development on a remote host where CUDA-Q is installed.
-
-The following sections contain instructions for how to install CUDA-Q on your machine using
-
-- :ref:`Docker <install-docker-image>`: A fully featured CUDA-Q installation including all C++ and Python tools is available as a `Docker <https://docs.docker.com/get-started/overview/>`__ image.
-- :ref:`Singularity <install-singularity-image>`: A `Singularity <https://docs.sylabs.io/guides/latest/user-guide/introduction.html>`__ container can easily be created based on our Docker images. 
-- :ref:`PyPI <install-python-wheels>`: Additionally, we distribute pre-built Python wheels via `PyPI <https://pypi.org>`__.
-- :ref:`Pre-built binaries <install-prebuilt-binaries>`: We also provide pre-built C++ binaries, bundled as `self-extracting archive <https://makeself.io/>`__, that work across a range of Linux operating systems.
-
-If you would like to build CUDA-Q from source to deploy on an HPC system without relying on a container runtime, please follow the instructions for :doc:`data_center_install`. 
-If, on the other hand, you want to contribute to the development of CUDA-Q itself and hence want to 
-build a custom version of CUDA-Q from source, follow the instructions on the 
-`CUDA-Q GitHub repository <https://github.com/NVIDIA/cuda-quantum/blob/main/Building.md>`__ instead.
-
-If you are unsure which option suits you best, we recommend using our :ref:`Docker image <install-docker-image>` to develop your applications in a controlled environment that does not depend on, or interfere with, other software
-that is installed on your system.
-
-.. _install-docker-image:
-
-Docker
-++++++++++++++++++++++++++++++++++++
-
-To download and use our Docker images, you will need to install and launch the Docker engine. 
-If you do not already have Docker installed on your system, you can get it by downloading and installing `Docker Desktop <https://docs.docker.com/get-docker/>`_. 
-If you do not have the necessary administrator permissions to install software on your machine,
-take a look at the section below on how to use `Singularity`_ instead.
-
-Docker images for all CUDA-Q releases are available on the `NGC Container Registry`_.
-In addition to publishing `stable releases <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__, 
-we also publish Docker images whenever we update certain branches on our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`_.
-These images are published in our `nightly channel on NGC <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum/tags>`__.
-To download the latest version on the main branch of our GitHub repository, built to work with CUDA 12, for example, use the command
-
-.. code-block:: console
-
-    docker pull nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-.. _NGC Container Registry: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum
-
-Early prototypes for features we are considering can be tried out by using the image tags starting 
-with `experimental`. The `README` in the `/home/cudaq` folder in the container gives more details 
-about the feature. We welcome and appreciate your feedback about these early prototypes; 
-how popular they are will help inform whether we should include them in future releases.
-
-Once you have downloaded an image, the container can be run using the command
-
-.. code-block:: console
-
-    docker run -it --name cuda-quantum nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-Replace the image name and/or tag in the command above, if necessary, with the one you want to use.
-This will give you terminal access to the created container. To enable support 
-for GPU-accelerated backends, you will need to pass the :code:`--gpus` flag when launching
-the container, for example:
-
-.. code-block:: console
-
-    docker run -it --gpus all --name cuda-quantum nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-.. note:: 
-
-  This command will fail if you do not have a suitable NVIDIA GPU available, or if your driver 
-  version is insufficient. To improve compatibility with older drivers, you may need to install the 
-  `NVIDIA container toolkit`_.
-
-.. _NVIDIA container toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
-
-You can stop and exit the container by typing the command :code:`exit`. If you did not specify
-:code:`--rm` flag when launching the container, the container still exists after exiting, as well as any 
-changes you made in it. You can get back to it using
-the command :code:`docker start -i cuda-quantum`. 
-You can delete an existing container and any changes you made using :code:`docker rm -v cuda-quantum`.
-
-When working with Docker images, the files inside the container are not visible outside the container environment. 
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-please take a look at the section on :ref:`Development with VS Code <docker-in-vscode>`.
-
-Alternatively, it is possible, but not recommended, to launch an SSH server inside the container environment and connect an IDE using SSH. To do so, make sure you have generated a suitable RSA key pair; if your `~/.ssh/` folder does not already contain the files `id_rsa.pub` and `id.rsa`,
-follow the instructions for generating a new SSH key on `this page <https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent>`__.
-You can then launch the container and connect to it via SSH by executing the following commands:
-
-.. code-block:: console
-
-    docker run -itd --gpus all --name cuda-quantum -p 2222:22 nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-    docker exec cuda-quantum bash -c "sudo apt-get install -y --no-install-recommends openssh-server"
-    docker exec cuda-quantum bash -c "sudo sed -i -E "s/#?\s*UsePAM\s+.+/UsePAM yes/g" /etc/ssh/sshd_config"
-    docker cp ~/.ssh/id_rsa.pub cuda-quantum:/home/cudaq/.ssh/authorized_keys
-    docker exec -d cuda-quantum bash -c "sudo -E /usr/sbin/sshd -D"
-    ssh cudaq@localhost -p 2222 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o GlobalKnownHostsFile=/dev/null
-
-
-.. _install-singularity-image:
-
-Singularity
-++++++++++++++++++++++++++++++++++++
-
-You can use `Singularity <https://github.com/sylabs/singularity>`__ to run a CUDA-Q container in a folder without needing administrator permissions.
-If you do not already have Singularity installed, you can build a relocatable installation from source. 
-To do so on Linux or WSL, make sure you have the `necessary prerequisites <https://docs.sylabs.io/guides/4.0/user-guide/quick_start.html#prerequisites>`__ installed, download a suitable version of the `go toolchain <https://docs.sylabs.io/guides/4.0/user-guide/quick_start.html#install-go>`__, and make sure the `go` binaries are on your :code:`PATH`. You can then build Singularity with the commands
-
-.. code-block:: console
-
-    wget https://github.com/sylabs/singularity/releases/download/v4.0.1/singularity-ce-4.0.1.tar.gz
-    tar -xzf singularity-ce-4.0.1.tar.gz singularity-ce-4.0.1/ && rm singularity-ce-4.0.1.tar.gz && cd singularity-ce-4.0.1/
-    ./mconfig --without-suid --prefix="$HOME/.local/singularity"
-    make -C ./builddir && make -C ./builddir install && cd .. && rm -rf singularity-ce-4.0.1/
-    echo 'PATH="$PATH:$HOME/.local/singularity/bin/"' >> ~/.profile && source ~/.profile
-
-For more information about using Singularity on other systems, take a look at the `admin guide <https://docs.sylabs.io/guides/4.0/admin-guide/installation.html#installation-on-windows-or-mac>`__.
-
-Once you have singularity installed, create a file `cuda-quantum.def` with the following content:
-
-.. code-block:: console
-
-    Bootstrap: docker
-    From: nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-    %runscript
-        mount devpts /dev/pts -t devpts
-        cp -r /home/cudaq/* .
-        bash
-
-Replace the image name and/or tag in the `From` line, if necessary, with the one you want to use;
-In addition to publishing `stable releases <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__, 
-we also publish Docker images whenever we update certain branches on our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`_.
-These images are published in our `nightly channel on NGC <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum/tags>`__.
-Early prototypes for features we are considering can be tried out by using the image tags starting 
-with `experimental`. We welcome and appreciate your feedback about these early prototypes; 
-how popular they are will help inform whether we should include them in future releases.
-
-You can then create a CUDA-Q container by running the following commands:
-
-.. code-block:: console
-
-    singularity build --fakeroot cuda-quantum.sif cuda-quantum.def
-    singularity run --writable --fakeroot cuda-quantum.sif
-
-In addition to the files in your current folder, you should now
-see a `README` file, as well as examples and tutorials.
-To enable support for GPU-accelerated backends, you will need to pass the
-the :code:`--nv` flag when running the container:
-
-.. code-block:: console
-
-    singularity run --writable --fakeroot --nv cuda-quantum.sif
-    nvidia-smi
-
-The output of the command above lists the GPUs that are visible and accessible in the container environment.
-
-.. note:: 
-
-  If you do not see any GPUs listed in the output of `nvidia-smi`, 
-  it means the container environment is unable to access a suitable NVIDIA GPU. 
-  This can happen if your driver version is insufficient, or if you are 
-  working on WSL. To improve compatibility with older drivers, or to enable GPU support
-  on WSL, please install the `NVIDIA container toolkit`_, and update the singularity configuration 
-  to set `use nvidia-container-cli` to `yes` and configure the correct `nvidia-container-cli path`. 
-  The two commands below use `sed` to do that:
-
-  .. code-block:: console
-
-    sed -i 's/use nvidia-container-cli = no/use nvidia-container-cli = yes/' "$HOME/.local/singularity/etc/singularity/singularity.conf"
-    sed -i 's/# nvidia-container-cli path =/nvidia-container-cli path = \/usr\/bin\/nvidia-container-cli/' "$HOME/.local/singularity/etc/singularity/singularity.conf"
-
-You can exit the container environment by typing the command :code:`exit`.
-Any changes you made will still be visible after you exit the container, and you can re-enable the 
-container environment at any time using the `run` command above.
-
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-please take a look at the section on :ref:`Development with VS Code <singularity-in-vscode>`.
-
-
-.. _install-python-wheels:
-
-Python wheels
-++++++++++++++++++++++++++++++++++++
-
-CUDA-Q Python wheels are available on `PyPI.org <https://pypi.org/project/cuda-quantum>`__. 
-Installation instructions can be found in the `project description <https://pypi.org/project/cuda-quantum/#description>`__.
-For more information about available versions and documentation,
-see :doc:`../../releases`.
-
-There are currently no source distributions available on PyPI, but you can download the 
-source code for the latest version of the CUDA-Q Python wheels from our 
-`GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__. 
-The source code for previous versions can be downloaded from the respective 
-`GitHub Release <https://github.com/NVIDIA/cuda-quantum/releases>`__.
-
-At this time, wheels are distributed for Linux operating systems only. 
-If your platform is not :ref:`officially supported <dependencies-and-compatibility>` and
-`pip` does not find a compatible wheel to install, you can build your own 
-wheel from source following the instructions here: :doc:`data_center_install`.
-
-To build the CUDA-Q Python API for the purpose of contributing to
-our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__,
-follow the instructions for 
-`Setting up your Environment <https://github.com/NVIDIA/cuda-quantum/blob/main/Dev_Setup.md>`__, 
-and then run the following commands in the repository root:
-
-.. code-block:: console
-
-    bash scripts/install_prerequisites.sh
-    pip install . --user
-
-.. _install-prebuilt-binaries:
-
-Pre-built binaries
-++++++++++++++++++++++++++++++++++++
-
-Starting with the 0.6.0 release, we provide pre-built binaries for using 
-CUDA-Q with C++. Support for using CUDA-Q with Python can be installed 
-side-by-side with the pre-built binaries for C++ by following the instructions on 
-`PyPI.org <https://pypi.org/project/cuda-quantum>`__.
-The pre-built binaries work across a range of Linux operating systems listed 
-under :ref:`dependencies-and-compatibility`. 
-
-Before installing our pre-built binaries, please make sure that your 
-operating system is using the `GNU C library <https://www.gnu.org/software/libc/>`__ 
-version 2.28 or newer. You can confirm this by checking the output of the command 
-`ldd --version`. If this command does not exist, or shows an older version than 2.28, 
-please double check that your operating system is listed as 
-:ref:`supported <dependencies-and-compatibility>`. If you use an operating system
-with an older GNU C library version, you will need to build the installer from 
-source following the instructions in :doc:`data_center_install`.
-
-You can download the `install_cuda_quantum` file for your processor architecture from
-the assets of the respective `GitHub release <https://github.com/NVIDIA/cuda-quantum/releases>`__. 
-The installer is a `self-extracting archive <https://makeself.io/>`__ that contains the 
-pre-built binaries as well as a script to move them to the correct locations. You will need
-`bash`, `tar`, and `gzip` (usually already installed on most Linux distributions) to run 
-the installer.
-The installation location of CUDA-Q is not currently configurable and using the installer
-hence requires admin privileges on the system. We may revise that in the future; please see and
-upvote the corresponding `GitHub issue <https://github.com/NVIDIA/cuda-quantum/issues/1075>`__.
-
-To install CUDA-Q, execute the command
-
-.. literalinclude:: ../../../../docker/test/installer/linux.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumInstall]
-    :end-before: [<CUDAQuantumInstall]
-
-.. note:: 
-
-  To use GPU-accelerated backends, you will need to install the necessary CUDA runtime libraries. 
-  For more information see the corresponding section on 
-  :ref:`Additional CUDA Tools <cuda-dependencies-prebuilt-binaries>`.
-
-The installation ensures that the necessary environment variables for
-using the CUDA-Q toolchain are set upon login for all POSIX shells.
-Confirm that the `nvq++` command is found. If it is not, please make sure 
-to set the environment variables defined by the `set_env.sh` script in the 
-CUDA-Q installation folder (usually `/usr/local/cudaq` or `/opt/nvidia/cudaq`).
-
-If an MPI installation is available in the directory defined by `MPI_PATH`, 
-the installer automatically enables MPI support in CUDA-Q.
-If you do not have MPI installed on your system, you can simply
-leave that path empty, and CUDA-Q will be installed without MPI support.
-If you install MPI at a later point in time, you can activate the MPI support in CUDA 
-Quantum by setting the `MPI_PATH` variable to its installation location and 
-executing the commands
-
-.. code-block:: console
-
-    MPI_PATH=/usr/local/openmpi # update this path as needed
-    bash "${CUDA_QUANTUM_PATH}/distributed_interfaces/activate_custom_mpi.sh"
-
-.. note::
-
-  Please make sure that you have the necessary development headers of the C standard 
-  library installed. You can check this by searching for `features.h`, commonly found
-  in `/usr/include/`. You can install the necessary headers via package manager 
-  (usually the package name is called something like `glibc-devel` or `libc6-dev`). 
-  These headers are also included with any installation of GCC.
-
-.. _local-development-with-vscode:
-
-Development with VS Code
-------------------------------------
-
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-we recommend using `VS Code <https://code.visualstudio.com/>`_. VS Code provides a seamless
-development experience on all platforms, and is also available without installation via web browser. 
-This sections describes how to connect VS Code to a running container on your machine.
-The section on :ref:`connecting to a remote host <connect-to-remote>` contains information on
-how to set up your development environment when accessing CUDA-Q on a remote host instead.
-
-.. _docker-in-vscode:
-
-Using a Docker container
-++++++++++++++++++++++++++++++++++++++++
-
-Before connecting VS Code, open a terminal/shell, 
-and start the CUDA-Q Docker container following the 
-instructions in the :ref:`section above <install-docker-image>`. 
-
-If you have a local installation of `VS Code <https://code.visualstudio.com/>`_ 
-you can connect to the running container using the  
-`Dev Containers <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers>`__ extension. If you want to use VS Code in the web browser, please follow the instructions
-in the section `Developing with Remote Tunnels`_ instead.
-
-.. |:spellcheck-disable:| replace:: \
-.. |:spellcheck-enable:| replace:: \
-
-After installing the
-`Dev Containers <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers>`__ extension, launch VS Code, open the Command Palette with `Ctrl+Shift+P`, and enter 
-|:spellcheck-disable:|"Dev Containers: Attach to Running Container"|:spellcheck-enable:|.
-You should see and select the running `cuda-quantum` container in the list. 
-After the window reloaded, enter "File: Open Folder" in the Command Palette to open the `/home/cudaq/` folder.
-
-To run the examples, open the Command Palette and enter "View: Show Terminal"
-to launch an integrated terminal. You are now all set to 
-:ref:`get started <post-installation>` with CUDA-Q development.
-
-.. _singularity-in-vscode:
-
-Using a Singularity container
-++++++++++++++++++++++++++++++++++++++++
-
-If you have a GitHub or Microsoft account, we recommend that you connect 
-to a CUDA-Q container using tunnels. To do so, launch a CUDA-Q Singularity 
-container following the instructions in the :ref:`section above <install-singularity-image>`,
-and then follow the instructions in the section `Developing with Remote Tunnels`_.
-
-If you cannot use tunnels, you need a local installation of 
-`VS Code <https://code.visualstudio.com/>`_ and you need to install 
-the `Remote - SSH <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh>`__ extension. 
-Make sure you also have a suitable SSH key pair; if your `~/.ssh/` folder does not already contain
-the files `id_rsa.pub` and `id.rsa`, follow the instructions for generating a new SSH key on 
-`this page <https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent>`__.
-
-To connect VS Code to a running CUDA-Q container, 
-the most convenient setup is to install and run an SSH server 
-in the Singularity container. Open a terminal/shell in a separate window,
-and enter the following commands to create a suitable sandbox:
-
-.. code-block:: console
-
-    singularity build --sandbox cuda-quantum-sandbox cuda-quantum.sif
-    singularity exec --writable --fakeroot cuda-quantum-sandbox \
-      apt-get install -y --no-install-recommends openssh-server
-    cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
-
-You can launch this sandbox by entering the commands below. Please see the `Singularity`_ section above
-for more information about how to get the `cuda-quantum.sif` image, and how to enable GPU-acceleration
-with the `--nv` flag.
-
-.. code-block:: console
-
-    singularity run --writable --fakeroot --nv --network-args="portmap=22:2222/tcp" cuda-quantum-sandbox
-    /usr/sbin/sshd -D -p 2222 -E sshd_output.txt
-
-.. note::
-
-  Make sure to use a free port. You can check if the SSH server is ready and listening
-  by looking at the log in `sshd_output.txt`. If the port is already in use, you can 
-  replace the number `2222` by any free TCP port in the range `1025-65535` in all
-  commands.
-
-Entering `Ctrl+C` followed by `exit` will stop the running container. You can re-start
-it at any time by entering the two commands above. While the container is running,
-open the Command Palette in VS Code with `Ctrl+Shift+P`, enter "Remote-SSH: Add new
-SSH Host", and enter the following SSH command:
-
-.. code-block:: console
-
-    ssh root@localhost -p 2222 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o GlobalKnownHostsFile=/dev/null
-
-.. note::
-
-  If you are working on Windows and are building and running the Singularity container in WSL,
-  make sure to copy the used SSH keys to the Windows partition, such that VS Code can connect with 
-  the expected key. Alternatively, add the used public key to the `/root/.ssh/authorized_keys` file in 
-  the Singularity container.
-
-You can then connect to the host by opening the Command Palette, entering
-"Remote SSH: Connect Current Window to Host", and choosing the newly created host.
-After the window reloaded, enter "File: Open Folder" in the 
-Command Palette to open the desired folder.
-
-To run the examples, open the Command Palette and enter "View: Show Terminal"
-to launch an integrated terminal. You are now all set to 
-:ref:`get started <post-installation>` with CUDA-Q development.
-
-
-.. _connect-to-remote:
-
-Connecting to a Remote Host
-------------------------------------
-
-Depending on the setup on the remote host, there are a couple of different options 
-for developing CUDA-Q applications.
-
-- If a CUDA-Q container is running on the remote host,
-  and you have a GitHub or Microsoft account, take a look at
-  `Developing with Remote Tunnels`_. This works for both Docker
-  and Singularity containers on the remote host, and should also
-  work for other containers.
-- If you cannot use tunnels, or if you want to work with an
-  existing CUDA-Q installation without using a container, 
-  take a look at `Remote Access via SSH`_ instead.
-
-.. _connect-vscode-via-tunnel:
-
-Developing with Remote Tunnels
-++++++++++++++++++++++++++++++++++++
-
-`Remote access via tunnel <https://code.visualstudio.com/blogs/2022/12/07/remote-even-better>`__
-can easily be enabled with the `VS Code CLI <https://code.visualstudio.com/docs/editor/command-line>`__.
-This allows to connect either a local installation of `VS Code <https://code.visualstudio.com/>`_, 
-or the `VS Code Web UI <https://vscode.dev/>`__, to a running CUDA-Q container on the same or a different machine. 
-
-Creating a secure connection requires authenticating with the same GitHub or Microsoft account on each end.
-Once authenticated, an SSH connection over the tunnel provides end-to-end encryption. To download the VS Code CLI, if necessary, and create a tunnel, execute the 
-following command in the running CUDA-Q container,
-and follow the instructions to authenticate:
-
-.. code-block:: console
-
-    vscode-setup tunnel --name cuda-quantum-remote --accept-server-license-terms
-
-You can then either `open VS Code in a web browser <https://vscode.dev/tunnel/cuda-quantum-remote/home/cudaq/>`__, or connect a local installation of VS Code.
-To connect a local installation of VS Code, make sure you have the 
-`Remote - Tunnels <https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-server>`__ extension installed,
-then open the Command Palette with `Ctrl+Shift+P`, enter "Remote Tunnels: Connect to Tunnel", 
-and enter `cuda-quantum-remote`. After the window reloaded, enter "File: Open Folder" in the Command Palette 
-to open the `/home/cudaq/` folder.
-
-You should see a pop up asking if you want to install the recommended extensions. Selecting to install them will
-configure VS Code with extensions for working with C++, Python, and Jupyter.
-You can always see the list of recommended extensions that aren't installed yet by clicking on the "Extensions" icon in the sidebar and navigating to the "Recommended" tab.
-
-Remote Access via SSH
-++++++++++++++++++++++++++++++++++++
-
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-you can connect a local installation of `VS Code <https://code.visualstudio.com/>`_ to a remote host via SSH. 
-
-.. note:: 
-
-  For the best user experience, we recommend to launch a CUDA-Q container on the remote host, 
-  and then connect :ref:`VS Code using tunnels <connect-vscode-via-tunnel>`.
-  If a connection via tunnel is not possible, this section describes using SSH instead.
-
-To do so, make sure you have `Remote - SSH <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh>`__ extension installed.
-Open the Command Palette with `Ctrl+Shift+P`, enter "Remote-SSH: Add new
-SSH Host", and enter the SSH command to connect to your account on the remote host.
-You can then connect to the host by opening the Command Palette, entering
-"Remote SSH: Connect Current Window to Host", and choosing the newly created host.
-
-When prompted, choose Linux as the operating system, and enter your
-password. After the window reloaded, enter "File: Open Folder" in the 
-Command Palette to open the desired folder. Our GitHub repository contains
-a folder with VS Code configurations including a list of recommended extensions for
-working with CUDA-Q; you can copy `these configurations <https://github.com/NVIDIA/cuda-quantum/tree/main/docker/release/config/.vscode>`__ into the a folder named `.vscode` in your workspace to use them.
-
-If you want to work with an existing CUDA-Q installation on the remote host, you are all set.
-Alternatively, you can use Singularity to build and run a container following the instructions in 
-:ref:`this section <install-singularity-image>`. Once the `cuda-quantum.sif` image is built and 
-available in your home directory on the remote host, you can update your VS Code configuration 
-to enable/improve completion, hover information, and other development tools within the container.
-
-To do so, open the Command Palette and enter "Remote-SSH: Open SSH Configuration File". 
-Add a new entry to that file with the command to launch the container, and edit the configuration 
-of the remote host, titled `remote-host` in the snippets below, to add a new identifier:
-
-.. code-block:: console
-
-    Host cuda-quantum~*
-      RemoteCommand singularity run --writable --fakeroot --nv ~/cuda-quantum.sif
-      RequestTTY yes
-
-    Host remote-host cuda-quantum~remote-host
-      HostName ...
-      ...
-
-You will need to edit a couple of VS Code setting to make use of the newly defined remote command;
-open the Command Palette, enter "Preferences: Open User Settings (JSON)", and add or update the 
-following configurations:
-
-.. code-block:: console
-
-    "remote.SSH.enableRemoteCommand": true,
-    "remote.SSH.useLocalServer": true,
-    "remote.SSH.remoteServerListenOnSocket": false,
-    "remote.SSH.connectTimeout": 120,
-    "remote.SSH.serverInstallPath": {
-        "cuda-quantum~remote-host": "~/.vscode-container/cuda-quantum",
-    },
-
-After saving the changes, you should now be able to select `cuda-quantum~remote-host` as the host
-when connecting via SSH, which will launch the CUDA-Q container and connect VS Code to it.
-
-.. note::
-
-  If the connection to `cuda-quantum~remote-host` fails, you may need to specify the full
-  path to the `singularity` executable on the remote host, since environment variables, 
-  and specifically the configured `PATH` may be different during launch than in your user account.
-
-DGX Cloud
---------------------------------
-
-If you are using `DGX Cloud <https://www.nvidia.com/en-us/data-center/dgx-cloud/>`__, 
-you can easily use it to run CUDA-Q applications.
-While submitting jobs to DGX Cloud directly from within CUDA-Q is not (yet) supported,
-you can use the NGC CLI to launch and interact with workloads in DGX Cloud.
-The following sections detail how to do that, and how to connect JupyterLab and/or VS Code
-to a running CUDA-Q job in DGX Cloud.
-
-.. _dgx-cloud-setup:
-
-Get Started
-+++++++++++++++++++++++++++++++
-
-To get started with DGX Cloud, you can 
-`request access here <https://www.nvidia.com/en-us/data-center/dgx-cloud/trial/>`__.
-Once you have access, `sign in <https://ngc.nvidia.com/signin>`__ to your account,
-and `generate an API key <https://ngc.nvidia.com/setup/api-key>`__. 
-`Install the NGC CLI <https://ngc.nvidia.com/setup/installers/cli>`__
-and configure it with 
-
-.. code-block:: console
-
-    ngc config set
-
-entering the API key you just generated when prompted, and configure other settings as appropriate.
-
-.. note::
-
-  The rest of this section assumes you have CLI version 3.33.0. If you 
-  have an older version installed, you can upgrade to the latest version using the command
-
-  .. code-block:: console
-
-      ngc version upgrade 3.33.0
-  
-  See also the `NGC CLI documentation <https://docs.ngc.nvidia.com/cli/index.html>`__
-  for more information about available commands.
-
-You can see all information about available compute resources and ace instances
-with the command 
-
-.. code-block:: console
-
-    ngc base-command ace list
-
-Confirm that you can submit a job with the command
-
-.. code-block:: console
-
-    ngc base-command job run \
-      --name Job-001 --total-runtime 60s \
-      --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \
-      --ace <ace_name> --instance <instance_name> \
-      --commandline 'echo "Hello from DGX Cloud!"'
-
-replacing `<ace_name>` and `<instance_name>` with the name of the ace and instance you want 
-to execute the job on.
-You should now see that job listed when you run the command
-
-.. code-block:: console
-
-    ngc base-command job list
-
-Once it has completed you can download the job results using the command
-
-.. code-block:: console
-
-    ngc base-command result download <job_id>
-
-replacing `<job_id>` with the id of the job you just submitted.
-You should see a new folder named `<job_id>` with the job log that contains 
-the output "Hello from DGX Cloud!".
-
-For more information about how to use the NGC CLI to interact with DGX Cloud, 
-we refer to the `NGC CLI documentation <https://docs.ngc.nvidia.com/cli/index.html>`__.
-
-Use JupyterLab
-+++++++++++++++++++++++++++++++
-
-Once you can :ref:`run jobs on DGX Cloud <dgx-cloud-setup>`, you can launch an interactive job 
-to use CUDA-Q with `JupyterLab <https://jupyterlab.readthedocs.io/en/latest/>`__ 
-running on DGX Cloud:
-
-.. code-block:: console
-
-    ngc base-command job run \
-      --name Job-interactive-001 --total-runtime 600s \
-      --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \
-      --ace <ace_name> --instance <instance_name> \
-      --port 8888 --commandline 'jupyter-lab-setup <my-custom-token> --port=8888'
-
-Replace `<my-custom-token>` in the command above with a custom token that you can freely choose.
-You will use this token to authenticate with JupyterLab;
-Go to the `job portal <https://bc.ngc.nvidia.com/jobs>`__, click on the job you just launched, and click on the link
-under |:spellcheck-disable:|"URL/Hostname"|:spellcheck-enable:| in Service Mapped Ports. 
-
-.. note::
-
-  It may take a couple of minutes for DGX Cloud to launch and for the URL to become active, even after it appears in the Service Mapped Ports section;
-  if you encounter a "404: Not Found" error, be patient and try again in a couple of minutes.
-
-Once this URL opens, you should see the JupyterLab authentication page; enter the 
-token you selected above to get access to the running CUDA-Q container.
-On the left you should see a folder with tutorials. Happy coding!
-
-Use VS Code
-+++++++++++++++++++++++++++++++
-
-Once you can :ref:`run jobs on DGX Cloud <dgx-cloud-setup>`, you can launch an interactive job 
-to use CUDA-Q with a local installation of `VS Code <https://code.visualstudio.com/>`_, 
-or the `VS Code Web UI <https://vscode.dev/>`__, running on DGX Cloud:
-
-.. code-block:: console
-
-    ngc base-command job run \
-      --name Job-interactive-001 --total-runtime 600s \
-      --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \
-      --ace <ace_name> --instance <instance_name> \
-      --commandline 'vscode-setup tunnel --name cuda-quantum-dgx --accept-server-license-terms'
-
-Go to the `job portal <https://bc.ngc.nvidia.com/jobs>`__, click on the job you just launched, and select the "Log"
-tab. Once the job is running, you should see instructions there for how to connect to the device the job is running on.
-These instructions include a link to open and the code to enter on that page; follow the instructions to authenticate. 
-Once you have authenticated, you can either 
-`open VS Code in a web browser <https://vscode.dev/tunnel/cuda-quantum-dgx/home/cudaq/>`__, 
-or connect a local installation of VS Code.
-To connect a local installation of VS Code, make sure you have the 
-`Remote - Tunnels <https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-server>`__ extension installed,
-then open the Command Palette with `Ctrl+Shift+P`, enter "Remote Tunnels: Connect to Tunnel", 
-and enter `cuda-quantum-remote`. After the window reloaded, enter "File: Open Folder" in the Command Palette 
-to open the `/home/cudaq/` folder.
-
-You should see a pop up asking if you want to install the recommended extensions. Selecting to install them will
-configure VS Code with extensions for working with C++, Python, and Jupyter.
-You can always see the list of recommended extensions that aren't installed yet by clicking on the "Extensions" icon in the sidebar and navigating to the "Recommended" tab.
-
-If you enter "View: Show Explorer" in the Command Palette, you should see a folder with tutorials and examples
-to help you get started. Take a look at `Next Steps`_ to dive into CUDA-Q development.
-
-.. _additional-cuda-tools:
-
-Additional CUDA Tools
-------------------------------------
-
-CUDA-Q makes use of GPU-acceleration in certain backends and components. 
-Depending on how you installed CUDA-Q, you may need to install 
-certain CUDA libraries separately to take advantage of these.
-
-Installation via PyPI
-++++++++++++++++++++++++++++++++++++
-
-If you installed CUDA-Q via `PyPI <https://pypi.org/project/cuda-quantum>`__, please follow the installation instructions there to install the necessary CUDA dependencies.
-
-Installation In Container Images
-++++++++++++++++++++++++++++++++++++
-
-If you are using the CUDA-Q container image, the image already contains all necessary runtime libraries to use all CUDA-Q components. To take advantage of GPU-acceleration, make sure
-to enable GPU support when you launch the container, that is pass the `--gpus all` flag when launching 
-the container with Docker and the `--nv` flag when launching the container with Singularity.
-
-Note that the image does not contain all development dependencies for CUDA, such as, for example the `nvcc` compiler. You can install all CUDA development dependencies by running the command 
-
-.. code-block:: console
-
-    sudo apt-get install cuda-toolkit-12.0
-
-inside the container. Make sure the toolkit version you install matches the CUDA runtime installation in the container.
-Most Python packages that use GPU-acceleration, such as for example `CuPy <https://cupy.dev>`__, require an existing CUDA installation. 
-After installing the `cuda-toolkit-12.0` you can install CuPy for CUDA 12 with the command
-
-.. code-block:: console
-
-    python3 -m pip install cupy-cuda12x
-
-.. _cuda-dependencies-prebuilt-binaries:
-
-Installing Pre-built Binaries
-++++++++++++++++++++++++++++++++++++
-
-If you installed pre-built binaries for CUDA-Q, you will need to install 
-the necessary CUDA runtime libraries to use GPU-acceleration in CUDA-Q. 
-If you prefer to only install the minimal set of runtime libraries, the following 
-commands, for example, install the necessary packages for RHEL 8:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>CUDARTInstall]
-    :end-before: [<CUDARTInstall]
-
-More detailed instructions for your platform can be found in the online documentation
-linked for that `CUDA version <https://developer.nvidia.com/cuda-toolkit-archive>`__. 
-Please make sure to install CUDA version 11.8 or newer, and confirm that your 
-`GPU driver <https://www.nvidia.com/download/index.aspx>`__ supports that version.
-While the above packages are sufficient to use GPU-acceleration within CUDA-Q, 
-we recommend installing the complete CUDA toolkit (`cuda-toolkit-12-0`) that also 
-includes the `nvcc` compiler. A separate CUDA-Q installer is available for CUDA 11, 
-built against version 11.8, and for CUDA 12, built against version 12.0.
-
-.. _distributed-computing-with-mpi:
-
-Distributed Computing with MPI
-------------------------------------
-
-CUDA-Q supports the Message Passing Interface (MPI) parallelism via a plugin interface.
-It is possible to activate or replace such an MPI plugin without re-installing or re-compiling CUDA-Q.
-MPI calls via CUDA-Q API for C++ and Python will be delegated to the currently activated plugin at runtime.
-
-.. tab:: Built-in MPI Support
-
-  The :ref:`CUDA-Q Docker image <install-docker-image>` is shipped with a pre-built MPI plugin based on an 
-  optimized OpenMPI installation included in the image. No action is required to use this plugin. 
-  We recommend using this plugin unless the container host has an existing MPI implementation other than OpenMPI.
-
-  If you are not using the Docker image, or are using the image on a system that has a
-  vendor-optimized MPI library pre-installed, please follow the instructions in the "Custom MPI Support" tab 
-  to enable MPI support.
-
-.. tab:: Custom MPI Support
-
-  If you are not using the Docker image, or are using the image on a system that has a
-  vendor-optimized MPI library pre-installed, CUDA-Q can be configured to use the local MPI installation by
-  manually activating a suitable plugin post-installation.
-  To do so, 
-
-  - Make sure the environment variable `CUDA_QUANTUM_PATH` points to the CUDA-Q installation directory. 
-    If you installed CUDA-Q using the `installer <install-prebuilt-binaries>`, or if you are using the CUDA-Q 
-    container image, this variable should already be defined. If you installed the CUDA-Q 
-    `Python wheels <install-python-wheels>`, set this variable to the directory listed under "Location" when you run the 
-    command `pip show cudaq`.
-
-  - Set the environment variable `MPI_PATH` to the location of your MPI installation. In particular, `${MPI_PATH}/include` 
-    is expected to contain the `mpi.h` header and `${MPI_PATH}/lib64` or `${MPI_PATH}/lib` is expected to contain `libmpi.so`.
-
-  - Execute the following command to complete the activation:
-
-    .. code-block:: console
-
-        bash $CUDA_QUANTUM_PATH/distributed_interfaces/activate_custom_mpi.sh
-
-  .. note::
-
-    HPC data centers often have a vendor-optimized MPI library pre-installed on their system. 
-    If you are using our container images, installing that MPI implementation in the container 
-    and manually activating the plugin following the steps above ensure the best performance,
-    and guarantee compatibility when MPI injection into a container occurs. 
-
-  Manually activating an MPI plugin replaces any existing plugin; After the initial activation, the newly built 
-  `libcudaq_distributed_interface_mpi.so` in the installation directory will subsequently always be used to 
-  handle CUDA-Q MPI calls.
-
-  .. note::
-
-    Executing the activation script from the CUDA-Q installation directory requires *write* permissions to that directory.
-    If you do not have the necessary permissions, copy the `distributed_interfaces` sub-directory to a local location and execute the 
-    activation script from there.
-
-    In this scenario, since the activated plugin (`libcudaq_distributed_interface_mpi.so`) is outside the CUDA-Q installation,
-    you must set the environment variable `$CUDAQ_MPI_COMM_LIB` to the path of that shared library.
-    This is done automatically when executing that activation script, but you may wish to persist that environment variable 
-    between bash sessions, e.g., by adding it to the `.bashrc` file.
-
-.. _updating-cuda-quantum:
-
-Updating CUDA-Q
---------------------------------
-
-If you installed the CUDA-Q Python wheels, you can update to the latest release
-by running the command 
-
-.. code-block:: console
-
-    python3 -m pip install --upgrade cudaq
-
-.. note::
-
-  Please check if you have an existing installation of the `cuda-quantum`, 
-  `cudaq-quantum-cu11`, or `cuda-quantum-cu12` package, 
-  and uninstall it prior to installing `cudaq`. The `cudaq` package supersedes the
-  `cuda-quantum` package and will install a suitable binary distribution (either 
-  `cuda-quantum-cu11` or `cuda-quantum-cu12`) for your system. Multiple versions 
-  of a CUDA-Q binary distribution will conflict with each other and not work properly.
-
-If you previously installed the CUDA-Q pre-built binaries, you should first uninstall your 
-current CUDA-Q installation before installing the new version using the installer. 
-To uninstall your current CUDA-Q version, run the command
-
-.. code-block:: console
-
-    sudo bash "${CUDA_QUANTUM_PATH}/uninstall.sh" -y
-
-The `uninstall.sh` script is generated during installation, and will remove all files 
-and folders that were created as part of the installation, whether they were modified 
-in the meantime or not. It does not remove any additional files that existed prior 
-to the installation or that you have added to the installation location since then. 
-You can then download and install the new version of CUDA-Q following the
-instructions :ref:`above <install-prebuilt-binaries>`.
-
-.. _dependencies-and-compatibility:
-
-Dependencies and Compatibility
---------------------------------
-
-CUDA-Q can be used to compile and run quantum programs on a CPU-only system, but a GPU is highly recommended and necessary to use the GPU-based simulators, see also :doc:`../backends/simulators`.
-
-The supported CPUs include x86_64 (x86-64-v3 architecture and newer) and ARM64 (ARM v8-A architecture and newer).
-
-.. note:: 
-
-  Some of the components included in the CUDA-Q Python wheels depend on an existing CUDA installation on your system. For more information about installing the CUDA-Q Python wheels, take a look at :ref:`this section <install-python-wheels>`.
-
-The following table summarizes the required components.
-
-.. list-table:: Supported Systems
-    :widths: 30 50
-    :header-rows: 0
-
-    * - CPU architectures
-      - x86_64, ARM64
-    * - Operating System
-      - Linux
-    * - Tested Distributions
-      - CentOS 8; Debian 11, 12; Fedora 38, 39; OpenSUSE/SLED/SLES 15.5, 15.6; RHEL 8, 9; Rocky 8, 9; Ubuntu 22.04, 24.04
-    * - Python versions
-      - 3.10+
-
-.. list-table:: Requirements for GPU Simulation
-    :widths: 30 50
-    :header-rows: 0
-
-    * - GPU Architectures
-      - Volta, Turing, Ampere, Ada, Hopper
-    * - NVIDIA GPU with Compute Capability
-      - 7.0+
-    * - CUDA
-      - 11.x (Driver 470.57.02+), 12.x (Driver 525.60.13+)
-
-Detailed information about supported drivers for different CUDA versions and be found `here <https://docs.nvidia.com/deploy/cuda-compatibility/>`__.
-
-
-.. _post-installation:
-
-Next Steps
-----------
-
-You can now compile and/or run the C++ and Python examples using the terminal.
-To open a terminal in VS Code, open the Command Palette with `Ctrl+Shift+P` and 
-enter "View: Show Terminal".
-
-.. image:: ../../_static/getToWork.png 
-
-The CUDA-Q image contains a folder with examples and tutorials in the :code:`/home/cudaq` directory. 
-These examples are provided to get you started with CUDA-Q and understanding 
-the programming and execution model. 
-If you are not using a container image, you can find these examples on our 
-`GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__.
-
-Let's start by running a simple program to validate your installation.
-The samples contain an implementation of a 
-`Bernstein-Vazirani algorithm <https://en.wikipedia.org/wiki/Bernstein%E2%80%93Vazirani_algorithm>`__. 
-To run the example, execute the command:
-
-.. tab:: Python
-
-  .. code-block:: console
-
-      python examples/python/bernstein_vazirani.py --size 5
-
-.. tab:: C++
-
-  .. code-block:: console
-
-      nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp && ./a.out
-
-This will execute the program on the :ref:`default simulator <default-simulator>`, which will use GPU-acceleration if 
-a suitable GPU has been detected. To confirm that the GPU acceleration works, you can 
-increase the size of the secret string, and pass the target as a command line argument:
-
-.. tab:: Python
-
-  .. code-block:: console
-
-      python examples/python/bernstein_vazirani.py --size 25 --target nvidia
-
-.. tab:: C++
-
-  .. code-block:: console
-
-      nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp -DSIZE=25 --target nvidia && ./a.out
-
-This program should complete fairly quickly. Depending on the available memory on your GPU,
-you can set the size of the secret string to up to 28-32 when running on the `nvidia` target. 
-
-.. note::
-
-  If you get an error that the CUDA driver version is insufficient or no GPU has been detected,
-  check that you have enabled GPU support when launching the container by passing the `--gpus all` flag
-  (for :ref:`Docker <install-docker-image>`) or the `--nv` flag (for :ref:`Singularity <install-singularity-image>`).
-  If you are not running a container, you can execute the command `nvidia-smi` to confirm your setup;
-  if the command is unknown or fails, you do not have a GPU or do not have a driver installed. If the command
-  succeeds, please confirm that your CUDA and driver version matches the 
-  :ref:`supported versions <dependencies-and-compatibility>`.
-
-Let's compare that to using only your CPU:
-
-.. tab:: Python
-
-  .. code-block:: console
-
-      python examples/python/bernstein_vazirani.py --size 25 --target qpp-cpu
-
-.. tab:: C++
-
-  .. code-block:: console
-
-      nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp -DSIZE=25 --target qpp-cpu && ./a.out
-
-When you execute this command, the program simply seems to hang; that is because it takes
-a long time for the CPU-only backend to simulate 28+ qubits! Cancel the execution with `Ctrl+C`.
-
-You are now all set to start developing quantum applications using CUDA-Q!
-Please proceed to :doc:`Basics <../basics/basics>` for an introduction
-to the fundamental features of CUDA-Q.
\ No newline at end of file
diff --git a/pr-2308/_sources/using/integration/cmake_app.rst.txt b/pr-2308/_sources/using/integration/cmake_app.rst.txt
deleted file mode 100644
index d54a761284..0000000000
--- a/pr-2308/_sources/using/integration/cmake_app.rst.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-CUDA-Q and CMake
-**********************
-
-CUDA-Q provides a full compiler toolchain for building 
-out quantum-classical applications. Since most C++ projects 
-provide some sort of integration with CMake, it becomes imperative 
-for our toolchain to integrate well with the CMake build system. 
-
-CUDA-Q provides CMake configuration files that allow downstream 
-application developers to create hybrid quantum-classical source code 
-and build it with the typical CMake workflow, which will natively 
-leverage :code:`nvq++` for compilation of CUDA-Q source files. 
-
-To see how this works, let's create a trivial example. Suppose we have 
-a folder named :code:`myCudaQApp` and within that folder we have 
-a simple CUDA-Q source file, like `ghz.cpp`. With a local 
-CUDA-Q installation present on the system, we can create the 
-following top-level :code:`CMakeLists.txt` file to build this file with 
-:code:`nvq++`:
-
-.. code:: bash 
-
-    cmake_minimum_required(VERSION 3.24 FATAL_ERROR)
-    project(myCudaQApp LANGUAGES CXX)
-    find_package(CUDAQ REQUIRED)
-    add_executable(ghz_exe ghz.cpp)
-
-To configure and build this file, all we need to do is execute the following from the top level of the :code:`myCudaQApp` folder:
-
-.. code:: bash 
-
-    mkdir build && cd build 
-    cmake .. -G Ninja -DCUDAQ_DIR="$CUDA_QUANTUM_PATH/lib/cmake/cudaq"
-    ninja
-    ./ghz_exe 
-
-Notice that by running the :code:`ninja` command with :code:`--verbose`, you'll see that 
-:code:`nvq++` was used to compile and link the executable. 
-
diff --git a/pr-2308/_sources/using/integration/cuda_gpu.rst.txt b/pr-2308/_sources/using/integration/cuda_gpu.rst.txt
deleted file mode 100644
index 4ad7c09f89..0000000000
--- a/pr-2308/_sources/using/integration/cuda_gpu.rst.txt
+++ /dev/null
@@ -1,114 +0,0 @@
-Using CUDA and CUDA-Q in a Project
-****************************************
-
-It may be the case that a project that uses CUDA-Q kernels may also
-want to use CUDA code to do computation on a GPU.  This is possible by using
-both the CUDA Toolkit and CUDA-Q tools. More about programming GPUs
-in CUDA can be found in the `Quick Start Guide
-<https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html>`__.
-
-Once the `nvcc` compiler is installed, it is possible to write
-CUDA kernels and have them execute on the system GPU. See NVIDIA's `An
-Easy Introduction to CUDA C and C++
-<https://developer.nvidia.com/blog/easy-introduction-cuda-c-and-c/>`__
-for more information on getting started with CUDA.
-
-CUDA code uses a unique syntax and is, typically, saved in a file with
-the extension `.cu`. For our example, assume we have written our
-CUDA code in the file `my_proj.cu`.
-
-CUDA-Q code is a library-based extension of C++ and uses
-standard conforming C++ syntax. Typically, a quantum kernel would be
-saved in a file with the `.cpp` extension. Again for our
-example, let's assume that we've written quantum kernels and saved
-them in the file `my_proj_quantum.cpp`.
-
-By default, CUDA-Q uses C++ 20 and builds source code against the 
-LLVM C++ standard library (`libc++`). To create a CUDA library that 
-can link against CUDA-Q code, make sure to define an API that does 
-not rely on C++ data structures that rely on a specific C++ toolchain
-for all functions intended to be called from CUDA-Q (see also 
-:ref:`clike-shim`). For example, if you define a CUDA kernel in `my_proj.cu`
-
-.. code-block:: cpp
-
-    template <typename CudaDataType>
-    __global__ void cudaSetFirstNElements(CudaDataType *sv, const CudaDataType *__restrict__ sv2, int64_t N) {
-    int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
-        if (i < N) {
-            sv[i].x = sv2[i].x;
-            sv[i].y = sv2[i].y;
-        } else {
-            sv[i].x = 0.0;
-            sv[i].y = 0.0;
-        }
-    }
-
-define the following template and all desired template specializations
-
-.. code-block:: cpp
-
-    template <typename CudaDataType>
-    void setFirstNElements(uint32_t n_blocks, 
-                           int32_t threads_per_block, 
-                           void *newDeviceStateVector, 
-                           void *deviceStateVector,
-                           std::size_t previousStateDimension) {
-        cudaSetFirstNElements<<<n_blocks, threads_per_block>>>(
-            reinterpret_cast<CudaDataType *>(newDeviceStateVector),
-            reinterpret_cast<CudaDataType *>(deviceStateVector),
-            previousStateDimension);
-    }
-
-    template void
-    setFirstNElements<cuFloatComplex>(uint32_t n_blocks, 
-                                      int32_t threads_per_block, 
-                                      void *newDeviceStateVector, 
-                                      void *deviceStateVector,
-                                      std::size_t previousStateDimension);
-
-    template void
-    setFirstNElements<cuDoubleComplex>(uint32_t n_blocks, 
-                                       int32_t threads_per_block, 
-                                       void *newDeviceStateVector, 
-                                       void *deviceStateVector,
-                                       std::size_t previousStateDimension);
-
-Create the corresponding header file `my_proj.h` declaring the `setFirstNElements`
-template. You can then call `setFirstNElements` from within `my_proj_quantum.cpp`
-by including `my_proj.h`, for example
-
-.. code-block:: cpp
-
-    #include "cuComplex.h"
-    #include "cuda_runtime.h"
-    #include "device_launch_parameters.h"
-    #include "my_proj.h"
-
-    using namespace my_kernels; // the namespace where you defined setFirstNElements
-
-    int main () {
-        const uint32_t n_blocks = 10;
-        const uint32_t threads_per_block = 5;
-
-        void *deviceStateVector;
-        cudaMalloc((void **)&deviceStateVector, 2 * sizeof(cuDoubleComplex));
-        // ...
-        void *newDeviceStateVector;
-        cudaMalloc((void **)&newDeviceStateVector, 2 * sizeof(cuDoubleComplex));
-        setFirstNElements<cuDoubleComplex>(n_blocks, threads_per_block,
-                                           newDeviceStateVector, deviceStateVector,
-                                           2);
-        return 0;
-    }
-
-To get an executable, compile the code with
-
-.. code-block:: bash
-
-    nvcc -c -std=c++17 -Xcompiler -fPIC my_proj.cu -o my_proj.o
-    nvq++ my_proj_quantum.cpp my_proj.o -I"${CUDA_HOME}/include/" -L"${CUDA_HOME}/lib64" -lcudart -o my_executable
-
-Above, :code:`nvq++` is used for the link step and will make sure the CUDA-Q
-runtime libraries are linked correctly to the executable program.
-The CUDA runtime is explicitly added to this command.
diff --git a/pr-2308/_sources/using/integration/integration.rst.txt b/pr-2308/_sources/using/integration/integration.rst.txt
deleted file mode 100644
index ce6d6ab094..0000000000
--- a/pr-2308/_sources/using/integration/integration.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-Integration with other Software Tools
-**************************************
-
-.. toctree::
-  :maxdepth: 1
-
-  Downstream CMake Integration <cmake_app.rst>
-  Combining CUDA with CUDA-Q <cuda_gpu.rst>
-  Integrating with Third-Party Libraries <libraries.rst>
-
-Information about customizing or extending the CUDA-Q toolchain can be found in :doc:`../extending/extending`.
\ No newline at end of file
diff --git a/pr-2308/_sources/using/integration/libraries.rst.txt b/pr-2308/_sources/using/integration/libraries.rst.txt
deleted file mode 100644
index 0e7c7c6fcd..0000000000
--- a/pr-2308/_sources/using/integration/libraries.rst.txt
+++ /dev/null
@@ -1,226 +0,0 @@
-Integrating with Third-Party Libraries
-****************************************
-
-CUDA-Q enables easily integrating quantum kernels with existing state-of-the-art tools
-for classical computing and high performance computing. While quantum kernels are expressed 
-in Python or C++, quantum hardware backends only support a very limited set of classical 
-instructions. Correspondingly, only a select set of libraries and functions that are 
-directly supported by the CUDA-Q compiler can be called from within quantum kernels.
-Code that is not marked as kernel code, on the other hand, can be used to invoke the 
-defined quantum kernels as well as any classical tools and libraries. CUDA-Q provides 
-a complete toolset to build complex applications running on multi-processor systems involving
-CPUs, GPUs and QPUs. 
-
-In Python, integration with any third-party Python package is handled
-automatically without any need for additional configuration. For C++, the rest of this
-document outlines how to integration with libraries that are compile with a different 
-compiler than `nvq++`.
-
-
-Calling a CUDA-Q library from C++
---------------------------------------
-
-The CUDA-Q compiler is a fully-featured C++ compiler able to process arbitrary C++ code outside
-of quantum kernels. In fact, the CUDA-Q installation includes and invokes a custom-built Clang 
-compiler under the hood. In general, it hence is not necessary to use a different compiler to 
-compile C++ host code that calls into quantum kernels; the host code is compiled along with 
-the quantum kernels into a single library or executable by the CUDA-Q toolchain.
-
-If you would still like to create a library that can be included in libraries and executables 
-built with a different toolchain, you can configure `nvq++` to include and encapsulate all its
-C++ dependencies, such that the CUDA-Q library only depends on C libraries. For example, 
-if you have a file `quantum_kernel.cpp` containing 
-
-.. code:: C++
-
-    #include "cudaq.h"
-    #include <iostream>
-    #include <sstream>
-
-    namespace quantum {
-
-    __qpu__ void bell() {
-      cudaq::qvector qs(2);
-      h(qs[0]);
-      cnot(qs[0], qs[1]);
-      mz(qs);
-    }
-
-    // Usually, one would probably return something like
-    // the most probably value in the distribution rather
-    // than the string dump. However, the bell kernel here
-    // has a 50/50 chance of returning 00 or 11.
-    const char* bell_distribution() {
-      auto dist = cudaq::sample(bell);
-
-      std::stringbuf buffer;
-      std::ostream os(&buffer);
-      dist.dump(os);
-      auto sample_results = buffer.str();
-
-      char* ptr = new char[sample_results.size() + 1];
-      strcpy(ptr, sample_results.c_str());
-      return ptr;
-    }
-
-    } // namespace quantum
-
-you can create such a library with the command
-
-.. code:: bash
-
-    nvq++ quantum_kernel.cpp -shared -static-libstdc++ -fPIC -o libquantum_kernel.so
-
-To use this library in a project built with an external toolchain, define a suitable 
-header file `quantum_kernel.h` containing
-
-.. code:: C++
-
-    namespace quantum {
-      const char* bell_distribution();
-    }
-
-and include it in your project. For example, if `external.cpp` contains
-
-.. code:: C++ 
-
-    #include <iostream>
-    #include "quantum_kernel.h"
-
-    int main () {
-      // some code
-      auto bell_dist = quantum::bell_distribution();
-      std::cout << bell_dist << std::endl;
-      delete[] bell_dist;
-      // more code
-      return 0;
-    }
-
-you can compile it with `g++` and link it with the built `libquantum_kernel.so` library
-using the command
-
-.. code:: bash
-
-    g++ external.cpp -lquantum_kernel -L"$(pwd)" -Wl,-rpath="$(pwd)" -I"$(pwd)"
-
-Note that the API defined in `quantum_kernel.h` does not depend on any data types that 
-rely on the specific implementation of the C++ standard library. For example, we return a 
-`const char*` instead of a `std::string` value. This is important to ensure that the data 
-is handled correctly regardless of which C++ standard library is used. If you include
-data types that depend on a specific C++ ABI, the linker should detect this incompatibility
-and should generate an error. See the section :ref:`clike-shim` below to learn more about 
-what to watch out for when defining an API to interface between libraries that are built 
-with different compilers. 
-
-
-Calling an C++ library from CUDA-Q
---------------------------------------
-
-CUDA-Q comes with a complete C++ toolchain, and is configured to use its own C++ standard
-library by default. When calling a third-party library from within a CUDA-Q application,
-the quantum kernels and the surrounding application code are compiled with the `nvq++` compiler, 
-whereas the third-party library may be compiled with a different toolchain. 
-For most widely distributed libraries, this will work out of the box. The exception are 
-third-party libraries that are pre-compiled (opposed to being header-only libraries) 
-and dynamically depend on a different C++ standard library than CUDA-Q uses. 
-This is the case for a library `third_party_library.so` if `libstdc++.so` is listed 
-in the output of the command
-
-.. code:: bash
-
-    ldd third_party_library.so
-
-To use such a library with CUDA-Q, you have to install the static version of the 
-library. The `nvq++` compiler can be configured to link a static library 
-`libexternal_library.a` instead of the shared one with the same flags as GCC/Clang:
-
-.. code:: bash
-
-    nvq++ my_code.cpp -Wl,-Bstatic -lexternal_library -Wl,-Bdynamic ...
-
-If the compilation fails due to undefined symbols during linking, then `libexternal_library.a`
-does not include the C++ standard library components it depends on. In this case, you can create a 
-suitable static library that contains the external library as well as its `libstdc++` dependencies
-by combining `libexternal_library.a` and `libstdc++.a` into a single flattened archive.
-For example, if the external library is built with GCC and located in `/usr/lib`,
-the following commands create a suitable archive using the GNU archiver:
-
-.. code:: bash
-
-    ar cruT libexternal_library_complete.a /usr/lib/libexternal_library.a "$(g++ -print-file-name=libstdc++.a)"
-    echo -e 'create libexternal_library_complete.a\naddlib libexternal_library_complete.a\nsave\nend' | ar -M
-
-The above commands create a new static library `libexternal_library_complete.a` in the current directory. 
-To use it, adjust the `nvq++` command accordingly:
-
-.. code:: bash
-
-    nvq++ my_code.cpp -L. -Wl,-Bstatic -lexternal_library_complete -Wl,-Bdynamic ...
-
-.. note::
-
-  If the third-party library functions you are calling from your CUDA-Q code 
-  consume or produce data types that explicitly depend on the C++ standard library implementation,
-  a small shim is needed to properly encapsulate these dependencies, as described in
-  :ref:`clike-shim`.
-
-
-.. _clike-shim:
-
-Interfacing between binaries compiled with a different toolchains
----------------------------------------------------------------------
-
-To avoid incompatibilities between different C++ implementations, 
-two binaries built using a different C++ compiler should be careful when passing data types between them
-that rely on a specific C++ implementation. While primitive data types can be passed without
-issues, attempting to pass a string, for example, will result in a linking failure; a `char*` must 
-be passed instead of a string.
-
-Most other data types, including templated types, can be passed as long as their alignment matches.
-While different compilers generally align data differently, it is possible to force a particular 
-alignment when defining the data type or value. For example, if we define a struct
-
-.. code:: C++
-
-    #pragma pack(push, 4)
-    template <typename ScalarType>
-    struct Entry {
-      bool is_integral;
-      ScalarType value;
-    };
-    #pragma pack(pop)
-
-the `pragma` directives ensure that each item will be 4-byte aligned, regardless of the 
-default setting that can differ between compilers.
-
-Even function pointers can usually be passed, as long as their argument and return types can be passed.
-When using templated functions and data types, the header file should only include their declaration as well 
-as explicit instantiation for all valid template arguments. For example,
-
-.. code:: C++
-
-    template <typename CudaDataType>
-    void initializeDeviceStateVector(uint32_t n_blocks, 
-                                    int32_t threads_per_block, 
-                                    void *deviceStateVector,
-                                    std::size_t stateDimension) {
-      cudaInitializeDeviceStateVector<<<n_blocks, threads_per_block>>>(
-        reinterpret_cast<CudaDataType *>(deviceStateVector), stateDimension);
-    }
-
-    template void
-    initializeDeviceStateVector<cuFloatComplex>(uint32_t n_blocks, 
-                                    int32_t threads_per_block, 
-                                    void *deviceStateVector,
-                                    std::size_t stateDimension);
-
-    template void
-    initializeDeviceStateVector<cuDoubleComplex>(uint32_t n_blocks, 
-                                    int32_t threads_per_block, 
-                                    void *deviceStateVector,
-                                    std::size_t stateDimension);
-
-The most cumbersome aspect when libraries built with different toolchains are combined into a single executable
-is exception handling. Since the handling relies on a specific ABI, an thrown exception is not necessarily 
-recognized as such in a library that uses a different ABI implementation. While matching the exception type 
-may not work as expected, a catch-all will reliably catch any thrown exception. 
diff --git a/pr-2308/_sources/using/quick_start.rst.txt b/pr-2308/_sources/using/quick_start.rst.txt
deleted file mode 100644
index d020dbbb19..0000000000
--- a/pr-2308/_sources/using/quick_start.rst.txt
+++ /dev/null
@@ -1,131 +0,0 @@
-Quick Start
-===================
-
-**NVIDIA CUDA-Q**
-
-CUDA-Q streamlines hybrid application development and promotes productivity and scalability
-in quantum computing. It offers a unified programming model designed for a hybrid
-setting |---| that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for 
-programming in Python and in C++. Learn more about the `key benefits of CUDA-Q <https://developer.nvidia.com/cuda-q>`_.
-
-This Quick Start page guides you through installing CUDA-Q and running your first program.
-If you have already installed and configured CUDA-Q, or if you are using our 
-`Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum>`_, you can move directly to our
-:doc:`Basics Section <basics/basics>`. More information about working with containers and Docker alternatives can be 
-found in our complete :doc:`Installation Guide <install/install>`.
-
-Install CUDA-Q
-----------------------------
-
-.. tab:: Python
-
-   To develop CUDA-Q applications using Python, 
-   please follow the instructions for `installing CUDA-Q <https://pypi.org/project/cuda-quantum/>`_ from PyPI. 
-   If you have an NVIDIA GPU, make sure to also follow the instructions for enabling GPU-acceleration.
-
-   .. include:: ../../../python/README.md
-      :parser: myst_parser.sphinx_
-      :start-after: (Begin complete install)
-      :end-before: (End complete install)
-
-   Once you completed the installation, please follow the instructions
-   :ref:`below <validate-installation>` to run your first CUDA-Q program!
-
-.. tab:: C++
-
-   To develop CUDA-Q applications using C++, please make sure you have a C++ toolchain installed
-   that supports C++20, for example `g++` version 11 or newer.
-   Download the `install_cuda_quantum` file for your processor architecture and CUDA version (`_cu11` suffix for CUDA 11 and `_cu12` suffix for CUDA 12) 
-   from the assets of the respective `GitHub release <https://github.com/NVIDIA/cuda-quantum/releases>`__; 
-   hat is the file with the `aarch64` extension for ARM processors, and the one with `x86_64` for, e.g., Intel and AMD processors.
-
-   To install CUDA-Q, execute the commands
-
-   .. code-block:: bash
-
-      sudo -E bash install_cuda_quantum*.$(uname -m) --accept 
-      . /etc/profile
-
-   If you have an NVIDIA GPU, please also install the `CUDA Toolkit <https://developer.nvidia.com/cuda-downloads>`__ to enable GPU-acceleration within CUDA-Q.
-
-   Please see the complete :ref:`installation guide <install-prebuilt-binaries>` for more details, including
-
-   - a list of :ref:`supported operating systems <dependencies-and-compatibility>`, 
-   - instructions on how to :ref:`enable MPI parallelization <distributed-computing-with-mpi>` within CUDA-Q, and
-   - information about :ref:`updating CUDA-Q <updating-cuda-quantum>`.
-
-   Once you completed the installation, please follow the instructions
-   :ref:`below <validate-installation>` to run your first CUDA-Q program!   
-
-.. |---|   unicode:: U+2014 .. EM DASH
-   :trim:
-
-.. _validate-installation:
-
-Validate your Installation
-----------------------------
-
-Let's run a simple program to validate your installation.
-The quantum kernel in the following program creates and measures the state 
-:math:`(|00\rangle + |11\rangle) / \sqrt{2}`. That means each kernel execution should 
-either yield `00` or `11`. The program samples, meaning it executes, the kernel 1000 times
-and prints how many times each output was measured. On average, the values `00` and `11`
-should be observed around 500 times each.
-
-.. tab:: Python
-
-   Create a file titled `program.py`, containing the following code:
-
-   .. literalinclude:: /snippets/python/quick_start.py
-      :language: python
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-   Run this program as you do any other Python program, for example:
-
-   .. code-block:: console
-
-      python3 program.py
-
-.. tab:: C++
-
-   Create a file titled `program.cpp`, containing the following code:
-
-   .. literalinclude:: /snippets/cpp/quick_start.cpp
-      :language: cpp
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-   Compile the program using the `nvq++` compiler and run the built application with the following command:
-
-   .. code-block:: console
-
-      nvq++ program.cpp -o program.x && ./program.x
-
-If you have an NVIDIA GPU the program uses GPU acceleration by default.
-To confirm that this works as expected and to see the effects of GPU acceleration, you can 
-increase the numbers of qubits the program uses to 28 and
-compare the time to execute the program on the 
-`nvidia` target (:ref:`GPU-accelerated statevector simulator <cuQuantum single-GPU>`) to the time when setting the target to `qpp-cpu` (:ref:`OpenMP parallelized CPU-only statevector simulator <OpenMP CPU-only>`):
-
-.. tab:: Python
-
-   .. code-block:: console
-
-      python3 program.py 28 --target nvidia
-
-.. tab:: C++
-
-   .. code-block:: console
-
-      nvq++ program.cpp -o program.x --target nvidia && ./program.x 28
-
-When you change the target to `qpp-cpu`, the program simply seems to hang; that is because it takes a long time for the CPU-only backend to simulate 28+ qubits! Cancel the execution with `Ctrl+C`.
-
-For more information about enabling GPU-acceleration, please see
-our complete :ref:`Installation Guide <additional-cuda-tools>`.
-For further information on available targets, see :doc:`Backends <backends/backends>`.
-
-You are now all set to start developing quantum applications using CUDA-Q!
-Please proceed to :doc:`Basics <basics/basics>` for an introduction
-to the fundamental features of CUDA-Q.
diff --git a/pr-2308/_sources/versions.rst.txt b/pr-2308/_sources/versions.rst.txt
deleted file mode 100644
index 9394f5a52e..0000000000
--- a/pr-2308/_sources/versions.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-************************
-CUDA-Q Versions
-************************
-
-.. include:: releases.rst
-    :start-line: 4
diff --git a/pr-2308/_static/_sphinx_javascript_frameworks_compat.js b/pr-2308/_static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8549469dc2..0000000000
--- a/pr-2308/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
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diff --git a/pr-2308/_static/attachToDevContainer.png b/pr-2308/_static/attachToDevContainer.png
deleted file mode 100644
index a841832ef8..0000000000
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diff --git a/pr-2308/_static/basic.css b/pr-2308/_static/basic.css
deleted file mode 100644
index 4e9a9f1fac..0000000000
--- a/pr-2308/_static/basic.css
+++ /dev/null
@@ -1,900 +0,0 @@
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\ No newline at end of file
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deleted file mode 100644
index 327f6b639a..0000000000
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+++ /dev/null
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diff --git a/pr-2308/_static/broken_example.png b/pr-2308/_static/broken_example.png
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index 92fad4b5c0..0000000000
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diff --git a/pr-2308/_static/clipboard.min.js b/pr-2308/_static/clipboard.min.js
deleted file mode 100644
index 54b3c46381..0000000000
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diff --git a/pr-2308/_static/copy-button.svg b/pr-2308/_static/copy-button.svg
deleted file mode 100644
index 9c074dae52..0000000000
--- a/pr-2308/_static/copy-button.svg
+++ /dev/null
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-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <rect x="8" y="8" width="12" height="12" rx="2" />
-  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
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diff --git a/pr-2308/_static/copybutton.css b/pr-2308/_static/copybutton.css
deleted file mode 100644
index f1916ec7d1..0000000000
--- a/pr-2308/_static/copybutton.css
+++ /dev/null
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-    position: absolute;
-    display: flex;
-    top: .3em;
-    right: .3em;
-    width: 1.7em;
-    height: 1.7em;
-	opacity: 0;
-    transition: opacity 0.3s, border .3s, background-color .3s;
-    user-select: none;
-    padding: 0;
-    border: none;
-    outline: none;
-    border-radius: 0.4em;
-    /* The colors that GitHub uses */
-    border: #1b1f2426 1px solid;
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-    color: #57606a;
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-    color: #22863a;
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-    height: 1.5em;
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-  position: relative;
- }
-
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-    padding: .2em;
-    font-size: .8em;
-    left: -.2em;
-    background: grey;
-    color: white;
-    white-space: nowrap;
-    z-index: 2;
-    border-radius: 2px;
-    transform: translateX(-102%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
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-    opacity: 1;
-    visibility: visible;
-    transform: translateX(-100%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-    transition-delay: .5s;
-}
-
-/* By default the copy button shouldn't show up when printing a page */
-@media print {
-    button.copybtn {
-        display: none;
-    }
-}
diff --git a/pr-2308/_static/copybutton.js b/pr-2308/_static/copybutton.js
deleted file mode 100644
index 1631e1138b..0000000000
--- a/pr-2308/_static/copybutton.js
+++ /dev/null
@@ -1,248 +0,0 @@
-// Localization support
-const messages = {
-  'en': {
-    'copy': 'Copy',
-    'copy_to_clipboard': 'Copy to clipboard',
-    'copy_success': 'Copied!',
-    'copy_failure': 'Failed to copy',
-  },
-  'es' : {
-    'copy': 'Copiar',
-    'copy_to_clipboard': 'Copiar al portapapeles',
-    'copy_success': '¡Copiado!',
-    'copy_failure': 'Error al copiar',
-  },
-  'de' : {
-    'copy': 'Kopieren',
-    'copy_to_clipboard': 'In die Zwischenablage kopieren',
-    'copy_success': 'Kopiert!',
-    'copy_failure': 'Fehler beim Kopieren',
-  },
-  'fr' : {
-    'copy': 'Copier',
-    'copy_to_clipboard': 'Copier dans le presse-papier',
-    'copy_success': 'Copié !',
-    'copy_failure': 'Échec de la copie',
-  },
-  'ru': {
-    'copy': 'Скопировать',
-    'copy_to_clipboard': 'Скопировать в буфер',
-    'copy_success': 'Скопировано!',
-    'copy_failure': 'Не удалось скопировать',
-  },
-  'zh-CN': {
-    'copy': '复制',
-    'copy_to_clipboard': '复制到剪贴板',
-    'copy_success': '复制成功!',
-    'copy_failure': '复制失败',
-  },
-  'it' : {
-    'copy': 'Copiare',
-    'copy_to_clipboard': 'Copiato negli appunti',
-    'copy_success': 'Copiato!',
-    'copy_failure': 'Errore durante la copia',
-  }
-}
-
-let locale = 'en'
-if( document.documentElement.lang !== undefined
-    && messages[document.documentElement.lang] !== undefined ) {
-  locale = document.documentElement.lang
-}
-
-let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
-if (doc_url_root == '#') {
-    doc_url_root = '';
-}
-
-/**
- * SVG files for our copy buttons
- */
-let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <title>${messages[locale]['copy_success']}</title>
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <path d="M5 12l5 5l10 -10" />
-</svg>`
-
-// If the user specified their own SVG use that, otherwise use the default
-let iconCopy = ``;
-if (!iconCopy) {
-  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <title>${messages[locale]['copy_to_clipboard']}</title>
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <rect x="8" y="8" width="12" height="12" rx="2" />
-  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
-</svg>`
-}
-
-/**
- * Set up copy/paste for code blocks
- */
-
-const runWhenDOMLoaded = cb => {
-  if (document.readyState != 'loading') {
-    cb()
-  } else if (document.addEventListener) {
-    document.addEventListener('DOMContentLoaded', cb)
-  } else {
-    document.attachEvent('onreadystatechange', function() {
-      if (document.readyState == 'complete') cb()
-    })
-  }
-}
-
-const codeCellId = index => `codecell${index}`
-
-// Clears selected text since ClipboardJS will select the text when copying
-const clearSelection = () => {
-  if (window.getSelection) {
-    window.getSelection().removeAllRanges()
-  } else if (document.selection) {
-    document.selection.empty()
-  }
-}
-
-// Changes tooltip text for a moment, then changes it back
-// We want the timeout of our `success` class to be a bit shorter than the
-// tooltip and icon change, so that we can hide the icon before changing back.
-var timeoutIcon = 2000;
-var timeoutSuccessClass = 1500;
-
-const temporarilyChangeTooltip = (el, oldText, newText) => {
-  el.setAttribute('data-tooltip', newText)
-  el.classList.add('success')
-  // Remove success a little bit sooner than we change the tooltip
-  // So that we can use CSS to hide the copybutton first
-  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
-}
-
-// Changes the copy button icon for two seconds, then changes it back
-const temporarilyChangeIcon = (el) => {
-  el.innerHTML = iconCheck;
-  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
-}
-
-const addCopyButtonToCodeCells = () => {
-  // If ClipboardJS hasn't loaded, wait a bit and try again. This
-  // happens because we load ClipboardJS asynchronously.
-  if (window.ClipboardJS === undefined) {
-    setTimeout(addCopyButtonToCodeCells, 250)
-    return
-  }
-
-  // Add copybuttons to all of our code cells
-  const COPYBUTTON_SELECTOR = 'div.highlight pre';
-  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
-  codeCells.forEach((codeCell, index) => {
-    const id = codeCellId(index)
-    codeCell.setAttribute('id', id)
-
-    const clipboardButton = id =>
-    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
-      ${iconCopy}
-    </button>`
-    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
-  })
-
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
-
-
-var copyTargetText = (trigger) => {
-  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-
-  // get filtered text
-  let exclude = '.linenos';
-
-  let text = filterText(target, exclude);
-  return formatCopyText(text, '', false, true, true, false, '', '')
-}
-
-  // Initialize with a callback so we can modify the text before copy
-  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
-
-  // Update UI with error/success messages
-  clipboard.on('success', event => {
-    clearSelection()
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
-    temporarilyChangeIcon(event.trigger)
-  })
-
-  clipboard.on('error', event => {
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
-  })
-}
-
-runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/pr-2308/_static/copybutton_funcs.js b/pr-2308/_static/copybutton_funcs.js
deleted file mode 100644
index dbe1aaad79..0000000000
--- a/pr-2308/_static/copybutton_funcs.js
+++ /dev/null
@@ -1,73 +0,0 @@
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-export function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
diff --git a/pr-2308/_static/css/badge_only.css b/pr-2308/_static/css/badge_only.css
deleted file mode 100644
index c718cee441..0000000000
--- a/pr-2308/_static/css/badge_only.css
+++ /dev/null
@@ -1 +0,0 @@
-.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
\ No newline at end of file
diff --git a/pr-2308/_static/css/fonts/Roboto-Slab-Bold.woff b/pr-2308/_static/css/fonts/Roboto-Slab-Bold.woff
deleted file mode 100644
index 6cb6000018..0000000000
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deleted file mode 100644
index 7059e23142..0000000000
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diff --git a/pr-2308/_static/css/fonts/Roboto-Slab-Regular.woff b/pr-2308/_static/css/fonts/Roboto-Slab-Regular.woff
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deleted file mode 100644
index f2c76e5bda..0000000000
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diff --git a/pr-2308/_static/css/fonts/fontawesome-webfont.eot b/pr-2308/_static/css/fonts/fontawesome-webfont.eot
deleted file mode 100644
index e9f60ca953..0000000000
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diff --git a/pr-2308/_static/css/fonts/fontawesome-webfont.svg b/pr-2308/_static/css/fonts/fontawesome-webfont.svg
deleted file mode 100644
index 855c845e53..0000000000
--- a/pr-2308/_static/css/fonts/fontawesome-webfont.svg
+++ /dev/null
@@ -1,2671 +0,0 @@
-<?xml version="1.0" standalone="no"?>
-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
-<svg>
-<metadata>
-Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
- By ,,,
-Copyright Dave Gandy 2016. All rights reserved.
-</metadata>
-<defs>
-<font id="FontAwesome" horiz-adv-x="1536" >
-  <font-face 
-    font-family="FontAwesome"
-    font-weight="400"
-    font-stretch="normal"
-    units-per-em="1792"
-    panose-1="0 0 0 0 0 0 0 0 0 0"
-    ascent="1536"
-    descent="-256"
-    bbox="-1.02083 -256.962 2304.6 1537.02"
-    underline-thickness="0"
-    underline-position="0"
-    unicode-range="U+0020-F500"
-  />
-<missing-glyph horiz-adv-x="896" 
-d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
-    <glyph glyph-name=".notdef" horiz-adv-x="896" 
-d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
-    <glyph glyph-name=".null" horiz-adv-x="0" 
- />
-    <glyph glyph-name="nonmarkingreturn" horiz-adv-x="597" 
- />
-    <glyph glyph-name="space" unicode=" " horiz-adv-x="448" 
- />
-    <glyph glyph-name="dieresis" unicode="&#xa8;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="copyright" unicode="&#xa9;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="registered" unicode="&#xae;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="acute" unicode="&#xb4;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="AE" unicode="&#xc6;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="Oslash" unicode="&#xd8;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="trademark" unicode="&#x2122;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="infinity" unicode="&#x221e;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="notequal" unicode="&#x2260;" horiz-adv-x="1792" 
- />
-    <glyph glyph-name="glass" unicode="&#xf000;" horiz-adv-x="1792" 
-d="M1699 1350q0 -35 -43 -78l-632 -632v-768h320q26 0 45 -19t19 -45t-19 -45t-45 -19h-896q-26 0 -45 19t-19 45t19 45t45 19h320v768l-632 632q-43 43 -43 78q0 23 18 36.5t38 17.5t43 4h1408q23 0 43 -4t38 -17.5t18 -36.5z" />
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deleted file mode 100644
index 4ab0ac179a..0000000000
--- a/pr-2308/_static/cudaq_override.css
+++ /dev/null
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-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- *                                                                             *
- * File: cudaq_override.css                                                    *
- * Created Date: 16 Feb 2023                                                   *
- * Last Modified: 16 Feb 2023                                                  *
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diff --git a/pr-2308/_static/doctools.js b/pr-2308/_static/doctools.js
deleted file mode 100644
index 527b876ca6..0000000000
--- a/pr-2308/_static/doctools.js
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
-  "TEXTAREA",
-  "INPUT",
-  "SELECT",
-  "BUTTON",
-]);
-
-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
-  }
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const Documentation = {
-  init: () => {
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
-  },
-
-  /**
-   * i18n support
-   */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
-
-  // gettext and ngettext don't access this so that the functions
-  // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
-  },
-
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
-  },
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-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
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-    Documentation.LOCALE = catalog.locale;
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-    document.querySelectorAll("input[name=q]")[0]?.focus();
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diff --git a/pr-2308/_static/documentation_options.js b/pr-2308/_static/documentation_options.js
deleted file mode 100644
index b57ae3b839..0000000000
--- a/pr-2308/_static/documentation_options.js
+++ /dev/null
@@ -1,14 +0,0 @@
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-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '',
-    LANGUAGE: 'en',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false,
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diff --git a/pr-2308/_static/getToWork.png b/pr-2308/_static/getToWork.png
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diff --git a/pr-2308/_static/jquery-3.6.0.js b/pr-2308/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b73..0000000000
--- a/pr-2308/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
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-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	}
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/pr-2308/_static/jquery.js b/pr-2308/_static/jquery.js
deleted file mode 100644
index c4c6022f29..0000000000
--- a/pr-2308/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
-/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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diff --git a/pr-2308/_static/language_data.js b/pr-2308/_static/language_data.js
deleted file mode 100644
index 2e22b06ab1..0000000000
--- a/pr-2308/_static/language_data.js
+++ /dev/null
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-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, is available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
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deleted file mode 100644
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deleted file mode 100644
index 4919ca8829..0000000000
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-  <style>
-    svg { fill: lightcoral; }
-    @media (prefers-color-scheme: dark) {
-      svg { fill: crimson; }
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diff --git a/pr-2308/_static/qpud_fig.png b/pr-2308/_static/qpud_fig.png
deleted file mode 100644
index 66080b1ff5..0000000000
Binary files a/pr-2308/_static/qpud_fig.png and /dev/null differ
diff --git a/pr-2308/_static/searchtools.js b/pr-2308/_static/searchtools.js
deleted file mode 100644
index e89e34d4e7..0000000000
--- a/pr-2308/_static/searchtools.js
+++ /dev/null
@@ -1,566 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
-  };
-}
-
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
-  const [docName, title, anchor, descr, score, _filename] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + anchor;
-  linkEl.dataset.score = score;
-  linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerHTML =
-      " (" + descr + ")";
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms)
-          );
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  searchTerms
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), searchTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString) => {
-    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
-    );
-    return "";
-  },
-
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
-  },
-
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
-    }
-  },
-
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
-
-  stopPulse: () => (Search._pulse_status = -1),
-
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
-
-    const pulse = () => {
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch: (query) => {
-    // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
-
-      // stem the word
-      let word = stemmer.stemWord(queryTermLower);
-      // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
-      else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
-      }
-    });
-
-    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
-      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
-    }
-
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
-
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
-    _removeChildren(document.getElementById("search-progress"));
-
-    const queryLower = query.toLowerCase();
-    for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
-        for (const [file, id] of foundTitles) {
-          let score = Math.round(100 * queryLower.length / title.length)
-          results.push([
-            docNames[file],
-            titles[file] !== title ? `${titles[file]} > ${title}` : title,
-            id !== null ? "#" + id : "",
-            null,
-            score,
-            filenames[file],
-          ]);
-        }
-      }
-    }
-
-    // search for explicit entries in index directives
-    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
-      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id] of foundEntries) {
-          let score = Math.round(100 * queryLower.length / entry.length)
-          results.push([
-            docNames[file],
-            titles[file],
-            id ? "#" + id : "",
-            null,
-            score,
-            filenames[file],
-          ]);
-        }
-      }
-    }
-
-    // lookup as object
-    objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
-    );
-
-    // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
-
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    results = results.reverse();
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
-
-    // print the results
-    _displayNextItem(results, results.length, searchTerms);
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
-      }
-
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-
-    const scoreMap = new Map();
-    const fileMap = new Map();
-
-    // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
-      ];
-      // add support for partial matches
-      if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
-      }
-
-      // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
-      // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
-
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
-
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
-      });
-
-      // create the mapping
-      files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
-      });
-    });
-
-    // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
-
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
-      if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
-
-      // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
-
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words.
-   */
-  makeSearchSummary: (htmlText, keywords) => {
-    const text = Search.htmlToText(htmlText);
-    if (text === "") return null;
-
-    const textLower = text.toLowerCase();
-    const actualStartPosition = [...keywords]
-      .map((k) => textLower.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("p");
-    summary.classList.add("context");
-    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
-    return summary;
-  },
-};
-
-_ready(Search.init);
diff --git a/pr-2308/_static/sg_gallery-binder.css b/pr-2308/_static/sg_gallery-binder.css
deleted file mode 100644
index 420005d227..0000000000
--- a/pr-2308/_static/sg_gallery-binder.css
+++ /dev/null
@@ -1,11 +0,0 @@
-/* CSS for binder integration */
-
-div.binder-badge {
-  margin: 1em auto;
-  vertical-align: middle;
-}
-
-div.lite-badge {
-  margin: 1em auto;
-  vertical-align: middle;
-}
diff --git a/pr-2308/_static/sg_gallery-dataframe.css b/pr-2308/_static/sg_gallery-dataframe.css
deleted file mode 100644
index 25be73092e..0000000000
--- a/pr-2308/_static/sg_gallery-dataframe.css
+++ /dev/null
@@ -1,46 +0,0 @@
-/* Pandas dataframe css */
-/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
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-  --sg-text-color: #000;
-  --sg-tr-odd-color: #f5f5f5;
-  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
-}
-html[data-theme="dark"] {
-  --sg-text-color: #fff;
-  --sg-tr-odd-color: #373737;
-  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
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-  font-size: 12px;
-  table-layout: fixed;
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-table.dataframe thead {
-  border-bottom: 1px solid var(--sg-text-color);
-  vertical-align: bottom;
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-table.dataframe tr,
-table.dataframe th,
-table.dataframe td {
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-  padding: 0.5em 0.5em;
-  line-height: normal;
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diff --git a/pr-2308/_static/sg_gallery-rendered-html.css b/pr-2308/_static/sg_gallery-rendered-html.css
deleted file mode 100644
index 93dc2ffb02..0000000000
--- a/pr-2308/_static/sg_gallery-rendered-html.css
+++ /dev/null
@@ -1,224 +0,0 @@
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diff --git a/pr-2308/_static/sg_gallery.css b/pr-2308/_static/sg_gallery.css
deleted file mode 100644
index 72227837d3..0000000000
--- a/pr-2308/_static/sg_gallery.css
+++ /dev/null
@@ -1,342 +0,0 @@
-/*
-Sphinx-Gallery has compatible CSS to fix default sphinx themes
-Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
-scrolls, agogo, traditional, nature, haiku, pyramid
-Tested for Read the Docs theme 0.1.7 */
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-/* Define light colors */
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-  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
-  --sg-tooltip-border: #ccc transparent;
-  --sg-thumb-box-shadow-color: #6c757d40;
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-  --sg-script-out: #888;
-  --sg-script-pre: #fafae2;
-  --sg-pytb-foreground: #000;
-  --sg-pytb-background: #ffe4e4;
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-  --sg-download-a-background-color: #ffc;
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-  --sg-download-a-border-color: 1px solid #c2c22d;
-  --sg-download-a-color: #000;
-  --sg-download-a-hover-background-color: #d5d57e;
-  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
-  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
-}
-@media(prefers-color-scheme: light) {
-  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
-    --sg-tooltip-foreground: black;
-    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
-    --sg-tooltip-border: #ccc transparent;
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-    --sg-download-a-border-color: 1px solid #c2c22d;
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-    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
-  }
-}
-
-html[data-theme="dark"], body[data-theme="dark"] {
-  --sg-tooltip-foreground: white;
-  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
-  --sg-tooltip-border: #333 transparent;
-  --sg-thumb-box-shadow-color: #79848d40;
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-@media(prefers-color-scheme: dark){
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-div.sphx-glr-animation .animation {
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diff --git a/pr-2308/_static/sphinx_highlight.js b/pr-2308/_static/sphinx_highlight.js
deleted file mode 100644
index aae669d7ea..0000000000
--- a/pr-2308/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,144 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
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-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
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-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
-  /**
-   * highlight the search words provided in localstorage in the text
-   */
-  highlightSearchWords: () => {
-    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
-
-    // get and clear terms from localstorage
-    const url = new URL(window.location);
-    const highlight =
-        localStorage.getItem("sphinx_highlight_terms")
-        || url.searchParams.get("highlight")
-        || "";
-    localStorage.removeItem("sphinx_highlight_terms")
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-
-    // get individual terms from highlight string
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
-            _("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    localStorage.removeItem("sphinx_highlight_terms")
-  },
-
-  initEscapeListener: () => {
-    // only install a listener if it is really needed
-    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
-    document.addEventListener("keydown", (event) => {
-      // bail for input elements
-      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
-      // bail with special keys
-      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
-      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
-        SphinxHighlight.hideSearchWords();
-        event.preventDefault();
-      }
-    });
-  },
-};
-
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
diff --git a/pr-2308/_static/startVscode.png b/pr-2308/_static/startVscode.png
deleted file mode 100644
index f7f78a1209..0000000000
Binary files a/pr-2308/_static/startVscode.png and /dev/null differ
diff --git a/pr-2308/_static/tabs.css b/pr-2308/_static/tabs.css
deleted file mode 100644
index 13042e5672..0000000000
--- a/pr-2308/_static/tabs.css
+++ /dev/null
@@ -1,110 +0,0 @@
-/* body[data-theme] { */
-:root {
-  --tabs--label-text: #4b5563;
-  --tabs--label-text--hover: #4b5563;
-  --tabs--label-text--active: #0ea5e9;
-  --tabs--label-text--active--hover: #0ea5e9;
-  --tabs--label-background: transparent;
-  --tabs--label-background--hover: transparent;
-  --tabs--label-background--active: transparent;
-  --tabs--label-background--active--hover: transparent;
-  --tabs--label-border: transparent;
-  --tabs--label-border--hover: #d1d5db;
-  --tabs--label-border--active: #0ea5e9;
-  --tabs--label-border--active--hover: #0ea5e9;
-  --tabs--padding-x: 1.25em;
-  --tabs--margin-x: 0;
-  --tabs--border: #e6e6e6;
-}
-
-/* Hide radio buttons */
-.tab-set > input {
-  position: absolute;
-  opacity: 0;
-}
-
-/* Tab set container */
-.tab-set {
-  border-radius: 2px;
-  display: flex;
-  flex-wrap: wrap;
-  margin: 0.75em 0;
-  position: relative;
-}
-
-/* Tab label */
-.tab-set > label {
-  z-index: 1;
-
-  width: auto;
-  border-bottom: 2px solid var(--tabs--label-border);
-  padding: 1em var(--tabs--padding-x) 0.5em;
-  margin-left: var(--tabs--margin-x);
-
-  color: var(--tabs--label-text);
-  background: var(--tabs--label-background);
-
-  transition: color 250ms;
-
-  cursor: pointer;
-
-  font-size: 0.875em;
-  font-weight: 700;
-}
-.tab-set > label:nth-child(2) {
-  margin-left: 0;
-}
-
-/* Hovered label */
-.tab-set > label:hover {
-  color: var(--tabs--label-text--hover);
-  background: var(--tabs--label-background--hover);
-  border-color: var(--tabs--label-border--hover);
-}
-
-/* Active tab label */
-.tab-set > input:checked + label {
-  color: var(--tabs--label-text--active);
-  background: var(--tabs--label-background--active);
-  border-color: var(--tabs--label-border--active);
-}
-.tab-set > input:checked + label:hover {
-  color: var(--tabs--label-text--active--hover);
-  background: var(--tabs--label-background--active--hover);
-  border-color: var(--tabs--label-border--active--hover);
-}
-
-/* Tab content */
-.tab-content {
-  order: 99;
-  display: none;
-  width: 100%;
-  box-shadow: 0 -0.0625rem var(--tabs--border);
-}
-
-/* Show content, when input is checked. */
-.tab-set > input:checked + label + .tab-content {
-  display: block;
-}
-.tab-content > p:first-child {
-  margin-top: 0.75rem;
-}
-/* Remove the top border on first code block */
-.tab-content > [class^="highlight-"]:first-child .highlight {
-  border-top: none;
-  border-top-left-radius: 0;
-  border-top-right-radius: 0;
-}
-
-/* Remove margins on children */
-.tab-content > *:first-child {
-  margin-top: 0;
-}
-.tab-content > *:last-child {
-  margin-bottom: 0;
-}
-
-/* Remove margins on nested tabs */
-.tab-content > .tab-set {
-  margin: 0;
-}
diff --git a/pr-2308/_static/tabs.js b/pr-2308/_static/tabs.js
deleted file mode 100644
index 58d2cdddd4..0000000000
--- a/pr-2308/_static/tabs.js
+++ /dev/null
@@ -1,30 +0,0 @@
-var labels_by_text = {};
-
-function ready() {
-  var li = document.getElementsByClassName("tab-label");
-  const urlParams = new URLSearchParams(window.location.search);
-  const tabs = urlParams.getAll("tabs");
-
-  for (const label of li) {
-    label.onclick = onLabelClick;
-    const text = label.textContent;
-    if (!labels_by_text[text]) {
-      labels_by_text[text] = [];
-    }
-    labels_by_text[text].push(label);
-  }
-
-  for (const tab of tabs) {
-    for (label of labels_by_text[tab]) {
-      label.previousSibling.checked = true;
-    }
-  }
-}
-
-function onLabelClick() {
-  // Activate other labels with the same text.
-  for (label of labels_by_text[this.textContent]) {
-    label.previousSibling.checked = true;
-  }
-}
-document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/pr-2308/_static/underscore-1.13.1.js b/pr-2308/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af964..0000000000
--- a/pr-2308/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.13.1
-  //     https://underscorejs.org
-  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.13.1';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
-
-  // Returns everything but the first entry of the `array`. Especially useful on
-  // the `arguments` object. Passing an **n** will return the rest N values in the
-  // `array`.
-  function rest(array, n, guard) {
-    return slice.call(array, n == null || guard ? 1 : n);
-  }
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array.
-  function last(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten(array, depth) {
-    return flatten$1(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
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-  // Zip together multiple lists into a single array -- elements that share
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diff --git a/pr-2308/_static/underscore.js b/pr-2308/_static/underscore.js
deleted file mode 100644
index cf177d4285..0000000000
--- a/pr-2308/_static/underscore.js
+++ /dev/null
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-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/quantum_operations.html#measurements">Measurements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/python/visualization.html">   Visualizing Kernels</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#features">Features</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id12">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="languages/python_api.html#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="languages/python_api.html#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="languages/python_api.html#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="languages/python_api.html#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="languages/python_api.html#cudaq.mpi.rank"><code class="docutils literal notranslate"><span class="pre">rank()</span></code></a></li>
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-</ul>
-</li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Quantum Operations</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="#x"><code class="code docutils literal notranslate"><span class="pre">x</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="#user-defined-custom-operations">User-Defined Custom Operations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
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-  <section id="quantum-operations">
-<h1>Quantum Operations<a class="headerlink" href="#quantum-operations" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q provides a default set of quantum operations on qubits.
-These operations can be used to define custom kernels and libraries.
-Since the set of quantum intrinsic operations natively supported on a specific target
-depends on the backends architecture, the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> compiler automatically
-decomposes the default operations into the appropriate set of intrinsic operations
-for that target.</p>
-<p>The sections <a class="reference internal" href="#unitary-operations-on-qubits">Unitary Operations on Qubits</a> and <a class="reference internal" href="#measurements-on-qubits">Measurements on Qubits</a> list the default set of quantum operations on qubits.</p>
-<p>Operations that implement unitary transformations of the quantum state are templated.
-The template argument allows to invoke the adjoint and controlled version of the quantum transformation, see the section on <a class="reference internal" href="#adjoint-and-controlled-operations">Adjoint and Controlled Operations</a>.</p>
-<p>CUDA-Q additionally provides overloads to support broadcasting of
-single-qubit operations across a vector of qubits.  For example,
-<code class="code docutils literal notranslate"><span class="pre">x(cudaq::qvector&lt;&gt;&amp;)</span></code> flips the state of each qubit in the provided
-<code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code>.</p>
-<section id="unitary-operations-on-qubits">
-<h2>Unitary Operations on Qubits<a class="headerlink" href="#unitary-operations-on-qubits" title="Permalink to this heading">¶</a></h2>
-<section id="x">
-<h3><code class="code docutils literal notranslate"><span class="pre">x</span></code><a class="headerlink" href="#x" title="Permalink to this heading">¶</a></h3>
-<p>This operation implements the transformation defined by the Pauli-X matrix. It is also known as the quantum version of a <code class="code docutils literal notranslate"><span class="pre">NOT</span></code>-gate.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># X = | 0  1 |</span>
-<span class="c1">#     | 1  0 |</span>
-<span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// X = | 0  1 |</span>
-<span class="c1">//     | 1  0 |</span>
-<span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="y">
-<h3><code class="code docutils literal notranslate"><span class="pre">y</span></code><a class="headerlink" href="#y" title="Permalink to this heading">¶</a></h3>
-<p>This operation implements the transformation defined by the Pauli-Y matrix.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># Y = | 0  -i |</span>
-<span class="c1">#     | i   0 |</span>
-<span class="n">y</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// Y = | 0  -i |</span>
-<span class="c1">//     | i   0 |</span>
-<span class="n">y</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="z">
-<h3><code class="code docutils literal notranslate"><span class="pre">z</span></code><a class="headerlink" href="#z" title="Permalink to this heading">¶</a></h3>
-<p>This operation implements the transformation defined by the Pauli-Z matrix.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># Z = | 1   0 |</span>
-<span class="c1">#     | 0  -1 |</span>
-<span class="n">z</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// Z = | 1   0 |</span>
-<span class="c1">//     | 0  -1 |</span>
-<span class="n">z</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="h">
-<h3><code class="code docutils literal notranslate"><span class="pre">h</span></code><a class="headerlink" href="#h" title="Permalink to this heading">¶</a></h3>
-<p>This operation is a rotation by π about the X+Z axis, and
-enables one to create a superposition of computational basis states.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># H = (1 / sqrt(2)) * | 1   1 |</span>
-<span class="c1">#                     | 1  -1 |</span>
-<span class="n">h</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// H = (1 / sqrt(2)) * | 1   1 |</span>
-<span class="c1">//                     | 1  -1 |</span>
-<span class="n">h</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="r1">
-<h3><code class="code docutils literal notranslate"><span class="pre">r1</span></code><a class="headerlink" href="#r1" title="Permalink to this heading">¶</a></h3>
-<p>This operation is an arbitrary rotation about the <code class="code docutils literal notranslate"><span class="pre">|1&gt;</span></code> state.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># R1(λ) = | 1     0    |</span>
-<span class="c1">#         | 0  exp(iλ) |</span>
-<span class="n">r1</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// R1(λ) = | 1     0    |</span>
-<span class="c1">//         | 0  exp(iλ) |</span>
-<span class="n">r1</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">numbers</span><span class="o">::</span><span class="n">pi</span><span class="p">,</span><span class="w"> </span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="rx">
-<h3><code class="code docutils literal notranslate"><span class="pre">rx</span></code><a class="headerlink" href="#rx" title="Permalink to this heading">¶</a></h3>
-<p>This operation is an arbitrary rotation about the X axis.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># Rx(θ) = |  cos(θ/2)  -isin(θ/2) |</span>
-<span class="c1">#         | -isin(θ/2)  cos(θ/2)  |</span>
-<span class="n">rx</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--5-input--2" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// Rx(θ) = |  cos(θ/2)  -isin(θ/2) |</span>
-<span class="c1">//         | -isin(θ/2)  cos(θ/2)  |</span>
-<span class="n">rx</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">numbers</span><span class="o">::</span><span class="n">pi</span><span class="p">,</span><span class="w"> </span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="ry">
-<h3><code class="code docutils literal notranslate"><span class="pre">ry</span></code><a class="headerlink" href="#ry" title="Permalink to this heading">¶</a></h3>
-<p>This operation is an arbitrary rotation about the Y axis.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--6-input--1" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># Ry(θ) = | cos(θ/2)  -sin(θ/2) |</span>
-<span class="c1">#         | sin(θ/2)   cos(θ/2) |</span>
-<span class="n">ry</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--6-input--2" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// Ry(θ) = | cos(θ/2)  -sin(θ/2) |</span>
-<span class="c1">//         | sin(θ/2)   cos(θ/2) |</span>
-<span class="n">ry</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">numbers</span><span class="o">::</span><span class="n">pi</span><span class="p">,</span><span class="w"> </span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="rz">
-<h3><code class="code docutils literal notranslate"><span class="pre">rz</span></code><a class="headerlink" href="#rz" title="Permalink to this heading">¶</a></h3>
-<p>This operation is an arbitrary rotation about the Z axis.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--7-input--1" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># Rz(λ) = | exp(-iλ/2)      0     |</span>
-<span class="c1">#         |     0       exp(iλ/2) |</span>
-<span class="n">rz</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--7-input--2" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// Rz(λ) = | exp(-iλ/2)      0     |</span>
-<span class="c1">//         |     0       exp(iλ/2) |</span>
-<span class="n">rz</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">numbers</span><span class="o">::</span><span class="n">pi</span><span class="p">,</span><span class="w"> </span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="s">
-<h3><code class="code docutils literal notranslate"><span class="pre">s</span></code><a class="headerlink" href="#s" title="Permalink to this heading">¶</a></h3>
-<p>This operation applies to its target a rotation by π/2 about the Z axis.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--8-input--1" name="tab-set--8" type="radio"><label class="tab-label" for="tab-set--8-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># S = | 1   0 |</span>
-<span class="c1">#     | 0   i |</span>
-<span class="n">s</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--8-input--2" name="tab-set--8" type="radio"><label class="tab-label" for="tab-set--8-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// S = | 1   0 |</span>
-<span class="c1">//     | 0   i |</span>
-<span class="n">s</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="t">
-<h3><code class="code docutils literal notranslate"><span class="pre">t</span></code><a class="headerlink" href="#t" title="Permalink to this heading">¶</a></h3>
-<p>This operation applies to its target a π/4 rotation about the Z axis.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--9-input--1" name="tab-set--9" type="radio"><label class="tab-label" for="tab-set--9-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># T = | 1      0     |</span>
-<span class="c1">#     | 0  exp(iπ/4) |</span>
-<span class="n">t</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--9-input--2" name="tab-set--9" type="radio"><label class="tab-label" for="tab-set--9-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// T = | 1      0     |</span>
-<span class="c1">//     | 0  exp(iπ/4) |</span>
-<span class="n">t</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="swap">
-<h3><code class="code docutils literal notranslate"><span class="pre">swap</span></code><a class="headerlink" href="#swap" title="Permalink to this heading">¶</a></h3>
-<p>This operation swaps the states of two qubits.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--10-input--1" name="tab-set--10" type="radio"><label class="tab-label" for="tab-set--10-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit_1</span><span class="p">,</span> <span class="n">qubit_2</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># Swap = | 1 0 0 0 |</span>
-<span class="c1">#        | 0 0 1 0 |</span>
-<span class="c1">#        | 0 1 0 0 |</span>
-<span class="c1">#        | 0 0 0 1 |</span>
-<span class="n">swap</span><span class="p">(</span><span class="n">qubit_1</span><span class="p">,</span> <span class="n">qubit_2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--10-input--2" name="tab-set--10" type="radio"><label class="tab-label" for="tab-set--10-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit_1</span><span class="p">,</span><span class="w"> </span><span class="n">qubit_2</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// Swap = | 1 0 0 0 |</span>
-<span class="c1">//        | 0 0 1 0 |</span>
-<span class="c1">//        | 0 1 0 0 |</span>
-<span class="c1">//        | 0 0 0 1 |</span>
-<span class="n">swap</span><span class="p">(</span><span class="n">qubit_1</span><span class="p">,</span><span class="w"> </span><span class="n">qubit_2</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="u3">
-<h3><code class="code docutils literal notranslate"><span class="pre">u3</span></code><a class="headerlink" href="#u3" title="Permalink to this heading">¶</a></h3>
-<p>This operation applies the universal three-parameters operator to target qubit. The three parameters are Euler angles - theta (θ), phi (φ), and lambda (λ).</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--11-input--1" name="tab-set--11" type="radio"><label class="tab-label" for="tab-set--11-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># U3(θ,φ,λ) = | cos(θ/2)            -exp(iλ) * sin(θ/2)       |</span>
-<span class="c1">#             | exp(iφ) * sin(θ/2)   exp(i(λ + φ)) * cos(θ/2) |</span>
-<span class="n">u3</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--11-input--2" name="tab-set--11" type="radio"><label class="tab-label" for="tab-set--11-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// U3(θ,φ,λ) = | cos(θ/2)            -exp(iλ) * sin(θ/2)       |</span>
-<span class="c1">//             | exp(iφ) * sin(θ/2)   exp(i(λ + φ)) * cos(θ/2) |</span>
-<span class="n">u3</span><span class="p">(</span><span class="n">M_PI</span><span class="p">,</span><span class="w"> </span><span class="n">M_PI</span><span class="p">,</span><span class="w"> </span><span class="n">M_PI_2</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="adjoint-and-controlled-operations">
-<h2>Adjoint and Controlled Operations<a class="headerlink" href="#adjoint-and-controlled-operations" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--12-input--1" name="tab-set--12" type="radio"><label class="tab-label" for="tab-set--12-input--1">Python</label><div class="tab-content docutils">
-<p>The <code class="code docutils literal notranslate"><span class="pre">adj</span></code> method of any gate can be used to invoke the
-<a class="reference external" href="https://en.wikipedia.org/wiki/Conjugate_transpose">adjoint</a> transformation:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a kernel and allocate a qubit in a |0&gt; state.</span>
-<span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-<span class="c1"># Apply the unitary transformation defined by the matrix</span>
-<span class="c1"># T = | 1      0     |</span>
-<span class="c1">#     | 0  exp(iπ/4) |</span>
-<span class="c1"># to the state of the qubit `q`:</span>
-<span class="n">t</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-<span class="c1"># Apply its adjoint transformation defined by the matrix</span>
-<span class="c1"># T† = | 1      0     |</span>
-<span class="c1">#      | 0  exp(-iπ/4) |</span>
-<span class="n">t</span><span class="o">.</span><span class="n">adj</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-<span class="c1"># `qubit` is now again in the initial state |0&gt;.</span>
-</pre></div>
-</div>
-<p>The <code class="code docutils literal notranslate"><span class="pre">ctrl</span></code> method of any gate can be used to apply the transformation
-conditional on the state of one or more control qubits, see also this
-<a class="reference external" href="https://en.wikipedia.org/wiki/Quantum_logic_gate#Controlled_gates">Wikipedia entry</a>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a kernel and allocate qubits in a |0&gt; state.</span>
-<span class="n">ctrl_1</span><span class="p">,</span> <span class="n">ctrl_2</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="c1"># Create a superposition.</span>
-<span class="n">h</span><span class="p">(</span><span class="n">ctrl_1</span><span class="p">)</span>
-<span class="c1"># `ctrl_1` is now in a state (|0&gt; + |1&gt;) / √2.</span>
-
-<span class="c1"># Apply the unitary transformation</span>
-<span class="c1"># | 1  0  0  0 |</span>
-<span class="c1"># | 0  1  0  0 |</span>
-<span class="c1"># | 0  0  0  1 |</span>
-<span class="c1"># | 0  0  1  0 |</span>
-<span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">ctrl_1</span><span class="p">,</span> <span class="n">ctrl_2</span><span class="p">)</span>
-<span class="c1"># `ctrl_1` and `ctrl_2` are in a state (|00&gt; + |11&gt;) / √2.</span>
-
-<span class="c1"># Set the state of `target` to |1&gt;:</span>
-<span class="n">x</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
-<span class="c1"># Apply the transformation T only if both</span>
-<span class="c1"># control qubits are in a |1&gt; state:</span>
-<span class="n">t</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">ctrl_1</span><span class="p">,</span> <span class="n">ctrl_2</span><span class="p">],</span> <span class="n">target</span><span class="p">)</span>
-<span class="c1"># The qubits ctrl_1, ctrl_2, and target are now in a state</span>
-<span class="c1"># (|000&gt; + exp(iπ/4)|111&gt;) / √2.</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--12-input--2" name="tab-set--12" type="radio"><label class="tab-label" for="tab-set--12-input--2">C++</label><div class="tab-content docutils">
-<p>The template argument <code class="code docutils literal notranslate"><span class="pre">cudaq::adj</span></code> can be used to invoke the
-<a class="reference external" href="https://en.wikipedia.org/wiki/Conjugate_transpose">adjoint</a> transformation:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Allocate a qubit in a |0&gt; state.</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-
-<span class="c1">// Apply the unitary transformation defined by the matrix</span>
-<span class="c1">// T = | 1      0     |</span>
-<span class="c1">//     | 0  exp(iπ/4) |</span>
-<span class="c1">// to the state of the qubit `q`:</span>
-<span class="n">t</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-
-<span class="c1">// Apply its adjoint transformation defined by the matrix</span>
-<span class="c1">// T† = | 1      0     |</span>
-<span class="c1">//      | 0  exp(-iπ/4) |</span>
-<span class="n">t</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">adj</span><span class="o">&gt;</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-<span class="c1">// Qubit `q` is now again in the initial state |0&gt;.</span>
-</pre></div>
-</div>
-<p>The template argument <code class="code docutils literal notranslate"><span class="pre">cudaq::ctrl</span></code> can be used to apply the transformation
-conditional on the state of one or more control qubits, see also this
-<a class="reference external" href="https://en.wikipedia.org/wiki/Quantum_logic_gate#Controlled_gates">Wikipedia entry</a>.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Allocate qubits in a |0&gt; state.</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">ctrl_1</span><span class="p">,</span><span class="w"> </span><span class="n">ctrl_2</span><span class="p">,</span><span class="w"> </span><span class="n">target</span><span class="p">;</span>
-<span class="c1">// Create a superposition.</span>
-<span class="n">h</span><span class="p">(</span><span class="n">ctrl_1</span><span class="p">);</span>
-<span class="c1">// Qubit ctrl_1 is now in a state (|0&gt; + |1&gt;) / √2.</span>
-
-<span class="c1">// Apply the unitary transformation</span>
-<span class="c1">// | 1  0  0  0 |</span>
-<span class="c1">// | 0  1  0  0 |</span>
-<span class="c1">// | 0  0  0  1 |</span>
-<span class="c1">// | 0  0  1  0 |</span>
-<span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ctrl_1</span><span class="p">,</span><span class="w"> </span><span class="n">ctrl_2</span><span class="p">);</span>
-<span class="c1">// The qubits ctrl_1 and ctrl_2 are in a state (|00&gt; + |11&gt;) / √2.</span>
-
-<span class="c1">// Set the state of `target` to |1&gt;:</span>
-<span class="n">x</span><span class="p">(</span><span class="n">target</span><span class="p">);</span>
-<span class="c1">// Apply the transformation T only if both</span>
-<span class="c1">// control qubits are in a |1&gt; state:</span>
-<span class="n">t</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ctrl_1</span><span class="p">,</span><span class="w"> </span><span class="n">ctrl_2</span><span class="p">,</span><span class="w"> </span><span class="n">target</span><span class="p">);</span>
-<span class="c1">// The qubits ctrl_1, ctrl_2, and target are now in a state</span>
-<span class="c1">// (|000&gt; + exp(iπ/4)|111&gt;) / √2.</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Following common convention, by default the transformation is applied to the target qubit(s)
-if all control qubits are in a <code class="code docutils literal notranslate"><span class="pre">|1&gt;</span></code> state.
-However, that behavior can be changed to instead apply the transformation when a control qubit is in
-a <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> state by negating the polarity of the control qubit.
-The syntax for negating the polarity is the not-operator preceding the
-control qubit:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--13-input--1" name="tab-set--13" type="radio"><label class="tab-label" for="tab-set--13-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">c</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="n">h</span><span class="p">(</span><span class="n">c</span><span class="p">);</span>
-<span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="o">!</span><span class="n">c</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-<span class="c1">// The qubits c and q are in a state (|01&gt; + |10&gt;) / √2.</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This notation is only supported in the context of applying a controlled operation and is only valid for control qubits. For example, negating either of the target qubits in the
-<code class="code docutils literal notranslate"><span class="pre">swap</span></code> operation is not allowed.
-Negating the polarity of control qubits is similarly supported when using <code class="code docutils literal notranslate"><span class="pre">cudaq::control</span></code> to conditionally apply a custom quantum kernel.</p>
-</section>
-<section id="measurements-on-qubits">
-<h2>Measurements on Qubits<a class="headerlink" href="#measurements-on-qubits" title="Permalink to this heading">¶</a></h2>
-<section id="mz">
-<h3><code class="code docutils literal notranslate"><span class="pre">mz</span></code><a class="headerlink" href="#mz" title="Permalink to this heading">¶</a></h3>
-<p>This operation measures a qubit with respect to the computational basis,
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-Z matrix.
-This is a non-linear transformation, and no template overloads are available.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--14-input--1" name="tab-set--14" type="radio"><label class="tab-label" for="tab-set--14-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="n">mz</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--14-input--2" name="tab-set--14" type="radio"><label class="tab-label" for="tab-set--14-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-<span class="n">mz</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="mx">
-<h3><code class="code docutils literal notranslate"><span class="pre">mx</span></code><a class="headerlink" href="#mx" title="Permalink to this heading">¶</a></h3>
-<p>This operation measures a qubit with respect to the Pauli-X basis,
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-X matrix.
-This is a non-linear transformation, and no template overloads are available.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--15-input--1" name="tab-set--15" type="radio"><label class="tab-label" for="tab-set--15-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="n">mx</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--15-input--2" name="tab-set--15" type="radio"><label class="tab-label" for="tab-set--15-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-<span class="n">mx</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="my">
-<h3><code class="code docutils literal notranslate"><span class="pre">my</span></code><a class="headerlink" href="#my" title="Permalink to this heading">¶</a></h3>
-<p>This operation measures a qubit with respect to the Pauli-Y basis,
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-Y matrix.
-This is a non-linear transformation, and no template overloads are available.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--16-input--1" name="tab-set--16" type="radio"><label class="tab-label" for="tab-set--16-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">my</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--16-input--2" name="tab-set--16" type="radio"><label class="tab-label" for="tab-set--16-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-<span class="n">my</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="user-defined-custom-operations">
-<h2>User-Defined Custom Operations<a class="headerlink" href="#user-defined-custom-operations" title="Permalink to this heading">¶</a></h2>
-<p>Users can define a custom quantum operation by its unitary matrix. First use
-the API to register a custom operation, outside of a CUDA-Q kernel. Then the
-operation can be used within a CUDA-Q kernel like any of the built-in operations
-defined above.
-Custom operations are supported on qubits only (<code class="code docutils literal notranslate"><span class="pre">qudit</span></code> with <code class="code docutils literal notranslate"><span class="pre">level</span> <span class="pre">=</span> <span class="pre">2</span></code>).</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--17-input--1" name="tab-set--17" type="radio"><label class="tab-label" for="tab-set--17-input--1">Python</label><div class="tab-content docutils">
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq.register_operation</span></code> API accepts an identifier string for
-the custom operation and its unitary matrix. The matrix can be a <code class="code docutils literal notranslate"><span class="pre">list</span></code> or
-<code class="code docutils literal notranslate"><span class="pre">numpy</span></code> array of complex numbers. A 1D matrix is interpreted as row-major.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">register_operation</span><span class="p">(</span><span class="s2">&quot;custom_h&quot;</span><span class="p">,</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">register_operation</span><span class="p">(</span><span class="s2">&quot;custom_x&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">bell</span><span class="p">():</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">custom_h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">custom_x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">bell</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--17-input--2" name="tab-set--17" type="radio"><label class="tab-label" for="tab-set--17-input--2">C++</label><div class="tab-content docutils">
-<p>The macro <code class="code docutils literal notranslate"><span class="pre">CUDAQ_REGISTER_OPERATION</span></code> accepts a unique name for the
-operation, the number of target qubits, the number of rotation parameters
-(can be 0), and the unitary matrix as a 1D row-major <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;complex&gt;</span></code>
-representation.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="n">CUDAQ_REGISTER_OPERATION</span><span class="p">(</span><span class="n">custom_h</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
-<span class="w">                        </span><span class="p">{</span><span class="n">M_SQRT1_2</span><span class="p">,</span><span class="w"> </span><span class="n">M_SQRT1_2</span><span class="p">,</span><span class="w"> </span><span class="n">M_SQRT1_2</span><span class="p">,</span><span class="w"> </span><span class="o">-</span><span class="n">M_SQRT1_2</span><span class="p">})</span>
-
-<span class="n">CUDAQ_REGISTER_OPERATION</span><span class="p">(</span><span class="n">custom_x</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">})</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">bell_pair</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">;</span>
-<span class="w">    </span><span class="n">custom_h</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="n">custom_x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="n">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">bell_pair</span><span class="p">);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="p">[</span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">counts</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;%s</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bits</span><span class="p">.</span><span class="n">data</span><span class="p">());</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>For multi-qubit operations, the matrix is interpreted with MSB qubit ordering,
-i.e. big-endian convention. The following example shows two different custom
-operations, each operating on 2 qubits.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--18-input--1" name="tab-set--18" type="radio"><label class="tab-label" for="tab-set--18-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Create and test a custom CNOT operation.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">register_operation</span><span class="p">(</span><span class="s2">&quot;my_cnot&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span>
-                                              <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span>
-                                              <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span>
-                                              <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">bell_pair</span><span class="p">():</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">my_cnot</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># `my_cnot(control, target)`</span>
-
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">bell_pair</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span> <span class="c1"># prints { 11:500 00:500 } (exact numbers will be random)</span>
-
-
-<span class="c1"># Construct a custom unitary matrix for X on the first qubit and Y</span>
-<span class="c1"># on the second qubit.</span>
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span>  <span class="mi">1</span> <span class="p">],</span> <span class="p">[</span><span class="mi">1</span> <span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="n">j</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">XY</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-<span class="c1"># Register the custom operation</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">register_operation</span><span class="p">(</span><span class="s2">&quot;my_XY&quot;</span><span class="p">,</span> <span class="n">XY</span><span class="p">)</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">custom_xy_test</span><span class="p">():</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">my_XY</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">y</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># undo the prior Y gate on qubit 1</span>
-
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">custom_xy_test</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span> <span class="c1"># prints { 10:1000 }</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--18-input--2" name="tab-set--18" type="radio"><label class="tab-label" for="tab-set--18-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="n">CUDAQ_REGISTER_OPERATION</span><span class="p">(</span><span class="n">MyCNOT</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
-<span class="w">                         </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">});</span>
-
-<span class="n">CUDAQ_REGISTER_OPERATION</span><span class="p">(</span>
-<span class="w">    </span><span class="n">MyXY</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
-<span class="w">    </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">-1</span><span class="p">},</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">},</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">-1</span><span class="p">},</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">},</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">});</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">bell_pair</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">;</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="n">MyCNOT</span><span class="p">(</span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span><span class="w"> </span><span class="c1">// MyCNOT(control, target)</span>
-<span class="p">}</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">custom_xy_test</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">;</span>
-<span class="w">  </span><span class="n">MyXY</span><span class="p">(</span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span>
-<span class="w">  </span><span class="n">y</span><span class="p">(</span><span class="n">r</span><span class="p">);</span><span class="w"> </span><span class="c1">// undo the prior Y gate on qubit 1</span>
-<span class="p">}</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="n">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">bell_pair</span><span class="p">);</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span><span class="w"> </span><span class="c1">// prints { 11:500 00:500 } (exact numbers will be random)</span>
-
-<span class="w">  </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">custom_xy_test</span><span class="p">);</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span><span class="w"> </span><span class="c1">// prints { 10:1000 }</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>When a custom operation is used on hardware backends, it is synthesized to a
-set of native quantum operations. Currently, only 1-qubit and 2-qubit custom
-operations are supported on hardware backends.</p>
-</div>
-</section>
-<section id="photonic-operations-on-qudits">
-<h2>Photonic Operations on Qudits<a class="headerlink" href="#photonic-operations-on-qudits" title="Permalink to this heading">¶</a></h2>
-<p>These operations are valid only on the <code class="code docutils literal notranslate"><span class="pre">orca-photonics</span></code> target which does not support
-the quantum operations above.</p>
-<section id="create">
-<h3><code class="code docutils literal notranslate"><span class="pre">create</span></code><a class="headerlink" href="#create" title="Permalink to this heading">¶</a></h3>
-<p>This operation increments the number of photons in a qumode up to a maximum value
-defined by the qudit level that represents the qumode. If it is applied to a qumode
-where the number of photons is already at the maximum value, the operation has no
-effect.</p>
-<p><span class="math notranslate nohighlight">\(U|0\rangle → |1\rangle, U|1\rangle → |2\rangle, U|2\rangle → |3\rangle, \cdots, U|d\rangle → |d\rangle\)</span>
-where <span class="math notranslate nohighlight">\(d\)</span> is the qudit level.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--19-input--1" name="tab-set--19" type="radio"><label class="tab-label" for="tab-set--19-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="n">qudit</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">create</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--19-input--2" name="tab-set--19" type="radio"><label class="tab-label" for="tab-set--19-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;</span><span class="mi">3</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-<span class="n">create</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="annihilate">
-<h3><code class="code docutils literal notranslate"><span class="pre">annihilate</span></code><a class="headerlink" href="#annihilate" title="Permalink to this heading">¶</a></h3>
-<p>This operation reduces the number of photons in a qumode up to a minimum value of
-0 representing the vacuum state. If it is applied to a qumode where the number of
-photons is already at the minimum value 0, the operation has no effect.</p>
-<p><span class="math notranslate nohighlight">\(U|0\rangle → |0\rangle, U|1\rangle → |0\rangle, U|2\rangle → |1\rangle, \cdots, U|d\rangle → |d-1\rangle\)</span>
-where <span class="math notranslate nohighlight">\(d\)</span> is the qudit level.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--20-input--1" name="tab-set--20" type="radio"><label class="tab-label" for="tab-set--20-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="n">qudit</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">annihilate</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--20-input--2" name="tab-set--20" type="radio"><label class="tab-label" for="tab-set--20-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;</span><span class="mi">3</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-<span class="n">annihilate</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="phase-shift">
-<h3><code class="code docutils literal notranslate"><span class="pre">phase_shift</span></code><a class="headerlink" href="#phase-shift" title="Permalink to this heading">¶</a></h3>
-<p>A phase shifter adds a phase <span class="math notranslate nohighlight">\(\phi\)</span> on a qumode. For the annihilation (<span class="math notranslate nohighlight">\(a_1\)</span>)
-and creation operators (<span class="math notranslate nohighlight">\(a_1^\dagger\)</span>) of a qumode, the phase shift operator
-is defined  by</p>
-<div class="math notranslate nohighlight">
-\[P(\phi) = \exp\left(i \phi a_1^\dagger a_1  \right)\]</div>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--21-input--1" name="tab-set--21" type="radio"><label class="tab-label" for="tab-set--21-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="n">qudit</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-<span class="n">phase_shift</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="mf">0.17</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--21-input--2" name="tab-set--21" type="radio"><label class="tab-label" for="tab-set--21-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;</span><span class="mi">4</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-<span class="n">phase_shift</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="mf">0.17</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="beam-splitter">
-<h3><code class="code docutils literal notranslate"><span class="pre">beam_splitter</span></code><a class="headerlink" href="#beam-splitter" title="Permalink to this heading">¶</a></h3>
-<p>Beam splitters act on two qumodes together and it is parameterized by a single angle
-<span class="math notranslate nohighlight">\(\theta\)</span>, relating to reflectivity.
-For the annihilation (<span class="math notranslate nohighlight">\(a_1\)</span> and <span class="math notranslate nohighlight">\(a_2\)</span>) and creation operators (<span class="math notranslate nohighlight">\(a_1^\dagger\)</span>
-and <span class="math notranslate nohighlight">\(a_2^\dagger\)</span>) of two qumodes, the beam splitter operator is defined by</p>
-<div class="math notranslate nohighlight">
-\[B(\theta) = \exp\left[i \theta (a_1^\dagger a_2 + a_1 a_2^\dagger) \right]\]</div>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--22-input--1" name="tab-set--22" type="radio"><label class="tab-label" for="tab-set--22-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="p">[</span><span class="n">qudit</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)]</span>
-<span class="n">beam_splitter</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mf">0.34</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--22-input--2" name="tab-set--22" type="radio"><label class="tab-label" for="tab-set--22-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;</span><span class="mi">3</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="n">beam_splitter</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="mf">0.34</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="id1">
-<h3><code class="code docutils literal notranslate"><span class="pre">mz</span></code><a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h3>
-<p>This operation returns the measurement results of the input qumode(s).</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--23-input--1" name="tab-set--23" type="radio"><label class="tab-label" for="tab-set--23-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qumodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">qudit</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)]</span>
-<span class="n">mz</span><span class="p">(</span><span class="n">qumodes</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--23-input--2" name="tab-set--23" type="radio"><label class="tab-label" for="tab-set--23-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;</span><span class="mi">3</span><span class="o">&gt;</span><span class="w"> </span><span class="n">qumodes</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="n">mz</span><span class="p">(</span><span class="n">qumodes</span><span class="p">);</span>
-</pre></div>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-  <section id="cuda-q-c-api">
-<h1>CUDA-Q C++ API<a class="headerlink" href="#cuda-q-c-api" title="Permalink to this heading">¶</a></h1>
-<section id="operators">
-<h2>Operators<a class="headerlink" href="#operators" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opE">
-<span class="target" id="classcudaq_1_1spin__op"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> represents a general sum of Pauli tensor products. It exposes the typical algebraic operations that allow programmers to define primitive Pauli operators and use them to compose larger, more complex Pauli tensor products and sums thereof. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op12spin_op_termE">
-<span class="target" id="classcudaq_1_1spin__op_1ae504b902ef95b21439e04d51143e3bc4"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">spin_op_term</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">bool</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>We represent the <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> terms in binary symplectic form, i.e. each term is a vector of 1s and 0s of size 2 * nQubits, where the first n elements represent X, the next n elements represent Z, and X=Z=1 -&gt; Y on site i, X=1, Z=0 -&gt; X on site i, and X=0, Z=1 -&gt; Z on site i. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op12csr_spmatrixE">
-<span class="target" id="classcudaq_1_1spin__op_1a3cc72dd5fc9a07f1c6912083f7012dcb"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">csr_spmatrix</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">tuple</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op12csr_spmatrixE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Typedef for a vector of non-zero sparse matrix elements. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERNSt4pairIK12spin_op_termNSt7complexIdEEEE">
-<span class="target" id="classcudaq_1_1spin__op_1aec0f837d1cb83ed2f0b73ad24a30763c"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="cudaq::spin_op::spin_op_term"><span class="n"><span class="pre">spin_op_term</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">termData</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERNSt4pairIK12spin_op_termNSt7complexIdEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes a single term / coefficient pair. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERKNSt4pairIK12spin_op_termNSt7complexIdEEEE">
-<span class="target" id="classcudaq_1_1spin__op_1ae1320f34fcd101466b5e6d6983bbeb6e"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="cudaq::spin_op::spin_op_term"><span class="n"><span class="pre">spin_op_term</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">termData</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERKNSt4pairIK12spin_op_termNSt7complexIdEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes a single term / coefficient constant pair. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opE5pauliKNSt6size_tENSt7complexIdEE">
-<span class="target" id="classcudaq_1_1spin__op_1aa563dacd8019e741e03fb2d85c0fa7e0"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">pauli</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">id</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">coeff</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1.0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opE5pauliKNSt6size_tENSt7complexIdEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the Pauli type, the qubit site, and the term coefficient. Constructs a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code> of one Pauli on one qubit. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERK12spin_op_termRKNSt7complexIdEE">
-<span class="target" id="classcudaq_1_1spin__op_1aaaf4fe4875eea54d2ca5844b5f69d77c"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="cudaq::spin_op::spin_op_term"><span class="n"><span class="pre">spin_op_term</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">term</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">coeff</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERK12spin_op_termRKNSt7complexIdEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the binary representation of a single term and its coefficient. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERKNSt13unordered_mapI12spin_op_termNSt7complexIdEEEE">
-<span class="target" id="classcudaq_1_1spin__op_1a5bb6434a82fe4479a72860ba5f1e7930"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">unordered_map</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="cudaq::spin_op::spin_op_term"><span class="n"><span class="pre">spin_op_term</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">_terms</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERKNSt13unordered_mapI12spin_op_termNSt7complexIdEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a full set of terms for the composite spin op as an unordered_map mapping individual terms to their coefficient. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERKNSt6vectorI12spin_op_termEERKNSt6vectorINSt7complexIdEEEE">
-<span class="target" id="classcudaq_1_1spin__op_1ae66b0d0da05c6566d3aa70473a035662"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="cudaq::spin_op::spin_op_term"><span class="n"><span class="pre">spin_op_term</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">bsf</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">coeffs</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERKNSt6vectorI12spin_op_termEERKNSt6vectorINSt7complexIdEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct from a vector of term data. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opEv">
-<span class="target" id="classcudaq_1_1spin__op_1a2eec6685f70a7e710460ec2c3813a149"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, creates the identity term. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opENSt6size_tE">
-<span class="target" id="classcudaq_1_1spin__op_1afe98dbe82f7f7792dc9c7002f8d980a8"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">numQubits</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct the identity term on the given number of qubits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERK7spin_op">
-<span class="target" id="classcudaq_1_1spin__op_1a5fbe20bdec4f57a728853f773b88d2da"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op7spin_opERK7spin_op" title="cudaq::spin_op::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">o</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op7spin_opERKNSt6vectorIdEENSt6size_tE">
-<span class="target" id="classcudaq_1_1spin__op_1a110eb5dc534fddf1277901c8663e08b4"></span><span class="sig-name descname"><span class="n"><span class="pre">spin_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">data_rep</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nQubits</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op7spin_opERKNSt6vectorIdEENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> from a serialized representation. Specifically, this encoding is via a vector of doubles. The encoding is as follows: for each term, a list of doubles where element <code class="docutils literal notranslate"><span class="pre">i</span></code> is a 3.0 for a Y, a 1.0 for a X, and a 2.0 for a Z on qubit i, followed by the real and imaginary part of the coefficient. Each term is appended to the array forming one large 1d array of doubles. The array is ended with the total number of terms represented as a double. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opD0Ev">
-<span class="target" id="classcudaq_1_1spin__op_1a11c0ef8ecb9670dec2a186a7589476cf"></span><span class="sig-name descname"><span class="n"><span class="pre">~spin_op</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opD0Ev" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The destructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op5beginEv">
-<span class="target" id="classcudaq_1_1spin__op_1aa44b7656a7aae6708fd76385cda157bd"></span><a class="reference internal" href="#_CPPv4I0EN5cudaq7spin_op8iteratorE" title="cudaq::spin_op::iterator"><span class="n"><span class="pre">iterator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">begin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op5beginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return iterator to start of <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> terms. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op3endEv">
-<span class="target" id="classcudaq_1_1spin__op_1a8f4570cf5e66c8b764756c6d939ba216"></span><a class="reference internal" href="#_CPPv4I0EN5cudaq7spin_op8iteratorE" title="cudaq::spin_op::iterator"><span class="n"><span class="pre">iterator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">end</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op3endEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return iterator to end of <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> terms. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op5beginEv">
-<span class="target" id="classcudaq_1_1spin__op_1a13fe416609d6696e805ee0e9ac189587"></span><a class="reference internal" href="#_CPPv4I0EN5cudaq7spin_op8iteratorE" title="cudaq::spin_op::iterator"><span class="n"><span class="pre">iterator</span></span></a><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">begin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op5beginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return constant iterator to start of <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code> terms. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op3endEv">
-<span class="target" id="classcudaq_1_1spin__op_1a1fe8f68270d5c9306dffb1f805944cdc"></span><a class="reference internal" href="#_CPPv4I0EN5cudaq7spin_op8iteratorE" title="cudaq::spin_op::iterator"><span class="n"><span class="pre">iterator</span></span></a><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">end</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op3endEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return constant iterator to end of <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code> terms. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opaSERK7spin_op">
-<span class="target" id="classcudaq_1_1spin__op_1ad18f3c40dbae543892e2b43aa21e4079"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opaSERK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the provided <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> equal to this one and return *this. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_oppLERK7spin_op">
-<span class="target" id="classcudaq_1_1spin__op_1a55cda5390f5f4ebd3cb41e5d749c86a8"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">+=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_oppLERK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> to this one and return *this. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opmIERK7spin_op">
-<span class="target" id="classcudaq_1_1spin__op_1a80fb43e29ae5fc1dce8804803879303b"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">-=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opmIERK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Subtract the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> from this one and return *this. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opmLERK7spin_op">
-<span class="target" id="classcudaq_1_1spin__op_1af79d857df22289d4b6791c2a711f8841"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opmLERK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> with this one and return *this. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_opeqERK7spin_op">
-<span class="target" id="classcudaq_1_1spin__op_1acb27a068fa567944e3b452892d2a4780"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_opeqERK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> is equal to the given one. Equality here does not consider the coefficients. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opmLEKd">
-<span class="target" id="classcudaq_1_1spin__op_1acfb4b53c76a0363ff89ac260fdbf21cf"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opmLEKd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> by the given double. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_opmLEKNSt7complexIdEE">
-<span class="target" id="classcudaq_1_1spin__op_1ae6d43a1e540454bc3b951c97bb8b3337"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4N5cudaq7spin_opmLEKNSt7complexIdEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> by the given complex value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op10num_qubitsEv">
-<span class="target" id="classcudaq_1_1spin__op_1a6bd7db85c9189b0e85245856bbd986cd"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">num_qubits</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op10num_qubitsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of qubits this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> is on. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op9num_termsEv">
-<span class="target" id="classcudaq_1_1spin__op_1ad75e50a6a1547192de5f4acd7ad3f2de"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">num_terms</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op9num_termsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of terms in this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op15get_coefficientEv">
-<span class="target" id="classcudaq_1_1spin__op_1a7a0579252345b90f6bc3b808724381f3"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_coefficient</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op15get_coefficientEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>For a <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> with 1 term, get that terms’ coefficient. Throws an exception for spin_ops with &gt; 1 terms. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op12get_raw_dataEv">
-<span class="target" id="classcudaq_1_1spin__op_1a7b693c256dda5c7e933a91f85d0455eb"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12spin_op_termE" title="cudaq::spin_op::spin_op_term"><span class="n"><span class="pre">spin_op_term</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_raw_data</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op12get_raw_dataEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the binary symplectic form data. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op11is_identityEv">
-<span class="target" id="classcudaq_1_1spin__op_1a95b0b23ea81105da74dc5d4d6a1fa07e"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">is_identity</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op11is_identityEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Is this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> == to the identity. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op4dumpEv">
-<span class="target" id="classcudaq_1_1spin__op_1abb3b7b89c257333db3fa0577712d77d2"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op4dumpEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump a string representation of this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> to standard out. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op9to_stringEb">
-<span class="target" id="classcudaq_1_1spin__op_1a72332ac65d2cc57ec6669dc6b4bcda8e"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">to_string</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">printCoefficients</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">true</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op9to_stringEb" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a string representation of this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op21getDataRepresentationEv">
-<span class="target" id="classcudaq_1_1spin__op_1a05ba20691749439da3866abda0a454e6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getDataRepresentation</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op21getDataRepresentationEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the vector&lt;double&gt; serialized representation of this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. (see the constructor for the encoding) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op12getDataTupleEv">
-<span class="target" id="classcudaq_1_1spin__op_1a997cacbd50b8ab6549a08e3fb959f876"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">tuple</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getDataTuple</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op12getDataTupleEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the serialized representation of this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>, such that it is fully constructible with a <a class="reference internal" href="#classcudaq_1_1spin__op_1a2eec6685f70a7e710460ec2c3813a149"><span class="std std-ref">spin_op()</span></a> constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op16distribute_termsENSt6size_tE">
-<span class="target" id="classcudaq_1_1spin__op_1a8fb7080ea8c312013fbbf2e530a1178f"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">distribute_terms</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">numChunks</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op16distribute_termsENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a vector of <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> representing a distribution of the terms in this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> into equally sized chunks. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op13for_each_termERRNSt8functionIFvR7spin_opEEE">
-<span class="target" id="classcudaq_1_1spin__op_1ac237866d1edf80aca645197c39e4c054"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">for_each_term</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op13for_each_termERRNSt8functionIFvR7spin_opEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Apply the give functor on each term of this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. This method can enable general reductions via lambda capture variables. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op14for_each_pauliERRNSt8functionIFv5pauliNSt6size_tEEEE">
-<span class="target" id="classcudaq_1_1spin__op_1a5e1833312240d4a94317a56db778c5f0"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">for_each_pauli</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">pauli</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op14for_each_pauliERRNSt8functionIFv5pauliNSt6size_tEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Apply the functor on each Pauli in this 1-term <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. An exception is thrown if there are more than 1 terms. Users should pass a functor that takes the <code class="docutils literal notranslate"><span class="pre">pauli</span></code> type and the qubit index. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op9to_matrixEv">
-<span class="target" id="classcudaq_1_1spin__op_1a5c3a338d5593536cceca3fb96958ae16"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrixE" title="cudaq::complex_matrix"><span class="n"><span class="pre">complex_matrix</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">to_matrix</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op9to_matrixEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a dense matrix representation of this <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7spin_op16to_sparse_matrixEv">
-<span class="target" id="classcudaq_1_1spin__op_1a2fccd38728e453a173e02b90c6a36257"></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_op12csr_spmatrixE" title="cudaq::spin_op::csr_spmatrix"><span class="n"><span class="pre">csr_spmatrix</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">to_sparse_matrix</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7spin_op16to_sparse_matrixEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a sparse matrix representation of this <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code>. The return type encodes all non-zero <code class="docutils literal notranslate"><span class="pre">(row,</span> <span class="pre">col,</span> <span class="pre">value)</span></code> elements. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-static-functions">Public Static Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op6randomENSt6size_tENSt6size_tEj">
-<span class="target" id="classcudaq_1_1spin__op_1a86568d7681059dc9f0cc3eb13d3286ed"></span><span class="k"><span class="pre">static</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">random</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nQubits</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nTerms</span></span>, <span class="kt"><span class="pre">unsigned</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">seed</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">random_device</span></span><span class="p"><span class="pre">{</span></span><span class="p"><span class="pre">}</span></span><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">)</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op6randomENSt6size_tENSt6size_tEj" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a random spin operator acting on the specified number of qubits and composed of the given number of terms. Override <code class="docutils literal notranslate"><span class="pre">seed</span></code> for repeatability. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7spin_op9from_wordERKNSt6stringE">
-<span class="target" id="classcudaq_1_1spin__op_1a6ad8a07f5a8f595f3fee45c9062c64a3"></span><span class="k"><span class="pre">static</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">from_word</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">pauliWord</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7spin_op9from_wordERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code> representative of the input Pauli word, e.g. XYX for a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code> on 3 qubits with a X support on the first and 3rd and Y support on the second. </p>
-</dd></dl>
-
-</div>
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq7spin_op8iteratorE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QualifiedSpinOp</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="structcudaq_1_1spin__op_1_1iterator"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">iterator</span></span></span><a class="headerlink" href="#_CPPv4I0EN5cudaq7spin_op8iteratorE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="quantum">
-<h2>Quantum<a class="headerlink" href="#quantum" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3dynE">
-<span class="target" id="qspan_8h_1a2c3fa68d916be379bab257d559f25d24"></span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">dyn</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="o"><span class="pre">~</span></span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq3dynE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I_NSt6size_tEEN5cudaq5quditE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Levels</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1qudit"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qudit</span></span></span><a class="headerlink" href="#_CPPv4I_NSt6size_tEEN5cudaq5quditE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The qudit models a general d-level quantum system. This type is templated on the number of levels d. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5qudit5quditEv">
-<span class="target" id="classcudaq_1_1qudit_1aae29ba66c8a87724c34d939787087608"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qudit</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5qudit5quditEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct a qudit, will allocated a new unique index. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">N</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq3dynE" title="cudaq::dyn"><span class="n"><span class="pre">dyn</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Levels</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">2</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="p"><span class="pre">(</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE" title="cudaq::qreg::N"><span class="n"><span class="pre">N</span></span></a><span class="w"> </span><span class="o"><span class="pre">&gt;</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1qreg"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qreg</span></span></span><a class="headerlink" href="#_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A <code class="docutils literal notranslate"><span class="pre">qreg</span></code> is a container for qudits. This container can be dynamic or compile-time-size specified. By default, the <code class="docutils literal notranslate"><span class="pre">qreg</span></code> is constructed as a dynamic register (vector-like) of qubits (2-level). This can be changed via the <code class="docutils literal notranslate"><span class="pre">qreg</span></code> type template parameters. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg10value_typeE">
-<span class="target" id="classcudaq_1_1qreg_1a609490620e68c0ec536e2ef6a3d921f4"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">value_type</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5quditE" title="cudaq::qudit"><span class="n"><span class="pre">qudit</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE" title="cudaq::qreg::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq4qreg10value_typeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Useful typedef indicating the underlying qudit type. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg5beginEv">
-<span class="target" id="classcudaq_1_1qreg_1aeaf9c41b1f57bb63ecf3acf411367bdc"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">begin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg5beginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Iterator interface, begin. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qregixEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1qreg_1a1dba67a7873d440e7b94f584914f0266"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq4qreg10value_typeE" title="cudaq::qreg::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">[]</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qregixEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the qudit at <code class="docutils literal notranslate"><span class="pre">idx</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg5frontENSt6size_tE">
-<span class="target" id="classcudaq_1_1qreg_1a1fd9a79ba06a022d5d8299bb00bc9669"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tEEN5cudaq5qspanE" title="cudaq::qspan"><span class="n"><span class="pre">qspan</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq3dynE" title="cudaq::dyn"><span class="n"><span class="pre">dyn</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE" title="cudaq::qreg::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">front</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">count</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg5frontENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">count)</span></code> qudits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg5frontEv">
-<span class="target" id="classcudaq_1_1qreg_1a32a03f7a1ca19a220db168caabdb4f71"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq4qreg10value_typeE" title="cudaq::qreg::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="n"><span class="pre">front</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg5frontEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the first qudit. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg4backENSt6size_tE">
-<span class="target" id="classcudaq_1_1qreg_1a176a76f8829ff346b7e5f9ca41e6d20b"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tEEN5cudaq5qspanE" title="cudaq::qspan"><span class="n"><span class="pre">qspan</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq3dynE" title="cudaq::dyn"><span class="n"><span class="pre">dyn</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE" title="cudaq::qreg::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">back</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">count</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg4backENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the <code class="docutils literal notranslate"><span class="pre">[count,</span> <a class="reference internal" href="#classcudaq_1_1qreg_1ac65686ebfda02852643371396350fba6"><span class="std std-ref"><span class="pre">size()</span></span></a><span class="pre">)</span></code> qudits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg4backEv">
-<span class="target" id="classcudaq_1_1qreg_1ad9adbd2e3a216840f470061c5b999a73"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq4qreg10value_typeE" title="cudaq::qreg::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="n"><span class="pre">back</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg4backEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the last qudit. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg5sliceENSt6size_tENSt6size_tE">
-<span class="target" id="classcudaq_1_1qreg_1a7dfda2cff5b477b77440f3a794d76e7b"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tEEN5cudaq5qspanE" title="cudaq::qspan"><span class="n"><span class="pre">qspan</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq3dynE" title="cudaq::dyn"><span class="n"><span class="pre">dyn</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tE_NSt6size_tE0EN5cudaq4qregE" title="cudaq::qreg::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">slice</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">start</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">size</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg5sliceENSt6size_tENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the <code class="docutils literal notranslate"><span class="pre">[start,</span> <span class="pre">start+size)</span></code> qudits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq4qreg4sizeEv">
-<span class="target" id="classcudaq_1_1qreg_1ac65686ebfda02852643371396350fba6"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">size</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq4qreg4sizeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the number of contained qudits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4qreg5clearEv">
-<span class="target" id="classcudaq_1_1qreg_1a7747f90309085d531f8fafa248e19c4d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clear</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4qreg5clearEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Destroys all contained qudits. Postcondition: <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1qreg_1ac65686ebfda02852643371396350fba6"><span class="std std-ref"><span class="pre">size()</span></span></a> <span class="pre">==</span> <span class="pre">0</span></code>. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I_NSt6size_tEEN5cudaq7qvectorE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Levels</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1qvector"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qvector</span></span></span><a class="headerlink" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A <code class="docutils literal notranslate"><span class="pre">qvector</span></code> is an owning, dynamically sized container for qudits. The semantics of the <code class="docutils literal notranslate"><span class="pre">qvector</span></code> follows that of a <code class="docutils literal notranslate"><span class="pre">std::vector</span></code> for qudits. It is templated on the number of levels for the held qudits. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector10value_typeE">
-<span class="target" id="classcudaq_1_1qvector_1ae682d4685f2f82df11ee2fba64b15979"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">value_type</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5quditE" title="cudaq::qudit"><span class="n"><span class="pre">qudit</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="cudaq::qvector::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq7qvector10value_typeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Useful typedef indicating the underlying qudit type. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector7qvectorENSt6size_tE">
-<span class="target" id="classcudaq_1_1qvector_1abae7b7b8afbba68eb00a0132242ca772"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qvector</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">size</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector7qvectorENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct a <code class="docutils literal notranslate"><span class="pre">qvector</span></code> with <code class="docutils literal notranslate"><span class="pre">size</span></code> qudits in the |0&gt; state. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector7qvectorERKNSt6vectorI7complexEEb">
-<span class="target" id="classcudaq_1_1qvector_1a1207f490dab570c43527a9563d29d57a"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qvector</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7complexE" title="cudaq::complex"><span class="n"><span class="pre">complex</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">vector</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">validate</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector7qvectorERKNSt6vectorI7complexEEb" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct a <code class="docutils literal notranslate"><span class="pre">qvector</span></code> from an input state vector. The number of qubits is determined by the size of the input vector. If <code class="docutils literal notranslate"><span class="pre">validate</span></code> is set, it will check the norm of input state vector. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector7qvectorERK5state">
-<span class="target" id="classcudaq_1_1qvector_1a0fd2545386af75c0d1aed18c8b3b50ef"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qvector</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7qvector7qvectorERK5state" title="cudaq::qvector::qvector::state"><span class="n"><span class="pre">state</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">state</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector7qvectorERK5state" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct a <code class="docutils literal notranslate"><span class="pre">qvector</span></code> from a pre-existing <code class="docutils literal notranslate"><span class="pre">state</span></code>. This <code class="docutils literal notranslate"><span class="pre">state</span></code> could be constructed with <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1state_1a2e9b7d521f974822246d180d43160d2e"><span class="std std-ref"><span class="pre">state::from_data</span></span></a></code> or retrieved from an cudaq::get_state. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector7qvectorERK7qvector">
-<span class="target" id="classcudaq_1_1qvector_1a3c728c95d13fa476066e3d4379b42577"></span><span class="sig-name descname"><span class="n"><span class="pre">qvector</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq7qvector7qvectorERK7qvector" title="cudaq::qvector::qvector"><span class="n"><span class="pre">qvector</span></span></a><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N5cudaq7qvector7qvectorERK7qvector" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">qvectors</span></code> cannot be copied </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector7qvectorERR7qvector">
-<span class="target" id="classcudaq_1_1qvector_1a82d3fe912eea5c553ca16535202a0153"></span><span class="sig-name descname"><span class="n"><span class="pre">qvector</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq7qvector7qvectorERR7qvector" title="cudaq::qvector::qvector"><span class="n"><span class="pre">qvector</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N5cudaq7qvector7qvectorERR7qvector" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">qvectors</span></code> cannot be moved </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvectoraSERK7qvector">
-<span class="target" id="classcudaq_1_1qvector_1a6c5ea26ff88b2878e489abe0decf0443"></span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="cudaq::qvector"><span class="n"><span class="pre">qvector</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="cudaq::qvector"><span class="n"><span class="pre">qvector</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N5cudaq7qvectoraSERK7qvector" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">qvectors</span></code> cannot be copy assigned. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector5beginEv">
-<span class="target" id="classcudaq_1_1qvector_1a70e0810339d14fd4dd2318e64ba0e8bf"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">begin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector5beginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Iterator interface, begin. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector3endEv">
-<span class="target" id="classcudaq_1_1qvector_1a318de5f80ce8de87d4ab93c23c7bb4e1"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">end</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector3endEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Iterator interface, end. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvectorixEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1qvector_1a0f523009b5ed3508b2ed82782519a162"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7qvector10value_typeE" title="cudaq::qvector::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">[]</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvectorixEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the qudit at <code class="docutils literal notranslate"><span class="pre">idx</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector5frontENSt6size_tE">
-<span class="target" id="classcudaq_1_1qvector_1a1a432e3062bdc3a20bb60325d13dc0f8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5qviewE" title="cudaq::qview"><span class="n"><span class="pre">qview</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="cudaq::qvector::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">front</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">count</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector5frontENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">count)</span></code> qudits as a non-owning <code class="docutils literal notranslate"><span class="pre">qview</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector5frontEv">
-<span class="target" id="classcudaq_1_1qvector_1a91033e3edd4c8d5bdeb8c4413b8dca46"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7qvector10value_typeE" title="cudaq::qvector::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="n"><span class="pre">front</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector5frontEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the first qudit. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector4backENSt6size_tE">
-<span class="target" id="classcudaq_1_1qvector_1a23e6307b94f7cfe671257e1aea073e3c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5qviewE" title="cudaq::qview"><span class="n"><span class="pre">qview</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="cudaq::qvector::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">back</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">count</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector4backENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the <code class="docutils literal notranslate"><span class="pre">[count,</span> <a class="reference internal" href="#classcudaq_1_1qvector_1a47fdee3b43a7df454bfc60e1095c12a1"><span class="std std-ref"><span class="pre">size()</span></span></a><span class="pre">)</span></code> qudits as a non-owning <code class="docutils literal notranslate"><span class="pre">qview</span></code> </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector4backEv">
-<span class="target" id="classcudaq_1_1qvector_1a661d4e8d60e511508e2fbae8089f0bb0"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7qvector10value_typeE" title="cudaq::qvector::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="n"><span class="pre">back</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector4backEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the last qudit. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector5sliceENSt6size_tENSt6size_tE">
-<span class="target" id="classcudaq_1_1qvector_1a0ce059332fd013ed919762961e306323"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5qviewE" title="cudaq::qview"><span class="n"><span class="pre">qview</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq7qvectorE" title="cudaq::qvector::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">slice</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">start</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">size</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector5sliceENSt6size_tENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the <code class="docutils literal notranslate"><span class="pre">[start,</span> <span class="pre">start+size)</span></code> qudits as a non-owning <code class="docutils literal notranslate"><span class="pre">qview</span></code> </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq7qvector4sizeEv">
-<span class="target" id="classcudaq_1_1qvector_1a47fdee3b43a7df454bfc60e1095c12a1"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">size</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq7qvector4sizeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns the number of contained qudits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7qvector5clearEv">
-<span class="target" id="classcudaq_1_1qvector_1a8b77c953437148f72de1a1150674f653"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clear</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7qvector5clearEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Destroys all contained qudits. Postcondition: <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1qvector_1a47fdee3b43a7df454bfc60e1095c12a1"><span class="std std-ref"><span class="pre">size()</span></span></a> <span class="pre">==</span> <span class="pre">0</span></code>. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I_NSt6size_tE_NSt6size_tEEN5cudaq5qspanE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">N</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq3dynE" title="cudaq::dyn"><span class="n"><span class="pre">dyn</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Levels</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1qspan"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qspan</span></span></span><a class="headerlink" href="#_CPPv4I_NSt6size_tE_NSt6size_tEEN5cudaq5qspanE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I_NSt6size_tEEN5cudaq5qviewE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Levels</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1qview"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qview</span></span></span><a class="headerlink" href="#_CPPv4I_NSt6size_tEEN5cudaq5qviewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><span class="pre">qview</span></code> represents a non-owning container of qudits. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5qview10value_typeE">
-<span class="target" id="classcudaq_1_1qview_1a6433c4ac3fb63793c67d05e48c5d7cd5"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">value_type</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5quditE" title="cudaq::qudit"><span class="n"><span class="pre">qudit</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5qviewE" title="cudaq::qview::Levels"><span class="n"><span class="pre">Levels</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq5qview10value_typeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Useful typedef exposing the underlying qudit type that this <code class="docutils literal notranslate"><span class="pre">qview</span></code> contains. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5qubitE">
-<span class="target" id="qudit_8h_1addd3b55f27803bd8ed9a66e354a951ee"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">qubit</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I_NSt6size_tEEN5cudaq5quditE" title="cudaq::qudit"><span class="n"><span class="pre">qudit</span></span></a><span class="p"><span class="pre">&lt;</span></span><span class="m"><span class="pre">2</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq5qubitE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-</section>
-<section id="common">
-<h2>Common<a class="headerlink" href="#common" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_resultE">
-<span class="target" id="classcudaq_1_1observe__result"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">observe_result</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq14observe_resultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1observe__result"><span class="std std-ref">observe_result</span></a> encapsulates all data generated from a cudaq”::”observe call. This includes all measurement counts from the execution of each ansatz + measure circuit, and the global expected value of the <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> passed to observe. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_result14observe_resultEdRK7spin_op">
-<span class="target" id="classcudaq_1_1observe__result_1aba859a67c28ee73dee0943043de4b419"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">observe_result</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">e</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">H</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_result14observe_resultEdRK7spin_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the precomputed expectation value for &lt;psi(x) | H | psi(x)&gt; for the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_result14observe_resultEdRK7spin_op13sample_result">
-<span class="target" id="classcudaq_1_1observe__result_1a0f3241a87d3924049763257e7f4caece"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">observe_result</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">e</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">H</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">counts</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_result14observe_resultEdRK7spin_op13sample_result" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the precomputed expectation value for &lt;psi(x) | H | psi(x)&gt; for the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. If this execution was shots based, also provide the <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> data containing counts for each term in H. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_result8raw_dataEv">
-<span class="target" id="classcudaq_1_1observe__result_1a1220b104814b47514af78f957cd3a543"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">raw_data</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_result8raw_dataEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the raw counts data for all terms. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_resultcvdEv">
-<span class="target" id="classcudaq_1_1observe__result_1aa3bb86339bb588bdc812ae27c9e8eb8a"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_resultcvdEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Conversion operator for this <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1observe__result"><span class="std std-ref"><span class="pre">observe_result</span></span></a></code> to <code class="docutils literal notranslate"><span class="pre">double</span></code>. Simply returns the precomputed expectation value for the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. This enables one to ignore the fine-grain <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> data, and explicitly request the expected value: double exp = cudaq”::”observe(…); as opposed to cudaq”::”observe_data data = cudaq::observe(…); auto exp = data.expectation();. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_result11expectationEv">
-<span class="target" id="classcudaq_1_1observe__result_1af008438d29b9d0c567a6d471f5e6a843"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">expectation</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_result11expectationEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the expected value for the provided <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq14observe_result11expectationEd10SpinOpType">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">SpinOpType</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1observe__result_1a3afd54ce62aa411173a1cb89d56d803d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">expectation</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0EN5cudaq14observe_result11expectationEd10SpinOpType" title="cudaq::observe_result::expectation::SpinOpType"><span class="n"><span class="pre">SpinOpType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">term</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN5cudaq14observe_result11expectationEd10SpinOpType" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the expectation value for a sub-term in the provided <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq14observe_result6countsE13sample_result10SpinOpType">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">SpinOpType</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1observe__result_1a3a2c8f6b6ddeb50d33b7d8adbb101db5"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">counts</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0EN5cudaq14observe_result6countsE13sample_result10SpinOpType" title="cudaq::observe_result::counts::SpinOpType"><span class="n"><span class="pre">SpinOpType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">term</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN5cudaq14observe_result6countsE13sample_result10SpinOpType" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the counts data for the given <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a>. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>term</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_result14id_coefficientEv">
-<span class="target" id="classcudaq_1_1observe__result_1a3a21317de754b336ff177896783906b8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">id_coefficient</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_result14id_coefficientEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the coefficient of the identity term. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14observe_result4dumpEv">
-<span class="target" id="classcudaq_1_1observe__result_1af43589c960a37f213670479486ae65f4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14observe_result4dumpEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump the counts data to standard out. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15observe_optionsE">
-<span class="target" id="structcudaq_1_1observe__options"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">observe_options</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15observe_optionsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Observe options to provide as an argument to the <code class="docutils literal notranslate"><span class="pre">observe()</span></code>, <code class="docutils literal notranslate"><span class="pre">async_observe()</span></code> functions. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Param shots<span class="colon">:</span></dt>
-<dd class="field-odd"><p>number of shots to run for the given kernel, or -1 if not applicable. </p>
-</dd>
-<dt class="field-even">Param noise<span class="colon">:</span></dt>
-<dd class="field-even"><p>noise model to use for the sample operation </p>
-</dd>
-<dt class="field-odd">Param num_trajectories<span class="colon">:</span></dt>
-<dd class="field-odd"><p>is the optional number of trajectories to be used when computing the expectation values in the presence of noise. This parameter is only applied to simulation backends that support noisy simulation of trajectories. </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRK15observe_optionsRR13QuantumKernel7spin_opDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRK15observe_optionsRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRK15observe_optionsRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="observe_8h_1affe3019eb4f1a2b8a335c379582dc7b4"></span><a class="reference internal" href="#_CPPv4N5cudaq14observe_resultE" title="cudaq::observe_result"><span class="n"><span class="pre">observe_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">observe</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15observe_optionsE" title="cudaq::observe_options"><span class="n"><span class="pre">observe_options</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">options</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRK15observe_optionsRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">H</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRK15observe_optionsRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRK15observe_optionsRR13QuantumKernel7spin_opDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the expected value of <code class="docutils literal notranslate"><span class="pre">H</span></code> with respect to <code class="docutils literal notranslate"><span class="pre">kernel(Args...)</span></code>. Specify the observation options. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq7observeE14observe_resultNSt6size_tERR13QuantumKernel7spin_opDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultNSt6size_tERR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultNSt6size_tERR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="observe_8h_1a4e4f8cd4261dfdfc68d67864d614cd89"></span><a class="reference internal" href="#_CPPv4N5cudaq14observe_resultE" title="cudaq::observe_result"><span class="n"><span class="pre">observe_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">observe</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shots</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultNSt6size_tERR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">H</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultNSt6size_tERR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultNSt6size_tERR13QuantumKernel7spin_opDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the expected value of <code class="docutils literal notranslate"><span class="pre">H</span></code> with respect to <code class="docutils literal notranslate"><span class="pre">kernel(Args...)</span></code>. Specify the number of shots. </p>
-<p>This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRR13QuantumKernel7spin_opDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="observe_8h_1afb629026ef792b365ae4d3abf985936e"></span><a class="reference internal" href="#_CPPv4N5cudaq14observe_resultE" title="cudaq::observe_result"><span class="n"><span class="pre">observe_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">observe</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">H</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRR13QuantumKernel7spin_opDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq7observeE14observe_resultRR13QuantumKernel7spin_opDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the expected value of <code class="docutils literal notranslate"><span class="pre">H</span></code> with respect to <code class="docutils literal notranslate"><span class="pre">kernel(Args...)</span></code>. </p>
-<p>This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">SpinOpContainer</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="observe_8h_1ab93faa23228bcaa0a433fd0bc0d8b019"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq14observe_resultE" title="cudaq::observe_result"><span class="n"><span class="pre">observe_result</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">observe</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args" title="cudaq::observe::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args" title="cudaq::observe::SpinOpContainer"><span class="n"><span class="pre">SpinOpContainer</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">termList</span></span>, <a class="reference internal" href="#_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args" title="cudaq::observe::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00Dp0EN5cudaq7observeENSt6vectorI14observe_resultEERR13QuantumKernelRK15SpinOpContainerDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the expected value of every <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref"><span class="pre">spin_op</span></span></a></code> provided in <code class="docutils literal notranslate"><span class="pre">SpinOpContainer</span></code> (a range concept) with respect to <code class="docutils literal notranslate"><span class="pre">kernel(Args...)</span></code>. Return a <code class="docutils literal notranslate"><span class="pre">std::vector&lt;</span><a class="reference internal" href="#classcudaq_1_1observe__result"><span class="std std-ref"><span class="pre">observe_result</span></span></a><span class="pre">&gt;</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContextE">
-<span class="target" id="classcudaq_1_1ExecutionContext"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionContext</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContextE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1ExecutionContext"><span class="std std-ref">ExecutionContext</span></a> is an abstraction to indicate how a CUDA-Q kernel should be executed. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext16ExecutionContextEKNSt6stringE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a81f2130cf99da78739b73ef090e5d0d7"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionContext</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext16ExecutionContextEKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The Constructor, takes the name of the context. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>n</strong> – The name of the context </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext16ExecutionContextEKNSt6stringENSt6size_tE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a0154a84697873509b800fd83d932df61"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionContext</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shots_</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext16ExecutionContextEKNSt6stringENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes the name and the number of shots. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>n</strong> – The name of the context </p></li>
-<li><p><strong>shots_</strong> – The number of shots </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext4nameE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a76b9d8d9cb2b6edd033519c0c31232df"></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">name</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext4nameE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The name of the context ({basic, sampling, observe}) </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext5shotsE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a427fb572338e5d8917ff37e0da27c70b"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">shots</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext5shotsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The number of execution shots. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext4spinE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a5a1a666610ce7feeaa990de0c77cb244"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">spin</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext4spinE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>An optional spin operator. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext6resultE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a427813b94672a42814459459e30e0930"></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">result</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext6resultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Measurement counts for a CUDA-Q kernel invocation. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext16expectationValueE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a6eefeeea0be24e461e56eb8150004cb9"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">expectationValue</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">nullopt</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext16expectationValueE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A computed expectation value. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext9optResultE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a7a508d29f07472fa255e3193ef3281dc"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq19optimization_resultE" title="cudaq::optimization_result"><span class="n"><span class="pre">optimization_result</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">optResult</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">nullopt</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext9optResultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>An optimization result. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext31hasConditionalsOnMeasureResultsE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a642d36be68417213256b572f0ba180cb"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hasConditionalsOnMeasureResults</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext31hasConditionalsOnMeasureResultsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The kernel being executed in this context has conditional statements on measure results. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext10noiseModelE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1aae5795038c44595b2af0b3297b2ef7c7"></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq11noise_modelE" title="cudaq::noise_model"><span class="n"><span class="pre">noise_model</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">noiseModel</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">nullptr</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext10noiseModelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Noise model to apply to the current execution. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext16canHandleObserveE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a2d9c2a355ea502e92b23a03ed2913005"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">canHandleObserve</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext16canHandleObserveE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Flag to indicate if backend can handle <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> observe task under this <a class="reference internal" href="#classcudaq_1_1ExecutionContext"><span class="std std-ref">ExecutionContext</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext9asyncExecE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a7bd212c313ec3a8800ad6193af5845c6"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">asyncExec</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext9asyncExecE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Flag indicating that the current execution should occur asynchronously. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext12futureResultE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a53330c89e4b1c462523dabe7edc6b696"></span><a class="reference internal" href="#_CPPv4N5cudaq7detailsE" title="cudaq::details"><span class="n"><span class="pre">details</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7details6futureE" title="cudaq::details::future"><span class="n"><span class="pre">future</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">futureResult</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext12futureResultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>When execution asynchronously, store the expected results as a cudaq::future here. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext11asyncResultE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a93bf2cc98e0912bcccaa269e3fad4f41"></span><a class="reference internal" href="#_CPPv4I0EN5cudaq12async_resultE" title="cudaq::async_result"><span class="n"><span class="pre">async_result</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">asyncResult</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext11asyncResultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct a <code class="docutils literal notranslate"><span class="pre">async_sample_result</span></code> so as to pass across Python boundary. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext15simulationStateE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1ad9af730512e65f603c4060850017f2a3"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">unique_ptr</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">simulationState</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext15simulationStateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Pointer to simulation-specific simulation data. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext13amplitudeMapsE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a225ca6c6264dc4bab3ebffd1d23252c5"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">map</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">int</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">amplitudeMaps</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext13amplitudeMapsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A map of basis-state amplitudes. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext20overlapComputeStatesE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1aa33674ead54268a410e60557fdaeb8e0"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">overlapComputeStates</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext20overlapComputeStatesE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>List of pairs of states to compute the overlap. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext13overlapResultE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a82f52405385514432d039465fde1ddee"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">overlapResult</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext13overlapResultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Overlap results. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext11kernelTraceE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a832079b8beaec29c27462f27704d6386"></span><a class="reference internal" href="#_CPPv4N5cudaq5TraceE" title="cudaq::Trace"><span class="n"><span class="pre">Trace</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kernelTrace</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext11kernelTraceE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>When run under the tracer context, persist the traced quantum resources here. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext10kernelNameE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a2c25b49d493c82f6a0660d0a41af6ea6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kernelName</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="s"><span class="pre">&quot;&quot;</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext10kernelNameE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The name of the kernel being executed. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext14batchIterationE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a0c0571f6674fe3e6af79fd064d828cb6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">batchIteration</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext14batchIterationE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The current iteration for a batch execution, used by observe_n and sample_n. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext15totalIterationsE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a688a9bdfda43ff68bb48124bedf6a9f4"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">totalIterations</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext15totalIterationsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>For batch execution, the total number of batch iterations. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext13registerNamesE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1aff429589472a75c69a48edd06ced97d6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">registerNames</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext13registerNamesE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>For mid-circuit measurements in library mode keep track of the register names. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext10reorderIdxE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a33d991dd8e3d37e040eb8fc625228c19"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">reorderIdx</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext10reorderIdxE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A vector containing information about how to reorder the global register after execution. Empty means no reordering. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext22invocationResultBufferE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a2cbc25e01de0353f7909f3e434cb2065"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">char</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">invocationResultBuffer</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext22invocationResultBufferE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A buffer containing the return value of a kernel invocation. Note: this is only needed for invocation not able to return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref"><span class="pre">sample_result</span></span></a></code>. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16ExecutionContext18numberTrajectoriesE">
-<span class="target" id="classcudaq_1_1ExecutionContext_1a1d8a8a13f8765a8fce850e1a124a5fbb"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">numberTrajectories</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16ExecutionContext18numberTrajectoriesE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The number of trajectories to be used for an expectation calculation on simulation backends that support trajectory simulation. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details6futureE">
-<span class="target" id="classcudaq_1_1details_1_1future"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">future</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq7details6futureE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The future type models the expected result of a CUDA-Q kernel execution under a specific execution context. This type is returned from asynchronous execution calls. It encapsulates the job-specific circuit execution identifiers, the name of the <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> the job executed on, and any extra configuration information needed to retrieve the results later from the server. This type can be persisted to file and read in later to retrieve execution results. It also optionally wraps a std::future&lt;T&gt; type, and in this case, persistence to file is not allowed, .get() must be invoked at some later point within the same runtime context. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details6future6futureEv">
-<span class="target" id="classcudaq_1_1details_1_1future_1a8a3afaa7207cba1092a88568d2bc2d14"></span><span class="sig-name descname"><span class="n"><span class="pre">future</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq7details6future6futureEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details6future6futureERR6future">
-<span class="target" id="classcudaq_1_1details_1_1future_1aefd8a6533355358a082435396fe3a56a"></span><span class="sig-name descname"><span class="n"><span class="pre">future</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq7details6future6futureERR6future" title="cudaq::details::future::future"><span class="n"><span class="pre">future</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq7details6future6futureERR6future" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>move constructor </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details6future6futureERNSt6vectorI3JobEERNSt6stringERNSt3mapINSt6stringENSt6stringEEE">
-<span class="target" id="classcudaq_1_1details_1_1future_1a19fc0a152a0df1e22d915f170c4e84d8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">future</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">Job</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">_jobs</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">qpuNameIn</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">map</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">config</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7details6future6futureERNSt6vectorI3JobEERNSt6stringERNSt3mapINSt6stringENSt6stringEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes all info required to be able to retrieve results at a later date, even after file persistence. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq12async_resultE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">T</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1async__result"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">async_result</span></span></span><a class="headerlink" href="#_CPPv4I0EN5cudaq12async_resultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>the <a class="reference internal" href="#classcudaq_1_1async__result"><span class="std std-ref">async_result</span></a> type is a user facing, future-like type that is returned from CUDA-Q public asynchronous API functions. It wraps a <a class="reference internal" href="#classcudaq_1_1details_1_1future"><span class="std std-ref">details::future</span></a> type, which can itself be constructed from a std::future or a collection of data pertinent to remote <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> REST invocation. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq12async_result3getEv">
-<span class="target" id="classcudaq_1_1async__result_1a0c48022f940cc2ed79ce4598636f1305"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0EN5cudaq12async_resultE" title="cudaq::async_result::T"><span class="n"><span class="pre">T</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq12async_result3getEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the asynchronously computed data, will wait until the data is ready. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq19async_sample_resultE">
-<span class="target" id="sample_8h_1aca3fc52fbabf920e1de791af75f5b47b"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">async_sample_result</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0EN5cudaq12async_resultE" title="cudaq::async_result"><span class="n"><span class="pre">async_result</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq19async_sample_resultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return type for asynchronous sampling. </p>
-</dd></dl>
-
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResultE">
-<span class="target" id="structcudaq_1_1ExecutionResult"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> models the result of a typical quantum state sampling task. It will contain the observed measurement bit strings and corresponding number of times observed, as well as an expected value with respect to the Z…Z operator. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq15ExecutionResult9serializeEv">
-<span class="target" id="structcudaq_1_1ExecutionResult_1acaf9ec8da53554bd1b695843cf91cd88"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">serialize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq15ExecutionResult9serializeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Serialize this sample result to a vector of integers. Encoding: 1st element is size of the register name N, then next N represent register name, next is the number of bitstrings M, then for each bit string a triple {string mapped to long, bit string length, count}. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult11deserializeERNSt6vectorINSt6size_tEEE">
-<span class="target" id="structcudaq_1_1ExecutionResult_1aa0fe4b59ed066f031e1064f1f5d8f8f7"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">deserialize</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">data</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult11deserializeERNSt6vectorINSt6size_tEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Deserialize a vector of integers to a <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>data</strong> – The data with encoding discussed in the brief for <code class="docutils literal notranslate"><span class="pre">serialize</span></code>. </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultEv">
-<span class="target" id="structcudaq_1_1ExecutionResult_1ae497123925b5f7dc7ef428b74ef88253"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultE16CountsDictionary">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a401208ff36537998547f2d5ade549529"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">CountsDictionary</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">c</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultE16CountsDictionary" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct from a CountsDictionary, assumes registerName == <strong>global</strong> </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>c</strong> – the counts </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultENSt6stringE">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a39c076328f75a06d1c3b3eb09507ed7c"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">name</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultENSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct from a register name. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultEd">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a2a6967f366c85814d8f85b5c566975b7"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">expVal</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct from a precomputed expectation value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultE16CountsDictionaryNSt6stringE">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a42e857f1f0c7fe45151f08dba075433a"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">CountsDictionary</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">c</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">name</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultE16CountsDictionaryNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct from a <code class="docutils literal notranslate"><span class="pre">CountsDictionary</span></code>, specify the register name. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>c</strong> – the counts </p></li>
-<li><p><strong>name</strong> – the register name </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultE16CountsDictionaryd">
-<span class="target" id="structcudaq_1_1ExecutionResult_1adb4703d48e39256d9f6cd53f0116f426"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">CountsDictionary</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">c</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">e</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultE16CountsDictionaryd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Construct from a <code class="docutils literal notranslate"><span class="pre">CountsDictionary</span></code> and expected value. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>c</strong> – The counts </p></li>
-<li><p><strong>e</strong> – The precomputed expected value </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult15ExecutionResultERK15ExecutionResult">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a87a8487490ca376b5501753b0deb5cbc"></span><span class="sig-name descname"><span class="n"><span class="pre">ExecutionResult</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultERK15ExecutionResult" title="cudaq::ExecutionResult::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult15ExecutionResultERK15ExecutionResult" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy constructor. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>other</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResultaSERK15ExecutionResult">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a46de0f6522e69f94787afb745768b0b8"></span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResultaSERK15ExecutionResult" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set this <a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref">ExecutionResult</span></a> equal to the provided one. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>other</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq15ExecutionResulteqERK15ExecutionResult">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a279c6d2312fd6805f8a8dabc746cd2e0"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">result</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq15ExecutionResulteqERK15ExecutionResult" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if the given <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> is the same as this one. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>result</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult12appendResultENSt6stringENSt6size_tE">
-<span class="target" id="structcudaq_1_1ExecutionResult_1aeacb5c5af18a345820c72c95b56891a2"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">appendResult</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">bitString</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">count</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult12appendResultENSt6stringENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Append the bitstring and count to this <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>bitString</strong> – </p></li>
-<li><p><strong>count</strong> – </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult12registerNameE">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a246582d8c877dd21fe814b467bc7ee25"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">registerName</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult12registerNameE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Register name for the classical bits. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15ExecutionResult14sequentialDataE">
-<span class="target" id="structcudaq_1_1ExecutionResult_1a9d8a73796ba7990851dec099bcfb5997"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">sequentialData</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15ExecutionResult14sequentialDataE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Sequential bit strings observed (not collated into a map) </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_resultE">
-<span class="target" id="classcudaq_1_1sample__result"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq13sample_resultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> abstraction wraps a set of <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code>s for a single quantum kernel execution under the sampling or observation <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1ExecutionContext"><span class="std std-ref"><span class="pre">ExecutionContext</span></span></a></code>. Each <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> is mapped to a register name, with a default <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> with name <strong>global</strong> representing the observed measurement results holistically for the quantum kernel. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13sample_resultEv">
-<span class="target" id="classcudaq_1_1sample__result_1ae8f611495ea923027a4008a4da8019a8"></span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13sample_resultEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Nullary constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13sample_resultERR15ExecutionResult">
-<span class="target" id="classcudaq_1_1sample__result_1ab6d5dd37a325832f89d8ee70130d6b80"></span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">result</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13sample_resultERR15ExecutionResult" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, sets the <strong>global</strong> sample result. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13sample_resultER15ExecutionResult">
-<span class="target" id="classcudaq_1_1sample__result_1a3435574a1e7adc5b540aa13649e4c202"></span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">result</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13sample_resultER15ExecutionResult" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, sets the <strong>global</strong> sample result. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>result</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13sample_resultERNSt6vectorI15ExecutionResultEE">
-<span class="target" id="classcudaq_1_1sample__result_1a85b8eb7d587ab955167da8757b9e2337"></span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">results</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13sample_resultERNSt6vectorI15ExecutionResultEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, appends all provided <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code>s. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13sample_resultEdRNSt6vectorI15ExecutionResultEE">
-<span class="target" id="classcudaq_1_1sample__result_1adbab6e399339691a0c26c97fc0f5157f"></span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">preComputedExp</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">results</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13sample_resultEdRNSt6vectorI15ExecutionResultEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes a precomputed expectation value and stores it with the <code class="docutils literal notranslate"><span class="pre">__global__</span></code> <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13sample_resultERK13sample_result">
-<span class="target" id="classcudaq_1_1sample__result_1aa4a1bd43b9b18cca2ef7a722ab6f8697"></span><span class="sig-name descname"><span class="n"><span class="pre">sample_result</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13sample_result13sample_resultERK13sample_result" title="cudaq::sample_result::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13sample_resultERK13sample_result" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy Constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_resultD0Ev">
-<span class="target" id="classcudaq_1_1sample__result_1adc32e8aaef0e8a7cd2a542c63c8c1ae6"></span><span class="sig-name descname"><span class="n"><span class="pre">~sample_result</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq13sample_resultD0Ev" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The destructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result15has_expectationEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a4289ace00d8ea97a2448491c4754fc67"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">has_expectation</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result15has_expectationEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if the <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> with the specified register name has a precomputed expectation value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result6appendER15ExecutionResult">
-<span class="target" id="classcudaq_1_1sample__result_1ad4f5a8c72c564a51709e426cb8ffafd4"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">append</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq15ExecutionResultE" title="cudaq::ExecutionResult"><span class="n"><span class="pre">ExecutionResult</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">result</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result6appendER15ExecutionResult" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add another <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref"><span class="pre">ExecutionResult</span></span></a></code> to this <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref"><span class="pre">sample_result</span></span></a></code>. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>result</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result14register_namesEv">
-<span class="target" id="classcudaq_1_1sample__result_1acf4d522001068f239b866577b2f66cb6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">register_names</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result14register_namesEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return all register names. Can be used in tandem with sample_result::to_map(regName : string) to retrieve the counts for each register. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_resultaSER13sample_result">
-<span class="target" id="classcudaq_1_1sample__result_1a8073f38162b3cc1fa4a11ccaae13e222"></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">counts</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_resultaSER13sample_result" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> equal to the provided one. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>counts</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_resultpLERK13sample_result">
-<span class="target" id="classcudaq_1_1sample__result_1a1c46b862576afe8241e79ab0fdb6b278"></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">+=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_resultpLERK13sample_result" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Append all the data from other to this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a>. Merge when necessary. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>other</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result9serializeEv">
-<span class="target" id="classcudaq_1_1sample__result_1a2bcff2b0f3cb9f46167643523308fc6b"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">serialize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result9serializeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Serialize this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a>. See <code class="docutils literal notranslate"><a class="reference internal" href="#structcudaq_1_1ExecutionResult_1acaf9ec8da53554bd1b695843cf91cd88"><span class="std std-ref"><span class="pre">ExecutionResult::serialize()</span></span></a></code> documentation for information. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result11deserializeERNSt6vectorINSt6size_tEEE">
-<span class="target" id="classcudaq_1_1sample__result_1a5504a7067b1c3df42f4a86cdf4fba742"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">deserialize</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">data</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result11deserializeERNSt6vectorINSt6size_tEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Create this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> from the serialized data. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>data</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_resulteqERK13sample_result">
-<span class="target" id="classcudaq_1_1sample__result_1af4d503a2b4753ef79760e96ad3e75039"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">counts</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_resulteqERK13sample_result" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> is the same as the given one. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>counts</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result11expectationEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a41787cee42b2648000f64dde69700171"></span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">expectation</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result11expectationEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the expected value &lt;Z…Z&gt; </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result9exp_val_zEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1aac21a1dd74311c9da0af96104e23fd51"></span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">exp_val_z</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result9exp_val_zEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Deprecated: Return the expected value &lt;Z…Z&gt; </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result11probabilityENSt11string_viewEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1ab0254228518d127b6d8c0dae37a5b7cb"></span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">probability</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">bitString</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result11probabilityENSt11string_viewEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the probability of observing the given bit string. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>bitString</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result13most_probableEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a87107f8bedab62a4cc94823b4a244ac5"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">most_probable</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result13most_probableEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the most probable bit string. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>registerName</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result5countENSt11string_viewEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a3397619ea681dfe67f30b5d9627a05e4"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">count</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">bitString</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result5countENSt11string_viewEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of times the given bitstring was observed. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>bitString</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result4sizeEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a892be2a88dec700cb2ad80c948d459dc"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">size</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result4sizeEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of observed bit strings. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result4dumpEv">
-<span class="target" id="classcudaq_1_1sample__result_1a2d4334a130fa2366c646308009cd87c5"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result4dumpEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> to standard out. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result4dumpERNSt7ostreamE">
-<span class="target" id="classcudaq_1_1sample__result_1a111c891c53b2e3c09a2dc8d3a2dfe381"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">os</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result4dumpERNSt7ostreamE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> to the given output stream. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result5clearEv">
-<span class="target" id="classcudaq_1_1sample__result_1a3caca71867a11d885f5deb13fe4a6d0c"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clear</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result5clearEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Clear this <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result6to_mapEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a0a60737d23dbfdf816e2c2e2cf79478e"></span><span class="n"><span class="pre">CountsDictionary</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">to_map</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result6to_mapEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Extract the ExecutionResults as a std::unordered&lt;string, size_t&gt; map. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>registerName</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result12get_marginalERRKNSt6vectorINSt6size_tEEEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1ad8e018d850d54e543bf3e7aa49256e62"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_marginal</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">marginalIndices</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result12get_marginalERRKNSt6vectorINSt6size_tEEEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Extract marginal counts, that is those counts for a subset of measured qubits. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>marginalIndices</strong> – The qubit indices as an <code class="docutils literal notranslate"><span class="pre">rvalue</span></code> </p></li>
-<li><p><strong>registerName</strong> – </p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result12get_marginalERKNSt6vectorINSt6size_tEEEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1a8629fa9d4639eb03fa89a4671611ecb6"></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_marginal</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">marginalIndices</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result12get_marginalERKNSt6vectorINSt6size_tEEEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Extract marginal counts, that is those counts for a subset of measured qubits. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>marginalIndices</strong> – The qubit indices as an reference </p></li>
-<li><p><strong>registerName</strong> – </p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result7reorderERKNSt6vectorINSt6size_tEEEKNSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1af47dab420f03957899761a4c98ed8d7f"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">reorder</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">index</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">registerName</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">GlobalRegisterName</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result7reorderERKNSt6vectorINSt6size_tEEEKNSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Reorder the bits in an <a class="reference internal" href="#structcudaq_1_1ExecutionResult"><span class="std std-ref">ExecutionResult</span></a>. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>index</strong> – Vector of indices such that <code class="docutils literal notranslate"><span class="pre">newBitStr(:)=oldBitStr(index(:))</span></code> </p></li>
-<li><p><strong>registerName</strong> – register name to process (defaults to global) </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result5beginEv">
-<span class="target" id="classcudaq_1_1sample__result_1a48c579a0025b54f83244d46f56de888d"></span><span class="n"><span class="pre">CountsDictionary</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">iterator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">begin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result5beginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Range-based iterator begin function. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result3endEv">
-<span class="target" id="classcudaq_1_1sample__result_1a7583d8f9a5149f591cc662f28f761a09"></span><span class="n"><span class="pre">CountsDictionary</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">iterator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">end</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result3endEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Range-based iterator end function. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result6cbeginEv">
-<span class="target" id="classcudaq_1_1sample__result_1a975023596652b87e6786fbfe2141facd"></span><span class="n"><span class="pre">CountsDictionary</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">const_iterator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">cbegin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result6cbeginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Range-based constant iterator begin function. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result4cendEv">
-<span class="target" id="classcudaq_1_1sample__result_1a0ee5a30d7055959acf917d58532a3800"></span><span class="n"><span class="pre">CountsDictionary</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">const_iterator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">cend</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result4cendEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Range-based constant iterator end function. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result5beginEv">
-<span class="target" id="classcudaq_1_1sample__result_1a8aab3104495d16b9663c9f0b13147ca6"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">CountsDictionary</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">const_iterator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">begin</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result5beginEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Range-based constant iterator begin function. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13sample_result3endEv">
-<span class="target" id="classcudaq_1_1sample__result_1a275bb2f44328b76376b706e742512a13"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">CountsDictionary</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">const_iterator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">end</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13sample_result3endEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Range-based constant iterator end function. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-static-functions">Public Static Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13sample_result15has_even_parityENSt11string_viewE">
-<span class="target" id="classcudaq_1_1sample__result_1ac3543c7649c64439ea15aba613223b35"></span><span class="k"><span class="pre">static</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">has_even_parity</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string_view</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">bitString</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13sample_result15has_even_parityENSt11string_viewE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if the bit string has even parity. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>bitString</strong> – </p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14sample_optionsE">
-<span class="target" id="structcudaq_1_1sample__options"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">sample_options</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq14sample_optionsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Sample options to provide to the sample() / async_sample() functions. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Param shots<span class="colon">:</span></dt>
-<dd class="field-odd"><p>number of shots to run for the given kernel </p>
-</dd>
-<dt class="field-even">Param noise<span class="colon">:</span></dt>
-<dd class="field-even"><p>noise model to use for the sample operation </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRK14sample_optionsRR13QuantumKernelDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRK14sample_optionsRR13QuantumKernelDpRR4Args" title="cudaq::sample::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRK14sample_optionsRR13QuantumKernelDpRR4Args" title="cudaq::sample::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="sample_8h_1a81d9b9c49afb4b58768cfdf4ba24b7ae"></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq14sample_optionsE" title="cudaq::sample_options"><span class="n"><span class="pre">sample_options</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">options</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRK14sample_optionsRR13QuantumKernelDpRR4Args" title="cudaq::sample::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRK14sample_optionsRR13QuantumKernelDpRR4Args" title="cudaq::sample::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRK14sample_optionsRR13QuantumKernelDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Sample the given quantum kernel expression and return the mapping of observed bit strings to corresponding number of times observed. </p>
-<p>
-Given a quantum kernel with void return type, sample the corresponding quantum circuit generated by the kernel expression, returning the mapping of bits observed to number of times it was observed. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>options</strong> – Sample options. </p></li>
-<li><p><strong>kernel</strong> – The kernel expression, must contain final measurements. </p></li>
-<li><p><strong>args</strong> – The variadic concrete arguments for evaluation of the kernel. </p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The counts dictionary.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq6sampleEDaNSt6size_tERR13QuantumKernelDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleEDaNSt6size_tERR13QuantumKernelDpRR4Args" title="cudaq::sample::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleEDaNSt6size_tERR13QuantumKernelDpRR4Args" title="cudaq::sample::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="sample_8h_1a1739a49bfb2fef6298023face97b8d6d"></span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shots</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleEDaNSt6size_tERR13QuantumKernelDpRR4Args" title="cudaq::sample::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleEDaNSt6size_tERR13QuantumKernelDpRR4Args" title="cudaq::sample::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq6sampleEDaNSt6size_tERR13QuantumKernelDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Sample the given quantum kernel expression and return the mapping of observed bit strings to corresponding number of times observed. Specify the number of shots. </p>
-<p>This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. </p>
-<p>
-Given a quantum kernel with void return type, sample the corresponding quantum circuit generated by the kernel expression, returning the mapping of bits observed to number of times it was observed. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>shots</strong> – The number of samples to collect. </p></li>
-<li><p><strong>kernel</strong> – The kernel expression, must contain final measurements. </p></li>
-<li><p><strong>args</strong> – The variadic concrete arguments for evaluation of the kernel. </p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The counts dictionary.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRR13QuantumKernelDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_r_v</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRR13QuantumKernelDpRR4Args" title="cudaq::sample::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRR13QuantumKernelDpRR4Args" title="cudaq::sample::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="sample_8h_1a8bc21fe7373f6097f1df2210f025549f"></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRR13QuantumKernelDpRR4Args" title="cudaq::sample::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRR13QuantumKernelDpRR4Args" title="cudaq::sample::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq6sampleE13sample_resultRR13QuantumKernelDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Sample the given quantum kernel expression and return the mapping of observed bit strings to corresponding number of times observed. </p>
-<p>This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. </p>
-<p>
-Given a quantum kernel with void return type, sample the corresponding quantum circuit generated by the kernel expression, returning the mapping of bits observed to number of times it was observed. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> – the kernel expression, must contain final measurements </p></li>
-<li><p><strong>args</strong> – the variadic concrete arguments for evaluation of the kernel. </p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>counts, The counts dictionary.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15SimulationStateE">
-<span class="target" id="classcudaq_1_1SimulationState"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">SimulationState</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15SimulationStateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1SimulationState"><span class="std std-ref"><span class="pre">SimulationState</span></span></a></code> interface provides and extension point for concrete circuit simulation sub-types to describe their underlying quantum state data in an efficient manner. The <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1SimulationState"><span class="std std-ref"><span class="pre">SimulationState</span></span></a></code> provides a handle on the data for clients, with data remaining on GPU device or CPU memory. The primary goal of this type and its sub-types is to minimize data transfers for the state. </p>
-<p>Subclassed by <a class="reference internal" href="#classcudaq_1_1CusvState"><span class="std std-ref">cudaq::CusvState&lt; ScalarType &gt;</span></a>, <a class="reference internal" href="#classcudaq_1_1RemoteSimulationState"><span class="std std-ref">cudaq::RemoteSimulationState</span></a>, <a class="reference internal" href="#classnvqir_1_1MPSSimulationState"><span class="std std-ref">nvqir::MPSSimulationState</span></a>, <a class="reference internal" href="#classnvqir_1_1TensorNetSimulationState"><span class="std std-ref">nvqir::TensorNetSimulationState</span></a></p>
-</dd></dl>
-
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15SimulationState6TensorE">
-<span class="target" id="structcudaq_1_1SimulationState_1_1Tensor"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15SimulationState6TensorE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Simulation states are made up of a vector of Tensors. Each tensor tracks its data pointer and the tensor extents. </p>
-</dd></dl>
-
-<dl class="cpp enum-class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15SimulationState9precisionE">
-<span class="target" id="classcudaq_1_1SimulationState_1a3fd90ce5445534e830b461b43b3dcf2f"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">precision</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15SimulationState9precisionE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Runtime-known precision for the simulation data. </p>
-<p><em>Values:</em></p>
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15SimulationState9precision4fp32E">
-<span class="target" id="classcudaq_1_1SimulationState_1a3fd90ce5445534e830b461b43b3dcf2fa4e1bd87474ad81110395b71013094b35"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp32</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15SimulationState9precision4fp32E" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15SimulationState9precision4fp64E">
-<span class="target" id="classcudaq_1_1SimulationState_1a3fd90ce5445534e830b461b43b3dcf2fa36dd759373a12bd5960718d0c482a2d4"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp64</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq15SimulationState9precision4fp64E" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="cpp enum-class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq20simulation_precisionE">
-<span class="target" id="host__config_8h_1ae727392959b51f54546b3bb55cfc31b5"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">simulation_precision</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq20simulation_precisionE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Define an enumeration of possible simulation floating point precision types. </p>
-<p><em>Values:</em></p>
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq20simulation_precision4fp32E">
-<span class="target" id="host__config_8h_1ae727392959b51f54546b3bb55cfc31b5a4e1bd87474ad81110395b71013094b35"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp32</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq20simulation_precision4fp32E" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq20simulation_precision4fp64E">
-<span class="target" id="host__config_8h_1ae727392959b51f54546b3bb55cfc31b5a36dd759373a12bd5960718d0c482a2d4"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp64</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq20simulation_precision4fp64E" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq6tensorE">
-<span class="target" id="qis_2state_8h_1a41df817a76288726556bc3340e71ee40"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">tensor</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationState6TensorE" title="cudaq::SimulationState::Tensor"><span class="n"><span class="pre">Tensor</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq6tensorE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15TensorStateDataE">
-<span class="target" id="SimulationState_8h_1a492cfc761cde8697c6e6263a23812c9e"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">TensorStateData</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq15TensorStateDataE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Encapsulates a list of tensors (data pointer and dimensions). </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10state_dataE">
-<span class="target" id="SimulationState_8h_1a4cec83b0c73799f1df02d25e49270ae2"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">state_data</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">variant</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">float</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">float</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq15TensorStateDataE" title="cudaq::TensorStateData"><span class="n"><span class="pre">TensorStateData</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq10state_dataE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>state_data is a variant type encoding different forms of user state vector data we support. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq9CusvStateE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ScalarType</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1CusvState"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CusvState</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><a class="headerlink" href="#_CPPv4I0EN5cudaq9CusvStateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><a class="reference internal" href="#classcudaq_1_1CusvState"><span class="std std-ref">CusvState</span></a> provides an implementation of <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1SimulationState"><span class="std std-ref"><span class="pre">SimulationState</span></span></a></code> that encapsulates the state data for the Custatevec Circuit Simulator. It attempts to keep the simulation data on GPU device and care is taken to ensure operations and comparisons with other states operate on compatible floating point element types. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5nvqir18MPSSimulationStateE">
-<span class="target" id="classnvqir_1_1MPSSimulationState"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">MPSSimulationState</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><a class="headerlink" href="#_CPPv4N5nvqir18MPSSimulationStateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5nvqir24TensorNetSimulationStateE">
-<span class="target" id="classnvqir_1_1TensorNetSimulationState"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">TensorNetSimulationState</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><a class="headerlink" href="#_CPPv4N5nvqir24TensorNetSimulationStateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq21RemoteSimulationStateE">
-<span class="target" id="classcudaq_1_1RemoteSimulationState"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">RemoteSimulationState</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq21RemoteSimulationStateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Implementation of <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1SimulationState"><span class="std std-ref"><span class="pre">SimulationState</span></span></a></code> for remote simulator backends. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq8registry14RegisteredTypeE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">T</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1registry_1_1RegisteredType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">RegisteredType</span></span></span><a class="headerlink" href="#_CPPv4I0EN5cudaq8registry14RegisteredTypeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><a class="reference internal" href="#classcudaq_1_1registry_1_1RegisteredType"><span class="std std-ref">RegisteredType</span></a> allows interface types to declare themselves as plugin interfaces. Used as follows class MyInterface : public RegisteredType&lt;MyInterface&gt; {…};. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrixE">
-<span class="target" id="classcudaq_1_1complex__matrix"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">complex_matrix</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrixE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1complex__matrix"><span class="std std-ref">complex_matrix</span></a> provides an abstraction for describing a matrix with N rows and M columns containing complex elements. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrix14complex_matrixEKNSt6size_tEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1complex__matrix_1afc709728054288800d83c362714d7571"></span><span class="sig-name descname"><span class="n"><span class="pre">complex_matrix</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">rows</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">cols</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrix14complex_matrixEKNSt6size_tEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Create a matrix of the given sizes, all elements initialized to 0.0. The <a class="reference internal" href="#classcudaq_1_1complex__matrix"><span class="std std-ref">complex_matrix</span></a> owns the matrix data and will delete it upon destruction. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrix14complex_matrixEP10value_typeKNSt6size_tEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1complex__matrix_1a680edda8dad30affa4b28c83d6b49573"></span><span class="sig-name descname"><span class="n"><span class="pre">complex_matrix</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="cudaq::complex_matrix::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">rawData</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">rows</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">cols</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrix14complex_matrixEP10value_typeKNSt6size_tEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Create a matrix from an existing data pointer. The <a class="reference internal" href="#classcudaq_1_1complex__matrix"><span class="std std-ref">complex_matrix</span></a> does not own this memory and will not delete it upon destruction. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrix4dataEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1a807081f855a6780dfeda25627c1d9cae"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="cudaq::complex_matrix::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">data</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrix4dataEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the internal data representation. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrix4rowsEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1a46f26ea0e1b0ab761b8bc16a25dc753c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">rows</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrix4rowsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of rows. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrix4colsEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1a7676167f15afd42963e189e96f1628f6"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">cols</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrix4colsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of columns. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrixmlER14complex_matrix">
-<span class="target" id="classcudaq_1_1complex__matrix_1a4bd641605635b405f6627f00198331f0"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrixE" title="cudaq::complex_matrix"><span class="n"><span class="pre">complex_matrix</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrixE" title="cudaq::complex_matrix"><span class="n"><span class="pre">complex_matrix</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrixmlER14complex_matrix" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply this matrix with the provided other matrix. This does not modify this matrix but instead returns a new matrix value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrixmlERNSt6vectorI10value_typeEE">
-<span class="target" id="classcudaq_1_1complex__matrix_1afc56d7e45004ac896d20f7b86aac63fb"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrixE" title="cudaq::complex_matrix"><span class="n"><span class="pre">complex_matrix</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="cudaq::complex_matrix::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrixmlERNSt6vectorI10value_typeEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply this matrix by a vector (A*x) such that x is provided as a std vector of the correct size. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrixclENSt6size_tENSt6size_tE">
-<span class="target" id="classcudaq_1_1complex__matrix_1a83cebaf41d1000f8e92078eb9f4a5018"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="cudaq::complex_matrix::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">()</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">i</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">j</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrixclENSt6size_tENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the element at the row <code class="docutils literal notranslate"><span class="pre">i</span></code> and column <code class="docutils literal notranslate"><span class="pre">j</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrix18minimal_eigenvalueEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1ad4e60a0ac829f448a26f7f5e869e540b"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="cudaq::complex_matrix::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">minimal_eigenvalue</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrix18minimal_eigenvalueEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the minimal eigenvalue for this matrix. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrix11eigenvaluesEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1a9fdab2211e97c8a54f2df7a35e19190e"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="cudaq::complex_matrix::value_type"><span class="n"><span class="pre">value_type</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">eigenvalues</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrix11eigenvaluesEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return this matrix’s eigenvalues. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq14complex_matrix12eigenvectorsEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1a8c4268a30ace97758990c16f2c6e6e21"></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrixE" title="cudaq::complex_matrix"><span class="n"><span class="pre">complex_matrix</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">eigenvectors</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq14complex_matrix12eigenvectorsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the eigenvectors of this matrix. They are returned as the columns of a new matrix. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrix8set_zeroEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1aa6f1e9da2a940a56319d97723dd694b8"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">set_zero</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrix8set_zeroEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set all elements in this matrix to 0.0. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrix4dumpERNSt7ostreamE">
-<span class="target" id="classcudaq_1_1complex__matrix_1af7a3f8bb36d85f5714cde14105aa6463"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">os</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrix4dumpERNSt7ostreamE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Print this matrix to the given output stream. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrix4dumpEv">
-<span class="target" id="classcudaq_1_1complex__matrix_1aa024a067b22061a372f51114e4ca94ff"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrix4dumpEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Print this matrix to standard out. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5TraceE">
-<span class="target" id="classcudaq_1_1Trace"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Trace</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq5TraceE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A trace is a circuit representation of the executed computation, as seen by the execution manager. (Here, a circuit is represented as a list of instructions on qudits). Since the execution manager cannot “see” control flow, the trace of a kernel with control flow represents a single execution path, and thus two calls to the same kernel might produce traces. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ElementType</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_integral_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="o"><span class="pre">&amp;&amp;</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_signed_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="cudaq__utils_8h_1ab5c4da9827fd0d2f3575706bda49941c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">range</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">N</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a vector of integers. The first element is zero, and the remaining elements are all values incremented by 1 to the total size value provided (exclusive). </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ElementType</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_integral_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="o"><span class="pre">&amp;&amp;</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_signed_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="cudaq__utils_8h_1aaf2865114850e54f70afe8b087297ce9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">range</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">start</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">stop</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="cudaq::range::ElementType"><span class="n"><span class="pre">ElementType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">step</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq5rangeENSt6vectorI11ElementTypeEE11ElementType11ElementType11ElementType" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a vector of integers. The first element is the user-specified <code class="docutils literal notranslate"><span class="pre">start</span></code> value. The remaining values are all values incremented by <code class="docutils literal notranslate"><span class="pre">step</span></code> (defaults to 1) until the <code class="docutils literal notranslate"><span class="pre">stop</span></code> value is reached (exclusive). </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0Dp0EN5cudaq4drawENSt6stringERR13QuantumKernelDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq4drawENSt6stringERR13QuantumKernelDpRR4Args" title="cudaq::draw::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq4drawENSt6stringERR13QuantumKernelDpRR4Args" title="cudaq::draw::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="draw_8h_1a3aa261b1dafc10d75e7fb62c84243b5c"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">draw</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq4drawENSt6stringERR13QuantumKernelDpRR4Args" title="cudaq::draw::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I0Dp0EN5cudaq4drawENSt6stringERR13QuantumKernelDpRR4Args" title="cudaq::draw::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0Dp0EN5cudaq4drawENSt6stringERR13QuantumKernelDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns a drawing of the execution path, i.e., the trace, of the kernel. The drawing is a UTF-8 encoded string. </p>
-<p>
-Usage: <div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/algorithms/draw.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-
-<span class="k">auto</span><span class="w"> </span><span class="n">bell_pair</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="p">};</span>
-<span class="p">...</span>
-<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">draw</span><span class="p">(</span><span class="n">bell_pair</span><span class="p">);</span>
-<span class="cm">/* Output:</span>
-<span class="cm">     ╭───╮     </span>
-<span class="cm">q0 : ┤ h ├──●──</span>
-<span class="cm">     ╰───╯╭─┴─╮</span>
-<span class="cm">q1 : ─────┤ x ├</span>
-<span class="cm">          ╰───╯</span>
-<span class="cm">*/</span>
-
-<span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">float</span><span class="w"> </span><span class="n">angle</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">ry</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="p">};</span>
-<span class="p">...</span>
-<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="mf">0.59</span><span class="p">);</span>
-<span class="cm">/* Output:</span>
-<span class="cm">     ╭───╮╭──────────╮</span>
-<span class="cm">q0 : ┤ h ├┤ ry(0.59) ├</span>
-<span class="cm">     ╰───╯╰──────────╯</span>
-<span class="cm">*/</span><span class="w">      </span>
-</pre></div>
-</div>
- </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> – The quantum callable with non-trivial function signature. </p></li>
-<li><p><strong>args</strong> – The arguments required for evaluation of the quantum kernel. </p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The UTF-8 encoded string of the circuit, without measurement operations.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0DpEN5cudaq9get_stateEDaRR13QuantumKernelDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="get__state_8h_1aa51698d34eb8301425750ad958514b93"></span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">get_state</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0DpEN5cudaq9get_stateEDaRR13QuantumKernelDpRR4Args" title="cudaq::get_state::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I0DpEN5cudaq9get_stateEDaRR13QuantumKernelDpRR4Args" title="cudaq::get_state::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0DpEN5cudaq9get_stateEDaRR13QuantumKernelDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the state representation generated by the kernel at the given runtime arguments. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9ResourcesE">
-<span class="target" id="classcudaq_1_1Resources"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Resources</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq9ResourcesE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1Resources"><span class="std std-ref">Resources</span></a> type encodes information regarding the currently executing kernel’s resource usage. This includes number and type of quantum operation, circuit depth, etc. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14complex_matrix10value_typeE">
-<span class="target" id="classcudaq_1_1complex__matrix_1ade050d2b1512d2fe61d2f69810419bc4"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq14complex_matrixE" title="cudaq::complex_matrix"><span class="n"><span class="pre">complex_matrix</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">value_type</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq14complex_matrix10value_typeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-</section>
-<section id="noise-modeling">
-<h2>Noise Modeling<a class="headerlink" href="#noise-modeling" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_opE">
-<span class="target" id="structcudaq_1_1kraus__op"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_op</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_opE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A <a class="reference internal" href="#structcudaq_1_1kraus__op"><span class="std std-ref">kraus_op</span></a> represents a single Kraus operation, described as a complex matrix of specific size. The matrix is represented here as a 1d array (specifically a std::vector). </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_op8kraus_opERK8kraus_op">
-<span class="target" id="structcudaq_1_1kraus__op_1a2f5c2b8930af146a4dbd3fadd7938fbc"></span><span class="sig-name descname"><span class="n"><span class="pre">kraus_op</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_op8kraus_opERK8kraus_op" title="cudaq::kraus_op::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_op8kraus_opERK8kraus_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_op8kraus_opENSt6vectorIN5cudaq7complexEEE">
-<span class="target" id="structcudaq_1_1kraus__op_1a14137e8fb637563b68c2353d2d6834cd"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_op</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7complexE" title="cudaq::complex"><span class="n"><span class="pre">complex</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">d</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_op8kraus_opENSt6vectorIN5cudaq7complexEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, initialize from vector data. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq8kraus_op8kraus_opERRNSt16initializer_listI1TEE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">T</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="structcudaq_1_1kraus__op_1ae05531f8a2507a0397e13250c622d512"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_op</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">initializer_list</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I0EN5cudaq8kraus_op8kraus_opERRNSt16initializer_listI1TEE" title="cudaq::kraus_op::kraus_op::T"><span class="n"><span class="pre">T</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">initList</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN5cudaq8kraus_op8kraus_opERRNSt16initializer_listI1TEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, initialize from initializer_list. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_opaSERK8kraus_op">
-<span class="target" id="structcudaq_1_1kraus__op_1ac7cdeb5fa87ffea343ea695710abb7a2"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_opaSERK8kraus_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set this <a class="reference internal" href="#structcudaq_1_1kraus__op"><span class="std std-ref">kraus_op</span></a> equal to the other. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq8kraus_op7adjointEv">
-<span class="target" id="structcudaq_1_1kraus__op_1a515b1d2dcfc3b07156eeee9ce394e706"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">adjoint</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq8kraus_op7adjointEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the adjoint of this <a class="reference internal" href="#structcudaq_1_1kraus__op"><span class="std std-ref">kraus_op</span></a>. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_op4dataE">
-<span class="target" id="structcudaq_1_1kraus__op_1a9ad38665040cf71ea3ce73d76ad4989a"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7complexE" title="cudaq::complex"><span class="n"><span class="pre">complex</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">data</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_op4dataE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Matrix data, represented as a 1d flattened. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_op5nRowsE">
-<span class="target" id="structcudaq_1_1kraus__op_1a6ae29957ac012da913831f2d3e8760a6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">nRows</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_op5nRowsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The number of rows in the matrix. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8kraus_op5nColsE">
-<span class="target" id="structcudaq_1_1kraus__op_1a037b5b46c6f29c3b93c70ce35c899709"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">nCols</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq8kraus_op5nColsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The number of columns in the matrix NOTE we currently assume nRows == nCols. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channelE">
-<span class="target" id="classcudaq_1_1kraus__channel"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_channel</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> represents a quantum noise channel on specific qubits. The action of the noise channel is described by a vector of Kraus operations - matrices with size equal to 2**nQubits x 2**nQubits, where the number of qubits is the number of provided qubit indices at construction. </p>
-<p>Subclassed by <a class="reference internal" href="#classcudaq_1_1amplitude__damping__channel"><span class="std std-ref">cudaq::amplitude_damping_channel</span></a>, <a class="reference internal" href="#classcudaq_1_1bit__flip__channel"><span class="std std-ref">cudaq::bit_flip_channel</span></a>, <a class="reference internal" href="#classcudaq_1_1depolarization__channel"><span class="std std-ref">cudaq::depolarization_channel</span></a>, <a class="reference internal" href="#classcudaq_1_1phase__flip__channel"><span class="std std-ref">cudaq::phase_flip_channel</span></a></p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel13kraus_channelEv">
-<span class="target" id="classcudaq_1_1kraus__channel_1acdd8db7bacc17f9913d4b137cb323445"></span><span class="sig-name descname"><span class="n"><span class="pre">kraus_channel</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel13kraus_channelEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The nullary constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel13kraus_channelERK13kraus_channel">
-<span class="target" id="classcudaq_1_1kraus__channel_1a5b4f645289e858e99a1e15d1d2e5794f"></span><span class="sig-name descname"><span class="n"><span class="pre">kraus_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channel13kraus_channelERK13kraus_channel" title="cudaq::kraus_channel::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel13kraus_channelERK13kraus_channel" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The copy constructor. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>other</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4IDpEN5cudaq13kraus_channel13kraus_channelEDpRRNSt16initializer_listI1TEE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">T</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1kraus__channel_1a78aab1847e17c4776ab2fb69ad82f5d9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_channel</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">initializer_list</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4IDpEN5cudaq13kraus_channel13kraus_channelEDpRRNSt16initializer_listI1TEE" title="cudaq::kraus_channel::kraus_channel::T"><span class="n"><span class="pre">T</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">inputLists</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4IDpEN5cudaq13kraus_channel13kraus_channelEDpRRNSt16initializer_listI1TEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, initializes kraus_ops internally from the provided initializer_lists. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel13kraus_channelERKNSt6vectorI8kraus_opEE">
-<span class="target" id="classcudaq_1_1kraus__channel_1a230e73f8a944398cb55331031f267ae2"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">inOps</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel13kraus_channelERKNSt6vectorI8kraus_opEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, take qubits and channel kraus_ops as lvalue reference. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel13kraus_channelERRNSt6vectorI8kraus_opEE">
-<span class="target" id="classcudaq_1_1kraus__channel_1ad31e6da920dad2e42f8564dc3552f776"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kraus_channel</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">ops</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel13kraus_channelERRNSt6vectorI8kraus_opEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, take qubits and channel kraus_ops as rvalue reference. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13kraus_channel4sizeEv">
-<span class="target" id="classcudaq_1_1kraus__channel_1ab48189c8c25838c7c485b29291c2a5fa"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">size</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13kraus_channel4sizeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of kraus_ops that make up this channel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq13kraus_channel5emptyEv">
-<span class="target" id="classcudaq_1_1kraus__channel_1a2f5ef656cfdae14b49667813159c1965"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">empty</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq13kraus_channel5emptyEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if there are no ops in this channel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channelixEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1kraus__channel_1a082da012db5a565347714f1e8c9aab99"></span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">[]</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channelixEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the <a class="reference internal" href="#structcudaq_1_1kraus__op"><span class="std std-ref">kraus_op</span></a> at the given index. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channelaSERK13kraus_channel">
-<span class="target" id="classcudaq_1_1kraus__channel_1abfdcf9c8f4a943e938744863d6cb1d59"></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channelaSERK13kraus_channel" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set this <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> equal to the given one. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel7get_opsEv">
-<span class="target" id="classcudaq_1_1kraus__channel_1ad86a7f7a00f7cea74d4d3ab684e32a35"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_ops</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel7get_opsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return all kraus_ops in this channel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel9push_backE8kraus_op">
-<span class="target" id="classcudaq_1_1kraus__channel_1a4c0ce018d87610e19c8783386211a6b9"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">push_back</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq8kraus_opE" title="cudaq::kraus_op"><span class="n"><span class="pre">kraus_op</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">op</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel9push_backE8kraus_op" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add a <a class="reference internal" href="#structcudaq_1_1kraus__op"><span class="std std-ref">kraus_op</span></a> to this channel. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel10noise_typeE">
-<span class="target" id="classcudaq_1_1kraus__channel_1a2c982452ceb13b1b518087f6a3ca32d0"></span><a class="reference internal" href="#_CPPv4N5cudaq16noise_model_typeE" title="cudaq::noise_model_type"><span class="n"><span class="pre">noise_model_type</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">noise_type</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq16noise_model_typeE" title="cudaq::noise_model_type"><span class="n"><span class="pre">noise_model_type</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq16noise_model_type7unknownE" title="cudaq::noise_model_type::unknown"><span class="n"><span class="pre">unknown</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel10noise_typeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Noise type enumeration. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq13kraus_channel10parametersE">
-<span class="target" id="classcudaq_1_1kraus__channel_1ad78d39ae4ebfcfcb2bfd91f9b5a4ebb5"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">parameters</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq13kraus_channel10parametersE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Noise parameter values. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq25amplitude_damping_channelE">
-<span class="target" id="classcudaq_1_1amplitude__damping__channel"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">amplitude_damping_channel</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq25amplitude_damping_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><a class="reference internal" href="#classcudaq_1_1amplitude__damping__channel"><span class="std std-ref">amplitude_damping_channel</span></a> is a <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> that automates the creation of the kraus_ops that make up a single-qubit amplitude damping error channel. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16bit_flip_channelE">
-<span class="target" id="classcudaq_1_1bit__flip__channel"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">bit_flip_channel</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq16bit_flip_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><a class="reference internal" href="#classcudaq_1_1bit__flip__channel"><span class="std std-ref">bit_flip_channel</span></a> is a <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> that automates the creation of the kraus_ops that make up a single-qubit bit flipping error channel. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18phase_flip_channelE">
-<span class="target" id="classcudaq_1_1phase__flip__channel"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">phase_flip_channel</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq18phase_flip_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><a class="reference internal" href="#classcudaq_1_1phase__flip__channel"><span class="std std-ref">phase_flip_channel</span></a> is a <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> that automates the creation of the kraus_ops that make up a single-qubit phase flip error channel. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq22depolarization_channelE">
-<span class="target" id="classcudaq_1_1depolarization__channel"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">depolarization_channel</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq22depolarization_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p><a class="reference internal" href="#classcudaq_1_1depolarization__channel"><span class="std std-ref">depolarization_channel</span></a> is a <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> that automates the creation of the kraus_ops that make up a single-qubit depolarization error channel. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11noise_modelE">
-<span class="target" id="classcudaq_1_1noise__model"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">noise_model</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq11noise_modelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1noise__model"><span class="std std-ref">noise_model</span></a> type keeps track of a set of kraus_channels to be applied after the execution of quantum operations. Each quantum operation maps to a Kraus channel containing a number of kraus_ops to be applied to the density matrix representation of the state. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11noise_model15PredicateFuncTyE">
-<span class="target" id="classcudaq_1_1noise__model_1aeb88e7dafa1e41c5925e30983b4eb36e"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">PredicateFuncTy</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="p"><span class="pre">(</span></span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq11noise_model15PredicateFuncTyE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Callback function type for noise channel. Given the qubit operands and gate parameters, this function should return a concrete noise channel. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11noise_model11noise_modelEv">
-<span class="target" id="classcudaq_1_1noise__model_1a5c366911696095b18a8bcf1c55fe1dac"></span><span class="sig-name descname"><span class="n"><span class="pre">noise_model</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq11noise_model11noise_modelEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>default constructor </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq11noise_model5emptyEv">
-<span class="target" id="classcudaq_1_1noise__model_1ae7a17c0d42e97d2750c9f2138e61b9d6"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">empty</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq11noise_model5emptyEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if there are no kraus_channels in this noise model. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11noise_model11add_channelERKNSt6stringERKNSt6vectorINSt6size_tEEERK13kraus_channel">
-<span class="target" id="classcudaq_1_1noise__model_1a179b0a307af0f9a056ce91852fd26e8d"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">add_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">quantumOp</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">qubits</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">channel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq11noise_model11add_channelERKNSt6stringERKNSt6vectorINSt6size_tEEERK13kraus_channel" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the Kraus channel to the specified one-qubit quantum operation. It applies to the quantumOp operation for the specified qubits in the <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11noise_model11add_channelERKNSt6stringERK15PredicateFuncTy">
-<span class="target" id="classcudaq_1_1noise__model_1af62dda77eade243217de8a8a0959d8c0"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">add_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">quantumOp</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq11noise_model15PredicateFuncTyE" title="cudaq::noise_model::PredicateFuncTy"><span class="n"><span class="pre">PredicateFuncTy</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">pred</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq11noise_model11add_channelERKNSt6stringERK15PredicateFuncTy" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the Kraus channel as a callback to the specified quantum operation. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>quantumOp</strong> – Quantum operation that the noise channel applies to. </p></li>
-<li><p><strong>pred</strong> – Callback function that generates a noise channel. </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11noise_model21add_all_qubit_channelERKNSt6stringERK13kraus_channeli">
-<span class="target" id="classcudaq_1_1noise__model_1ae7127d90f75c57418d423d717a04abcf"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">add_all_qubit_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">quantumOp</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">channel</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">numControls</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq11noise_model21add_all_qubit_channelERKNSt6stringERK13kraus_channeli" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the Kraus channel that applies to a quantum operation on any arbitrary qubits. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>quantumOp</strong> – Quantum operation that the noise channel applies to. </p></li>
-<li><p><strong>channel</strong> – The Kraus channel to apply. </p></li>
-<li><p><strong>numControls</strong> – Number of control qubits for the gate. Default is 0 (gate without a control modifier). </p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4IDpEN5cudaq11noise_model11add_channelEvRKNSt6vectorINSt6size_tEEERK13kraus_channel">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">QuantumOp</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1noise__model_1a4cd1510fa429dd4a3a51ce7364dc4c4d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">add_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">qubits</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">channel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4IDpEN5cudaq11noise_model11add_channelEvRKNSt6vectorINSt6size_tEEERK13kraus_channel" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the provided <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> to all specified quantum operations. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4IDpEN5cudaq11noise_model11add_channelEvRK15PredicateFuncTy">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">QuantumOp</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1noise__model_1a64a6ad51e15b64cce3970c4c6d27044e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">add_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq11noise_model15PredicateFuncTyE" title="cudaq::noise_model::PredicateFuncTy"><span class="n"><span class="pre">PredicateFuncTy</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">pred</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4IDpEN5cudaq11noise_model11add_channelEvRK15PredicateFuncTy" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the provided <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> callback to all specified quantum operations. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4IDpEN5cudaq11noise_model21add_all_qubit_channelEvRK13kraus_channeli">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">QuantumOp</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1noise__model_1a5613f092904482de026ca4594e436341"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">add_all_qubit_channel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">channel</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">numControls</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4IDpEN5cudaq11noise_model21add_all_qubit_channelEvRK13kraus_channeli" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add the provided <a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref">kraus_channel</span></a> to all specified quantum operations applying on arbitrary qubits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq11noise_model12get_channelsERKNSt6stringERKNSt6vectorINSt6size_tEEERKNSt6vectorINSt6size_tEEERKNSt6vectorIdEE">
-<span class="target" id="classcudaq_1_1noise__model_1a49ca0172344eb9663fc818a3c4b68d7b"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_channels</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">quantumOp</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">targetQubits</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">controlQubits</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><span class="p"><span class="pre">}</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">params</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><span class="p"><span class="pre">}</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq11noise_model12get_channelsERKNSt6stringERKNSt6vectorINSt6size_tEEERKNSt6vectorINSt6size_tEEERKNSt6vectorIdEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return relevant kraus_channels on the specified qubits for. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0ENK5cudaq11noise_model12get_channelsENSt6vectorI13kraus_channelEERKNSt6vectorINSt6size_tEEERKNSt6vectorINSt6size_tEEERKNSt6vectorIdEE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumOp</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1noise__model_1aab36695296be377853de07e763bccb93"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13kraus_channelE" title="cudaq::kraus_channel"><span class="n"><span class="pre">kraus_channel</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_channels</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">targetQubits</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">controlQubits</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><span class="p"><span class="pre">}</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">params</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><span class="p"><span class="pre">}</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4I0ENK5cudaq11noise_model12get_channelsENSt6vectorI13kraus_channelEERKNSt6vectorINSt6size_tEEERKNSt6vectorINSt6size_tEEERKNSt6vectorIdEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get all kraus_channels on the given qubits. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp enum-class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16noise_model_typeE">
-<span class="target" id="NoiseModel_8h_1a8410c9abbed0e7dfcf2c5db0919e46e5"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">noise_model_type</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16noise_model_typeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Noise model enumerated type that allows downstream simulators of <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1kraus__channel"><span class="std std-ref"><span class="pre">kraus_channel</span></span></a></code> objects to apply simulator-specific logic for well-known noise models. </p>
-<p><em>Values:</em></p>
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16noise_model_type7unknownE">
-<span class="target" id="NoiseModel_8h_1a8410c9abbed0e7dfcf2c5db0919e46e5aad921d60486366258809553a3db49a4a"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">unknown</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16noise_model_type7unknownE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16noise_model_type22depolarization_channelE">
-<span class="target" id="NoiseModel_8h_1a8410c9abbed0e7dfcf2c5db0919e46e5ae929ee38cfe5edcb239b9c21fb3e3387"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">depolarization_channel</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16noise_model_type22depolarization_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16noise_model_type25amplitude_damping_channelE">
-<span class="target" id="NoiseModel_8h_1a8410c9abbed0e7dfcf2c5db0919e46e5a319331f39218db335141c4e93138836f"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">amplitude_damping_channel</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16noise_model_type25amplitude_damping_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16noise_model_type16bit_flip_channelE">
-<span class="target" id="NoiseModel_8h_1a8410c9abbed0e7dfcf2c5db0919e46e5a1b278bac9f8ec713b5d25f994d5522d9"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">bit_flip_channel</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16noise_model_type16bit_flip_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp enumerator">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16noise_model_type18phase_flip_channelE">
-<span class="target" id="NoiseModel_8h_1a8410c9abbed0e7dfcf2c5db0919e46e5a47d7b6d93e6a7373d773f10e9971fba6"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">phase_flip_channel</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16noise_model_type18phase_flip_channelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="kernel-builder">
-<h2>Kernel Builder<a class="headerlink" href="#kernel-builder" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4IDpEN5cudaq14kernel_builderE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1kernel__builder"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kernel_builder</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7detailsE" title="cudaq::details"><span class="n"><span class="pre">details</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7details19kernel_builder_baseE" title="cudaq::details::kernel_builder_base"><span class="n"><span class="pre">kernel_builder_base</span></span></a><a class="headerlink" href="#_CPPv4IDpEN5cudaq14kernel_builderE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder14kernel_builderERNSt6vectorIN7details17KernelBuilderTypeEEE">
-<span class="target" id="classcudaq_1_1kernel__builder_1abeef4b926dd7f39ec1264903d92be8a0"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kernel_builder</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq7detailsE" title="cudaq::details"><span class="n"><span class="pre">details</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7details17KernelBuilderTypeE" title="cudaq::details::KernelBuilderType"><span class="n"><span class="pre">KernelBuilderType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">types</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder14kernel_builderERNSt6vectorIN7details17KernelBuilderTypeEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes the input <code class="docutils literal notranslate"><span class="pre">KernelBuilderType</span></code>s which is used to create the MLIR function type. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder12getArgumentsEv">
-<span class="target" id="classcudaq_1_1kernel__builder_1a9da8c65636070b6e4d96a34792317819"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="n"><span class="pre">getArguments</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder12getArgumentsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> arguments. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder11isArgStdVecENSt6size_tE">
-<span class="target" id="classcudaq_1_1kernel__builder_1a93e1e94ce81d273f342834d75ecfbe55"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">isArgStdVec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder11isArgStdVecENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return <code class="docutils literal notranslate"><span class="pre">true</span></code> if the argument to the kernel is a <code class="docutils literal notranslate"><span class="pre">std::vector</span></code>, <code class="docutils literal notranslate"><span class="pre">false</span></code> otherwise. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder4nameEv">
-<span class="target" id="classcudaq_1_1kernel__builder_1a4b181a0f89e0c03025646fcc868f1d64"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">name</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder4nameEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the name of this kernel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder12getNumParamsEv">
-<span class="target" id="classcudaq_1_1kernel__builder_1a9b3e71aecd1f34f4f1ef30aa98a72b57"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getNumParams</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder12getNumParamsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of function arguments. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder6qallocEv">
-<span class="target" id="classcudaq_1_1kernel__builder_1ace7af6cc7bcb2e14f2b81aa23a256f83"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qalloc</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder6qallocEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> representing the allocated qubit. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder6qallocEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1kernel__builder_1a0233bdb257aa60a73b0eaee70fd2ab89"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qalloc</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nQubits</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder6qallocEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> representing the allocated <code class="docutils literal notranslate"><span class="pre">QVec</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder6qallocE10QuakeValue">
-<span class="target" id="classcudaq_1_1kernel__builder_1a0d0bd9a759aaf3bbafae68d5ab78377e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qalloc</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">size</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder6qallocE10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> representing the allocated <code class="docutils literal notranslate"><span class="pre">Veq</span></code>, size is from a pre-allocated size <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> or <code class="docutils literal notranslate"><span class="pre">BlockArgument</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder6qallocERNSt6vectorINSt7complexIdEEEE">
-<span class="target" id="classcudaq_1_1kernel__builder_1a1823a80e0f41cab9edcd403d39c3e219"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">qalloc</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">state</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder6qallocERNSt6vectorINSt7complexIdEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> representing the allocated quantum register, initialized to the given state vector, <code class="docutils literal notranslate"><span class="pre">state</span></code>.</p>
-<p>Note: input argument is a <em>true</em> reference here, the calling context has to own the data. Specifically, the builder object will capture variables by reference (implemented as a container of pointers for simplicity) but the builder does not create, own, or copy these vectors. This implies that if the captured vector goes out of scope before the kernel is invoked, the reference may contain garbage. This behavior is identical to a C++ lambda capture by reference. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder11constantValEd">
-<span class="target" id="classcudaq_1_1kernel__builder_1aaad4791d9bdb3ae7a855f8ea35defcc3"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">constantVal</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">val</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder11constantValEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref"><span class="pre">QuakeValue</span></span></a></code> representing the constant floating-point value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq14kernel_builder4swapERK10QuakeValueRK10QuakeValue">
-<span class="target" id="classcudaq_1_1kernel__builder_1a27162cfcecff29cb112fc946e98527b8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">swap</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">first</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">second</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq14kernel_builder4swapERK10QuakeValueRK10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>SWAP operation for swapping the quantum states of two qubits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq14kernel_builder4swapEvRK10QuakeValueRK10QuakeValueRK10QuakeValue">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">mod</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_same_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq14kernel_builder4swapEvRK10QuakeValueRK10QuakeValueRK10QuakeValue" title="cudaq::kernel_builder::swap::mod"><span class="n"><span class="pre">mod</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ctrl</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1kernel__builder_1ac2a97ab1f89609efc12dad9a77e6759c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">swap</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">control</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">first</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">second</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq14kernel_builder4swapEvRK10QuakeValueRK10QuakeValueRK10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>SWAP operation for performing a Fredkin gate between two qubits, based on the state of input <code class="docutils literal notranslate"><span class="pre">control</span></code> qubit/s. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq14kernel_builder4swapEvRKNSt6vectorI10QuakeValueEERK10QuakeValueRK10QuakeValue">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">mod</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_same_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq14kernel_builder4swapEvRKNSt6vectorI10QuakeValueEERK10QuakeValueRK10QuakeValue" title="cudaq::kernel_builder::swap::mod"><span class="n"><span class="pre">mod</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ctrl</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1kernel__builder_1a2950291e3c57ed184cf5e7bcd4b61f35"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">swap</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">controls</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">first</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">second</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq14kernel_builder4swapEvRKNSt6vectorI10QuakeValueEERK10QuakeValueRK10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>SWAP operation for performing a Fredkin gate between two qubits, based on the state of an input vector of <code class="docutils literal notranslate"><span class="pre">controls</span></code>. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValueE">
-<span class="target" id="classcudaq_1_1QuakeValue"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuakeValue</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValueE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> is meant to provide a thin wrapper around an mlir::Value instance. These QuakeValues represent handles to function arguments and return values from MLIR Operations, specifically Quake Dialect operations. The <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> also exposes and algebraic API enabling one to negate, add, subtract, and multiply QuakeValues with each other as well as with primitive arithmetic types, e.g. double. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq10QuakeValue8getValueEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1a405ee59cc3ef467a1bcedac32012c8a9"></span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Value</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getValue</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq10QuakeValue8getValueEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the actual MLIR Value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue10QuakeValueERN4mlir20ImplicitLocOpBuilderEN4mlir5ValueE">
-<span class="target" id="classcudaq_1_1QuakeValue_1a7fd98aad59579fb7df2a82ff298abf5c"></span><span class="sig-name descname"><span class="n"><span class="pre">QuakeValue</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ImplicitLocOpBuilder</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">builder</span></span>, <span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Value</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue10QuakeValueERN4mlir20ImplicitLocOpBuilderEN4mlir5ValueE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes the builder and the value to wrap. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue10QuakeValueERN4mlir20ImplicitLocOpBuilderEd">
-<span class="target" id="classcudaq_1_1QuakeValue_1a80819c12221b08a9746e9058bb32b013"></span><span class="sig-name descname"><span class="n"><span class="pre">QuakeValue</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ImplicitLocOpBuilder</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">builder</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">v</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue10QuakeValueERN4mlir20ImplicitLocOpBuilderEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes the builder and a constant double value, which will map to an arith::ConstantFloatOp Value. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue4dumpEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1a8244774853762e984d8cc17e49094951"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue4dumpEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump the <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> to standard out. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue4dumpERNSt7ostreamE">
-<span class="target" id="classcudaq_1_1QuakeValue_1aa07fe57eeb25b3e7119f00ddad8f40e2"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue4dumpERNSt7ostreamE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump the <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> to the given output stream. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue22canValidateNumElementsEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1acc754ef5c14737881caf8bc44d06a761"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">canValidateNumElements</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue22canValidateNumElementsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> of StdVecType can validate its number of unique elements. We cannot do this in the case of <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> extractions within for loops where we do not know the bounds of the loop. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue5sliceEKNSt6size_tEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1QuakeValue_1a4d77a06940f26f478b781b3f6b4672b9"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">slice</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">startIdx</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">count</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue5sliceEKNSt6size_tEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>For a subscriptable <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>, extract a sub set of the elements starting at the given startIdx and including the following count elements. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue4sizeEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1af7d27118ed68e75175ab7e7927e16000"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">size</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue4sizeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>For a <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> with type StdVec or Veq, return the size <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue12constantSizeEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1ab54c3d0ca18fc34f09e6e8f664cb6c67"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">constantSize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue12constantSizeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the constant size of this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> if it is of Veq type. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue8isStdVecEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1a9a744ebbea0a51a5cda2f90684ab6ec8"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">isStdVec</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue8isStdVecEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> is of type StdVec. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValue19getRequiredElementsEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1af63960107eb641d65b04e0eff5889d68"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getRequiredElements</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValue19getRequiredElementsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>For a <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> of type StdVec, return the number of required elements, i.e. the number of unique extractions observed. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValueixEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1QuakeValue_1ad6347f7655d24d2b540783a91fef8274"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">[]</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValueixEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a new <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> when the current value is indexed, specifically for QuakeValues of type StdVecType and VeqType. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValueixERK10QuakeValue">
-<span class="target" id="classcudaq_1_1QuakeValue_1a055e4cc59ef7a83cffdf2a6d90d56060"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">[]</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValueixERK10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a new <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> when the current value is indexed, specifically for QuakeValues of type StdVecType and VeqType. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq10QuakeValuemiEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1a63a98bacfbe17a183206883f0f1b45f6"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">-</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq10QuakeValuemiEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the negation of this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuemlEKd">
-<span class="target" id="classcudaq_1_1QuakeValue_1a24abc47f5ce02e98717d349e8d3e3094"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuemlEKd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> by the given double. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuemlE10QuakeValue">
-<span class="target" id="classcudaq_1_1QuakeValue_1ac30ca2eea7293d25ae0126503b6fd002"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">*</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuemlE10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Multiply this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> by the given <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuedvEKd">
-<span class="target" id="classcudaq_1_1QuakeValue_1ab3a2057299dd94693dd8d32a1b48061f"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">/</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuedvEKd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Divide this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> by the given double. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuedvE10QuakeValue">
-<span class="target" id="classcudaq_1_1QuakeValue_1a8a34931fe0357e9072cb0abfd9cfd2ac"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">/</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuedvE10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Divide this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> by the given <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValueplEKd">
-<span class="target" id="classcudaq_1_1QuakeValue_1a32e330261b099d9e58cefda0f9ab95d3"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">+</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValueplEKd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> with the given double. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValueplEKi">
-<span class="target" id="classcudaq_1_1QuakeValue_1a760df950070c0dba099a48f42f65e030"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">+</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValueplEKi" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> with the given int. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValueplE10QuakeValue">
-<span class="target" id="classcudaq_1_1QuakeValue_1a75db83861eb822ab9daebedaa08b0afa"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">+</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValueplE10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Add this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> with the given <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuemiEKd">
-<span class="target" id="classcudaq_1_1QuakeValue_1afc857a20787542c9c802952cdc5a9e38"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">-</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuemiEKd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Subtract the given double from this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuemiEKi">
-<span class="target" id="classcudaq_1_1QuakeValue_1a9048069e1023244e4a5200ddd82fcc71"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">-</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuemiEKi" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Subtract the given int from this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq10QuakeValuemiE10QuakeValue">
-<span class="target" id="classcudaq_1_1QuakeValue_1acdaddb1a8d5f3c8608a50e5ebbc3bca6"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">-</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq10QuakeValuemiE10QuakeValue" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Subtract the given <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a> from this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq10QuakeValue7inverseEv">
-<span class="target" id="classcudaq_1_1QuakeValue_1a68fb96c779ac2645503c6aaf72322675"></span><a class="reference internal" href="#_CPPv4N5cudaq10QuakeValueE" title="cudaq::QuakeValue"><span class="n"><span class="pre">QuakeValue</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">inverse</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq10QuakeValue7inverseEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the inverse (1/x) of this <a class="reference internal" href="#classcudaq_1_1QuakeValue"><span class="std std-ref">QuakeValue</span></a>. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details19kernel_builder_baseE">
-<span class="target" id="classcudaq_1_1details_1_1kernel__builder__base"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kernel_builder_base</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq7details19kernel_builder_baseE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1details_1_1kernel__builder__base"><span class="std std-ref"><span class="pre">kernel_builder_base</span></span></a></code> provides a base type for the templated kernel builder so that we can get a single handle on an instance within the runtime. </p>
-<p>Subclassed by <a class="reference internal" href="#classcudaq_1_1kernel__builder"><span class="std std-ref">cudaq::kernel_builder&lt; Args &gt;</span></a></p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-friends">Friends</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details19kernel_builder_baselsERNSt7ostreamERK19kernel_builder_base">
-<span class="target" id="classcudaq_1_1details_1_1kernel__builder__base_1a3b4d8b805718a31695e63c798775ab55"></span><span class="k"><span class="pre">friend</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">&lt;&lt;</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">stream</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7details19kernel_builder_baseE" title="cudaq::details::kernel_builder_base"><span class="n"><span class="pre">kernel_builder_base</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">builder</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7details19kernel_builder_baselsERNSt7ostreamERK19kernel_builder_base" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Write the <a class="reference internal" href="#classcudaq_1_1kernel__builder"><span class="std std-ref">kernel_builder</span></a> to the given output stream. This outputs the Quake representation. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details17KernelBuilderTypeE">
-<span class="target" id="classcudaq_1_1details_1_1KernelBuilderType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelBuilderType</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq7details17KernelBuilderTypeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><span class="pre">kernel_builder::Type</span></code> allows us to track input C++ types representing the quake function argument types in a way that does not expose MLIR Type to the CUDA-Q code. This type keeps track of a functor that generates the MLIR Type in implementation code when <a class="reference internal" href="#classcudaq_1_1details_1_1KernelBuilderType_1ae62efe009d225bfc935230fd0d74fca7"><span class="std std-ref">create()</span></a> is invoked. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details17KernelBuilderType17KernelBuilderTypeERRNSt8functionIFN4mlir4TypeEPN4mlir11MLIRContextEEEE">
-<span class="target" id="classcudaq_1_1details_1_1KernelBuilderType_1a4dadceff6c1aaa5620aa246e037c4856"></span><span class="sig-name descname"><span class="n"><span class="pre">KernelBuilderType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Type</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">MLIRContext</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">ctx</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">f</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7details17KernelBuilderType17KernelBuilderTypeERRNSt8functionIFN4mlir4TypeEPN4mlir11MLIRContextEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, take the Type generation functor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7details17KernelBuilderType6createEPN4mlir11MLIRContextE">
-<span class="target" id="classcudaq_1_1details_1_1KernelBuilderType_1ae62efe009d225bfc935230fd0d74fca7"></span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Type</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">create</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">mlir</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">MLIRContext</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">ctx</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq7details17KernelBuilderType6createEPN4mlir11MLIRContextE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Create the MLIR Type. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-</section>
-<section id="algorithms">
-<h2>Algorithms<a class="headerlink" href="#algorithms" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9optimizerE">
-<span class="target" id="classcudaq_1_1optimizer"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">optimizer</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq9optimizerE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1optimizer"><span class="std std-ref">cudaq::optimizer</span></a> provides a high-level interface for general optimization of user-specified objective functions. This is meant to serve an interface for clients working with concrete subtypes providing specific optimization algorithms possibly delegating to third party libraries. This interface provides an optimize(…) method that takes the number of objective function input parameters (the dimension), and a user-specified objective function that takes the function input parameters as a immutable (<code class="docutils literal notranslate"><span class="pre">const</span></code>) vector&lt;double&gt; reference and a mutable vector&lt;double&gt; reference modeling the current iteration gradient vector (dF / dx_i, for all i parameters). This function must return a scalar double, the value of this function at the current input parameters. The optimizer also exposes a method for querying whether the current optimization strategy requires gradients or not. Parameterizing optimization strategies is left as a task for sub-types (things like initial parameters, max function evaluations, etc.).</p>
-<p><div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">optimizers</span><span class="o">::</span><span class="n">cobyla</span><span class="w"> </span><span class="n">opt</span><span class="p">;</span>
-<span class="n">assert</span><span class="p">(</span><span class="o">!</span><span class="n">opt</span><span class="p">.</span><span class="n">requiresGradients</span><span class="p">()</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="s">&quot;Cobyla algo does not use gradients.&quot;</span><span class="p">);</span>
-<span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">opt_val</span><span class="p">,</span><span class="w"> </span><span class="n">opt_params</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">opt</span><span class="p">.</span><span class="n">optimize</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span>
-<span class="w">              </span><span class="p">[</span><span class="o">&amp;</span><span class="p">](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">dx</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">                  </span><span class="p">...</span><span class="w"> </span><span class="n">use</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">modify</span><span class="w"> </span><span class="n">dx</span><span class="p">,</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">scalar</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="p">...</span>
-<span class="w">              </span><span class="p">}</span>
-<span class="w">            </span><span class="p">);</span>
-</pre></div>
-</div>
- </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9optimizer17requiresGradientsEv">
-<span class="target" id="classcudaq_1_1optimizer_1a872567d3394d98475967a00fdade1e7f"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">requiresGradients</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq9optimizer17requiresGradientsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Returns true if this optimization strategy requires gradients to achieve its optimization goals. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9optimizer8optimizeEKiRR20optimizable_function">
-<span class="target" id="classcudaq_1_1optimizer_1af1f7e250c72ce293529ba760f162d4eb"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq19optimization_resultE" title="cudaq::optimization_result"><span class="n"><span class="pre">optimization_result</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">optimize</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">dim</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq20optimizable_functionE" title="cudaq::optimizable_function"><span class="n"><span class="pre">optimizable_function</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">opt_function</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq9optimizer8optimizeEKiRR20optimizable_function" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Run the optimization strategy defined by concrete sub-type implementations. Takes the number of variational parameters, and a custom objective function that takes the function input parameters as a immutable (<code class="docutils literal notranslate"><span class="pre">const</span></code>) vector&lt;double&gt; reference and a mutable vector&lt;double&gt; reference modeling the current iteration gradient vector (dF / dx_i, for all i parameters). This function must return a scalar double, the value of this function at the current input parameters. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq20optimizable_functionE">
-<span class="target" id="classcudaq_1_1optimizable__function"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">optimizable_function</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq20optimizable_functionE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>An <a class="reference internal" href="#classcudaq_1_1optimizable__function"><span class="std std-ref">optimizable_function</span></a> wraps a user-provided objective function to be optimized. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq19optimization_resultE">
-<span class="target" id="optimizer_8h_1a93240fa136c0cb1c044af3126c958b92"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">optimization_result</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">tuple</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq19optimization_resultE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Typedef modeling the result of an optimization strategy, a double representing the optimal value and the corresponding optimal parameters. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5stateE">
-<span class="target" id="classcudaq_1_1state"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">state</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq5stateE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1state"><span class="std std-ref">cudaq::state</span></a> encapsulate backend simulation state vector or density matrix data. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5state5stateEP15SimulationState">
-<span class="target" id="classcudaq_1_1state_1ae07a132f3563b069e3e2acd494ec4eec"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">state</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">ptrToOwn</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5state5stateEP15SimulationState" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, takes the simulation data and owns it. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5state5stateERK5state">
-<span class="target" id="classcudaq_1_1state_1a4037eab50e004c821c32010a043461cc"></span><span class="sig-name descname"><span class="n"><span class="pre">state</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq5state5stateERK5state" title="cudaq::state::state"><span class="n"><span class="pre">state</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq5state5stateERK5state" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy constructor (default) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5stateaSERR5state">
-<span class="target" id="classcudaq_1_1state_1acbe39e2bb1d4d4180dd4055be938eeff"></span><a class="reference internal" href="#_CPPv4N5cudaq5stateE" title="cudaq::state"><span class="n"><span class="pre">state</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq5stateE" title="cudaq::state"><span class="n"><span class="pre">state</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5stateaSERR5state" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy assignment. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5stateixENSt6size_tE">
-<span class="target" id="classcudaq_1_1state_1a09a14ee2f7ea1ac49caca188341e8aa4"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">[]</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5stateixENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Convenience function for extracting from a known vector. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5stateclENSt6size_tENSt6size_tE">
-<span class="target" id="classcudaq_1_1state_1a9abced2d45ce495bf0bb3d4c0391a929"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">()</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">idx</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">jdx</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5stateclENSt6size_tENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Convenience function for extracting from a known matrix. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5stateclERKNSt16initializer_listINSt6size_tEEENSt6size_tE">
-<span class="target" id="classcudaq_1_1state_1a09276630f1773cfffc4346dd0df34057"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">()</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">initializer_list</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&amp;</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensorIdx</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5stateclERKNSt16initializer_listINSt6size_tEEENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>General extraction operator for state data. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state10get_tensorENSt6size_tE">
-<span class="target" id="classcudaq_1_1state_1aa59a231f12caabb305ed2053913290b1"></span><a class="reference internal" href="#_CPPv4N5cudaq6tensorE" title="cudaq::tensor"><span class="n"><span class="pre">tensor</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_tensor</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensorIdx</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state10get_tensorENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the tensor at the given index for this state representation. For state-vector and density matrix simulation states, there is just one tensor with rank 1 or 2 respectively. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state11get_tensorsEv">
-<span class="target" id="classcudaq_1_1state_1af9f27534930537d903acfe367e174745"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq6tensorE" title="cudaq::tensor"><span class="n"><span class="pre">tensor</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_tensors</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state11get_tensorsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return all tensors that represent this simulation state. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state15get_num_tensorsEv">
-<span class="target" id="classcudaq_1_1state_1ac7031054d0e32f7957ec9478f40d1497"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_num_tensors</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state15get_num_tensorsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of tensors that represent this state. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state14get_num_qubitsEv">
-<span class="target" id="classcudaq_1_1state_1a79d449c7e66ae1a83dacbf6ea2573a2c"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_num_qubits</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state14get_num_qubitsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of qubits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state13get_precisionEv">
-<span class="target" id="classcudaq_1_1state_1a72d54ecbe1db8b8f49865d14da424243"></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationStateE" title="cudaq::SimulationState"><span class="n"><span class="pre">SimulationState</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq15SimulationState9precisionE" title="cudaq::SimulationState::precision"><span class="n"><span class="pre">precision</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_precision</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state13get_precisionEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the underlying floating point precision for this state. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state9is_on_gpuEv">
-<span class="target" id="classcudaq_1_1state_1a47e0b8240490f3b5e6045c3baf936a69"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">is_on_gpu</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state9is_on_gpuEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if this a state on the GPU. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0ENK5cudaq5state7to_hostEvPNSt7complexI10ScalarTypeEENSt6size_tE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ScalarType</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1state_1aacdfdccf258f11a78270fcdba4a87005"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">to_host</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I0ENK5cudaq5state7to_hostEvPNSt7complexI10ScalarTypeEENSt6size_tE" title="cudaq::state::to_host::ScalarType"><span class="n"><span class="pre">ScalarType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">hostPtr</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">numElements</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4I0ENK5cudaq5state7to_hostEvPNSt7complexI10ScalarTypeEENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Copy this state from device to. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state4dumpEv">
-<span class="target" id="classcudaq_1_1state_1a8b3cca5e2ba7672a8fc23a92f33700ca"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state4dumpEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump the state to standard out. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq5state4dumpERNSt7ostreamE">
-<span class="target" id="classcudaq_1_1state_1aaffdb68ddd91dfe2527f3e906f4d53e5"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dump</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">os</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq5state4dumpERNSt7ostreamE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Dump the state to given output stream. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5state7overlapERK5state">
-<span class="target" id="classcudaq_1_1state_1ac52a47246232d55fff20665e2d8139da"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">overlap</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq5stateE" title="cudaq::state"><span class="n"><span class="pre">state</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5state7overlapERK5state" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the overlap of this state with the other one. For state vectors (pure states), it is computed as <code class="docutils literal notranslate"><span class="pre">|&lt;this</span> <span class="pre">|</span> <span class="pre">other&gt;|</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5state9amplitudeERKNSt6vectorIiEE">
-<span class="target" id="classcudaq_1_1state_1a17f056d04cecd168f83cfec31f25f1c5"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">amplitude</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">int</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">basisState</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5state9amplitudeERKNSt6vectorIiEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the amplitude of the given computational basis state. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5state10amplitudesERKNSt6vectorINSt6vectorIiEEEE">
-<span class="target" id="classcudaq_1_1state_1a3341e4701bfc271215631ac9a7c73c82"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">amplitudes</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">int</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">basisStates</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5state10amplitudesERKNSt6vectorINSt6vectorIiEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the amplitudes of the given list of computational basis states. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-static-functions">Public Static Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5state9from_dataERK10state_data">
-<span class="target" id="classcudaq_1_1state_1a2e9b7d521f974822246d180d43160d2e"></span><span class="k"><span class="pre">static</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq5stateE" title="cudaq::state"><span class="n"><span class="pre">state</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">from_data</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq10state_dataE" title="cudaq::state_data"><span class="n"><span class="pre">state_data</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">data</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5state9from_dataERK10state_data" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Create a new state from user-provided data. The data can be host or device data. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8gradientE">
-<span class="target" id="classcudaq_1_1gradient"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq8gradientE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1gradient"><span class="std std-ref">cudaq::gradient</span></a> represents a base type for all gradient strategies leveraged by variational algorithms. </p>
-<p>The <a class="reference internal" href="#classcudaq_1_1gradient"><span class="std std-ref">cudaq::gradient</span></a> tracks a std::function with signature void(std::vector&lt;double&gt;) representing the parameterized kernel ansatz. For ansatzes that do not follow this signature, a separate Argument Mapper must be provided which takes std::vector&lt;double&gt; to a tuple of custom function arguments. All gradient subtypes should inherit the base class constructors (using gradient::gradient), but provide a concrete implementation of <a class="reference internal" href="#classcudaq_1_1gradient_1a00ec8d88812a7d459ee134e4b5c9593c"><span class="std std-ref">gradient::compute()</span></a>. The compute method takes as input the current iteration parameters, a reference to the gradient vector which <a class="reference internal" href="#classcudaq_1_1gradient_1a00ec8d88812a7d459ee134e4b5c9593c"><span class="std std-ref">compute()</span></a> must update, the <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">cudaq::spin_op</span></a> for this variational algorithm, and the expected value of the <a class="reference internal" href="#classcudaq_1_1spin__op"><span class="std std-ref">spin_op</span></a> at the current iteration’s parameters. Subtypes can leverage the protected gradient::getExpectedValue() method to compute &lt;psi(x) | H | psi(x)&gt; at the provided set of variational parameters. </p>
-<p>Subclassed by <a class="reference internal" href="#classcudaq_1_1gradients_1_1central__difference"><span class="std std-ref">cudaq::gradients::central_difference</span></a>, <a class="reference internal" href="#classcudaq_1_1gradients_1_1forward__difference"><span class="std std-ref">cudaq::gradients::forward_difference</span></a>, <a class="reference internal" href="#classcudaq_1_1gradients_1_1parameter__shift"><span class="std std-ref">cudaq::gradients::parameter_shift</span></a></p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8gradient8gradientERRNSt8functionIFvNSt6vectorIdEEEEE">
-<span class="target" id="classcudaq_1_1gradient_1ab284b63287df8a9c5f30a10c472e77eb"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq8gradient8gradientERRNSt8functionIFvNSt6vectorIdEEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with prescribed signature. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8gradient8gradientEv">
-<span class="target" id="classcudaq_1_1gradient_1acc76a46b709b332d3475ce585abf2e99"></span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq8gradient8gradientEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Empty constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq8gradient8gradientER7KernelTRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradient_1adcc5f751cb413adec4a5baccd652a4d9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq8gradient8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradient::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq8gradient8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradient::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq8gradient8gradientER7KernelTRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0DpEN5cudaq8gradient7setArgsEvR13QuantumKernelDpRR4Args">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="sig-name descname"><span class="n"><span class="pre">Args</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradient_1ad768f534886884dd9b53efd61a6dfed4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setArgs</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0DpEN5cudaq8gradient7setArgsEvR13QuantumKernelDpRR4Args" title="cudaq::gradient::setArgs::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I0DpEN5cudaq8gradient7setArgsEvR13QuantumKernelDpRR4Args" title="cudaq::gradient::setArgs::Args"><span class="n"><span class="pre">Args</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">...</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">args</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0DpEN5cudaq8gradient7setArgsEvR13QuantumKernelDpRR4Args" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Take the quantum kernel and concrete arguments for all arguments except the first std::vector&lt;double&gt; argument, which is used for the variational parameters for the gradient. Serialize and save those arguments into this object. (Useful for NVQC.) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I0EN5cudaq8gradient9setKernelEvR13QuantumKernel">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradient_1afdec4982d0785dc116c1e63ccc4c1ce4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setKernel</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I0EN5cudaq8gradient9setKernelEvR13QuantumKernel" title="cudaq::gradient::setKernel::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN5cudaq8gradient9setKernelEvR13QuantumKernel" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the kernel after the gradient has been constructed. Use of this function requires that the kernel ONLY accept the variational parameters. It cannot have any non-variational parameters. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq8gradient8gradientER7KernelT">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq8gradient8gradientER7KernelT" title="cudaq::gradient::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradient_1a7470fe58b0990b6f097d476470a337ff"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq8gradient8gradientER7KernelT" title="cudaq::gradient::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq8gradient8gradientER7KernelT" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable that must have the prescribed call signature (void(std::vector&lt;double&gt;)) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq8gradient8gradientERR13QuantumKernelRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradient_1a1ed77d0851f57c219123cf612c096177"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq8gradient8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradient::gradient::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq8gradient8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradient::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq8gradient8gradientERR13QuantumKernelRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8gradient7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd">
-<span class="target" id="classcudaq_1_1gradient_1a00ec8d88812a7d459ee134e4b5c9593c"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">dx</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">h</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">funcAtX</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq8gradient7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the current iterations gradient vector and update the provided vector&lt;double reference (<code class="docutils literal notranslate"><span class="pre">dx</span></code>). </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8gradient7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd">
-<span class="target" id="classcudaq_1_1gradient_1ab8cb7ac40ff72370b2ac1f29da889518"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">func</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">funcAtX</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq8gradient7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the gradient vector for the provided objective function, <code class="docutils literal notranslate"><span class="pre">func</span></code>, at the given set of parameters, <code class="docutils literal notranslate"><span class="pre">x</span></code>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8gradient5cloneEv">
-<span class="target" id="classcudaq_1_1gradient_1a3cd5c1a4a12b19aa3078e49f9ecaa886"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">unique_ptr</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clone</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq8gradient5cloneEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Clone the object. Must be implemented by derived classes. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18central_differenceE">
-<span class="target" id="classcudaq_1_1gradients_1_1central__difference"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">central_difference</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq9gradients18central_differenceE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18central_difference5cloneEv">
-<span class="target" id="classcudaq_1_1gradients_1_1central__difference_1a687f4a1033a66fa3755c95a6d0e46c7c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">unique_ptr</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clone</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18central_difference5cloneEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Clone the object. Must be implemented by derived classes. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18central_difference7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd">
-<span class="target" id="classcudaq_1_1gradients_1_1central__difference_1a10744d416a2c6b72cc1932907761b079"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">dx</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">h</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">exp_h</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18central_difference7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the current iterations gradient vector and update the provided vector&lt;double reference (<code class="docutils literal notranslate"><span class="pre">dx</span></code>). </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18central_difference7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd">
-<span class="target" id="classcudaq_1_1gradients_1_1central__difference_1a972e199322d35bf761b46f5ccf563691"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">func</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">funcAtX</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18central_difference7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1gradients_1_1central__difference"><span class="std std-ref"><span class="pre">central_difference</span></span></a></code> gradient for the arbitrary function, <code class="docutils literal notranslate"><span class="pre">func</span></code>, passed in by the user. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18central_difference8gradientERRNSt8functionIFvNSt6vectorIdEEEEE">
-<span class="target" id="classcudaq_1_1gradients_1_1central__difference_1ab284b63287df8a9c5f30a10c472e77eb"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18central_difference8gradientERRNSt8functionIFvNSt6vectorIdEEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with prescribed signature. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18central_difference8gradientEv">
-<span class="target" id="classcudaq_1_1gradients_1_1central__difference_1acc76a46b709b332d3475ce585abf2e99"></span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18central_difference8gradientEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Empty constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelTRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1central__difference_1adcc5f751cb413adec4a5baccd652a4d9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradients::central_difference::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradients::central_difference::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelTRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelT">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelT" title="cudaq::gradients::central_difference::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1central__difference_1a7470fe58b0990b6f097d476470a337ff"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelT" title="cudaq::gradients::central_difference::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientER7KernelT" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable that must have the prescribed call signature (void(std::vector&lt;double&gt;)) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients18central_difference8gradientERR13QuantumKernelRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1central__difference_1a1ed77d0851f57c219123cf612c096177"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradients::central_difference::gradient::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradients::central_difference::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients18central_difference8gradientERR13QuantumKernelRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients15parameter_shiftE">
-<span class="target" id="classcudaq_1_1gradients_1_1parameter__shift"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">parameter_shift</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq9gradients15parameter_shiftE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients15parameter_shift5cloneEv">
-<span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1aa4a4d2be3b9c98c41376617c5a4dff51"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">unique_ptr</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clone</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients15parameter_shift5cloneEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Clone the object. Must be implemented by derived classes. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients15parameter_shift7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd">
-<span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1a83289c003b373ee92073b7327f250cb4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">dx</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">h</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">exp_h</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients15parameter_shift7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the current iterations gradient vector and update the provided vector&lt;double reference (<code class="docutils literal notranslate"><span class="pre">dx</span></code>). </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients15parameter_shift7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd">
-<span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1a5b92a24b1b7bfed20963a5e4affa0e75"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">func</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">funcAtX</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients15parameter_shift7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1gradients_1_1parameter__shift"><span class="std std-ref"><span class="pre">parameter_shift</span></span></a></code> gradient for the arbitrary function, <code class="docutils literal notranslate"><span class="pre">func</span></code>, passed in by the user. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients15parameter_shift8gradientERRNSt8functionIFvNSt6vectorIdEEEEE">
-<span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1ab284b63287df8a9c5f30a10c472e77eb"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq9gradients15parameter_shift8gradientERRNSt8functionIFvNSt6vectorIdEEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with prescribed signature. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients15parameter_shift8gradientEv">
-<span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1acc76a46b709b332d3475ce585abf2e99"></span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients15parameter_shift8gradientEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Empty constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelTRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1adcc5f751cb413adec4a5baccd652a4d9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradients::parameter_shift::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradients::parameter_shift::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelTRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelT">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelT" title="cudaq::gradients::parameter_shift::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1a7470fe58b0990b6f097d476470a337ff"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelT" title="cudaq::gradients::parameter_shift::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientER7KernelT" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable that must have the prescribed call signature (void(std::vector&lt;double&gt;)) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientERR13QuantumKernelRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1parameter__shift_1a1ed77d0851f57c219123cf612c096177"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradients::parameter_shift::gradient::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradients::parameter_shift::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients15parameter_shift8gradientERR13QuantumKernelRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18forward_differenceE">
-<span class="target" id="classcudaq_1_1gradients_1_1forward__difference"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">forward_difference</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq9gradients18forward_differenceE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the first order forward difference approximation for the gradient. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18forward_difference5cloneEv">
-<span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1aabbca1c2829aa67ed5902c9e7c4284cd"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">unique_ptr</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clone</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18forward_difference5cloneEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Clone the object. Must be implemented by derived classes. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18forward_difference7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd">
-<span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1a601410b8137564ce7fb76891322a2a7f"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">dx</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">h</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">funcAtX</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18forward_difference7computeERKNSt6vectorIdEERNSt6vectorIdEERK7spin_opd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1gradients_1_1forward__difference"><span class="std std-ref"><span class="pre">forward_difference</span></span></a></code> gradient. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18forward_difference7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd">
-<span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1a2b21090e517c7cec2b3d776e60f35419"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">compute</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">x</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">func</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">funcAtX</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18forward_difference7computeERKNSt6vectorIdEERKNSt8functionIFdNSt6vectorIdEEEEEd" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Compute the <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1gradients_1_1forward__difference"><span class="std std-ref"><span class="pre">forward_difference</span></span></a></code> gradient for the arbitrary function, <code class="docutils literal notranslate"><span class="pre">func</span></code>, passed in by the user. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18forward_difference8gradientERRNSt8functionIFvNSt6vectorIdEEEEE">
-<span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1ab284b63287df8a9c5f30a10c472e77eb"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18forward_difference8gradientERRNSt8functionIFvNSt6vectorIdEEEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with prescribed signature. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9gradients18forward_difference8gradientEv">
-<span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1acc76a46b709b332d3475ce585abf2e99"></span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq9gradients18forward_difference8gradientEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Empty constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelTRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1adcc5f751cb413adec4a5baccd652a4d9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradients::forward_difference::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelTRR10ArgsMapper" title="cudaq::gradients::forward_difference::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelTRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelT">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelT</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">enable_if_t</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">is_invocable_v</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelT" title="cudaq::gradients::forward_difference::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1a7470fe58b0990b6f097d476470a337ff"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelT" title="cudaq::gradients::forward_difference::gradient::KernelT"><span class="n"><span class="pre">KernelT</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientER7KernelT" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes a callable that must have the prescribed call signature (void(std::vector&lt;double&gt;)) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq9gradients18forward_difference8gradientERR13QuantumKernelRR10ArgsMapper">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuantumKernel</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">ArgsMapper</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classcudaq_1_1gradients_1_1forward__difference_1a1ed77d0851f57c219123cf612c096177"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gradient</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradients::forward_difference::gradient::QuantumKernel"><span class="n"><span class="pre">QuantumKernel</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">kernel</span></span>, <a class="reference internal" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientERR13QuantumKernelRR10ArgsMapper" title="cudaq::gradients::forward_difference::gradient::ArgsMapper"><span class="n"><span class="pre">ArgsMapper</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">argsMapper</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq9gradients18forward_difference8gradientERR13QuantumKernelRR10ArgsMapper" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor, takes the quantum kernel with non-standard signature the Argument Mapper function object that maps the parameters to concrete arguments for the quantum kernel. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-</section>
-<section id="platform">
-<h2>Platform<a class="headerlink" href="#platform" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPUE">
-<span class="target" id="classcudaq_1_1QPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8registryE" title="cudaq::registry"><span class="n"><span class="pre">registry</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4I0EN5cudaq8registry14RegisteredTypeE" title="cudaq::registry::RegisteredType"><span class="n"><span class="pre">RegisteredType</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq3QPUE" title="cudaq::QPU"><span class="n"><span class="pre">QPU</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A CUDA-Q <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> is an abstraction on the quantum processing unit which executes quantum kernel expressions. The <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> exposes certain information about the <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> being targeting, such as the number of available qubits, the logical ID for this <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> in a set of available QPUs, and its qubit connectivity. The <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> keeps track of an execution queue for enqueuing asynchronous tasks that execute quantum kernel expressions. The <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> also tracks the client-provided execution context to enable quantum kernel related tasks such as sampling and observation.</p>
-<p>This type is meant to be subtyped by concrete <a class="reference internal" href="#classcudaq_1_1quantum__platform"><span class="std std-ref">quantum_platform</span></a> subtypes. </p>
-<p>Subclassed by <a class="reference internal" href="#classcudaq_1_1BaseRemoteRESTQPU"><span class="std std-ref">cudaq::BaseRemoteRESTQPU</span></a>, <a class="reference internal" href="#classcudaq_1_1BaseRemoteSimulatorQPU"><span class="std std-ref">cudaq::BaseRemoteSimulatorQPU</span></a>, <a class="reference internal" href="#classcudaq_1_1OrcaRemoteRESTQPU"><span class="std std-ref">cudaq::OrcaRemoteRESTQPU</span></a></p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU3QPUEv">
-<span class="target" id="classcudaq_1_1QPU_1a6ede419124b2032208c082990a6113d2"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QPU</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU3QPUEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, initializes the execution queue. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU3QPUENSt6size_tE">
-<span class="target" id="classcudaq_1_1QPU_1ab3fcfc8b6722f3ee1d28e0e84138d3ff"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QPU</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">_qpuId</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU3QPUENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The constructor, sets the current <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> Id and initializes the execution queue </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU3QPUERR3QPU">
-<span class="target" id="classcudaq_1_1QPU_1a3b371e46e9435f8c563870e640d38e1a"></span><span class="sig-name descname"><span class="n"><span class="pre">QPU</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq3QPU3QPUERR3QPU" title="cudaq::QPU::QPU"><span class="n"><span class="pre">QPU</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPU3QPUERR3QPU" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Move constructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPUD0Ev">
-<span class="target" id="classcudaq_1_1QPU_1a3a66227bdbd3c46981cd068ae1814057"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">~QPU</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPUD0Ev" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The destructor. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU5setIdENSt6size_tE">
-<span class="target" id="classcudaq_1_1QPU_1a839ee0e4a992dc05855f5c31892b0ad7"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setId</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">_qpuId</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU5setIdENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the current <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> Id. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq3QPU20getExecutionThreadIdEv">
-<span class="target" id="classcudaq_1_1QPU_1a79378c8539dd40594956b157b31d469e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">thread</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">id</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getExecutionThreadId</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq3QPU20getExecutionThreadIdEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get id of the thread this <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>’s queue executes on. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU12getNumQubitsEv">
-<span class="target" id="classcudaq_1_1QPU_1aed29436f93c96339d245b906862e0443"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getNumQubits</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU12getNumQubitsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of qubits. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU15getConnectivityEv">
-<span class="target" id="classcudaq_1_1QPU_1a783a0450e73f35edad0322e1c07bc702"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">auto</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getConnectivity</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU15getConnectivityEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the qubit connectivity. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU11isSimulatorEv">
-<span class="target" id="classcudaq_1_1QPU_1a7c1850650ce0720da66de1e3e4b460df"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">isSimulator</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU11isSimulatorEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Is this <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> a simulator ? </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU27supportsConditionalFeedbackEv">
-<span class="target" id="classcudaq_1_1QPU_1ac9fdfb6b9fd22acc4207c48a0162c480"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">supportsConditionalFeedback</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU27supportsConditionalFeedbackEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return whether this <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> has conditional feedback support. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq3QPU21getRemoteCapabilitiesEv">
-<span class="target" id="classcudaq_1_1QPU_1ada538a4355a21ae9d4920238bed65f30"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq18RemoteCapabilitiesE" title="cudaq::RemoteCapabilities"><span class="n"><span class="pre">RemoteCapabilities</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getRemoteCapabilities</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq3QPU21getRemoteCapabilitiesEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the remote capabilities for this platform. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU8setShotsEi">
-<span class="target" id="classcudaq_1_1QPU_1a0a2adbf1c6212a49b36ed30f06626982"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setShots</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">_nShots</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU8setShotsEi" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Base class handling of shots is do-nothing, subclasses can handle as they wish </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU10isEmulatedEv">
-<span class="target" id="classcudaq_1_1QPU_1a04df7e40bc230f5a27afcf72ac8cf23f"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">isEmulated</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU10isEmulatedEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Is this a local emulator of a remote <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>? </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU7enqueueER11QuantumTask">
-<span class="target" id="classcudaq_1_1QPU_1a369c5f7ebc7d5ff79e2e55210bd1ec1b"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enqueue</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq11QuantumTaskE" title="cudaq::QuantumTask"><span class="n"><span class="pre">QuantumTask</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">task</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPU7enqueueER11QuantumTask" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Enqueue a quantum task on the asynchronous execution queue. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU19setExecutionContextEP16ExecutionContext">
-<span class="target" id="classcudaq_1_1QPU_1a796e2d78252c0b990bcbdda4512cad14"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setExecutionContext</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N5cudaq16ExecutionContextE" title="cudaq::ExecutionContext"><span class="n"><span class="pre">ExecutionContext</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">context</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPU19setExecutionContextEP16ExecutionContext" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the execution context, meant for subtype specification. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU21resetExecutionContextEv">
-<span class="target" id="classcudaq_1_1QPU_1af4a531bc79686767ffe8b412c22567e2"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">resetExecutionContext</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPU21resetExecutionContextEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Reset the execution context, meant for subtype specification. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU12launchKernelERKNSt6stringE15KernelThunkTypePvNSt8uint64_tENSt8uint64_tERKNSt6vectorIPvEE">
-<span class="target" id="classcudaq_1_1QPU_1a52aad45bea52aaec4dcdf03506dd61f1"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq21KernelThunkResultTypeE" title="cudaq::KernelThunkResultType"><span class="n"><span class="pre">KernelThunkResultType</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">launchKernel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">name</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq15KernelThunkTypeE" title="cudaq::KernelThunkType"><span class="n"><span class="pre">KernelThunkType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">kernelFunc</span></span>, <span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">args</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">uint64_t</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">uint64_t</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">rawArgs</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N5cudaq3QPU12launchKernelERKNSt6stringE15KernelThunkTypePvNSt8uint64_tENSt8uint64_tERKNSt6vectorIPvEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Launch the kernel with given name (to extract its Quake representation). The raw function pointer is also provided, as are the runtime arguments, as a struct-packed void pointer and its corresponding size. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU12launchKernelERKNSt6stringERKNSt6vectorIPvEE">
-<span class="target" id="classcudaq_1_1QPU_1a5a02ed81eab2209910562c4161660404"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">launchKernel</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">name</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">rawArgs</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU12launchKernelERKNSt6stringERKNSt6vectorIPvEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Launch the kernel with given name and argument arrays. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU29launchSerializedCodeExecutionERKNSt6stringERN5cudaq30SerializedCodeExecutionContextE">
-<span class="target" id="classcudaq_1_1QPU_1a1d10a4aec2af11778d55f97cd959243f"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">launchSerializedCodeExecution</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">name</span></span>, <a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq30SerializedCodeExecutionContextE" title="cudaq::SerializedCodeExecutionContext"><span class="n"><span class="pre">SerializedCodeExecutionContext</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">serializeCodeExecutionObject</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU29launchSerializedCodeExecutionERKNSt6stringERN5cudaq30SerializedCodeExecutionContextE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Launch serialized code for remote execution. Subtypes that support this should override this function. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3QPU15onRandomSeedSetENSt6size_tE">
-<span class="target" id="classcudaq_1_1QPU_1ab4915615be5d425122d93d59ac1a22cd"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">onRandomSeedSet</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">seed</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3QPU15onRandomSeedSetENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Notify the <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> that a new random seed value is set. By default do nothing, let subclasses override. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq17BaseRemoteRESTQPUE">
-<span class="target" id="classcudaq_1_1BaseRemoteRESTQPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">BaseRemoteRESTQPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3QPUE" title="cudaq::QPU"><span class="n"><span class="pre">QPU</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq17BaseRemoteRESTQPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Subclassed by <a class="reference internal" href="#classcudaq_1_1FermioniqBaseQPU"><span class="std std-ref">cudaq::FermioniqBaseQPU</span></a>, <a class="reference internal" href="#classcudaq_1_1QuEraBaseQPU"><span class="std std-ref">cudaq::QuEraBaseQPU</span></a></p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq22BaseRemoteSimulatorQPUE">
-<span class="target" id="classcudaq_1_1BaseRemoteSimulatorQPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">BaseRemoteSimulatorQPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3QPUE" title="cudaq::QPU"><span class="n"><span class="pre">QPU</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq22BaseRemoteSimulatorQPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Subclassed by <a class="reference internal" href="#classcudaq_1_1BaseNvcfSimulatorQPU"><span class="std std-ref">cudaq::BaseNvcfSimulatorQPU</span></a></p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq20BaseNvcfSimulatorQPUE">
-<span class="target" id="classcudaq_1_1BaseNvcfSimulatorQPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">BaseNvcfSimulatorQPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq22BaseRemoteSimulatorQPUE" title="cudaq::BaseRemoteSimulatorQPU"><span class="n"><span class="pre">BaseRemoteSimulatorQPU</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq20BaseNvcfSimulatorQPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Implementation of base <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> subtype that submits simulation request to NVCF. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16FermioniqBaseQPUE">
-<span class="target" id="classcudaq_1_1FermioniqBaseQPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">FermioniqBaseQPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq17BaseRemoteRESTQPUE" title="cudaq::BaseRemoteRESTQPU"><span class="n"><span class="pre">BaseRemoteRESTQPU</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq16FermioniqBaseQPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1FermioniqBaseQPU"><span class="std std-ref"><span class="pre">FermioniqBaseQPU</span></span></a></code> is a <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> that allows users to. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq17OrcaRemoteRESTQPUE">
-<span class="target" id="classcudaq_1_1OrcaRemoteRESTQPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">OrcaRemoteRESTQPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3QPUE" title="cudaq::QPU"><span class="n"><span class="pre">QPU</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq17OrcaRemoteRESTQPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1OrcaRemoteRESTQPU"><span class="std std-ref">OrcaRemoteRESTQPU</span></a> is a subtype of <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> that enables the execution of CUDA-Q kernels on remotely hosted quantum computing services via a REST Client / Server interaction. This type is meant to be general enough to support any remotely hosted service. Moreover, this <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> handles launching kernels under the Execution Context that includes sampling via synchronous client invocations. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq12QuEraBaseQPUE">
-<span class="target" id="classcudaq_1_1QuEraBaseQPU"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">QuEraBaseQPU</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq17BaseRemoteRESTQPUE" title="cudaq::BaseRemoteRESTQPU"><span class="n"><span class="pre">BaseRemoteRESTQPU</span></span></a><a class="headerlink" href="#_CPPv4N5cudaq12QuEraBaseQPUE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <code class="docutils literal notranslate"><a class="reference internal" href="#classcudaq_1_1QuEraBaseQPU"><span class="std std-ref"><span class="pre">QuEraBaseQPU</span></span></a></code> is a <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> that allows users to. </p>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platformE">
-<span class="target" id="classcudaq_1_1quantum__platform"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">quantum_platform</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platformE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1quantum__platform"><span class="std std-ref">quantum_platform</span></a> corresponds to a specific quantum architecture. The <a class="reference internal" href="#classcudaq_1_1quantum__platform"><span class="std std-ref">quantum_platform</span></a> exposes a public API for programmers to query specific information about the targeted QPU(s) (e.g. number of qubits, qubit connectivity, etc.). This type is meant to be subclassed for concrete realizations of quantum platforms, which are intended to populate this platformQPUs member of this base class. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform12connectivityEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1aa0162be5a19283a7fe9a933ff93425c2"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq17QubitConnectivityE" title="cudaq::QubitConnectivity"><span class="n"><span class="pre">QubitConnectivity</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">connectivity</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform12connectivityEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Fetch the connectivity info. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform14get_num_qubitsEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a3daf9ab69de7ba94d639043de23ba977"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_num_qubits</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform14get_num_qubitsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get the number of qubits for the current <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform26supports_task_distributionEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1aa3371a29fdf210c5a0831f199c2604a7"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">supports_task_distribution</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform26supports_task_distributionEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if this platform exposes multiple QPUs and supports parallel distribution of quantum tasks. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform14get_num_qubitsENSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a01da4519c0c507cd59cfea0e7f493608"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_num_qubits</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform14get_num_qubitsENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get the number of qubits for the <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> with ID qpu_id. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform9get_shotsEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a73c885b661d50e6fc7315270846facb8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">optional</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">int</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_shots</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform9get_shotsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Getter for the shots. This will be deprecated once <code class="docutils literal notranslate"><span class="pre">set_shots</span></code> and <code class="docutils literal notranslate"><span class="pre">clear_shots</span></code> are removed. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform9set_shotsEi">
-<span class="target" id="classcudaq_1_1quantum__platform_1a102472165450ce351417e897d813b9f3"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">set_shots</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">numShots</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform9set_shotsEi" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Setter for the shots. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform11clear_shotsEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a996c2cc3b40e8c531ad1504af1d212de"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">clear_shots</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform11clear_shotsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Reset shots. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform12set_exec_ctxEPN5cudaq16ExecutionContextENSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a6f25ae9f5846cf99e58794b604a1b0d2"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">set_exec_ctx</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq16ExecutionContextE" title="cudaq::ExecutionContext"><span class="n"><span class="pre">ExecutionContext</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">ctx</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform12set_exec_ctxEPN5cudaq16ExecutionContextENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Specify the execution context for this platform. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform12get_exec_ctxEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a96f71234cc01a1def47d0093e1315e61"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq16ExecutionContextE" title="cudaq::ExecutionContext"><span class="n"><span class="pre">ExecutionContext</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">get_exec_ctx</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform12get_exec_ctxEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the current execution context. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform14reset_exec_ctxENSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1ac98cd229d35948309412a2139ec5fc80"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">reset_exec_ctx</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform14reset_exec_ctxENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Reset the execution context for this platform. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform8num_qpusEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a6c795a09a2880fc7d7e973fc40c9f976"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">num_qpus</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform8num_qpusEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get the number of QPUs available with this platform. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform12is_simulatorEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a2cbc4b549fe8a3e1e9c8edc1193135a3"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">is_simulator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform12is_simulatorEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return whether this platform is simulating the architecture. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform29supports_conditional_feedbackEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1ac477ee8827975113e29a01fc638bcd5a"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">supports_conditional_feedback</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform29supports_conditional_feedbackEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return whether the <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> has conditional feedback support. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform4nameEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a16b8cb93fb7a325cc1bd78076c832674"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">name</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform4nameEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The name of the platform, which also corresponds to the name of the platform file. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform15get_current_qpuEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1ad6cc6e01d40b010e8ae07e0332c61656"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_current_qpu</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform15get_current_qpuEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get the ID of the current <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform15set_current_qpuEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a67006ab2622bc51fa3f92310d65137f1"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">set_current_qpu</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">device_id</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform15set_current_qpuEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the current <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> via its device ID. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform9is_remoteEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a61163027d2aa2e064e0d1ff2e65e4d5a"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">is_remote</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpuId</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform9is_remoteEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if the <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> is remote. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform11is_emulatedEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1aaa21661369c0930137d6e6c0f4d98ec5"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">is_emulated</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpuId</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform11is_emulatedEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a> is locally emulating a remote <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform9set_noiseEPK11noise_model">
-<span class="target" id="classcudaq_1_1quantum__platform_1a6e798c291d46aff40ce8fdad40e6fb90"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">set_noise</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq11noise_modelE" title="cudaq::noise_model"><span class="n"><span class="pre">noise_model</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">model</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform9set_noiseEPK11noise_model" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the noise model for future invocations of quantum kernels. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4NK5cudaq16quantum_platform23get_remote_capabilitiesEKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a1459a03708bb0236c804148fb6775cbf"></span><a class="reference internal" href="#_CPPv4N5cudaq18RemoteCapabilitiesE" title="cudaq::RemoteCapabilities"><span class="n"><span class="pre">RemoteCapabilities</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">get_remote_capabilities</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpuId</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK5cudaq16quantum_platform23get_remote_capabilitiesEKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get the remote capabilities (only applicable for remote platforms) </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform11reset_noiseEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a39b49eb99f389ffbf2d6aa64698e6110"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">reset_noise</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform11reset_noiseEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Turn off any noise models. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform16enqueueAsyncTaskEKNSt6size_tER19KernelExecutionTask">
-<span class="target" id="classcudaq_1_1quantum__platform_1a3d25480de207ff5d5ebffb0c93551470"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">future</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enqueueAsyncTask</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span>, <a class="reference internal" href="#_CPPv4N5cudaq19KernelExecutionTaskE" title="cudaq::KernelExecutionTask"><span class="n"><span class="pre">KernelExecutionTask</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform16enqueueAsyncTaskEKNSt6size_tER19KernelExecutionTask" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Enqueue an asynchronous sampling task. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform16enqueueAsyncTaskEKNSt6size_tERNSt8functionIFvvEEE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a1a72901becdfed7bca93b52a0805534b"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enqueueAsyncTask</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">f</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform16enqueueAsyncTaskEKNSt6size_tERNSt8functionIFvvEEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Enqueue a general task that runs on the specified <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform9launchVQEEKNSt6stringEPKvPN5cudaq8gradientEN5cudaq7spin_opERN5cudaq9optimizerEKiKNSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a100d2ad5ac344001c5842a32e4788649"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">launchVQE</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">kernelName</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">kernelArgs</span></span>, <a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq8gradientE" title="cudaq::gradient"><span class="n"><span class="pre">gradient</span></span></a><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">gradient</span></span>, <a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq7spin_opE" title="cudaq::spin_op"><span class="n"><span class="pre">spin_op</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">H</span></span>, <a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq9optimizerE" title="cudaq::optimizer"><span class="n"><span class="pre">optimizer</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">optimizer</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n_params</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shots</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform9launchVQEEKNSt6stringEPKvPN5cudaq8gradientEN5cudaq7spin_opERN5cudaq9optimizerEKiKNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Launch a VQE operation on the platform. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform16setTargetBackendERKNSt6stringE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a237421abcf74b7ac220aa84f6fccc3d2"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setTargetBackend</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">name</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform16setTargetBackendERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the target backend, by default do nothing, let subclasses override. </p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>name</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform15onRandomSeedSetENSt6size_tE">
-<span class="target" id="classcudaq_1_1quantum__platform_1af1694e06135ecdfab51f20ecc87f7ee8"></span><span class="k"><span class="pre">virtual</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">onRandomSeedSet</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">seed</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform15onRandomSeedSetENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Called by the runtime to notify that a new random seed value is set. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform14resetLogStreamEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a02bbc42f21f59ca9fa992db36e9dfd7b"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">resetLogStream</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform14resetLogStreamEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Turn off any custom logging stream. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform12getLogStreamEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1ab77a8ffa73c4f4097f6374095e96bd1f"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">getLogStream</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform12getLogStreamEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Get the stream for info logging. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform12setLogStreamERNSt7ostreamE">
-<span class="target" id="classcudaq_1_1quantum__platform_1a0939ab8f5192e22be54aa85f741f9c49"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">setLogStream</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ostream</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">logStream</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform12setLogStreamERNSt7ostreamE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set the info logging stream. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-static-functions">Public Static Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq16quantum_platform14list_platformsEv">
-<span class="target" id="classcudaq_1_1quantum__platform_1a75f4168edb243d52ca8df2e78ad905f9"></span><span class="k"><span class="pre">static</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">list_platforms</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq16quantum_platform14list_platformsEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>List all available platforms. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18RemoteCapabilitiesE">
-<span class="target" id="structcudaq_1_1RemoteCapabilities"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">RemoteCapabilities</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq18RemoteCapabilitiesE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A structure of boolean fields to indicate whether a given remote server has specific capabilities. </p>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18RemoteCapabilities18RemoteCapabilitiesEb">
-<span class="target" id="structcudaq_1_1RemoteCapabilities_1a436f02286cef89972c62bc8e3cc98122"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">RemoteCapabilities</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">initValue</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq18RemoteCapabilities18RemoteCapabilitiesEb" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Constructor that broadcasts <code class="docutils literal notranslate"><span class="pre">initValue</span></code> to all fields. </p>
-</dd></dl>
-
-</div>
-<div class="breathe-sectiondef docutils container">
-<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18RemoteCapabilities12stateOverlapE">
-<span class="target" id="structcudaq_1_1RemoteCapabilities_1a534e4bef1df86badcf37620ec8e41aec"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">stateOverlap</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq18RemoteCapabilities12stateOverlapE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>True if the remote can perform state overlap operations. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18RemoteCapabilities18serializedCodeExecE">
-<span class="target" id="structcudaq_1_1RemoteCapabilities_1a59e51afc4c9baeefbad76528f0291857"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">serializedCodeExec</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq18RemoteCapabilities18serializedCodeExecE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>True if the remote can perform serialized code execution (raw Python commands). </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18RemoteCapabilities3vqeE">
-<span class="target" id="structcudaq_1_1RemoteCapabilities_1a9747aef4cba0c08f8a8c762848663c64"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">vqe</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq18RemoteCapabilities3vqeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>True if the remote can perform an entire VQE operation without and back-and-forth client/server communications. </p>
-</dd></dl>
-
-<dl class="cpp var">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18RemoteCapabilities17isRemoteSimulatorE">
-<span class="target" id="structcudaq_1_1RemoteCapabilities_1ae0e06cc1b438c44d0751a43688407a8e"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">isRemoteSimulator</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N5cudaq18RemoteCapabilities17isRemoteSimulatorE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>True if execution is performed on a remote simulator - i.e. IR generation (including synthesis) and execution are performed in different processes. </p>
-</dd></dl>
-
-</div>
-</dd></dl>
-
-<dl class="cpp class">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq30SerializedCodeExecutionContextE">
-<span class="target" id="classcudaq_1_1SerializedCodeExecutionContext"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">SerializedCodeExecutionContext</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq30SerializedCodeExecutionContextE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The <a class="reference internal" href="#classcudaq_1_1SerializedCodeExecutionContext"><span class="std std-ref">SerializedCodeExecutionContext</span></a> is an abstraction to indicate how a serialized code should be executed. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11QuantumTaskE">
-<span class="target" id="QuantumExecutionQueue_8h_1a234ec4683d248518f1c276dc20fc3472"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">QuantumTask</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq11QuantumTaskE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The QuantumTask is ultimately what gets added to the execution queue. It is meant to wrap any Sampling or Observe task with an appropriate std::promise instance being provided and set. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq17QubitConnectivityE">
-<span class="target" id="quantum__platform_8h_1a104e794b18a96a94b7b3ee6fbd96698d"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">QubitConnectivity</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq9QubitEdgeE" title="cudaq::QubitEdge"><span class="n"><span class="pre">QubitEdge</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq17QubitConnectivityE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9QubitEdgeE">
-<span class="target" id="quantum__platform_8h_1aa2ee059741b84fd4577879dad8e672ef"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">QubitEdge</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">pair</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq9QubitEdgeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Typedefs for defining the connectivity structure of a <a class="reference internal" href="#classcudaq_1_1QPU"><span class="std std-ref">QPU</span></a>. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq19KernelExecutionTaskE">
-<span class="target" id="quantum__platform_8h_1a2126c891aff3a4b8d4e49df0bdac3a78"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">KernelExecutionTask</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq19KernelExecutionTaskE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A sampling tasks takes no input arguments and returns a <a class="reference internal" href="#classcudaq_1_1sample__result"><span class="std std-ref">sample_result</span></a> instance. </p>
-</dd></dl>
-
-<dl class="cpp struct">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq21KernelThunkResultTypeE">
-<span class="target" id="structcudaq_1_1KernelThunkResultType"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">KernelThunkResultType</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq21KernelThunkResultTypeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>A kernel may return results dynamically if the size of the result is not a constant at compile-time. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15KernelThunkTypeE">
-<span class="target" id="ThunkInterface_8h_1a94674b30c58e63bc8a5ad3d9f24c3f71"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">KernelThunkType</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N5cudaq21KernelThunkResultTypeE" title="cudaq::KernelThunkResultType"><span class="n"><span class="pre">KernelThunkResultType</span></span></a><span class="w"> </span><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">)</span></span><span class="p"><span class="pre">(</span></span><span class="kt"><span class="pre">void</span></span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="p"><span class="pre">)</span></span><a class="headerlink" href="#_CPPv4N5cudaq15KernelThunkTypeE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The universal signature of a kernel thunk. </p>
-</dd></dl>
-
-</section>
-<section id="utilities">
-<h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7complexE">
-<span class="target" id="host__config_8h_1aed5a2a46851a0e70522e1f2ff4201094"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">complex</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">complex</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4N5cudaq4realE" title="cudaq::real"><span class="n"><span class="pre">real</span></span></a><span class="p"><span class="pre">&gt;</span></span><a class="headerlink" href="#_CPPv4N5cudaq7complexE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4realE">
-<span class="target" id="host__config_8h_1a4b7ad2191c1c3ad1233eb18016821c99"></span><span class="k"><span class="pre">using</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">real</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="kt"><span class="pre">double</span></span><a class="headerlink" href="#_CPPv4N5cudaq4realE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq5rangeENSt6size_tE">
-<span class="target" id="cudaq__utils_8h_1afba25b385ea0f6fa692a7c4baad5f4bc"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">range</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">N</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq5rangeENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return a vector of unsigned integers. The first element is zero, and the remaining elements are all values incremented by 1 to the total size value provided (exclusive). </p>
-</dd></dl>
-
-</section>
-<section id="namespaces">
-<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this heading">¶</a></h2>
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv45cudaq">
-<span class="target" id="namespacecudaq"></span><span class="k"><span class="pre">namespace</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">cudaq</span></span></span><a class="headerlink" href="#_CPPv45cudaq" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq18num_available_gpusEv">
-<span class="target" id="cudaq_8h_1a1f10ecfee89d8754d4e899d178aa1040"></span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">num_available_gpus</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq18num_available_gpusEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>The number of available GPUs. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq15set_random_seedENSt6size_tE">
-<span class="target" id="cudaq_8h_1a0635ad8b98868ed14865bab138e23ff0"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">set_random_seed</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">seed</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq15set_random_seedENSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set a seed for any random number generators used in backend simulations. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq9set_noiseERKN5cudaq11noise_modelE">
-<span class="target" id="cudaq_8h_1a11add49cd36f05bfd240d84a35678868"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">set_noise</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq11noise_modelE" title="cudaq::noise_model"><span class="n"><span class="pre">noise_model</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">model</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq9set_noiseERKN5cudaq11noise_modelE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Set a custom noise model for simulation. The caller must also call <code class="docutils literal notranslate"><span class="pre">cudaq::unset_noise</span></code> before <code class="docutils literal notranslate"><span class="pre">model</span></code> gets deallocated or goes out of scope. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq11unset_noiseEv">
-<span class="target" id="cudaq_8h_1afbecd5e530b3ef32c7872b87541c0a43"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">unset_noise</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq11unset_noiseEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Remove an existing noise model from simulation. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq7detailsE">
-<span class="target" id="namespacecudaq_1_1details"></span><span class="k"><span class="pre">namespace</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">details</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq7detailsE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq8registryE">
-<span class="target" id="namespacecudaq_1_1registry"></span><span class="k"><span class="pre">namespace</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">registry</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq8registryE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpiE">
-<span class="target" id="namespacecudaq_1_1mpi"></span><span class="k"><span class="pre">namespace</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">mpi</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq3mpiE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi10initializeEv">
-<span class="target" id="cudaq_8h_1a9d165737129a8ca7423ce1e0b0c9ca7e"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">initialize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi10initializeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Initialize MPI if available. This function is a no-op if there CUDA-Q has not been built against MPI. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi10initializeEiPPc">
-<span class="target" id="cudaq_8h_1a6c944ecd26dfb16504034fb023a04a99"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">initialize</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">argc</span></span>, <span class="kt"><span class="pre">char</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">argv</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi10initializeEiPPc" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Initialize MPI if available. This function is a no-op if there CUDA-Q has not been built against MPI. Takes program arguments as input. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi14is_initializedEv">
-<span class="target" id="cudaq_8h_1a2c7d083e6db96bf244206e1da6f44774"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">is_initialized</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi14is_initializedEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return true if MPI is already initialized, false otherwise. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi8finalizeEv">
-<span class="target" id="cudaq_8h_1adbce8f0a621b7c6073e3f358d452b5ed"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">finalize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi8finalizeEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Finalize MPI. This function is a no-op if there CUDA-Q has not been built against MPI. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi4rankEv">
-<span class="target" id="cudaq_8h_1ae314e6dc9f293eebe6b9dcb44efef7c3"></span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">rank</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi4rankEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the rank of the calling process. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi9num_ranksEv">
-<span class="target" id="cudaq_8h_1ae3a3c06ca899063fbabb0f0c936d3c41"></span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">num_ranks</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi9num_ranksEv" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Return the number of MPI ranks. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi10all_gatherERNSt6vectorIdEERKNSt6vectorIdEE">
-<span class="target" id="cudaq_8h_1aaa5aebe9903c057fa3f1ae8fce98a50b"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">all_gather</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">global</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">local</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi10all_gatherERNSt6vectorIdEERKNSt6vectorIdEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Gather all vector data (floating point numbers) locally into the provided global vector. </p>
-<p>Global vector must be sized to fit all vector elements coming from individual ranks. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi10all_gatherERNSt6vectorIiEERKNSt6vectorIiEE">
-<span class="target" id="cudaq_8h_1ac382bb3637b38312a88615b20a4f8b3f"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">all_gather</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">int</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">global</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">int</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">local</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi10all_gatherERNSt6vectorIiEERKNSt6vectorIiEE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Gather all vector data (integers) locally into the provided global vector. </p>
-<p>Global vector must be sized to fit all vector elements coming from individual ranks. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK4Func">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">T</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Func</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="observe_8h_1a872bbab143f7cd999c07290b023ecb81"></span><a class="reference internal" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK4Func" title="cudaq::mpi::all_reduce::T"><span class="n"><span class="pre">T</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">all_reduce</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK4Func" title="cudaq::mpi::all_reduce::T"><span class="n"><span class="pre">T</span></span></a><span class="p"><span class="pre">&amp;</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK4Func" title="cudaq::mpi::all_reduce::Func"><span class="n"><span class="pre">Func</span></span></a><span class="p"><span class="pre">&amp;</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK4Func" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK14BinaryFunction">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">T</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">BinaryFunction</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="cudaq_8h_1acf510f9fe029c39b4d475c75fb876dc3"></span><a class="reference internal" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK14BinaryFunction" title="cudaq::mpi::all_reduce::T"><span class="n"><span class="pre">T</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">all_reduce</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK14BinaryFunction" title="cudaq::mpi::all_reduce::T"><span class="n"><span class="pre">T</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">localValue</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK14BinaryFunction" title="cudaq::mpi::all_reduce::BinaryFunction"><span class="n"><span class="pre">BinaryFunction</span></span></a><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">function</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN5cudaq3mpi10all_reduceE1TRK1TRK14BinaryFunction" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Reduce all values across ranks with the specified binary function. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi9broadcastERNSt6vectorIdEEi">
-<span class="target" id="cudaq_8h_1a16e7663348b7ff4e38d5a2177881c232"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">broadcast</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">data</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">rootRank</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi9broadcastERNSt6vectorIdEEi" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Broadcast a vector from a process (rootRank) to all other processes. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq3mpi9broadcastERNSt6stringEi">
-<span class="target" id="cudaq_8h_1abf82bc930287d1b8f8411c98bb38a384"></span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq3mpiE" title="cudaq::mpi"><span class="n"><span class="pre">mpi</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">broadcast</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">data</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">rootRank</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq3mpi9broadcastERNSt6stringEi" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Broadcast a string from a process (rootRank) to all other processes. </p>
-</dd></dl>
-
-<dl class="cpp type">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4orcaE">
-<span class="target" id="namespacecudaq_1_1orca"></span><span class="k"><span class="pre">namespace</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">orca</span></span></span><a class="headerlink" href="#_CPPv4N5cudaq4orcaE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4orca6sampleERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEEiNSt6size_tE">
-<span class="target" id="orca__qpu_8h_1a54f956b7c787432c3ae17f134115962e"></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq4orcaE" title="cudaq::orca"><span class="n"><span class="pre">orca</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">input_state</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">loop_lengths</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">bs_angles</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n_samples</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">10000</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4orca6sampleERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEEiNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4orca6sampleERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEERNSt6vectorIdEEiNSt6size_tE">
-<span class="target" id="orca__qpu_8h_1aeec6022fc6de40baecc78ee8373888e6"></span><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq13sample_resultE" title="cudaq::sample_result"><span class="n"><span class="pre">sample_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq4orcaE" title="cudaq::orca"><span class="n"><span class="pre">orca</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">input_state</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">loop_lengths</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">bs_angles</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">ps_angles</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n_samples</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">10000</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4orca6sampleERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEERNSt6vectorIdEEiNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd><p>Implementation of the sample method of the cudaq::orca namespace. </p>
-</dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4orca12sample_asyncERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEEiNSt6size_tE">
-<span class="target" id="orca__qpu_8h_1a071cbf531541a00d1e37f19d14d52a84"></span><a class="reference internal" href="#_CPPv4N5cudaq19async_sample_resultE" title="cudaq::async_sample_result"><span class="n"><span class="pre">async_sample_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq4orcaE" title="cudaq::orca"><span class="n"><span class="pre">orca</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample_async</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">input_state</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">loop_lengths</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">bs_angles</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n_samples</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">10000</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4orca12sample_asyncERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEEiNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
-<dd></dd></dl>
-
-<dl class="cpp function">
-<dt class="sig sig-object cpp" id="_CPPv4N5cudaq4orca12sample_asyncERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEERNSt6vectorIdEEiNSt6size_tE">
-<span class="target" id="orca__qpu_8h_1a76a830568c0bad0a5ddcd4007de6281a"></span><a class="reference internal" href="#_CPPv4N5cudaq19async_sample_resultE" title="cudaq::async_sample_result"><span class="n"><span class="pre">async_sample_result</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><a class="reference internal" href="#_CPPv45cudaq" title="cudaq"><span class="n"><span class="pre">cudaq</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N5cudaq4orcaE" title="cudaq::orca"><span class="n"><span class="pre">orca</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">sample_async</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">input_state</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">loop_lengths</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">bs_angles</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">ps_angles</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">n_samples</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">10000</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">qpu_id</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N5cudaq4orca12sample_asyncERNSt6vectorINSt6size_tEEERNSt6vectorINSt6size_tEEERNSt6vectorIdEERNSt6vectorIdEEiNSt6size_tE" title="Permalink to this definition">¶</a><br /></dt>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-</li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../api.html">   API Reference</a><ul class="current">
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-<li class="toctree-l3"><a class="reference internal" href="cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="cpp_api.html#algorithms">Algorithms</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.translate"><code class="docutils literal notranslate"><span class="pre">translate()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.evolve_async"><code class="docutils literal notranslate"><span class="pre">evolve_async()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#backend-configuration">Backend Configuration</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.has_target"><code class="docutils literal notranslate"><span class="pre">has_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.get_target"><code class="docutils literal notranslate"><span class="pre">get_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.get_targets"><code class="docutils literal notranslate"><span class="pre">get_targets()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.set_target"><code class="docutils literal notranslate"><span class="pre">set_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.reset_target"><code class="docutils literal notranslate"><span class="pre">reset_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.set_noise"><code class="docutils literal notranslate"><span class="pre">set_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.unset_noise"><code class="docutils literal notranslate"><span class="pre">unset_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.initialize_cudaq"><code class="docutils literal notranslate"><span class="pre">initialize_cudaq()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.num_available_gpus"><code class="docutils literal notranslate"><span class="pre">num_available_gpus()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.set_random_seed"><code class="docutils literal notranslate"><span class="pre">set_random_seed()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#data-types">Data Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.SimulationPrecision"><code class="docutils literal notranslate"><span class="pre">SimulationPrecision</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.Target"><code class="docutils literal notranslate"><span class="pre">Target</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.State"><code class="docutils literal notranslate"><span class="pre">State</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.Tensor"><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.ComplexMatrix"><code class="docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.Schedule"><code class="docutils literal notranslate"><span class="pre">Schedule</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.integrator.BaseIntegrator"><code class="docutils literal notranslate"><span class="pre">BaseIntegrator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.AsyncEvolveResult"><code class="docutils literal notranslate"><span class="pre">AsyncEvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum"><code class="docutils literal notranslate"><span class="pre">OperatorSum</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator"><code class="docutils literal notranslate"><span class="pre">ElementaryOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator"><code class="docutils literal notranslate"><span class="pre">ProductOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.expressions.RydbergHamiltonian"><code class="docutils literal notranslate"><span class="pre">RydbergHamiltonian</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator"><code class="docutils literal notranslate"><span class="pre">ScalarOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.definitions.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.spin.x"><code class="docutils literal notranslate"><span class="pre">spin.x()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.spin.y"><code class="docutils literal notranslate"><span class="pre">spin.y()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.spin.z"><code class="docutils literal notranslate"><span class="pre">spin.z()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.cudm_state.CuDensityMatState"><code class="docutils literal notranslate"><span class="pre">CuDensityMatState</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.operator.cudm_state.to_cupy_array"><code class="docutils literal notranslate"><span class="pre">to_cupy_array()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.SampleResult"><code class="docutils literal notranslate"><span class="pre">SampleResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.AsyncSampleResult"><code class="docutils literal notranslate"><span class="pre">AsyncSampleResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.ObserveResult"><code class="docutils literal notranslate"><span class="pre">ObserveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.AsyncObserveResult"><code class="docutils literal notranslate"><span class="pre">AsyncObserveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.AsyncStateResult"><code class="docutils literal notranslate"><span class="pre">AsyncStateResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.OptimizationResult"><code class="docutils literal notranslate"><span class="pre">OptimizationResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#optimizers">Optimizers</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#gradients">Gradients</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#noisy-simulation">Noisy Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#mpi-submodule">MPI Submodule</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.initialize"><code class="docutils literal notranslate"><span class="pre">initialize()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.rank"><code class="docutils literal notranslate"><span class="pre">rank()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.num_ranks"><code class="docutils literal notranslate"><span class="pre">num_ranks()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.all_gather"><code class="docutils literal notranslate"><span class="pre">all_gather()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.broadcast"><code class="docutils literal notranslate"><span class="pre">broadcast()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.is_initialized"><code class="docutils literal notranslate"><span class="pre">is_initialized()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.mpi.finalize"><code class="docutils literal notranslate"><span class="pre">finalize()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#orca-submodule">ORCA Submodule</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq.orca.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../default_ops.html">Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../default_ops.html#unitary-operations-on-qubits">Unitary Operations on Qubits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#x"><code class="code docutils literal notranslate"><span class="pre">x</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#y"><code class="code docutils literal notranslate"><span class="pre">y</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#z"><code class="code docutils literal notranslate"><span class="pre">z</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#r1"><code class="code docutils literal notranslate"><span class="pre">r1</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#rx"><code class="code docutils literal notranslate"><span class="pre">rx</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#rz"><code class="code docutils literal notranslate"><span class="pre">rz</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#swap"><code class="code docutils literal notranslate"><span class="pre">swap</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#u3"><code class="code docutils literal notranslate"><span class="pre">u3</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../default_ops.html#adjoint-and-controlled-operations">Adjoint and Controlled Operations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../default_ops.html#measurements-on-qubits">Measurements on Qubits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#mz"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#mx"><code class="code docutils literal notranslate"><span class="pre">mx</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#my"><code class="code docutils literal notranslate"><span class="pre">my</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../default_ops.html#user-defined-custom-operations">User-Defined Custom Operations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#annihilate"><code class="code docutils literal notranslate"><span class="pre">annihilate</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#phase-shift"><code class="code docutils literal notranslate"><span class="pre">phase_shift</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#beam-splitter"><code class="code docutils literal notranslate"><span class="pre">beam_splitter</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../default_ops.html#id1"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
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-  <section id="module-cudaq">
-<span id="cuda-q-python-api"></span><h1>CUDA-Q Python API<a class="headerlink" href="#module-cudaq" title="Permalink to this heading">¶</a></h1>
-<section id="program-construction">
-<h2>Program Construction<a class="headerlink" href="#program-construction" title="Permalink to this heading">¶</a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.make_kernel">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">make_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.make_kernel" title="Permalink to this definition">¶</a></dt>
-<dd><blockquote>
-<div><p>Create a <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>: An empty kernel function to be used for quantum
-program construction. This kernel is non-parameterized if it accepts no
-arguments, else takes the provided types as arguments.</p>
-<p>Returns a kernel if it is non-parameterized, else a tuple containing the
-kernel and a <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> for each kernel argument.</p>
-</div></blockquote>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="c1"># Non-parameterized kernel.</span>
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-
-<span class="c1"># Example:</span>
-<span class="c1"># Parameterized kernel that accepts an `int` and `float` as arguments.</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">int_value</span><span class="p">,</span> <span class="n">float_value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.PyKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">PyKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">argTypeList</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernel" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> provides an API for dynamically constructing quantum
-circuits. The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> programmatically represents the circuit as an MLIR
-function using the Quake dialect.</p>
-<dl class="py attribute">
-<dt class="sig sig-object py" id="cudaq.PyKernel.name">
-<span class="sig-name descname"><span class="pre">name</span></span><a class="headerlink" href="#cudaq.PyKernel.name" title="Permalink to this definition">¶</a></dt>
-<dd><p>The name of the <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> function. Read-only.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="cudaq.PyKernel.arguments">
-<span class="sig-name descname"><span class="pre">arguments</span></span><a class="headerlink" href="#cudaq.PyKernel.arguments" title="Permalink to this definition">¶</a></dt>
-<dd><p>The arguments accepted by the
-<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> function. Read-only.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>List[<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>]</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="cudaq.PyKernel.argument_count">
-<span class="sig-name descname"><span class="pre">argument_count</span></span><a class="headerlink" href="#cudaq.PyKernel.argument_count" title="Permalink to this definition">¶</a></dt>
-<dd><p>The number of arguments accepted by the
-<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> function. Read-only.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="cudaq.Kernel">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><a class="headerlink" href="#cudaq.Kernel" title="Permalink to this definition">¶</a></dt>
-<dd><p>alias of <a class="reference internal" href="#cudaq.PyKernel" title="cudaq.kernel.kernel_builder.PyKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyKernel</span></code></a></p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">PyKernelDecorator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernelName</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funcSrc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overrideGlobalScopedVars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code> serves as a standard Python decorator that takes
-the decorated function as input and optionally lowers its  AST
-representation to executable code via MLIR. This decorator enables full JIT
-compilation mode, where the function is lowered to an MLIR representation.</p>
-<p>This decorator exposes a call overload that executes the code via the
-MLIR <code class="code docutils literal notranslate"><span class="pre">ExecutionEngine</span></code> for the MLIR mode.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Invoke the CUDA-Q kernel. JIT compilation of the kernel AST to MLIR
-will occur here if it has not already occurred, except when the target
-requires custom handling.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.__str__">
-<span class="sig-name descname"><span class="pre">__str__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.__str__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the MLIR Module string representation for this kernel.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.compile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compile the Python function AST to MLIR. This is a no-op
-if the kernel is already compiled.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.extract_c_function_pointer">
-<span class="sig-name descname"><span class="pre">extract_c_function_pointer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.extract_c_function_pointer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the C function pointer for the function with given name, or
-with the name of this kernel if not provided.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">jStr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overrideDict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a JSON string into a new PyKernelDecorator object.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.merge_kernel">
-<span class="sig-name descname"><span class="pre">merge_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">otherMod</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.merge_kernel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Merge the kernel in this PyKernelDecorator (the ModuleOp) with
-the provided ModuleOp.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.synthesize_callable_arguments">
-<span class="sig-name descname"><span class="pre">synthesize_callable_arguments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">funcNames</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.synthesize_callable_arguments" title="Permalink to this definition">¶</a></dt>
-<dd><p>Given this Kernel has callable block arguments, synthesize away these
-callable arguments with the in-module FuncOps with given names. The
-name at index 0 in the list corresponds to the first callable block
-argument, index 1 to the second callable block argument, etc.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert <code class="code docutils literal notranslate"><span class="pre">self</span></code> to a JSON-serialized version of the kernel such that
-<code class="code docutils literal notranslate"><span class="pre">from_json</span></code> can reconstruct it elsewhere.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PyKernelDecorator.type_to_str">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">type_to_str</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PyKernelDecorator.type_to_str" title="Permalink to this definition">¶</a></dt>
-<dd><p>This converts types to strings in a clean JSON-compatible way.
-int -&gt; ‘int’
-list[float] -&gt; ‘list[float]’
-List[float] -&gt; ‘list[float]’</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.kernel">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.kernel" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">cudaq.kernel</span></code> represents the CUDA-Q language function
-attribute that programmers leverage to indicate the following function
-is a CUDA-Q kernel and should be compile and executed on
-an available quantum coprocessor.</p>
-<p>Verbose logging can be enabled via <code class="code docutils literal notranslate"><span class="pre">verbose=True</span></code>.</p>
-</dd></dl>
-
-</section>
-<section id="kernel-execution">
-<h2>Kernel Execution<a class="headerlink" href="#kernel-execution" title="Permalink to this heading">¶</a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.sample">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.sample" title="Permalink to this definition">¶</a></dt>
-<dd><p>Sample the state generated by the provided <code class="code docutils literal notranslate"><span class="pre">kernel</span></code> at the given kernel
-<code class="code docutils literal notranslate"><span class="pre">arguments</span></code> over the specified number of circuit executions (<code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>).
-Each argument in <code class="code docutils literal notranslate"><span class="pre">arguments</span></code> provided can be a list or <code class="code docutils literal notranslate"><span class="pre">ndarray</span></code> of arguments
-of the specified kernel argument type, and in this case, the <code class="code docutils literal notranslate"><span class="pre">sample</span></code>
-functionality will be broadcasted over all argument sets and a list of
-<code class="code docutils literal notranslate"><span class="pre">sample_result</span></code> instances will be returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to execute <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>
-times on the QPU.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments. For
-example, if the kernel takes two <code class="code docutils literal notranslate"><span class="pre">float</span></code> values as input, the <code class="code docutils literal notranslate"><span class="pre">sample</span></code> call
-should be structured as <code class="code docutils literal notranslate"><span class="pre">cudaq.sample(kernel,</span> <span class="pre">firstFloat,</span> <span class="pre">secondFloat)</span></code>. For
-broadcasting of the <code class="code docutils literal notranslate"><span class="pre">sample</span></code> function, the arguments should be structured as a
-<code class="code docutils literal notranslate"><span class="pre">list</span></code> or <code class="code docutils literal notranslate"><span class="pre">ndarray</span></code> of argument values of the specified kernel argument type.</p></li>
-<li><p><strong>shots_count</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The number of kernel executions on the QPU.
-Defaults to 1000. Key-word only.</p></li>
-<li><p><strong>noise_model</strong> (Optional[<code class="code docutils literal notranslate"><span class="pre">NoiseModel</span></code>]) – The optional <a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.NoiseModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">NoiseModel</span></code></a>
-to add noise to the kernel execution on the simulator. Defaults to
-an empty noise model.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A dictionary containing the measurement count results for the <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>,
-or a list of such results in the case of <code class="code docutils literal notranslate"><span class="pre">sample</span></code> function broadcasting.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> or <code class="code docutils literal notranslate"><span class="pre">list[SampleResult]</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.sample_async">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">sample_async</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.sample_async" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">sample_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots_count:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qpu_id:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.AsyncSampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Asynchronously sample the state of the provided <code class="code docutils literal notranslate"><span class="pre">kernel</span></code> at the
-specified number of circuit executions (<code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>).
-When targeting a quantum platform with more than one QPU, the optional
-<code class="code docutils literal notranslate"><span class="pre">qpu_id</span></code> allows for control over which QPU to enable. Will return a
-future whose results can be retrieved via <code class="code docutils literal notranslate"><span class="pre">future.get()</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to execute <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>
-times on the QPU.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-<li><p><strong>shots_count</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The number of kernel executions on the
-QPU. Defaults to 1000. Key-word only.</p></li>
-<li><p><strong>qpu_id</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The optional identification for which QPU
-on the platform to target. Defaults to zero. Key-word only.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A dictionary containing the measurement count results for the <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.AsyncSampleResult" title="cudaq.AsyncSampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">AsyncSampleResult</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.observe">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">observe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spin_operator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.observe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the expected value of the <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> with respect to
-the <code class="code docutils literal notranslate"><span class="pre">kernel</span></code>. If the input <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> is a list of <code class="code docutils literal notranslate"><span class="pre">SpinOperator</span></code> then compute
-the expected value of every operator in the list and return a list of results.
-If the kernel accepts arguments, it will be evaluated
-with respect to <code class="code docutils literal notranslate"><span class="pre">kernel(*arguments)</span></code>. Each argument in <code class="code docutils literal notranslate"><span class="pre">arguments</span></code> provided
-can be a list or <code class="code docutils literal notranslate"><span class="pre">ndarray</span></code> of arguments of the specified kernel argument
-type, and in this case, the <code class="code docutils literal notranslate"><span class="pre">observe</span></code> functionality will be broadcasted over
-all argument sets and a list of <code class="code docutils literal notranslate"><span class="pre">observe_result</span></code> instances will be returned.
-If both the input <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> and <code class="code docutils literal notranslate"><span class="pre">arguments</span></code> are broadcast lists,
-a nested list of results over <code class="code docutils literal notranslate"><span class="pre">arguments</span></code> then <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> will be returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to evaluate the
-expectation value with respect to.</p></li>
-<li><p><strong>spin_operator</strong> (<a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinOperator</span></code></a> or <code class="code docutils literal notranslate"><span class="pre">list[SpinOperator]</span></code>) – The Hermitian spin operator to
-calculate the expectation of, or a list of such operators.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the
-kernel function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-<li><p><strong>shots_count</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The number of shots to use for QPU
-execution. Defaults to -1 implying no shots-based sampling. Key-word only.</p></li>
-<li><p><strong>noise_model</strong> (Optional[<code class="code docutils literal notranslate"><span class="pre">NoiseModel</span></code>]) – The optional <a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.NoiseModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">NoiseModel</span></code></a> to add
-noise to the kernel execution on the simulator. Defaults to an empty
-noise model.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A data-type containing the expectation value of the <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> with
-respect to the <code class="code docutils literal notranslate"><span class="pre">kernel(*arguments)</span></code>, or a list of such results in the case
-of <code class="code docutils literal notranslate"><span class="pre">observe</span></code> function broadcasting. If <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code> was provided, the
-<a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.ObserveResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">ObserveResult</span></code></a> will also contain a <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> dictionary.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.ObserveResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">ObserveResult</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.observe_async">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">observe_async</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.observe_async" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">observe_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spin_operator:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qpu_id:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots_count:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.AsyncObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncObserveResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Compute the expected value of the <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> with respect to
-the <code class="code docutils literal notranslate"><span class="pre">kernel</span></code> asynchronously. If the kernel accepts arguments, it will
-be evaluated with respect to <code class="code docutils literal notranslate"><span class="pre">kernel(*arguments)</span></code>. When targeting a
-quantum platform with more than one QPU, the optional <code class="code docutils literal notranslate"><span class="pre">qpu_id</span></code> allows
-for control over which QPU to enable. Will return a future whose results
-can be retrieved via <code class="code docutils literal notranslate"><span class="pre">future.get()</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to evaluate the
-expectation value with respect to.</p></li>
-<li><p><strong>spin_operator</strong> (<a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinOperator</span></code></a>) – The Hermitian spin operator to
-calculate the expectation of.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the
-kernel function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-<li><p><strong>qpu_id</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The optional identification for which QPU on
-the platform to target. Defaults to zero. Key-word only.</p></li>
-<li><p><strong>shots_count</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The number of shots to use for QPU
-execution. Defaults to -1 implying no shots-based sampling. Key-word only.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A future containing the result of the call to observe.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.AsyncObserveResult" title="cudaq.AsyncObserveResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">AsyncObserveResult</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.get_state">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.get_state" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <a class="reference internal" href="#cudaq.State" title="cudaq.State"><code class="xref py py-class docutils literal notranslate"><span class="pre">State</span></code></a> of the system after execution of the provided <code class="code docutils literal notranslate"><span class="pre">kernel</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to execute on the QPU.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-</ul>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Define a kernel that will produced the all |11...1&gt; state.</span>
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-<span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-<span class="c1"># Prepare qubits in the 1-state.</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-<span class="c1"># Get the state of the system. This will execute the provided kernel</span>
-<span class="c1"># and, depending on the selected target, will return the state as a</span>
-<span class="c1"># vector or matrix.</span>
-<span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.get_state_async">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_state_async</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.get_state_async" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_state_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qpu_id:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.AsyncStateResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncStateResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncStateResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Asynchronously retrieve the state generated by the given quantum kernel.
-When targeting a quantum platform with more than one QPU, the optional
-<code class="code docutils literal notranslate"><span class="pre">qpu_id</span></code> allows for control over which QPU to enable. Will return a
-future whose results can be retrieved via <code class="code docutils literal notranslate"><span class="pre">future.get()</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to execute on the QPU.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-<li><p><strong>qpu_id</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The optional identification for which QPU
-on the platform to target. Defaults to zero. Key-word only.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Quantum state (state vector or density matrix) data).</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.AsyncStateResult" title="cudaq.AsyncStateResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">AsyncStateResult</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.vqe">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">vqe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.vqe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">vqe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">spin_operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.optimizer" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_count</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">vqe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">spin_operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.optimizer" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_count</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">argument_mapper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">vqe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.gradient" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">spin_operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.optimizer" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_count</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">vqe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.gradient" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">spin_operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.optimizer" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.optimizer</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_count</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">argument_mapper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.draw">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.draw" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a string representing the drawing of the execution path,
-in the format specified as the first argument. If the format is
-‘ascii’, the output will be a UTF-8 encoded string. If the format
-is ‘latex’, the output will be a LaTeX string.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>format</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The format of the output. Can be ‘ascii’ or ‘latex’.</p></li>
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to draw.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-</ul>
-</dd>
-</dl>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a UTF-8 encoded string representing drawing of the execution
-path, i.e., the trace, of the provided <code class="code docutils literal notranslate"><span class="pre">kernel</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to draw.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The UTF-8 encoded string of the circuit, without measurement operations.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">bell_pair</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">cx</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">bell_pair</span><span class="p">))</span>
-<span class="c1"># Output</span>
-<span class="c1">#      ╭───╮</span>
-<span class="c1"># q0 : ┤ h ├──●──</span>
-<span class="c1">#      ╰───╯╭─┴─╮</span>
-<span class="c1"># q1 : ─────┤ x ├</span>
-<span class="c1">#           ╰───╯</span>
-
-<span class="c1"># Example with arguments</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">angle</span><span class="p">:</span><span class="nb">float</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="mf">0.59</span><span class="p">))</span>
-<span class="c1"># Output</span>
-<span class="c1">#      ╭───╮╭──────────╮</span>
-<span class="c1"># q0 : ┤ h ├┤ ry(0.59) ├</span>
-<span class="c1">#      ╰───╯╰──────────╯</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.translate">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">translate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.translate" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">translate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel:</span> <span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">format:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'qir'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return a UTF-8 encoded string representing drawing of the execution
-path, i.e., the trace, of the provided <code class="code docutils literal notranslate"><span class="pre">kernel</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>format</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – format to translate to. Available formats: <code class="code docutils literal notranslate"><span class="pre">qir</span></code>, <code class="code docutils literal notranslate"><span class="pre">qir-base</span></code>,
-<code class="code docutils literal notranslate"><span class="pre">qir-adaptive</span></code>, <code class="code docutils literal notranslate"><span class="pre">openqasm2</span></code>.</p></li>
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>) – The <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> to translate.</p></li>
-<li><p><strong>*arguments</strong> (<em>Optional</em><em>[</em><em>Any</em><em>]</em>) – The concrete values to evaluate the kernel
-function at. Leave empty if the kernel doesn’t accept any arguments.</p></li>
-<li><p><strong>Note</strong> – Translating functions with arguments to OpenQASM 2.0 is not supported.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The UTF-8 encoded string of the circuit, without measurement operations.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">bell_pair</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">cx</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">translate</span><span class="p">(</span><span class="n">bell_pair</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;qir&quot;</span><span class="p">))</span>
-
-<span class="c1"># Output</span>
-<span class="p">;</span> <span class="n">ModuleID</span> <span class="o">=</span> <span class="s1">&#39;LLVMDialectModule&#39;</span>
-<span class="n">source_filename</span> <span class="o">=</span> <span class="s1">&#39;LLVMDialectModule&#39;</span>
-
-<span class="o">%</span><span class="n">Array</span> <span class="o">=</span> <span class="nb">type</span> <span class="n">opaque</span>
-<span class="o">%</span><span class="n">Result</span> <span class="o">=</span> <span class="nb">type</span> <span class="n">opaque</span>
-<span class="o">%</span><span class="n">Qubit</span> <span class="o">=</span> <span class="nb">type</span> <span class="n">opaque</span>
-
-<span class="o">...</span>
-<span class="o">...</span>
-
-<span class="n">define</span> <span class="n">void</span> <span class="nd">@__nvqpp__mlirgen__function_variable_qreg</span><span class="o">.</span><span class="n">_Z13variable_qregv</span><span class="p">()</span> <span class="n">local_unnamed_addr</span> <span class="p">{</span>
-  <span class="o">%</span><span class="mi">1</span> <span class="o">=</span> <span class="n">tail</span> <span class="n">call</span> <span class="o">%</span><span class="n">Array</span><span class="o">*</span> <span class="nd">@__quantum__rt__qubit_allocate_array</span><span class="p">(</span><span class="n">i64</span> <span class="mi">2</span><span class="p">)</span>
-  <span class="o">...</span>
-  <span class="o">%</span><span class="mi">8</span> <span class="o">=</span> <span class="n">tail</span> <span class="n">call</span> <span class="o">%</span><span class="n">Result</span><span class="o">*</span> <span class="nd">@__quantum__qis__mz</span><span class="p">(</span><span class="o">%</span><span class="n">Qubit</span><span class="o">*</span> <span class="o">%</span><span class="mi">4</span><span class="p">)</span>
-  <span class="o">%</span><span class="mi">9</span> <span class="o">=</span> <span class="n">tail</span> <span class="n">call</span> <span class="o">%</span><span class="n">Result</span><span class="o">*</span> <span class="nd">@__quantum__qis__mz</span><span class="p">(</span><span class="o">%</span><span class="n">Qubit</span><span class="o">*</span> <span class="o">%</span><span class="mi">7</span><span class="p">)</span>
-  <span class="n">tail</span> <span class="n">call</span> <span class="n">void</span> <span class="nd">@__quantum__rt__qubit_release_array</span><span class="p">(</span><span class="o">%</span><span class="n">Array</span><span class="o">*</span> <span class="o">%</span><span class="mi">1</span><span class="p">)</span>
-  <span class="n">ret</span> <span class="n">void</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</section>
-<section id="dynamics">
-<h2>Dynamics<a class="headerlink" href="#dynamics" title="Permalink to this heading">¶</a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.evolve">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">evolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hamiltonian</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum" title="cudaq.operator.expressions.OperatorSum"><span class="pre">cudaq.operator.expressions.OperatorSum</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">cudaq.operator.expressions.ProductOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.Schedule" title="cudaq.operator.schedule.Schedule"><span class="pre">Schedule</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">State</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">State</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collapse_operators</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum" title="cudaq.operator.expressions.OperatorSum"><span class="pre">cudaq.operator.expressions.OperatorSum</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">cudaq.operator.expressions.ProductOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observables</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum" title="cudaq.operator.expressions.OperatorSum"><span class="pre">cudaq.operator.expressions.OperatorSum</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">cudaq.operator.expressions.ProductOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_intermediate_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">integrator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.integrator.BaseIntegrator" title="cudaq.operator.integrator.BaseIntegrator"><span class="pre">BaseIntegrator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots_count</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">EvolveResult</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">EvolveResult</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#cudaq.evolve" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the time evolution of one or more initial state(s) under the defined
-operator(s).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>hamiltonian</strong> – Operator that describes the behavior of a quantum system
-without noise.</p></li>
-<li><p><strong>dimensions</strong> – A mapping that specifies the number of levels, that is
-the dimension, of each degree of freedom that any of the operator
-arguments acts on.</p></li>
-<li><p><strong>schedule</strong> – A sequence that generates a mapping of keyword arguments
-to their respective value. The keyword arguments are the parameters
-needed to evaluate any of the operators passed to <code class="code docutils literal notranslate"><span class="pre">evolve</span></code>.
-All required parameters for evaluating an operator and their
-documentation, if available, can be queried by accessing the
-<code class="code docutils literal notranslate"><span class="pre">parameter</span></code> property of the operator.</p></li>
-<li><p><strong>initial_state</strong> – A single state or a sequence of states of a quantum system.</p></li>
-<li><p><strong>collapse_operators</strong> – A sequence of operators that describe the influence of
-noise on the quantum system.</p></li>
-<li><p><strong>observables</strong> – A sequence of operators for which to compute their expectation
-value during evolution. If <code class="code docutils literal notranslate"><span class="pre">store_intermediate_results</span></code> is set to True,
-the expectation values are computed after each step in the schedule,
-and otherwise only the final expectation values at the end of the
-evolution are computed.</p></li>
-<li><p><strong>shots_count</strong> – Optional integer, if provided, it is the number of shots to use
-for QPU execution.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A single evolution result if a single initial state is provided, or a sequence
-of evolution results representing the data computed during the evolution of each
-initial state. See <code class="code docutils literal notranslate"><span class="pre">EvolveResult</span></code> for more information about the data computed
-during evolution.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.evolve_async">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">evolve_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hamiltonian</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum" title="cudaq.operator.expressions.OperatorSum"><span class="pre">cudaq.operator.expressions.OperatorSum</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">cudaq.operator.expressions.ProductOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.Schedule" title="cudaq.operator.schedule.Schedule"><span class="pre">Schedule</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">State</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">State</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collapse_operators</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum" title="cudaq.operator.expressions.OperatorSum"><span class="pre">cudaq.operator.expressions.OperatorSum</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">cudaq.operator.expressions.ProductOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observables</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.OperatorSum" title="cudaq.operator.expressions.OperatorSum"><span class="pre">cudaq.operator.expressions.OperatorSum</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">cudaq.operator.expressions.ProductOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_intermediate_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">integrator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.integrator.BaseIntegrator" title="cudaq.operator.integrator.BaseIntegrator"><span class="pre">BaseIntegrator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shots_count</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.AsyncEvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncEvolveResult"><span class="pre">AsyncEvolveResult</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.AsyncEvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncEvolveResult"><span class="pre">AsyncEvolveResult</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#cudaq.evolve_async" title="Permalink to this definition">¶</a></dt>
-<dd><p>Asynchronously computes the time evolution of one or more initial state(s)
-under the defined operator(s). See <code class="code docutils literal notranslate"><span class="pre">cudaq.evolve</span></code> for more details about the
-parameters passed here.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p>The handle to a single evolution result if a single initial state is provided,
-or a sequence of handles to the evolution results representing the data computed
-during the evolution of each initial state. See the <code class="code docutils literal notranslate"><span class="pre">EvolveResult</span></code> for more
-information about the data computed during evolution.</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-<section id="backend-configuration">
-<h2>Backend Configuration<a class="headerlink" href="#backend-configuration" title="Permalink to this heading">¶</a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.has_target">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">has_target</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.has_target" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">has_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return true if the <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> with the given name exists.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.get_target">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.get_target" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> with the given name. Will raise an exception if the name is not valid.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_target</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> with the given name. Will raise an exception if the name is not valid.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.get_targets">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_targets</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.get_targets" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">get_targets</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Return all available <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> instances on the current system.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.set_target">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.set_target" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0:</span> <span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*\*kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Set the <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> to be used for CUDA-Q kernel execution. Can provide optional, target-specific configuration data via Python kwargs.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">\*\*kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Set the <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> with given name to be used for CUDA-Q kernel execution. Can provide optional, target-specific configuration data via Python kwargs.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.reset_target">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">reset_target</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.reset_target" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">reset_target</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Reset the current <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> to the default.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.set_noise">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_noise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.set_noise" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.NoiseModel"><span class="pre">cudaq.NoiseModel</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Set the underlying noise model.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.unset_noise">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">unset_noise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.unset_noise" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">unset_noise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Clear backend simulation from any existing noise models.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.initialize_cudaq">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">initialize_cudaq</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.initialize_cudaq" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">initialize_cudaq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">\*\*kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Initialize the CUDA-Q environment.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.num_available_gpus">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">num_available_gpus</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.num_available_gpus" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">num_available_gpus</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>The number of available GPUs detected on the system.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.set_random_seed">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_random_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.set_random_seed" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">set_random_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Provide the seed for backend quantum kernel simulation.</p>
-</dd></dl>
-
-</section>
-<section id="data-types">
-<h2>Data Types<a class="headerlink" href="#data-types" title="Permalink to this heading">¶</a></h2>
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.SimulationPrecision">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">SimulationPrecision</span></span><a class="headerlink" href="#cudaq.SimulationPrecision" title="Permalink to this definition">¶</a></dt>
-<dd><p>Enumeration describing the precision of the underyling simulation.</p>
-<p>Members:</p>
-<blockquote>
-<div><p>fp32</p>
-<p>fp64</p>
-</div></blockquote>
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.SimulationPrecision.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><a class="headerlink" href="#cudaq.SimulationPrecision.name" title="Permalink to this definition">¶</a></dt>
-<dd><p>object) -&gt; str
-:noindex:</p>
-<dl class="field-list">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">name(self</span></span></dt>
-<dd></dd></dl>
-
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.Target">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">Target</span></span><a class="headerlink" href="#cudaq.Target" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> represents the underlying infrastructure that CUDA-Q kernels will execute on. Instances of <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> describe what simulator they may leverage, the quantum_platform required for execution, and a description for the target.</p>
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.Target.description">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#cudaq.Target.description" title="Permalink to this definition">¶</a></dt>
-<dd><p>A string describing the features for this <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.Target.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.Target.get_precision" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SimulationPrecision" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SimulationPrecision"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SimulationPrecision</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the simulation precision for the current target.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.Target.is_emulated">
-<span class="sig-name descname"><span class="pre">is_emulated</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.Target.is_emulated" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">is_emulated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns true if the emulation mode for the target has been activated.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.Target.is_remote">
-<span class="sig-name descname"><span class="pre">is_remote</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.Target.is_remote" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">is_remote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns true if the target consists of a remote REST QPU.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.Target.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><a class="headerlink" href="#cudaq.Target.name" title="Permalink to this definition">¶</a></dt>
-<dd><p>The name of the <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.Target.num_qpus">
-<span class="sig-name descname"><span class="pre">num_qpus</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.Target.num_qpus" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">num_qpus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.Target" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Target</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the number of QPUs available in this <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code>.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.Target.platform">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">platform</span></span><a class="headerlink" href="#cudaq.Target.platform" title="Permalink to this definition">¶</a></dt>
-<dd><p>The name of the quantum_platform implementation this <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> leverages.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.Target.simulator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">simulator</span></span><a class="headerlink" href="#cudaq.Target.simulator" title="Permalink to this definition">¶</a></dt>
-<dd><p>The name of the simulator this <code class="code docutils literal notranslate"><span class="pre">cudaq.Target</span></code> leverages. This will be empty for physical QPUs.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.State">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">State</span></span><a class="headerlink" href="#cudaq.State" title="Permalink to this definition">¶</a></dt>
-<dd><p>A data-type representing the quantum state of the internal simulator. This type is not user-constructible and instances can only be retrieved via the <code class="code docutils literal notranslate"><span class="pre">cudaq.get_state(...)</span></code> function or the static cudaq.State.from_data() method.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.amplitude">
-<span class="sig-name descname"><span class="pre">amplitude</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.amplitude" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">amplitude</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the amplitude of a state in computational basis.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="c1"># Create a simulation state.</span>
-<span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># Return the amplitude of |0101&gt;, assuming this is a 4-qubit state.</span>
-<span class="n">amplitude</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">amplitude</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">amplitude</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the amplitude of a state in computational basis.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="c1"># Create a simulation state.</span>
-<span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># Return the amplitude of |0101&gt;, assuming this is a 4-qubit state.</span>
-<span class="n">amplitude</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">amplitude</span><span class="p">(</span><span class="s1">&#39;0101&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.amplitudes">
-<span class="sig-name descname"><span class="pre">amplitudes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.amplitudes" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">amplitudes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the amplitude of a list of states in computational basis.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="c1"># Create a simulation state.</span>
-<span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># Return the amplitude of |0101&gt; and |1010&gt;, assuming this is a 4-qubit state.</span>
-<span class="n">amplitudes</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">amplitudes</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]])</span>
-</pre></div>
-</div>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">amplitudes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the amplitudes of a list of states in computational basis.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="c1"># Create a simulation state.</span>
-<span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># Return the amplitudes of |0101&gt; and |1010&gt;, assuming this is a 4-qubit state.</span>
-<span class="n">amplitudes</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">amplitudes</span><span class="p">([</span><span class="s1">&#39;0101&#39;</span><span class="p">,</span> <span class="s1">&#39;1010&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.dump">
-<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.dump" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Print the state to the console.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.from_data">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.from_data" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">numpy.ndarray</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a state from data.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a state from matrix product state tensor data.</p>
-<ol class="arabic" start="3">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.Tensor" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Tensor"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Tensor</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a state from matrix product state tensor data.</p>
-<ol class="arabic" start="4">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a state from matrix product state tensor data (as CuPy ndarray).</p>
-<ol class="arabic" start="5">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return a state from CuPy device array.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.getTensor">
-<span class="sig-name descname"><span class="pre">getTensor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.getTensor" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">getTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.Tensor" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Tensor"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Tensor</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <code class="code docutils literal notranslate"><span class="pre">idx</span></code> tensor making up this state representation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.getTensors">
-<span class="sig-name descname"><span class="pre">getTensors</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.getTensors" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">getTensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.Tensor" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Tensor"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.Tensor</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Return all the tensors that comprise this state representation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.is_on_gpu">
-<span class="sig-name descname"><span class="pre">is_on_gpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.is_on_gpu" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">is_on_gpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return True if this state is on the GPU.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.num_qubits">
-<span class="sig-name descname"><span class="pre">num_qubits</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.num_qubits" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">num_qubits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns the number of qubits represented by this state.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.State.overlap">
-<span class="sig-name descname"><span class="pre">overlap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.State.overlap" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">overlap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Compute the overlap between the provided <a class="reference internal" href="#cudaq.State" title="cudaq.State"><code class="xref py py-class docutils literal notranslate"><span class="pre">State</span></code></a>’s.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">overlap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">numpy.ndarray</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Compute the overlap between the provided <a class="reference internal" href="#cudaq.State" title="cudaq.State"><code class="xref py py-class docutils literal notranslate"><span class="pre">State</span></code></a>’s.</p>
-<ol class="arabic" start="3">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">overlap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Compute overlap with general CuPy device array.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.Tensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">Tensor</span></span><a class="headerlink" href="#cudaq.Tensor" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">Tensor</span></code> describes a pointer to simulation data as well as the rank and extents for that tensorial data it represents.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.QuakeValue">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">QuakeValue</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mlirValue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pyKernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue" title="Permalink to this definition">¶</a></dt>
-<dd><p>A <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> represents a handle to an individual function
-argument of a <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a>, or a return value from an operation within
-it. As documented in <a class="reference internal" href="#cudaq.make_kernel" title="cudaq.make_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_kernel()</span></code></a>, a <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> can hold
-values of the following types: int, float, list/List, <a class="reference internal" href="#cudaq.qubit" title="cudaq.qubit"><code class="xref py py-class docutils literal notranslate"><span class="pre">qubit</span></code></a>, or
-<a class="reference internal" href="#cudaq.qvector" title="cudaq.qvector"><code class="xref py py-class docutils literal notranslate"><span class="pre">qvector</span></code></a>. The <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> can also hold kernel operations
-such as qubit allocations and measurements.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__add__">
-<span class="sig-name descname"><span class="pre">__add__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__add__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the sum of <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>) and <code class="code docutils literal notranslate"><span class="pre">other</span></code> (float).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="n">value</span> <span class="o">+</span> <span class="mf">5.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__radd__">
-<span class="sig-name descname"><span class="pre">__radd__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__radd__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the sum of <code class="code docutils literal notranslate"><span class="pre">other</span></code> (float) and <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="mf">5.0</span> <span class="o">+</span> <span class="n">value</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__sub__">
-<span class="sig-name descname"><span class="pre">__sub__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__sub__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the difference of <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>) and <code class="code docutils literal notranslate"><span class="pre">other</span></code> (float).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="n">value</span> <span class="o">-</span> <span class="mf">5.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__rsub__">
-<span class="sig-name descname"><span class="pre">__rsub__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__rsub__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the difference of <code class="code docutils literal notranslate"><span class="pre">other</span></code> (float) and <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="mf">5.0</span> <span class="o">-</span> <span class="n">value</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__neg__">
-<span class="sig-name descname"><span class="pre">__neg__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__neg__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the negation of <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="o">-</span><span class="n">value</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__mul__">
-<span class="sig-name descname"><span class="pre">__mul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__mul__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the product of <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>) with <code class="code docutils literal notranslate"><span class="pre">other</span></code> (float).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="n">value</span> <span class="o">*</span> <span class="mf">5.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__rmul__">
-<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__rmul__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the product of <code class="code docutils literal notranslate"><span class="pre">other</span></code> (float) with <code class="code docutils literal notranslate"><span class="pre">self</span></code> (<a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if the underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> type is not a float.</p>
-</dd>
-</dl>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example:</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">new_value</span><span class="p">:</span> <span class="n">QuakeValue</span> <span class="o">=</span> <span class="mf">5.0</span> <span class="o">*</span> <span class="n">value</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.__getitem__">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.__getitem__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return the element of <code class="code docutils literal notranslate"><span class="pre">self</span></code> at the provided <code class="code docutils literal notranslate"><span class="pre">index</span></code>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Only <code class="code docutils literal notranslate"><span class="pre">list</span></code> or <a class="reference internal" href="#cudaq.qvector" title="cudaq.qvector"><code class="xref py py-class docutils literal notranslate"><span class="pre">qvector</span></code></a> type <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>’s may be indexed.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – The element of <code class="code docutils literal notranslate"><span class="pre">self</span></code> that you’d like to return.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A new <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> for the <code class="code docutils literal notranslate"><span class="pre">index</span></code> element of <code class="code docutils literal notranslate"><span class="pre">self</span></code>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></p>
-</dd>
-<dt class="field-even">Raises<span class="colon">:</span></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.13)"><strong>RuntimeError</strong></a> – if <code class="code docutils literal notranslate"><span class="pre">self</span></code> is a non-subscriptable <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.QuakeValue.slice">
-<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">startIdx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.QuakeValue.slice" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return a slice of the given <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> as a new <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The underlying <a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a> must be a <code class="code docutils literal notranslate"><span class="pre">list</span></code> or <code class="code docutils literal notranslate"><span class="pre">veq</span></code>.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – The index to begin the slice from.</p></li>
-<li><p><strong>count</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – The number of elements to extract after the <code class="code docutils literal notranslate"><span class="pre">start</span></code> index.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A new <code class="code docutils literal notranslate"><span class="pre">QuakeValue</span></code> containing a slice of <code class="code docutils literal notranslate"><span class="pre">self</span></code>
-from the <code class="code docutils literal notranslate"><span class="pre">start</span></code> element to the <code class="code docutils literal notranslate"><span class="pre">start</span> <span class="pre">+</span> <span class="pre">count</span></code> element.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.QuakeValue" title="cudaq.QuakeValue"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.qubit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">qubit</span></span><a class="headerlink" href="#cudaq.qubit" title="Permalink to this definition">¶</a></dt>
-<dd><p>The qubit is the primary unit of information in a quantum computer. Qubits can be created individually or as part of larger registers.</p>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="cudaq.qreg">
-<span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">qreg</span></span><a class="headerlink" href="#cudaq.qreg" title="Permalink to this definition">¶</a></dt>
-<dd><p>alias of <a class="reference internal" href="#cudaq.qvector" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.qvector"><code class="xref py py-class docutils literal notranslate"><span class="pre">qvector</span></code></a></p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.qvector">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">qvector</span></span><a class="headerlink" href="#cudaq.qvector" title="Permalink to this definition">¶</a></dt>
-<dd><p>An owning, dynamically sized container for qubits. The semantics of the <code class="code docutils literal notranslate"><span class="pre">qvector</span></code> follows that of a <code class="code docutils literal notranslate"><span class="pre">std::vector</span></code> or <code class="code docutils literal notranslate"><span class="pre">list</span></code> for qubits.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">ComplexMatrix</span></span><a class="headerlink" href="#cudaq.ComplexMatrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.ComplexMatrix"><code class="xref py py-class docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a> is a thin wrapper around a matrix of complex&lt;double&gt; elements.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix.__getitem__">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ComplexMatrix.__getitem__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the matrix element at i, j.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the matrix element at i, j.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix.__str__">
-<span class="sig-name descname"><span class="pre">__str__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ComplexMatrix.__str__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__str__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Write this matrix to a string representation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix.minimal_eigenvalue">
-<span class="sig-name descname"><span class="pre">minimal_eigenvalue</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ComplexMatrix.minimal_eigenvalue" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">minimal_eigenvalue</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the lowest eigenvalue for this <a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.ComplexMatrix"><code class="xref py py-class docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix.num_columns">
-<span class="sig-name descname"><span class="pre">num_columns</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ComplexMatrix.num_columns" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">num_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns the number of columns in the matrix.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix.num_rows">
-<span class="sig-name descname"><span class="pre">num_rows</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ComplexMatrix.num_rows" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">num_rows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns the number of rows in the matrix.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ComplexMatrix.to_numpy">
-<span class="sig-name descname"><span class="pre">to_numpy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ComplexMatrix.to_numpy" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ComplexMatrix</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray[]</span></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert <a class="reference internal" href="#cudaq.ComplexMatrix" title="cudaq.ComplexMatrix"><code class="xref py py-class docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a> to numpy.ndarray.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.Schedule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">Schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">steps</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">get_value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.complexfloating" title="(in NumPy v2.1)"><span class="pre">numpy.complexfloating</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.Schedule" title="Permalink to this definition">¶</a></dt>
-<dd><p>Represents an iterator that produces all values needed for evaluating
-an operator expression at different time steps.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.integrator.BaseIntegrator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.integrator.</span></span><span class="sig-name descname"><span class="pre">BaseIntegrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.integrator.BaseIntegrator" title="Permalink to this definition">¶</a></dt>
-<dd><p>An abstract wrapper around ODE integrator to ensure a common interface for master equation solver usage.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.EvolveResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">EvolveResult</span></span><a class="headerlink" href="#cudaq.EvolveResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>Stores the execution data from an invocation of <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.EvolveResult.expectation_values">
-<span class="sig-name descname"><span class="pre">expectation_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.EvolveResult.expectation_values" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Stores the expectation values, that is the results from the calls to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>, at each step in the schedule produced by a call to <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>, including the final expectation values. Each entry corresponds to one observable provided in the <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a> call. This property is only populated saving intermediate results was requested in the call to <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>. This value will be None if no intermediate results were requested, or if no observables were specified in the call.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.EvolveResult.final_expectation_values">
-<span class="sig-name descname"><span class="pre">final_expectation_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.EvolveResult.final_expectation_values" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">final_expectation_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Stores the final expectation values, that is the results produced by calls to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>, triggered by a call to <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>. Each entry corresponds to one observable provided in the <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a> call. This value will be None if no observables were specified in the call.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.EvolveResult.final_state">
-<span class="sig-name descname"><span class="pre">final_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.EvolveResult.final_state" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">final_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Stores the final state produced by a call to <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>. Represent the state of a quantum system after time evolution under a set of operators, see the <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a> documentation for more detail.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.EvolveResult.intermediate_states">
-<span class="sig-name descname"><span class="pre">intermediate_states</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.EvolveResult.intermediate_states" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">intermediate_states</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Stores all intermediate states, meaning the state after each step in a defined schedule, produced by a call to <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>, including the final state. This property is only populated if saving intermediate results was requested in the call to <a class="reference internal" href="#cudaq.evolve" title="cudaq.evolve"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve()</span></code></a>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.AsyncEvolveResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">AsyncEvolveResult</span></span><a class="headerlink" href="#cudaq.AsyncEvolveResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>Stores the execution data from an invocation of <a class="reference internal" href="#cudaq.evolve_async" title="cudaq.evolve_async"><code class="xref py py-func docutils literal notranslate"><span class="pre">evolve_async()</span></code></a>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.AsyncEvolveResult.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.AsyncEvolveResult.get" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.AsyncEvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncEvolveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncEvolveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.EvolveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.EvolveResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Retrieve the evolution result from the asynchronous evolve execution
-.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.SpinOperator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">SpinOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SpinOperator" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.OperatorSum">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.expressions.</span></span><span class="sig-name descname"><span class="pre">OperatorSum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">terms</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.ProductOperator" title="cudaq.operator.expressions.ProductOperator"><span class="pre">ProductOperator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.expressions.OperatorSum" title="Permalink to this definition">¶</a></dt>
-<dd><p>Represents an operator expression consisting of a sum of terms,
-where each term is a product of elementary and scalar operators.</p>
-<p>Operator expressions cannot be used within quantum kernels, but
-they provide methods to convert them to data types that can.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.ElementaryOperator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.expressions.</span></span><span class="sig-name descname"><span class="pre">ElementaryOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">degrees</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.expressions.ElementaryOperator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Represents an elementary operator that acts on one or more degrees of freedom,
-and cannot be further simplified to be the product or sum of other operators.
-An elementary operator is defined as a function of zero or more complex-valued
-parameters using the <a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator.define" title="cudaq.operator.expressions.ElementaryOperator.define"><code class="xref py py-func docutils literal notranslate"><span class="pre">ElementaryOperator.define()</span></code></a> class method.</p>
-<p>Operator expressions cannot be used within quantum kernels, but
-they provide methods to convert them to data types that can.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.ElementaryOperator.define">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.1)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.complexfloating" title="(in NumPy v2.1)"><span class="pre">complexfloating</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><a class="headerlink" href="#cudaq.operator.expressions.ElementaryOperator.define" title="Permalink to this definition">¶</a></dt>
-<dd><p>Adds the definition of an elementary operator with the given id to the class.
-After definition, an the defined elementary operator can be instantiated by
-providing the operator id as well as the degree(s) of freedom that it acts on.</p>
-<p>An elementary operator is a parameterized object acting on certain degrees of
-freedom. To evaluate an operator, for example to compute its matrix, the level,
-that is the dimension, for each degree of freedom it acts on must be provided,
-as well as all additional parameters. Additional parameters must be provided in
-the form of keyword arguments.</p>
-<p>Note:
-The dimensions passed during operator evaluation are automatically validated
-against the expected dimensions specified during definition - the <code class="code docutils literal notranslate"><span class="pre">create</span></code>
-function does not need to do this.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>op_id</strong> – A string that uniquely identifies the defined operator.</p></li>
-<li><p><strong>expected_dimensions</strong> – defines the number of levels, that is the dimension,
-for each degree of freedom in canonical (that is sorted) order. A
-negative or zero value for one (or more) of the expected dimensions
-indicates that the operator is defined for any dimension of the
-corresponding degree of freedom.</p></li>
-<li><p><strong>create</strong> – Takes any number of complex-valued arguments and returns the
-matrix representing the operator in canonical order. If the matrix can
-be defined for any number of levels for one or more degree of freedom,
-the <code class="code docutils literal notranslate"><span class="pre">create</span></code> function must take an argument called <code class="code docutils literal notranslate"><span class="pre">dimension</span></code> (or <code class="code docutils literal notranslate"><span class="pre">dim</span></code>
-for short), if the operator acts on a single degree of freedom, and an
-argument called <code class="code docutils literal notranslate"><span class="pre">dimensions</span></code> (or <code class="code docutils literal notranslate"><span class="pre">dims</span></code> for short), if the operator acts
-on multiple degrees of freedom.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.ProductOperator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.expressions.</span></span><span class="sig-name descname"><span class="pre">ProductOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">atomic_operators</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">cudaq.operator.expressions.ElementaryOperator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.expressions.ProductOperator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Represents an operator expression consisting of a product of elementary
-and scalar operators.</p>
-<p>Operator expressions cannot be used within quantum kernels, but
-they provide methods to convert them to data types that can.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.RydbergHamiltonian">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.expressions.</span></span><span class="sig-name descname"><span class="pre">RydbergHamiltonian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">atom_sites</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amplitude</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_global</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">atom_filling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_local</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.expressions.RydbergHamiltonian" title="Permalink to this definition">¶</a></dt>
-<dd><p>Representation for the time-dependent Hamiltonian which is simulated by
-QuEra’s Aquila machine.
-Ref: <a class="reference external" href="https://docs.aws.amazon.com/braket/latest/developerguide/braket-quera-submitting-analog-program-aquila.html#braket-quera-ahs-program-schema">https://docs.aws.amazon.com/braket/latest/developerguide/braket-quera-submitting-analog-program-aquila.html#braket-quera-ahs-program-schema</a></p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.RydbergHamiltonian.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">atom_sites</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amplitude</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_global</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">ScalarOperator</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">atom_filling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_local</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.operator.expressions.ScalarOperator" title="cudaq.operator.expressions.ScalarOperator"><span class="pre">cudaq.operator.expressions.ScalarOperator</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.13)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.expressions.RydbergHamiltonian.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Instantiate an operator consumable by <code class="code docutils literal notranslate"><span class="pre">evolve</span></code> API using the supplied
-parameters.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>atom_sites</strong> – List of 2-d coordinates where the tweezers trap atoms.</p></li>
-<li><p><strong>amplitude</strong> – time and value points of driving amplitude, Omega(t).</p></li>
-<li><p><strong>phase</strong> – time and value points of driving phase, phi(t).</p></li>
-<li><p><strong>delta_global</strong> – time and value points of driving detuning,
-Delta_global(t).</p></li>
-<li><p><strong>atom_filling</strong> – Optional. Marks atoms that occupy the trap sites with
-1, and empty sites with 0. If not provided, all are
-set to 1, i.e. filled.</p></li>
-<li><p><strong>delta_local</strong> – Optional. A tuple of time and value points of the
-time-dependent factor of the local detuning magnitude,
-Delta_local(t), and site-dependent factor of the local
-detuning  magnitude, h_k, a dimensionless number
-between 0.0 and 1.0</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.expressions.ScalarOperator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.expressions.</span></span><span class="sig-name descname"><span class="pre">ScalarOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.complexfloating" title="(in NumPy v2.1)"><span class="pre">numpy.complexfloating</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><span class="pre">complex</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_info</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.expressions.ScalarOperator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Represents a scalar operator defined as a function of zero or more
-complex-valued parameters.</p>
-<p>Operator expressions cannot be used within quantum kernels, but
-they provide methods to convert them to data types that can.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.definitions.SpinOperator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.definitions.</span></span><span class="sig-name descname"><span class="pre">SpinOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.definitions.SpinOperator" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.spin.i">
-<span class="sig-prename descclassname"><span class="pre">spin.</span></span><span class="sig-name descname"><span class="pre">i</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">ElementaryOperator</span></a></span></span><a class="headerlink" href="#cudaq.spin.i" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.spin.x">
-<span class="sig-prename descclassname"><span class="pre">spin.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">ElementaryOperator</span></a></span></span><a class="headerlink" href="#cudaq.spin.x" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.spin.y">
-<span class="sig-prename descclassname"><span class="pre">spin.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">ElementaryOperator</span></a></span></span><a class="headerlink" href="#cudaq.spin.y" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.spin.z">
-<span class="sig-prename descclassname"><span class="pre">spin.</span></span><span class="sig-name descname"><span class="pre">z</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.operator.expressions.ElementaryOperator" title="cudaq.operator.expressions.ElementaryOperator"><span class="pre">ElementaryOperator</span></a></span></span><a class="headerlink" href="#cudaq.spin.z" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.operator.cudm_state.CuDensityMatState">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.operator.cudm_state.</span></span><span class="sig-name descname"><span class="pre">CuDensityMatState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.cudm_state.CuDensityMatState" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="cudaq.operator.cudm_state.to_cupy_array">
-<span class="sig-prename descclassname"><span class="pre">cudaq.operator.cudm_state.</span></span><span class="sig-name descname"><span class="pre">to_cupy_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.operator.cudm_state.to_cupy_array" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.SampleResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">SampleResult</span></span><a class="headerlink" href="#cudaq.SampleResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>A data-type containing the results of a call to <a class="reference internal" href="#cudaq.sample" title="cudaq.sample"><code class="xref py py-func docutils literal notranslate"><span class="pre">sample()</span></code></a>.
-This includes all measurement counts data from both mid-circuit and
-terminal measurements.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>At this time, mid-circuit measurements are not directly supported.
-Mid-circuit measurements may only be used if they are passed through
-to <code class="code docutils literal notranslate"><span class="pre">c_if</span></code>.</p>
-</div>
-<dl class="py attribute">
-<dt class="sig sig-object py" id="cudaq.SampleResult.register_names">
-<span class="sig-name descname"><span class="pre">register_names</span></span><a class="headerlink" href="#cudaq.SampleResult.register_names" title="Permalink to this definition">¶</a></dt>
-<dd><p>A list of the names of each measurement
-register that are stored in <code class="code docutils literal notranslate"><span class="pre">self</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>List[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>]</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.__getitem__">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.__getitem__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">bitstring</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the measurement counts for the given <code class="code docutils literal notranslate"><span class="pre">bitstring</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>bitstring</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The binary string to return the measurement data of.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The number of times the given <code class="code docutils literal notranslate"><span class="pre">bitstring</span></code> was measured
-during the <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code> number of executions on the QPU.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.__iter__">
-<span class="sig-name descname"><span class="pre">__iter__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.__iter__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__iter__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Iterator</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Iterate through the <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> dictionary.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.__len__">
-<span class="sig-name descname"><span class="pre">__len__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.__len__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__len__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the number of elements in <code class="code docutils literal notranslate"><span class="pre">self</span></code>. Equivalent to the number of uniquely measured bitstrings.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.clear" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Clear out all metadata from <code class="code docutils literal notranslate"><span class="pre">self</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.count">
-<span class="sig-name descname"><span class="pre">count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.count" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">bitstring</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'__global__'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the number of times the given bitstring was observed.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>bitstring</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The binary string to return the measurement counts for.</p></li>
-<li><p><strong>register_name</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em>) – The optional measurement register name to
-extract the probability from. Defaults to the ‘__global__’ register.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The number of times the given bitstring was measured during the experiment.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.deserialize">
-<span class="sig-name descname"><span class="pre">deserialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.deserialize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">deserialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Deserialize this SampleResult from an existing vector of integers adhering to the implicit encoding.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.dump">
-<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.dump" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Print a string of the raw measurement counts data to the terminal.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.expectation">
-<span class="sig-name descname"><span class="pre">expectation</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.expectation" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'__global__'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the expectation value in the Z-basis of the <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> that was sampled.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.expectation_z">
-<span class="sig-name descname"><span class="pre">expectation_z</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.expectation_z" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation_z</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'__global__'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the expectation value in the Z-basis of the <a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> that was sampled.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.get_marginal_counts">
-<span class="sig-name descname"><span class="pre">get_marginal_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.get_marginal_counts" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get_marginal_counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">self:</span> <span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult,</span> <span class="pre">marginal_indices:</span> <span class="pre">list[int],</span> <span class="pre">\*,</span> <span class="pre">register_name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'__global__'</span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Extract the measurement counts data for the provided subset of
-qubits (<code class="code docutils literal notranslate"><span class="pre">marginal_indices</span></code>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>marginal_indices</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – A list of the qubit indices to extract the
-measurement data from.</p></li>
-<li><p><strong>register_name</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em>) – The optional measurement register name to extract
-the counts data from. Defaults to the ‘__global__’ register.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A new <code class="code docutils literal notranslate"><span class="pre">SampleResult</span></code> dictionary containing the extracted measurement data.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.get_register_counts">
-<span class="sig-name descname"><span class="pre">get_register_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.get_register_counts" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get_register_counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Extract the provided sub-register (<code class="code docutils literal notranslate"><span class="pre">register_name</span></code>) as a new <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.get_sequential_data">
-<span class="sig-name descname"><span class="pre">get_sequential_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.get_sequential_data" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get_sequential_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'__global__'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the data from the given register (<code class="code docutils literal notranslate"><span class="pre">register_name</span></code>) as it was collected sequentially. A list of measurement results, not collated into a map.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.items" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Iterator</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the key/value pairs in this <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> dictionary.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.most_probable">
-<span class="sig-name descname"><span class="pre">most_probable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.most_probable" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">most_probable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'__global__'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the bitstring that was measured most frequently in the
-experiment.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>register_name</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em>) – The optional measurement register
-name to extract the most probable bitstring from. Defaults to the
-‘__global__’ register.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The most frequently measured binary string during the experiment.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.probability">
-<span class="sig-name descname"><span class="pre">probability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.probability" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">probability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">bitstring</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">register_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'__global__'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the probability of measuring the given <code class="code docutils literal notranslate"><span class="pre">bitstring</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>bitstring</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The binary string to return the measurement
-probability of.</p></li>
-<li><p><strong>register_name</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em>) – The optional measurement register
-name to extract the probability from. Defaults to the ‘__global__’
-register.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The probability of measuring the given <code class="code docutils literal notranslate"><span class="pre">bitstring</span></code>. Equivalent
-to the proportion of the total times the bitstring was measured
-vs. the number of experiments (<code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>).</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.serialize">
-<span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.serialize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Serialize this SampleResult to a vector of integer encoding.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.SampleResult.values">
-<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.SampleResult.values" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Iterator</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Return all values (the counts) in this <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> dictionary.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.AsyncSampleResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">AsyncSampleResult</span></span><a class="headerlink" href="#cudaq.AsyncSampleResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>A data-type containing the results of a call to <a class="reference internal" href="#cudaq.sample_async" title="cudaq.sample_async"><code class="xref py py-func docutils literal notranslate"><span class="pre">sample_async()</span></code></a>.
-The <code class="code docutils literal notranslate"><span class="pre">AsyncSampleResult</span></code> models a future-like type, whose
-<a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> may be returned via an invocation of the <code class="code docutils literal notranslate"><span class="pre">get</span></code> method. This
-kicks off a wait on the current thread until the results are available.
-See <a class="reference external" href="https://en.cppreference.com/w/cpp/thread/future">future</a>
-for more information on this programming pattern.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.AsyncSampleResult.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.AsyncSampleResult.get" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.AsyncSampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> from the asynchronous sample execution.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.ObserveResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">ObserveResult</span></span><a class="headerlink" href="#cudaq.ObserveResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>A data-type containing the results of a call to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>. This includes any measurement counts data, as well as the global expectation value of the user-defined <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ObserveResult.counts">
-<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ObserveResult.counts" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Returns a <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> dictionary with the measurement results from the experiment. The result for each individual term of the <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> is stored in its own measurement register. Each register name corresponds to the string representation of the spin term (without any coefficients).</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_term</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><span class="pre">cudaq.SpinOperator</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Given a <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code> of the global <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> that was passed
-to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>, return its measurement counts.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>sub_term</strong> (<a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinOperator</span></code></a>) – An individual sub-term of the
-<code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The measurement counts data for the individual <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a></p>
-</dd>
-</dl>
-<ol class="arabic" start="3">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_term</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Given a <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code> of the global <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> that was passed
-to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>, return its measurement counts.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>sub_term</strong> (<a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinOperator</span></code></a>) – An individual sub-term of the
-<code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The measurement counts data for the individual <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ObserveResult.dump">
-<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ObserveResult.dump" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Dump the raw data from the <a class="reference internal" href="#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">SampleResult</span></code></a> that are stored in <a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.ObserveResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">ObserveResult</span></code></a> to the terminal.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ObserveResult.expectation">
-<span class="sig-name descname"><span class="pre">expectation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ObserveResult.expectation" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the expectation value of the <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> that was provided in <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_term</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the expectation value of an individual <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code> of the
-global <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> that was passed to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>sub_term</strong> (<a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinOperator</span></code></a>) – An individual sub-term of the
-<code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The expectation value of the <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code> with respect to the
-<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> that was passed to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ObserveResult.expectation_z">
-<span class="sig-name descname"><span class="pre">expectation_z</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ObserveResult.expectation_z" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation_z</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the expectation value of the <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> that was
-provided in <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p><code class="code docutils literal notranslate"><span class="pre">expectation_z</span></code> has been deprecated in favor of <code class="code docutils literal notranslate"><span class="pre">expectation</span></code>.</p>
-</div>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">expectation_z</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_term</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Return the expectation value of an individual <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code> of the
-global <code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code> that was passed to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p><code class="code docutils literal notranslate"><span class="pre">expectation_z</span></code> has been deprecated in favor of <code class="code docutils literal notranslate"><span class="pre">expectation</span></code>.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>sub_term</strong> (<a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinOperator</span></code></a>) – An individual sub-term of the
-<code class="code docutils literal notranslate"><span class="pre">spin_operator</span></code>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The expectation value of the <code class="code docutils literal notranslate"><span class="pre">sub_term</span></code> with respect to the
-<a class="reference internal" href="#cudaq.Kernel" title="cudaq.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> that was passed to <a class="reference internal" href="#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe()</span></code></a>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.ObserveResult.get_spin">
-<span class="sig-name descname"><span class="pre">get_spin</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.ObserveResult.get_spin" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get_spin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SpinOperator" title="cudaq.SpinOperator"><span class="pre">cudaq.SpinOperator</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <code class="code docutils literal notranslate"><span class="pre">SpinOperator</span></code> corresponding to this <code class="code docutils literal notranslate"><span class="pre">ObserveResult</span></code>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.AsyncObserveResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">AsyncObserveResult</span></span><a class="headerlink" href="#cudaq.AsyncObserveResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>A data-type containing the results of a call to <a class="reference internal" href="#cudaq.observe_async" title="cudaq.observe_async"><code class="xref py py-func docutils literal notranslate"><span class="pre">observe_async()</span></code></a>.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">AsyncObserveResult</span></code> contains a future, whose <a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.ObserveResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">ObserveResult</span></code></a>
-may be returned via an invocation of the <code class="code docutils literal notranslate"><span class="pre">get</span></code> method.</p>
-<p>This kicks off a wait on the current thread until the results are available.</p>
-<p>See <a class="reference external" href="https://en.cppreference.com/w/cpp/thread/future">future</a>
-for more information on this programming pattern.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.AsyncObserveResult.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.AsyncObserveResult.get" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.AsyncObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncObserveResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.ObserveResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns the <a class="reference internal" href="#cudaq.ObserveResult" title="cudaq.ObserveResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">ObserveResult</span></code></a> from the asynchronous observe execution.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.AsyncStateResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">AsyncStateResult</span></span><a class="headerlink" href="#cudaq.AsyncStateResult" title="Permalink to this definition">¶</a></dt>
-<dd><p>A data-type containing the results of a call to <a class="reference internal" href="#cudaq.get_state_async" title="cudaq.get_state_async"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_state_async()</span></code></a>.
-The <code class="code docutils literal notranslate"><span class="pre">AsyncStateResult</span></code> models a future-like type, whose
-<a class="reference internal" href="#cudaq.State" title="cudaq.State"><code class="xref py py-class docutils literal notranslate"><span class="pre">State</span></code></a> may be returned via an invocation of the <code class="code docutils literal notranslate"><span class="pre">get</span></code> method. This
-kicks off a wait on the current thread until the results are available.
-See <a class="reference external" href="https://en.cppreference.com/w/cpp/thread/future">future</a>
-for more information on this programming pattern.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.AsyncStateResult.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.AsyncStateResult.get" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.AsyncStateResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncStateResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncStateResult</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.State" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <a class="reference internal" href="#cudaq.State" title="cudaq.State"><code class="xref py py-class docutils literal notranslate"><span class="pre">State</span></code></a> from the asynchronous <code class="code docutils literal notranslate"><span class="pre">get_state</span></code> accessor execution.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.OptimizationResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">OptimizationResult</span></span><a class="headerlink" href="#cudaq.OptimizationResult" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<section id="optimizers">
-<h3>Optimizers<a class="headerlink" href="#optimizers" title="Permalink to this heading">¶</a></h3>
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.optimizers.optimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.optimizers.</span></span><span class="sig-name descname"><span class="pre">optimizer</span></span><a class="headerlink" href="#cudaq.optimizers.optimizer" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.optimizers.</span></span><span class="sig-name descname"><span class="pre">GradientDescent</span></span><a class="headerlink" href="#cudaq.optimizers.GradientDescent" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.optimizers.GradientDescent" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to optimizer</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.initial_parameters">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">initial_parameters</span></span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.initial_parameters" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the initial parameter values for the optimization.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.lower_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">lower_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.lower_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the lower value bound for the optimization parameters.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.max_iterations">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iterations</span></span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.max_iterations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the maximum number of optimizer iterations.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.optimize">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.optimize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.GradientDescent" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Run <code class="code docutils literal notranslate"><span class="pre">cudaq.optimize()</span></code> on the provided objective function.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.requires_gradients">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.requires_gradients" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.GradientDescent" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns whether the optimizer requires gradient.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.GradientDescent" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.GradientDescent</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert optimizer to JSON string</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.GradientDescent.upper_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">upper_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.GradientDescent.upper_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the upper value bound for the optimization parameters.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.optimizers.</span></span><span class="sig-name descname"><span class="pre">COBYLA</span></span><a class="headerlink" href="#cudaq.optimizers.COBYLA" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.COBYLA.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.optimizers.COBYLA" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to optimizer</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.initial_parameters">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">initial_parameters</span></span><a class="headerlink" href="#cudaq.optimizers.COBYLA.initial_parameters" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the initial parameter values for the optimization.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.lower_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">lower_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.COBYLA.lower_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the lower value bound for the optimization parameters.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.max_iterations">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iterations</span></span><a class="headerlink" href="#cudaq.optimizers.COBYLA.max_iterations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the maximum number of optimizer iterations.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.optimize">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.COBYLA.optimize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.COBYLA" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Run <code class="code docutils literal notranslate"><span class="pre">cudaq.optimize()</span></code> on the provided objective function.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.requires_gradients">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.COBYLA.requires_gradients" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.COBYLA" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns whether the optimizer requires gradient.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.COBYLA.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.COBYLA" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.COBYLA</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert optimizer to JSON string</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.COBYLA.upper_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">upper_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.COBYLA.upper_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the upper value bound for the optimization parameters.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.optimizers.</span></span><span class="sig-name descname"><span class="pre">NelderMead</span></span><a class="headerlink" href="#cudaq.optimizers.NelderMead" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.NelderMead.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.optimizers.NelderMead" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to optimizer</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.initial_parameters">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">initial_parameters</span></span><a class="headerlink" href="#cudaq.optimizers.NelderMead.initial_parameters" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the initial parameter values for the optimization.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.lower_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">lower_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.NelderMead.lower_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the lower value bound for the optimization parameters.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.max_iterations">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iterations</span></span><a class="headerlink" href="#cudaq.optimizers.NelderMead.max_iterations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the maximum number of optimizer iterations.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.optimize">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.NelderMead.optimize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.NelderMead" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Run <code class="code docutils literal notranslate"><span class="pre">cudaq.optimize()</span></code> on the provided objective function.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.requires_gradients">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.NelderMead.requires_gradients" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.NelderMead" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns whether the optimizer requires gradient.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.NelderMead.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.NelderMead" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.NelderMead</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert optimizer to JSON string</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.NelderMead.upper_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">upper_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.NelderMead.upper_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the upper value bound for the optimization parameters.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.optimizers.</span></span><span class="sig-name descname"><span class="pre">LBFGS</span></span><a class="headerlink" href="#cudaq.optimizers.LBFGS" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.LBFGS.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.optimizers.LBFGS" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to optimizer</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.initial_parameters">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">initial_parameters</span></span><a class="headerlink" href="#cudaq.optimizers.LBFGS.initial_parameters" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the initial parameter values for the optimization.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.lower_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">lower_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.LBFGS.lower_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the lower value bound for the optimization parameters.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.max_iterations">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iterations</span></span><a class="headerlink" href="#cudaq.optimizers.LBFGS.max_iterations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the maximum number of optimizer iterations.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.optimize">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.LBFGS.optimize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.LBFGS" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Run <code class="code docutils literal notranslate"><span class="pre">cudaq.optimize()</span></code> on the provided objective function.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.requires_gradients">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.LBFGS.requires_gradients" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">requires_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.LBFGS" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns whether the optimizer requires gradient.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.optimizers.LBFGS.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.optimizers.LBFGS" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.optimizers.LBFGS</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert optimizer to JSON string</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.optimizers.LBFGS.upper_bounds">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">upper_bounds</span></span><a class="headerlink" href="#cudaq.optimizers.LBFGS.upper_bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the upper value bound for the optimization parameters.</p>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="gradients">
-<h3>Gradients<a class="headerlink" href="#gradients" title="Permalink to this heading">¶</a></h3>
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.gradients.gradient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.gradients.</span></span><span class="sig-name descname"><span class="pre">gradient</span></span><a class="headerlink" href="#cudaq.gradients.gradient" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.gradients.CentralDifference">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.gradients.</span></span><span class="sig-name descname"><span class="pre">CentralDifference</span></span><a class="headerlink" href="#cudaq.gradients.CentralDifference" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.CentralDifference.compute">
-<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.CentralDifference.compute" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.gradient" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_vector</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funcAtX</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Compute the gradient of the provided <code class="code docutils literal notranslate"><span class="pre">parameter_vector</span></code> with respect to its loss function, using the <code class="code docutils literal notranslate"><span class="pre">CentralDifference</span></code> method.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.CentralDifference.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.CentralDifference.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.gradients.CentralDifference" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.CentralDifference"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.CentralDifference</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to gradient</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.CentralDifference.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.CentralDifference.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.CentralDifference" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.CentralDifference"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.CentralDifference</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert gradient to JSON string</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.gradients.ForwardDifference">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.gradients.</span></span><span class="sig-name descname"><span class="pre">ForwardDifference</span></span><a class="headerlink" href="#cudaq.gradients.ForwardDifference" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.ForwardDifference.compute">
-<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.ForwardDifference.compute" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.gradient" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_vector</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funcAtX</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Compute the gradient of the provided <code class="code docutils literal notranslate"><span class="pre">parameter_vector</span></code> with respect to its loss function, using the <code class="code docutils literal notranslate"><span class="pre">ForwardDifference</span></code> method.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.ForwardDifference.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.ForwardDifference.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.gradients.ForwardDifference" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ForwardDifference"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ForwardDifference</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to gradient</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.ForwardDifference.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.ForwardDifference.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.ForwardDifference" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ForwardDifference"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ForwardDifference</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert gradient to JSON string</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.gradients.ParameterShift">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.gradients.</span></span><span class="sig-name descname"><span class="pre">ParameterShift</span></span><a class="headerlink" href="#cudaq.gradients.ParameterShift" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.ParameterShift.compute">
-<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.ParameterShift.compute" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.gradient" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.gradient</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_vector</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funcAtX</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Compute the gradient of the provided <code class="code docutils literal notranslate"><span class="pre">parameter_vector</span></code> with respect to its loss function, using the <code class="code docutils literal notranslate"><span class="pre">ParameterShift</span></code> method.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.ParameterShift.from_json">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.ParameterShift.from_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.gradients.ParameterShift" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ParameterShift"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ParameterShift</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert JSON string to gradient</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.gradients.ParameterShift.to_json">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.gradients.ParameterShift.to_json" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.gradients.ParameterShift" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ParameterShift"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.gradients.ParameterShift</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Convert gradient to JSON string</p>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-<section id="noisy-simulation">
-<h3>Noisy Simulation<a class="headerlink" href="#noisy-simulation" title="Permalink to this heading">¶</a></h3>
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.NoiseModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">NoiseModel</span></span><a class="headerlink" href="#cudaq.NoiseModel" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">NoiseModel</span></code> defines a set of <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a>’s applied to specific qubits after the invocation of specified quantum operations.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.NoiseModel.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.NoiseModel.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Construct an empty noise model.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.NoiseModel.add_all_qubit_channel">
-<span class="sig-name descname"><span class="pre">add_all_qubit_channel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.NoiseModel.add_all_qubit_channel" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">add_all_qubit_channel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><span class="pre">cudaq.KrausChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_controls</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Add the given <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a> to be applied after invocation
-of the specified quantum operation on arbitrary qubits.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>operator</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The quantum operator to apply the noise channel to.</p></li>
-<li><p><strong>channel</strong> (<a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><em>cudaq.KrausChannel</em></a>) – The <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a> to apply
-to the specified <code class="code docutils literal notranslate"><span class="pre">operator</span></code> on any arbitrary qubits.</p></li>
-<li><p><strong>num_controls</strong> – Number of control bits. Default is 0 (no control bits).</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.NoiseModel.add_channel">
-<span class="sig-name descname"><span class="pre">add_channel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.NoiseModel.add_channel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">add_channel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">qubits</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><span class="pre">cudaq.KrausChannel</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Add the given <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a> to be applied after invocation
-of the specified quantum operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>operator</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The quantum operator to apply the noise channel to.</p></li>
-<li><p><strong>qubits</strong> (<em>List</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – The qubit/s to apply the noise channel to.</p></li>
-<li><p><strong>channel</strong> (<a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><em>cudaq.KrausChannel</em></a>) – The <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a> to apply
-to the specified <code class="code docutils literal notranslate"><span class="pre">operator</span></code> on the specified <code class="code docutils literal notranslate"><span class="pre">qubits</span></code>.</p></li>
-</ul>
-</dd>
-</dl>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">add_channel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><span class="pre">cudaq.KrausChannel</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Add the given <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a> generator callback to be applied after invocation
-of the specified quantum operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>operator</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The quantum operator to apply the noise channel to.</p></li>
-<li><p><strong>pre</strong> (<em>Callable</em>) – The callback which takes qubits operands and gate parameters and returns a concrete <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a> to apply
-to the specified <code class="code docutils literal notranslate"><span class="pre">operator</span></code>.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.NoiseModel.get_channels">
-<span class="sig-name descname"><span class="pre">get_channels</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.NoiseModel.get_channels" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">get_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.NoiseModel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">qubits</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><span class="pre">cudaq.KrausChannel</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a>’s that make up this noise model.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.BitFlipChannel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">BitFlipChannel</span></span><a class="headerlink" href="#cudaq.BitFlipChannel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Models the decoherence of the qubit state. Its constructor expects a
-float value, <code class="code docutils literal notranslate"><span class="pre">probability</span></code>, representing the probability that the qubit
-flips from the 1-state to the 0-state, or vice versa. E.g, the
-probability of a random X-180 rotation being applied to the qubit.</p>
-<p>The Kraus Channels are thereby defined to be:</p>
-<p>K_0 = sqrt(1 - probability) * I</p>
-<p>K_1 = sqrt(probability ) * X</p>
-<p>The probability of the qubit remaining in the same state is therefore <code class="code docutils literal notranslate"><span class="pre">1</span> <span class="pre">-</span>
-<span class="pre">probability</span></code>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.BitFlipChannel.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.BitFlipChannel.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.BitFlipChannel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.BitFlipChannel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.BitFlipChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Initialize the <code class="code docutils literal notranslate"><span class="pre">BitFlipChannel</span></code> with the provided <code class="code docutils literal notranslate"><span class="pre">probability</span></code>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.PhaseFlipChannel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">PhaseFlipChannel</span></span><a class="headerlink" href="#cudaq.PhaseFlipChannel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Models the decoherence of the qubit phase. Its constructor expects a
-float value, <code class="code docutils literal notranslate"><span class="pre">probability</span></code>, representing the probability of a random
-Z-180 rotation being applied to the qubit.</p>
-<p>The Kraus Channels are thereby defined to be:</p>
-<p>K_0 = sqrt(1 - probability) * I</p>
-<p>K_1 = sqrt(probability ) * Z</p>
-<p>The probability of the qubit phase remaining untouched is therefore
-<code class="code docutils literal notranslate"><span class="pre">1</span> <span class="pre">-</span> <span class="pre">probability</span></code>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.PhaseFlipChannel.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.PhaseFlipChannel.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.PhaseFlipChannel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.PhaseFlipChannel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.PhaseFlipChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Initialize the <code class="code docutils literal notranslate"><span class="pre">PhaseFlipChannel</span></code> with the provided <code class="code docutils literal notranslate"><span class="pre">probability</span></code>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.DepolarizationChannel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">DepolarizationChannel</span></span><a class="headerlink" href="#cudaq.DepolarizationChannel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Models the decoherence of the qubit state and phase into a mixture ”
-of the computational basis states, <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> and <code class="code docutils literal notranslate"><span class="pre">|1&gt;</span></code>.</p>
-<p>The Kraus Channels are thereby defined to be:</p>
-<p>K_0 = sqrt(1 - probability) * I</p>
-<p>K_1 = sqrt(probability / 3) * X</p>
-<p>K_2 = sqrt(probability / 3) * Y</p>
-<p>K_3 = sqrt(probability / 3) * Z</p>
-<p>where I, X, Y, Z are the 2x2 Pauli matrices.</p>
-<p>The constructor expects a float value, <code class="code docutils literal notranslate"><span class="pre">probability</span></code>, representing the
-probability the state decay will occur. The qubit will remain untouched,
-therefore, with a probability of <code class="code docutils literal notranslate"><span class="pre">1</span> <span class="pre">-</span> <span class="pre">probability</span></code>. And the X,Y,Z operators
-will be applied with a probability of <code class="code docutils literal notranslate"><span class="pre">probability</span> <span class="pre">/</span> <span class="pre">3</span></code>.</p>
-<p>For <code class="code docutils literal notranslate"><span class="pre">probability</span> <span class="pre">=</span> <span class="pre">0.0</span></code>, the channel will behave noise-free.
-For <code class="code docutils literal notranslate"><span class="pre">probability</span> <span class="pre">=</span> <span class="pre">0.75</span></code>, the channel will fully depolarize the state.
-For <code class="code docutils literal notranslate"><span class="pre">probability</span> <span class="pre">=</span> <span class="pre">1.0</span></code>, the channel will be uniform.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.DepolarizationChannel.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.DepolarizationChannel.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.DepolarizationChannel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.DepolarizationChannel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.DepolarizationChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Initialize the <code class="code docutils literal notranslate"><span class="pre">DepolarizationChannel</span></code> with the provided <code class="code docutils literal notranslate"><span class="pre">probability</span></code>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.AmplitudeDampingChannel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">AmplitudeDampingChannel</span></span><a class="headerlink" href="#cudaq.AmplitudeDampingChannel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Models the dissipation of energy due to system interactions with the
-environment.</p>
-<p>The Kraus Channels are thereby defined to be:</p>
-<p>K_0 = sqrt(1 - probability) * I</p>
-<p>K_1 = sqrt(probability) * 0.5 * (X + iY)</p>
-<p>Its constructor expects a float value, <code class="code docutils literal notranslate"><span class="pre">probability</span></code>,
-representing the probablity that the qubit will decay to its ground
-state. The probability of the qubit remaining in the same state is
-therefore <code class="code docutils literal notranslate"><span class="pre">1</span> <span class="pre">-</span> <span class="pre">probability</span></code>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.AmplitudeDampingChannel.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.AmplitudeDampingChannel.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.AmplitudeDampingChannel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AmplitudeDampingChannel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AmplitudeDampingChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Initialize the <code class="code docutils literal notranslate"><span class="pre">AmplitudeDampingChannel</span></code> with the provided <code class="code docutils literal notranslate"><span class="pre">probability</span></code>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.KrausChannel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">KrausChannel</span></span><a class="headerlink" href="#cudaq.KrausChannel" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">KrausChannel</span></code> is composed of a list of <a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.KrausOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausOperator</span></code></a>’s and is applied to a specific qubit or set of qubits.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.KrausChannel.__getitem__">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.KrausChannel.__getitem__" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausChannel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausOperator"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausOperator</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the <a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.KrausOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausOperator</span></code></a> at the given index in this <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.KrausChannel.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.KrausChannel.append" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausChannel"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausChannel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausOperator"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.KrausOperator</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Add a <a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.KrausOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausOperator</span></code></a> to this <a class="reference internal" href="#cudaq.KrausChannel" title="cudaq.KrausChannel"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausChannel</span></code></a>.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="cudaq.KrausOperator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cudaq.</span></span><span class="sig-name descname"><span class="pre">KrausOperator</span></span><a class="headerlink" href="#cudaq.KrausOperator" title="Permalink to this definition">¶</a></dt>
-<dd><p>The <code class="code docutils literal notranslate"><span class="pre">KrausOperator</span></code> is represented by a matrix and serves as an element of a quantum channel such that <code class="code docutils literal notranslate"><span class="pre">Sum</span> <span class="pre">Ki</span> <span class="pre">Ki^dag</span> <span class="pre">=</span> <span class="pre">I.</span></code></p>
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.KrausOperator.col_count">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">col_count</span></span><a class="headerlink" href="#cudaq.KrausOperator.col_count" title="Permalink to this definition">¶</a></dt>
-<dd><p>The number of columns in the matrix representation of this <a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.KrausOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausOperator</span></code></a>.</p>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="cudaq.KrausOperator.row_count">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">row_count</span></span><a class="headerlink" href="#cudaq.KrausOperator.row_count" title="Permalink to this definition">¶</a></dt>
-<dd><p>The number of rows in the matrix representation of this <a class="reference internal" href="#cudaq.KrausOperator" title="cudaq.KrausOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">KrausOperator</span></code></a>.</p>
-</dd></dl>
-
-</dd></dl>
-
-</section>
-</section>
-<section id="mpi-submodule">
-<h2>MPI Submodule<a class="headerlink" href="#mpi-submodule" title="Permalink to this heading">¶</a></h2>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.initialize">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">initialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.initialize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">initialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Initialize MPI if available.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.rank">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">rank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.rank" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">rank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the rank of this process.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.num_ranks">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">num_ranks</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.num_ranks" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">num_ranks</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Return the total number of ranks.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.all_gather">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.all_gather" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Gather and scatter the <code class="code docutils literal notranslate"><span class="pre">local</span></code> list of floating-point numbers, returning a concatenation of all lists across all ranks. The total global list size must be provided.</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Gather and scatter the <code class="code docutils literal notranslate"><span class="pre">local</span></code> list of integers, returning a concatenation of all lists across all ranks. The total global list size must be provided.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.broadcast">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.broadcast" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
-<dd></dd></dl>
-
-<p>Broadcast an array from a process (rootRank) to all other processes. The size of broadcast array must be provided.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.is_initialized" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Returns true if MPI has already been initialized.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.mpi.finalize">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">finalize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.mpi.finalize" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.mpi.</span></span><span class="sig-name descname"><span class="pre">finalize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></dt>
-<dd></dd></dl>
-
-<p>Finalize MPI.</p>
-</dd></dl>
-
-</section>
-<section id="orca-submodule">
-<h2>ORCA Submodule<a class="headerlink" href="#orca-submodule" title="Permalink to this heading">¶</a></h2>
-<dl class="py method">
-<dt class="sig sig-object py" id="cudaq.orca.sample">
-<span class="sig-prename descclassname"><span class="pre">cudaq.orca.</span></span><span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cudaq.orca.sample" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overloaded function.</p>
-<ol class="arabic">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.orca.</span></span><span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loop_lengths</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bs_angles</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ps_angles</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qpu_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Performs Time Bin Interferometer (TBI) boson sampling experiments on ORCA’s backends</p>
-<ol class="arabic" start="2">
-<li><dl class="py function">
-<dt class="sig sig-object py">
-<span class="sig-prename descclassname"><span class="pre">cudaq.orca.</span></span><span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loop_lengths</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bs_angles</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qpu_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#cudaq.SampleResult" title="cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult"><span class="pre">cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.SampleResult</span></a></span></span></dt>
-<dd></dd></dl>
-
-</li>
-</ol>
-<p>Performs Time Bin Interferometer (TBI) boson sampling experiments on ORCA’s backends</p>
-</dd></dl>
-
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-</section>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-  <section id="Quantum-Enhanced-Auxiliary-Field-Quantum-Monte-Carlo">
-<h1>Quantum Enhanced Auxiliary Field Quantum Monte Carlo<a class="headerlink" href="#Quantum-Enhanced-Auxiliary-Field-Quantum-Monte-Carlo" title="Permalink to this heading">¶</a></h1>
-<p>This work was done in collaboration with the Next Generation Computing team at <a class="reference external" href="https://www.basf.com/global/en.html">BASF</a>.</p>
-<p>In this tutorial we implement a quantum-classical hybrid workflow for computing the ground state energies of a strongly interacting molecular system. The algorithm consists of two parts:</p>
-<ol class="arabic simple">
-<li><p>A variational quantum eigensolver that uses the quantum-number-preserving ansatz proposed by <a class="reference external" href="https://doi.org/10.1088/1367-2630/ac2cb3">Anselmetti et al. (2021)</a> to generate a quantum trial wave function <span class="math notranslate nohighlight">\(|\Psi_T\rangle\)</span> using CUDA Quantum.</p></li>
-<li><p>An Auxiliary-Field Quantum Monte Carlo simulation that realizes a classical imaginary time evolution and collects the ground state energy estimates.</p></li>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Package installs</span>
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pyscf</span><span class="o">==</span><span class="m">2</span>.6.2<span class="w"> </span><span class="nv">openfermion</span><span class="o">==</span><span class="m">1</span>.6.1<span class="w"> </span><span class="nv">ipie</span><span class="o">==</span><span class="m">0</span>.7.1<span class="w"> </span>-q
-</pre></div>
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-<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.</span><span class="ansi-yellow-fg">
-</span>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Relevant imports</span>
-
-<span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">from</span> <span class="nn">pyscf</span> <span class="kn">import</span> <span class="n">gto</span><span class="p">,</span> <span class="n">scf</span><span class="p">,</span> <span class="n">ao2mo</span><span class="p">,</span> <span class="n">mcscf</span>
-
-<span class="kn">from</span> <span class="nn">afqmc_src.vqe_cudaq_qnp</span> <span class="kn">import</span> <span class="n">VQE</span><span class="p">,</span> <span class="n">get_cudaq_hamiltonian</span>
-<span class="kn">from</span> <span class="nn">afqmc_src.utils_ipie</span> <span class="kn">import</span> <span class="n">get_coeff_wf</span><span class="p">,</span> <span class="n">gen_ipie_input_from_pyscf_chk</span>
-
-<span class="kn">from</span> <span class="nn">ipie.hamiltonians.generic</span> <span class="kn">import</span> <span class="n">Generic</span> <span class="k">as</span> <span class="n">HamGeneric</span>
-<span class="kn">from</span> <span class="nn">ipie.qmc.afqmc</span> <span class="kn">import</span> <span class="n">AFQMC</span>
-<span class="kn">from</span> <span class="nn">ipie.systems.generic</span> <span class="kn">import</span> <span class="n">Generic</span>
-<span class="kn">from</span> <span class="nn">ipie.trial_wavefunction.particle_hole</span> <span class="kn">import</span> <span class="n">ParticleHole</span>
-<span class="kn">from</span> <span class="nn">ipie.analysis.extraction</span> <span class="kn">import</span> <span class="n">extract_observable</span>
-
-<span class="kn">from</span> <span class="nn">ipie.config</span> <span class="kn">import</span> <span class="n">config</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-
-<span class="c1"># Ipie has recently added GPU support however this remains a bit tricky to use as it requires manual installation of several packages.</span>
-<span class="c1"># Once this is streamlined, we can set the GPU option to True in the tutorial.</span>
-<span class="n">config</span><span class="o">.</span><span class="n">update_option</span><span class="p">(</span><span class="s2">&quot;use_gpu&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (&gt;= 3.0.0) is not supported.
-  warnings.warn(
-</pre></div></div>
-</div>
-<p>We start by defining the structure of the molecule, the basis set, and its spin. We build the molecule object with PySCF and run a preliminary Hartree-Fock computation. Here we choose as an example a <a class="reference external" href="https://doi.org/10.1021/acs.jctc.3c01375">chelating agent</a> representing a relevant class of substances industrially produced at large scales. Their use ranges, among the others, from water softeners in cleaning applications, modulators of redox behaviour in oxidative bleaching, scale
-suppressants, soil remediation and ligands for catalysts. In particular we focus here in a Fe(III)-NTA complex whose structure is given in the file imported below.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the molecular structure and the basis set for the Fenta molecule.</span>
-
-<span class="n">atom</span> <span class="o">=</span> <span class="s2">&quot;afqmc_src/geo_fenta.xyz&quot;</span>
-<span class="n">basis</span> <span class="o">=</span> <span class="s2">&quot;cc-pVTZ&quot;</span>
-<span class="n">spin</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">num_active_orbitals</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">num_active_electrons</span> <span class="o">=</span> <span class="mi">5</span>
-
-<span class="c1"># You can swap to O3 which is a smaller system and takes less computational resources and time to run.</span>
-
-<span class="n">atom</span> <span class="o">=</span> <span class="s2">&quot;afqmc_src/geo_o3.xyz&quot;</span>
-<span class="n">basis</span> <span class="o">=</span> <span class="s2">&quot;cc-pVTZ&quot;</span>
-<span class="n">spin</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">num_active_orbitals</span> <span class="o">=</span> <span class="mi">9</span>
-<span class="n">num_active_electrons</span> <span class="o">=</span> <span class="mi">12</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># PYSCF helps to build the molecule and run Hartree-Fock.</span>
-
-<span class="c1"># Define the molecule.</span>
-<span class="n">molecule</span> <span class="o">=</span> <span class="n">gto</span><span class="o">.</span><span class="n">M</span><span class="p">(</span><span class="n">atom</span><span class="o">=</span><span class="n">atom</span><span class="p">,</span> <span class="n">spin</span><span class="o">=</span><span class="n">spin</span><span class="p">,</span> <span class="n">basis</span><span class="o">=</span><span class="n">basis</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="c1"># Restriced open shell HF.</span>
-<span class="n">hartee_fock</span> <span class="o">=</span> <span class="n">scf</span><span class="o">.</span><span class="n">ROHF</span><span class="p">(</span><span class="n">molecule</span><span class="p">)</span>
-<span class="n">hartee_fock</span><span class="o">.</span><span class="n">chkfile</span> <span class="o">=</span> <span class="s2">&quot;afqmc_src/output.chk&quot;</span>
-
-<span class="c1"># Run Hartree-Fock.</span>
-<span class="n">hartee_fock</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
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-<div class="highlight"><pre>
--224.34048064812222
-</pre></div></div>
-</div>
-<section id="Hamiltonian-preparation-for-VQE">
-<h2>Hamiltonian preparation for VQE<a class="headerlink" href="#Hamiltonian-preparation-for-VQE" title="Permalink to this heading">¶</a></h2>
-<p>Since this molecule contains of around 600 orbitals (which would correspond to 1200 qubits) and 143 total electrons, it is impossible to perform a full VQE with full statevector simulation. Therefore, we need to identify an active space with fewer orbitals and electrons that contribute to the strongly interacting part of the whole molecule. We then run a post Hartree-Fock computation with the PySCF’s built-in CASCI method in order to obtain the one-body (<span class="math notranslate nohighlight">\(t_{pq}\)</span>) and two-body
-(<span class="math notranslate nohighlight">\(V_{prqs}\)</span>) integrals that define the molecular Hamiltonian in the active space:</p>
-<div class="math notranslate nohighlight">
-\[H= \sum_{pq}t_{pq}\hat{a}_{p}^\dagger \hat {a}_{q}+\sum_{pqrs}  V_{prqs}\hat a_{p}^\dagger \hat a_{q}^\dagger \hat a_{s}\hat a_{r} \tag{1}\]</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">openfermion.transforms</span> <span class="kn">import</span> <span class="n">jordan_wigner</span>
-<span class="kn">from</span> <span class="nn">openfermion</span> <span class="kn">import</span> <span class="n">generate_hamiltonian</span>
-
-<span class="c1"># Run a CASCI simulation for computing the Hamiltonian in the active space.</span>
-<span class="n">casci</span> <span class="o">=</span> <span class="n">mcscf</span><span class="o">.</span><span class="n">CASCI</span><span class="p">(</span><span class="n">hartee_fock</span><span class="p">,</span> <span class="n">num_active_orbitals</span><span class="p">,</span> <span class="n">num_active_electrons</span><span class="p">)</span>
-<span class="n">casci</span><span class="o">.</span><span class="n">fix_spin_</span><span class="p">(</span><span class="n">ss</span><span class="o">=</span><span class="p">(</span><span class="n">molecule</span><span class="o">.</span><span class="n">spin</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">molecule</span><span class="o">.</span><span class="n">spin</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)))</span>
-
-<span class="c1"># Executes the kernel to compute the hamiltonian in the active space.</span>
-<span class="n">casci</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-
-<span class="c1"># Compute the one-body (h1) and two-body integrals (tbi) as shown in equation 1.</span>
-<span class="n">h1</span><span class="p">,</span> <span class="n">energy_core</span> <span class="o">=</span> <span class="n">casci</span><span class="o">.</span><span class="n">get_h1eff</span><span class="p">()</span>
-
-<span class="n">h2</span> <span class="o">=</span> <span class="n">casci</span><span class="o">.</span><span class="n">get_h2eff</span><span class="p">()</span>
-<span class="n">h2_no_symmetry</span> <span class="o">=</span> <span class="n">ao2mo</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="n">h2</span><span class="p">,</span> <span class="n">num_active_orbitals</span><span class="p">)</span>
-
-<span class="c1"># V_pqrs terms in H.</span>
-<span class="n">tbi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">h2_no_symmetry</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
-
-<span class="c1"># Compute the hamiltonian and convert it to a CUDA-Q operator.</span>
-<span class="n">mol_ham</span> <span class="o">=</span> <span class="n">generate_hamiltonian</span><span class="p">(</span><span class="n">h1</span><span class="p">,</span> <span class="n">tbi</span><span class="p">,</span> <span class="n">energy_core</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-<span class="n">jw_hamiltonian</span> <span class="o">=</span> <span class="n">jordan_wigner</span><span class="p">(</span><span class="n">mol_ham</span><span class="p">)</span>
-<span class="n">hamiltonian</span><span class="p">,</span> <span class="n">constant_term</span> <span class="o">=</span> <span class="n">get_cudaq_hamiltonian</span><span class="p">(</span><span class="n">jw_hamiltonian</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Run-VQE-with-CUDA-Q">
-<h2>Run VQE with CUDA-Q<a class="headerlink" href="#Run-VQE-with-CUDA-Q" title="Permalink to this heading">¶</a></h2>
-<p>We can now execute the VQE algorithm using the quantum number preserving ansatz. At the end of the VQE, we store the final statevector that will be used in the classical AFQMC computation as an initial guess.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define some options for the VQE.</span>
-<span class="n">options</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">&#39;n_vqe_layers&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
-    <span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
-    <span class="s1">&#39;energy_core&#39;</span><span class="p">:</span> <span class="n">constant_term</span><span class="p">,</span>
-    <span class="s1">&#39;return_final_state_vec&#39;</span><span class="p">:</span> <span class="kc">True</span>
-<span class="p">}</span>
-
-<span class="n">n_qubits</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_active_orbitals</span>
-
-<span class="n">vqe</span> <span class="o">=</span> <span class="n">VQE</span><span class="p">(</span><span class="n">n_qubits</span><span class="o">=</span><span class="n">n_qubits</span><span class="p">,</span>
-          <span class="n">num_active_electrons</span><span class="o">=</span><span class="n">num_active_electrons</span><span class="p">,</span>
-          <span class="n">spin</span><span class="o">=</span><span class="n">spin</span><span class="p">,</span>
-          <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">vqe</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="n">hamiltonian</span><span class="p">)</span>
-
-<span class="c1"># Best energy from VQE.</span>
-<span class="n">optimized_energy</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;energy_optimized&#39;</span><span class="p">]</span>
-
-<span class="c1"># Final state vector.</span>
-<span class="n">final_state_vector</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;state_vec&quot;</span><span class="p">]</span>
-
-<span class="c1"># Energies during the optimization loop.</span>
-<span class="n">vqe_energies</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;callback_energies&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-# Using cudaq optimizer
-# Num Params: 16
-# Qubits: 18
-# N_layers: 1
-# Energy after the VQE: -224.3881035525103
-</pre></div></div>
-</div>
-</section>
-<section id="Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">
-<h2>Auxiliary Field Quantum Monte Carlo (AFQMC)<a class="headerlink" href="#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)" title="Permalink to this heading">¶</a></h2>
-<p>AFQMC is a numerical method for computing relevant properties of strongly interacting molecules. AFQMC is a type of Quantum Monte Carlo method that combines the use of random walks with an auxiliary field to simulate the imaginary-time evolution of a quantum system and drive it to the lowest energy state. This method can provide accurate results for ground-state properties of a wide range of physical systems, including atoms, molecules, and solids. Here we summarize the main features of AFQMC
-while a detailed introduction to can be found <a class="reference external" href="https://www.cond-mat.de/events/correl13/manuscripts/zhang.pdf">here</a>.</p>
-<p>We consider the electronic Hamiltonian in the second quantization <span class="math">\begin{equation}
-H = {H}_1 + {H}_2
-=\sum_{pq} h_{pq} {a}_{p}^{\dagger} {a}_{q} + \frac{1}{2} \sum_{pqrs} v_{pqrs}{a}_{p}^{\dagger} {a}_r {a}^{\dagger}_{q} {a}_s \tag{2}
-\end{equation}</span> where <span class="math notranslate nohighlight">\({a}_{p}^{\dagger}\)</span> and <span class="math notranslate nohighlight">\({a}_{q}\)</span> are fermionic creation and annihilation operators of orbitals <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(q\)</span>, respectively. The terms $h_{pq} $ and <span class="math notranslate nohighlight">\(v_{pqrs}\)</span> are the matrix elements of the one-body, <span class="math notranslate nohighlight">\(H_1\)</span>, and two-body, <span class="math notranslate nohighlight">\(H_2\)</span>, interactions of <span class="math notranslate nohighlight">\(H\)</span>, respectively. Here, we omit the spin indices for simplicity.</p>
-<p>AFQMC realizes an imaginary time propagation of an initial state (chosen as a Slater determinant) <span class="math notranslate nohighlight">\(\ket{\Psi_{I}}\)</span> towards the ground state <span class="math notranslate nohighlight">\(\ket{\Psi_0}\)</span> of a given hamiltonian, <span class="math notranslate nohighlight">\(H\)</span>, with <span class="math">\begin{equation}
-\ket{\Psi_0} \sim\lim_{n \to \infty} \left[ e^{-\Delta\tau H  }  \right]^{n} \ket{\Psi_{I}}
-\tag{3}
-\end{equation}</span> where <span class="math notranslate nohighlight">\(\Delta\tau\)</span> is the imaginary time step.</p>
-<p>AFQMC relies on decomposing the two-body interactions <span class="math notranslate nohighlight">\(H_2\)</span> in terms of sum of squares of one-body operators <span class="math notranslate nohighlight">\({v}_\gamma\)</span> such that the Hamiltonian <span class="math notranslate nohighlight">\({H}\)</span> becomes <span class="math">\begin{equation}
-H = v_0 - \frac{1}{2}\sum_{\gamma=1}^{N_\gamma} {v}_\gamma^2
-\tag{4}
-\end{equation}</span> with ${v}<em>0 = {H}1 $ and $ {v}:nbsphinx-math:`gamma `= i :nbsphinx-math:`sum`</em>{pq} L<sup>{:nbsphinx-math:</sup>gamma`}{pq} {a}{p}`{<span class="math">\dagger</span>}{a}_{q}. $ The <span class="math notranslate nohighlight">\(N_\gamma\)</span> matrices <span class="math notranslate nohighlight">\(L^{\gamma}_{pq}\)</span> are called Cholesky vectors as they are obtained via a Cholesky decomposition of the two-body matrix elements <span class="math notranslate nohighlight">\(v_{pqrs}\)</span> via <span class="math notranslate nohighlight">\(v_{pqrs} = \sum_{\gamma=1}^{N_\gamma} L^{\gamma}_{pr} L^{\gamma}_{qs}\)</span>.</p>
-<p>The imaginary time propagation evolves an ensemble of walkers <span class="math notranslate nohighlight">\(\{\phi^{(n)}\}\)</span> (that are Slater determinants) and allows one to access observables of the system. For example, the local energy <span class="math">\begin{equation}
-\mathcal{E}_{\text{loc}}(\phi^{(n)}) = \frac{\bra{\Psi_\mathrm{T}}H\ket{\phi^{(n)}}}{\braket{\Psi_\mathrm{T}| \phi^{(n)}}}
-\tag{5}
-\end{equation}</span> defined as the mixed expectation value of the Hamiltonian with the trial wave function <span class="math notranslate nohighlight">\(\ket{\Psi_\mathrm{T}}\)</span>.</p>
-<p>The trial wavefunction can be in general a single or a multi-Slater determinant coming from VQE for example. This might help in achieving more accurate ground state energy estimates.</p>
-<p>The implementation of AFQMC we use here is from <a class="reference external" href="https://github.com/JoonhoLee-Group/ipie">ipie</a> that supports both CPUs and GPUs and requires the following steps:</p>
-<ol class="arabic simple">
-<li><p>Preparation of the molecular Hamiltonian by performing the Cholesky decomposition</p></li>
-<li><p>Preparation of the trial state from the VQE wavefunction</p></li>
-<li><p>Executing AFQMC</p></li>
-</ol>
-</section>
-<section id="Preparation-of-the-molecular-Hamiltonian">
-<h2>Preparation of the molecular Hamiltonian<a class="headerlink" href="#Preparation-of-the-molecular-Hamiltonian" title="Permalink to this heading">¶</a></h2>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># AFQMC.</span>
-
-<span class="c1"># Generate the input Hamiltonian for ipie from the checkpoint file from pyscf.</span>
-<span class="n">ipie_hamiltonian</span> <span class="o">=</span> <span class="n">gen_ipie_input_from_pyscf_chk</span><span class="p">(</span><span class="n">hartee_fock</span><span class="o">.</span><span class="n">chkfile</span><span class="p">,</span>
-                                                 <span class="n">mcscf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                 <span class="n">chol_cut</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">)</span>
-
-<span class="n">h1e</span><span class="p">,</span> <span class="n">cholesky_vectors</span><span class="p">,</span> <span class="n">e0</span> <span class="o">=</span> <span class="n">ipie_hamiltonian</span>
-
-<span class="n">num_basis</span> <span class="o">=</span> <span class="n">cholesky_vectors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="n">num_chol</span> <span class="o">=</span> <span class="n">cholesky_vectors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-<span class="n">system</span> <span class="o">=</span> <span class="n">Generic</span><span class="p">(</span><span class="n">nelec</span><span class="o">=</span><span class="n">molecule</span><span class="o">.</span><span class="n">nelec</span><span class="p">)</span>
-
-<span class="n">afqmc_hamiltonian</span> <span class="o">=</span> <span class="n">HamGeneric</span><span class="p">(</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">h1e</span><span class="p">,</span> <span class="n">h1e</span><span class="p">]),</span>
-    <span class="n">cholesky_vectors</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">num_basis</span> <span class="o">*</span> <span class="n">num_basis</span><span class="p">,</span> <span class="n">num_chol</span><span class="p">)),</span> <span class="n">e0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-# Number of electrons in simulation: (12, 12)
-</pre></div></div>
-</div>
-</section>
-<section id="Preparation-of-the-trial-wave-function">
-<h2>Preparation of the trial wave function<a class="headerlink" href="#Preparation-of-the-trial-wave-function" title="Permalink to this heading">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build the trial wavefunction from the state vector computed via VQE.</span>
-<span class="n">wavefunction</span> <span class="o">=</span> <span class="n">get_coeff_wf</span><span class="p">(</span><span class="n">final_state_vector</span><span class="p">,</span>
-                            <span class="n">n_active_elec</span><span class="o">=</span><span class="n">num_active_electrons</span><span class="p">,</span>
-                            <span class="n">spin</span><span class="o">=</span><span class="n">spin</span><span class="p">)</span>
-
-<span class="n">trial</span> <span class="o">=</span> <span class="n">ParticleHole</span><span class="p">(</span><span class="n">wavefunction</span><span class="p">,</span>
-                     <span class="n">molecule</span><span class="o">.</span><span class="n">nelec</span><span class="p">,</span>
-                     <span class="n">num_basis</span><span class="p">,</span>
-                     <span class="n">num_dets_for_props</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">wavefunction</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-                     <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="n">trial</span><span class="o">.</span><span class="n">compute_trial_energy</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="n">trial</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
-<span class="n">trial</span><span class="o">.</span><span class="n">half_rotate</span><span class="p">(</span><span class="n">afqmc_hamiltonian</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Setup-of-the-AFQMC-parameters">
-<h2>Setup of the AFQMC parameters<a class="headerlink" href="#Setup-of-the-AFQMC-parameters" title="Permalink to this heading">¶</a></h2>
-<p>Here we can choose the input options like the timestep <span class="math notranslate nohighlight">\(\Delta\tau\)</span>, the total number of walkers <code class="docutils literal notranslate"><span class="pre">num_walkers</span></code> and the total number of AFQMC iterations <code class="docutils literal notranslate"><span class="pre">num_blocks</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Setup the AFQMC parameters.</span>
-<span class="n">afqmc_msd</span> <span class="o">=</span> <span class="n">AFQMC</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">molecule</span><span class="o">.</span><span class="n">nelec</span><span class="p">,</span>
-                        <span class="n">afqmc_hamiltonian</span><span class="p">,</span>
-                        <span class="n">trial</span><span class="p">,</span>
-                        <span class="n">num_walkers</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                        <span class="n">num_steps_per_block</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
-                        <span class="n">num_blocks</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-                        <span class="n">timestep</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span>
-                        <span class="n">stabilize_freq</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                        <span class="n">seed</span><span class="o">=</span><span class="mi">96264512</span><span class="p">,</span>
-                        <span class="n">pop_control_freq</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                        <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># Run the AFQMC.</span>
-<span class="n">afqmc_msd</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">estimator_filename</span><span class="o">=</span><span class="s1">&#39;afqmc_src/estimates.0.h5&#39;</span><span class="p">)</span>
-<span class="n">afqmc_msd</span><span class="o">.</span><span class="n">finalise</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># Extract the energies.</span>
-<span class="n">qmc_data</span> <span class="o">=</span> <span class="n">extract_observable</span><span class="p">(</span><span class="n">afqmc_msd</span><span class="o">.</span><span class="n">estimators</span><span class="o">.</span><span class="n">filename</span><span class="p">,</span> <span class="s2">&quot;energy&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-# random seed is 96264512
-            Block                   Weight            WeightFactor            HybridEnergy                  ENumer                  EDenom                  ETotal                  E1Body                  E2Body
-                0   1.0000000000000000e+02  1.0000000000000000e+02  0.0000000000000000e+00 -2.2437583763935545e+04  1.0000000000000000e+02 -2.2437583763935547e+02 -3.7639365190228011e+02  1.5201781426292453e+02
-                1   4.2276634193515412e+02  1.4127560668989827e+03 -1.1711742028818304e+02 -2.2473358126540003e+04  9.9999999999999986e+01 -2.2473358126540006e+02 -3.7646854013277283e+02  1.5173495886737268e+02
-                2   1.0031922288872407e+02  3.8320523739865604e+02 -1.1743088014788954e+02 -2.2489226882493567e+04  1.0000000000000001e+02 -2.2489226882493563e+02 -3.7650504938463922e+02  1.5161278055970348e+02
-                3   9.9900990681040355e+01  1.0008400623205630e+02 -1.1736864885170948e+02 -2.2495677577437204e+04  9.9999999999999972e+01 -2.2495677577437212e+02 -3.7659644834889821e+02  1.5163967257452603e+02
-                4   1.0009188692360159e+02  1.0005173726372723e+02 -1.1748969527283802e+02 -2.2495531836556856e+04  1.0000000000000001e+02 -2.2495531836556853e+02 -3.7675907314082951e+02  1.5180375477526098e+02
-                5   9.9997269300807844e+01  1.0010618465796188e+02 -1.1752703012577417e+02 -2.2502732667629320e+04  1.0000000000000001e+02 -2.2502732667629317e+02 -3.7663343013337044e+02  1.5160610345707727e+02
-                6   1.0012131352337956e+02  1.0019003056579172e+02 -1.1770170647504112e+02 -2.2513369839216481e+04  1.0000000000000000e+02 -2.2513369839216480e+02 -3.7660812717909516e+02  1.5147442878693036e+02
-                7   9.9936984461419740e+01  9.9929966800671224e+01 -1.1765353928750643e+02 -2.2516138533920657e+04  1.0000000000000000e+02 -2.2516138533920659e+02 -3.7660292355465600e+02  1.5144153821544941e+02
-                8   9.9902337463172714e+01  9.9910800755312891e+01 -1.1761532255317621e+02 -2.2518524275281430e+04  9.9999999999999986e+01 -2.2518524275281433e+02 -3.7674246483479845e+02  1.5155722208198404e+02
-                9   1.0012943675389775e+02  1.0013880643723378e+02 -1.1780913595074867e+02 -2.2512465963277762e+04  1.0000000000000000e+02 -2.2512465963277762e+02 -3.7677999264623367e+02  1.5165533301345607e+02
-               10   9.9628730363609819e+01  9.9223106824565718e+01 -1.1749814144939067e+02 -2.2517668156221851e+04  1.0000000000000000e+02 -2.2517668156221850e+02 -3.7688306341863290e+02  1.5170638185641434e+02
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot the energies.</span>
-
-<span class="n">vqe_y</span> <span class="o">=</span> <span class="n">vqe_energies</span>
-<span class="n">vqe_x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vqe_y</span><span class="p">)))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vqe_x</span><span class="p">,</span> <span class="n">vqe_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;VQE&quot;</span><span class="p">)</span>
-
-<span class="n">afqmc_y</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">qmc_data</span><span class="p">[</span><span class="s2">&quot;ETotal&quot;</span><span class="p">])</span>
-<span class="n">afqmc_x</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">vqe_x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">afqmc_y</span><span class="p">)))]</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">afqmc_x</span><span class="p">,</span> <span class="n">afqmc_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;AFQMC&quot;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Optimization steps&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Energy [Ha]&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.legend.Legend at 0x7fea847f35e0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_afqmc_22_1.png" src="../../_images/applications_python_afqmc_22_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)
-</pre></div></div>
-</div>
-</section>
-</section>
-
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diff --git a/pr-2308/applications/python/afqmc.ipynb b/pr-2308/applications/python/afqmc.ipynb
deleted file mode 100644
index 6f150dca1d..0000000000
--- a/pr-2308/applications/python/afqmc.ipynb
+++ /dev/null
@@ -1,533 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quantum Enhanced Auxiliary Field Quantum Monte Carlo\n",
-    "\n",
-    "This work was done in collaboration with the Next Generation Computing team at [BASF](https://www.basf.com/global/en.html).\n",
-    "\n",
-    "In this tutorial we implement a quantum-classical hybrid workflow for computing the ground state energies of a strongly interacting molecular system. The algorithm consists of two parts:\n",
-    "\n",
-    "\n",
-    "1. A variational quantum eigensolver that uses the quantum-number-preserving ansatz proposed by [Anselmetti et al. (2021)](https://doi.org/10.1088/1367-2630/ac2cb3) to generate a quantum trial wave function $|\\Psi_T\\rangle$ using CUDA Quantum.\n",
-    "\n",
-    "2. An Auxiliary-Field Quantum Monte Carlo simulation that realizes a classical imaginary time evolution and collects the ground state energy estimates.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "# Package installs\n",
-    "!pip install pyscf==2.6.2 openfermion==1.6.1 ipie==0.7.1 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (>= 3.0.0) is not supported.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Relevant imports\n",
-    "\n",
-    "import cudaq\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from pyscf import gto, scf, ao2mo, mcscf\n",
-    "\n",
-    "from afqmc_src.vqe_cudaq_qnp import VQE, get_cudaq_hamiltonian\n",
-    "from afqmc_src.utils_ipie import get_coeff_wf, gen_ipie_input_from_pyscf_chk\n",
-    "\n",
-    "from ipie.hamiltonians.generic import Generic as HamGeneric\n",
-    "from ipie.qmc.afqmc import AFQMC\n",
-    "from ipie.systems.generic import Generic\n",
-    "from ipie.trial_wavefunction.particle_hole import ParticleHole\n",
-    "from ipie.analysis.extraction import extract_observable\n",
-    "\n",
-    "from ipie.config import config\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "\n",
-    "# Ipie has recently added GPU support however this remains a bit tricky to use as it requires manual installation of several packages.\n",
-    "# Once this is streamlined, we can set the GPU option to True in the tutorial.\n",
-    "config.update_option(\"use_gpu\", False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start by defining the structure of the molecule, the basis set, and its spin. We build the molecule object with PySCF and run a preliminary Hartree-Fock computation. Here we choose as an example a [chelating agent](https://doi.org/10.1021/acs.jctc.3c01375) representing a relevant class of substances industrially produced at large scales. Their use ranges, among the others, from water softeners in cleaning applications, modulators of redox behaviour in oxidative bleaching, scale suppressants, soil remediation and ligands for catalysts. In particular we focus here in a Fe(III)-NTA complex whose structure is given in the file imported below.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the molecular structure and the basis set for the Fenta molecule.\n",
-    "\n",
-    "atom = \"afqmc_src/geo_fenta.xyz\"\n",
-    "basis = \"cc-pVTZ\"\n",
-    "spin = 1\n",
-    "num_active_orbitals = 5\n",
-    "num_active_electrons = 5\n",
-    "\n",
-    "# You can swap to O3 which is a smaller system and takes less computational resources and time to run.\n",
-    "\n",
-    "atom = \"afqmc_src/geo_o3.xyz\"\n",
-    "basis = \"cc-pVTZ\"\n",
-    "spin = 0\n",
-    "num_active_orbitals = 9\n",
-    "num_active_electrons = 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-224.34048064812222"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# PYSCF helps to build the molecule and run Hartree-Fock.\n",
-    "\n",
-    "# Define the molecule.\n",
-    "molecule = gto.M(atom=atom, spin=spin, basis=basis, verbose=0)\n",
-    "\n",
-    "# Restriced open shell HF.\n",
-    "hartee_fock = scf.ROHF(molecule)\n",
-    "hartee_fock.chkfile = \"afqmc_src/output.chk\"\n",
-    "\n",
-    "# Run Hartree-Fock.\n",
-    "hartee_fock.kernel()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hamiltonian preparation for VQE\n",
-    "\n",
-    "Since this molecule contains of around 600 orbitals (which would correspond to 1200 qubits) and 143 total electrons, it is impossible to perform a full VQE with full statevector simulation. Therefore, we need to identify an active space with fewer orbitals and electrons that contribute to the strongly interacting part of the whole molecule. We then run a post Hartree-Fock computation with the PySCF's built-in CASCI method in order to obtain the one-body ($t_{pq}$) and two-body \n",
-    "($V_{prqs}$) integrals that define the molecular Hamiltonian in the active space:\n",
-    "\n",
-    "$$ H= \\sum_{pq}t_{pq}\\hat{a}_{p}^\\dagger \\hat {a}_{q}+\\sum_{pqrs}  V_{prqs}\\hat a_{p}^\\dagger \\hat a_{q}^\\dagger \\hat a_{s}\\hat a_{r} \\tag{1}$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from openfermion.transforms import jordan_wigner\n",
-    "from openfermion import generate_hamiltonian\n",
-    "\n",
-    "# Run a CASCI simulation for computing the Hamiltonian in the active space.\n",
-    "casci = mcscf.CASCI(hartee_fock, num_active_orbitals, num_active_electrons)\n",
-    "casci.fix_spin_(ss=(molecule.spin / 2 * (molecule.spin / 2 + 1)))\n",
-    "\n",
-    "# Executes the kernel to compute the hamiltonian in the active space.\n",
-    "casci.kernel()\n",
-    "\n",
-    "# Compute the one-body (h1) and two-body integrals (tbi) as shown in equation 1.\n",
-    "h1, energy_core = casci.get_h1eff()\n",
-    "\n",
-    "h2 = casci.get_h2eff()\n",
-    "h2_no_symmetry = ao2mo.restore('1', h2, num_active_orbitals)\n",
-    "\n",
-    "# V_pqrs terms in H.\n",
-    "tbi = np.asarray(h2_no_symmetry.transpose(0, 2, 3, 1), order='C')\n",
-    "\n",
-    "# Compute the hamiltonian and convert it to a CUDA-Q operator.\n",
-    "mol_ham = generate_hamiltonian(h1, tbi, energy_core.item())\n",
-    "jw_hamiltonian = jordan_wigner(mol_ham)\n",
-    "hamiltonian, constant_term = get_cudaq_hamiltonian(jw_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run VQE with CUDA-Q\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now execute the VQE algorithm using the quantum number preserving ansatz. At the end of the VQE, we store the final statevector that will be used in the classical AFQMC computation as an initial guess.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Using cudaq optimizer\n",
-      "# Num Params: 16\n",
-      "# Qubits: 18\n",
-      "# N_layers: 1\n",
-      "# Energy after the VQE: -224.3881035525103\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define some options for the VQE.\n",
-    "options = {\n",
-    "    'n_vqe_layers': 1,\n",
-    "    'maxiter': 100,\n",
-    "    'energy_core': constant_term,\n",
-    "    'return_final_state_vec': True\n",
-    "}\n",
-    "\n",
-    "n_qubits = 2 * num_active_orbitals\n",
-    "\n",
-    "vqe = VQE(n_qubits=n_qubits,\n",
-    "          num_active_electrons=num_active_electrons,\n",
-    "          spin=spin,\n",
-    "          options=options)\n",
-    "\n",
-    "results = vqe.execute(hamiltonian)\n",
-    "\n",
-    "# Best energy from VQE.\n",
-    "optimized_energy = results['energy_optimized']\n",
-    "\n",
-    "# Final state vector.\n",
-    "final_state_vector = results[\"state_vec\"]\n",
-    "\n",
-    "# Energies during the optimization loop.\n",
-    "vqe_energies = results[\"callback_energies\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Auxiliary Field Quantum Monte Carlo (AFQMC)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "AFQMC is a numerical method for computing relevant properties of strongly interacting molecules. AFQMC is a type of Quantum Monte Carlo method that combines the use of random walks with an auxiliary field to simulate the imaginary-time evolution of a quantum system and drive it to the lowest energy state. This method can provide accurate results for ground-state properties of a wide range of physical systems, including atoms, molecules, and solids. Here we summarize the main features of AFQMC while a detailed introduction to can be found [here](https://www.cond-mat.de/events/correl13/manuscripts/zhang.pdf).\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We consider the electronic Hamiltonian in the second quantization\n",
-    "\\begin{equation}\n",
-    "H = {H}_1 + {H}_2 \n",
-    "=\\sum_{pq} h_{pq} {a}_{p}^{\\dagger} {a}_{q} + \\frac{1}{2} \\sum_{pqrs} v_{pqrs}{a}_{p}^{\\dagger} {a}_r {a}^{\\dagger}_{q} {a}_s \\tag{2}\n",
-    "\\end{equation}\n",
-    "where ${a}_{p}^{\\dagger}$ and ${a}_{q}$ are fermionic creation and annihilation operators of orbitals $p$ and $q$, respectively. The terms $h_{pq} $ and \n",
-    "$v_{pqrs}$ are the matrix elements of the one-body, $H_1$, and two-body, $H_2$, interactions of $H$, respectively. Here, we omit the spin indices for simplicity.\n",
-    "\n",
-    "AFQMC realizes an imaginary time propagation of an initial state (chosen as a Slater determinant) $\\ket{\\Psi_{I}}$ towards the ground state $\\ket{\\Psi_0}$ of a given hamiltonian, $H$, with\n",
-    "\\begin{equation}\n",
-    "\\ket{\\Psi_0} \\sim\\lim_{n \\to \\infty} \\left[ e^{-\\Delta\\tau H  }  \\right]^{n} \\ket{\\Psi_{I}}\n",
-    "\\tag{3}\n",
-    "\\end{equation} \n",
-    "where $\\Delta\\tau$ is the imaginary time step.\n",
-    "\n",
-    "AFQMC relies on decomposing the two-body interactions $H_2$ in terms of sum of squares of one-body operators ${v}_\\gamma$ such that the Hamiltonian ${H}$ becomes\n",
-    "\\begin{equation}\n",
-    "H = v_0 - \\frac{1}{2}\\sum_{\\gamma=1}^{N_\\gamma} {v}_\\gamma^2\n",
-    "\\tag{4}\n",
-    "\\end{equation}\n",
-    "with ${v}_0 = {H}_1 $ and $\n",
-    "{v}_\\gamma = i \\sum_{pq} L^{\\gamma}_{pq} {a}_{p}^{\\dagger}{a}_{q}.\n",
-    "$\n",
-    "The $N_\\gamma$ matrices $L^{\\gamma}_{pq}$ are called Cholesky vectors as they are  obtained via a Cholesky decomposition of the two-body matrix elements \n",
-    "$v_{pqrs}$ via $v_{pqrs} = \\sum_{\\gamma=1}^{N_\\gamma} L^{\\gamma}_{pr} L^{\\gamma}_{qs}$.\n",
-    "\n",
-    "The imaginary time propagation evolves an ensemble of walkers $\\{\\phi^{(n)}\\}$ (that are Slater determinants) and allows one to access observables of the system. For example, the local energy\n",
-    "\\begin{equation}\n",
-    "\\mathcal{E}_{\\text{loc}}(\\phi^{(n)}) = \\frac{\\bra{\\Psi_\\mathrm{T}}H\\ket{\\phi^{(n)}}}{\\braket{\\Psi_\\mathrm{T}| \\phi^{(n)}}}\n",
-    "\\tag{5}\n",
-    "\\end{equation}\n",
-    "defined as the mixed expectation value of the Hamiltonian with the trial wave function $\\ket{\\Psi_\\mathrm{T}}$.\n",
-    "\n",
-    "\n",
-    "The trial wavefunction can be in general a single or a multi-Slater determinant coming from VQE for example. This might help in achieving more accurate ground state energy estimates.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The implementation of AFQMC we use here is from [ipie](https://github.com/JoonhoLee-Group/ipie) that supports both CPUs and GPUs and requires the following steps:\n",
-    "\n",
-    "\n",
-    "1. Preparation of the molecular Hamiltonian by performing the Cholesky decomposition\n",
-    "\n",
-    "2. Preparation of the trial state from the VQE wavefunction\n",
-    "\n",
-    "3. Executing AFQMC\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparation of the molecular Hamiltonian\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Number of electrons in simulation: (12, 12)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# AFQMC.\n",
-    "\n",
-    "# Generate the input Hamiltonian for ipie from the checkpoint file from pyscf.\n",
-    "ipie_hamiltonian = gen_ipie_input_from_pyscf_chk(hartee_fock.chkfile,\n",
-    "                                                 mcscf=True,\n",
-    "                                                 chol_cut=1e-5)\n",
-    "\n",
-    "h1e, cholesky_vectors, e0 = ipie_hamiltonian\n",
-    "\n",
-    "num_basis = cholesky_vectors.shape[1]\n",
-    "num_chol = cholesky_vectors.shape[0]\n",
-    "\n",
-    "system = Generic(nelec=molecule.nelec)\n",
-    "\n",
-    "afqmc_hamiltonian = HamGeneric(\n",
-    "    np.array([h1e, h1e]),\n",
-    "    cholesky_vectors.transpose((1, 2, 0)).reshape(\n",
-    "        (num_basis * num_basis, num_chol)), e0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparation of the trial wave function\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build the trial wavefunction from the state vector computed via VQE.\n",
-    "wavefunction = get_coeff_wf(final_state_vector,\n",
-    "                            n_active_elec=num_active_electrons,\n",
-    "                            spin=spin)\n",
-    "\n",
-    "trial = ParticleHole(wavefunction,\n",
-    "                     molecule.nelec,\n",
-    "                     num_basis,\n",
-    "                     num_dets_for_props=len(wavefunction[0]),\n",
-    "                     verbose=False)\n",
-    "\n",
-    "trial.compute_trial_energy = True\n",
-    "trial.build()\n",
-    "trial.half_rotate(afqmc_hamiltonian)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup of the AFQMC parameters\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we can choose the input options like the timestep $\\Delta\\tau$, the total number of walkers `num_walkers` and the total number of AFQMC iterations `num_blocks`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# random seed is 96264512\n",
-      "            Block                   Weight            WeightFactor            HybridEnergy                  ENumer                  EDenom                  ETotal                  E1Body                  E2Body\n",
-      "                0   1.0000000000000000e+02  1.0000000000000000e+02  0.0000000000000000e+00 -2.2437583763935545e+04  1.0000000000000000e+02 -2.2437583763935547e+02 -3.7639365190228011e+02  1.5201781426292453e+02\n",
-      "                1   4.2276634193515412e+02  1.4127560668989827e+03 -1.1711742028818304e+02 -2.2473358126540003e+04  9.9999999999999986e+01 -2.2473358126540006e+02 -3.7646854013277283e+02  1.5173495886737268e+02\n",
-      "                2   1.0031922288872407e+02  3.8320523739865604e+02 -1.1743088014788954e+02 -2.2489226882493567e+04  1.0000000000000001e+02 -2.2489226882493563e+02 -3.7650504938463922e+02  1.5161278055970348e+02\n",
-      "                3   9.9900990681040355e+01  1.0008400623205630e+02 -1.1736864885170948e+02 -2.2495677577437204e+04  9.9999999999999972e+01 -2.2495677577437212e+02 -3.7659644834889821e+02  1.5163967257452603e+02\n",
-      "                4   1.0009188692360159e+02  1.0005173726372723e+02 -1.1748969527283802e+02 -2.2495531836556856e+04  1.0000000000000001e+02 -2.2495531836556853e+02 -3.7675907314082951e+02  1.5180375477526098e+02\n",
-      "                5   9.9997269300807844e+01  1.0010618465796188e+02 -1.1752703012577417e+02 -2.2502732667629320e+04  1.0000000000000001e+02 -2.2502732667629317e+02 -3.7663343013337044e+02  1.5160610345707727e+02\n",
-      "                6   1.0012131352337956e+02  1.0019003056579172e+02 -1.1770170647504112e+02 -2.2513369839216481e+04  1.0000000000000000e+02 -2.2513369839216480e+02 -3.7660812717909516e+02  1.5147442878693036e+02\n",
-      "                7   9.9936984461419740e+01  9.9929966800671224e+01 -1.1765353928750643e+02 -2.2516138533920657e+04  1.0000000000000000e+02 -2.2516138533920659e+02 -3.7660292355465600e+02  1.5144153821544941e+02\n",
-      "                8   9.9902337463172714e+01  9.9910800755312891e+01 -1.1761532255317621e+02 -2.2518524275281430e+04  9.9999999999999986e+01 -2.2518524275281433e+02 -3.7674246483479845e+02  1.5155722208198404e+02\n",
-      "                9   1.0012943675389775e+02  1.0013880643723378e+02 -1.1780913595074867e+02 -2.2512465963277762e+04  1.0000000000000000e+02 -2.2512465963277762e+02 -3.7677999264623367e+02  1.5165533301345607e+02\n",
-      "               10   9.9628730363609819e+01  9.9223106824565718e+01 -1.1749814144939067e+02 -2.2517668156221851e+04  1.0000000000000000e+02 -2.2517668156221850e+02 -3.7688306341863290e+02  1.5170638185641434e+02\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Setup the AFQMC parameters.\n",
-    "afqmc_msd = AFQMC.build(molecule.nelec,\n",
-    "                        afqmc_hamiltonian,\n",
-    "                        trial,\n",
-    "                        num_walkers=100,\n",
-    "                        num_steps_per_block=25,\n",
-    "                        num_blocks=10,\n",
-    "                        timestep=0.005,\n",
-    "                        stabilize_freq=5,\n",
-    "                        seed=96264512,\n",
-    "                        pop_control_freq=5,\n",
-    "                        verbose=False)\n",
-    "\n",
-    "# Run the AFQMC.\n",
-    "afqmc_msd.run(estimator_filename='afqmc_src/estimates.0.h5')\n",
-    "afqmc_msd.finalise(verbose=False)\n",
-    "\n",
-    "# Extract the energies.\n",
-    "qmc_data = extract_observable(afqmc_msd.estimators.filename, \"energy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fea847f35e0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the energies.\n",
-    "\n",
-    "vqe_y = vqe_energies\n",
-    "vqe_x = list(range(len(vqe_y)))\n",
-    "plt.plot(vqe_x, vqe_y, label=\"VQE\")\n",
-    "\n",
-    "afqmc_y = list(qmc_data[\"ETotal\"])\n",
-    "afqmc_x = [i + vqe_x[-1] for i in list(range(len(afqmc_y)))]\n",
-    "plt.plot(afqmc_x, afqmc_y, label=\"AFQMC\")\n",
-    "\n",
-    "plt.xlabel(\"Optimization steps\")\n",
-    "plt.ylabel(\"Energy [Ha]\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
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-  <section id="Bernstein-Vazirani-Algorithm">
-<h1>Bernstein-Vazirani Algorithm<a class="headerlink" href="#Bernstein-Vazirani-Algorithm" title="Permalink to this heading">¶</a></h1>
-<p>The Bernstein-Vazirani (BV) algorithm demonstrates an exponential speedup over classical methods for the particular task:</p>
-<p>Suppose we have an oracle that implements the function <span class="math notranslate nohighlight">\(f: \{0,1\}^n \longrightarrow \{0,1\}\)</span>.</p>
-<p><span class="math notranslate nohighlight">\(f(x)\)</span> is promised to be the dot product between <span class="math notranslate nohighlight">\(x\)</span> and a secret <span class="math notranslate nohighlight">\(n\)</span>-bit binary string <span class="math notranslate nohighlight">\(s\)</span>:</p>
-<p><span class="math notranslate nohighlight">\(f(x) = x \cdot s (mod \space 2) = (x_1s_1 + x_2s_2 + ... + x_ns_n) (mod \space 2)\)</span>.</p>
-<p>Our goal is to find the secret string <span class="math notranslate nohighlight">\(s\)</span>.</p>
-<section id="Classical-case">
-<h2>Classical case<a class="headerlink" href="#Classical-case" title="Permalink to this heading">¶</a></h2>
-<p>For the case of <span class="math notranslate nohighlight">\(n=3\)</span> consider <span class="math notranslate nohighlight">\(s=101\)</span>, which is a secret string and hence we dont have access to it.</p>
-<p>We have access to the function: <span class="math notranslate nohighlight">\(f(x) = x\cdot s (mod \space 2) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \space 2)\)</span></p>
-<p>Let us strategically query the function to determine <span class="math notranslate nohighlight">\(s\)</span>.</p>
-<p><span class="math notranslate nohighlight">\(1^{st}\)</span> query: <span class="math notranslate nohighlight">\(f(001) = 001 \cdot s (mod \space 2) = (0 \cdot 1 + 0 \cdot 0 + 1 \cdot 1)(mod \space 2) = 1 (mod \space 2) = 1\)</span></p>
-<p><span class="math notranslate nohighlight">\(2^{nd}\)</span> query: <span class="math notranslate nohighlight">\(f(010) = 010 \cdot s (mod \space 2) = (0 \cdot 1 + 1 \cdot 0 + 0 \cdot 1)(mod \space 2) = 0 (mod \space 2) = 0\)</span></p>
-<p><span class="math notranslate nohighlight">\(3^{rd}\)</span> query: <span class="math notranslate nohighlight">\(f(100) = 100 \cdot s (mod \space 2) = (1 \cdot 1 + 0 \cdot 0 + 0 \cdot 1)(mod \space 2) = 1 (mod \space 2) = 1\)</span></p>
-<p>Remember that from a user perspective, you only see <span class="math notranslate nohighlight">\(f(001) = 1\)</span> since the inner mechanisms of the oracle and the value of <span class="math notranslate nohighlight">\(s\)</span> is hidden from you.</p>
-<p>Why did we query <span class="math notranslate nohighlight">\(f(001)\)</span> and not <span class="math notranslate nohighlight">\(f(011)\)</span>?</p>
-<p>For <span class="math notranslate nohighlight">\(f(001) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \space 2) = (0 + 0 + x_3s_3) (mod \space 2) = (x_3s_3) (mod \space 2) = 1 \cdot s_3 (mod \space 2) = s_3 (mod \space 2) = 1\)</span></p>
-<p>We have now isolated <span class="math notranslate nohighlight">\(s_3\)</span> and know that <span class="math notranslate nohighlight">\(s_3 (mod \space 2) = 1\)</span>. <span class="math notranslate nohighlight">\(s_3\)</span> can only be 0 or 1 and in this case it has to be <span class="math notranslate nohighlight">\(1\)</span> since <span class="math notranslate nohighlight">\(0 (mod \space 2) = 0\)</span>.</p>
-<p>For <span class="math notranslate nohighlight">\(f(011) = (x_2s_2 + x_3s_3) (mod \space 2)\)</span>, we will be left with <span class="math notranslate nohighlight">\(s_2\)</span> and <span class="math notranslate nohighlight">\(s_3\)</span> each of which will be more difficult to isolate via a combination of linear equations. Hence why we have to strategically query the function.</p>
-<p>In the classical case, we see that the secret string <span class="math notranslate nohighlight">\(s\)</span> can be calculated in 3 queries. More generally speaking, it would take <span class="math notranslate nohighlight">\(n\)</span> queries to generate enough information to determine <span class="math notranslate nohighlight">\(s\)</span></p>
-</section>
-<section id="Quantum-case">
-<h2>Quantum case<a class="headerlink" href="#Quantum-case" title="Permalink to this heading">¶</a></h2>
-<p>The BV quantum algorithm can take advantage of superposition and entanglement to encode the problem and produce an answer with a single oracle (black box function) query. The setup is a register of <span class="math notranslate nohighlight">\(n\)</span> qubits in the <span class="math notranslate nohighlight">\(\ket{0}\)</span> state and a single auxiliary qubit in the <span class="math notranslate nohighlight">\(\ket{1}\)</span> state. The auxiliary qubit enables the phase kickback. Consider the register of <span class="math notranslate nohighlight">\(n\)</span> qubits initialized here:</p>
-<div class="math notranslate nohighlight">
-\[\ket{0}\]</div>
-<p>Application of a Hadamard gate to each qubit results in:</p>
-<div class="math notranslate nohighlight">
-\[H \ket{0}  = \frac{1}{\sqrt{2^n}}\sum_x^{n-1} \ket{x}\]</div>
-<p>Next, the oracle, <span class="math notranslate nohighlight">\(U_f\)</span>, performs maps <span class="math notranslate nohighlight">\(\ket{x}\)</span> to <span class="math notranslate nohighlight">\((-1)^{f(x)}\ket{x}\)</span> resulting in:</p>
-<div class="math notranslate nohighlight">
-\[U_f \frac{1}{\sqrt{2^n}}\sum_x^{n-1} \ket{x} = \frac{1}{\sqrt{2^n}}\sum_x^{n-1} (-1)^{f(x)}\ket{x} =\frac{1}{\sqrt{2^n}}\sum_x^{n-1} (-1)^{a*x}\ket{x}\]</div>
-<p>Applying Hadamard gates again returns the following state:</p>
-<div class="math notranslate nohighlight">
-\[H \frac{1}{\sqrt{2^n}}\sum_x^{n-1} (-1)^{a*x}\ket{x}  =  \frac{1}{\sqrt{2^n}}\sum_{x,y}^{n-1} (-1)^{a*x} (-1)^{y*x} \ket{y}  = \frac{1}{\sqrt{2^n}}\sum_{y}^{n-1} \sum_{x}^{n-1} (-1)^{(a \oplus y)*x}\ket{y}\]</div>
-<p>In the case of <span class="math notranslate nohighlight">\(a \neq y\)</span>, the entire term becomes zero. In the case of <span class="math notranslate nohighlight">\(a = y\)</span>, the state <span class="math notranslate nohighlight">\(\ket{a}\)</span> is returned with an amplitude of 1. This means there is a 100% chance of the measurement result being the hidden bitstring <span class="math notranslate nohighlight">\(a\)</span> with only one call to the oracle!</p>
-</section>
-<section id="Implementing-in-CUDA-Q">
-<h2>Implementing in CUDA-Q<a class="headerlink" href="#Implementing-in-CUDA-Q" title="Permalink to this heading">¶</a></h2>
-<p>The cell below generates a random bitstring of length <span class="math notranslate nohighlight">\(n\)</span>. If you are running this on your CPU, keep the qubit count small and increase to around 30 if you are running on a GPU with the <code class="docutils literal notranslate"><span class="pre">nvidia</span></code> backend.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;qpp-cpu&#39;</span><span class="p">)</span>
-<span class="c1">#cudaq.set_target(&#39;nvidia&#39;)  # GPU backend which enables scaling to large problem sizes</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">5</span>  <span class="c1"># Set to around 30 qubits if you have GPU access</span>
-
-<span class="n">secret_string</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span>
-                 <span class="mi">0</span><span class="p">]</span>  <span class="c1"># Change the secret string to whatever you prefer</span>
-
-<span class="k">assert</span> <span class="n">qubit_count</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">secret_string</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, the oracle kernel is defined. This will be used inside of the primary BV kernel, so it needs to take both the main register and auxiliary qubit as inputs as well as the secret bitstring. The oracle loops through the bits and applies a CNOT on the auxiliary qubit if the register qubit is a 1.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">oracle</span><span class="p">(</span><span class="n">register</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">auxiliary_qubit</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">,</span>
-           <span class="n">secret_string</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-
-    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">bit</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">secret_string</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">bit</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">register</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="n">auxiliary_qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The code below performs the steps described above for the BV algorithm, calling the oracle kernel as needed. You can see the circuit created below and verify that the procedure guessed the correct bitstring.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">bernstein_vazirani</span><span class="p">(</span><span class="n">secret_string</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">secret_string</span><span class="p">))</span>  <span class="c1"># register of size n</span>
-    <span class="n">auxiliary_qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>  <span class="c1"># auxiliary qubit</span>
-
-    <span class="c1"># Prepare the auxillary qubit.</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">auxiliary_qubit</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">auxiliary_qubit</span><span class="p">)</span>
-
-    <span class="c1"># Place the rest of the register in a superposition state.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-    <span class="c1"># Query the oracle.</span>
-    <span class="n">oracle</span><span class="p">(</span><span class="n">qubits</span><span class="p">,</span> <span class="n">auxiliary_qubit</span><span class="p">,</span> <span class="n">secret_string</span><span class="p">)</span>
-
-    <span class="c1"># Apply another set of Hadamards to the register.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>  <span class="c1"># measures only the main register</span>
-
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">bernstein_vazirani</span><span class="p">,</span> <span class="n">secret_string</span><span class="p">))</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">bernstein_vazirani</span><span class="p">,</span> <span class="n">secret_string</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;secret bitstring = </span><span class="si">{</span><span class="n">secret_string</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;measured state = </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">most_probable</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Were we successful? </span><span class="si">{</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="n">secret_string</span><span class="p">])</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">result</span><span class="o">.</span><span class="n">most_probable</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮          ╭───╮
-q0 : ┤ h ├───────●──┤ h ├──────────
-     ├───┤       │  ╰───╯╭───╮
-q1 : ┤ h ├───────┼────●──┤ h ├─────
-     ├───┤       │    │  ├───┤
-q2 : ┤ h ├───────┼────┼──┤ h ├─────
-     ├───┤       │    │  ╰───╯╭───╮
-q3 : ┤ h ├───────┼────┼────●──┤ h ├
-     ├───┤       │    │    │  ├───┤
-q4 : ┤ h ├───────┼────┼────┼──┤ h ├
-     ├───┤╭───╮╭─┴─╮╭─┴─╮╭─┴─╮╰───╯
-q5 : ┤ x ├┤ h ├┤ x ├┤ x ├┤ x ├─────
-     ╰───╯╰───╯╰───╯╰───╯╰───╯
-
-secret bitstring = [1, 1, 0, 1, 0]
-measured state = 11010
-Were we successful? True
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)
-</pre></div></div>
-</div>
-</section>
-</section>
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diff --git a/pr-2308/applications/python/bernstein_vazirani.ipynb b/pr-2308/applications/python/bernstein_vazirani.ipynb
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index b0333b5cf8..0000000000
--- a/pr-2308/applications/python/bernstein_vazirani.ipynb
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "29b3f948-cc6a-4776-ad75-1dc259e0c298",
-   "metadata": {},
-   "source": [
-    "# Bernstein-Vazirani Algorithm\n",
-    "\n",
-    "The Bernstein-Vazirani (BV) algorithm demonstrates an exponential speedup over classical methods for the particular task: \n",
-    "\n",
-    "Suppose we have an oracle that implements the function $f: \\{0,1\\}^n \\longrightarrow \\{0,1\\}$. \n",
-    "\n",
-    "$f(x)$ is promised to be the dot product between $x$ and a secret $n$-bit binary string $s$:\n",
-    "\n",
-    "$f(x) = x \\cdot s (mod \\space 2) =  (x_1s_1 + x_2s_2 + ... + x_ns_n) (mod \\space 2)$.\n",
-    "\n",
-    "Our goal is to find the secret string $s$.\n",
-    "\n",
-    "### Classical case\n",
-    "\n",
-    "For the case of $n=3$ consider $s=101$, which is a secret string and hence we dont have access to it. \n",
-    "\n",
-    "We have access to the function: $f(x) = x\\cdot s (mod \\space 2) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \\space 2)$\n",
-    "\n",
-    "Let us strategically query the function to determine $s$. \n",
-    "\n",
-    "$1^{st}$ query: $f(001) = 001 \\cdot s (mod \\space 2) = (0 \\cdot 1 + 0 \\cdot 0 + 1 \\cdot 1)(mod \\space 2) = 1 (mod \\space 2) = 1$\n",
-    "\n",
-    "$2^{nd}$ query: $f(010) = 010 \\cdot s (mod \\space 2) = (0 \\cdot 1 + 1 \\cdot 0 + 0 \\cdot 1)(mod \\space 2) = 0 (mod \\space 2) = 0$\n",
-    "\n",
-    "$3^{rd}$ query: $f(100) = 100 \\cdot s (mod \\space 2) = (1 \\cdot 1 + 0 \\cdot 0 + 0 \\cdot 1)(mod \\space 2) = 1 (mod \\space 2) = 1$\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Remember that from a user perspective, you only see $f(001) = 1$ since the inner mechanisms of the oracle and the value of $s$ is hidden from you. \n",
-    "\n",
-    "Why did we query $f(001)$ and not $f(011)$?\n",
-    "\n",
-    "For $f(001) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \\space 2) = (0 + 0 + x_3s_3) (mod \\space 2) = (x_3s_3) (mod \\space 2) = 1 \\cdot s_3 (mod \\space 2) = s_3 (mod \\space 2) = 1$\n",
-    "\n",
-    "We have now isolated $s_3$ and know that $s_3 (mod \\space 2) = 1$. $s_3$ can only be 0 or 1 and in this case it has to be $1$ since $0 (mod \\space 2) = 0$. \n",
-    "\n",
-    "For $f(011) = (x_2s_2 + x_3s_3) (mod \\space 2)$, we will be left with $s_2$ and $s_3$ each of which will be more difficult to isolate via a combination of linear equations. Hence why we have to strategically query the function. \n",
-    "\n",
-    "In the classical case, we see that the secret string $s$ can be calculated in 3 queries. More generally speaking, it would take $n$ queries to generate enough information to determine $s$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebdfe818",
-   "metadata": {},
-   "source": [
-    "### Quantum case\n",
-    "\n",
-    "The BV quantum algorithm can take advantage of superposition and entanglement to encode the problem and produce an answer with a single oracle (black box function) query. The setup is a register of $n$ qubits in the $\\ket{0}$ state and a single auxiliary qubit in the $\\ket{1}$ state. The auxiliary qubit enables the phase kickback. Consider the register of $n$ qubits initialized here:\n",
-    "\n",
-    "$$ \\ket{0} $$\n",
-    "\n",
-    "Application of a Hadamard gate to each qubit results in:\n",
-    "$$ H \\ket{0}  = \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} \\ket{x}$$\n",
-    "\n",
-    "Next, the oracle, $U_f$, performs maps $\\ket{x}$ to $(-1)^{f(x)}\\ket{x}$ resulting in:\n",
-    "\n",
-    "$$ U_f \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} \\ket{x} = \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{f(x)}\\ket{x} =\\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{a*x}\\ket{x} $$\n",
-    "\n",
-    "Applying Hadamard gates again returns the following state:\n",
-    "$$ H \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{a*x}\\ket{x}  =  \\frac{1}{\\sqrt{2^n}}\\sum_{x,y}^{n-1} (-1)^{a*x} (-1)^{y*x} \\ket{y}  = \\frac{1}{\\sqrt{2^n}}\\sum_{y}^{n-1} \\sum_{x}^{n-1} (-1)^{(a \\oplus y)*x}\\ket{y} $$\n",
-    "\n",
-    "In the case of $a \\neq y$, the entire term becomes zero. In the case of $a = y$, the state $\\ket{a}$ is returned with an amplitude of 1.  This means there is a 100% chance of the measurement result being the hidden bitstring $a$ with only one call to the oracle!\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fdbba1e-8eab-42a2-8964-6d7e86db0ad4",
-   "metadata": {},
-   "source": [
-    "### Implementing in CUDA-Q\n",
-    "\n",
-    "The cell below generates a random bitstring of length $n$. If you are running this on your CPU, keep the qubit count small and increase to around 30 if you are running on a GPU with the `nvidia` backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "883762a1-d271-4692-adad-d64daa20805d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "#cudaq.set_target('nvidia')  # GPU backend which enables scaling to large problem sizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d72225e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qubit_count = 5  # Set to around 30 qubits if you have GPU access\n",
-    "\n",
-    "secret_string = [1, 1, 0, 1,\n",
-    "                 0]  # Change the secret string to whatever you prefer\n",
-    "\n",
-    "assert qubit_count == len(secret_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3df178fe-ccf3-41aa-b5f7-037a1a1d4a69",
-   "metadata": {},
-   "source": [
-    "Next, the oracle kernel is defined. This will be used inside of the primary BV kernel, so it needs to take both the main register and auxiliary qubit as inputs as well as the secret bitstring. The oracle loops through the bits and applies a CNOT on the auxiliary qubit if the register qubit is a 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "91dbde75-486b-4015-9627-9ff404bdb475",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def oracle(register: cudaq.qview, auxiliary_qubit: cudaq.qubit,\n",
-    "           secret_string: List[int]):\n",
-    "\n",
-    "    for index, bit in enumerate(secret_string):\n",
-    "        if bit == 1:\n",
-    "            x.ctrl(register[index], auxiliary_qubit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "685bb02e-afdc-4b69-afb4-81af9c2fafbd",
-   "metadata": {},
-   "source": [
-    "The code below performs the steps described above for the BV algorithm, calling the oracle kernel as needed. You can see the circuit created below and verify that the procedure guessed the correct bitstring. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d93cb1db-b5c6-45ef-9efc-c426895aac18",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮          ╭───╮          \n",
-      "q0 : ┤ h ├───────●──┤ h ├──────────\n",
-      "     ├───┤       │  ╰───╯╭───╮     \n",
-      "q1 : ┤ h ├───────┼────●──┤ h ├─────\n",
-      "     ├───┤       │    │  ├───┤     \n",
-      "q2 : ┤ h ├───────┼────┼──┤ h ├─────\n",
-      "     ├───┤       │    │  ╰───╯╭───╮\n",
-      "q3 : ┤ h ├───────┼────┼────●──┤ h ├\n",
-      "     ├───┤       │    │    │  ├───┤\n",
-      "q4 : ┤ h ├───────┼────┼────┼──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╭─┴─╮╭─┴─╮╰───╯\n",
-      "q5 : ┤ x ├┤ h ├┤ x ├┤ x ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯╰───╯╰───╯     \n",
-      "\n",
-      "secret bitstring = [1, 1, 0, 1, 0]\n",
-      "measured state = 11010\n",
-      "Were we successful? True\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def bernstein_vazirani(secret_string: List[int]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(len(secret_string))  # register of size n\n",
-    "    auxiliary_qubit = cudaq.qubit()  # auxiliary qubit\n",
-    "\n",
-    "    # Prepare the auxillary qubit.\n",
-    "    x(auxiliary_qubit)\n",
-    "    h(auxiliary_qubit)\n",
-    "\n",
-    "    # Place the rest of the register in a superposition state.\n",
-    "    h(qubits)\n",
-    "\n",
-    "    # Query the oracle.\n",
-    "    oracle(qubits, auxiliary_qubit, secret_string)\n",
-    "\n",
-    "    # Apply another set of Hadamards to the register.\n",
-    "    h(qubits)\n",
-    "\n",
-    "    mz(qubits)  # measures only the main register\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(bernstein_vazirani, secret_string))\n",
-    "result = cudaq.sample(bernstein_vazirani, secret_string)\n",
-    "\n",
-    "print(f\"secret bitstring = {secret_string}\")\n",
-    "print(f\"measured state = {result.most_probable()}\")\n",
-    "print(\n",
-    "    f\"Were we successful? {''.join([str(i) for i in secret_string]) == result.most_probable()}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "388592de-3b47-45f4-b687-72abed6fdf8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/cost_minimization.html b/pr-2308/applications/python/cost_minimization.html
deleted file mode 100644
index a9ce381dbd..0000000000
--- a/pr-2308/applications/python/cost_minimization.html
+++ /dev/null
@@ -1,910 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="Cost-Minimization">
-<h1>Cost Minimization<a class="headerlink" href="#Cost-Minimization" title="Permalink to this heading">¶</a></h1>
-<p>Below we start with a basic example of a hybrid variational algorithm which involves flipping the Bloch vector of a qubit from the <span class="math notranslate nohighlight">\(\ket{0}\)</span> to the <span class="math notranslate nohighlight">\(\ket{1}\)</span> state. First we import the relevant packages and set our backend to simulate our workflow on NVIDIA GPUs.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize a kernel/ ansatz and variational parameters.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">angles</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-    <span class="c1"># Allocate a qubit that is initialized to the |0&gt; state.</span>
-    <span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="c1"># Define gates and the qubits they act upon.</span>
-    <span class="n">rx</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubit</span><span class="p">)</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="c1"># Our Hamiltonian will be the Z expectation value of our qubit.</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="c1"># Initial gate parameters which intialize the qubit in the zero state</span>
-<span class="n">initial_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">initial_parameters</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
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-<div class="highlight"><pre>
-     ╭───────╮╭───────╮
-q0 : ┤ rx(0) ├┤ ry(0) ├
-     ╰───────╯╰───────╯
-
-</pre></div></div>
-</div>
-<p>We build our cost function such that its minimal value corresponds to the qubit being in the <span class="math notranslate nohighlight">\(\ket{1}\)</span> state. The observe call below allows us to simulate our statevector <span class="math notranslate nohighlight">\(\ket{\psi}\)</span>, and calculate <span class="math notranslate nohighlight">\(\bra{\psi}Z\ket{\psi}\)</span>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cost_values</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the expectation value as our cost.&quot;&quot;&quot;</span>
-    <span class="n">expectation_value</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span>
-                                      <span class="n">parameters</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-    <span class="n">cost_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">expectation_value</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">expectation_value</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We see that the initial value of our cost function is one, demonstrating that our qubit is in the zero state</span>
-<span class="n">initial_cost_value</span> <span class="o">=</span> <span class="n">cost</span><span class="p">(</span><span class="n">initial_parameters</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">initial_cost_value</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-1.0
-</pre></div></div>
-</div>
-<p>Below we use our built-in optimization suite to minimize the cost function. We will be using the gradient-free COBYLA alogrithm.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define a CUDA-Q optimizer.</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">COBYLA</span><span class="p">()</span>
-<span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span> <span class="o">=</span> <span class="n">initial_parameters</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">dimensions</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">cost</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting how the value of the cost function decreases during the minimization procedure.</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="n">x_values</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cost_values</span><span class="p">)))</span>
-<span class="n">y_values</span> <span class="o">=</span> <span class="n">cost_values</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_values</span><span class="p">,</span> <span class="n">y_values</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Cost Value&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
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-Text(0, 0.5, &#39;Cost Value&#39;)
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-<img alt="../../_images/applications_python_cost_minimization_8_1.png" src="../../_images/applications_python_cost_minimization_8_1.png" />
-</div>
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-<p>We see that the final value or our cost function, <span class="math notranslate nohighlight">\(\bra{\psi}Z\ket{\psi} = -1\)</span> demonstrating that the qubit is in the <span class="math notranslate nohighlight">\(\ket{1}\)</span> state.</p>
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diff --git a/pr-2308/applications/python/cost_minimization.ipynb b/pr-2308/applications/python/cost_minimization.ipynb
deleted file mode 100644
index cd55790624..0000000000
--- a/pr-2308/applications/python/cost_minimization.ipynb
+++ /dev/null
@@ -1,206 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cost Minimization\n",
-    "\n",
-    "Below we start with a basic example of a hybrid variational algorithm which involves flipping the Bloch vector of a qubit from the $\\ket{0}$ to the $\\ket{1}$ state. First we import the relevant packages and set our backend to simulate our workflow on NVIDIA GPUs. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───────╮╭───────╮\n",
-      "q0 : ┤ rx(0) ├┤ ry(0) ├\n",
-      "     ╰───────╯╰───────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Initialize a kernel/ ansatz and variational parameters.\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: List[float]):\n",
-    "    # Allocate a qubit that is initialized to the |0> state.\n",
-    "    qubit = cudaq.qubit()\n",
-    "    # Define gates and the qubits they act upon.\n",
-    "    rx(angles[0], qubit)\n",
-    "    ry(angles[1], qubit)\n",
-    "\n",
-    "\n",
-    "# Our Hamiltonian will be the Z expectation value of our qubit.\n",
-    "hamiltonian = cudaq.spin.z(0)\n",
-    "\n",
-    "# Initial gate parameters which intialize the qubit in the zero state\n",
-    "initial_parameters = [0, 0]\n",
-    "\n",
-    "print(cudaq.draw(kernel, initial_parameters))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We build our cost function such that its minimal value corresponds to the qubit being in the $\\ket{1}$ state. The observe call below allows us to simulate our statevector $\\ket{\\psi}$, and calculate $\\bra{\\psi}Z\\ket{\\psi}$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cost_values = []\n",
-    "\n",
-    "\n",
-    "def cost(parameters):\n",
-    "    \"\"\"Returns the expectation value as our cost.\"\"\"\n",
-    "    expectation_value = cudaq.observe(kernel, hamiltonian,\n",
-    "                                      parameters).expectation()\n",
-    "    cost_values.append(expectation_value)\n",
-    "    return expectation_value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# We see that the initial value of our cost function is one, demonstrating that our qubit is in the zero state\n",
-    "initial_cost_value = cost(initial_parameters)\n",
-    "print(initial_cost_value)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we use our built-in optimization suite to minimize the cost function. We will be using the gradient-free COBYLA alogrithm. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define a CUDA-Q optimizer.\n",
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "optimizer.initial_parameters = initial_parameters\n",
-    "\n",
-    "result = optimizer.optimize(dimensions=2, function=cost)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cost Value')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting how the value of the cost function decreases during the minimization procedure.\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x_values = list(range(len(cost_values)))\n",
-    "y_values = cost_values\n",
-    "\n",
-    "plt.plot(x_values, y_values)\n",
-    "\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Cost Value\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the final value or our cost function, $\\bra{\\psi}Z\\ket{\\psi} = -1$ demonstrating that the qubit is in the $\\ket{1}$ state."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-      <li class="breadcrumb-item active">Deutsch’s Algorithm</li>
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-  <section id="Deutsch's-Algorithm">
-<h1>Deutsch’s Algorithm<a class="headerlink" href="#Deutsch's-Algorithm" title="Permalink to this heading">¶</a></h1>
-<p>We have a function which takes in a bit and outputs a bit. This can be represented as <span class="math notranslate nohighlight">\(f: \{0,1\} \longrightarrow \{0,1\}\)</span>.</p>
-<p>The function <span class="math notranslate nohighlight">\(f\)</span> has a property; either it is constant or balanced.</p>
-<p>If constant, the outputs are the same regardless of the inputs, i.e., <span class="math notranslate nohighlight">\(f(0) = f(1) = 0\)</span> or <span class="math notranslate nohighlight">\(f(0) = f(1) = 1\)</span>.</p>
-<p>If balanced, the ouputs are balanced across their possibilities, i.e, if <span class="math notranslate nohighlight">\(f(0) = 0\)</span> then <span class="math notranslate nohighlight">\(f(1) = 1\)</span> or if <span class="math notranslate nohighlight">\(f(0) = 1\)</span> then <span class="math notranslate nohighlight">\(f(1) = 0\)</span>.</p>
-<p>The question we would like to answer is if the function is constant or balanced.</p>
-<p>Classically, if we are given a function <span class="math notranslate nohighlight">\(f\)</span>, we can solve to find its property via the code below:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Treat the code snippet below like a black box, i.e., you don&#39;t have access to the value of the property argument and can just query f with different inputs.</span>
-
-
-<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">property</span><span class="o">=</span><span class="s1">&#39;constant&#39;</span><span class="p">):</span>
-
-    <span class="k">if</span> <span class="nb">property</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-
-        <span class="c1"># The output is a 1 regardless of the input; we can also make the output to be 0.</span>
-
-        <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">1</span>
-        <span class="k">elif</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">1</span>
-
-    <span class="k">if</span> <span class="nb">property</span> <span class="o">==</span> <span class="s1">&#39;balanced&#39;</span><span class="p">:</span>
-
-        <span class="c1"># The output depends on the input.</span>
-
-        <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">1</span>
-        <span class="k">elif</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">0</span>
-</pre></div>
-</div>
-</div>
-<p>Let us now classically find out if the function we defined above is constant or balanced.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">f</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">f</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The function is constant&#39;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">f</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The function is balanced&#39;</span><span class="p">)</span>
-
-<span class="k">elif</span> <span class="n">f</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">f</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The function is constant&#39;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">f</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The function is balanced&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The function is constant
-</pre></div></div>
-</div>
-<p>If you step through the <code class="docutils literal notranslate"><span class="pre">if</span></code> statements above, one can see that we require 2 calls to the function to determine its property. That is, we have to query <span class="math notranslate nohighlight">\(f\)</span> twice.</p>
-<p>The claim is that Deutsch’s algorithm can solve for this property with 1 function evalulation, demonstrating quantum advantage.</p>
-<p>Below we first go through the math and then the implementation in CUDA Quantum.</p>
-<section id="XOR-\oplus">
-<h2>XOR <span class="math notranslate nohighlight">\(\oplus\)</span><a class="headerlink" href="#XOR-\oplus" title="Permalink to this heading">¶</a></h2>
-<p>You may have seen the <span class="math notranslate nohighlight">\(\oplus\)</span> symbol floating around. This is the exclusive OR or the XOR operation. It follows the rules of addition modulo 2. For example,</p>
-<ol class="arabic simple">
-<li><p><span class="math notranslate nohighlight">\(3\oplus5 = (3+5)_{\%2} = 8_{\%2} = 0\)</span></p></li>
-<li><p><span class="math notranslate nohighlight">\(5\oplus4 = (5+4)_{\%2} = 9_{\%2} = 1\)</span></p></li>
-</ol>
-<p>A quick way to perform the <span class="math notranslate nohighlight">\(\oplus\)</span> calculation is if the result of the addition is an even number, the result is 0 otherwise 1.</p>
-</section>
-<section id="Quantum-oracles">
-<h2>Quantum oracles<a class="headerlink" href="#Quantum-oracles" title="Permalink to this heading">¶</a></h2>
-<p><img alt="2f12dad57a45430db563acdc7dfaf8e6" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
-<p>Suppose we have <span class="math notranslate nohighlight">\(f(x): \{0,1\} \longrightarrow \{0,1\}\)</span>. We can compute this function on a quantum computer using oracles which we treat as black box functions that yield the output with an appropriate sequence of logic gates.</p>
-<p>Above you see an oracle represented as <span class="math notranslate nohighlight">\(U_f\)</span> which allows us to transform the state <span class="math notranslate nohighlight">\(\ket{x}\ket{y}\)</span> into:</p>
-<div class="math notranslate nohighlight">
-\[\begin{aligned}
-U_f\ket{x}\ket{y}  =  \ket{x}\ket{y \oplus f(x)}
-\end{aligned}\]</div>
-<p>If <span class="math notranslate nohighlight">\(y = 0\)</span>, then <span class="math notranslate nohighlight">\(U_f\ket{x}\ket{y} = U_f\ket{x}\ket{0} = \ket{x}\ket{0 \oplus f(x)} = \ket{x}\ket{f(x)}\)</span> since <span class="math notranslate nohighlight">\(f(x)\)</span> can either be <span class="math notranslate nohighlight">\(0/1\)</span> and <span class="math notranslate nohighlight">\(0 \oplus 0 = 0\)</span> and <span class="math notranslate nohighlight">\(0 \oplus 1 = 1\)</span>.</p>
-<p>This is remarkable because by setting <span class="math notranslate nohighlight">\(\ket{y} = \ket{0}\)</span>, we can extract the value of <span class="math notranslate nohighlight">\(f(x)\)</span> by measuring the value of the second qubit.</p>
-</section>
-<section id="Phase-oracle">
-<h2>Phase oracle<a class="headerlink" href="#Phase-oracle" title="Permalink to this heading">¶</a></h2>
-<p>Since <span class="math notranslate nohighlight">\(f(x)\)</span> can be either <span class="math notranslate nohighlight">\(0\)</span> or <span class="math notranslate nohighlight">\(1\)</span>, we have that <span class="math notranslate nohighlight">\(0 \oplus f(x) = 0 \oplus 0 = 0 = f(x)\)</span> or <span class="math notranslate nohighlight">\(0 \oplus f(x) = 0 \oplus 1 = 1 = f(x)\)</span>.</p>
-<p>Similarly, <span class="math notranslate nohighlight">\(1 \oplus f(x) = 1 \oplus 0 = 1 = \overline{f(x)}\)</span> or <span class="math notranslate nohighlight">\(1 \oplus f(x) = 1 \oplus 1 = 0 = \overline{f(x)}\)</span> where the bar on top of <span class="math notranslate nohighlight">\(f(x)\)</span> denotes ‘not <span class="math notranslate nohighlight">\(f(x)\)</span>’.</p>
-<p>Let us now prove a result which we shall use later:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-U_f\ket{x}\ket{-}  &amp;=   U_f \ket{x} \tfrac{1}{\sqrt{2}}(\ket{0} - \ket{1}) \\
-&amp;= \tfrac{1}{\sqrt{2}}  (U_f\ket{x}\ket{0} - U_f\ket{x}\ket{1}) \\
-&amp;= \tfrac{1}{\sqrt{2}}  (\ket{x}\ket{0 \oplus f(x)} - \ket{x}\ket{1 \oplus f(x)}) \\
-&amp;= \tfrac{1}{\sqrt{2}}  (\ket{x}\ket{f(x)} - \ket{x}\ket{\overline{f(x)}}) \\
-&amp;=
-\begin{cases}
-      \tfrac{1}{\sqrt{2}}  (\ket{x}\ket{0} - \ket{x}\ket{1}) = \ket{x}\ket{-} &amp;   f(x) = 0 \\
-      \tfrac{1}{\sqrt{2}}  (\ket{x}\ket{1} - \ket{x}\ket{0}) = -\ket{x}\ket{-} &amp;   f(x) = 1 \\
-\end{cases} \\
-&amp;= (-1)^{f(x)}\ket{x}\ket{-}
-\end{aligned}\end{split}\]</div>
-<p>In summary, <span class="math notranslate nohighlight">\(U_f\ket{x}\ket{-} = (-1)^{f(x)}\ket{x}\ket{-}\)</span> where the <span class="math notranslate nohighlight">\(\ket{-}\)</span> qubit was left unchanged and a phase was applied to <span class="math notranslate nohighlight">\(\ket{x}\)</span>.</p>
-</section>
-<section id="Quantum-parallelism">
-<h2>Quantum parallelism<a class="headerlink" href="#Quantum-parallelism" title="Permalink to this heading">¶</a></h2>
-<p>Consider:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-U_f\ket{+}\ket{0} &amp;= U_f \tfrac{1}{\sqrt{2}}(\ket{0}+\ket{1}) \ket{0} \\
-&amp;= U_f \tfrac{1}{\sqrt{2}}(\ket{0}\ket{0}+\ket{1}\ket{0}) \\
-&amp;= \tfrac{1}{\sqrt{2}}(U_f \ket{0}\ket{0} + U_f \ket{1}\ket{0})  \\
-&amp;= \tfrac{1}{\sqrt{2}}(U_f \ket{0}\ket{0} + U_f \ket{1}\ket{0})  \\
-&amp;= \tfrac{1}{\sqrt{2}}(\ket{0}\ket{0 \oplus f(0)} + \ket{1}\ket{0 \oplus f(1)})  \\
-&amp;= \tfrac{1}{\sqrt{2}}(\ket{0}\ket{f(0)} + \ket{1}\ket{f(1)})  \\
-\end{aligned}\end{split}\]</div>
-<p>We have calculated information about both <span class="math notranslate nohighlight">\(f(0)\)</span> and <span class="math notranslate nohighlight">\(f(1)\)</span> simultaneously. Quantum mechanics allows for parallelism by exploiting the ability of superposition states.</p>
-</section>
-<section id="Deutschs'-Algorithm:">
-<h2>Deutschs’ Algorithm:<a class="headerlink" href="#Deutschs'-Algorithm:" title="Permalink to this heading">¶</a></h2>
-<p>Our aim is to find out if <span class="math notranslate nohighlight">\(f: \{0,1\} \longrightarrow \{0,1\}\)</span> is a constant or a balanced function? If constant, <span class="math notranslate nohighlight">\(f(0) = f(1)\)</span>, and if balanced, <span class="math notranslate nohighlight">\(f(0) \neq f(1)\)</span>.</p>
-<p>We step through the circuit diagram below and follow the math after the application of each gate.</p>
-<p><img alt="4522114be5fe45578bce3cdfc4d56a80" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
-<div class="math notranslate nohighlight">
-\[\ket{\psi_0}  =  \ket{01}
-\tag{1}\]</div>
-<div class="math notranslate nohighlight">
-\[\ket{\psi_1}  =   H_1H_0\ket{\psi_0} = H_1H_0\ket{01} = \ket{+-} = \frac{1}{\sqrt{2}} (\ket{0}+\ket{1})\ket{-}
-\tag{2}\]</div>
-<div class="math notranslate nohighlight">
-\[\ket{\psi_2}  =   U_f\ket{\psi_1} = \frac{1}{\sqrt{2}} (U_f\ket{0}\ket{-} + U_f\ket{1}\ket{-})
-\tag{3}\]</div>
-<p>using <span class="math notranslate nohighlight">\(U_f\ket{x}\ket{-} = (-1)^{f(x)}\ket{x}\ket{-}\)</span> which we proved in the phase oracle section above:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_2}  =  \frac{1}{\sqrt{2}} ((-1)^{f(0)}\ket{0}\ket{-} + (-1)^{f(1)}\ket{1}\ket{-}) \\
-= \frac{1}{\sqrt{2}} ((-1)^{f(0)}\ket{0} + (-1)^{f(1)}\ket{1}) \ket{-} \\
-\end{aligned}\end{split}\]</div>
-<p>We now drop the second, qubit in the <span class="math notranslate nohighlight">\(\ket{-}\)</span> state from our derivations as the figure above has no further gate operations acting on it. Remember our aim is to find out if <span class="math notranslate nohighlight">\(f(x)\)</span> is a constant or a balanced function. We have the following options:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{2}}=
-\begin{cases}
-      \tfrac{1}{\sqrt{2}} (\ket{0} + \ket{1})  &amp;   f(0) = 0, f(1) = 0, f(x)  \text{ is constant} \\
-      -\tfrac{1}{\sqrt{2}} (\ket{0} + \ket{1})  &amp;   f(0) = 1, f(1) = 1, f(x)  \text{ is constant} \\
-      \tfrac{1}{\sqrt{2}} (\ket{0} - \ket{1})  &amp;   f(0) = 0, f(1) = 1, f(x)  \text{ is balanced} \\
-      -\tfrac{1}{\sqrt{2}} (\ket{0} - \ket{1})  &amp;   f(0) = 1, f(1) = 0, f(x)  \text{ is balanced} \\
-\end{cases}
-\end{aligned}\end{split}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{2}} =
-\begin{cases}
-      \pm \tfrac{1}{\sqrt{2}} (\ket{0} + \ket{1}) = \pm \ket{+}  &amp;    f(x)  \text{ is constant} \\
-      \pm \tfrac{1}{\sqrt{2}} (\ket{0} - \ket{1})= \pm \ket{-}  &amp;    f(x)  \text{ is balanced} \\
-\end{cases}
-\end{aligned}\end{split}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{3}}= H\ket{\psi_{2}}
-\begin{cases}
-      \pm H\ket{+} =  \pm \ket{0} &amp;    f(x)  \text{ is constant} \\
-      \pm H\ket{-}  = \pm \ket{1}  &amp;   f(x)  \text{ is balanced} \\
-\end{cases}
-\end{aligned}\end{split}\]</div>
-<p>We can now measure the first qubit to yield either a <span class="math notranslate nohighlight">\(0\)</span> or a <span class="math notranslate nohighlight">\(1\)</span> to determine if <span class="math notranslate nohighlight">\(f(x)\)</span> is constant or balanced.</p>
-<p>Deutsch’s algorithm may not be practically useful but does demonstrate quantum advantage since it uses one function call to determine the property of <span class="math notranslate nohighlight">\(f\)</span> in comparison to two for classical methods.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import the CUDA-Q package and set the target to run on NVIDIA GPUs.</span>
-
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (&gt;= 3.0.0) is not supported.
-  warnings.warn(
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here we input the values of [f(0), f(1)] which allows us to represent constant or balanced functions.</span>
-
-<span class="n">fx</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Let us now code up the circuit shown above following the state Psi after each step.</span>
-
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">fx</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-    <span class="n">qubit_0</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">qubit_1</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-    <span class="c1"># Psi 0</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubit_1</span><span class="p">)</span>
-
-    <span class="c1"># Psi 1</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubit_1</span><span class="p">)</span>
-
-    <span class="c1"># Psi 2 - oracle</span>
-    <span class="k">if</span> <span class="n">fx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">)</span>
-        <span class="n">x</span><span class="p">(</span><span class="n">qubit_1</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">fx</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">)</span>
-
-    <span class="c1"># Psi 3</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">)</span>
-
-    <span class="c1"># Measure the qubit to yield if the function is constant or balanced.</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">)</span>
-
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">fx</span><span class="p">))</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">fx</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">result</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;0&#39;</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;f(x) is a constant function&#39;</span><span class="p">)</span>
-<span class="k">elif</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">result</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;1&#39;</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;f(x) is a balanced function&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮          ╭───╮
-q0 : ┤ h ├───────●──┤ h ├
-     ├───┤╭───╮╭─┴─╮╰───╯
-q1 : ┤ x ├┤ h ├┤ x ├─────
-     ╰───╯╰───╯╰───╯
-
-f(x) is a balanced function
-</pre></div></div>
-</div>
-</section>
-</section>
-
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diff --git a/pr-2308/applications/python/deutschs_algorithm.ipynb b/pr-2308/applications/python/deutschs_algorithm.ipynb
deleted file mode 100644
index eb3bb88f1f..0000000000
--- a/pr-2308/applications/python/deutschs_algorithm.ipynb
+++ /dev/null
@@ -1,422 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deutsch's Algorithm "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a function which takes in a bit and outputs a bit. This can be represented as $f: \\{0,1\\} \\longrightarrow \\{0,1\\}$. \n",
-    "\n",
-    "The function $f$ has a property; either it is constant or balanced. \n",
-    "\n",
-    "If constant, the outputs are the same regardless of the inputs, i.e., $f(0) = f(1) = 0$ or $f(0) = f(1) = 1$.\n",
-    "\n",
-    "If balanced, the ouputs are balanced across their possibilities, i.e,  if $f(0) = 0$ then $f(1) = 1$ or if $f(0) = 1$ then $f(1) = 0$.\n",
-    "\n",
-    "The question we would like to answer is if the function is constant or balanced. \n",
-    " \n",
-    "Classically, if we are given a function $f$, we can solve to find its property via the code below: \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Treat the code snippet below like a black box, i.e., you don't have access to the value of the property argument and can just query f with different inputs.\n",
-    "\n",
-    "\n",
-    "def f(x, property='constant'):\n",
-    "\n",
-    "    if property == 'constant':\n",
-    "\n",
-    "        # The output is a 1 regardless of the input; we can also make the output to be 0.\n",
-    "\n",
-    "        if x == 0:\n",
-    "            return 1\n",
-    "        elif x == 1:\n",
-    "            return 1\n",
-    "\n",
-    "    if property == 'balanced':\n",
-    "\n",
-    "        # The output depends on the input.\n",
-    "\n",
-    "        if x == 0:\n",
-    "            return 1\n",
-    "        elif x == 1:\n",
-    "            return 0"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us now classically find out if the function we defined above is constant or balanced. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The function is constant\n"
-     ]
-    }
-   ],
-   "source": [
-    "if f(0) == 0:\n",
-    "    if f(1) == 0:\n",
-    "        print('The function is constant')\n",
-    "    elif f(1) == 1:\n",
-    "        print('The function is balanced')\n",
-    "\n",
-    "elif f(0) == 1:\n",
-    "    if f(1) == 1:\n",
-    "        print('The function is constant')\n",
-    "    elif f(1) == 0:\n",
-    "        print('The function is balanced')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you step through the `if` statements above, one can see that we require 2 calls to the function to determine its property. That is, we have to query $f$ twice.\n",
-    "\n",
-    "The claim is that Deutsch's algorithm can solve for this property with 1 function evalulation, demonstrating quantum advantage. \n",
-    "\n",
-    "Below we first go through the math and then the implementation in CUDA Quantum. \n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## XOR $\\oplus$\n",
-    "\n",
-    "You may have seen the $\\oplus$ symbol floating around. This is the exclusive OR or the XOR operation. It follows the rules of addition modulo 2. For example, \n",
-    "\n",
-    "\n",
-    "1. $3\\oplus5 = (3+5)_{\\%2} = 8_{\\%2} = 0$\n",
-    "\n",
-    "2. $5\\oplus4 = (5+4)_{\\%2} = 9_{\\%2} = 1$\n",
-    "\n",
-    "A quick way to perform the $\\oplus$ calculation is if the result of the addition is an even number, the result is 0 otherwise 1. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum oracles\n",
-    "\n",
-    "<img src=\"images/oracle.png\"  width=\"300\" height=\"150\">\n",
-    "\n",
-    "Suppose we have $f(x): \\{0,1\\} \\longrightarrow \\{0,1\\}$. We can compute this function on a quantum computer using oracles which we treat as black box functions that yield the output with an appropriate sequence of logic gates. \n",
-    "\n",
-    "Above you see an oracle represented as $U_f$ which allows us to transform the state $\\ket{x}\\ket{y}$ into: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{x}\\ket{y}  =  \\ket{x}\\ket{y \\oplus f(x)}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "If $y = 0$, then $U_f\\ket{x}\\ket{y} = U_f\\ket{x}\\ket{0}  =   \\ket{x}\\ket{0 \\oplus f(x)} =  \\ket{x}\\ket{f(x)}$ since $f(x)$ can either be $0/1$ and $0 \\oplus 0 = 0$ and $0 \\oplus 1 = 1$.\n",
-    "\n",
-    "This is remarkable because by setting $\\ket{y} = \\ket{0}$, we can extract the value of $f(x)$ by measuring the value of the second qubit. \n",
-    " \n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phase oracle\n",
-    "\n",
-    "\n",
-    "Since $f(x)$ can be either $0$ or $1$, we have that $0 \\oplus f(x) = 0 \\oplus 0 =  0 = f(x)$ or $0 \\oplus f(x) = 0 \\oplus 1 =  1 = f(x)$. \n",
-    "\n",
-    "Similarly, $1 \\oplus f(x) = 1 \\oplus 0 =  1 = \\overline{f(x)}$ or $1 \\oplus f(x) = 1 \\oplus 1 =  0 = \\overline{f(x)}$ where the bar on top of $f(x)$ denotes 'not $f(x)$'. \n",
-    "\n",
-    "\n",
-    "Let us now prove a result which we shall use later: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{x}\\ket{-}  &=   U_f \\ket{x} \\tfrac{1}{\\sqrt{2}}(\\ket{0} - \\ket{1}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (U_f\\ket{x}\\ket{0} - U_f\\ket{x}\\ket{1}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{0 \\oplus f(x)} - \\ket{x}\\ket{1 \\oplus f(x)}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{f(x)} - \\ket{x}\\ket{\\overline{f(x)}}) \\\\\n",
-    "&= \n",
-    "\\begin{cases}\n",
-    "      \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{0} - \\ket{x}\\ket{1}) = \\ket{x}\\ket{-} &   f(x) = 0 \\\\\n",
-    "      \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{1} - \\ket{x}\\ket{0}) = -\\ket{x}\\ket{-} &   f(x) = 1 \\\\\n",
-    "\\end{cases} \\\\\n",
-    "&= (-1)^{f(x)}\\ket{x}\\ket{-}    \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "In summary, $U_f\\ket{x}\\ket{-} = (-1)^{f(x)}\\ket{x}\\ket{-}$ where the $\\ket{-}$ qubit was left unchanged and a phase was applied to $\\ket{x}$. \n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Quantum parallelism\n",
-    "\n",
-    "Consider: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{+}\\ket{0} &= U_f \\tfrac{1}{\\sqrt{2}}(\\ket{0}+\\ket{1}) \\ket{0} \\\\\n",
-    "&= U_f \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{0}+\\ket{1}\\ket{0}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}(U_f \\ket{0}\\ket{0} + U_f \\ket{1}\\ket{0})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(U_f \\ket{0}\\ket{0} + U_f \\ket{1}\\ket{0})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{0 \\oplus f(0)} + \\ket{1}\\ket{0 \\oplus f(1)})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{f(0)} + \\ket{1}\\ket{f(1)})  \\\\   \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We have calculated information about both $f(0)$ and $f(1)$ simultaneously. Quantum mechanics allows for parallelism by exploiting the ability of superposition states.  "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deutschs' Algorithm: \n",
-    "\n",
-    "Our aim is to find out if $f: \\{0,1\\} \\longrightarrow \\{0,1\\}$ is a constant or a balanced function? If constant, $f(0) = f(1)$, and if balanced, $f(0) \\neq f(1)$.\n",
-    "\n",
-    "We step through the circuit diagram below and follow the math after the application of each gate.\n",
-    "\n",
-    "<img src=\"images/deutsch.png\"  width=\"500\" height=\"210\">\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\ket{\\psi_0}  =  \\ket{01}\n",
-    "\\tag{1}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\\ket{\\psi_1}  =   H_1H_0\\ket{\\psi_0} = H_1H_0\\ket{01} = \\ket{+-} = \\frac{1}{\\sqrt{2}} (\\ket{0}+\\ket{1})\\ket{-}\n",
-    "\\tag{2}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\ket{\\psi_2}  =   U_f\\ket{\\psi_1} = \\frac{1}{\\sqrt{2}} (U_f\\ket{0}\\ket{-} + U_f\\ket{1}\\ket{-})\n",
-    "\\tag{3}\n",
-    "$$\n",
-    "\n",
-    "using $U_f\\ket{x}\\ket{-} = (-1)^{f(x)}\\ket{x}\\ket{-}$ which we proved in the phase oracle section above: \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_2}  =  \\frac{1}{\\sqrt{2}} ((-1)^{f(0)}\\ket{0}\\ket{-} + (-1)^{f(1)}\\ket{1}\\ket{-}) \\\\\n",
-    "= \\frac{1}{\\sqrt{2}} ((-1)^{f(0)}\\ket{0} + (-1)^{f(1)}\\ket{1}) \\ket{-} \\\\\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We now drop the second, qubit in the $\\ket{-}$ state from our derivations as the figure above has no further gate operations acting on it. Remember our aim is to find out if $f(x)$ is a constant or a balanced function. We have the following options: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\ket{\\psi_{2}}=\n",
-    "\\begin{cases}\n",
-    "      \\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1})  &   f(0) = 0, f(1) = 0, f(x)  \\text{ is constant} \\\\\n",
-    "      -\\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1})  &   f(0) = 1, f(1) = 1, f(x)  \\text{ is constant} \\\\\n",
-    "      \\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})  &   f(0) = 0, f(1) = 1, f(x)  \\text{ is balanced} \\\\\n",
-    "      -\\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})  &   f(0) = 1, f(1) = 0, f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} =\n",
-    "\\begin{cases}\n",
-    "      \\pm \\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1}) = \\pm \\ket{+}  &    f(x)  \\text{ is constant} \\\\\n",
-    "      \\pm \\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})= \\pm \\ket{-}  &    f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{3}}= H\\ket{\\psi_{2}}\n",
-    "\\begin{cases}\n",
-    "      \\pm H\\ket{+} =  \\pm \\ket{0} &    f(x)  \\text{ is constant} \\\\\n",
-    "      \\pm H\\ket{-}  = \\pm \\ket{1}  &   f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We can now measure the first qubit to yield either a $0$ or a $1$ to determine if $f(x)$ is constant or balanced. \n",
-    "\n",
-    "Deutsch's algorithm may not be practically useful but does demonstrate quantum advantage since it uses one function call to determine the property of $f$ in comparison to two for classical methods. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (>= 3.0.0) is not supported.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import the CUDA-Q package and set the target to run on NVIDIA GPUs.\n",
-    "\n",
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here we input the values of [f(0), f(1)] which allows us to represent constant or balanced functions.\n",
-    "\n",
-    "fx = [0, 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮          ╭───╮\n",
-      "q0 : ┤ h ├───────●──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╰───╯\n",
-      "q1 : ┤ x ├┤ h ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯     \n",
-      "\n",
-      "f(x) is a balanced function\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let us now code up the circuit shown above following the state Psi after each step.\n",
-    "\n",
-    "qubit_count = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(fx: List[int]):\n",
-    "    qubit_0 = cudaq.qubit()\n",
-    "    qubit_1 = cudaq.qubit()\n",
-    "\n",
-    "    # Psi 0\n",
-    "    x(qubit_1)\n",
-    "\n",
-    "    # Psi 1\n",
-    "    h(qubit_0)\n",
-    "    h(qubit_1)\n",
-    "\n",
-    "    # Psi 2 - oracle\n",
-    "    if fx[0] == 1:\n",
-    "        x.ctrl(qubit_0, qubit_1)\n",
-    "        x(qubit_1)\n",
-    "\n",
-    "    if fx[1] == 1:\n",
-    "        x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "    # Psi 3\n",
-    "    h(qubit_0)\n",
-    "\n",
-    "    # Measure the qubit to yield if the function is constant or balanced.\n",
-    "    mz(qubit_0)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, fx))\n",
-    "\n",
-    "result = cudaq.sample(kernel, fx, shots_count=1)\n",
-    "\n",
-    "if np.array(result)[0] == '0':\n",
-    "    print('f(x) is a constant function')\n",
-    "elif np.array(result)[0] == '1':\n",
-    "    print('f(x) is a balanced function')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/digitized_counterdiabatic_qaoa.html b/pr-2308/applications/python/digitized_counterdiabatic_qaoa.html
deleted file mode 100644
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--- a/pr-2308/applications/python/digitized_counterdiabatic_qaoa.html
+++ /dev/null
@@ -1,1088 +0,0 @@
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-  <section id="Molecular-docking-via-DC-QAOA">
-<h1>Molecular docking via DC-QAOA<a class="headerlink" href="#Molecular-docking-via-DC-QAOA" title="Permalink to this heading">¶</a></h1>
-<p>Drugs often work by binding to an active site of a protein, inhibiting or activating its function for some therapeutic purpose. Finding new candidate drugs is extremely difficult. The study of molecular docking helps guide this search and involves the prediction of how strongly a certain ligand (drug) will bind to its target (usually a protein).</p>
-<p>One of the primary challenges to molecular docking arises from the many geometric degrees of freedom present in proteins and ligands, making it difficult to predict the optimal orientation and assess if the drug is a good candidate or not. One solution is to formulate the problem as a mathematical optimization problem where the optimal solution corresponds to the most likely ligand-protein configuration. This optimization problem can be solved on a quantum computer using methods like the Quantum
-Approximate Optimization Algorithm (QAOA). This tutorial demonstrates how this <a class="reference external" href="https://arxiv.org/pdf/2308.04098">paper</a> used digitized-counteradiabatic (DC) QAOA to study molecular docking. This tutorial assumes you have an understanding of QAOA, if not, please see the CUDA-Q MaxCut tutorial found <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/applications/python/qaoa.html">here</a>.</p>
-<p>The next section provides more detail on the problem setup followed by CUDA-Q implementations below.</p>
-<section id="Setting-up-the-Molecular-Docking-Problem">
-<h2>Setting up the Molecular Docking Problem<a class="headerlink" href="#Setting-up-the-Molecular-Docking-Problem" title="Permalink to this heading">¶</a></h2>
-<p>The figure from the <a class="reference external" href="https://arxiv.org/pdf/2308.04098">paper</a> provides a helpful diagram for understanding the workflow.</p>
-<p><img alt="docking" src="../../_images/docking.png" /></p>
-<p>There are 6 key steps: 1. The experimental protein and ligand structures are determined and used to select pharmacores, or an important chemical group that will govern the chemical interactions. 2. Two labeled distance graphs (LAGs) of size <span class="math notranslate nohighlight">\(N\)</span> and <span class="math notranslate nohighlight">\(M\)</span> represent the protein and the ligand, respectively. Each node corresponds to a pharmacore and each edge weight corresponds to the distance between pharmacores. 3. A <span class="math notranslate nohighlight">\(M*N\)</span> node binding interaction graph (BIG) is created from the
-LAGs. Each node in the BIG graph corresponds to a pair of pharmacores, one from the ligand and the other from the protein. The existence of edges between nodes in the BIG graph are determined from the LAGs and correspond to interactions that can feesibly coexist. Therefore, cliques in the graph correspond to mutually possible interactions. 4. The problem is mapped to a QAOA circuit and corresponding Hamiltonian. From there, the ground state solution is determined. 5. The ground state will
-produce the maximum weighted clique which corresponds to the best (most strongly bound) orientation of the ligand and protein. 6. The predicted docking structure is interpreted from the QAOA result and is used for further analysis.</p>
-</section>
-<section id="CUDA-Q-Implementation">
-<h2>CUDA-Q Implementation<a class="headerlink" href="#CUDA-Q-Implementation" title="Permalink to this heading">¶</a></h2>
-<p>First, the appropriate libraries are imported and the <code class="docutils literal notranslate"><span class="pre">nvidia</span></code> backend is selected to run on GPUs if available.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<br/></pre></div>
-</div>
-</div>
-<p>The block below defines two of the BIG data sets from the paper. The first is a smaller example, but it can be swapped with the commented out example below at your discretion. The weights are specified for each node based on the nature of the ligand and protein pharmacores represented by the node.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The two graph inputs from the paper</span>
-
-<span class="c1"># BIG 1</span>
-
-<span class="n">nodes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
-<span class="n">qubit_num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
-<span class="n">edges</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
-         <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
-<span class="n">non_edges</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">nodes</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">nodes</span> <span class="k">if</span> <span class="n">u</span> <span class="o">&lt;</span> <span class="n">v</span> <span class="ow">and</span> <span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">edges</span>
-<span class="p">]</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Edges: &#39;</span><span class="p">,</span> <span class="n">edges</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Non-Edges: &#39;</span><span class="p">,</span> <span class="n">non_edges</span><span class="p">)</span>
-
-<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.6686</span><span class="p">,</span> <span class="mf">0.6686</span><span class="p">,</span> <span class="mf">0.6686</span><span class="p">,</span> <span class="mf">0.1453</span><span class="p">,</span> <span class="mf">0.1453</span><span class="p">,</span> <span class="mf">0.1453</span><span class="p">]</span>
-<span class="n">penalty</span> <span class="o">=</span> <span class="mf">6.0</span>
-<span class="n">num_layers</span> <span class="o">=</span> <span class="mi">3</span>
-
-<span class="c1"># BIG 2 (More expensive simulation)</span>
-<span class="c1">#nodes=[0,1,2,3,4,5,6,7]</span>
-<span class="c1">#qubit_num=len(nodes)</span>
-<span class="c1">#edges=[[0,1],[0,2],[0,5],[0,6],[0,7],[1,2],[1,4],[1,6],[1,7],[2,4],[2,5],[2,7],[3,4],[3,5],[3,6],\</span>
-<span class="c1">#    [4,5],[4,6],[5,6]]</span>
-<span class="c1">#non_edges=[[u,v] for u in nodes for v in nodes if u&lt;v and [u,v] not in edges]</span>
-<span class="c1">#print(&#39;Edges: &#39;, edges)</span>
-<span class="c1">#print(&#39;Non-edges: &#39;, non_edges)</span>
-<span class="c1">#weights=[0.6686,0.6686,0.6886,0.1091,0.0770,0.0770,0.0770,0.0770]</span>
-<span class="c1">#penalty=8.0</span>
-<span class="c1">#num_layers=8</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Edges:  [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4], [3, 4], [3, 5], [4, 5]]
-Non-Edges:  [[0, 3], [1, 4], [2, 5]]
-</pre></div></div>
-</div>
-<p>Next, the Hamiltonian is constructed:</p>
-<div class="math notranslate nohighlight">
-\[H = \frac{1}{2}\sum_{i \in V}w_i(\sigma^z_i - 1) + \frac{P}{4} \sum_{(i,j) \notin E, i \neq j} (\sigma^z_i -1)(\sigma^z_j - 1)\]</div>
-<p>The first term concerns the vertices and the weights of the given pharmacores. The second term is a penalty term that penalizes edges of the graph with no interactions. The penalty <span class="math notranslate nohighlight">\(P\)</span> is set by the user and is defined as 6 in the cell above. The function below returns the Hamiltonian as a CUDA-Q <code class="docutils literal notranslate"><span class="pre">spin_op</span></code> object.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Generate the Hamiltonian</span>
-<span class="k">def</span> <span class="nf">ham_clique</span><span class="p">(</span><span class="n">penalty</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">non_edges</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">:</span>
-
-    <span class="n">spin_ham</span> <span class="o">=</span> <span class="mf">0.0</span>
-    <span class="k">for</span> <span class="n">wt</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span>
-        <span class="c1">#print(wt,node)</span>
-        <span class="n">spin_ham</span> <span class="o">+=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">wt</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
-        <span class="n">spin_ham</span> <span class="o">-=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">wt</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">non_edge</span> <span class="ow">in</span> <span class="n">non_edges</span><span class="p">:</span>
-        <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="p">(</span><span class="n">non_edge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">non_edge</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="c1">#print(u,v)</span>
-        <span class="n">spin_ham</span> <span class="o">+=</span> <span class="n">penalty</span> <span class="o">/</span> <span class="mf">4.0</span> <span class="o">*</span> <span class="p">(</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">-</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="o">-</span>
-                                     <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">+</span> <span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">spin_ham</span>
-</pre></div>
-</div>
-</div>
-<p>The code below strips the Hamiltonian into a list of coefficients and corresponding Pauli words which can be passed into a quantum kernel.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Collect coefficients from a spin operator so we can pass them to a kernel</span>
-<span class="k">def</span> <span class="nf">term_coefficients</span><span class="p">(</span><span class="n">ham</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">]:</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ham</span><span class="o">.</span><span class="n">for_each_term</span><span class="p">(</span><span class="k">lambda</span> <span class="n">term</span><span class="p">:</span> <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">term</span><span class="o">.</span><span class="n">get_coefficient</span><span class="p">()))</span>
-    <span class="k">return</span> <span class="n">result</span>
-
-    <span class="c1"># Collect Pauli words from a spin operator so we can pass them to a kernel</span>
-
-
-<span class="k">def</span> <span class="nf">term_words</span><span class="p">(</span><span class="n">ham</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ham</span><span class="o">.</span><span class="n">for_each_term</span><span class="p">(</span><span class="k">lambda</span> <span class="n">term</span><span class="p">:</span> <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">term</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="kc">False</span><span class="p">)))</span>
-    <span class="k">return</span> <span class="n">result</span>
-
-
-<span class="n">ham</span> <span class="o">=</span> <span class="n">ham_clique</span><span class="p">(</span><span class="n">penalty</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">non_edges</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">ham</span><span class="p">)</span>
-
-<span class="n">coef</span> <span class="o">=</span> <span class="n">term_coefficients</span><span class="p">(</span><span class="n">ham</span><span class="p">)</span>
-<span class="n">words</span> <span class="o">=</span> <span class="n">term_words</span><span class="p">(</span><span class="n">ham</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">term_coefficients</span><span class="p">(</span><span class="n">ham</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">term_words</span><span class="p">(</span><span class="n">ham</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[1.5+0j] IIZIIZ
-[1.5+0j] ZIIZII
-[-1.1657+0j] IZIIII
-[1.5+0j] IZIIZI
-[-1.42735+0j] IIIZII
-[3.2791499999999996+0j] IIIIII
-[-1.1657+0j] IIZIII
-[-1.42735+0j] IIIIIZ
-[-1.1657+0j] ZIIIII
-[-1.42735+0j] IIIIZI
-
-[(1.5+0j), (1.5+0j), (-1.1657+0j), (1.5+0j), (-1.42735+0j), (3.2791499999999996+0j), (-1.1657+0j), (-1.42735+0j), (-1.1657+0j), (-1.42735+0j)]
-[&#39;IIZIIZ&#39;, &#39;ZIIZII&#39;, &#39;IZIIII&#39;, &#39;IZIIZI&#39;, &#39;IIIZII&#39;, &#39;IIIIII&#39;, &#39;IIZIII&#39;, &#39;IIIIIZ&#39;, &#39;ZIIIII&#39;, &#39;IIIIZI&#39;]
-</pre></div></div>
-</div>
-<p>The kernel below defines a DC-QAOA circuit. What makes the approach “DC” is the inclusion of additional counteradiabatic terms to better drive the optimization to the ground state. These terms are digitized and applied as additional operations following each QAOA layer. The increase in parameters is hopefully offset by requiring fewer layers. In this example, the DC terms are the additional parameterized <span class="math notranslate nohighlight">\(Y\)</span> operations applied to each qubit. These can be commented out to run conventional
-QAOA.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">dc_qaoa</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">thetas</span><span class="p">:</span><span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>\
-    <span class="n">coef</span><span class="p">:</span><span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">],</span> <span class="n">words</span><span class="p">:</span><span class="nb">list</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">pauli_word</span><span class="p">]):</span>
-
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">)</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-    <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">coef</span><span class="p">)):</span>
-            <span class="n">exp_pauli</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="o">*</span> <span class="n">coef</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">qubits</span><span class="p">,</span> <span class="n">words</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">):</span>
-            <span class="n">rx</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="c1">#Comment out this for loop for conventional QAOA</span>
-        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">):</span>
-            <span class="n">ry</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
-</pre></div>
-</div>
-</div>
-<p>The classical optimizer for the QAOA procedure can be specified as one of the built-in CUDA-Q optimizers, in this case Nelder Mead. The parameter count is defined for DC-QAOA, but can be swapped with the commented line below for conventional QAOA.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify the optimizer and its initial parameters.</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">NelderMead</span><span class="p">()</span>
-
-<span class="c1">#Specify random seeds</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_random_seed</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
-
-<span class="c1"># if dc_qaoa used</span>
-<span class="n">parameter_count</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">qubit_num</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">coef</span><span class="p">))</span> <span class="o">*</span> <span class="n">num_layers</span>
-
-<span class="c1"># if qaoa used</span>
-<span class="c1"># parameter_count=(qubit_num+len(coef))*num_layers</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of parameters: &#39;</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-<span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">8</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">8</span><span class="p">,</span>
-                                                 <span class="n">parameter_count</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Initial parameters = &quot;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Total number of parameters:  66
-Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064, 0.37118004688049144, -0.03656087558321203, 0.08564174998504231, 0.21639801853794682, 0.11122286088634259, 0.1743727097033635, -0.36518146001762486, -0.15829741539542244, -0.3467434780387345, 0.28043500852894776, -0.09986021299050934, 0.14125225086023052, -0.19141728018199775, -0.11970943368650361, -0.3853063093646483, -0.1112643868789806, 0.3527177454825464, -0.22156160012057186, -0.1418496891385843, 0.32811766468303116, -0.367642000671186, -0.34158180583996006, 0.10196745745501312, 0.29359239180502594, -0.3858537615546677, 0.19366130907065582, 0.24570488114056754, -0.3332307385378807, 0.12287973244618389, 0.007274514934614895, -0.015799547372526146, 0.3578070967202224, -0.39268963055535144, -0.19872246354138554, 0.16668715544467982, -0.13777293592446055, -0.17514665212709513, 0.15350249947988204, 0.32872977428061945, -0.20068831419712105, -0.032919322131134854, -0.19399909325771983, -0.09477141125241506, 0.08210460401106645, 0.21392577760158515, -0.3393568044538389, 0.14615087942938465, 0.03790339186006314, -0.2843250892879255, -0.3151384847055956, -0.19983741137121905, -0.27348611567665115, 0.33457528180906904, 0.14145414847455462, -0.20604220093940323, 0.05410235084309195, 0.04447870918600966, -0.3355714098595045, 0.266806440171265, -0.07436189654442632, -0.2789176729721685, -0.2427508182662484]
-</pre></div></div>
-</div>
-<p>A cost function is specified which computes the expectation value of the DC-QAOA circuit and the Hamiltonian using the <code class="docutils literal notranslate"><span class="pre">observe</span></code> function. Running the optimization returns the minimized expectation value and the optimal parameters.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cost_values</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
-
-    <span class="n">cost</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">dc_qaoa</span><span class="p">,</span> <span class="n">ham</span><span class="p">,</span> <span class="n">qubit_num</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">coef</span><span class="p">,</span>
-                         <span class="n">words</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-    <span class="n">cost_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">cost</span>
-
-
-<span class="c1"># Optimize!</span>
-<span class="n">optimal_expectation</span><span class="p">,</span> <span class="n">optimal_parameters</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span>
-    <span class="n">dimensions</span><span class="o">=</span><span class="n">parameter_count</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">objective</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimal_expectation =&#39;</span><span class="p">,</span> <span class="n">optimal_expectation</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimal_parameters =&#39;</span><span class="p">,</span> <span class="n">optimal_parameters</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-optimal_expectation = -2.0057493966746804
-optimal_parameters = [2.0508763934174787, 0.013930789730781493, 0.5793211220774144, 0.878009560684498, 0.5277129177248182, 0.4404810513078178, 0.5755552245467919, 0.14125558672355468, 0.3724262117066903, 0.1318978057007808, -1.1228708513911436, 0.932342804955409, -0.8478237950658537, 0.46345886313018125, -0.5809397306340341, 0.2408342488137229, 0.11216088888484882, -0.009704173265255175, 0.4757346661223584, -0.7281211610985926, 0.06051951319169091, -0.7794512146826196, 0.09249435261907034, 0.09998378319110682, 1.255349350720572, 1.2607038244228248, 0.2060124032311757, 0.13991934581192997, 0.9874814082082164, -0.1591291464755939, 0.30815482837046393, -0.9701804681517978, -0.002609462845755913, 0.43533533568363353, 0.642630110681613, 0.6137063363954748, -0.7204687246344496, 0.08390768435524378, 0.5480630700433249, -0.38905723227347905, -0.6837811162838194, -0.17239016898719284, 0.1649341118754853, -0.46771209183422724, -0.008565327035838663, 1.982230359328883, -0.4232972687799105, 0.22765896988428905, 0.04207923928239914, -0.36758378917672285, -0.01825447063622079, -0.059755059728027485, -0.6849697218162497, 0.2711684382411018, -0.2904257415666667, -0.16359529445017368, -0.09168623367396612, 0.5786087806926155, -0.3476755367718726, 0.1209273564533628, 0.605136043801364, -0.19128215816141694, 0.16756583092588012, 1.0715488214105267, -0.5269641128095075, -0.3029128369198704]
-</pre></div></div>
-</div>
-<p>Sampling the circuit with the optimal parameters allows for the <code class="docutils literal notranslate"><span class="pre">most_probable</span></code> command to reveal the bitsting corresponding to the ideal graph partitioning solution. This indicates what sort of interactions are present in the ideal docking configuration</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shots</span> <span class="o">=</span> <span class="mi">200000</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">dc_qaoa</span><span class="p">,</span>
-                      <span class="n">qubit_num</span><span class="p">,</span>
-                      <span class="n">num_layers</span><span class="p">,</span>
-                      <span class="n">optimal_parameters</span><span class="p">,</span>
-                      <span class="n">coef</span><span class="p">,</span>
-                      <span class="n">words</span><span class="p">,</span>
-                      <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The MVWCP is given by the partition: &#39;</span><span class="p">,</span> <span class="n">counts</span><span class="o">.</span><span class="n">most_probable</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{ 110001:16 011100:4 111000:199979 011000:1 }
-
-The MVWCP is given by the partition:  111000
-</pre></div></div>
-</div>
-<p><img alt="dockin" class="no-scaled-link" src="../../_images/partition.png" style="width: 300px;" /></p>
-<p>The convergence of the optimization can be plotted below.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="n">x_values</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cost_values</span><span class="p">)))</span>
-<span class="n">y_values</span> <span class="o">=</span> <span class="n">cost_values</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_values</span><span class="p">,</span> <span class="n">y_values</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Cost Value&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_digitized_counterdiabatic_qaoa_20_0.png" src="../../_images/applications_python_digitized_counterdiabatic_qaoa_20_0.png" />
-</div>
-</div>
-</section>
-</section>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="qaoa.html" class="btn btn-neutral float-left" title="Max-Cut with QAOA" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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-  }
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-    color: #8c0;
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diff --git a/pr-2308/applications/python/digitized_counterdiabatic_qaoa.ipynb b/pr-2308/applications/python/digitized_counterdiabatic_qaoa.ipynb
deleted file mode 100644
index a3043b7a1b..0000000000
--- a/pr-2308/applications/python/digitized_counterdiabatic_qaoa.ipynb
+++ /dev/null
@@ -1,427 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Molecular docking via DC-QAOA\n",
-    "\n",
-    "Drugs often work by binding to an active site of a protein, inhibiting or activating its function for some therapeutic purpose. Finding new candidate drugs is extremely difficult. The study of molecular docking helps guide this search and involves the prediction of how strongly a certain ligand (drug) will bind to its target (usually a protein).  \n",
-    "\n",
-    "One of the primary challenges to molecular docking arises from the many geometric degrees of freedom present in proteins and ligands, making it difficult to predict the optimal orientation and assess if the drug is a good candidate or not. One solution is to formulate the problem as a mathematical optimization problem where the optimal solution corresponds to the most likely ligand-protein configuration. This optimization problem can be solved on a quantum computer using methods like the Quantum Approximate Optimization Algorithm (QAOA). This tutorial demonstrates how this [paper](https://arxiv.org/pdf/2308.04098) used digitized-counteradiabatic (DC) QAOA to study molecular docking.  This tutorial assumes you have an understanding of QAOA, if not, please see the CUDA-Q MaxCut tutorial found [here](https://nvidia.github.io/cuda-quantum/latest/applications/python/qaoa.html).\n",
-    "\n",
-    "The next section provides more detail on the problem setup followed by CUDA-Q implementations below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setting up the Molecular Docking Problem\n",
-    "\n",
-    "The figure from the [paper](https://arxiv.org/pdf/2308.04098) provides a helpful diagram for understanding the workflow.\n",
-    "\n",
-    "![docking](./images/docking.png)\n",
-    "\n",
-    "\n",
-    "There are 6 key steps:\n",
-    "1.  The experimental protein and ligand structures are determined and used to select pharmacores, or an important chemical group that will govern the chemical interactions.\n",
-    "2. Two labeled distance graphs (LAGs) of size $N$ and $M$ represent the protein and the ligand, respectively. Each node corresponds to a pharmacore and each edge weight corresponds to the distance between pharmacores.\n",
-    "3.  A $M*N$ node binding interaction graph (BIG) is created from the LAGs. Each node in the BIG graph corresponds to a pair of pharmacores, one from the ligand and the other from the protein. The existence of edges between nodes in the BIG graph are determined from the LAGs and correspond to interactions that can feesibly coexist. Therefore, cliques in the graph correspond to mutually possible interactions. \n",
-    "4. The problem is mapped to a QAOA circuit and corresponding Hamiltonian. From there, the ground state solution is determined.\n",
-    "5.  The ground state will produce the maximum weighted clique which corresponds to the best (most strongly bound) orientation of the ligand and protein.\n",
-    "6.  The predicted docking structure is interpreted from the QAOA result and is used for further analysis.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### CUDA-Q Implementation\n",
-    "\n",
-    "First, the appropriate libraries are imported and the `nvidia` backend is selected to run on GPUs if available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "import numpy as np\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The block below defines two of the BIG data sets from the paper. The first is a smaller example, but it can be swapped with the commented out example below at your discretion. The weights are specified for each node based on the nature of the ligand and protein pharmacores represented by the node."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Edges:  [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4], [3, 4], [3, 5], [4, 5]]\n",
-      "Non-Edges:  [[0, 3], [1, 4], [2, 5]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The two graph inputs from the paper\n",
-    "\n",
-    "# BIG 1\n",
-    "\n",
-    "nodes = [0, 1, 2, 3, 4, 5]\n",
-    "qubit_num = len(nodes)\n",
-    "edges = [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4],\n",
-    "         [3, 4], [3, 5], [4, 5]]\n",
-    "non_edges = [\n",
-    "    [u, v] for u in nodes for v in nodes if u < v and [u, v] not in edges\n",
-    "]\n",
-    "\n",
-    "print('Edges: ', edges)\n",
-    "print('Non-Edges: ', non_edges)\n",
-    "\n",
-    "weights = [0.6686, 0.6686, 0.6686, 0.1453, 0.1453, 0.1453]\n",
-    "penalty = 6.0\n",
-    "num_layers = 3\n",
-    "\n",
-    "# BIG 2 (More expensive simulation)\n",
-    "#nodes=[0,1,2,3,4,5,6,7]\n",
-    "#qubit_num=len(nodes)\n",
-    "#edges=[[0,1],[0,2],[0,5],[0,6],[0,7],[1,2],[1,4],[1,6],[1,7],[2,4],[2,5],[2,7],[3,4],[3,5],[3,6],\\\n",
-    "#    [4,5],[4,6],[5,6]]\n",
-    "#non_edges=[[u,v] for u in nodes for v in nodes if u<v and [u,v] not in edges]\n",
-    "#print('Edges: ', edges)\n",
-    "#print('Non-edges: ', non_edges)\n",
-    "#weights=[0.6686,0.6686,0.6886,0.1091,0.0770,0.0770,0.0770,0.0770]\n",
-    "#penalty=8.0\n",
-    "#num_layers=8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the Hamiltonian is constructed: \n",
-    "\n",
-    "$$H = \\frac{1}{2}\\sum_{i \\in V}w_i(\\sigma^z_i - 1) + \\frac{P}{4} \\sum_{(i,j) \\notin E, i \\neq j} (\\sigma^z_i -1)(\\sigma^z_j - 1) $$\n",
-    "\n",
-    "\n",
-    "The first term concerns the vertices and the weights of the given pharmacores.  The second term is a penalty term that penalizes edges of the graph with no interactions.  The penalty $P$ is set by the user and is defined as 6 in the cell above. The function below returns the Hamiltonian as a CUDA-Q `spin_op` object.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate the Hamiltonian\n",
-    "def ham_clique(penalty, nodes, weights, non_edges) -> cudaq.SpinOperator:\n",
-    "\n",
-    "    spin_ham = 0.0\n",
-    "    for wt, node in zip(weights, nodes):\n",
-    "        #print(wt,node)\n",
-    "        spin_ham += 0.5 * wt * spin.z(node)\n",
-    "        spin_ham -= 0.5 * wt * spin.i(node)\n",
-    "\n",
-    "    for non_edge in non_edges:\n",
-    "        u, v = (non_edge[0], non_edge[1])\n",
-    "        #print(u,v)\n",
-    "        spin_ham += penalty / 4.0 * (spin.z(u) * spin.z(v) - spin.z(u) -\n",
-    "                                     spin.z(v) + spin.i(u) * spin.i(v))\n",
-    "\n",
-    "    return spin_ham"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below strips the Hamiltonian into a list of coefficients and corresponding Pauli words which can be passed into a quantum kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.5+0j] IIZIIZ\n",
-      "[1.5+0j] ZIIZII\n",
-      "[-1.1657+0j] IZIIII\n",
-      "[1.5+0j] IZIIZI\n",
-      "[-1.42735+0j] IIIZII\n",
-      "[3.2791499999999996+0j] IIIIII\n",
-      "[-1.1657+0j] IIZIII\n",
-      "[-1.42735+0j] IIIIIZ\n",
-      "[-1.1657+0j] ZIIIII\n",
-      "[-1.42735+0j] IIIIZI\n",
-      "\n",
-      "[(1.5+0j), (1.5+0j), (-1.1657+0j), (1.5+0j), (-1.42735+0j), (3.2791499999999996+0j), (-1.1657+0j), (-1.42735+0j), (-1.1657+0j), (-1.42735+0j)]\n",
-      "['IIZIIZ', 'ZIIZII', 'IZIIII', 'IZIIZI', 'IIIZII', 'IIIIII', 'IIZIII', 'IIIIIZ', 'ZIIIII', 'IIIIZI']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Collect coefficients from a spin operator so we can pass them to a kernel\n",
-    "def term_coefficients(ham: cudaq.SpinOperator) -> list[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "    # Collect Pauli words from a spin operator so we can pass them to a kernel\n",
-    "\n",
-    "\n",
-    "def term_words(ham: cudaq.SpinOperator) -> list[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "ham = ham_clique(penalty, nodes, weights, non_edges)\n",
-    "print(ham)\n",
-    "\n",
-    "coef = term_coefficients(ham)\n",
-    "words = term_words(ham)\n",
-    "\n",
-    "print(term_coefficients(ham))\n",
-    "print(term_words(ham))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The kernel below defines a DC-QAOA circuit.  What makes the approach \"DC\" is the inclusion of additional counteradiabatic terms to better drive the optimization to the ground state. These terms are digitized and applied as additional operations following each QAOA layer.  The increase in parameters is hopefully offset by requiring fewer layers. In this example, the DC terms are the additional parameterized $Y$ operations applied to each qubit. These can be commented out to run conventional QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def dc_qaoa(qubit_num:int, num_layers:int, thetas:list[float],\\\n",
-    "    coef:list[complex], words:list[cudaq.pauli_word]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    h(qubits)\n",
-    "\n",
-    "    count = 0\n",
-    "    for p in range(num_layers):\n",
-    "\n",
-    "        for i in range(len(coef)):\n",
-    "            exp_pauli(thetas[count] * coef[i].real, qubits, words[i])\n",
-    "            count += 1\n",
-    "\n",
-    "        for j in range(qubit_num):\n",
-    "            rx(thetas[count], qubits[j])\n",
-    "            count += 1\n",
-    "\n",
-    "        #Comment out this for loop for conventional QAOA\n",
-    "        for k in range(qubit_num):\n",
-    "            ry(thetas[count], qubits[k])\n",
-    "            count += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The classical optimizer for the QAOA procedure can be specified as one of the built-in CUDA-Q optimizers, in this case Nelder Mead. The parameter count is defined for DC-QAOA, but can be swapped with the commented line below for conventional QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of parameters:  66\n",
-      "Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064, 0.37118004688049144, -0.03656087558321203, 0.08564174998504231, 0.21639801853794682, 0.11122286088634259, 0.1743727097033635, -0.36518146001762486, -0.15829741539542244, -0.3467434780387345, 0.28043500852894776, -0.09986021299050934, 0.14125225086023052, -0.19141728018199775, -0.11970943368650361, -0.3853063093646483, -0.1112643868789806, 0.3527177454825464, -0.22156160012057186, -0.1418496891385843, 0.32811766468303116, -0.367642000671186, -0.34158180583996006, 0.10196745745501312, 0.29359239180502594, -0.3858537615546677, 0.19366130907065582, 0.24570488114056754, -0.3332307385378807, 0.12287973244618389, 0.007274514934614895, -0.015799547372526146, 0.3578070967202224, -0.39268963055535144, -0.19872246354138554, 0.16668715544467982, -0.13777293592446055, -0.17514665212709513, 0.15350249947988204, 0.32872977428061945, -0.20068831419712105, -0.032919322131134854, -0.19399909325771983, -0.09477141125241506, 0.08210460401106645, 0.21392577760158515, -0.3393568044538389, 0.14615087942938465, 0.03790339186006314, -0.2843250892879255, -0.3151384847055956, -0.19983741137121905, -0.27348611567665115, 0.33457528180906904, 0.14145414847455462, -0.20604220093940323, 0.05410235084309195, 0.04447870918600966, -0.3355714098595045, 0.266806440171265, -0.07436189654442632, -0.2789176729721685, -0.2427508182662484]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Specify the optimizer and its initial parameters.\n",
-    "optimizer = cudaq.optimizers.NelderMead()\n",
-    "\n",
-    "#Specify random seeds\n",
-    "np.random.seed(13)\n",
-    "cudaq.set_random_seed(13)\n",
-    "\n",
-    "# if dc_qaoa used\n",
-    "parameter_count = (2 * qubit_num + len(coef)) * num_layers\n",
-    "\n",
-    "# if qaoa used\n",
-    "# parameter_count=(qubit_num+len(coef))*num_layers\n",
-    "\n",
-    "print('Total number of parameters: ', parameter_count)\n",
-    "optimizer.initial_parameters = np.random.uniform(-np.pi / 8, np.pi / 8,\n",
-    "                                                 parameter_count)\n",
-    "print(\"Initial parameters = \", optimizer.initial_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A cost function is specified which computes the expectation value of the DC-QAOA circuit and the Hamiltonian using the `observe` function. Running the optimization returns the minimized expectation value and the optimal parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "optimal_expectation = -2.0057493966746804\n",
-      "optimal_parameters = [2.0508763934174787, 0.013930789730781493, 0.5793211220774144, 0.878009560684498, 0.5277129177248182, 0.4404810513078178, 0.5755552245467919, 0.14125558672355468, 0.3724262117066903, 0.1318978057007808, -1.1228708513911436, 0.932342804955409, -0.8478237950658537, 0.46345886313018125, -0.5809397306340341, 0.2408342488137229, 0.11216088888484882, -0.009704173265255175, 0.4757346661223584, -0.7281211610985926, 0.06051951319169091, -0.7794512146826196, 0.09249435261907034, 0.09998378319110682, 1.255349350720572, 1.2607038244228248, 0.2060124032311757, 0.13991934581192997, 0.9874814082082164, -0.1591291464755939, 0.30815482837046393, -0.9701804681517978, -0.002609462845755913, 0.43533533568363353, 0.642630110681613, 0.6137063363954748, -0.7204687246344496, 0.08390768435524378, 0.5480630700433249, -0.38905723227347905, -0.6837811162838194, -0.17239016898719284, 0.1649341118754853, -0.46771209183422724, -0.008565327035838663, 1.982230359328883, -0.4232972687799105, 0.22765896988428905, 0.04207923928239914, -0.36758378917672285, -0.01825447063622079, -0.059755059728027485, -0.6849697218162497, 0.2711684382411018, -0.2904257415666667, -0.16359529445017368, -0.09168623367396612, 0.5786087806926155, -0.3476755367718726, 0.1209273564533628, 0.605136043801364, -0.19128215816141694, 0.16756583092588012, 1.0715488214105267, -0.5269641128095075, -0.3029128369198704]\n"
-     ]
-    }
-   ],
-   "source": [
-    "cost_values = []\n",
-    "\n",
-    "\n",
-    "def objective(parameters):\n",
-    "\n",
-    "    cost = cudaq.observe(dc_qaoa, ham, qubit_num, num_layers, parameters, coef,\n",
-    "                         words).expectation()\n",
-    "    cost_values.append(cost)\n",
-    "    return cost\n",
-    "\n",
-    "\n",
-    "# Optimize!\n",
-    "optimal_expectation, optimal_parameters = optimizer.optimize(\n",
-    "    dimensions=parameter_count, function=objective)\n",
-    "\n",
-    "print('optimal_expectation =', optimal_expectation)\n",
-    "print('optimal_parameters =', optimal_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sampling the circuit with the optimal parameters allows for the `most_probable` command to reveal the bitsting corresponding to the ideal graph partitioning solution. This indicates what sort of interactions are present in the ideal docking configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 110001:16 011100:4 111000:199979 011000:1 }\n",
-      "\n",
-      "The MVWCP is given by the partition:  111000\n"
-     ]
-    }
-   ],
-   "source": [
-    "shots = 200000\n",
-    "\n",
-    "counts = cudaq.sample(dc_qaoa,\n",
-    "                      qubit_num,\n",
-    "                      num_layers,\n",
-    "                      optimal_parameters,\n",
-    "                      coef,\n",
-    "                      words,\n",
-    "                      shots_count=shots)\n",
-    "print(counts)\n",
-    "\n",
-    "print('The MVWCP is given by the partition: ', counts.most_probable())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"./images/partition.png\" alt=\"dockin\" width=\"300\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The convergence of the optimization can be plotted below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x_values = list(range(len(cost_values)))\n",
-    "y_values = cost_values\n",
-    "\n",
-    "plt.plot(x_values, y_values)\n",
-    "\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Cost Value\")\n",
-    "plt.show()"
-   ]
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- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
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-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
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- },
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- "nbformat_minor": 4
-}
diff --git a/pr-2308/applications/python/divisive_clustering_coresets.html b/pr-2308/applications/python/divisive_clustering_coresets.html
deleted file mode 100644
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-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
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-  <section id="Divisive-Clustering-With-Coresets-Using-CUDA-Q">
-<h1>Divisive Clustering With Coresets Using CUDA-Q<a class="headerlink" href="#Divisive-Clustering-With-Coresets-Using-CUDA-Q" title="Permalink to this heading">¶</a></h1>
-<p>This tutorial will explore a CUDA-Q implementation of recent research (ArXiv Paper: <a class="reference external" href="https://arxiv.org/pdf/2402.01529.pdf">https://arxiv.org/pdf/2402.01529.pdf</a>) performed by a team from the University of Edinburgh. This tutorial was jointly developed by NVIDIA and the authors so users can better understand their method and explore how CUDA-Q removed barriers to scaling.</p>
-<p>The code for this tutorial is based off the MIT licensed code found here: <a class="reference external" href="https://github.com/Boniface316/bigdata_vqa">https://github.com/Boniface316/bigdata_vqa</a></p>
-<p>Clustering is a common unsupervised learning technique aimed at grouping data with similar characteristics. The unique properties of quantum computers could allow for enhanced pattern finding in clustering applications and enable more reliable data analysis. However, quantum computers today are severely limited by qubit count and noise. Performing practical clustering applications would require far too many qubits. The Edinburgh team developed a new method (extending the work of Harrow) to
-leverage coresets for clustering applications on quantum computers and use far fewer qubits. This tutorial will walk through an example using this approach for divisive clustering and emphasize the utility of CUDA-Q for scaling quantum simulations.</p>
-<p>The goal of divisive clustering is to begin with all data points as one set, and iteratively bipartition the data until each point is its own cluster. The branching behavior of this process can be used to understand similarities in the data points.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If you are running outside of a CUDA-Q container or CUDA-Q directory tree, you may need to uncomment these lines to fetch the files.</span>
-<span class="c1"># If you are running inside a CUDA-Q tree, then this step can be skipped.</span>
-<span class="c1"># !mkdir divisive_clustering_src</span>
-<span class="c1"># !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py</span>
-<span class="c1"># !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the relevant packages.</span>
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">mpi4py</span><span class="o">==</span><span class="m">3</span>.1.6<span class="w"> </span><span class="nv">networkx</span><span class="o">==</span><span class="m">2</span>.8.8<span class="w"> </span><span class="nv">pandas</span><span class="o">==</span><span class="m">2</span>.2.2<span class="w"> </span>scikit-learn<span class="o">==</span><span class="m">1</span>.4.2<span class="w"> </span><span class="nv">tqdm</span><span class="o">==</span><span class="m">4</span>.66.2<span class="w"> </span>-q
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="c1"># Auxillary Imports</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
-<span class="kn">from</span> <span class="nn">divisive_clustering_src.divisive_clustering</span> <span class="kn">import</span> <span class="n">Coreset</span><span class="p">,</span> <span class="n">DivisiveClustering</span><span class="p">,</span> <span class="n">Dendrogram</span><span class="p">,</span> <span class="n">Voironi_Tessalation</span>
-
-<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The settings below are global parameters for the quantum simulation and can be toggled by the user.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">circuit_depth</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">max_iterations</span> <span class="o">=</span> <span class="mi">75</span>
-<span class="n">max_shots</span> <span class="o">=</span> <span class="mi">1000</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Given a data set <span class="math notranslate nohighlight">\(X = (x_1, x_2, \cdots, x_N)\)</span>, a coreset is weighted data set of much smaller size (<span class="math notranslate nohighlight">\(X', w\)</span>) that represents <span class="math notranslate nohighlight">\(X\)</span> enough such that analysis of (<span class="math notranslate nohighlight">\(X', w\)</span>) can allow us to draw reasonable approximate conclusions about <span class="math notranslate nohighlight">\(X\)</span>. There are various approaches to build coresets. They can be found in Practical Coreset Construction for Machine Learning (<a class="reference external" href="https://arxiv.org/pdf/1703.06476.pdf">https://arxiv.org/pdf/1703.06476.pdf</a>) and New Streaming Algorithms for Coresets in Machine Learning
-(<a class="reference external" href="https://arxiv.org/pdf/1703.06476.pdf">https://arxiv.org/pdf/1703.06476.pdf</a>).</p>
-<p>Essentially, coreset construction boils down to finding the optimal coreset size and weights given some error tolerance. Given the constraints of a quantum computer, in this work, a coreset size is selected <span class="math notranslate nohighlight">\(a\)</span> <span class="math notranslate nohighlight">\(priori\)</span>, and the error is determined for each model.</p>
-<p>The following is an example <span class="math notranslate nohighlight">\(M=10\)</span> coreset constructed from a 1000-point data set and loaded into a pandas data frame. See the image below where the coreset is represented by the black stars, the size of which corresponds to the weights.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">raw_data</span> <span class="o">=</span> <span class="n">Coreset</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
-<span class="n">coreset</span> <span class="o">=</span> <span class="n">Coreset</span><span class="p">(</span>
-    <span class="n">raw_data</span><span class="o">=</span><span class="n">raw_data</span><span class="p">,</span>
-    <span class="n">number_of_sampling_for_centroids</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">coreset_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">number_of_coresets_to_evaluate</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">coreset_method</span><span class="o">=</span><span class="s2">&quot;BFL2&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">coreset_vectors</span><span class="p">,</span> <span class="n">coreset_weights</span> <span class="o">=</span> <span class="n">coreset</span><span class="o">.</span><span class="n">get_best_coresets</span><span class="p">()</span>
-
-<span class="n">coreset_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">coreset_vectors</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
-    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">coreset_vectors</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="s2">&quot;weights&quot;</span><span class="p">:</span> <span class="n">coreset_weights</span>
-<span class="p">})</span>
-<span class="n">coreset_df</span><span class="p">[</span><span class="s2">&quot;Name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">chr</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">65</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">coreset_df</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">coreset_df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Using BFL2 method to generate coresets
-          X         Y     weights Name
-0  7.028364  1.669787  234.230716    A
-1  7.167441  0.354792  101.319288    B
-2  1.022889 -0.921443  125.158339    C
-3  2.706134 -2.636852   13.650774    D
-4  6.998497  0.455847  116.758239    E
-5  7.507918  0.630311  120.727176    F
-6 -2.102508  2.297727   53.294127    G
-7  5.722463  1.400433   77.415840    H
-8 -1.425868  2.341136   42.847985    I
-9  7.985373 -0.063209  240.116237    J
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">raw_data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">raw_data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Raw Data&quot;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;#7eba00&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-    <span class="n">coreset_df</span><span class="p">[</span><span class="s2">&quot;X&quot;</span><span class="p">],</span>
-    <span class="n">coreset_df</span><span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">],</span>
-    <span class="n">s</span><span class="o">=</span><span class="n">coreset_df</span><span class="p">[</span><span class="s2">&quot;weights&quot;</span><span class="p">],</span>
-    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Coreset&quot;</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Raw data and its best 10 coreset using BFL2&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_divisive_clustering_coresets_11_0.png" src="../../_images/applications_python_divisive_clustering_coresets_11_0.png" />
-</div>
-</div>
-<section id="Data-preprocessing">
-<h2>Data preprocessing<a class="headerlink" href="#Data-preprocessing" title="Permalink to this heading">¶</a></h2>
-<p>In order to cluster data on a quantum computer, the task needs to be cast into the form of a binary optimization problem. Each qubit represents a coreset point, and the quantum algorithm determines how to bipartition the coreset points at each iteration of the divisive clustering routine.</p>
-<p>The first step is to convert coreset points into a fully connected graph. The edge weight is calculated by:</p>
-<p><span class="math notranslate nohighlight">\(e_{ij} = w_iw_jd_{ij}\)</span> where <span class="math notranslate nohighlight">\(d_{ij}\)</span> is the Euclidean distance between points <span class="math notranslate nohighlight">\(i\)</span> and <span class="math notranslate nohighlight">\(j\)</span>.</p>
-<p>This process is handled by <code class="docutils literal notranslate"><span class="pre">Coreset.coreset_to_graph()</span></code>. The function returns a fully connected graph <span class="math notranslate nohighlight">\(G\)</span> with edge weights.</p>
-</section>
-<section id="Quantum-functions">
-<h2>Quantum functions<a class="headerlink" href="#Quantum-functions" title="Permalink to this heading">¶</a></h2>
-<p>The divisive clustering problem will be implemented on a quantum computer using a variational quantum algorithm (VQA) approach. A VQA takes a Hamiltonian (encoded with the optimization problem) and a parameterized ansatz and evaluates expectation values (quantum computer) that inform updates to the ansatz parameters (classical computer). The graph <span class="math notranslate nohighlight">\(G\)</span> (Code in the “src” file) is used to construct the Hamiltonian, derived specifically for the divisive clustering problem, and motivated by a
-max-cut Hamiltonian. The <code class="docutils literal notranslate"><span class="pre">spin.z(i)</span></code> method in CUDA-Q adds a Pauli Z operation that acts on qubit <span class="math notranslate nohighlight">\(i\)</span> to the Hamiltonian.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_K2_Hamiltonian</span><span class="p">(</span><span class="n">G</span><span class="p">:</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the K2 Hamiltonian for the given graph G</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        G (nx.Graph): Weighted graph</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">H</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">():</span>
-        <span class="n">weight</span> <span class="o">=</span> <span class="n">G</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">][</span><span class="s2">&quot;weight&quot;</span><span class="p">]</span>
-        <span class="n">H</span> <span class="o">+=</span> <span class="n">weight</span> <span class="o">*</span> <span class="p">(</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">j</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">H</span>
-</pre></div>
-</div>
-</div>
-<p>The code below constructs a quantum kernel, defining the circuit which will serve as an ansatz. The structure of the circuit is a hardware efficient ansatz consisting of layers of parameterized <span class="math notranslate nohighlight">\(R_Z\)</span> and <span class="math notranslate nohighlight">\(R_Y\)</span> gate acting on each qubit, followed by a linear cascade of CNOT gates, and two more rotation gates.</p>
-<p>The <code class="docutils literal notranslate"><span class="pre">&#64;cudaq.kernel</span></code> decorator allows us to define a quantum circuit in the new kernel mode syntax which provides performance benefits to JIT compilation.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_VQE_circuit</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the VQE circuit for the given number of qubits and circuit depth</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        number_of_qubits (int): Number of qubits</span>
-<span class="sd">        circuit_depth (int): Circuit depth</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        cudaq.Kernel: VQE Circuit</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-    <span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">number_of_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;VQE Circuit</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            thetas (list[float]): List of parameters</span>
-<span class="sd">            number_of_qubits (int): Number of qubits</span>
-<span class="sd">            circuit_depth (int): Circuit depth</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">)</span>
-
-        <span class="n">theta_position</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">circuit_depth</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">):</span>
-                <span class="n">ry</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">theta_position</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-                <span class="n">rz</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">theta_position</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-
-                <span class="n">theta_position</span> <span class="o">+=</span> <span class="mi">2</span>
-
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_qubits</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">):</span>
-                <span class="n">ry</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">theta_position</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-                <span class="n">rz</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="n">theta_position</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-
-                <span class="n">theta_position</span> <span class="o">+=</span> <span class="mi">2</span>
-
-    <span class="k">return</span> <span class="n">kernel</span>
-</pre></div>
-</div>
-</div>
-<p>We can visualize the circuit using the <code class="docutils literal notranslate"><span class="pre">cudaq.draw()</span></code> method. Below, we are drawing the circuit for 5 qubits.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameter_count</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">circuit_depth</span> <span class="o">*</span> <span class="mi">5</span>
-<span class="n">parameters</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">parameter_count</span><span class="p">)</span>
-
-<span class="n">circuit</span> <span class="o">=</span> <span class="n">get_VQE_circuit</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭────────────╮ ╭────────────╮      ╭────────────╮╭────────────╮»
-q0 : ┤ ry(0.8904) ├─┤ rz(0.7335) ├───●──┤ ry(0.4343) ├┤ rz(0.2236) ├»
-     ├────────────┤ ├────────────┤ ╭─┴─╮╰────────────╯├────────────┤»
-q1 : ┤ ry(0.7937) ├─┤ rz(0.9981) ├─┤ x ├──────●───────┤ ry(0.3945) ├»
-     ├───────────┬╯ ├────────────┤ ╰───╯    ╭─┴─╮     ╰────────────╯»
-q2 : ┤ ry(0.696) ├──┤ rz(0.3352) ├──────────┤ x ├───────────●───────»
-     ├───────────┴╮╭┴────────────┤          ╰───╯         ╭─┴─╮     »
-q3 : ┤ ry(0.6658) ├┤ rz(0.05277) ├────────────────────────┤ x ├─────»
-     ├───────────┬╯├─────────────┴╮                       ╰───╯     »
-q4 : ┤ ry(0.791) ├─┤ rz(0.003569) ├─────────────────────────────────»
-     ╰───────────╯ ╰──────────────╯                                 »
-
-################################################################################
-
-
-─────────────────────────────────────────────
-╭────────────╮
-┤ rz(0.4119) ├───────────────────────────────
-├────────────┤╭────────────╮
-┤ ry(0.3205) ├┤ rz(0.3504) ├─────────────────
-╰────────────╯├────────────┤ ╭────────────╮
-──────●───────┤ ry(0.3913) ├─┤ rz(0.7392) ├──
-    ╭─┴─╮     ├────────────┤╭┴────────────┴─╮
-────┤ x ├─────┤ ry(0.3171) ├┤ rz(0.0008056) ├
-    ╰───╯     ╰────────────╯╰───────────────╯
-
-</pre></div></div>
-</div>
-<p>The next step is to select a classical optimizer. There are multiple <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#optimizers">optimizers</a> built-in to CUDA-Q that can be selected. The code below returns the optimizer with the proper number of initial parameters.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_optimizer</span><span class="p">(</span><span class="n">optimizer</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">max_iterations</span><span class="p">,</span>
-                  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the optimizer with the given parameters</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        optimizer (cudaq.optimizers.optimizer): Optimizer</span>
-<span class="sd">        max_iterations (int): Maximum number of iterations</span>
-<span class="sd">        **kwargs: Additional arguments</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">parameter_count</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;circuit_depth&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;qubits&quot;</span><span class="p">]</span>
-    <span class="n">initial_params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">8.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">8.0</span><span class="p">,</span>
-                                       <span class="n">parameter_count</span><span class="p">)</span>
-    <span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span> <span class="o">=</span> <span class="n">initial_params</span>
-
-    <span class="n">optimizer</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="n">max_iterations</span>
-    <span class="k">return</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">parameter_count</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Divisive-Clustering-Function">
-<h2>Divisive Clustering Function<a class="headerlink" href="#Divisive-Clustering-Function" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">DivisiveClusteringVQA</span></code> class enables the procedure to iteratively bipartition the coreset points until each is its own cluster. If you wish to develop on top of this or see how the underlying code works, please see the <code class="docutils literal notranslate"><span class="pre">divisive_clustering.py</span></code> file in the src directory.</p>
-<p><code class="docutils literal notranslate"><span class="pre">run_divisive_clustering</span></code>, takes the current iteration’s coreset points that will be bipartitioned as inputs, extracts the appropriate weights, and builds a graph <span class="math notranslate nohighlight">\(G\)</span>. The graph is then an input into the <code class="docutils literal notranslate"><span class="pre">get_counts_from_simulation</span></code> function.</p>
-<p><code class="docutils literal notranslate"><span class="pre">get_counts_from_simulation</span></code> handles preparation and execution of the quantum simulation. First, it takes <span class="math notranslate nohighlight">\(G\)</span> and from it builds a spin Hamiltonian. Second, it defines a cost function, which in this case is a lambda function that returns the expectation value of our parameterized quantum circuit and the Hamiltonian. This value is obtained using the CUDA-Q <code class="docutils literal notranslate"><span class="pre">observe</span></code> command, accelerated by GPUs. After the expectation value is minimized, the quantum circuit corresponding to the optimal
-parameters is sampled using the CUDA-Q <code class="docutils literal notranslate"><span class="pre">sample</span></code> function. The bitstrings and their associated counts are returned by <code class="docutils literal notranslate"><span class="pre">get_counts_from_simulation</span></code>.</p>
-<p>A subset of these counts is evaluated to compute their exact cost. The best bitstring is returned and later used to assign the coreset points to one of two clusters.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DivisiveClusteringVQA</span><span class="p">(</span><span class="n">DivisiveClustering</span><span class="p">):</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">run_divisive_clustering</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">coreset_vectors_df_for_iteration</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Runs the Divisive Clustering algorithm</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            coreset_vectors_df_for_iteration (pd.DataFrame): Coreset vectors for the iteration</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            str: Best bitstring</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">coreset_vectors_for_iteration_np</span><span class="p">,</span> <span class="n">coreset_weights_for_iteration_np</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_get_iteration_coreset_vectors_and_weights</span><span class="p">(</span>
-                <span class="n">coreset_vectors_df_for_iteration</span><span class="p">))</span>
-
-        <span class="n">G</span> <span class="o">=</span> <span class="n">Coreset</span><span class="o">.</span><span class="n">coreset_to_graph</span><span class="p">(</span>
-            <span class="n">coreset_vectors_for_iteration_np</span><span class="p">,</span>
-            <span class="n">coreset_weights_for_iteration_np</span><span class="p">,</span>
-            <span class="n">metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">coreset_to_graph_metric</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="n">counts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_counts_from_simulation</span><span class="p">(</span>
-            <span class="n">G</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">circuit_depth</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">max_shots</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_best_bitstring</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">G</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">get_counts_from_simulation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">:</span> <span class="n">nx</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-                                   <span class="n">max_iterations</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-                                   <span class="n">max_shots</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SampleResult</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Runs the VQA simulation</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            G (nx.graph): Graph</span>
-<span class="sd">            circuit_depth (int): Circuit depth</span>
-<span class="sd">            max_iterations (int): Maximum number of iterations</span>
-<span class="sd">            max_shots (int): Maximum number of shots</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            cudaq.SampleResult: Measurement from the experiment</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">qubits</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span>
-        <span class="n">Hamiltonian</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_Hamiltonian</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
-        <span class="n">optimizer</span><span class="p">,</span> <span class="n">parameter_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_function</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span>
-            <span class="n">max_iterations</span><span class="p">,</span>
-            <span class="n">qubits</span><span class="o">=</span><span class="n">qubits</span><span class="p">,</span>
-            <span class="n">circuit_depth</span><span class="o">=</span><span class="n">circuit_depth</span><span class="p">)</span>
-
-        <span class="n">kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_circuit</span><span class="p">(</span><span class="n">qubits</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">objective_function</span><span class="p">(</span>
-            <span class="n">parameter_vector</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
-            <span class="n">hamiltonian</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span> <span class="o">=</span> <span class="n">Hamiltonian</span><span class="p">,</span>
-            <span class="n">kernel</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">Kernel</span> <span class="o">=</span> <span class="n">kernel</span><span class="p">,</span>
-        <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">            Objective function that returns the cost of the simulation</span>
-
-<span class="sd">            Args:</span>
-<span class="sd">                parameter_vector (List[float]):</span>
-<span class="sd">                hamiltonian (cudaq.SpinOperator): Circuit parameter values as a vector</span>
-<span class="sd">                kernel (cudaq.Kernel) : Circuit configuration</span>
-
-<span class="sd">            Returns:</span>
-<span class="sd">                float: Expectation value of the circuit</span>
-
-<span class="sd">            &quot;&quot;&quot;</span>
-
-            <span class="n">get_result</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">parameter_vector</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span>
-                <span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">parameter_vector</span><span class="p">,</span> <span class="n">qubits</span><span class="p">,</span> <span class="n">circuit_depth</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-            <span class="n">cost</span> <span class="o">=</span> <span class="n">get_result</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">)</span>
-
-            <span class="k">return</span> <span class="n">cost</span>
-
-        <span class="n">energy</span><span class="p">,</span> <span class="n">optimal_parameters</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span>
-            <span class="n">dimensions</span><span class="o">=</span><span class="n">parameter_count</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">objective_function</span><span class="p">)</span>
-
-        <span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                              <span class="n">optimal_parameters</span><span class="p">,</span>
-                              <span class="n">qubits</span><span class="p">,</span>
-                              <span class="n">circuit_depth</span><span class="p">,</span>
-                              <span class="n">shots_count</span><span class="o">=</span><span class="n">max_shots</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">counts</span>
-</pre></div>
-</div>
-</div>
-<p>An instance of the <code class="docutils literal notranslate"><span class="pre">DivisiveClusteringVQA</span></code> class is mostly constructed from variables previously discussed like the functions for building the Hamiltonians and quantum circuits. Parameters related to the quantum simulation can also be specified here such as <code class="docutils literal notranslate"><span class="pre">circuit_depth</span></code> and <code class="docutils literal notranslate"><span class="pre">max_shots</span></code>. The <code class="docutils literal notranslate"><span class="pre">threshold_for_max_cut</span></code> parameter specifies what percent of the sample results from the quantum computer are checked for the best bitstring value.</p>
-<p>The other options specify advanced features like if the data is normalized and how the graph weights are computed.</p>
-<p>Finally, the <code class="docutils literal notranslate"><span class="pre">get_divisive_sequence</span></code> method performs the iterations and produces the clustering data which we will analyze below. Note that this postprocessing code is not exposed in this tutorial but can be found in the source code.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">COBYLA</span><span class="p">()</span>
-
-<span class="n">divisive_clustering</span> <span class="o">=</span> <span class="n">DivisiveClusteringVQA</span><span class="p">(</span>
-    <span class="n">circuit_depth</span><span class="o">=</span><span class="n">circuit_depth</span><span class="p">,</span>
-    <span class="n">max_iterations</span><span class="o">=</span><span class="n">max_iterations</span><span class="p">,</span>
-    <span class="n">max_shots</span><span class="o">=</span><span class="n">max_shots</span><span class="p">,</span>
-    <span class="n">threshold_for_max_cut</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span>
-    <span class="n">create_Hamiltonian</span><span class="o">=</span><span class="n">get_K2_Hamiltonian</span><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
-    <span class="n">optimizer_function</span><span class="o">=</span><span class="n">get_optimizer</span><span class="p">,</span>
-    <span class="n">create_circuit</span><span class="o">=</span><span class="n">get_VQE_circuit</span><span class="p">,</span>
-    <span class="n">normalize_vectors</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">sort_by_descending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">coreset_to_graph_metric</span><span class="o">=</span><span class="s2">&quot;dist&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">hierarchial_clustering_sequence</span> <span class="o">=</span> <span class="n">divisive_clustering</span><span class="o">.</span><span class="n">get_divisive_sequence</span><span class="p">(</span>
-    <span class="n">coreset_df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
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-100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18/18 [00:00&lt;00:00, 44254.09it/s]
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-</pre></div></div>
-</div>
-<p>The data can be nicely visualized with a Dendrogram which maps where the bipartitionings occurred. Early splits generally mark divisions between the least similar data.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dendo</span> <span class="o">=</span> <span class="n">Dendrogram</span><span class="p">(</span><span class="n">coreset_df</span><span class="p">,</span> <span class="n">hierarchial_clustering_sequence</span><span class="p">)</span>
-<span class="n">dendo</span><span class="o">.</span><span class="n">plot_dendrogram</span><span class="p">(</span><span class="n">plot_title</span><span class="o">=</span><span class="s2">&quot;Dendrogram of Coreset using VQE&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_divisive_clustering_coresets_29_0.png" src="../../_images/applications_python_divisive_clustering_coresets_29_0.png" />
-</div>
-</div>
-<p>Each branch point in the dendrogram aboves corresponds to one of the plots below. Notice the first iterations are the most complicated, and the final iterations become trivial bipartitioning of two points. Occasionally, especially in the first iteration, the partitioning might be puzzling at first glance. The data might seem to naturally cluster into two groups. However, there are cases where a stray point seems to belong in the wrong cluster. There are two explanations for this. 1) The quantum
-sampling is approximate and stochastic. It is possible that too few shots were taken to sample the ground state of the problem. 2) It is important to remember that we are clustering coresets and not data points. There can be cases where it is optimal to pay a penalty by excluding a point based on proximity if the weights are small enough that the penalty has less impact. Usually, if a point looks unusually clustered and you go look at the original coresets plotted above, that point will have a
-small weight.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Dendrogram</span><span class="o">.</span><span class="n">plot_hierarchial_split</span><span class="p">(</span><span class="n">hierarchial_clustering_sequence</span><span class="p">,</span> <span class="n">coreset_df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_divisive_clustering_coresets_31_0.png" src="../../_images/applications_python_divisive_clustering_coresets_31_0.png" />
-</div>
-</div>
-<p>The hierarchical clustering can be converted to flat clustering by drawing a line perpendicular to the branches. Any data point that intersects the line is considered to be in the same cluster. The function below performs this task at threshold height of 1.5. If you want to use the number of clusters instead of height, you can use <code class="docutils literal notranslate"><span class="pre">dendo.get_clusters_using_k()</span></code> method. You pass the number of desired clusters as an argument. The figure below shows the clusters that are formed at threshold
-height of 1.5.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">threshold_height</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">clusters</span> <span class="o">=</span> <span class="n">dendo</span><span class="o">.</span><span class="n">get_clusters_using_height</span><span class="p">(</span><span class="n">threshold_height</span><span class="p">)</span>
-<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;purple&quot;</span><span class="p">,</span> <span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="s2">&quot;yellow&quot;</span><span class="p">]</span>
-<span class="n">dendo</span><span class="o">.</span><span class="n">plot_dendrogram</span><span class="p">(</span>
-    <span class="n">plot_title</span><span class="o">=</span><span class="s2">&quot;Dendrogram of Coreset using VQE&quot;</span><span class="p">,</span>
-    <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
-    <span class="n">clusters</span><span class="o">=</span><span class="n">clusters</span><span class="p">,</span>
-    <span class="n">color_threshold</span><span class="o">=</span><span class="n">threshold_height</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_divisive_clustering_coresets_33_0.png" src="../../_images/applications_python_divisive_clustering_coresets_33_0.png" />
-</div>
-</div>
-<p>You can visualize the flat clusters using <code class="docutils literal notranslate"><span class="pre">dendo.plot_clusters()</span></code> method. The function takes the clusters and colors as arguments. The clusters are represented by different colors.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dendo</span><span class="o">.</span><span class="n">plot_clusters</span><span class="p">(</span><span class="n">clusters</span><span class="p">,</span>
-                    <span class="n">colors</span><span class="p">,</span>
-                    <span class="n">plot_title</span><span class="o">=</span><span class="s2">&quot;Clusters of Coreset using VQE&quot;</span><span class="p">,</span>
-                    <span class="n">show_annotation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_divisive_clustering_coresets_35_0.png" src="../../_images/applications_python_divisive_clustering_coresets_35_0.png" />
-</div>
-</div>
-<p>The function below uses the <code class="docutils literal notranslate"><span class="pre">dendo.get_voronoi_tessalation()</span></code> method to convert the clusters into regions. <code class="docutils literal notranslate"><span class="pre">coreset_df</span></code>, <code class="docutils literal notranslate"><span class="pre">clusters</span></code> and <code class="docutils literal notranslate"><span class="pre">colors</span></code> need to be passed as the arguments to create the regions. This function creates a region for each coreset point separately and then colors them according to the clusters with colors passed as arguments. Another option is to create regions using the centroids of the clusters. You need to pass <code class="docutils literal notranslate"><span class="pre">tesslation_by_cluster=True</span></code> to the function to
-perform this task.</p>
-<p>Once the region creation is complete, you can use <code class="docutils literal notranslate"><span class="pre">plot_voironi()</span></code> method to plot the regions. The function takes the clusters and colors as arguments.</p>
-<p>Remembering that these regions were based on coresets, they can overlay the original data set and be used to cluster the data based on the coreset analysis.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vt</span> <span class="o">=</span> <span class="n">Voironi_Tessalation</span><span class="p">(</span><span class="n">coreset_df</span><span class="p">,</span>
-                         <span class="n">clusters</span><span class="p">,</span>
-                         <span class="n">colors</span><span class="p">,</span>
-                         <span class="n">tesslation_by_cluster</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">vt</span><span class="o">.</span><span class="n">plot_voironi</span><span class="p">(</span><span class="n">plot_title</span><span class="o">=</span><span class="s2">&quot;Voironi Tessalation of Coreset using VQE&quot;</span><span class="p">,</span>
-                <span class="n">show_annotation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_divisive_clustering_coresets_37_0.png" src="../../_images/applications_python_divisive_clustering_coresets_37_0.png" />
-</div>
-</div>
-</section>
-<section id="QAOA-Implementation">
-<h2>QAOA Implementation<a class="headerlink" href="#QAOA-Implementation" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q is designed to be a flexible tool for developers so they can test different implementations of the same code. For example, one can perform the same analysis, instead using a QAOA approach. Only the kernel needs to be changed as is done below.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_QAOA_circuit</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the QAOA circuit for the given number of qubits and circuit depth</span>
-
-
-<span class="sd">    Args:</span>
-<span class="sd">        number_of_qubits (int): Number of qubits</span>
-<span class="sd">        circuit_depth (int): Circuit depth</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        cudaq.Kernel: QAOA Circuit</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-    <span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">number_of_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">)</span>
-
-        <span class="n">layers</span> <span class="o">=</span> <span class="n">circuit_depth</span>
-
-        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layers</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">qubit</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_qubits</span><span class="p">):</span>
-                <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">qubit</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[(</span><span class="n">qubit</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">number_of_qubits</span><span class="p">])</span>
-                <span class="n">rz</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">thetas</span><span class="p">[</span><span class="n">layer</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[(</span><span class="n">qubit</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">number_of_qubits</span><span class="p">])</span>
-                <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">qubit</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[(</span><span class="n">qubit</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">number_of_qubits</span><span class="p">])</span>
-
-            <span class="n">rx</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">thetas</span><span class="p">[</span><span class="n">layer</span> <span class="o">+</span> <span class="n">layers</span><span class="p">],</span> <span class="n">qubits</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">kernel</span>
-
-
-<span class="n">circuit</span> <span class="o">=</span> <span class="n">get_QAOA_circuit</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">circuit_depth</span><span class="p">),</span> <span class="mi">5</span><span class="p">,</span> <span class="n">circuit_depth</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-                                                                             »
-q0 : ──●──────────────────●──────────────────────────────────────────────────»
-     ╭─┴─╮╭────────────╮╭─┴─╮                                                »
-q1 : ┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──────────────────────────»
-     ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮                        »
-q2 : ────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──»
-                             ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮»
-q3 : ────────────────────────────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├»
-                                                     ╰───╯╰────────────╯╰───╯»
-q4 : ────────────────────────────────────────────────────────────────────────»
-                                                                             »
-
-################################################################################
-
-                        ╭───╮╭────────────╮╭───╮╭───────────╮
-────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├┤ rx(1.111) ├
-                        ╰─┬─╯╰────────────╯╰─┬─╯├───────────┤
-──────────────────────────┼──────────────────┼──┤ rx(1.111) ├
-                          │                  │  ├───────────┤
-──────────────────────────┼──────────────────┼──┤ rx(1.111) ├
-                          │                  │  ├───────────┤
-──●──────────────────●────┼──────────────────┼──┤ rx(1.111) ├
-╭─┴─╮╭────────────╮╭─┴─╮  │                  │  ├───────────┤
-┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──┤ rx(1.111) ├
-╰───╯╰────────────╯╰───╯                        ╰───────────╯
-
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_optimizer</span><span class="p">(</span><span class="n">optimizer</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">max_iterations</span><span class="p">,</span>
-                  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns the optimizer with the given parameters</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        optimizer (cudaq.optimizers.optimizer): Optimizer</span>
-<span class="sd">        max_iterations (int): Maximum number of iterations</span>
-<span class="sd">        **kwargs: Additional arguments</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">parameter_count</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;circuit_depth&quot;</span><span class="p">]</span>
-    <span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">8.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">8.0</span><span class="p">,</span>
-                                                     <span class="n">parameter_count</span><span class="p">)</span>
-    <span class="n">optimizer</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="n">max_iterations</span>
-    <span class="k">return</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">parameter_count</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">COBYLA</span><span class="p">()</span>
-
-<span class="n">divisive_clustering</span> <span class="o">=</span> <span class="n">DivisiveClusteringVQA</span><span class="p">(</span>
-    <span class="n">circuit_depth</span><span class="o">=</span><span class="n">circuit_depth</span><span class="p">,</span>
-    <span class="n">max_iterations</span><span class="o">=</span><span class="n">max_iterations</span><span class="p">,</span>
-    <span class="n">max_shots</span><span class="o">=</span><span class="n">max_shots</span><span class="p">,</span>
-    <span class="n">threshold_for_max_cut</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span>
-    <span class="n">create_Hamiltonian</span><span class="o">=</span><span class="n">get_K2_Hamiltonian</span><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
-    <span class="n">optimizer_function</span><span class="o">=</span><span class="n">get_optimizer</span><span class="p">,</span>
-    <span class="n">create_circuit</span><span class="o">=</span><span class="n">get_QAOA_circuit</span><span class="p">,</span>
-    <span class="n">normalize_vectors</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">sort_by_descending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">coreset_to_graph_metric</span><span class="o">=</span><span class="s2">&quot;dist&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">hierarchial_clustering_sequence</span> <span class="o">=</span> <span class="n">divisive_clustering</span><span class="o">.</span><span class="n">get_divisive_sequence</span><span class="p">(</span>
-    <span class="n">coreset_df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
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-100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00&lt;00:00, 42473.96it/s]
-</pre></div></div>
-</div>
-</section>
-<section id="Scaling-simulations-with-CUDA-Q">
-<h2>Scaling simulations with CUDA-Q<a class="headerlink" href="#Scaling-simulations-with-CUDA-Q" title="Permalink to this heading">¶</a></h2>
-<p>The University of Edinburgh team quickly encountered scaling challenges when they were developing this method. By developing with CUDA-Q they were able to port their code to an HPC environment once GPUs became available. GPUs massively accelerated their development and testing and allowed them to produce the 25 qubit experiments presented in their publication. If you have a GPU available, run the following code examples and see how the times compare on your device. Each call executes the
-divisive clustering procedure using the QAOA method, 100000 simulated shots for each VQE loop, and maximum 75 VQE iterations.</p>
-<p>First, try a slightly larger N=18 problem using the CPU (<code class="docutils literal notranslate"><span class="pre">qpp-cpu</span></code>) backend.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Uncomment the following line if you want to explicitly execute this step on your system.</span>
-<span class="c1"># !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 18</span>
-</pre></div>
-</div>
-</div>
-<p>Now try the N=18 example on the GPU backend (<code class="docutils literal notranslate"><span class="pre">nvidia</span></code>).</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>python3<span class="w"> </span>divisive_clustering_src/main_divisive_clustering.py<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--M<span class="w"> </span><span class="m">18</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Using BFL2 method to generate coresets
-100%|███████████████████████████████████████| 751/751 [00:00&lt;00:00, 3460.26it/s]
-100%|████████████████████████████████████████| 16/16 [00:00&lt;00:00, 42771.74it/s]
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-100%|████████████████████████████████████████| 16/16 [00:00&lt;00:00, 44979.13it/s]
-100%|██████████████████████████████████████| 128/128 [00:00&lt;00:00, 19366.94it/s]
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-100%|██████████████████████████████████████████| 8/8 [00:00&lt;00:00, 51941.85it/s]
-100%|██████████████████████████████████████████| 4/4 [00:00&lt;00:00, 56111.09it/s]
-Total time for the execution: 461.866833317
-Total time spent on CUDA-Q: 10.452308367999706
-</pre></div></div>
-</div>
-<p>Scaling up to N=25, the task becomes even more onerous on a CPU. Depending on your device, the simulation may not even run. Try it and feel free to interrupt after your patience has worn out.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># target = &#39;qpp-cpu&#39;</span>
-<span class="c1"># Uncomment the following line if you want to explicitly execute this step on your system.</span>
-<span class="c1"># !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 25</span>
-</pre></div>
-</div>
-</div>
-<p>N=25 can still easily be completed by a single GPU.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># target = &#39;nvidia&#39;</span>
-<span class="o">!</span>python3<span class="w"> </span>divisive_clustering_src/main_divisive_clustering.py<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--M<span class="w"> </span><span class="m">25</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Using BFL2 method to generate coresets
-100%|█████████████████████████████████████| 7352/7352 [00:03&lt;00:00, 2063.82it/s]
-100%|███████████████████████████████████| 16492/16492 [00:03&lt;00:00, 4739.44it/s]
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-100%|██████████████████████████████████████████| 4/4 [00:00&lt;00:00, 54120.05it/s]
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-100%|██████████████████████████████████████████| 8/8 [00:00&lt;00:00, 55924.05it/s]
-100%|████████████████████████████████████████| 16/16 [00:00&lt;00:00, 42717.29it/s]
-100%|██████████████████████████████████████████| 4/4 [00:00&lt;00:00, 55007.27it/s]
-100%|██████████████████████████████████████████| 4/4 [00:00&lt;00:00, 53601.33it/s]
-100%|██████████████████████████████████████████| 4/4 [00:00&lt;00:00, 47127.01it/s]
-Total time for the execution: 67.61674502899999
-Total time spent on CUDA-Q: 21.439895901
-</pre></div></div>
-</div>
-<p>If we want to push the simulation to an <span class="math notranslate nohighlight">\(N=34\)</span> coreset, a single GPU (assuming A100) will run out of memory. Run the code below to see for yourself.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># target = &#39;nvidia&#39;</span>
-<span class="o">!</span>python3<span class="w"> </span>divisive_clustering_src/main_divisive_clustering.py<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--M<span class="w"> </span><span class="m">34</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Using BFL2 method to generate coresets
-RuntimeError: NLOpt runtime error: nlopt failure
-</pre></div></div>
-</div>
-<p>To compute a problem with 34 qubits, we need to pool the memory of multiple GPUs. If you have multiple GPUs available, try the code below to run the same computation on 4 GPUs. You do not need to wait for the code to finish, just note how it does not fail immediately due to memory issues.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># target = &#39;nvidia-mgpu&#39;</span>
-<span class="n">gpu_count</span> <span class="o">=</span> <span class="o">!</span>nvidia-smi<span class="w"> </span>-L<span class="w"> </span><span class="p">|</span><span class="w"> </span>wc<span class="w"> </span>-l
-<span class="k">try</span><span class="p">:</span>
-    <span class="n">gpu_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">gpu_count</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="k">except</span><span class="p">:</span>
-    <span class="n">gpu_count</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="k">if</span> <span class="n">gpu_count</span> <span class="o">&gt;=</span> <span class="mi">4</span><span class="p">:</span>
-    <span class="o">!</span>mpirun<span class="w"> </span>-np<span class="w"> </span><span class="m">4</span><span class="w"> </span>python3<span class="w"> </span>divisive_clustering_src/main_divisive_clustering.py<span class="w"> </span>--target<span class="w"> </span>nvidia-mgpu<span class="w"> </span>--M<span class="w"> </span><span class="m">34</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span>
-        <span class="sa">f</span><span class="s1">&#39;Not enough GPUs found on this system (</span><span class="si">{</span><span class="n">gpu_count</span><span class="si">}</span><span class="s1">) to run this step&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Using BFL2 method to generate coresets
-Using BFL2 method to generate coresets
-Using BFL2 method to generate coresets
-Using BFL2 method to generate coresets
-^C
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
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diff --git a/pr-2308/applications/python/divisive_clustering_coresets.ipynb b/pr-2308/applications/python/divisive_clustering_coresets.ipynb
deleted file mode 100644
index 0cffbd7110..0000000000
--- a/pr-2308/applications/python/divisive_clustering_coresets.ipynb
+++ /dev/null
@@ -1,1097 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Divisive Clustering With Coresets Using CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial will explore a CUDA-Q implementation of recent research (ArXiv Paper: https://arxiv.org/pdf/2402.01529.pdf) performed by a team from the University of Edinburgh. This tutorial was jointly developed by NVIDIA and the authors so users can better understand their method and explore how CUDA-Q removed barriers to scaling. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code for this tutorial is based off the MIT licensed code found here: https://github.com/Boniface316/bigdata_vqa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Clustering is a common unsupervised learning technique aimed at grouping data with similar characteristics. The unique properties of quantum computers could allow for enhanced pattern finding in clustering applications and enable more reliable data analysis. However, quantum computers today are severely limited by qubit count and noise.  Performing practical clustering applications would require far too many qubits.  The Edinburgh team developed a new method (extending the work of Harrow) to leverage coresets for clustering applications on quantum computers and use far fewer qubits.  This tutorial will walk through an example using this approach for divisive clustering and emphasize the utility of CUDA-Q for scaling quantum simulations.\n",
-    "\n",
-    "The goal of divisive clustering is to begin with all data points as one set, and iteratively bipartition the data until each point is its own cluster.  The branching behavior of this process can be used to understand similarities in the data points.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If you are running outside of a CUDA-Q container or CUDA-Q directory tree, you may need to uncomment these lines to fetch the files.\n",
-    "# If you are running inside a CUDA-Q tree, then this step can be skipped.\n",
-    "# !mkdir divisive_clustering_src\n",
-    "# !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py\n",
-    "# !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "!pip install mpi4py==3.1.6 networkx==2.8.8 pandas==2.2.2 scikit-learn==1.4.2 tqdm==4.66.2 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "# Auxillary Imports\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import warnings\n",
-    "from typing import Tuple\n",
-    "from divisive_clustering_src.divisive_clustering import Coreset, DivisiveClustering, Dendrogram, Voironi_Tessalation\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The settings below are global parameters for the quantum simulation and can be toggled by the user.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "circuit_depth = 1\n",
-    "max_iterations = 75\n",
-    "max_shots = 1000\n",
-    "np.random.seed(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Given a data set $X = (x_1, x_2, \\cdots, x_N)$, a coreset is weighted data set of much smaller size ($X', w$) that represents $X$ enough such that analysis of ($X', w$) can allow us to draw reasonable approximate conclusions about $X$. There are various approaches to build coresets. They can be found in Practical Coreset Construction for Machine Learning (https://arxiv.org/pdf/1703.06476.pdf) and New Streaming Algorithms for Coresets in Machine Learning (https://arxiv.org/pdf/1703.06476.pdf).\n",
-    "\n",
-    "\n",
-    "Essentially, coreset construction boils down to finding the optimal coreset size and weights given some error tolerance.  Given the constraints of a quantum computer, in this work, a coreset size is selected $a$ $priori$, and the error is determined for each model.\n",
-    "\n",
-    "\n",
-    "The following is an example $M=10$ coreset constructed from a 1000-point data set and loaded into a pandas data frame. See the image below where the coreset is represented by the black stars, the size of which corresponds to the weights.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "          X         Y     weights Name\n",
-      "0  7.028364  1.669787  234.230716    A\n",
-      "1  7.167441  0.354792  101.319288    B\n",
-      "2  1.022889 -0.921443  125.158339    C\n",
-      "3  2.706134 -2.636852   13.650774    D\n",
-      "4  6.998497  0.455847  116.758239    E\n",
-      "5  7.507918  0.630311  120.727176    F\n",
-      "6 -2.102508  2.297727   53.294127    G\n",
-      "7  5.722463  1.400433   77.415840    H\n",
-      "8 -1.425868  2.341136   42.847985    I\n",
-      "9  7.985373 -0.063209  240.116237    J\n"
-     ]
-    }
-   ],
-   "source": [
-    "raw_data = Coreset.create_dataset(1000)\n",
-    "coreset = Coreset(\n",
-    "    raw_data=raw_data,\n",
-    "    number_of_sampling_for_centroids=10,\n",
-    "    coreset_size=10,\n",
-    "    number_of_coresets_to_evaluate=4,\n",
-    "    coreset_method=\"BFL2\",\n",
-    ")\n",
-    "\n",
-    "coreset_vectors, coreset_weights = coreset.get_best_coresets()\n",
-    "\n",
-    "coreset_df = pd.DataFrame({\n",
-    "    \"X\": coreset_vectors[:, 0],\n",
-    "    \"Y\": coreset_vectors[:, 1],\n",
-    "    \"weights\": coreset_weights\n",
-    "})\n",
-    "coreset_df[\"Name\"] = [chr(i + 65) for i in coreset_df.index]\n",
-    "print(coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(raw_data[:, 0], raw_data[:, 1], label=\"Raw Data\", c=\"#7eba00\")\n",
-    "plt.scatter(\n",
-    "    coreset_df[\"X\"],\n",
-    "    coreset_df[\"Y\"],\n",
-    "    s=coreset_df[\"weights\"],\n",
-    "    label=\"Coreset\",\n",
-    "    color=\"black\",\n",
-    "    marker=\"*\",\n",
-    ")\n",
-    "plt.xlabel(\"X\")\n",
-    "plt.ylabel(\"Y\")\n",
-    "plt.title(\"Raw data and its best 10 coreset using BFL2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to cluster data on a quantum computer, the task needs to be cast into the form of a binary optimization problem.  Each qubit represents a coreset point, and the quantum algorithm determines how to bipartition the coreset points at each iteration of the divisive clustering routine. \n",
-    "\n",
-    "The first step is to convert coreset points into a fully connected graph. The edge weight is calculated by:\n",
-    "\n",
-    "$e_{ij} = w_iw_jd_{ij}$ where $d_{ij}$ is the Euclidean distance between points $i$ and $j$. \n",
-    "\n",
-    "This process is handled by `Coreset.coreset_to_graph()`. The function returns a fully connected graph $G$ with edge weights."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The divisive clustering problem will be implemented on a quantum computer using a variational quantum algorithm (VQA) approach.  A VQA takes a Hamiltonian (encoded with the optimization problem) and a parameterized ansatz and evaluates expectation values (quantum computer) that inform updates to the ansatz parameters (classical computer).  The graph $G$ (Code in the \"src\" file) is used to construct the Hamiltonian, derived specifically for the divisive clustering problem, and motivated by a max-cut Hamiltonian.  The `spin.z(i)` method in CUDA-Q adds a Pauli Z operation that acts on qubit $i$ to the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_K2_Hamiltonian(G: nx.Graph) -> cudaq.SpinOperator:\n",
-    "    \"\"\"Returns the K2 Hamiltonian for the given graph G\n",
-    "\n",
-    "    Args:\n",
-    "        G (nx.Graph): Weighted graph\n",
-    "    \"\"\"\n",
-    "    H = 0\n",
-    "\n",
-    "    for i, j in G.edges():\n",
-    "        weight = G[i][j][\"weight\"]\n",
-    "        H += weight * (spin.z(i) * spin.z(j))\n",
-    "\n",
-    "    return H"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below constructs a quantum kernel, defining the circuit which will serve as an ansatz.  The structure of the circuit is a hardware efficient ansatz consisting of layers of parameterized $R_Z$ and $R_Y$ gate acting on each qubit, followed by a linear cascade of CNOT gates, and two more rotation gates.\n",
-    "\n",
-    "The `@cudaq.kernel` decorator allows us to define a quantum circuit in the new kernel mode syntax which provides performance benefits to JIT compilation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_VQE_circuit(number_of_qubits: int, circuit_depth: int) -> cudaq.Kernel:\n",
-    "    \"\"\"Returns the VQE circuit for the given number of qubits and circuit depth\n",
-    "\n",
-    "    Args:\n",
-    "        number_of_qubits (int): Number of qubits\n",
-    "        circuit_depth (int): Circuit depth\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq.Kernel: VQE Circuit\n",
-    "    \"\"\"\n",
-    "\n",
-    "    @cudaq.kernel\n",
-    "    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):\n",
-    "        \"\"\"VQE Circuit\n",
-    "\n",
-    "        Args:\n",
-    "            thetas (list[float]): List of parameters\n",
-    "            number_of_qubits (int): Number of qubits\n",
-    "            circuit_depth (int): Circuit depth\n",
-    "        \"\"\"\n",
-    "        qubits = cudaq.qvector(number_of_qubits)\n",
-    "\n",
-    "        theta_position = 0\n",
-    "\n",
-    "        for i in range(circuit_depth):\n",
-    "            for j in range(number_of_qubits):\n",
-    "                ry(thetas[theta_position], qubits[j])\n",
-    "                rz(thetas[theta_position + 1], qubits[j])\n",
-    "\n",
-    "                theta_position += 2\n",
-    "\n",
-    "            for j in range(number_of_qubits - 1):\n",
-    "                cx(qubits[j], qubits[j + 1])\n",
-    "\n",
-    "            for j in range(number_of_qubits):\n",
-    "                ry(thetas[theta_position], qubits[j])\n",
-    "                rz(thetas[theta_position + 1], qubits[j])\n",
-    "\n",
-    "                theta_position += 2\n",
-    "\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualize the circuit using the `cudaq.draw()` method. Below, we are drawing the circuit for 5 qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭────────────╮ ╭────────────╮      ╭────────────╮╭────────────╮»\n",
-      "q0 : ┤ ry(0.8904) ├─┤ rz(0.7335) ├───●──┤ ry(0.4343) ├┤ rz(0.2236) ├»\n",
-      "     ├────────────┤ ├────────────┤ ╭─┴─╮╰────────────╯├────────────┤»\n",
-      "q1 : ┤ ry(0.7937) ├─┤ rz(0.9981) ├─┤ x ├──────●───────┤ ry(0.3945) ├»\n",
-      "     ├───────────┬╯ ├────────────┤ ╰───╯    ╭─┴─╮     ╰────────────╯»\n",
-      "q2 : ┤ ry(0.696) ├──┤ rz(0.3352) ├──────────┤ x ├───────────●───────»\n",
-      "     ├───────────┴╮╭┴────────────┤          ╰───╯         ╭─┴─╮     »\n",
-      "q3 : ┤ ry(0.6658) ├┤ rz(0.05277) ├────────────────────────┤ x ├─────»\n",
-      "     ├───────────┬╯├─────────────┴╮                       ╰───╯     »\n",
-      "q4 : ┤ ry(0.791) ├─┤ rz(0.003569) ├─────────────────────────────────»\n",
-      "     ╰───────────╯ ╰──────────────╯                                 »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                                             \n",
-      "─────────────────────────────────────────────\n",
-      "╭────────────╮                               \n",
-      "┤ rz(0.4119) ├───────────────────────────────\n",
-      "├────────────┤╭────────────╮                 \n",
-      "┤ ry(0.3205) ├┤ rz(0.3504) ├─────────────────\n",
-      "╰────────────╯├────────────┤ ╭────────────╮  \n",
-      "──────●───────┤ ry(0.3913) ├─┤ rz(0.7392) ├──\n",
-      "    ╭─┴─╮     ├────────────┤╭┴────────────┴─╮\n",
-      "────┤ x ├─────┤ ry(0.3171) ├┤ rz(0.0008056) ├\n",
-      "    ╰───╯     ╰────────────╯╰───────────────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "parameter_count = 4 * circuit_depth * 5\n",
-    "parameters = np.random.rand(parameter_count)\n",
-    "\n",
-    "circuit = get_VQE_circuit(5, circuit_depth)\n",
-    "print(cudaq.draw(circuit, parameters, 5, circuit_depth))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next step is to select a classical optimizer. There are multiple [optimizers](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#optimizers) built-in to CUDA-Q that can be selected. The code below returns the optimizer with the proper number of initial parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,\n",
-    "                  **kwargs) -> Tuple[cudaq.optimizers.optimizer, int]:\n",
-    "    \"\"\"Returns the optimizer with the given parameters\n",
-    "\n",
-    "    Args:\n",
-    "        optimizer (cudaq.optimizers.optimizer): Optimizer\n",
-    "        max_iterations (int): Maximum number of iterations\n",
-    "        **kwargs: Additional arguments\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count\n",
-    "    \"\"\"\n",
-    "    parameter_count = 4 * kwargs[\"circuit_depth\"] * kwargs[\"qubits\"]\n",
-    "    initial_params = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,\n",
-    "                                       parameter_count)\n",
-    "    optimizer.initial_parameters = initial_params\n",
-    "\n",
-    "    optimizer.max_iterations = max_iterations\n",
-    "    return optimizer, parameter_count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Divisive Clustering Function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `DivisiveClusteringVQA` class enables the procedure to iteratively bipartition the coreset points until each is its own cluster. If you wish to develop on top of this or see how the underlying code works, please see the `divisive_clustering.py` file in the src directory. \n",
-    "\n",
-    "`run_divisive_clustering`, takes the current iteration's coreset points that will be bipartitioned as inputs, extracts the appropriate weights, and builds a graph $G$. The graph is then an input into the `get_counts_from_simulation` function. \n",
-    "\n",
-    "\n",
-    "`get_counts_from_simulation` handles preparation and execution of the quantum simulation. First, it takes $G$ and from it builds a spin Hamiltonian. Second, it defines a cost function, which in this case is a lambda function that returns the expectation value of our parameterized quantum circuit and the Hamiltonian. This value is obtained using the CUDA-Q `observe` command, accelerated by GPUs.  After the expectation value is minimized, the quantum circuit corresponding to the optimal parameters is sampled using the CUDA-Q `sample` function. The bitstrings and their associated counts are returned by `get_counts_from_simulation`.\n",
-    "\n",
-    "A subset of these counts is evaluated to compute their exact cost. The best bitstring is returned and later used to assign the coreset points to one of two clusters.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class DivisiveClusteringVQA(DivisiveClustering):\n",
-    "\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        **kwargs,\n",
-    "    ):\n",
-    "        super().__init__(**kwargs)\n",
-    "\n",
-    "    def run_divisive_clustering(\n",
-    "        self,\n",
-    "        coreset_vectors_df_for_iteration: pd.DataFrame,\n",
-    "    ):\n",
-    "        \"\"\"Runs the Divisive Clustering algorithm\n",
-    "\n",
-    "        Args:\n",
-    "            coreset_vectors_df_for_iteration (pd.DataFrame): Coreset vectors for the iteration\n",
-    "\n",
-    "        Returns:\n",
-    "            str: Best bitstring\n",
-    "\n",
-    "        \"\"\"\n",
-    "        coreset_vectors_for_iteration_np, coreset_weights_for_iteration_np = (\n",
-    "            self._get_iteration_coreset_vectors_and_weights(\n",
-    "                coreset_vectors_df_for_iteration))\n",
-    "\n",
-    "        G = Coreset.coreset_to_graph(\n",
-    "            coreset_vectors_for_iteration_np,\n",
-    "            coreset_weights_for_iteration_np,\n",
-    "            metric=self.coreset_to_graph_metric,\n",
-    "        )\n",
-    "\n",
-    "        counts = self.get_counts_from_simulation(\n",
-    "            G,\n",
-    "            self.circuit_depth,\n",
-    "            self.max_iterations,\n",
-    "            self.max_shots,\n",
-    "        )\n",
-    "\n",
-    "        return self._get_best_bitstring(counts, G)\n",
-    "\n",
-    "    def get_counts_from_simulation(self, G: nx.graph, circuit_depth: int,\n",
-    "                                   max_iterations: int,\n",
-    "                                   max_shots: int) -> cudaq.SampleResult:\n",
-    "        \"\"\"\n",
-    "        Runs the VQA simulation\n",
-    "\n",
-    "        Args:\n",
-    "            G (nx.graph): Graph\n",
-    "            circuit_depth (int): Circuit depth\n",
-    "            max_iterations (int): Maximum number of iterations\n",
-    "            max_shots (int): Maximum number of shots\n",
-    "\n",
-    "        Returns:\n",
-    "            cudaq.SampleResult: Measurement from the experiment\n",
-    "        \"\"\"\n",
-    "\n",
-    "        qubits = len(G.nodes)\n",
-    "        Hamiltonian = self.create_Hamiltonian(G)\n",
-    "        optimizer, parameter_count = self.optimizer_function(\n",
-    "            self.optimizer,\n",
-    "            max_iterations,\n",
-    "            qubits=qubits,\n",
-    "            circuit_depth=circuit_depth)\n",
-    "\n",
-    "        kernel = self.create_circuit(qubits, circuit_depth)\n",
-    "\n",
-    "        def objective_function(\n",
-    "            parameter_vector: list[float],\n",
-    "            hamiltonian: cudaq.SpinOperator = Hamiltonian,\n",
-    "            kernel: cudaq.Kernel = kernel,\n",
-    "        ) -> float:\n",
-    "            \"\"\"\n",
-    "\n",
-    "            Objective function that returns the cost of the simulation\n",
-    "\n",
-    "            Args:\n",
-    "                parameter_vector (List[float]):\n",
-    "                hamiltonian (cudaq.SpinOperator): Circuit parameter values as a vector\n",
-    "                kernel (cudaq.Kernel) : Circuit configuration\n",
-    "\n",
-    "            Returns:\n",
-    "                float: Expectation value of the circuit\n",
-    "\n",
-    "            \"\"\"\n",
-    "\n",
-    "            get_result = lambda parameter_vector: cudaq.observe(\n",
-    "                kernel, hamiltonian, parameter_vector, qubits, circuit_depth\n",
-    "            ).expectation()\n",
-    "\n",
-    "            cost = get_result(parameter_vector)\n",
-    "\n",
-    "            return cost\n",
-    "\n",
-    "        energy, optimal_parameters = optimizer.optimize(\n",
-    "            dimensions=parameter_count, function=objective_function)\n",
-    "\n",
-    "        counts = cudaq.sample(kernel,\n",
-    "                              optimal_parameters,\n",
-    "                              qubits,\n",
-    "                              circuit_depth,\n",
-    "                              shots_count=max_shots)\n",
-    "\n",
-    "        return counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An instance of the `DivisiveClusteringVQA` class is mostly constructed from variables previously discussed like the functions for building the Hamiltonians and quantum circuits. Parameters related to the quantum simulation can also be specified here such as `circuit_depth` and `max_shots`.  The ` threshold_for_max_cut` parameter specifies what percent of the sample results from the quantum computer are checked for the best bitstring value.\n",
-    "\n",
-    "The other options specify advanced features like if the data is normalized and how the graph weights are computed.\n",
-    "\n",
-    "\n",
-    "Finally, the `get_divisive_sequence` method performs the iterations and produces the clustering data which we will analyze below. Note that this postprocessing code is not exposed in this tutorial but can be found in the source code. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 129/129 [00:00<00:00, 12075.19it/s]\n",
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-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15827.56it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 13617.87it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "divisive_clustering = DivisiveClusteringVQA(\n",
-    "    circuit_depth=circuit_depth,\n",
-    "    max_iterations=max_iterations,\n",
-    "    max_shots=max_shots,\n",
-    "    threshold_for_max_cut=0.75,\n",
-    "    create_Hamiltonian=get_K2_Hamiltonian,\n",
-    "    optimizer=optimizer,\n",
-    "    optimizer_function=get_optimizer,\n",
-    "    create_circuit=get_VQE_circuit,\n",
-    "    normalize_vectors=True,\n",
-    "    sort_by_descending=True,\n",
-    "    coreset_to_graph_metric=\"dist\",\n",
-    ")\n",
-    "\n",
-    "hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(\n",
-    "    coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data can be nicely visualized with a Dendrogram which maps where the bipartitionings occurred. Early splits generally mark divisions between the least similar data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dendo = Dendrogram(coreset_df, hierarchial_clustering_sequence)\n",
-    "dendo.plot_dendrogram(plot_title=\"Dendrogram of Coreset using VQE\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each branch point in the dendrogram aboves corresponds to one of the plots below. Notice the first iterations are the most complicated, and the final iterations become trivial bipartitioning of two points. Occasionally, especially in the first iteration, the partitioning might be puzzling at first glance. The data might seem to naturally cluster into two groups. However, there are cases where a stray point seems to belong in the wrong cluster.  There are two explanations for this.  1) The quantum sampling is approximate and stochastic. It is possible that too few shots were taken to sample the ground state of the problem. 2) It is important to remember that we are clustering coresets and not data points. There can be cases where it is optimal to pay a penalty by excluding a point based on proximity if the weights are small enough that the penalty has less impact. Usually, if a point looks unusually clustered and you go look at the original coresets plotted above, that point will have a small weight."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x1200 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Dendrogram.plot_hierarchial_split(hierarchial_clustering_sequence, coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The hierarchical clustering can be converted to flat clustering by drawing a line perpendicular to the branches. Any data point that intersects the line is considered to be in the same cluster. The function below performs this task at threshold height of 1.5. If you want to use the number of clusters instead of height, you can use `dendo.get_clusters_using_k()` method. You pass the number of desired clusters as an argument. The figure below shows the clusters that are formed at threshold height of 1.5."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "threshold_height = 1\n",
-    "clusters = dendo.get_clusters_using_height(threshold_height)\n",
-    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\"]\n",
-    "dendo.plot_dendrogram(\n",
-    "    plot_title=\"Dendrogram of Coreset using VQE\",\n",
-    "    colors=colors,\n",
-    "    clusters=clusters,\n",
-    "    color_threshold=threshold_height,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can visualize the flat clusters using `dendo.plot_clusters()` method. The function takes the clusters and colors as arguments. The clusters are represented by different colors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dendo.plot_clusters(clusters,\n",
-    "                    colors,\n",
-    "                    plot_title=\"Clusters of Coreset using VQE\",\n",
-    "                    show_annotation=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function below uses the `dendo.get_voronoi_tessalation()` method to convert the clusters into regions. `coreset_df`, `clusters` and `colors` need to be passed as the arguments to create the regions. This function creates a region for each coreset point separately and then colors them according to the clusters with colors passed as arguments. Another option is to create regions using the centroids of the clusters. You need to pass `tesslation_by_cluster=True` to the function to perform this task.\n",
-    "\n",
-    "Once the region creation is complete, you can use `plot_voironi()` method to plot the regions. The function takes the clusters and colors as arguments. \n",
-    "\n",
-    "Remembering that these regions were based on coresets, they can overlay the original data set and be used to cluster the data based on the coreset analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "vt = Voironi_Tessalation(coreset_df,\n",
-    "                         clusters,\n",
-    "                         colors,\n",
-    "                         tesslation_by_cluster=False)\n",
-    "vt.plot_voironi(plot_title=\"Voironi Tessalation of Coreset using VQE\",\n",
-    "                show_annotation=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## QAOA Implementation\n",
-    "\n",
-    "CUDA-Q is designed to be a flexible tool for developers so they can test different implementations of the same code. For example, one can perform the same analysis, instead using a QAOA approach. Only the kernel needs to be changed as is done below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                             »\n",
-      "q0 : ──●──────────────────●──────────────────────────────────────────────────»\n",
-      "     ╭─┴─╮╭────────────╮╭─┴─╮                                                »\n",
-      "q1 : ┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──────────────────────────»\n",
-      "     ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮                        »\n",
-      "q2 : ────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──»\n",
-      "                             ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮»\n",
-      "q3 : ────────────────────────────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├»\n",
-      "                                                     ╰───╯╰────────────╯╰───╯»\n",
-      "q4 : ────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                             »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                        ╭───╮╭────────────╮╭───╮╭───────────╮\n",
-      "────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├┤ rx(1.111) ├\n",
-      "                        ╰─┬─╯╰────────────╯╰─┬─╯├───────────┤\n",
-      "──────────────────────────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "                          │                  │  ├───────────┤\n",
-      "──────────────────────────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "                          │                  │  ├───────────┤\n",
-      "──●──────────────────●────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "╭─┴─╮╭────────────╮╭─┴─╮  │                  │  ├───────────┤\n",
-      "┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──┤ rx(1.111) ├\n",
-      "╰───╯╰────────────╯╰───╯                        ╰───────────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_QAOA_circuit(number_of_qubits, circuit_depth) -> cudaq.Kernel:\n",
-    "    \"\"\"Returns the QAOA circuit for the given number of qubits and circuit depth\n",
-    "\n",
-    "\n",
-    "    Args:\n",
-    "        number_of_qubits (int): Number of qubits\n",
-    "        circuit_depth (int): Circuit depth\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq.Kernel: QAOA Circuit\n",
-    "    \"\"\"\n",
-    "\n",
-    "    @cudaq.kernel\n",
-    "    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):\n",
-    "        qubits = cudaq.qvector(number_of_qubits)\n",
-    "\n",
-    "        layers = circuit_depth\n",
-    "\n",
-    "        for layer in range(layers):\n",
-    "            for qubit in range(number_of_qubits):\n",
-    "                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])\n",
-    "                rz(2.0 * thetas[layer], qubits[(qubit + 1) % number_of_qubits])\n",
-    "                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])\n",
-    "\n",
-    "            rx(2.0 * thetas[layer + layers], qubits)\n",
-    "\n",
-    "    return kernel\n",
-    "\n",
-    "\n",
-    "circuit = get_QAOA_circuit(5, circuit_depth)\n",
-    "\n",
-    "print(cudaq.draw(circuit, np.random.rand(2 * circuit_depth), 5, circuit_depth))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,\n",
-    "                  **kwargs) -> Tuple[cudaq.optimizers.optimizer, int]:\n",
-    "    \"\"\"\n",
-    "    Returns the optimizer with the given parameters\n",
-    "\n",
-    "    Args:\n",
-    "        optimizer (cudaq.optimizers.optimizer): Optimizer\n",
-    "        max_iterations (int): Maximum number of iterations\n",
-    "        **kwargs: Additional arguments\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count\n",
-    "    \"\"\"\n",
-    "\n",
-    "    parameter_count = 2 * kwargs[\"circuit_depth\"]\n",
-    "    optimizer.initial_parameters = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,\n",
-    "                                                     parameter_count)\n",
-    "    optimizer.max_iterations = max_iterations\n",
-    "    return optimizer, parameter_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 484/484 [00:00<00:00, 12163.89it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 52703.30it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 48/48 [00:00<00:00, 31987.07it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 36393.09it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 37957.50it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 42473.96it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "divisive_clustering = DivisiveClusteringVQA(\n",
-    "    circuit_depth=circuit_depth,\n",
-    "    max_iterations=max_iterations,\n",
-    "    max_shots=max_shots,\n",
-    "    threshold_for_max_cut=0.75,\n",
-    "    create_Hamiltonian=get_K2_Hamiltonian,\n",
-    "    optimizer=optimizer,\n",
-    "    optimizer_function=get_optimizer,\n",
-    "    create_circuit=get_QAOA_circuit,\n",
-    "    normalize_vectors=True,\n",
-    "    sort_by_descending=True,\n",
-    "    coreset_to_graph_metric=\"dist\",\n",
-    ")\n",
-    "\n",
-    "hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(\n",
-    "    coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scaling simulations with CUDA-Q\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The University of Edinburgh team quickly encountered scaling challenges when they were developing this method. By developing with CUDA-Q they were able to port their code to an HPC environment once GPUs became available. GPUs massively accelerated their development and testing and allowed them to produce the 25 qubit experiments presented in their publication. If you have a GPU available, run the following code examples and see how the times compare on your device. Each call executes the divisive clustering procedure using the QAOA method, 100000 simulated shots for each VQE loop, and maximum 75 VQE iterations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, try a slightly larger N=18 problem using the CPU (`qpp-cpu`) backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Uncomment the following line if you want to explicitly execute this step on your system.\n",
-    "# !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now try the N=18 example on the GPU backend (`nvidia`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "100%|███████████████████████████████████████| 751/751 [00:00<00:00, 3460.26it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 42771.74it/s]\n",
-      "100%|█████████████████████████████████████| 4064/4064 [00:00<00:00, 6862.37it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 56871.92it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 44979.13it/s]\n",
-      "100%|██████████████████████████████████████| 128/128 [00:00<00:00, 19366.94it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 53773.13it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 54648.91it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 51941.85it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 56111.09it/s]\n",
-      "Total time for the execution: 461.866833317\n",
-      "Total time spent on CUDA-Q: 10.452308367999706\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Scaling up to N=25, the task becomes even more onerous on a CPU. Depending on your device, the simulation may not even run. Try it and feel free to interrupt after your patience has worn out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# target = 'qpp-cpu'\n",
-    "# Uncomment the following line if you want to explicitly execute this step on your system.\n",
-    "# !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "N=25 can still easily be completed by a single GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "100%|█████████████████████████████████████| 7352/7352 [00:03<00:00, 2063.82it/s]\n",
-      "100%|███████████████████████████████████| 16492/16492 [00:03<00:00, 4739.44it/s]\n",
-      "100%|██████████████████████████████████████| 256/256 [00:00<00:00, 15185.58it/s]\n",
-      "100%|████████████████████████████████████████| 64/64 [00:00<00:00, 23728.05it/s]\n",
-      "100%|██████████████████████████████████████| 256/256 [00:00<00:00, 15437.97it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 50840.05it/s]\n",
-      "100%|████████████████████████████████████████| 32/32 [00:00<00:00, 33562.82it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 54120.05it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 54560.05it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 55924.05it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 42717.29it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 55007.27it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 53601.33it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 47127.01it/s]\n",
-      "Total time for the execution: 67.61674502899999\n",
-      "Total time spent on CUDA-Q: 21.439895901\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia'\n",
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to push the simulation to an $N=34$ coreset, a single GPU (assuming A100) will run out of memory.  Run the code below to see for yourself. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "RuntimeError: NLOpt runtime error: nlopt failure\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia'\n",
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 34"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To compute a problem with 34 qubits, we need to pool the memory of multiple GPUs.  If you have multiple GPUs available, try the code below to run the same computation on 4 GPUs. You do not need to wait for the code to finish, just note how it does not fail immediately due to memory issues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "^C\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia-mgpu'\n",
-    "gpu_count = !nvidia-smi -L | wc -l\n",
-    "try:\n",
-    "    gpu_count = int(gpu_count[0])\n",
-    "except:\n",
-    "    gpu_count = 0\n",
-    "if gpu_count >= 4:\n",
-    "    !mpirun -np 4 python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia-mgpu --M 34\n",
-    "else:\n",
-    "    print(\n",
-    "        f'Not enough GPUs found on this system ({gpu_count}) to run this step')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/applications/python/hadamard_test.html b/pr-2308/applications/python/hadamard_test.html
deleted file mode 100644
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-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-  <section id="Using-the-Hadamard-Test-to-Determine-Quantum-Krylov-Subspace-Decomposition-Matrix-Elements">
-<h1>Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements<a class="headerlink" href="#Using-the-Hadamard-Test-to-Determine-Quantum-Krylov-Subspace-Decomposition-Matrix-Elements" title="Permalink to this heading">¶</a></h1>
-<p>The Hadamard test is a quantum algorithm for estimating expectation values and is a useful subroutine for a number of quantum applications ranging from estimation of molecular ground state energies to quantum semidefinite programming. This tutorial will briefly introduce the Hadamard test, demonstrate how it can be implemented in CUDA-Q, and then parallelized for a Quantum Krylov Subspace Diagonalization application.</p>
-<p>The Hadamard test is performed using a register with an ancilla qubit in the <span class="math notranslate nohighlight">\(\ket{0}\)</span> state and a prepared quantum state <span class="math notranslate nohighlight">\(\ket{\psi}\)</span>, the following circuit can be used to extract the expectation value from measurement of the ancilla.</p>
-<p><img alt="Htest" src="../../_images/htest.png" /></p>
-<p>The key insight is that</p>
-<div class="math notranslate nohighlight">
-\[P(0) = \frac{1}{2} \left[ I + Re \bra{\psi} O \ket{\phi} \right]\]</div>
-<p>and</p>
-<div class="math notranslate nohighlight">
-\[P(1) = \frac{1}{2} \left[ I - Re \bra{\psi} O \ket{\phi} \right]\]</div>
-<p>so their difference is equal to</p>
-<div class="math notranslate nohighlight">
-\[P(0)-P(1) = Re \bra{\psi} O \ket{\phi}.\]</div>
-<p>More details and a short derivation can be found <a class="reference external" href="https://en.wikipedia.org/wiki/Hadamard_test">here</a>.</p>
-<p>What if you want to perform the Hadamard test to compute an expectation value like <span class="math notranslate nohighlight">\(\bra{\psi} O \ket{\phi}\)</span>, where <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> and <span class="math notranslate nohighlight">\(\ket{\phi}\)</span> are different states and <span class="math notranslate nohighlight">\(O\)</span> is a Pauli Operator? This is a common subroutine for the QKSD, where matrix elements are determined by computing expectation values between different states.</p>
-<p>Defining <span class="math notranslate nohighlight">\(O\)</span> as</p>
-<div class="math notranslate nohighlight">
-\[O = X_1X_2,\]</div>
-<p>and given the fact that</p>
-<div class="math notranslate nohighlight">
-\[\ket{\psi} = U_{\psi}\ket{0} \qquad \ket{\phi} = U_{\phi}\ket{0},\]</div>
-<p>we can combine the state preparation steps into the operator resulting in</p>
-<div class="math notranslate nohighlight">
-\[\bra{\psi}O\ket{\phi} = \bra{0}U_\psi^\dagger O U_\phi\ket{0},\]</div>
-<p>which corresponds to the following circuit. <img alt="Htest2" src="../../_images/htestfactored.png" /></p>
-<p>By preparing this circuit, and repeatedly measuring the ancilla qubit, we estimate the expectation value as</p>
-<div class="math notranslate nohighlight">
-\[P(0)-P(1) = Re \bra{\psi} O \ket{\phi}.\]</div>
-<p>The following sections demonstrate how this can be performed in CUDA-Q.</p>
-<section id="Numerical-result-as-a-reference:">
-<h2>Numerical result as a reference:<a class="headerlink" href="#Numerical-result-as-a-reference:" title="Permalink to this heading">¶</a></h2>
-<p>Before performing the Hadamard test, let’s determine the exact expectation value by performing the matrix multiplications explicitly. The code below builds two CUDA-Q kernels corresponding to <span class="math notranslate nohighlight">\(\ket{\psi} = \frac{1}{\sqrt{2}}\begin{pmatrix}1 \\ 0 \\ 1 \\ 0\end{pmatrix}\)</span> and <span class="math notranslate nohighlight">\(\ket{\phi} = \begin{pmatrix}0 \\ 1 \\ 0 \\ 0\end{pmatrix}.\)</span></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">)</span>
-
-<span class="n">qubit_num</span> <span class="o">=</span> <span class="mi">2</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">psi</span><span class="p">(</span><span class="n">num</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">num</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">phi</span><span class="p">(</span><span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<p>The state vectors can be accessed using the <code class="docutils literal notranslate"><span class="pre">get_state</span></code> command and printed as numpy arrays:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">psi</span><span class="p">,</span> <span class="n">qubit_num</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Psi state: &#39;</span><span class="p">,</span> <span class="n">psi_state</span><span class="p">)</span>
-
-<span class="n">phi_state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">phi</span><span class="p">,</span> <span class="n">qubit_num</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Phi state: &#39;</span><span class="p">,</span> <span class="n">phi_state</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Psi state:  SV: [(0.707107,0), (0,0), (0.707107,0), (0,0)]
-
-Phi state:  SV: [(0,0), (1,0), (0,0), (0,0)]
-
-</pre></div></div>
-</div>
-<p>The Hamiltonian operator (<span class="math notranslate nohighlight">\(O\)</span> in the derivation above) is defined as a CUDA-Q spin operator and converted to a matrix with <code class="docutils literal notranslate"><span class="pre">to_matrix</span></code>. The following line of code performs the explicit matrix multiplications to produce the exact expectation value.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ham</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ham_matrix</span> <span class="o">=</span> <span class="n">ham</span><span class="o">.</span><span class="n">to_matrix</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;hamiltonian: &#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ham_matrix</span><span class="p">),</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">exp_val</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psi_state</span><span class="p">)</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">ham_matrix</span><span class="p">,</span> <span class="n">phi_state</span><span class="p">))</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Numerical expectation value: &#39;</span><span class="p">,</span> <span class="n">exp_val</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-hamiltonian:  [[0.+0.j 0.+0.j 0.+0.j 1.+0.j]
- [0.+0.j 0.+0.j 1.+0.j 0.+0.j]
- [0.+0.j 1.+0.j 0.+0.j 0.+0.j]
- [1.+0.j 0.+0.j 0.+0.j 0.+0.j]]
-
-Numerical expectation value:  (0.7071067690849304+0j)
-</pre></div></div>
-</div>
-</section>
-<section id="Using-Sample-to-perform-the-Hadamard-test">
-<h2>Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test<a class="headerlink" href="#Using-Sample-to-perform-the-Hadamard-test" title="Permalink to this heading">¶</a></h2>
-<p>Three CUDA-Q kernels are constructed below corresponding to <span class="math notranslate nohighlight">\(\ket{\psi}\)</span>, <span class="math notranslate nohighlight">\(\ket{\phi}\)</span>, and the Hamiltonian. A fourth kernel constructs the Hadamard test circuit and completes with a measurement of the ancilla qubit in the computational basis.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">U_psi</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">U_phi</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">ham_cir</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">ancilla</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">U_phi</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">ham_cir</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">U_psi</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-
-    <span class="n">mz</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The CUDA-Q <code class="docutils literal notranslate"><span class="pre">sample</span></code> method computes 100000 sample ancilla measurements, and from them we can estimate the expectation value. The standard error is provided as well. Try increasing the sample size and note the convergence of the expectation value and the standard error towards the numerical result.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shots</span> <span class="o">=</span> <span class="mi">100000</span>
-<span class="n">qubit_num</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">count</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_num</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">count</span><span class="p">)</span>
-
-<span class="n">mean_val</span> <span class="o">=</span> <span class="p">(</span><span class="n">count</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">count</span><span class="p">[</span><span class="s1">&#39;1&#39;</span><span class="p">])</span> <span class="o">/</span> <span class="n">shots</span>
-<span class="n">error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">count</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">count</span><span class="p">[</span><span class="s1">&#39;1&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">shots</span><span class="p">)</span> <span class="o">/</span> <span class="n">shots</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Observable QC: &#39;</span><span class="p">,</span> <span class="n">mean_val</span><span class="p">,</span> <span class="s1">&#39;+ -&#39;</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Numerical result&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">exp_val</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{ 0:85281 1:14719 }
-
-Observable QC:  0.70562 + - 0.0015844563982640861
-Numerical result 0.7071067690849304
-</pre></div></div>
-</div>
-</section>
-<section id="Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">
-<h2>Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test<a class="headerlink" href="#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test" title="Permalink to this heading">¶</a></h2>
-<p>This example is small, but a more practical application of the Hadamard test such as QKSD will require much larger circuits. The QKSD method works by reducing the exponential <span class="math notranslate nohighlight">\(2^N\)</span> Hilbert space into an exponentially smaller subspace using a set of non-orthogonal states which are easy to prepare on a quantum computer. The Hadamard test is used to compute the matrix elements of this smaller subspace which is then diagonalized using a classical method to produce the eigenvalues. <a class="reference external" href="https://www.osti.gov/servlets/purl/1962060">This
-paper</a> described the method in more detail and is the source of the figure below.</p>
-<p><img alt="Htest3" src="../../_images/QKSD.png" /></p>
-<p>This method can be easily parallelized, and multiple QPUs, if available, could compute the matrix elements asynchronously. The CUDA-Q <code class="docutils literal notranslate"><span class="pre">mqpu</span></code> backend allows you to simulate a computation across multiple simulated QPUs. The code below demonstrates how.</p>
-<p>First, the Hadamard test circuit is defined, but this time the <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> and <span class="math notranslate nohighlight">\(\ket{\phi}\)</span> states contain parameterized rotations so that multiple states can be quickly generated, for the sake of example.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">U_psi</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">theta</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">U_phi</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">theta</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-    <span class="n">rx</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">ham_cir</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">angle</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">theta</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-    <span class="n">ancilla</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">U_phi</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">ham_cir</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">U_psi</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-
-    <span class="n">mz</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, the <code class="docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> backend is specified and the number of GPUs available is determined.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-
-<span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span>
-<span class="n">qpu_count</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of QPUs:&quot;</span><span class="p">,</span> <span class="n">qpu_count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Number of QPUs: 5
-</pre></div></div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">sample_async</span></code> command is then used to distribute the Hadamard test computations across multiple simulated QPUs. The results are saved in a list and accessed below.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shots</span> <span class="o">=</span> <span class="mi">100000</span>
-<span class="n">angle</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">3.14</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">]</span>
-<span class="n">theta</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]</span>
-<span class="n">qubit_num</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-    <span class="n">count</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                               <span class="n">qubit_num</span><span class="p">,</span>
-                               <span class="n">angle</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
-                               <span class="n">theta</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
-                               <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">,</span>
-                               <span class="n">qpu_id</span><span class="o">=</span><span class="n">i</span> <span class="o">%</span> <span class="n">qpu_count</span><span class="p">)</span>
-    <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The four matrix elements are shown below and can be classically processed to produce the eigenvalues.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mean_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">angle</span><span class="p">))</span>
-<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">result</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-    <span class="n">i_result</span> <span class="o">=</span> <span class="n">count</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">i_result</span><span class="p">)</span>
-    <span class="n">mean_val</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">i_result</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">i_result</span><span class="p">[</span><span class="s1">&#39;1&#39;</span><span class="p">])</span> <span class="o">/</span> <span class="n">shots</span>
-    <span class="n">error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">i_result</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">i_result</span><span class="p">[</span><span class="s1">&#39;1&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">shots</span><span class="p">)</span> <span class="o">/</span> <span class="n">shots</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;QKSD Matrix Element: &#39;</span><span class="p">,</span> <span class="n">mean_val</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="s1">&#39;+ -&#39;</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
-    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-0
-{ 0:49865 1:50135 }
-
-QKSD Matrix Element:  -0.0027 + - 0.0022360598270171573
-1
-{ 0:49796 1:50204 }
-
-QKSD Matrix Element:  -0.00408 + - 0.002236049366181346
-2
-{ 0:49695 1:50305 }
-
-QKSD Matrix Element:  -0.0061 + - 0.002236026375068058
-3
-{ 0:49972 1:50028 }
-
-QKSD Matrix Element:  -0.00056 + - 0.002236067626884303
-</pre></div></div>
-</div>
-<section id="Classically-Diagonalize-the-Subspace-Matrix">
-<h3>Classically Diagonalize the Subspace Matrix<a class="headerlink" href="#Classically-Diagonalize-the-Subspace-Matrix" title="Permalink to this heading">¶</a></h3>
-<p>For a problem of this size, numpy can be used to diagonalize the subspace and produce the eigenvalues and eigenvectors in the basis of non-orthogonal states.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">my_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">m</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-        <span class="n">my_mat</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_val</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
-        <span class="n">m</span> <span class="o">+=</span> <span class="mi">1</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">my_mat</span><span class="p">)</span>
-
-<span class="n">E</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">my_mat</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Eigenvalues: &#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">E</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Eigenvector: &#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">V</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[[-0.0027  -0.00408]
- [-0.0061  -0.00056]]
-Eigenvalues:
-[-0.00782313  0.00456313]
-Eigenvector:
-[[-0.76575845  0.64312829]
- [-0.64312829 -0.76575845]]
-</pre></div></div>
-</div>
-</section>
-</section>
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diff --git a/pr-2308/applications/python/hadamard_test.ipynb b/pr-2308/applications/python/hadamard_test.ipynb
deleted file mode 100644
index 1d1f781d59..0000000000
--- a/pr-2308/applications/python/hadamard_test.ipynb
+++ /dev/null
@@ -1,472 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements\n",
-    "\n",
-    "The Hadamard test is a quantum algorithm for estimating expectation values and is a useful subroutine for a number of quantum applications ranging from estimation of molecular ground state energies to quantum semidefinite programming. This tutorial will briefly introduce the Hadamard test, demonstrate how it can be implemented in CUDA-Q, and then parallelized for a Quantum Krylov Subspace Diagonalization application.\n",
-    "\n",
-    "The Hadamard test is performed using a register with an ancilla qubit in the $\\ket{0}$ state and a prepared quantum state $\\ket{\\psi}$, the following circuit can be used to extract the expectation value from measurement of the ancilla.\n",
-    "\n",
-    "\n",
-    "![Htest](./images/htest.png)\n",
-    "\n",
-    "The key insight is that $$P(0) = \\frac{1}{2} \\left[ I + Re \\bra{\\psi} O \\ket{\\phi} \\right]$$ and $$P(1) = \\frac{1}{2} \\left[ I - Re \\bra{\\psi} O \\ket{\\phi} \\right]$$ so their difference is equal to $$P(0)-P(1) = Re \\bra{\\psi} O \\ket{\\phi}.$$\n",
-    "\n",
-    "\n",
-    "More details and a short derivation can be found [here](https://en.wikipedia.org/wiki/Hadamard_test)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if you want to perform the Hadamard test to compute an expectation value like $\\bra{\\psi} O \\ket{\\phi}$, where $\\ket{\\psi}$ and $\\ket{\\phi}$ are different states and $O$ is a Pauli Operator? This is a common subroutine for the QKSD, where matrix elements are determined by computing expectation values between different states.\n",
-    "\n",
-    "Defining $O$ as \n",
-    "$$O = X_1X_2,$$\n",
-    "\n",
-    "and given the fact that\n",
-    "$$\\ket{\\psi} = U_{\\psi}\\ket{0} \\qquad \\ket{\\phi} = U_{\\phi}\\ket{0},$$\n",
-    "\n",
-    "we can combine the state preparation steps into the operator resulting in\n",
-    "$$\\bra{\\psi}O\\ket{\\phi} = \\bra{0}U_\\psi^\\dagger O U_\\phi\\ket{0},$$\n",
-    "which corresponds to the following circuit.\n",
-    "![Htest2](./images/htestfactored.png)\n",
-    "\n",
-    "By preparing this circuit, and repeatedly measuring the ancilla qubit, we estimate the expectation value as $$P(0)-P(1) = Re \\bra{\\psi} O \\ket{\\phi}.$$\n",
-    "\n",
-    "\n",
-    "The following sections demonstrate how this can be performed in CUDA-Q."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical result as a reference: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before performing the Hadamard test, let's determine the exact expectation value by performing the matrix multiplications explicitly. The code below builds two CUDA-Q kernels corresponding to $\\ket{\\psi} = \\frac{1}{\\sqrt{2}}\\begin{pmatrix}1 \\\\ 0 \\\\ 1 \\\\ 0\\end{pmatrix}$ and $\\ket{\\phi} = \\begin{pmatrix}0 \\\\ 1 \\\\ 0 \\\\ 0\\end{pmatrix}.$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "from functools import reduce\n",
-    "\n",
-    "cudaq.set_target('nvidia')\n",
-    "\n",
-    "qubit_num = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def psi(num: int):\n",
-    "    q = cudaq.qvector(num)\n",
-    "    h(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def phi(n: int):\n",
-    "    q = cudaq.qvector(n)\n",
-    "    x(q[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The state vectors can be accessed using the `get_state` command and printed as numpy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Psi state:  SV: [(0.707107,0), (0,0), (0.707107,0), (0,0)]\n",
-      "\n",
-      "Phi state:  SV: [(0,0), (1,0), (0,0), (0,0)]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "psi_state = cudaq.get_state(psi, qubit_num)\n",
-    "print('Psi state: ', psi_state)\n",
-    "\n",
-    "phi_state = cudaq.get_state(phi, qubit_num)\n",
-    "print('Phi state: ', phi_state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian operator ($O$ in the derivation above) is defined as a CUDA-Q spin operator and converted to a matrix with `to_matrix`. The following line of code performs the explicit matrix multiplications to produce the exact expectation value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hamiltonian:  [[0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n",
-      " [0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n",
-      " [0.+0.j 1.+0.j 0.+0.j 0.+0.j]\n",
-      " [1.+0.j 0.+0.j 0.+0.j 0.+0.j]] \n",
-      "\n",
-      "Numerical expectation value:  (0.7071067690849304+0j)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ham = cudaq.spin.x(0) * cudaq.spin.x(1)\n",
-    "ham_matrix = ham.to_matrix()\n",
-    "print('hamiltonian: ', np.array(ham_matrix), '\\n')\n",
-    "\n",
-    "exp_val = reduce(np.dot, (np.array(psi_state).conj().T, ham_matrix, phi_state))\n",
-    "\n",
-    "print('Numerical expectation value: ', exp_val)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `Sample` to perform the Hadamard test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Three CUDA-Q kernels are constructed below corresponding to $\\ket{\\psi}$, $\\ket{\\phi}$, and the Hamiltonian. A fourth kernel constructs the Hadamard test circuit and completes with a measurement of the ancilla qubit in the computational basis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "cudaq.set_target('nvidia')\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_psi(q: cudaq.qview):\n",
-    "    h(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_phi(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def ham_cir(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "    x(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(n: int):\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    q = cudaq.qvector(n)\n",
-    "    h(ancilla)\n",
-    "    cudaq.control(U_phi, ancilla, q)\n",
-    "    cudaq.control(ham_cir, ancilla, q)\n",
-    "    cudaq.control(U_psi, ancilla, q)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    mz(ancilla)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CUDA-Q `sample` method computes 100000 sample ancilla measurements, and from them we can estimate the expectation value. The standard error is provided as well. Try increasing the sample size and note the convergence of the expectation value and the standard error towards the numerical result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 0:85281 1:14719 }\n",
-      "\n",
-      "Observable QC:  0.70562 + - 0.0015844563982640861\n",
-      "Numerical result 0.7071067690849304\n"
-     ]
-    }
-   ],
-   "source": [
-    "shots = 100000\n",
-    "qubit_num = 2\n",
-    "count = cudaq.sample(kernel, qubit_num, shots_count=shots)\n",
-    "print(count)\n",
-    "\n",
-    "mean_val = (count['0'] - count['1']) / shots\n",
-    "error = np.sqrt(2 * count['0'] * count['1'] / shots) / shots\n",
-    "print('Observable QC: ', mean_val, '+ -', error)\n",
-    "print('Numerical result', np.real(exp_val))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test\n",
-    "\n",
-    "This example is small, but a more practical application of the Hadamard test such as QKSD will require much larger circuits. The QKSD method works by reducing the exponential $2^N$ Hilbert space into an exponentially smaller subspace using a set of non-orthogonal states which are easy to prepare on a quantum computer. The Hadamard test is used to compute the matrix elements of this smaller subspace which is then diagonalized using a classical method to produce the eigenvalues. [This paper](https://www.osti.gov/servlets/purl/1962060) described the method in more detail and is the source of the figure below.\n",
-    "\n",
-    "![Htest3](./images/QKSD.png)\n",
-    "\n",
-    "This method can be easily parallelized, and multiple QPUs, if available, could compute the matrix elements asynchronously.  The CUDA-Q `mqpu` backend allows you to simulate a computation across multiple simulated QPUs. The code below demonstrates how.\n",
-    "\n",
-    "First, the Hadamard test circuit is defined, but this time the $\\ket{\\psi}$ and $\\ket{\\phi}$ states contain parameterized rotations so that multiple states can be quickly generated, for the sake of example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_psi(q: cudaq.qview, theta: float):\n",
-    "    ry(theta, q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_phi(q: cudaq.qview, theta: float):\n",
-    "    rx(theta, q[0])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def ham_cir(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "    x(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(n: int, angle: float, theta: float):\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    q = cudaq.qvector(n)\n",
-    "    h(ancilla)\n",
-    "    cudaq.control(U_phi, ancilla, q, theta)\n",
-    "    cudaq.control(ham_cir, ancilla, q)\n",
-    "    cudaq.control(U_psi, ancilla, q, angle)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    mz(ancilla)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the `nvidia-mqpu` backend is specified and the number of GPUs available is determined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of QPUs: 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "cudaq.set_target(\"nvidia\", option=\"mqpu\")\n",
-    "\n",
-    "target = cudaq.get_target()\n",
-    "qpu_count = target.num_qpus()\n",
-    "print(\"Number of QPUs:\", qpu_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `sample_async` command is then used to distribute the Hadamard test computations across multiple simulated QPUs. The results are saved in a list and accessed below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shots = 100000\n",
-    "angle = [0.0, 1.5, 3.14, 0.7]\n",
-    "theta = [0.6, 1.2, 2.2, 3.0]\n",
-    "qubit_num = 2\n",
-    "\n",
-    "result = []\n",
-    "for i in range(4):\n",
-    "    count = cudaq.sample_async(kernel,\n",
-    "                               qubit_num,\n",
-    "                               angle[i],\n",
-    "                               theta[i],\n",
-    "                               shots_count=shots,\n",
-    "                               qpu_id=i % qpu_count)\n",
-    "    result.append(count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The four matrix elements are shown below and can be classically processed to produce the eigenvalues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "{ 0:49865 1:50135 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.0027 + - 0.0022360598270171573\n",
-      "1\n",
-      "{ 0:49796 1:50204 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.00408 + - 0.002236049366181346\n",
-      "2\n",
-      "{ 0:49695 1:50305 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.0061 + - 0.002236026375068058\n",
-      "3\n",
-      "{ 0:49972 1:50028 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.00056 + - 0.002236067626884303\n"
-     ]
-    }
-   ],
-   "source": [
-    "mean_val = np.zeros(len(angle))\n",
-    "i = 0\n",
-    "for count in result:\n",
-    "    print(i)\n",
-    "    i_result = count.get()\n",
-    "    print(i_result)\n",
-    "    mean_val[i] = (i_result['0'] - i_result['1']) / shots\n",
-    "    error = np.sqrt(2 * i_result['0'] * i_result['1'] / shots) / shots\n",
-    "    print('QKSD Matrix Element: ', mean_val[i], '+ -', error)\n",
-    "    i += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classically Diagonalize the Subspace Matrix\n",
-    "\n",
-    "For a problem of this size, numpy can be used to diagonalize the subspace and produce the eigenvalues and eigenvectors in the basis of non-orthogonal states. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[-0.0027  -0.00408]\n",
-      " [-0.0061  -0.00056]]\n",
-      "Eigenvalues: \n",
-      "[-0.00782313  0.00456313]\n",
-      "Eigenvector: \n",
-      "[[-0.76575845  0.64312829]\n",
-      " [-0.64312829 -0.76575845]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "my_mat = np.zeros((2, 2), dtype=float)\n",
-    "m = 0\n",
-    "for k in range(2):\n",
-    "    for j in range(2):\n",
-    "        my_mat[k, j] = mean_val[m]\n",
-    "        m += 1\n",
-    "\n",
-    "print(my_mat)\n",
-    "\n",
-    "E, V = np.linalg.eigh(my_mat)\n",
-    "\n",
-    "print('Eigenvalues: ')\n",
-    "print(E)\n",
-    "\n",
-    "print('Eigenvector: ')\n",
-    "print(V)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
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-  <section id="Hybrid-Quantum-Neural-Networks">
-<h1>Hybrid Quantum Neural Networks<a class="headerlink" href="#Hybrid-Quantum-Neural-Networks" title="Permalink to this heading">¶</a></h1>
-<p>The example below highlights a hybrid quantum neural network workflow with CUDA-Q and PyTorch where both layers can GPU accelerated to maximise performance.</p>
-<p><img alt="hybrid" class="no-scaled-link" src="../../_images/hybrid.png" style="width: 600px;" /></p>
-<p>We perform binary classification on the MNIST dataset where data flows through the neural network architecture to the quantum circuit whose output is used to classify hand written digits.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the relevant packages.</span>
-
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">matplotlib</span><span class="o">==</span><span class="m">3</span>.8.4<span class="w"> </span><span class="nv">torch</span><span class="o">==</span><span class="m">2</span>.0.1+cu118<span class="w"> </span><span class="nv">torchvision</span><span class="o">==</span><span class="m">0</span>.15.2+cu118<span class="w"> </span>scikit-learn<span class="o">==</span><span class="m">1</span>.4.2<span class="w"> </span>-q<span class="w"> </span>--extra-index-url<span class="w"> </span>https://download.pytorch.org/whl/cu118
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import the relevant libraries</span>
-
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">Function</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
-<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torchvision</span>
-
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-
-<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">22</span><span class="p">)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_random_seed</span><span class="p">(</span><span class="mi">44</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set CUDAQ and PyTorch to run on either CPU or GPU.</span>
-
-<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;qpp-cpu&quot;</span><span class="p">)</span>
-
-<span class="c1">#cudaq.set_target(&quot;nvidia&quot;)</span>
-<span class="c1">#device = torch.device(&quot;cuda:0&quot;)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">prepare_data</span><span class="p">(</span><span class="n">target_digits</span><span class="p">,</span> <span class="n">sample_count</span><span class="p">,</span> <span class="n">test_size</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Load and prepare the MNIST dataset to be used</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        target_digits (list): digits to perform classification of</span>
-<span class="sd">        sample_count (int): total number of images to be used</span>
-<span class="sd">        test_size (float): percentage of sample_count to be used as test set, the remainder is the training set</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        dataset in train, test format with targets</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
-        <span class="p">[</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
-         <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">))])</span>
-
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s2">&quot;./data&quot;</span><span class="p">,</span>
-                             <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                             <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                             <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
-
-    <span class="c1"># Filter out the required labels.</span>
-    <span class="n">idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span> <span class="o">==</span> <span class="n">target_digits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">|</span> <span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span>
-                                                   <span class="o">==</span> <span class="n">target_digits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">targets</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
-
-    <span class="c1"># Select a subset based on number of datapoints specified by sample_count.</span>
-    <span class="n">subset_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))[:</span><span class="n">sample_count</span><span class="p">]</span>
-
-    <span class="n">x</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">subset_indices</span><span class="p">]</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="n">y</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">[</span><span class="n">subset_indices</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="c1"># Relabel the targets as a 0 or a 1.</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="nb">min</span><span class="p">(</span><span class="n">target_digits</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
-
-    <span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span>
-                                                        <span class="n">y</span><span class="p">,</span>
-                                                        <span class="n">test_size</span><span class="o">=</span><span class="n">test_size</span> <span class="o">/</span>
-                                                        <span class="mi">100</span><span class="p">,</span>
-                                                        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                        <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Classical parameters.</span>
-
-<span class="n">sample_count</span> <span class="o">=</span> <span class="mi">1000</span>  <span class="c1"># Total number of images to use.</span>
-<span class="n">target_digits</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>  <span class="c1"># Hand written digits to classify.</span>
-<span class="n">test_size</span> <span class="o">=</span> <span class="mi">30</span>  <span class="c1"># Percentage of dataset to be used for testing.</span>
-<span class="n">classification_threshold</span> <span class="o">=</span> <span class="mf">0.5</span>  <span class="c1"># Classification boundary used to measure accuracy.</span>
-<span class="n">epochs</span> <span class="o">=</span> <span class="mi">1000</span>  <span class="c1"># Number of epochs to train for.</span>
-
-<span class="c1"># Quantum parmeters.</span>
-
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># Measurement operator.</span>
-<span class="n">shift</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>  <span class="c1"># Magnitude of parameter shift.</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">prepare_data</span><span class="p">(</span><span class="n">target_digits</span><span class="p">,</span> <span class="n">sample_count</span><span class="p">,</span>
-                                                <span class="n">test_size</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot some images from the training set to visualise.</span>
-<span class="k">if</span> <span class="n">device</span> <span class="o">!=</span> <span class="s1">&#39;cpu&#39;</span><span class="p">:</span>
-    <span class="n">sample_to_plot</span> <span class="o">=</span> <span class="n">x_train</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">))</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">sample_to_plot</span> <span class="o">=</span> <span class="n">x_train</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
-
-<span class="n">grid_img</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">sample_to_plot</span><span class="p">,</span>
-                                       <span class="n">nrow</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                                       <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-                                       <span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">grid_img</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_hybrid_qnns_9_0.png" src="../../_images/applications_python_hybrid_qnns_9_0.png" />
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">QuantumFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Allows the quantum circuit to input data, output expectation values</span>
-<span class="sd">    and calculate gradients of variational parameters via finite difference&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Define the quantum circuit in CUDA Quantum&quot;&quot;&quot;</span>
-
-        <span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-        <span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">thetas</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-
-            <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-
-            <span class="n">ry</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">rx</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="n">kernel</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">qubit_count</span> <span class="o">=</span> <span class="n">qubit_count</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">hamiltonian</span>
-
-    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta_vals</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Excetute the quantum circuit to output an expectation value&quot;&quot;&quot;</span>
-
-        <span class="c1">#If running on GPU, thetas is a torch.tensor that will live on GPU memory. The observe function calls a .tolist() method on inputs which moves thetas from GPU to CPU.</span>
-
-        <span class="n">qubit_count</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">qubit_count</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">theta_vals</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
-
-        <span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hamiltonian</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span>
-                                <span class="n">theta_vals</span><span class="p">)</span>
-
-        <span class="n">exp_vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">))]</span>
-        <span class="n">exp_vals</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">exp_vals</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">exp_vals</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">thetas</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">,</span> <span class="n">quantum_circuit</span><span class="p">,</span>
-                <span class="n">shift</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">:</span>
-
-        <span class="c1"># Save shift and quantum_circuit in context to use in backward.</span>
-        <span class="n">ctx</span><span class="o">.</span><span class="n">shift</span> <span class="o">=</span> <span class="n">shift</span>
-        <span class="n">ctx</span><span class="o">.</span><span class="n">quantum_circuit</span> <span class="o">=</span> <span class="n">quantum_circuit</span>
-
-        <span class="c1"># Calculate expectation value.</span>
-        <span class="n">exp_vals</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">quantum_circuit</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">thetas</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">thetas</span><span class="p">,</span> <span class="n">exp_vals</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">exp_vals</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Backward pass computation via finite difference&quot;&quot;&quot;</span>
-
-        <span class="n">thetas</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
-
-        <span class="n">gradients</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">thetas</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">thetas</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-
-            <span class="n">thetas_plus</span> <span class="o">=</span> <span class="n">thetas</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-            <span class="n">thetas_plus</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">shift</span>
-            <span class="n">exp_vals_plus</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">quantum_circuit</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">thetas_plus</span><span class="p">)</span>
-
-            <span class="n">thetas_minus</span> <span class="o">=</span> <span class="n">thetas</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-            <span class="n">thetas_minus</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">-=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">shift</span>
-            <span class="n">exp_vals_minus</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">quantum_circuit</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">thetas_minus</span><span class="p">)</span>
-
-            <span class="n">gradients</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">exp_vals_plus</span> <span class="o">-</span> <span class="n">exp_vals_minus</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">ctx</span><span class="o">.</span><span class="n">shift</span><span class="p">)</span>
-
-        <span class="n">gradients</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">grad_output</span><span class="p">,</span> <span class="n">gradients</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">gradients</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">QuantumLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Encapsulates a quantum circuit into a quantum layer adhering PyTorch convention&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">shift</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">QuantumLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">quantum_circuit</span> <span class="o">=</span> <span class="n">QuantumFunction</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shift</span> <span class="o">=</span> <span class="n">shift</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
-
-        <span class="n">result</span> <span class="o">=</span> <span class="n">QuantumFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantum_circuit</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shift</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">result</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Hybrid_QNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Structure of the hybrid neural network with classical fully connected layers and quantum layers&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Hybrid_QNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">256</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc4</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc5</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
-
-        <span class="c1"># The 2 outputs from PyTorch fc5 layer feed into the 2 variational gates in the quantum circuit.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">quantum</span> <span class="o">=</span> <span class="n">QuantumLayer</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">shift</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">)</span>  <span class="c1"># Turns images into vectors.</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc4</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc5</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="c1"># Quantum circuit outputs an expectation value which is fed into the sigmoid activation function to perform classification.</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quantum</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">accuracy_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">sum</span><span class="p">((</span><span class="n">y</span> <span class="o">==</span> <span class="p">(</span><span class="n">y_hat</span> <span class="o">&gt;=</span> <span class="n">classification_threshold</span><span class="p">)))</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">hybrid_model</span> <span class="o">=</span> <span class="n">Hybrid_QNN</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adadelta</span><span class="p">(</span><span class="n">hybrid_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span>
-                           <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
-                           <span class="n">weight_decay</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
-
-<span class="n">loss_function</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCELoss</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-<span class="n">training_cost</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">testing_cost</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">training_accuracy</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">testing_accuracy</span> <span class="o">=</span> <span class="p">[]</span>
-
-<span class="n">hybrid_model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
-
-    <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-
-    <span class="n">y_hat_train</span> <span class="o">=</span> <span class="n">hybrid_model</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="n">train_cost</span> <span class="o">=</span> <span class="n">loss_function</span><span class="p">(</span><span class="n">y_hat_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="n">train_cost</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-
-    <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
-
-    <span class="n">training_accuracy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_hat_train</span><span class="p">))</span>
-    <span class="n">training_cost</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_cost</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-
-    <span class="n">hybrid_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-
-        <span class="n">y_hat_test</span> <span class="o">=</span> <span class="n">hybrid_model</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="n">test_cost</span> <span class="o">=</span> <span class="n">loss_function</span><span class="p">(</span><span class="n">y_hat_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="n">testing_accuracy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_hat_test</span><span class="p">))</span>
-        <span class="n">testing_cost</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">test_cost</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">training_cost</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">testing_cost</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Cost&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">training_accuracy</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">testing_accuracy</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_hybrid_qnns_15_0.png" src="../../_images/applications_python_hybrid_qnns_15_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-<div class="highlight"><pre>
-CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)
-</pre></div></div>
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diff --git a/pr-2308/applications/python/hybrid_qnns.ipynb b/pr-2308/applications/python/hybrid_qnns.ipynb
deleted file mode 100644
index 4d8852fa91..0000000000
--- a/pr-2308/applications/python/hybrid_qnns.ipynb
+++ /dev/null
@@ -1,477 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hybrid Quantum Neural Networks\n",
-    "\n",
-    "The example below highlights a hybrid quantum neural network workflow with CUDA-Q and PyTorch where both layers can GPU accelerated to maximise performance."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"images/hybrid.png\" alt=\"hybrid\" width=\"600\">\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We perform binary classification on the MNIST dataset where data flows through the neural network architecture to the quantum circuit whose output is used to classify hand written digits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "\n",
-    "!pip install matplotlib==3.8.4 torch==2.0.1+cu118 torchvision==0.15.2+cu118 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu118"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the relevant libraries\n",
-    "\n",
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch\n",
-    "from torch.autograd import Function\n",
-    "from torchvision import datasets, transforms\n",
-    "import torch.optim as optim\n",
-    "import torch.nn as nn\n",
-    "import torchvision\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "torch.manual_seed(22)\n",
-    "cudaq.set_random_seed(44)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set CUDAQ and PyTorch to run on either CPU or GPU.\n",
-    "\n",
-    "device = torch.device('cpu')\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "#cudaq.set_target(\"nvidia\")\n",
-    "#device = torch.device(\"cuda:0\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def prepare_data(target_digits, sample_count, test_size):\n",
-    "    \"\"\"Load and prepare the MNIST dataset to be used \n",
-    "    \n",
-    "    Args: \n",
-    "        target_digits (list): digits to perform classification of \n",
-    "        sample_count (int): total number of images to be used\n",
-    "        test_size (float): percentage of sample_count to be used as test set, the remainder is the training set\n",
-    "        \n",
-    "    Returns: \n",
-    "        dataset in train, test format with targets \n",
-    "    \n",
-    "    \"\"\"\n",
-    "\n",
-    "    transform = transforms.Compose(\n",
-    "        [transforms.ToTensor(),\n",
-    "         transforms.Normalize((0.1307), (0.3081))])\n",
-    "\n",
-    "    dataset = datasets.MNIST(\"./data\",\n",
-    "                             train=True,\n",
-    "                             download=True,\n",
-    "                             transform=transform)\n",
-    "\n",
-    "    # Filter out the required labels.\n",
-    "    idx = (dataset.targets == target_digits[0]) | (dataset.targets\n",
-    "                                                   == target_digits[1])\n",
-    "    dataset.data = dataset.data[idx]\n",
-    "    dataset.targets = dataset.targets[idx]\n",
-    "\n",
-    "    # Select a subset based on number of datapoints specified by sample_count.\n",
-    "    subset_indices = torch.randperm(dataset.data.size(0))[:sample_count]\n",
-    "\n",
-    "    x = dataset.data[subset_indices].float().unsqueeze(1).to(device)\n",
-    "\n",
-    "    y = dataset.targets[subset_indices].to(device).float().to(device)\n",
-    "\n",
-    "    # Relabel the targets as a 0 or a 1.\n",
-    "    y = torch.where(y == min(target_digits), 0.0, 1.0)\n",
-    "\n",
-    "    x_train, x_test, y_train, y_test = train_test_split(x,\n",
-    "                                                        y,\n",
-    "                                                        test_size=test_size /\n",
-    "                                                        100,\n",
-    "                                                        shuffle=True,\n",
-    "                                                        random_state=42)\n",
-    "\n",
-    "    return x_train, x_test, y_train, y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Classical parameters.\n",
-    "\n",
-    "sample_count = 1000  # Total number of images to use.\n",
-    "target_digits = [5, 6]  # Hand written digits to classify.\n",
-    "test_size = 30  # Percentage of dataset to be used for testing.\n",
-    "classification_threshold = 0.5  # Classification boundary used to measure accuracy.\n",
-    "epochs = 1000  # Number of epochs to train for.\n",
-    "\n",
-    "# Quantum parmeters.\n",
-    "\n",
-    "qubit_count = 1\n",
-    "hamiltonian = spin.z(0)  # Measurement operator.\n",
-    "shift = torch.tensor(torch.pi / 2)  # Magnitude of parameter shift."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train, x_test, y_train, y_test = prepare_data(target_digits, sample_count,\n",
-    "                                                test_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot some images from the training set to visualise.\n",
-    "if device != 'cpu':\n",
-    "    sample_to_plot = x_train[:10].to(torch.device('cpu'))\n",
-    "else:\n",
-    "    sample_to_plot = x_train[:10]\n",
-    "\n",
-    "grid_img = torchvision.utils.make_grid(sample_to_plot,\n",
-    "                                       nrow=5,\n",
-    "                                       padding=3,\n",
-    "                                       normalize=True)\n",
-    "plt.imshow(grid_img.permute(1, 2, 0))\n",
-    "plt.axis('off')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class QuantumFunction(Function):\n",
-    "    \"\"\"Allows the quantum circuit to input data, output expectation values \n",
-    "    and calculate gradients of variational parameters via finite difference\"\"\"\n",
-    "\n",
-    "    def __init__(self, qubit_count: int, hamiltonian: cudaq.SpinOperator):\n",
-    "        \"\"\"Define the quantum circuit in CUDA Quantum\"\"\"\n",
-    "\n",
-    "        @cudaq.kernel\n",
-    "        def kernel(qubit_count: int, thetas: np.ndarray):\n",
-    "\n",
-    "            qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "            ry(thetas[0], qubits[0])\n",
-    "            rx(thetas[1], qubits[0])\n",
-    "\n",
-    "        self.kernel = kernel\n",
-    "        self.qubit_count = qubit_count\n",
-    "        self.hamiltonian = hamiltonian\n",
-    "\n",
-    "    def run(self, theta_vals: torch.tensor) -> torch.tensor:\n",
-    "        \"\"\"Excetute the quantum circuit to output an expectation value\"\"\"\n",
-    "\n",
-    "        #If running on GPU, thetas is a torch.tensor that will live on GPU memory. The observe function calls a .tolist() method on inputs which moves thetas from GPU to CPU.\n",
-    "\n",
-    "        qubit_count = [self.qubit_count for _ in range(theta_vals.shape[0])]\n",
-    "\n",
-    "        results = cudaq.observe(self.kernel, self.hamiltonian, qubit_count,\n",
-    "                                theta_vals)\n",
-    "\n",
-    "        exp_vals = [results[i].expectation() for i in range(len(results))]\n",
-    "        exp_vals = torch.Tensor(exp_vals).to(device)\n",
-    "\n",
-    "        return exp_vals\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def forward(ctx, thetas: torch.tensor, quantum_circuit,\n",
-    "                shift) -> torch.tensor:\n",
-    "\n",
-    "        # Save shift and quantum_circuit in context to use in backward.\n",
-    "        ctx.shift = shift\n",
-    "        ctx.quantum_circuit = quantum_circuit\n",
-    "\n",
-    "        # Calculate expectation value.\n",
-    "        exp_vals = ctx.quantum_circuit.run(thetas).reshape(-1, 1)\n",
-    "\n",
-    "        ctx.save_for_backward(thetas, exp_vals)\n",
-    "\n",
-    "        return exp_vals\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def backward(ctx, grad_output):\n",
-    "        \"\"\"Backward pass computation via finite difference\"\"\"\n",
-    "\n",
-    "        thetas, _ = ctx.saved_tensors\n",
-    "\n",
-    "        gradients = torch.zeros(thetas.shape, device=device)\n",
-    "\n",
-    "        for i in range(thetas.shape[1]):\n",
-    "\n",
-    "            thetas_plus = thetas.clone()\n",
-    "            thetas_plus[:, i] += ctx.shift\n",
-    "            exp_vals_plus = ctx.quantum_circuit.run(thetas_plus)\n",
-    "\n",
-    "            thetas_minus = thetas.clone()\n",
-    "            thetas_minus[:, i] -= ctx.shift\n",
-    "            exp_vals_minus = ctx.quantum_circuit.run(thetas_minus)\n",
-    "\n",
-    "            gradients[:, i] = (exp_vals_plus - exp_vals_minus) / (2 * ctx.shift)\n",
-    "\n",
-    "        gradients = torch.mul(grad_output, gradients)\n",
-    "\n",
-    "        return gradients, None, None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class QuantumLayer(nn.Module):\n",
-    "    \"\"\"Encapsulates a quantum circuit into a quantum layer adhering PyTorch convention\"\"\"\n",
-    "\n",
-    "    def __init__(self, qubit_count: int, hamiltonian, shift: torch.tensor):\n",
-    "        super(QuantumLayer, self).__init__()\n",
-    "\n",
-    "        self.quantum_circuit = QuantumFunction(qubit_count, hamiltonian)\n",
-    "        self.shift = shift\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "\n",
-    "        result = QuantumFunction.apply(input, self.quantum_circuit, self.shift)\n",
-    "\n",
-    "        return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Hybrid_QNN(nn.Module):\n",
-    "    \"\"\"Structure of the hybrid neural network with classical fully connected layers and quantum layers\"\"\"\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        super(Hybrid_QNN, self).__init__()\n",
-    "\n",
-    "        self.fc1 = nn.Linear(28 * 28, 256)\n",
-    "        self.fc2 = nn.Linear(256, 128)\n",
-    "        self.dropout = nn.Dropout(0.25)\n",
-    "\n",
-    "        self.fc3 = nn.Linear(128, 64)\n",
-    "        self.fc4 = nn.Linear(64, 32)\n",
-    "        self.fc5 = nn.Linear(32, 2)\n",
-    "        self.dropout = nn.Dropout(0.25)\n",
-    "\n",
-    "        # The 2 outputs from PyTorch fc5 layer feed into the 2 variational gates in the quantum circuit.\n",
-    "        self.quantum = QuantumLayer(qubit_count, hamiltonian, shift)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "\n",
-    "        x = x.view(-1, 28 * 28)  # Turns images into vectors.\n",
-    "\n",
-    "        x = torch.relu(self.fc1(x))\n",
-    "        x = torch.relu(self.fc2(x))\n",
-    "        x = self.dropout(x)\n",
-    "\n",
-    "        x = torch.relu(self.fc3(x))\n",
-    "        x = torch.relu(self.fc4(x))\n",
-    "        x = torch.relu(self.fc5(x))\n",
-    "        x = self.dropout(x)\n",
-    "\n",
-    "        # Quantum circuit outputs an expectation value which is fed into the sigmoid activation function to perform classification.\n",
-    "        x = torch.sigmoid(self.quantum(x))\n",
-    "\n",
-    "        return x.view(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def accuracy_score(y, y_hat):\n",
-    "    return sum((y == (y_hat >= classification_threshold))) / len(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hybrid_model = Hybrid_QNN().to(device)\n",
-    "\n",
-    "optimizer = optim.Adadelta(hybrid_model.parameters(),\n",
-    "                           lr=0.001,\n",
-    "                           weight_decay=0.8)\n",
-    "\n",
-    "loss_function = nn.BCELoss().to(device)\n",
-    "\n",
-    "training_cost = []\n",
-    "testing_cost = []\n",
-    "training_accuracy = []\n",
-    "testing_accuracy = []\n",
-    "\n",
-    "hybrid_model.train()\n",
-    "for epoch in range(epochs):\n",
-    "\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "    y_hat_train = hybrid_model(x_train).to(device)\n",
-    "\n",
-    "    train_cost = loss_function(y_hat_train, y_train).to(device)\n",
-    "\n",
-    "    train_cost.backward()\n",
-    "\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    training_accuracy.append(accuracy_score(y_train, y_hat_train))\n",
-    "    training_cost.append(train_cost.item())\n",
-    "\n",
-    "    hybrid_model.eval()\n",
-    "    with torch.no_grad():\n",
-    "\n",
-    "        y_hat_test = hybrid_model(x_test).to(device)\n",
-    "\n",
-    "        test_cost = loss_function(y_hat_test, y_test).to(device)\n",
-    "\n",
-    "        testing_accuracy.append(accuracy_score(y_test, y_hat_test))\n",
-    "        testing_cost.append(test_cost.item())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(training_cost, label='Train')\n",
-    "plt.plot(testing_cost, label='Test')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Cost')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(training_accuracy, label='Train')\n",
-    "plt.plot(testing_accuracy, label='Test')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/krylov.html b/pr-2308/applications/python/krylov.html
deleted file mode 100644
index d472411ff5..0000000000
--- a/pr-2308/applications/python/krylov.html
+++ /dev/null
@@ -1,1276 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
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-  <title>Multi-reference Quantum Krylov Algorithm - \(H_2\) Molecule &mdash; NVIDIA CUDA-Q  documentation</title>
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-                amd64-pr-2308
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#utilities">Utilities</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
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-  <section id="Multi-reference-Quantum-Krylov-Algorithm---H_2-Molecule">
-<h1>Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule<a class="headerlink" href="#Multi-reference-Quantum-Krylov-Algorithm---H_2-Molecule" title="Permalink to this heading">¶</a></h1>
-<p>The multireference selected quantum Krylov (MRSQK) algorithm is defined in <a class="reference external" href="https://arxiv.org/pdf/1911.05163">this paper</a> and was developed as a low-cost alternative to quantum phase estimation. This tutorial will demonstrate how this algorithm can be implemented in CUDA-Q and accelerated using multiple GPUs. The <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/applications/python/hadamard_test.html">CUDA-Q Hadamard test tutorial</a> might provide helpful background information for understanding
-this tutorial.</p>
-<p>The algorithm works by preparing an initial state, and then defining this state in a smaller subspace constructed with a basis that corresponds to Trotter steps of the initial state. This subspace can be diagonalized to produce an approximate energy for the system without variational optimization of any parameters.</p>
-<p>In the example below, the initial guess is the ground state of the diagonalized Hamiltonian for demonstration purposes. In practice one could use a number of heuristics to prepare the ground state such as Hartree Fock or CISD. A very promising ground state preparation method which can leverage quantum computers is the linear combination of unitaries (LCU). LCU would allow for the state preparation to occur completely on the quantum computer and avoid storing an inputting the exponentially large
-state vector.</p>
-<p>Regardless of the method used for state preparation, the procedure begins by selecting a <span class="math notranslate nohighlight">\(d\)</span>-dimensional basis of reference states <span class="math notranslate nohighlight">\({\Phi_0 \cdots \Phi_d},\)</span> where each is a linear combination of Slater determinants:</p>
-<div class="math notranslate nohighlight">
-\[\ket{\Phi_I}  =  \sum_{\mu} d_{\mu I}\ket{\phi_{\mu}}.\]</div>
-<p>From this, a non-orthogonal Krylov Space <span class="math notranslate nohighlight">\(\mathcal{K} = \{\psi_{0} \cdots \psi_{N}\}\)</span> is constructed by applying a family of <span class="math notranslate nohighlight">\(s\)</span> unitary operators on each of the <span class="math notranslate nohighlight">\(d\)</span> reference states resulting in <span class="math notranslate nohighlight">\(d*s = N\)</span> elements in the Krylov space where</p>
-<div class="math notranslate nohighlight">
-\[\ket{\psi_{\alpha}} \equiv \ket{\psi_I^{(n)}} = \hat{U}_n\ket{\Phi_I},\]</div>
-<p>Therefore, the general quantum state that we originally set out to describe is</p>
-<div class="math notranslate nohighlight">
-\[\ket{\Psi} = \sum_{\alpha} c_{\alpha}\ket{\psi_{\alpha}} = \sum_{I=0}^d \sum_{n=0}^s c_I^{(n)}\hat{U}_n\ket{\Phi_I}.\]</div>
-<p>The energy of this state can be obtained by solving the generalized eigenvalue problem</p>
-<div class="math notranslate nohighlight">
-\[\boldsymbol{Hc}=\boldsymbol{Sc}E,\]</div>
-<p>where the elements of the overlap are</p>
-<div class="math notranslate nohighlight">
-\[S_{\alpha \beta} = \braket{\psi_{\alpha}|\psi_{\beta}} =   \braket{\Phi_I|\hat{U}_m^{\dagger}\hat{U}_n|\Phi_J}\]</div>
-<p>and Hamiltonian matrix is</p>
-<div class="math notranslate nohighlight">
-\[H_{\alpha \beta} = \braket{\psi_{\alpha}|\hat{H}|\psi_{\beta}} =   \braket{\Phi_I|\hat{U}_m^{\dagger}\hat{H}\hat{U}_n|\Phi_J}.\]</div>
-<p>The matrix elements for <span class="math notranslate nohighlight">\(S\)</span> are computed with the Hadamard test with a circuit shown below for the case of the overlap matrix elements.</p>
-<p><img alt="Htest" src="../../_images/krylovcircuit.png" /></p>
-<p>The <span class="math notranslate nohighlight">\(2\sigma_+\)</span> term refers to measurement of the expectation value of this circuit with the <span class="math notranslate nohighlight">\(X+iY\)</span> operator.</p>
-<p>The Hamiltonian matrix elements are computed with a circuit that includes controlled application of the Hamiltonian. Once the <span class="math notranslate nohighlight">\(H\)</span> and <span class="math notranslate nohighlight">\(S\)</span> matrices are constructed, the diagonalization is performed classically to produce an estimate for the ground state in question.</p>
-<section id="Setup">
-<h2>Setup<a class="headerlink" href="#Setup" title="Permalink to this heading">¶</a></h2>
-<p>This cell installs the necessary packages. This application can be parallelized, so please uncomment the <code class="docutils literal notranslate"><span class="pre">mgpu</span></code> specification below if you would like to run on more than one GPU.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy</span>
-
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">openfermion</span><span class="o">==</span><span class="m">1</span>.6.1<span class="w"> </span><span class="nv">pyscf</span><span class="o">==</span><span class="m">2</span>.6.2<span class="w"> </span><span class="nv">openfermionpyscf</span><span class="o">==</span><span class="m">0</span>.5<span class="w"> </span>-q
-
-<span class="c1"># Single-node, single gpu</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-<span class="n">multi_gpu</span> <span class="o">=</span> <span class="kc">False</span>
-
-<span class="c1"># Single-node, multi-GPU</span>
-<span class="c1">#cudaq.set_target(&quot;nvidia&quot;, option=&#39;mqpu,fp64&#39;)</span>
-<span class="c1">#multi_gpu = True</span>
-</pre></div>
-</div>
-</div>
-<p>The molecule is defined below and its Hamiltonian is extracted as a matrix. The matrix is diagonalized to produce the ground state. The corresponding state vector will be used as the initial state to ensure good results for this demonstration.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define H2 molecule</span>
-<span class="n">geometry</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)),</span> <span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.7474</span><span class="p">))]</span>
-
-<span class="n">hamiltonian</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">chemistry</span><span class="o">.</span><span class="n">create_molecular_hamiltonian</span><span class="p">(</span>
-    <span class="n">geometry</span><span class="p">,</span> <span class="s1">&#39;sto-3g&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-<span class="n">electron_count</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_electrons</span>
-<span class="n">qubits_num</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">n_orbitals</span>
-
-<span class="c1"># Diagonalize Hamiltonian</span>
-<span class="n">spin_ham_matrix</span> <span class="o">=</span> <span class="n">hamiltonian</span><span class="o">.</span><span class="n">to_matrix</span><span class="p">()</span>
-<span class="n">e</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">spin_ham_matrix</span><span class="p">)</span>
-
-<span class="c1"># Find the ground state energy and the corresponding eigenvector</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Ground state energy (classical simulation)= &#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">e</span><span class="p">),</span> <span class="s1">&#39;, index= &#39;</span><span class="p">,</span>
-      <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
-<span class="n">min_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
-
-<span class="c1"># Eigenvector can be used to initialize the qubits</span>
-<span class="n">vec</span> <span class="o">=</span> <span class="n">c</span><span class="p">[:,</span> <span class="n">min_indices</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<p>The functions below take the original Hamiltonian defined above, and strip it into a list of Pauli words and a list of coefficients which will be uses later.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Collect coefficients from a spin operator so they can pass to a kernel</span>
-<span class="k">def</span> <span class="nf">termCoefficients</span><span class="p">(</span><span class="n">ham</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">]:</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ham</span><span class="o">.</span><span class="n">for_each_term</span><span class="p">(</span><span class="k">lambda</span> <span class="n">term</span><span class="p">:</span> <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">term</span><span class="o">.</span><span class="n">get_coefficient</span><span class="p">()))</span>
-    <span class="k">return</span> <span class="n">result</span>
-
-
-<span class="c1"># Collect Pauli words from a spin operator so they can pass to a kernel</span>
-<span class="k">def</span> <span class="nf">termWords</span><span class="p">(</span><span class="n">ham</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ham</span><span class="o">.</span><span class="n">for_each_term</span><span class="p">(</span><span class="k">lambda</span> <span class="n">term</span><span class="p">:</span> <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">term</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="kc">False</span><span class="p">)))</span>
-    <span class="k">return</span> <span class="n">result</span>
-
-
-<span class="c1"># Build the lists of coefficients and Pauli Words from the H2 Hamiltonian</span>
-<span class="n">coefficient</span> <span class="o">=</span> <span class="n">termCoefficients</span><span class="p">(</span><span class="n">hamiltonian</span><span class="p">)</span>
-<span class="n">pauli_string</span> <span class="o">=</span> <span class="n">termWords</span><span class="p">(</span><span class="n">hamiltonian</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">coefficient</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">pauli_string</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>In this example, the unitary operators that build the Krylov subspace are first-order Trotter operations at different time steps. The performance here could potentially be improved by increasing the size of the time step, using a higher order Trotter approximation, or using other sorts of approximations. The CUDA-Q kernels below define the unitary operations that construct the <span class="math notranslate nohighlight">\(\psi\)</span> basis. Each receives the target qubits, the time step, and components of the Hamiltonian.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Applies Unitary operation corresponding to Um</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">U_m</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">],</span>
-        <span class="n">words</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">pauli_word</span><span class="p">]):</span>
-    <span class="c1"># Compute U_m = exp(-i m dt H)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)):</span>
-        <span class="n">exp_pauli</span><span class="p">(</span><span class="n">dt</span> <span class="o">*</span> <span class="n">coefficients</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">qubits</span><span class="p">,</span> <span class="n">words</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-
-<span class="c1"># Applies Unitary operation corresponding to Un</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">U_n</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">],</span>
-        <span class="n">words</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">pauli_word</span><span class="p">]):</span>
-    <span class="c1"># Compute U_n = exp(-i n dt H)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)):</span>
-        <span class="n">exp_pauli</span><span class="p">(</span><span class="n">dt</span> <span class="o">*</span> <span class="n">coefficients</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">qubits</span><span class="p">,</span> <span class="n">words</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-
-<span class="c1"># Applies the gate operations for a Hamiltonian Pauli Word</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">apply_pauli</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">word</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-    <span class="c1"># Add H (Hamiltonian operator)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">word</span><span class="p">)):</span>
-        <span class="k">if</span> <span class="n">word</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">word</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="n">y</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">word</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="n">z</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-
-<span class="c1"># Performs Hadamard test circuit which determines matrix elements of S and H of subspace</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">qfd_kernel</span><span class="p">(</span><span class="n">dt_alpha</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">dt_beta</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">],</span>
-               <span class="n">words</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">pauli_word</span><span class="p">],</span> <span class="n">word_list</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
-               <span class="n">vec</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">]):</span>
-
-    <span class="n">ancilla</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">qreg</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-
-    <span class="n">x</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">U_m</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">qreg</span><span class="p">,</span> <span class="n">dt_alpha</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">,</span> <span class="n">words</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">apply_pauli</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">qreg</span><span class="p">,</span> <span class="n">word_list</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">U_n</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">qreg</span><span class="p">,</span> <span class="n">dt_beta</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">,</span> <span class="n">words</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The auxillary function below takes a Pauli word, and converts it to a list of integers which informs applications of this word to a circuit with the <code class="docutils literal notranslate"><span class="pre">apply_pauli</span></code> kernel.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">pauli_str</span><span class="p">(</span><span class="n">pauli_word</span><span class="p">,</span> <span class="n">qubits_num</span><span class="p">):</span>
-
-    <span class="n">my_list</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubits_num</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">pauli_word</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="s1">&#39;I&#39;</span><span class="p">:</span>
-            <span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">pauli_word</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="s1">&#39;X&#39;</span><span class="p">:</span>
-            <span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">pauli_word</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="s1">&#39;Y&#39;</span><span class="p">:</span>
-            <span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">pauli_word</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="s1">&#39;Z&#39;</span><span class="p">:</span>
-            <span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">my_list</span>
-</pre></div>
-</div>
-</div>
-<p>The spin operators necessary for the Hadamard test are defined below.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the spin-op x for real component and y for the imaginary component.</span>
-<span class="n">x_0</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">y_0</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Finally, the time step for the unitary operations that build the Krylov space is defined as well as the dimension of the Krylov space.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Define parameters for the quantum Krylov space</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="mf">0.5</span>
-
-<span class="c1"># Dimension of the Krylov space</span>
-<span class="n">m_qfd</span> <span class="o">=</span> <span class="mi">4</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Computing-the-matrix-elements">
-<h2>Computing the matrix elements<a class="headerlink" href="#Computing-the-matrix-elements" title="Permalink to this heading">¶</a></h2>
-<p>The cell below computes the overlap matrix. This can be done in serial or in parallel, depending on the <code class="docutils literal notranslate"><span class="pre">multi_gpu</span></code> specification. First, an operator is built to apply the identity to the overlap matrix circuit when <code class="docutils literal notranslate"><span class="pre">apply_pauli</span></code> is called. Next, the <code class="docutils literal notranslate"><span class="pre">wf_overlap</span></code> array is constructed which will hold the matrix elements.</p>
-<p>Next, a pair of nested loops iterate over the time steps defined by the dimension of the subspace. Each m,n combination corresponds to computation of an off-diagonal matrix element of the overlap matrix <span class="math notranslate nohighlight">\(S\)</span> using the Hadamard test. This is accomplished by calling the CUDA-Q <code class="docutils literal notranslate"><span class="pre">observe</span></code> function with the X and Y operators, along with the time steps, the components of the Hamiltonian matrix, and the initial state vector <code class="docutils literal notranslate"><span class="pre">vec</span></code>.</p>
-<p>The observe function broadcasts over the two provided operators <span class="math notranslate nohighlight">\(X\)</span> and <span class="math notranslate nohighlight">\(Y\)</span> and returns a list of results. The <code class="docutils literal notranslate"><span class="pre">expectation</span></code> function returns the expectation values which are summed and stored in the matrix.</p>
-<p>The multi-gpu case completes the same steps, except the <code class="docutils literal notranslate"><span class="pre">observe_async</span></code> command is used. This allows for the <span class="math notranslate nohighlight">\(X\)</span> and <span class="math notranslate nohighlight">\(Y\)</span> observables to be evaluated at the same time on two different simulated QPUs. In this case, the results are stored in lists corresponding to the real and imaginary parts. These are then accessed later with the <code class="docutils literal notranslate"><span class="pre">get</span></code> command to build <span class="math notranslate nohighlight">\(S\)</span>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## Single GPU:</span>
-<span class="k">if</span> <span class="ow">not</span> <span class="n">multi_gpu</span><span class="p">:</span>
-
-    <span class="c1"># Add identity operator to compute overlap of basis</span>
-    <span class="n">observe_op</span> <span class="o">=</span> <span class="mf">1.0</span>
-    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubits_num</span><span class="p">):</span>
-        <span class="n">observe_op</span> <span class="o">*=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    <span class="n">identity_word</span> <span class="o">=</span> <span class="n">observe_op</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">pauli_list</span> <span class="o">=</span> <span class="n">pauli_str</span><span class="p">(</span><span class="n">identity_word</span><span class="p">,</span> <span class="n">qubits_num</span><span class="p">)</span>
-
-    <span class="c1"># Empty overlap matrix S</span>
-    <span class="n">wf_overlap</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">m_qfd</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
-
-    <span class="c1"># Loop to solve for S matrix elements</span>
-    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m_qfd</span><span class="p">):</span>
-        <span class="n">dt_m</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">m</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">):</span>
-            <span class="n">dt_n</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">n</span>
-            <span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">qfd_kernel</span><span class="p">,</span> <span class="p">[</span><span class="n">x_0</span><span class="p">,</span> <span class="n">y_0</span><span class="p">],</span> <span class="n">dt_m</span><span class="p">,</span> <span class="n">dt_n</span><span class="p">,</span>
-                                    <span class="n">coefficient</span><span class="p">,</span> <span class="n">pauli_string</span><span class="p">,</span> <span class="n">pauli_list</span><span class="p">,</span> <span class="n">vec</span><span class="p">)</span>
-            <span class="n">temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="k">for</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]</span>
-            <span class="n">wf_overlap</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span>
-            <span class="k">if</span> <span class="n">n</span> <span class="o">!=</span> <span class="n">m</span><span class="p">:</span>
-                <span class="n">wf_overlap</span><span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">wf_overlap</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">])</span>
-
-<span class="k">else</span><span class="p">:</span>
-
-    <span class="c1">## Multi-GPU</span>
-
-    <span class="c1"># Add identity operator to compute overlap of basis</span>
-    <span class="n">observe_op</span> <span class="o">=</span> <span class="mf">1.0</span>
-
-    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubits_num</span><span class="p">):</span>
-        <span class="n">observe_op</span> <span class="o">*=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-
-    <span class="n">identity_word</span> <span class="o">=</span> <span class="n">observe_op</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">pauli_list</span> <span class="o">=</span> <span class="n">pauli_str</span><span class="p">(</span><span class="n">identity_word</span><span class="p">,</span> <span class="n">qubits_num</span><span class="p">)</span>
-
-    <span class="c1"># Empty overlap matrix S</span>
-    <span class="n">wf_overlap</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">m_qfd</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
-
-    <span class="c1"># Empty lists to store results</span>
-    <span class="n">collect_overlap_real</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">collect_overlap_img</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="c1"># Loop to compute matrix elements of S</span>
-    <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m_qfd</span><span class="p">):</span>
-        <span class="n">dt_m</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">m</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">):</span>
-            <span class="n">dt_n</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">n</span>
-
-            <span class="c1"># The count variable determines which (simulated) QPU the computation is sent to</span>
-            <span class="c1"># The results are stored in a list corresponding to the real and imaginary parts</span>
-            <span class="n">count_id</span> <span class="o">=</span> <span class="n">count</span> <span class="o">%</span> <span class="mi">2</span>
-            <span class="n">collect_overlap_real</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">qfd_kernel</span><span class="p">,</span>
-                                    <span class="n">x_0</span><span class="p">,</span>
-                                    <span class="n">dt_m</span><span class="p">,</span>
-                                    <span class="n">dt_n</span><span class="p">,</span>
-                                    <span class="n">coefficient</span><span class="p">,</span>
-                                    <span class="n">pauli_string</span><span class="p">,</span>
-                                    <span class="n">pauli_list</span><span class="p">,</span>
-                                    <span class="n">vec</span><span class="p">,</span>
-                                    <span class="n">qpu_id</span><span class="o">=</span><span class="n">count_id</span><span class="p">))</span>
-
-            <span class="n">collect_overlap_img</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">qfd_kernel</span><span class="p">,</span>
-                                    <span class="n">y_0</span><span class="p">,</span>
-                                    <span class="n">dt_m</span><span class="p">,</span>
-                                    <span class="n">dt_n</span><span class="p">,</span>
-                                    <span class="n">coefficient</span><span class="p">,</span>
-                                    <span class="n">pauli_string</span><span class="p">,</span>
-                                    <span class="n">pauli_list</span><span class="p">,</span>
-                                    <span class="n">vec</span><span class="p">,</span>
-                                    <span class="n">qpu_id</span><span class="o">=</span><span class="n">count_id</span> <span class="o">+</span> <span class="mi">2</span><span class="p">))</span>
-            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="n">tot_dim</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="c1"># A second loop takes the lists of results and, using `get`, populates the matrix elements</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m_qfd</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">):</span>
-            <span class="n">observe_result</span> <span class="o">=</span> <span class="n">collect_overlap_real</span><span class="p">[</span><span class="n">tot_dim</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-            <span class="n">real_val</span> <span class="o">=</span> <span class="n">observe_result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-            <span class="n">observe_result</span> <span class="o">=</span> <span class="n">collect_overlap_img</span><span class="p">[</span><span class="n">tot_dim</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-            <span class="n">img_val</span> <span class="o">=</span> <span class="n">observe_result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-            <span class="n">wf_overlap</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">real_val</span> <span class="o">+</span> <span class="n">img_val</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span>
-            <span class="k">if</span> <span class="n">n</span> <span class="o">!=</span> <span class="n">m</span><span class="p">:</span>
-                <span class="n">wf_overlap</span><span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">wf_overlap</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">])</span>
-
-            <span class="n">tot_dim</span> <span class="o">+=</span> <span class="mi">1</span>
-</pre></div>
-</div>
-</div>
-<p>The Hamiltonian matrix elements are computed in the same way, except this time the Hamiltonian is applied as part of the circuit. This is accomplished with the extra for loop, which iterates through the terms in the Hamiltonian, computing an expectation value for each one and then summing the results to produce one matrix element.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## Single GPU:</span>
-<span class="k">if</span> <span class="ow">not</span> <span class="n">multi_gpu</span><span class="p">:</span>
-
-    <span class="c1"># Empty H</span>
-    <span class="n">ham_matrx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">m_qfd</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
-
-    <span class="c1"># Loops over H matrix terms</span>
-    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m_qfd</span><span class="p">):</span>
-        <span class="n">dt_m</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">m</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">):</span>
-            <span class="n">dt_n</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">n</span>
-
-            <span class="c1"># 2 entry array that stores real and imaginary part of matrix element</span>
-            <span class="n">tot_e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-
-            <span class="c1"># Loops over the terms in the Hamiltonian, computing expectation values</span>
-            <span class="k">for</span> <span class="n">coef</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">coefficient</span><span class="p">,</span> <span class="n">pauli_string</span><span class="p">):</span>
-                <span class="c1">#print(coef,word)</span>
-
-                <span class="n">pauli_list</span> <span class="o">=</span> <span class="n">pauli_str</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">qubits_num</span><span class="p">)</span>
-                <span class="c1">#print(pauli_list)</span>
-
-                <span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">qfd_kernel</span><span class="p">,</span> <span class="p">[</span><span class="n">x_0</span><span class="p">,</span> <span class="n">y_0</span><span class="p">],</span> <span class="n">dt_m</span><span class="p">,</span> <span class="n">dt_n</span><span class="p">,</span>
-                                        <span class="n">coefficient</span><span class="p">,</span> <span class="n">pauli_string</span><span class="p">,</span> <span class="n">pauli_list</span><span class="p">,</span>
-                                        <span class="n">vec</span><span class="p">)</span>
-
-                <span class="n">temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="k">for</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]</span>
-
-                <span class="c1"># Multiplies result by coefficient corresponding to Pauli Word</span>
-                <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">coef</span><span class="o">.</span><span class="n">real</span> <span class="o">*</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">coef</span><span class="o">.</span><span class="n">imag</span> <span class="o">*</span> <span class="n">temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-                <span class="c1"># Accumulates results for each Pauli Word</span>
-                <span class="n">tot_e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="n">tot_e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="c1"># Sums real and imaginary totals to specify Hamiltonian entry</span>
-            <span class="n">ham_matrx</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">tot_e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">tot_e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span>
-            <span class="k">if</span> <span class="n">n</span> <span class="o">!=</span> <span class="n">m</span><span class="p">:</span>
-                <span class="n">ham_matrx</span><span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">ham_matrx</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">])</span>
-<span class="k">else</span><span class="p">:</span>
-
-    <span class="c1">## Multi-GPU</span>
-
-    <span class="c1"># Empty H</span>
-    <span class="n">ham_matrx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">m_qfd</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
-
-    <span class="c1"># Loops over H matrix terms</span>
-    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m_qfd</span><span class="p">):</span>
-        <span class="n">dt_m</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">m</span>
-        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">m_qfd</span><span class="p">):</span>
-            <span class="n">dt_n</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">n</span>
-
-            <span class="c1">#List storing results from real and imaginary circuit evaluations for each Pauli word</span>
-            <span class="n">ham_matrix_real</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">ham_matrix_imag</span> <span class="o">=</span> <span class="p">[]</span>
-
-            <span class="c1"># Loop to asynchronously compute real and imaginary parts for each Pauli word on two GPUs</span>
-            <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">tot_e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">coef</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">coefficient</span><span class="p">,</span> <span class="n">pauli_string</span><span class="p">):</span>
-                <span class="n">count_id</span> <span class="o">=</span> <span class="n">count</span> <span class="o">%</span> <span class="mi">2</span>
-                <span class="c1">#print(coef,word)</span>
-
-                <span class="n">pauli_list</span> <span class="o">=</span> <span class="n">pauli_str</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">qubits_num</span><span class="p">)</span>
-                <span class="c1">#print(pauli_list)</span>
-
-                <span class="n">ham_matrix_real</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">qfd_kernel</span><span class="p">,</span>
-                                        <span class="n">x_0</span><span class="p">,</span>
-                                        <span class="n">dt_m</span><span class="p">,</span>
-                                        <span class="n">dt_n</span><span class="p">,</span>
-                                        <span class="n">coefficient</span><span class="p">,</span>
-                                        <span class="n">pauli_string</span><span class="p">,</span>
-                                        <span class="n">pauli_list</span><span class="p">,</span>
-                                        <span class="n">vec</span><span class="p">,</span>
-                                        <span class="n">qpu_id</span><span class="o">=</span><span class="n">count_id</span><span class="p">))</span>
-                <span class="n">ham_matrix_imag</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">qfd_kernel</span><span class="p">,</span>
-                                        <span class="n">y_0</span><span class="p">,</span>
-                                        <span class="n">dt_m</span><span class="p">,</span>
-                                        <span class="n">dt_n</span><span class="p">,</span>
-                                        <span class="n">coefficient</span><span class="p">,</span>
-                                        <span class="n">pauli_string</span><span class="p">,</span>
-                                        <span class="n">pauli_list</span><span class="p">,</span>
-                                        <span class="n">vec</span><span class="p">,</span>
-                                        <span class="n">qpu_id</span><span class="o">=</span><span class="n">count_id</span> <span class="o">+</span> <span class="mi">2</span><span class="p">))</span>
-
-                <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
-
-            <span class="c1"># Loops through coefficients to sum the real and imaginary parts of H matrix element</span>
-            <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">coef</span> <span class="ow">in</span> <span class="n">coefficient</span><span class="p">:</span>
-
-                <span class="n">observe_result</span> <span class="o">=</span> <span class="n">ham_matrix_real</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-                <span class="n">real_val</span> <span class="o">=</span> <span class="n">observe_result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-                <span class="n">observe_result</span> <span class="o">=</span> <span class="n">ham_matrix_imag</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-                <span class="n">img_val</span> <span class="o">=</span> <span class="n">observe_result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-                <span class="n">tot_e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">real_val</span> <span class="o">*</span> <span class="n">coef</span><span class="o">.</span><span class="n">real</span>
-                <span class="n">tot_e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">img_val</span> <span class="o">*</span> <span class="n">coef</span><span class="o">.</span><span class="n">imag</span>
-
-                <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
-
-            <span class="c1"># Enter final H matrix element</span>
-            <span class="n">ham_matrx</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">tot_e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">tot_e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span>
-            <span class="k">if</span> <span class="n">n</span> <span class="o">!=</span> <span class="n">m</span><span class="p">:</span>
-                <span class="n">ham_matrx</span><span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">ham_matrx</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Determining-the-ground-state-energy-of-the-subspace">
-<h2>Determining the ground state energy of the subspace<a class="headerlink" href="#Determining-the-ground-state-energy-of-the-subspace" title="Permalink to this heading">¶</a></h2>
-<p>The final step is to solve the generalized eigenvaulue problem with the overlap and Hamiltonian matrices constructed using the quantum computer. The procedure begins by diagonalizing <span class="math notranslate nohighlight">\(S\)</span> with the transform</p>
-<div class="math notranslate nohighlight">
-\[S = U\Sigma U^{\dagger}\]</div>
-<p>The eigenvectors <span class="math notranslate nohighlight">\(v\)</span> and eigenvalues <span class="math notranslate nohighlight">\(s\)</span> are used to construct a new matrix <span class="math notranslate nohighlight">\(X':\)</span></p>
-<div class="math notranslate nohighlight">
-\[X' = S ^{\frac{-1}{2}} = \sum_k v_{ki} \frac{1}{\sqrt{s_k}} v_{kj}.\]</div>
-<p>The matrix <span class="math notranslate nohighlight">\(X'\)</span> diagonalizes <span class="math notranslate nohighlight">\(H:\)</span></p>
-<div class="math notranslate nohighlight">
-\[X'^{\dagger}HX' = ES^{\frac{1}{2}}C.\]</div>
-<p>Using the eigenvectors of <span class="math notranslate nohighlight">\(H'\)</span>, (<span class="math notranslate nohighlight">\(^{\frac{1}{2}}C\)</span>), the original eigenvectors to the problem can be found by left multiplying by <span class="math notranslate nohighlight">\(S^{\frac{-1}{2}}C.\)</span></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">eig</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
-    <span class="c1">#Solver for generalized eigenvalue problem</span>
-    <span class="c1"># HC = SCE</span>
-
-    <span class="n">THRESHOLD</span> <span class="o">=</span> <span class="mf">1e-20</span>
-    <span class="n">s_diag</span><span class="p">,</span> <span class="n">u</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-    <span class="n">s_prime</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">sii</span> <span class="ow">in</span> <span class="n">s_diag</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">sii</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">1e-7</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;S may not be hermitian, large imag. eval component.&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">sii</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">THRESHOLD</span><span class="p">:</span>
-            <span class="n">s_prime</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">sii</span><span class="p">))</span>
-
-    <span class="n">X_prime</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">s_diag</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">s_prime</span><span class="p">)),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s_diag</span><span class="p">)):</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s_prime</span><span class="p">)):</span>
-            <span class="n">X_prime</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">u</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">s_prime</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-
-    <span class="n">H_prime</span> <span class="o">=</span> <span class="p">(((</span><span class="n">X_prime</span><span class="o">.</span><span class="n">conjugate</span><span class="p">())</span><span class="o">.</span><span class="n">transpose</span><span class="p">())</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">H</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X_prime</span><span class="p">)</span>
-    <span class="n">e_prime</span><span class="p">,</span> <span class="n">C_prime</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">H_prime</span><span class="p">)</span>
-    <span class="n">C</span> <span class="o">=</span> <span class="n">X_prime</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">C_prime</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">e_prime</span><span class="p">,</span> <span class="n">C</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">eigen_value</span><span class="p">,</span> <span class="n">eigen_vect</span> <span class="o">=</span> <span class="n">eig</span><span class="p">(</span><span class="n">ham_matrx</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">m_qfd</span><span class="p">,</span> <span class="mi">0</span><span class="p">:</span><span class="n">m_qfd</span><span class="p">],</span> <span class="n">wf_overlap</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">m_qfd</span><span class="p">,</span>
-                                                                      <span class="mi">0</span><span class="p">:</span><span class="n">m_qfd</span><span class="p">])</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Energy from QFD:&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">eigen_value</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-
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diff --git a/pr-2308/applications/python/krylov.ipynb b/pr-2308/applications/python/krylov.ipynb
deleted file mode 100644
index 183a13d691..0000000000
--- a/pr-2308/applications/python/krylov.ipynb
+++ /dev/null
@@ -1,634 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "16e3036d-b1be-43b8-8838-95dfc14cad60",
-   "metadata": {},
-   "source": [
-    "# Multi-reference Quantum Krylov Algorithm - $H_2$ Molecule\n",
-    "\n",
-    "The multireference selected quantum Krylov (MRSQK) algorithm is defined in [this paper](https://arxiv.org/pdf/1911.05163) and was developed as a low-cost alternative to quantum phase estimation. This tutorial will demonstrate how this algorithm can be implemented in CUDA-Q and accelerated using multiple GPUs. The [CUDA-Q Hadamard test tutorial](https://nvidia.github.io/cuda-quantum/latest/applications/python/hadamard_test.html) might provide helpful background information for understanding this tutorial.\n",
-    "\n",
-    "The algorithm works by preparing an initial state, and then defining this state in a smaller subspace constructed with a basis that corresponds to Trotter steps of the initial state. This subspace can be diagonalized to produce an approximate energy for the system without variational optimization of any parameters.\n",
-    "\n",
-    "In the example below, the initial guess is the ground state of the diagonalized Hamiltonian for demonstration purposes.  In practice one could use a number of heuristics to prepare the ground state such as Hartree Fock or CISD. A very promising ground state preparation method which can leverage quantum computers is the linear combination of unitaries (LCU). LCU would allow for the state preparation to occur completely on the quantum computer and avoid storing an inputting the exponentially large state vector.\n",
-    "\n",
-    "\n",
-    "Regardless of the method used for state preparation, the procedure begins by selecting a $d$-dimensional basis of reference states ${\\Phi_0 \\cdots \\Phi_d},$ where each is a linear combination of Slater determinants: \n",
-    "\n",
-    "$$ \\ket{\\Phi_I}  =  \\sum_{\\mu} d_{\\mu I}\\ket{\\phi_{\\mu}}. $$\n",
-    "\n",
-    "\n",
-    "From this, a non-orthogonal Krylov Space $\\mathcal{K} = \\{\\psi_{0} \\cdots \\psi_{N}\\}$ is constructed by applying a family of $s$ unitary operators on each of the $d$ reference states resulting in $d*s = N$ elements in the Krylov space where \n",
-    "$$ \\ket{\\psi_{\\alpha}} \\equiv \\ket{\\psi_I^{(n)}} = \\hat{U}_n\\ket{\\Phi_I}, $$\n",
-    "\n",
-    "Therefore, the general quantum state that we originally set out to describe is\n",
-    "\n",
-    "$$ \\ket{\\Psi} = \\sum_{\\alpha} c_{\\alpha}\\ket{\\psi_{\\alpha}} = \\sum_{I=0}^d \\sum_{n=0}^s c_I^{(n)}\\hat{U}_n\\ket{\\Phi_I}.  $$\n",
-    "\n",
-    "The energy of this state can be obtained by solving the generalized eigenvalue problem\n",
-    "$$ \\boldsymbol{Hc}=\\boldsymbol{Sc}E, $$\n",
-    "\n",
-    "where the elements of the overlap are\n",
-    "\n",
-    "$$S_{\\alpha \\beta} = \\braket{\\psi_{\\alpha}|\\psi_{\\beta}} =   \\braket{\\Phi_I|\\hat{U}_m^{\\dagger}\\hat{U}_n|\\Phi_J}$$ \n",
-    "\n",
-    "and Hamiltonian matrix is\n",
-    "\n",
-    "$$H_{\\alpha \\beta} = \\braket{\\psi_{\\alpha}|\\hat{H}|\\psi_{\\beta}} =   \\braket{\\Phi_I|\\hat{U}_m^{\\dagger}\\hat{H}\\hat{U}_n|\\Phi_J}.$$\n",
-    "\n",
-    "The matrix elements for $S$ are computed with the Hadamard test with a circuit shown below for the case of the overlap matrix elements. \n",
-    "\n",
-    "![Htest](./images/krylovcircuit.png)\n",
-    "\n",
-    "The $2\\sigma_+$ term refers to measurement of the expectation value of this circuit with the $X+iY$ operator.\n",
-    "\n",
-    "The Hamiltonian matrix elements are computed with a circuit that includes controlled application of the Hamiltonian. Once the $H$ and $S$ matrices are constructed, the diagonalization is performed classically to produce an estimate for the ground state in question.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b90de056-4dc5-473f-bf50-29c7d8b2ad05",
-   "metadata": {},
-   "source": [
-    "### Setup\n",
-    "\n",
-    "This cell installs the necessary packages. This application can be parallelized, so please uncomment the `mgpu` specification below if you would like to run on more than one GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba61665c-dc3b-4e43-b1cf-340855ea68fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "import scipy\n",
-    "\n",
-    "!pip install openfermion==1.6.1 pyscf==2.6.2 openfermionpyscf==0.5 -q\n",
-    "\n",
-    "# Single-node, single gpu\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "multi_gpu = False\n",
-    "\n",
-    "# Single-node, multi-GPU\n",
-    "#cudaq.set_target(\"nvidia\", option='mqpu,fp64')\n",
-    "#multi_gpu = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ba6e94e-a3e6-48aa-8392-e598429b0be7",
-   "metadata": {},
-   "source": [
-    "The molecule is defined below and its Hamiltonian is extracted as a matrix. The matrix is diagonalized to produce the ground state. The corresponding state vector will be used as the initial state to ensure good results for this demonstration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fb4192c-054a-4bff-b86f-a8d58cf8bac6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define H2 molecule\n",
-    "geometry = [('H', (0.0, 0.0, 0.0)), ('H', (0.0, 0.0, 0.7474))]\n",
-    "\n",
-    "hamiltonian, data = cudaq.chemistry.create_molecular_hamiltonian(\n",
-    "    geometry, 'sto-3g', 1, 0)\n",
-    "\n",
-    "electron_count = data.n_electrons\n",
-    "qubits_num = 2 * data.n_orbitals\n",
-    "\n",
-    "# Diagonalize Hamiltonian\n",
-    "spin_ham_matrix = hamiltonian.to_matrix()\n",
-    "e, c = np.linalg.eig(spin_ham_matrix)\n",
-    "\n",
-    "# Find the ground state energy and the corresponding eigenvector\n",
-    "print('Ground state energy (classical simulation)= ', np.min(e), ', index= ',\n",
-    "      np.argmin(e))\n",
-    "min_indices = np.argmin(e)\n",
-    "\n",
-    "# Eigenvector can be used to initialize the qubits\n",
-    "vec = c[:, min_indices]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "562f795e-cf4c-4e43-a0d9-3a3ae346277d",
-   "metadata": {},
-   "source": [
-    "The functions below take the original Hamiltonian defined above, and strip it into a list of Pauli words and a list of coefficients which will be uses later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "014e72eb-ee66-48f5-bb58-73210e621fad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Collect coefficients from a spin operator so they can pass to a kernel\n",
-    "def termCoefficients(ham: cudaq.SpinOperator) -> list[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Collect Pauli words from a spin operator so they can pass to a kernel\n",
-    "def termWords(ham: cudaq.SpinOperator) -> list[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Build the lists of coefficients and Pauli Words from the H2 Hamiltonian\n",
-    "coefficient = termCoefficients(hamiltonian)\n",
-    "pauli_string = termWords(hamiltonian)\n",
-    "\n",
-    "print(coefficient)\n",
-    "print(pauli_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74b4c079-8837-47ae-a484-52ec5f4a160e",
-   "metadata": {},
-   "source": [
-    "In this example, the unitary operators that build the Krylov subspace are first-order Trotter operations at different time steps. The performance here could potentially be improved by increasing the size of the time step, using a higher order Trotter approximation, or using other sorts of approximations. The CUDA-Q kernels below define the unitary operations that construct the $\\psi$ basis.  Each receives the target qubits, the time step, and components of the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c98db2b7-eaaf-4e3a-83bb-4f7ad0730471",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Applies Unitary operation corresponding to Um\n",
-    "@cudaq.kernel\n",
-    "def U_m(qubits: cudaq.qview, dt: float, coefficients: list[complex],\n",
-    "        words: list[cudaq.pauli_word]):\n",
-    "    # Compute U_m = exp(-i m dt H)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(dt * coefficients[i].real, qubits, words[i])\n",
-    "\n",
-    "\n",
-    "# Applies Unitary operation corresponding to Un\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_n(qubits: cudaq.qview, dt: float, coefficients: list[complex],\n",
-    "        words: list[cudaq.pauli_word]):\n",
-    "    # Compute U_n = exp(-i n dt H)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(dt * coefficients[i].real, qubits, words[i])\n",
-    "\n",
-    "\n",
-    "# Applies the gate operations for a Hamiltonian Pauli Word\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def apply_pauli(qubits: cudaq.qview, word: list[int]):\n",
-    "    # Add H (Hamiltonian operator)\n",
-    "    for i in range(len(word)):\n",
-    "        if word[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "        if word[i] == 2:\n",
-    "            y(qubits[i])\n",
-    "        if word[i] == 3:\n",
-    "            z(qubits[i])\n",
-    "\n",
-    "\n",
-    "# Performs Hadamard test circuit which determines matrix elements of S and H of subspace\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qfd_kernel(dt_alpha: float, dt_beta: float, coefficients: list[complex],\n",
-    "               words: list[cudaq.pauli_word], word_list: list[int],\n",
-    "               vec: list[complex]):\n",
-    "\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    qreg = cudaq.qvector(vec)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    x(ancilla)\n",
-    "    cudaq.control(U_m, ancilla, qreg, dt_alpha, coefficients, words)\n",
-    "    x(ancilla)\n",
-    "\n",
-    "    cudaq.control(apply_pauli, ancilla, qreg, word_list)\n",
-    "    cudaq.control(U_n, ancilla, qreg, dt_beta, coefficients, words)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c8f9fb4-4974-44bf-aa8c-c9e86ffb0dd4",
-   "metadata": {},
-   "source": [
-    "The auxillary function below takes a Pauli word, and converts it to a list of integers which informs applications of this word to a circuit with the `apply_pauli` kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5bed072a-9fb6-4394-9759-b45ba292d0a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pauli_str(pauli_word, qubits_num):\n",
-    "\n",
-    "    my_list = []\n",
-    "    for i in range(qubits_num):\n",
-    "        if str(pauli_word[i]) == 'I':\n",
-    "            my_list.append(0)\n",
-    "        if str(pauli_word[i]) == 'X':\n",
-    "            my_list.append(1)\n",
-    "        if str(pauli_word[i]) == 'Y':\n",
-    "            my_list.append(2)\n",
-    "        if str(pauli_word[i]) == 'Z':\n",
-    "            my_list.append(3)\n",
-    "    return my_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "999675e2-a5b0-43d1-ba80-827f1c81b3dc",
-   "metadata": {},
-   "source": [
-    "The spin operators necessary for the Hadamard test are defined below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "297dbc29-e48a-411b-bc42-4fabe180a641",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the spin-op x for real component and y for the imaginary component.\n",
-    "x_0 = cudaq.spin.x(0)\n",
-    "y_0 = cudaq.spin.y(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1880e50d-6d27-44e4-b27e-329d8c499bfb",
-   "metadata": {},
-   "source": [
-    "Finally, the time step for the unitary operations that build the Krylov space is defined as well as the dimension of the Krylov space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3ae6b210-13c4-40ba-be73-4fb906a1f7ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Define parameters for the quantum Krylov space\n",
-    "dt = 0.5\n",
-    "\n",
-    "# Dimension of the Krylov space\n",
-    "m_qfd = 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d59f13b-e9f8-44e6-b270-be4fc6a80c01",
-   "metadata": {},
-   "source": [
-    "### Computing the matrix elements \n",
-    "\n",
-    "The cell below computes the overlap matrix. This can be done in serial or in parallel, depending on the `multi_gpu` specification.  First, an operator is built to apply the identity to the overlap matrix circuit when `apply_pauli` is called. Next, the `wf_overlap` array is constructed which will hold the matrix elements. \n",
-    "\n",
-    "Next, a pair of nested loops iterate over the time steps defined by the dimension of the subspace.  Each m,n combination corresponds to computation of an off-diagonal matrix element of the overlap matrix $S$ using the Hadamard test.  This is accomplished by calling the CUDA-Q `observe` function with the X and Y operators, along with the time steps, the components of the Hamiltonian matrix, and the initial state vector `vec`.\n",
-    "\n",
-    "The observe function broadcasts over the two provided operators $X$ and $Y$ and returns a list of results. The `expectation` function returns the expectation values which are summed and stored in the matrix.\n",
-    "\n",
-    "The multi-gpu case completes the same steps, except the `observe_async` command is used. This allows for the $X$ and $Y$ observables to be evaluated at the same time on two different simulated QPUs. In this case, the results are stored in lists corresponding to the real and imaginary parts. These are then accessed later with the `get` command to build $S$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "875124e8-dba8-4ace-828b-d4b03352f9d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Single GPU:\n",
-    "if not multi_gpu:\n",
-    "\n",
-    "    # Add identity operator to compute overlap of basis\n",
-    "    observe_op = 1.0\n",
-    "    for m in range(qubits_num):\n",
-    "        observe_op *= cudaq.spin.i(m)\n",
-    "    identity_word = observe_op.to_string(False)\n",
-    "    pauli_list = pauli_str(identity_word, qubits_num)\n",
-    "\n",
-    "    # Empty overlap matrix S\n",
-    "    wf_overlap = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loop to solve for S matrix elements\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "            results = cudaq.observe(qfd_kernel, [x_0, y_0], dt_m, dt_n,\n",
-    "                                    coefficient, pauli_string, pauli_list, vec)\n",
-    "            temp = [result.expectation() for result in results]\n",
-    "            wf_overlap[m, n] = temp[0] + temp[1] * 1j\n",
-    "            if n != m:\n",
-    "                wf_overlap[n, m] = np.conj(wf_overlap[m, n])\n",
-    "\n",
-    "else:\n",
-    "\n",
-    "    ## Multi-GPU\n",
-    "\n",
-    "    # Add identity operator to compute overlap of basis\n",
-    "    observe_op = 1.0\n",
-    "\n",
-    "    for m in range(qubits_num):\n",
-    "        observe_op *= cudaq.spin.i(m)\n",
-    "\n",
-    "    identity_word = observe_op.to_string(False)\n",
-    "    pauli_list = pauli_str(identity_word, qubits_num)\n",
-    "\n",
-    "    # Empty overlap matrix S\n",
-    "    wf_overlap = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Empty lists to store results\n",
-    "    collect_overlap_real = []\n",
-    "    collect_overlap_img = []\n",
-    "\n",
-    "    # Loop to compute matrix elements of S\n",
-    "    count = 0\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            # The count variable determines which (simulated) QPU the computation is sent to\n",
-    "            # The results are stored in a list corresponding to the real and imaginary parts\n",
-    "            count_id = count % 2\n",
-    "            collect_overlap_real.append(\n",
-    "                cudaq.observe_async(qfd_kernel,\n",
-    "                                    x_0,\n",
-    "                                    dt_m,\n",
-    "                                    dt_n,\n",
-    "                                    coefficient,\n",
-    "                                    pauli_string,\n",
-    "                                    pauli_list,\n",
-    "                                    vec,\n",
-    "                                    qpu_id=count_id))\n",
-    "\n",
-    "            collect_overlap_img.append(\n",
-    "                cudaq.observe_async(qfd_kernel,\n",
-    "                                    y_0,\n",
-    "                                    dt_m,\n",
-    "                                    dt_n,\n",
-    "                                    coefficient,\n",
-    "                                    pauli_string,\n",
-    "                                    pauli_list,\n",
-    "                                    vec,\n",
-    "                                    qpu_id=count_id + 2))\n",
-    "            count += 1\n",
-    "\n",
-    "    tot_dim = 0\n",
-    "\n",
-    "    # A second loop takes the lists of results and, using `get`, populates the matrix elements\n",
-    "    for n in range(m_qfd):\n",
-    "        for m in range(n, m_qfd):\n",
-    "            observe_result = collect_overlap_real[tot_dim].get()\n",
-    "            real_val = observe_result.expectation()\n",
-    "\n",
-    "            observe_result = collect_overlap_img[tot_dim].get()\n",
-    "            img_val = observe_result.expectation()\n",
-    "\n",
-    "            wf_overlap[m, n] = real_val + img_val * 1j\n",
-    "            if n != m:\n",
-    "                wf_overlap[n, m] = np.conj(wf_overlap[m, n])\n",
-    "\n",
-    "            tot_dim += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fa0e0f1-0705-4dd6-a354-30db1526e266",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian matrix elements are computed in the same way, except this time the Hamiltonian is applied as part of the circuit. This is accomplished with the extra for loop, which iterates through the terms in the Hamiltonian, computing an expectation value for each one and then summing the results to produce one matrix element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "562a6063-b0fe-4d69-a4f4-c7d5309436da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Single GPU:\n",
-    "if not multi_gpu:\n",
-    "\n",
-    "    # Empty H\n",
-    "    ham_matrx = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loops over H matrix terms\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            # 2 entry array that stores real and imaginary part of matrix element\n",
-    "            tot_e = np.zeros(2)\n",
-    "\n",
-    "            # Loops over the terms in the Hamiltonian, computing expectation values\n",
-    "            for coef, word in zip(coefficient, pauli_string):\n",
-    "                #print(coef,word)\n",
-    "\n",
-    "                pauli_list = pauli_str(word, qubits_num)\n",
-    "                #print(pauli_list)\n",
-    "\n",
-    "                results = cudaq.observe(qfd_kernel, [x_0, y_0], dt_m, dt_n,\n",
-    "                                        coefficient, pauli_string, pauli_list,\n",
-    "                                        vec)\n",
-    "\n",
-    "                temp = [result.expectation() for result in results]\n",
-    "\n",
-    "                # Multiplies result by coefficient corresponding to Pauli Word\n",
-    "                temp[0] = coef.real * temp[0]\n",
-    "                temp[1] = coef.imag * temp[1]\n",
-    "\n",
-    "                # Accumulates results for each Pauli Word\n",
-    "                tot_e[0] += temp[0]\n",
-    "                tot_e[1] += temp[1]\n",
-    "\n",
-    "            # Sums real and imaginary totals to specify Hamiltonian entry\n",
-    "            ham_matrx[m, n] = tot_e[0] + tot_e[1] * 1j\n",
-    "            if n != m:\n",
-    "                ham_matrx[n, m] = np.conj(ham_matrx[m, n])\n",
-    "else:\n",
-    "\n",
-    "    ## Multi-GPU\n",
-    "\n",
-    "    # Empty H\n",
-    "    ham_matrx = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loops over H matrix terms\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            #List storing results from real and imaginary circuit evaluations for each Pauli word\n",
-    "            ham_matrix_real = []\n",
-    "            ham_matrix_imag = []\n",
-    "\n",
-    "            # Loop to asynchronously compute real and imaginary parts for each Pauli word on two GPUs\n",
-    "            count = 0\n",
-    "            tot_e = np.zeros(2)\n",
-    "            for coef, word in zip(coefficient, pauli_string):\n",
-    "                count_id = count % 2\n",
-    "                #print(coef,word)\n",
-    "\n",
-    "                pauli_list = pauli_str(word, qubits_num)\n",
-    "                #print(pauli_list)\n",
-    "\n",
-    "                ham_matrix_real.append(\n",
-    "                    cudaq.observe_async(qfd_kernel,\n",
-    "                                        x_0,\n",
-    "                                        dt_m,\n",
-    "                                        dt_n,\n",
-    "                                        coefficient,\n",
-    "                                        pauli_string,\n",
-    "                                        pauli_list,\n",
-    "                                        vec,\n",
-    "                                        qpu_id=count_id))\n",
-    "                ham_matrix_imag.append(\n",
-    "                    cudaq.observe_async(qfd_kernel,\n",
-    "                                        y_0,\n",
-    "                                        dt_m,\n",
-    "                                        dt_n,\n",
-    "                                        coefficient,\n",
-    "                                        pauli_string,\n",
-    "                                        pauli_list,\n",
-    "                                        vec,\n",
-    "                                        qpu_id=count_id + 2))\n",
-    "\n",
-    "                count += 1\n",
-    "\n",
-    "            # Loops through coefficients to sum the real and imaginary parts of H matrix element\n",
-    "            i = 0\n",
-    "            for coef in coefficient:\n",
-    "\n",
-    "                observe_result = ham_matrix_real[i].get()\n",
-    "                real_val = observe_result.expectation()\n",
-    "\n",
-    "                observe_result = ham_matrix_imag[i].get()\n",
-    "                img_val = observe_result.expectation()\n",
-    "\n",
-    "                tot_e[0] += real_val * coef.real\n",
-    "                tot_e[1] += img_val * coef.imag\n",
-    "\n",
-    "                i += 1\n",
-    "\n",
-    "            # Enter final H matrix element\n",
-    "            ham_matrx[m, n] = tot_e[0] + tot_e[1] * 1j\n",
-    "            if n != m:\n",
-    "                ham_matrx[n, m] = np.conj(ham_matrx[m, n])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1917bd58-4ce3-4e3e-9ccf-096577c0da14",
-   "metadata": {},
-   "source": [
-    "### Determining the ground state energy of the subspace\n",
-    "\n",
-    "The final step is to solve the generalized eigenvaulue problem with the overlap and Hamiltonian matrices constructed using the quantum computer. The procedure begins by diagonalizing  $S$ with the transform $$S = U\\Sigma U^{\\dagger}$$\n",
-    "\n",
-    "The eigenvectors $v$ and eigenvalues $s$ are used to construct a new matrix $X':$\n",
-    "\n",
-    "$$ X' = S ^{\\frac{-1}{2}} = \\sum_k v_{ki} \\frac{1}{\\sqrt{s_k}} v_{kj}.$$\n",
-    "\n",
-    "The matrix $X'$ diagonalizes $H:$\n",
-    "\n",
-    "$$ X'^{\\dagger}HX' = ES^{\\frac{1}{2}}C.$$\n",
-    "\n",
-    "Using the eigenvectors of $H'$, ($^{\\frac{1}{2}}C$), the original eigenvectors to the problem can be found by left multiplying by $S^{\\frac{-1}{2}}C.$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8b281533-bf3d-4aed-823b-9d2f926dfe3e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def eig(H, s):\n",
-    "    #Solver for generalized eigenvalue problem\n",
-    "    # HC = SCE\n",
-    "\n",
-    "    THRESHOLD = 1e-20\n",
-    "    s_diag, u = scipy.linalg.eig(s)\n",
-    "    s_prime = []\n",
-    "    for sii in s_diag:\n",
-    "        if np.imag(sii) > 1e-7:\n",
-    "            raise ValueError(\n",
-    "                \"S may not be hermitian, large imag. eval component.\")\n",
-    "        if np.real(sii) > THRESHOLD:\n",
-    "            s_prime.append(np.real(sii))\n",
-    "\n",
-    "    X_prime = np.zeros((len(s_diag), len(s_prime)), dtype=complex)\n",
-    "\n",
-    "    for i in range(len(s_diag)):\n",
-    "        for j in range(len(s_prime)):\n",
-    "            X_prime[i][j] = u[i][j] / np.sqrt(s_prime[j])\n",
-    "\n",
-    "    H_prime = (((X_prime.conjugate()).transpose()).dot(H)).dot(X_prime)\n",
-    "    e_prime, C_prime = scipy.linalg.eig(H_prime)\n",
-    "    C = X_prime.dot(C_prime)\n",
-    "\n",
-    "    return e_prime, C"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74c1fa51-4fae-412b-b90d-262e0b3aaf53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eigen_value, eigen_vect = eig(ham_matrx[0:m_qfd, 0:m_qfd], wf_overlap[0:m_qfd,\n",
-    "                                                                      0:m_qfd])\n",
-    "print('Energy from QFD:')\n",
-    "print(np.min(eigen_value))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/logical_aim_sqale.html b/pr-2308/applications/python/logical_aim_sqale.html
deleted file mode 100644
index ab5b6d4055..0000000000
--- a/pr-2308/applications/python/logical_aim_sqale.html
+++ /dev/null
@@ -1,1930 +0,0 @@
-<!DOCTYPE html>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-  <section id="Anderson-Impurity-Model-ground-state-solver-on-Infleqtion's-Sqale">
-<h1>Anderson Impurity Model ground state solver on Infleqtion’s Sqale<a class="headerlink" href="#Anderson-Impurity-Model-ground-state-solver-on-Infleqtion's-Sqale" title="Permalink to this heading">¶</a></h1>
-<p>Ground state quantum chemistry—computing total energies of molecular configurations to within chemical accuracy—is perhaps the most highly-touted industrial application of fault-tolerant quantum computers. Strongly correlated materials, for example, are particularly interesting, and tools like dynamical mean-field theory (DMFT) allow one to account for the effect of their strong, localized electronic correlations. These DMFT models help predict material properties by approximating the system as
-a single site impurity inside a “bath” that encompasses the rest of the system. Simulating such dynamics can be a tough task using classical methods, but can be done efficiently on a quantum computer via quantum simulation.</p>
-<p>In this notebook, we showcase a workflow for preparing the ground state of the minimal single-impurity Anderson model (SIAM) using the Hamiltonian Variational Ansatz for a range of realistic parameters. As a first step towards running DMFT on a fault-tolerant quantum computer, we will use logical qubits encoded in the <code class="docutils literal notranslate"><span class="pre">[[4,</span> <span class="pre">2,</span> <span class="pre">2]]</span></code> code. Using this workflow, we will obtain the ground state energy estimates via noisy simulation, and then also execute the corresponding optimized circuits on
-Infleqtion’s gate-based neutral-atom quantum computer, making the benefits of logical qubits apparent. More details can be found in our <a class="reference external" href="https://arxiv.org/abs/2412.07670">paper</a>.</p>
-<p>This demo notebook uses CUDA-Q (<code class="docutils literal notranslate"><span class="pre">cudaq</span></code>) and a CUDA-QX library, <code class="docutils literal notranslate"><span class="pre">cudaq-solvers</span></code>; let us first begin by importing (and installing as needed) these packages:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
-    <span class="kn">import</span> <span class="nn">cudaq_solvers</span> <span class="k">as</span> <span class="nn">solvers</span>
-    <span class="kn">import</span> <span class="nn">cudaq</span>
-    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Installing required packages...&quot;</span><span class="p">)</span>
-    <span class="o">%</span><span class="k">pip</span> install --quiet &#39;cudaq-solvers&#39; &#39;matplotlib&#39;
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Installed `cudaq`, `cudaq-solvers`, and `matplotlib` packages.&quot;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;You may need to restart the kernel to import newly installed packages.&quot;</span><span class="p">)</span>
-    <span class="kn">import</span> <span class="nn">cudaq_solvers</span> <span class="k">as</span> <span class="nn">solvers</span>
-    <span class="kn">import</span> <span class="nn">cudaq</span>
-    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Mapping</span><span class="p">,</span> <span class="n">Sequence</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
-<span class="kn">import</span> <span class="nn">os</span>
-</pre></div>
-</div>
-</div>
-<section id="Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">
-<h2>Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX<a class="headerlink" href="#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX" title="Permalink to this heading">¶</a></h2>
-<p>To prepare our ground state quantum Anderson impurity model circuits (referred to as AIM circuits in this notebook for short), we use VQE to train an ansatz to minimize a Hamiltonian and obtain optimal angles that can be used to set the AIM circuits. As described in our <a class="reference external" href="https://arxiv.org/abs/2412.07670">paper</a>, the associated restricted Hamiltonian for our SIAM can be reduced to,</p>
-<div class="math notranslate nohighlight">
-\[\begin{equation}
-H_{(U, V)} = U (Z_0 Z_2 - 1) / 4 + V (X_0 + X_2),
-\end{equation}\]</div>
-<p>where <span class="math notranslate nohighlight">\(U\)</span> is the Coulomb interaction and <span class="math notranslate nohighlight">\(V\)</span> the hybridization strength. In this notebook workflow, we will optimize over a 2-dimensional grid of Hamiltonian parameter values, namely <span class="math notranslate nohighlight">\(U\in \{1, 5, 9\}\)</span> and <span class="math notranslate nohighlight">\(V\in \{-9, -1, 7\}\)</span> (with all values assumed to be in units of eV), to ensure that the ansatz is generally trainable and expressive, and obtain 9 different circuit layers identified by the key <span class="math notranslate nohighlight">\((U, V)\)</span>. We will simulate the VQE on GPU (or optionally on CPU if you
-do not have GPU access), enabled by CUDA-Q, in the absence of noise:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;qpp-cpu&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;fp64&quot;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;fp64&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>This workflow can be easily defined in CUDA-Q as shown in the cell below, using the CUDA-QX Solvers library (which accelerates quantum algorithms like the VQE):</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">ansatz</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
-    <span class="c1"># Create a CUDA-Q parameterized kernel</span>
-    <span class="n">paramterized_ansatz</span><span class="p">,</span> <span class="n">variational_angles</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">)</span>
-
-    <span class="c1"># Using |+&gt; as the initial state:</span>
-    <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">rx</span><span class="p">(</span><span class="n">variational_angles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">rz</span><span class="p">(</span><span class="n">variational_angles</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">paramterized_ansatz</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">paramterized_ansatz</span>
-
-
-<span class="k">def</span> <span class="nf">run_logical_vqe</span><span class="p">(</span><span class="n">cudaq_hamiltonian</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]]:</span>
-    <span class="c1"># Set seed for easier reproduction</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-
-    <span class="c1"># Initial angles for the optimizer</span>
-    <span class="n">init_angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e-1</span>
-
-    <span class="c1"># Obtain CUDA-Q Ansatz</span>
-    <span class="n">num_qubits</span> <span class="o">=</span> <span class="n">cudaq_hamiltonian</span><span class="o">.</span><span class="n">get_qubit_count</span><span class="p">()</span>
-    <span class="n">variational_kernel</span> <span class="o">=</span> <span class="n">ansatz</span><span class="p">(</span><span class="n">num_qubits</span><span class="p">)</span>
-
-    <span class="c1"># Perform VQE optimization</span>
-    <span class="n">energy</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">solvers</span><span class="o">.</span><span class="n">vqe</span><span class="p">(</span>
-        <span class="n">variational_kernel</span><span class="p">,</span>
-        <span class="n">cudaq_hamiltonian</span><span class="p">,</span>
-        <span class="n">init_angles</span><span class="p">,</span>
-        <span class="n">optimizer</span><span class="o">=</span><span class="n">minimize</span><span class="p">,</span>
-        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;SLSQP&quot;</span><span class="p">,</span>
-        <span class="n">tol</span><span class="o">=</span><span class="mf">1e-10</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">energy</span><span class="p">,</span> <span class="n">params</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Constructing-circuits-in-the-[[4,2,2]]-encoding">
-<h2>Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding<a class="headerlink" href="#Constructing-circuits-in-the-[[4,2,2]]-encoding" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> code is a quantum error detection code that uses four physical qubits to encode two logical qubits. In this notebook, we will construct two variants of quantum circuits: physical (bare, unencoded) and logical (encoded). These circuits will be informed by the Hamiltonian Variational Ansatz described earlier. To measure all the terms in our Hamiltonian, we will measure the data qubits in both the <span class="math notranslate nohighlight">\(Z\)</span>- and <span class="math notranslate nohighlight">\(X\)</span>-basis, as allowed by the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> logical gateset.
-Full details on the circuit constructions are outlined in our <a class="reference external" href="https://arxiv.org/abs/2412.07670">paper</a>.</p>
-<p>Below, we create functions to build our CUDA-Q AIM circuits, both physical and logical versions. As we consider noisy simulation in this notebook, we will include some noisy gates. Here, for simplicity, we will just register a custom identity gate – to be later used as a noisy operation to model readout error:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">register_operation</span><span class="p">(</span><span class="s2">&quot;meas_id&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">aim_physical_circuit</span><span class="p">(</span>
-    <span class="n">angles</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">basis</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">ignore_meas_id</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
-    <span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="c1"># Bell state prep</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="c1"># Rx Gate</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">rx</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># ZZ rotation</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">rz</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">basis</span> <span class="o">==</span> <span class="s2">&quot;z_basis&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_meas_id</span><span class="p">:</span>
-            <span class="n">kernel</span><span class="o">.</span><span class="n">for_loop</span><span class="p">(</span>
-                <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="k">lambda</span> <span class="n">q_idx</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="s2">&quot;meas_id&quot;</span><span class="p">)(</span><span class="n">qubits</span><span class="p">[</span><span class="n">q_idx</span><span class="p">])</span>
-            <span class="p">)</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">basis</span> <span class="o">==</span> <span class="s2">&quot;x_basis&quot;</span><span class="p">:</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_meas_id</span><span class="p">:</span>
-            <span class="n">kernel</span><span class="o">.</span><span class="n">for_loop</span><span class="p">(</span>
-                <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="k">lambda</span> <span class="n">q_idx</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="s2">&quot;meas_id&quot;</span><span class="p">)(</span><span class="n">qubits</span><span class="p">[</span><span class="n">q_idx</span><span class="p">])</span>
-            <span class="p">)</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unsupported basis provided:&quot;</span><span class="p">,</span> <span class="n">basis</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">kernel</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">aim_logical_circuit</span><span class="p">(</span>
-    <span class="n">angles</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">basis</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">ignore_meas_id</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
-    <span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
-
-    <span class="n">kernel</span><span class="o">.</span><span class="n">for_loop</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> <span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-
-    <span class="c1"># Rx teleportation</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">rx</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">basis</span> <span class="o">==</span> <span class="s2">&quot;z_basis&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_meas_id</span><span class="p">:</span>
-            <span class="n">kernel</span><span class="o">.</span><span class="n">for_loop</span><span class="p">(</span>
-                <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="s2">&quot;meas_id&quot;</span><span class="p">)(</span><span class="n">qubits</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
-            <span class="p">)</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">basis</span> <span class="o">==</span> <span class="s2">&quot;x_basis&quot;</span><span class="p">:</span>
-        <span class="c1"># ZZ rotation and teleportation</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">rz</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">for_loop</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> <span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_meas_id</span><span class="p">:</span>
-            <span class="n">kernel</span><span class="o">.</span><span class="n">for_loop</span><span class="p">(</span>
-                <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="s2">&quot;meas_id&quot;</span><span class="p">)(</span><span class="n">qubits</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
-            <span class="p">)</span>
-        <span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unsupported basis provided:&quot;</span><span class="p">,</span> <span class="n">basis</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">kernel</span>
-</pre></div>
-</div>
-</div>
-<p>With the circuit definitions above, we can now define a function that automatically runs the VQE and constructs a dictionary containing all the AIM circuits we want to submit to hardware (or noisily simulate):</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">generate_circuit_set</span><span class="p">(</span><span class="n">ignore_meas_id</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">object</span><span class="p">:</span>
-    <span class="n">u_vals</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
-    <span class="n">v_vals</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
-    <span class="n">circuit_dict</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">u_vals</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">v_vals</span><span class="p">:</span>
-            <span class="n">qubit_hamiltonian</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="mf">0.25</span> <span class="o">*</span> <span class="n">u</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-                <span class="o">-</span> <span class="mf">0.25</span> <span class="o">*</span> <span class="n">u</span>
-                <span class="o">+</span> <span class="n">v</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-                <span class="o">+</span> <span class="n">v</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-            <span class="p">)</span>
-            <span class="n">_</span><span class="p">,</span> <span class="n">opt_params</span> <span class="o">=</span> <span class="n">run_logical_vqe</span><span class="p">(</span><span class="n">qubit_hamiltonian</span><span class="p">)</span>
-            <span class="n">angles</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span> <span class="k">for</span> <span class="n">angle</span> <span class="ow">in</span> <span class="n">opt_params</span><span class="p">]</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Computed optimal angles=</span><span class="si">{</span><span class="n">angles</span><span class="si">}</span><span class="s2"> for U=</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">, V=</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-            <span class="n">tmp_physical_dict</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">tmp_logical_dict</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">basis</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;z_basis&quot;</span><span class="p">,</span> <span class="s2">&quot;x_basis&quot;</span><span class="p">):</span>
-                <span class="n">tmp_physical_dict</span><span class="p">[</span><span class="n">basis</span><span class="p">]</span> <span class="o">=</span> <span class="n">aim_physical_circuit</span><span class="p">(</span>
-                    <span class="n">angles</span><span class="p">,</span> <span class="n">basis</span><span class="p">,</span> <span class="n">ignore_meas_id</span><span class="o">=</span><span class="n">ignore_meas_id</span>
-                <span class="p">)</span>
-                <span class="n">tmp_logical_dict</span><span class="p">[</span><span class="n">basis</span><span class="p">]</span> <span class="o">=</span> <span class="n">aim_logical_circuit</span><span class="p">(</span>
-                    <span class="n">angles</span><span class="p">,</span> <span class="n">basis</span><span class="p">,</span> <span class="n">ignore_meas_id</span><span class="o">=</span><span class="n">ignore_meas_id</span>
-                <span class="p">)</span>
-
-            <span class="n">circuit_dict</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-                <span class="s2">&quot;physical&quot;</span><span class="p">:</span> <span class="n">tmp_physical_dict</span><span class="p">,</span>
-                <span class="s2">&quot;logical&quot;</span><span class="p">:</span> <span class="n">tmp_logical_dict</span><span class="p">,</span>
-            <span class="p">}</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Finished building optimized circuits!&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">circuit_dict</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sim_circuit_dict</span> <span class="o">=</span> <span class="n">generate_circuit_set</span><span class="p">()</span>
-<span class="n">circuit_layers</span> <span class="o">=</span> <span class="n">sim_circuit_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9
-Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1
-Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7
-Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9
-Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1
-Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7
-Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9
-Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1
-Computed optimal angles=[-1.7301462729177735, 1.570796033796985] for U=9, V=7
-
-Finished building optimized circuits!
-</pre></div></div>
-</div>
-</section>
-<section id="Setting-up-submission-and-decoding-workflow">
-<h2>Setting up submission and decoding workflow<a class="headerlink" href="#Setting-up-submission-and-decoding-workflow" title="Permalink to this heading">¶</a></h2>
-<p>In this section, we define various helper functions that will play a role in generating the associated energies of the AIM circuits based on the circuit samples (in the different bases), as well as decode the logical circuits with post-selection informed by the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> code:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">_num_qubits</span><span class="p">(</span><span class="n">counts</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">counts</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">isdecimal</span><span class="p">():</span>
-            <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-    <span class="k">return</span> <span class="mi">0</span>
-
-
-<span class="k">def</span> <span class="nf">process_counts</span><span class="p">(</span>
-    <span class="n">counts</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span>
-    <span class="n">data_qubits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
-    <span class="n">flag_qubits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(),</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-    <span class="n">new_data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;0&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">flag_qubits</span><span class="p">):</span>
-            <span class="k">continue</span>
-
-        <span class="n">new_key</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data_qubits</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="s2">&quot;01&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">issuperset</span><span class="p">(</span><span class="n">new_key</span><span class="p">):</span>
-            <span class="k">continue</span>
-
-        <span class="n">new_data</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">new_key</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">new_data</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">+=</span> <span class="n">val</span>
-
-    <span class="k">return</span> <span class="n">new_data</span>
-
-
-<span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="n">counts</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Decode physical counts into logical counts. Should be called after `process_counts`.&quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">counts</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">{}</span>
-
-    <span class="n">num_qubits</span> <span class="o">=</span> <span class="n">_num_qubits</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">num_qubits</span> <span class="o">%</span> <span class="mi">4</span> <span class="o">==</span> <span class="mi">0</span>
-
-    <span class="n">physical_to_logical</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;0000&quot;</span><span class="p">:</span> <span class="s2">&quot;00&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;1111&quot;</span><span class="p">:</span> <span class="s2">&quot;00&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;0011&quot;</span><span class="p">:</span> <span class="s2">&quot;01&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;1100&quot;</span><span class="p">:</span> <span class="s2">&quot;01&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;0101&quot;</span><span class="p">:</span> <span class="s2">&quot;10&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;1010&quot;</span><span class="p">:</span> <span class="s2">&quot;10&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;0110&quot;</span><span class="p">:</span> <span class="s2">&quot;11&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;1001&quot;</span><span class="p">:</span> <span class="s2">&quot;11&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-
-    <span class="n">new_data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">physical_keys</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">4</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_qubits</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span>
-        <span class="n">logical_keys</span> <span class="o">=</span> <span class="p">[</span><span class="n">physical_to_logical</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">physical_key</span><span class="p">)</span> <span class="k">for</span> <span class="n">physical_key</span> <span class="ow">in</span> <span class="n">physical_keys</span><span class="p">]</span>
-        <span class="k">if</span> <span class="kc">None</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">logical_keys</span><span class="p">:</span>
-            <span class="n">new_key</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">logical_keys</span><span class="p">)</span>
-            <span class="n">new_data</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">new_key</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="n">new_data</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">+=</span> <span class="n">val</span>
-
-    <span class="k">return</span> <span class="n">new_data</span>
-
-
-<span class="k">def</span> <span class="nf">ev_x</span><span class="p">(</span><span class="n">counts</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-    <span class="n">ev</span> <span class="o">=</span> <span class="mf">0.0</span>
-
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">ev</span> <span class="o">+=</span> <span class="n">val</span> <span class="o">*</span> <span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-
-    <span class="n">total</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-    <span class="n">ev</span> <span class="o">/=</span> <span class="n">total</span>
-    <span class="k">return</span> <span class="n">ev</span>
-
-
-<span class="k">def</span> <span class="nf">ev_xx</span><span class="p">(</span><span class="n">counts</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-    <span class="n">ev</span> <span class="o">=</span> <span class="mf">0.0</span>
-
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">ev</span> <span class="o">+=</span> <span class="n">val</span> <span class="o">*</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="n">k</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">&quot;1&quot;</span><span class="p">)</span>
-
-    <span class="n">total</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-    <span class="n">ev</span> <span class="o">/=</span> <span class="n">total</span>
-    <span class="k">return</span> <span class="n">ev</span>
-
-
-<span class="k">def</span> <span class="nf">ev_zz</span><span class="p">(</span><span class="n">counts</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-    <span class="n">ev</span> <span class="o">=</span> <span class="mf">0.0</span>
-
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">ev</span> <span class="o">+=</span> <span class="n">val</span> <span class="o">*</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="n">k</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">&quot;1&quot;</span><span class="p">)</span>
-
-    <span class="n">total</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-    <span class="n">ev</span> <span class="o">/=</span> <span class="n">total</span>
-    <span class="k">return</span> <span class="n">ev</span>
-
-
-<span class="k">def</span> <span class="nf">aim_logical_energies</span><span class="p">(</span>
-    <span class="n">data_ordering</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">counts_list</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]]:</span>
-    <span class="n">counts_data</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="n">data_ordering</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">decode</span><span class="p">(</span>
-            <span class="n">process_counts</span><span class="p">(</span>
-                <span class="n">counts</span><span class="p">,</span>
-                <span class="n">data_qubits</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
-                <span class="n">flag_qubits</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">counts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">counts_list</span><span class="p">)</span>
-    <span class="p">}</span>
-    <span class="k">return</span> <span class="n">_aim_energies</span><span class="p">(</span><span class="n">counts_data</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">aim_physical_energies</span><span class="p">(</span>
-    <span class="n">data_ordering</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">counts_list</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]]:</span>
-    <span class="n">counts_data</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="n">data_ordering</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">process_counts</span><span class="p">(</span>
-            <span class="n">counts</span><span class="p">,</span>
-            <span class="n">data_qubits</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-        <span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">counts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">counts_list</span><span class="p">)</span>
-    <span class="p">}</span>
-    <span class="k">return</span> <span class="n">_aim_energies</span><span class="p">(</span><span class="n">counts_data</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_aim_energies</span><span class="p">(</span>
-    <span class="n">counts_data</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]],</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]]:</span>
-    <span class="n">evxs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">evxxs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">evzzs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">totals</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">counts</span> <span class="ow">in</span> <span class="n">counts_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">h_params</span><span class="p">,</span> <span class="n">basis</span> <span class="o">=</span> <span class="n">key</span>
-        <span class="n">key_a</span><span class="p">,</span> <span class="n">key_b</span> <span class="o">=</span> <span class="n">h_params</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
-        <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">key_a</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">key_b</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">basis</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">):</span>
-            <span class="n">evxs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="n">ev_x</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-            <span class="n">evxxs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="n">ev_xx</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">evzzs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="n">ev_zz</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-        <span class="n">totals</span><span class="o">.</span><span class="n">setdefault</span><span class="p">((</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">totals</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-
-    <span class="n">energies</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">uncertainties</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">evxs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="o">&amp;</span> <span class="n">evzzs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="n">string_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="n">energies</span><span class="p">[</span><span class="n">string_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">u</span> <span class="o">*</span> <span class="p">(</span><span class="n">evzzs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">4</span> <span class="o">+</span> <span class="n">v</span> <span class="o">*</span> <span class="n">evxs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span>
-
-        <span class="n">uncertainty_xx</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">v</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">evxxs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">])</span> <span class="o">-</span> <span class="n">u</span> <span class="o">*</span> <span class="n">v</span> <span class="o">*</span> <span class="n">evxs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
-        <span class="n">uncertainty_zz</span> <span class="o">=</span> <span class="n">u</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">evzzs</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
-
-        <span class="n">uncertainties</span><span class="p">[</span><span class="n">string_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span>
-            <span class="p">(</span><span class="n">uncertainty_zz</span> <span class="o">+</span> <span class="n">uncertainty_xx</span> <span class="o">-</span> <span class="n">energies</span><span class="p">[</span><span class="n">string_key</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">totals</span><span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">energies</span><span class="p">,</span> <span class="n">uncertainties</span>
-
-
-<span class="k">def</span> <span class="nf">_get_energy_diff</span><span class="p">(</span>
-    <span class="n">bf_energies</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span>
-    <span class="n">physical_energies</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span>
-    <span class="n">logical_energies</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]]:</span>
-    <span class="n">physical_energy_diff</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">logical_energy_diff</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="c1"># Data ordering following `bf_energies` keys</span>
-    <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">bf_energies</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="n">physical_sim_energy</span> <span class="o">=</span> <span class="n">physical_energies</span><span class="p">[</span><span class="n">layer</span><span class="p">]</span>
-        <span class="n">logical_sim_energy</span> <span class="o">=</span> <span class="n">logical_energies</span><span class="p">[</span><span class="n">layer</span><span class="p">]</span>
-        <span class="n">true_energy</span> <span class="o">=</span> <span class="n">bf_energies</span><span class="p">[</span><span class="n">layer</span><span class="p">]</span>
-        <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Layer=(</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">) has brute-force energy of: </span><span class="si">{</span><span class="n">true_energy</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Physical circuit of layer=(</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">) got an energy of: </span><span class="si">{</span><span class="n">physical_sim_energy</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Logical circuit of layer=(</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">) got an energy of: </span><span class="si">{</span><span class="n">logical_sim_energy</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="mi">72</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">logical_sim_energy</span> <span class="o">&lt;</span> <span class="n">physical_sim_energy</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Logical circuit achieved the lower energy!&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Physical circuit achieved the lower energy&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="mi">72</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-        <span class="n">physical_energy_diff</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="p">(</span><span class="n">true_energy</span> <span class="o">-</span> <span class="n">physical_sim_energy</span><span class="p">)</span>
-        <span class="p">)</span>  <span class="c1"># Multiply by -1 since negative energies</span>
-        <span class="n">logical_energy_diff</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="p">(</span><span class="n">true_energy</span> <span class="o">-</span> <span class="n">logical_sim_energy</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">physical_energy_diff</span><span class="p">,</span> <span class="n">logical_energy_diff</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">submit_aim_circuits</span><span class="p">(</span>
-    <span class="n">circuit_dict</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">folder_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;future_aim_results&quot;</span><span class="p">,</span>
-    <span class="n">shots_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
-    <span class="n">noise_model</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">mlir</span><span class="o">.</span><span class="n">_mlir_libs</span><span class="o">.</span><span class="n">_quakeDialects</span><span class="o">.</span><span class="n">cudaq_runtime</span><span class="o">.</span><span class="n">NoiseModel</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">run_async</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]]</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">run_async</span><span class="p">:</span>
-        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">folder_path</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">aim_results</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;physical&quot;</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">&quot;logical&quot;</span><span class="p">:</span> <span class="p">[]}</span>
-
-    <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">circuit_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="k">if</span> <span class="n">run_async</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Posting circuits associated with layer=(&#39;</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">&#39;)&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Running circuits associated with layer=(&#39;</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">&#39;)&quot;</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">basis</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;z_basis&quot;</span><span class="p">,</span> <span class="s2">&quot;x_basis&quot;</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">run_async</span><span class="p">:</span>
-                <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
-
-                <span class="n">tmp_physical_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span>
-                    <span class="n">circuit_dict</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s2">&quot;physical&quot;</span><span class="p">][</span><span class="n">basis</span><span class="p">],</span> <span class="n">shots_count</span><span class="o">=</span><span class="n">shots_count</span>
-                <span class="p">)</span>
-                <span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder_path</span><span class="si">}</span><span class="s2">/physical_</span><span class="si">{</span><span class="n">basis</span><span class="si">}</span><span class="s2">_job_u=</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">_v=</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">_result.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-                <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">tmp_physical_results</span><span class="p">))</span>
-                <span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-                <span class="n">tmp_logical_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span>
-                    <span class="n">circuit_dict</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s2">&quot;logical&quot;</span><span class="p">][</span><span class="n">basis</span><span class="p">],</span> <span class="n">shots_count</span><span class="o">=</span><span class="n">shots_count</span>
-                <span class="p">)</span>
-                <span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder_path</span><span class="si">}</span><span class="s2">/logical_</span><span class="si">{</span><span class="n">basis</span><span class="si">}</span><span class="s2">_job_u=</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">_v=</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">_result.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-                <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">tmp_logical_results</span><span class="p">))</span>
-                <span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">tmp_physical_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span>
-                    <span class="n">circuit_dict</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s2">&quot;physical&quot;</span><span class="p">][</span><span class="n">basis</span><span class="p">],</span>
-                    <span class="n">shots_count</span><span class="o">=</span><span class="n">shots_count</span><span class="p">,</span>
-                    <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_model</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">tmp_logical_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span>
-                    <span class="n">circuit_dict</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s2">&quot;logical&quot;</span><span class="p">][</span><span class="n">basis</span><span class="p">],</span>
-                    <span class="n">shots_count</span><span class="o">=</span><span class="n">shots_count</span><span class="p">,</span>
-                    <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_model</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">aim_results</span><span class="p">[</span><span class="s2">&quot;physical&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">tmp_physical_results</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
-                <span class="n">aim_results</span><span class="p">[</span><span class="s2">&quot;logical&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">tmp_logical_results</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">run_async</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Completed all circuit sampling!&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">aim_results</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">All circuits submitted for async sampling!&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">_get_async_results</span><span class="p">(</span>
-    <span class="n">layers</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">folder_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;future_aim_results&quot;</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]]:</span>
-    <span class="n">aim_results</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;physical&quot;</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">&quot;logical&quot;</span><span class="p">:</span> <span class="p">[]}</span>
-    <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Retrieving all circuits counts associated with layer=(&#39;</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">&#39;)&quot;</span><span class="p">)</span>
-        <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">basis</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;z_basis&quot;</span><span class="p">,</span> <span class="s2">&quot;x_basis&quot;</span><span class="p">):</span>
-            <span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder_path</span><span class="si">}</span><span class="s2">/physical_</span><span class="si">{</span><span class="n">basis</span><span class="si">}</span><span class="s2">_job_u=</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">_v=</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">_result.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-            <span class="n">tmp_physical_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-            <span class="n">physical_counts</span> <span class="o">=</span> <span class="n">tmp_physical_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-
-            <span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder_path</span><span class="si">}</span><span class="s2">/logical_</span><span class="si">{</span><span class="n">basis</span><span class="si">}</span><span class="s2">_job_u=</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">_v=</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">_result.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-            <span class="n">tmp_logical_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-            <span class="n">logical_counts</span> <span class="o">=</span> <span class="n">tmp_logical_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-
-            <span class="n">aim_results</span><span class="p">[</span><span class="s2">&quot;physical&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">physical_counts</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
-            <span class="n">aim_results</span><span class="p">[</span><span class="s2">&quot;logical&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">logical_counts</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Obtained all circuit samples!&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">aim_results</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Running-a-CUDA-Q-noisy-simulation">
-<h2>Running a CUDA-Q noisy simulation<a class="headerlink" href="#Running-a-CUDA-Q-noisy-simulation" title="Permalink to this heading">¶</a></h2>
-<p>In this section, we will first explore the performance of the physical and logical circuits under the influence of a device noise model. This will help us predict experimental results, as well as understand the dominant error sources at play. Such a simulation can be achieved via CUDA-Q’s density matrix simulator:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">reset_target</span><span class="p">()</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;density-matrix-cpu&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_device_noise</span><span class="p">(</span>
-    <span class="n">depolar_prob_1q</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="n">depolar_prob_2q</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">readout_error_prob</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">custom_gates</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">mlir</span><span class="o">.</span><span class="n">_mlir_libs</span><span class="o">.</span><span class="n">_quakeDialects</span><span class="o">.</span><span class="n">cudaq_runtime</span><span class="o">.</span><span class="n">NoiseModel</span><span class="p">:</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">NoiseModel</span><span class="p">()</span>
-    <span class="n">depolar_noise</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">DepolarizationChannel</span><span class="p">(</span><span class="n">depolar_prob_1q</span><span class="p">)</span>
-
-    <span class="n">noisy_ops</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;z&quot;</span><span class="p">,</span> <span class="s2">&quot;s&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="s2">&quot;h&quot;</span><span class="p">,</span> <span class="s2">&quot;rx&quot;</span><span class="p">,</span> <span class="s2">&quot;rz&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="n">noisy_ops</span><span class="p">:</span>
-        <span class="n">noise</span><span class="o">.</span><span class="n">add_all_qubit_channel</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">depolar_noise</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">custom_gates</span><span class="p">:</span>
-        <span class="n">custom_depolar_channel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">DepolarizationChannel</span><span class="p">(</span><span class="n">depolar_prob_1q</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="n">custom_gates</span><span class="p">:</span>
-            <span class="n">noise</span><span class="o">.</span><span class="n">add_all_qubit_channel</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">custom_depolar_channel</span><span class="p">)</span>
-
-    <span class="c1"># Two qubit depolarization error</span>
-    <span class="n">p_0</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">depolar_prob_2q</span>
-    <span class="n">p_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p_0</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
-
-    <span class="n">k0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="p">[[</span><span class="n">p_0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">p_0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">p_0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">p_0</span><span class="p">]],</span>
-        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">k1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">p_1</span><span class="p">],</span> <span class="p">[</span><span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span>
-        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">k2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="p">[</span>
-            <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span>
-            <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">p_1</span><span class="p">],</span>
-            <span class="p">[</span><span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span>
-            <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span>
-        <span class="p">],</span>
-        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">k3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="p">[[</span><span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="n">p_1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="n">p_1</span><span class="p">]],</span>
-        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">kraus_channel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">KrausChannel</span><span class="p">([</span><span class="n">k0</span><span class="p">,</span> <span class="n">k1</span><span class="p">,</span> <span class="n">k2</span><span class="p">,</span> <span class="n">k3</span><span class="p">])</span>
-
-    <span class="n">noise</span><span class="o">.</span><span class="n">add_all_qubit_channel</span><span class="p">(</span><span class="s2">&quot;cz&quot;</span><span class="p">,</span> <span class="n">kraus_channel</span><span class="p">)</span>
-    <span class="n">noise</span><span class="o">.</span><span class="n">add_all_qubit_channel</span><span class="p">(</span><span class="s2">&quot;cx&quot;</span><span class="p">,</span> <span class="n">kraus_channel</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">readout_error_prob</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="c1"># Readout error modeled with a Bit flip channel on identity before measurement</span>
-        <span class="n">bit_flip</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">BitFlipChannel</span><span class="p">(</span><span class="n">readout_error_prob</span><span class="p">)</span>
-        <span class="n">noise</span><span class="o">.</span><span class="n">add_all_qubit_channel</span><span class="p">(</span><span class="s2">&quot;meas_id&quot;</span><span class="p">,</span> <span class="n">bit_flip</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">noise</span>
-</pre></div>
-</div>
-</div>
-<p>Finally, with our example noise model defined above, we can synchronously &amp; noisily sample all of our AIM circuits by passing <code class="docutils literal notranslate"><span class="pre">noise_model=cudaq_noise_model</span></code> to the workflow containing function <code class="docutils literal notranslate"><span class="pre">submit_aim_circuits()</span></code>:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example parameters that can model execution on hardware at the high, simulation, level:</span>
-<span class="c1"># Take single-qubit gate depolarization rate: ~0.2% or better (fidelity ≥99.8%)</span>
-<span class="c1"># Take two-qubit gate depolarization rate: ~1–2% (fidelity ~98–99%)</span>
-<span class="n">cudaq_noise_model</span> <span class="o">=</span> <span class="n">get_device_noise</span><span class="p">(</span><span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">,</span> <span class="n">readout_error_prob</span><span class="o">=</span><span class="mf">0.02</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">aim_sim_data</span> <span class="o">=</span> <span class="n">submit_aim_circuits</span><span class="p">(</span><span class="n">sim_circuit_dict</span><span class="p">,</span> <span class="n">noise_model</span><span class="o">=</span><span class="n">cudaq_noise_model</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Running circuits associated with layer=(&#39;1:-9&#39;)
-Running circuits associated with layer=(&#39;1:-1&#39;)
-Running circuits associated with layer=(&#39;1:7&#39;)
-Running circuits associated with layer=(&#39;5:-9&#39;)
-Running circuits associated with layer=(&#39;5:-1&#39;)
-Running circuits associated with layer=(&#39;5:7&#39;)
-Running circuits associated with layer=(&#39;9:-9&#39;)
-Running circuits associated with layer=(&#39;9:-1&#39;)
-Running circuits associated with layer=(&#39;9:7&#39;)
-
-Completed all circuit sampling!
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_ordering</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">circuit_layers</span><span class="p">:</span>
-    <span class="k">for</span> <span class="n">basis</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;z_basis&quot;</span><span class="p">,</span> <span class="s2">&quot;x_basis&quot;</span><span class="p">):</span>
-        <span class="n">data_ordering</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">key</span><span class="p">,</span> <span class="n">basis</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sim_physical_energies</span><span class="p">,</span> <span class="n">sim_physical_uncertainties</span> <span class="o">=</span> <span class="n">aim_physical_energies</span><span class="p">(</span>
-    <span class="n">data_ordering</span><span class="p">,</span> <span class="n">aim_sim_data</span><span class="p">[</span><span class="s2">&quot;physical&quot;</span><span class="p">]</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sim_logical_energies</span><span class="p">,</span> <span class="n">sim_logical_uncertainties</span> <span class="o">=</span> <span class="n">aim_logical_energies</span><span class="p">(</span>
-    <span class="n">data_ordering</span><span class="p">,</span> <span class="n">aim_sim_data</span><span class="p">[</span><span class="s2">&quot;logical&quot;</span><span class="p">]</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>To analyze our simulated energy results in the above cells, we will compare them to the brute-force computed exact ground state energies for the AIM Hamiltonian. For simplicity, these are already stored in the dictionary <code class="docutils literal notranslate"><span class="pre">bf_energies</span></code> below:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bf_energies</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;1:-9&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">18.251736027394713</span><span class="p">,</span>
-    <span class="s2">&quot;1:-1&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">2.265564437074638</span><span class="p">,</span>
-    <span class="s2">&quot;1:7&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">14.252231964940428</span><span class="p">,</span>
-    <span class="s2">&quot;5:-9&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">19.293350575766127</span><span class="p">,</span>
-    <span class="s2">&quot;5:-1&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">3.608495283014149</span><span class="p">,</span>
-    <span class="s2">&quot;5:7&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">15.305692796870582</span><span class="p">,</span>
-    <span class="s2">&quot;9:-9&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">20.39007993367173</span><span class="p">,</span>
-    <span class="s2">&quot;9:-1&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">5.260398644698076</span><span class="p">,</span>
-    <span class="s2">&quot;9:7&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">16.429650912487233</span><span class="p">,</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<p>With the above metric, we can assess the performance of the logical circuits against the physical circuits by considering how far away the respective energies are from the brute-force expected energies. The cell below computes these energy deviations:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sim_physical_energy_diff</span><span class="p">,</span> <span class="n">sim_logical_energy_diff</span> <span class="o">=</span> <span class="n">_get_energy_diff</span><span class="p">(</span>
-    <span class="n">bf_energies</span><span class="p">,</span> <span class="n">sim_physical_energies</span><span class="p">,</span> <span class="n">sim_logical_energies</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Layer=(1, -9) has brute-force energy of: -18.251736027394713
-Physical circuit of layer=(1, -9) got an energy of: -15.929
-Logical circuit of layer=(1, -9) got an energy of: -17.46016175277361
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(1, -1) has brute-force energy of: -2.265564437074638
-Physical circuit of layer=(1, -1) got an energy of: -1.97
-Logical circuit of layer=(1, -1) got an energy of: -2.176531948420889
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(1, 7) has brute-force energy of: -14.252231964940428
-Physical circuit of layer=(1, 7) got an energy of: -12.268
-Logical circuit of layer=(1, 7) got an energy of: -13.26321740664324
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(5, -9) has brute-force energy of: -19.293350575766127
-Physical circuit of layer=(5, -9) got an energy of: -16.8495
-Logical circuit of layer=(5, -9) got an energy of: -18.46681284816878
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(5, -1) has brute-force energy of: -3.608495283014149
-Physical circuit of layer=(5, -1) got an energy of: -3.1965000000000003
-Logical circuit of layer=(5, -1) got an energy of: -3.4531715120183297
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(5, 7) has brute-force energy of: -15.305692796870582
-Physical circuit of layer=(5, 7) got an energy of: -13.336
-Logical circuit of layer=(5, 7) got an energy of: -14.341784541550897
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(9, -9) has brute-force energy of: -20.39007993367173
-Physical circuit of layer=(9, -9) got an energy of: -17.802
-Logical circuit of layer=(9, -9) got an energy of: -19.339249509416753
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(9, -1) has brute-force energy of: -5.260398644698076
-Physical circuit of layer=(9, -1) got an energy of: -4.8580000000000005
-Logical circuit of layer=(9, -1) got an energy of: -5.1227150992242025
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(9, 7) has brute-force energy of: -16.429650912487233
-Physical circuit of layer=(9, 7) got an energy of: -14.3635
-Logical circuit of layer=(9, 7) got an energy of: -15.448422736181264
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-</pre></div></div>
-</div>
-<p>Both physical and logical circuits were subject to the same noise model, but the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> provides additional information that can help overcome some errors. Visualizing the computed energy differences from the above the cell, our noisy simulation provides a preview of the benefits logical qubits can offer:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
-
-<span class="n">layer_labels</span> <span class="o">=</span> <span class="p">[(</span><span class="nb">int</span><span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]))</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">bf_energies</span><span class="o">.</span><span class="n">keys</span><span class="p">()]</span>
-<span class="n">plot_labels</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">layer_labels</span><span class="p">]</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span>
-    <span class="n">plot_labels</span><span class="p">,</span>
-    <span class="n">sim_physical_energy_diff</span><span class="p">,</span>
-    <span class="n">yerr</span><span class="o">=</span><span class="n">sim_physical_uncertainties</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span>
-    <span class="n">ecolor</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">26</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">94</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">26</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">94</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">capsize</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">elinewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
-    <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-    <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-    <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Physical&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span>
-    <span class="n">plot_labels</span><span class="p">,</span>
-    <span class="n">sim_logical_energy_diff</span><span class="p">,</span>
-    <span class="n">yerr</span><span class="o">=</span><span class="n">sim_logical_uncertainties</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span>
-    <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">177</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">152</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">ecolor</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">177</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">152</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">capsize</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">elinewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
-    <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-    <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-    <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Logical&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Hamiltonian Parameters (U, V)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Energy above true ground state (in eV)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;CUDA-Q AIM Circuits Simulation (lower is better)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mf">18.5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span>
-    <span class="n">top</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">sim_physical_energy_diff</span><span class="p">)</span> <span class="o">+</span> <span class="nb">max</span><span class="p">(</span><span class="n">sim_physical_uncertainties</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="o">+</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=-</span><span class="mf">0.2</span>
-<span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_logical_aim_sqale_38_0.png" src="../../_images/applications_python_logical_aim_sqale_38_0.png" />
-</div>
-</div>
-</section>
-<section id="Running-logical-AIM-on-Infleqtion's-hardware">
-<h2>Running logical AIM on Infleqtion’s hardware<a class="headerlink" href="#Running-logical-AIM-on-Infleqtion's-hardware" title="Permalink to this heading">¶</a></h2>
-<p>The entire workflow we’ve seen thus far can be seamlessly executed on real quantum hardware as well. CUDA-Q has integration with Infleqtion’s gate-based neutral atom quantum computer, <a class="reference external" href="https://arxiv.org/html/2408.08288v2">Sqale</a>, allowing execution of CUDA-Q kernels on neutral-atom hardware via Infleqtion’s cross-platform Superstaq compiler API that performs low-level compilation and optimization under the hood. Indeed, the AIM research results seen in <a class="reference external" href="https://arxiv.org/abs/2412.07670">our
-paper</a> were obtained via this complete end-to-end workflow.</p>
-<p>To do so, users can obtain a Superstaq API key from <a class="reference external" href="https://superstaq.infleqtion.com/">superstaq.infleqtion.com</a> to gain access to Infleqtion’s neutral-atom simulator, with <a class="reference external" href="https://www.infleqtion.com/sqale-preregistration">pre-registration</a> open for access to Infleqtion’s neutral atom QPU.</p>
-<p>As a tutorial, let us reproduce the workflow we’ve run so far but on Infleqtion’s QPU. We begin with the same GPU-enhanced VQE to generate the AIM circuits:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">reset_target</span><span class="p">()</span>
-
-<span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;qpp-cpu&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;fp64&quot;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;fp64&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device_circuit_dict</span> <span class="o">=</span> <span class="n">generate_circuit_set</span><span class="p">(</span>
-    <span class="n">ignore_meas_id</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)</span>  <span class="c1"># Setting `ignore_meas_id=True` drops the noisy-identity gate from earlier</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9
-Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1
-Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7
-Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9
-Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1
-Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7
-Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9
-Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1
-Computed optimal angles=[-1.7301462945564499, 1.570796044872433] for U=9, V=7
-
-Finished building optimized circuits!
-</pre></div></div>
-</div>
-<p>And now, we change backends! Before selecting an Infleqtion machine in CUDA-Q, we must first set our Superstaq API key, like so:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># os.environ[&#39;SUPERSTAQ_API_KEY&#39;] = &quot;api_key&quot;</span>
-</pre></div>
-</div>
-</div>
-<p>Next, we declare the type of execution we would like on Infleqtion’s machine based on the keyword options specified:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">reset_target</span><span class="p">()</span>
-
-<span class="c1"># Set the following to run on Infleqtion&#39;s Sqale QPU:</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s2">&quot;cq_sqale_qpu&quot;</span><span class="p">)</span>
-
-<span class="c1"># Set the following to run an ideal dry-run on Infleqtion&#39;s Sqale QPU:</span>
-<span class="c1"># cudaq.set_target(&quot;infleqtion&quot;, machine=&quot;cq_sqale_qpu&quot;, method=&quot;dry-run&quot;)</span>
-
-<span class="c1"># Set the following to run a device-realistic noisy simulation of Infleqtion&#39;s Sqale QPU:</span>
-<span class="c1"># cudaq.set_target(&quot;infleqtion&quot;, machine=&quot;cq_sqale_qpu&quot;, method=&quot;noise-sim&quot;)</span>
-
-<span class="c1"># Set the following to run a local, ideal emulation:</span>
-<span class="c1"># cudaq.set_target(&quot;infleqtion&quot;, emulate=True)</span>
-</pre></div>
-</div>
-</div>
-<p>With that, we’re all set! That simple change instructs our AIM circuits to execute on Infleqtion’s QPU (or simulator). Due to the general queue wait time of running on hardware, we optionally recommend enabling the <code class="docutils literal notranslate"><span class="pre">run_async=True</span></code> flag to asynchronously sample the circuits. This will allow the cell to be executed and not wait synchronously until all the jobs are complete, allowing other classical code to be run in the meantime. When using <code class="docutils literal notranslate"><span class="pre">run_async</span></code>, an optional directory to store the job
-information can be specified with <code class="docutils literal notranslate"><span class="pre">folder_path</span></code> (this will be important to later retrieve the job results from the same directory)</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">submit_aim_circuits</span><span class="p">(</span>
-    <span class="n">device_circuit_dict</span><span class="p">,</span> <span class="n">folder_path</span><span class="o">=</span><span class="s2">&quot;hardware_aim_future_results&quot;</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">run_async</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Posting circuits associated with layer=(&#39;1:-9&#39;)
-Posting circuits associated with layer=(&#39;1:-1&#39;)
-Posting circuits associated with layer=(&#39;1:7&#39;)
-Posting circuits associated with layer=(&#39;5:-9&#39;)
-Posting circuits associated with layer=(&#39;5:-1&#39;)
-Posting circuits associated with layer=(&#39;5:7&#39;)
-Posting circuits associated with layer=(&#39;9:-9&#39;)
-Posting circuits associated with layer=(&#39;9:-1&#39;)
-Posting circuits associated with layer=(&#39;9:7&#39;)
-
-All circuits submitted for async sampling!
-</pre></div></div>
-</div>
-<p>With the above cell execution, all the circuits will post to execute on QPU. We can then return at a later time to retrieve the job results with the cell below:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">aim_device_data</span> <span class="o">=</span> <span class="n">_get_async_results</span><span class="p">(</span><span class="n">circuit_layers</span><span class="p">,</span> <span class="n">folder_path</span><span class="o">=</span><span class="s2">&quot;hardware_aim_future_results&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Retrieving all circuits counts associated with layer=(&#39;1:-9&#39;)
-Retrieving all circuits counts associated with layer=(&#39;1:-1&#39;)
-Retrieving all circuits counts associated with layer=(&#39;1:7&#39;)
-Retrieving all circuits counts associated with layer=(&#39;5:-9&#39;)
-Retrieving all circuits counts associated with layer=(&#39;5:-1&#39;)
-Retrieving all circuits counts associated with layer=(&#39;5:7&#39;)
-Retrieving all circuits counts associated with layer=(&#39;9:-9&#39;)
-Retrieving all circuits counts associated with layer=(&#39;9:-1&#39;)
-Retrieving all circuits counts associated with layer=(&#39;9:7&#39;)
-
-Obtained all circuit samples!
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">physical_energies</span><span class="p">,</span> <span class="n">physical_uncertainties</span> <span class="o">=</span> <span class="n">aim_physical_energies</span><span class="p">(</span>
-    <span class="n">data_ordering</span><span class="p">,</span> <span class="n">aim_device_data</span><span class="p">[</span><span class="s2">&quot;physical&quot;</span><span class="p">]</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">logical_energies</span><span class="p">,</span> <span class="n">logical_uncertainties</span> <span class="o">=</span> <span class="n">aim_logical_energies</span><span class="p">(</span>
-    <span class="n">data_ordering</span><span class="p">,</span> <span class="n">aim_device_data</span><span class="p">[</span><span class="s2">&quot;logical&quot;</span><span class="p">]</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">physical_energy_diff</span><span class="p">,</span> <span class="n">logical_energy_diff</span> <span class="o">=</span> <span class="n">_get_energy_diff</span><span class="p">(</span>
-    <span class="n">bf_energies</span><span class="p">,</span> <span class="n">physical_energies</span><span class="p">,</span> <span class="n">logical_energies</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Layer=(1, -9) has brute-force energy of: -18.251736027394713
-Physical circuit of layer=(1, -9) got an energy of: -17.626499999999997
-Logical circuit of layer=(1, -9) got an energy of: -17.69666562801761
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(1, -1) has brute-force energy of: -2.265564437074638
-Physical circuit of layer=(1, -1) got an energy of: -2.1415
-Logical circuit of layer=(1, -1) got an energy of: -2.2032104443266585
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(1, 7) has brute-force energy of: -14.252231964940428
-Physical circuit of layer=(1, 7) got an energy of: -12.9955
-Logical circuit of layer=(1, 7) got an energy of: -13.76919450035401
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(5, -9) has brute-force energy of: -19.293350575766127
-Physical circuit of layer=(5, -9) got an energy of: -18.331
-Logical circuit of layer=(5, -9) got an energy of: -18.85730052910377
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(5, -1) has brute-force energy of: -3.608495283014149
-Physical circuit of layer=(5, -1) got an energy of: -3.476
-Logical circuit of layer=(5, -1) got an energy of: -3.5425689231532203
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(5, 7) has brute-force energy of: -15.305692796870582
-Physical circuit of layer=(5, 7) got an energy of: -14.043500000000002
-Logical circuit of layer=(5, 7) got an energy of: -14.795918428433312
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(9, -9) has brute-force energy of: -20.39007993367173
-Physical circuit of layer=(9, -9) got an energy of: -19.4715
-Logical circuit of layer=(9, -9) got an energy of: -19.96524696701215
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(9, -1) has brute-force energy of: -5.260398644698076
-Physical circuit of layer=(9, -1) got an energy of: -4.973
-Logical circuit of layer=(9, -1) got an energy of: -5.207315773582224
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-Layer=(9, 7) has brute-force energy of: -16.429650912487233
-Physical circuit of layer=(9, 7) got an energy of: -15.182
-Logical circuit of layer=(9, 7) got an energy of: -16.241375689575516
-------------------------------------------------------------------------
-Logical circuit achieved the lower energy!
-------------------------------------------------------------------------
-
-</pre></div></div>
-</div>
-<p>As before, we use the same metric of comparing against the true ground state energies; however, this time, both the physical and logical circuits are fully exposed to real hardware noise. Yet, we expect the use of logical qubits afforded to us by the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> code to achieve energies closer to the true ground state than the bare physical circuits (up to a certain error threshold). And indeed they do! Visually, we can plot the energy deviations of both the physical and logical circuits from
-the cell above and observe that the logical circuits are able to outperform the physical circuits by obtaining much lower energies, demonstrating the power of error detection and the beginning possibilities of fault-tolerant quantum computation:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span>
-    <span class="n">plot_labels</span><span class="p">,</span>
-    <span class="n">physical_energy_diff</span><span class="p">,</span>
-    <span class="n">yerr</span><span class="o">=</span><span class="n">physical_uncertainties</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span>
-    <span class="n">ecolor</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">26</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">94</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">26</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">94</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">capsize</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">elinewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
-    <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-    <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-    <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Physical&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span>
-    <span class="n">plot_labels</span><span class="p">,</span>
-    <span class="n">logical_energy_diff</span><span class="p">,</span>
-    <span class="n">yerr</span><span class="o">=</span><span class="n">logical_uncertainties</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span>
-    <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">177</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">152</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">ecolor</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">177</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="mi">152</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">),</span>
-    <span class="n">capsize</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-    <span class="n">elinewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
-    <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
-    <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-    <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Logical&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Hamiltonian Parameters (U, V)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Energy above true ground state (in eV)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;CUDA-Q AIM Infleqtion Hardware Execution (lower is better)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mf">18.5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">physical_energy_diff</span><span class="p">)</span> <span class="o">+</span> <span class="nb">max</span><span class="p">(</span><span class="n">physical_uncertainties</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="o">+</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=-</span><span class="mf">0.2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_logical_aim_sqale_56_0.png" src="../../_images/applications_python_logical_aim_sqale_56_0.png" />
-</div>
-</div>
-</section>
-</section>
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-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="hadamard_test.html" class="btn btn-neutral float-left" title="Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/pr-2308/applications/python/logical_aim_sqale.ipynb b/pr-2308/applications/python/logical_aim_sqale.ipynb
deleted file mode 100644
index f51bd10294..0000000000
--- a/pr-2308/applications/python/logical_aim_sqale.ipynb
+++ /dev/null
@@ -1,1356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Anderson Impurity Model ground state solver on Infleqtion's Sqale"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ground state quantum chemistry—computing total energies of molecular configurations to within chemical accuracy—is perhaps the most highly-touted industrial application of fault-tolerant quantum computers. Strongly correlated materials, for example, are particularly interesting, and tools like dynamical mean-field theory (DMFT) allow one to account for the effect of their strong, localized electronic correlations. These DMFT models help predict material properties by approximating the system as a single site impurity inside a “bath” that encompasses the rest of the system. Simulating such dynamics can be a tough task using classical methods, but can be done efficiently on a quantum computer via quantum simulation.\n",
-    "\n",
-    "In this notebook, we showcase a workflow for preparing the ground state of the minimal single-impurity Anderson model (SIAM) using the Hamiltonian Variational Ansatz for a range of realistic parameters. As a first step towards running DMFT on a fault-tolerant quantum computer, we will use logical qubits encoded in the `[[4, 2, 2]]` code. Using this workflow, we will obtain the ground state energy estimates via noisy simulation, and then also execute the corresponding optimized circuits on Infleqtion's gate-based neutral-atom quantum computer, making the benefits of logical qubits apparent. More details can be found in our [paper](https://arxiv.org/abs/2412.07670)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This demo notebook uses CUDA-Q (`cudaq`) and a CUDA-QX library, `cudaq-solvers`; let us first begin by importing (and installing as needed) these packages:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    import cudaq_solvers as solvers\n",
-    "    import cudaq\n",
-    "    import matplotlib.pyplot as plt\n",
-    "except ImportError:\n",
-    "    print(\"Installing required packages...\")\n",
-    "    %pip install --quiet 'cudaq-solvers' 'matplotlib'\n",
-    "    print(\"Installed `cudaq`, `cudaq-solvers`, and `matplotlib` packages.\")\n",
-    "    print(\"You may need to restart the kernel to import newly installed packages.\")\n",
-    "    import cudaq_solvers as solvers\n",
-    "    import cudaq\n",
-    "    import matplotlib.pyplot as plt\n",
-    "\n",
-    "from collections.abc import Mapping, Sequence\n",
-    "import numpy as np\n",
-    "from scipy.optimize import minimize\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To prepare our ground state quantum Anderson impurity model circuits (referred to as AIM circuits in this notebook for short), we use VQE to train an ansatz to minimize a Hamiltonian and obtain optimal angles that can be used to set the AIM circuits. As described in our [paper](https://arxiv.org/abs/2412.07670), the associated restricted Hamiltonian for our SIAM can be reduced to,\n",
-    "$$  \n",
-    "\\begin{equation}\n",
-    "H_{(U, V)} = U (Z_0 Z_2 - 1) / 4 + V (X_0 + X_2),\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "where $U$ is the Coulomb interaction and $V$ the hybridization strength. In this notebook workflow, we will optimize over a 2-dimensional grid of Hamiltonian parameter values, namely $U\\in \\{1, 5, 9\\}$ and $V\\in \\{-9, -1, 7\\}$ (with all values assumed to be in units of eV), to ensure that the ansatz is generally trainable and expressive, and obtain 9 different circuit layers identified by the key $(U, V)$. We will simulate the VQE on GPU (or optionally on CPU if you do not have GPU access), enabled by CUDA-Q, in the absence of noise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if cudaq.num_available_gpus() == 0:\n",
-    "    cudaq.set_target(\"qpp-cpu\", option=\"fp64\")\n",
-    "else:\n",
-    "    cudaq.set_target(\"nvidia\", option=\"fp64\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This workflow can be easily defined in CUDA-Q as shown in the cell below, using the CUDA-QX Solvers library (which accelerates quantum algorithms like the VQE):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def ansatz(n_qubits: int) -> cudaq.Kernel:\n",
-    "    # Create a CUDA-Q parameterized kernel\n",
-    "    paramterized_ansatz, variational_angles = cudaq.make_kernel(list)\n",
-    "    qubits = paramterized_ansatz.qalloc(n_qubits)\n",
-    "\n",
-    "    # Using |+> as the initial state:\n",
-    "    paramterized_ansatz.h(qubits[0])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    paramterized_ansatz.rx(variational_angles[0], qubits[0])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "    paramterized_ansatz.rz(variational_angles[1], qubits[1])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "    return paramterized_ansatz\n",
-    "\n",
-    "\n",
-    "def run_logical_vqe(cudaq_hamiltonian: cudaq.SpinOperator) -> tuple[float, list[float]]:\n",
-    "    # Set seed for easier reproduction\n",
-    "    np.random.seed(42)\n",
-    "\n",
-    "    # Initial angles for the optimizer\n",
-    "    init_angles = np.random.random(2) * 1e-1\n",
-    "\n",
-    "    # Obtain CUDA-Q Ansatz\n",
-    "    num_qubits = cudaq_hamiltonian.get_qubit_count()\n",
-    "    variational_kernel = ansatz(num_qubits)\n",
-    "\n",
-    "    # Perform VQE optimization\n",
-    "    energy, params, _ = solvers.vqe(\n",
-    "        variational_kernel,\n",
-    "        cudaq_hamiltonian,\n",
-    "        init_angles,\n",
-    "        optimizer=minimize,\n",
-    "        method=\"SLSQP\",\n",
-    "        tol=1e-10,\n",
-    "    )\n",
-    "    return energy, params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing circuits in the `[[4,2,2]]` encoding"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `[[4,2,2]]` code is a quantum error detection code that uses four physical qubits to encode two logical qubits. In this notebook, we will construct two variants of quantum circuits: physical (bare, unencoded) and logical (encoded). These circuits will be informed by the Hamiltonian Variational Ansatz described earlier. To measure all the terms in our Hamiltonian, we will measure the data qubits in both the $Z$- and $X$-basis, as allowed by the `[[4,2,2]]` logical gateset. Full details on the circuit constructions are outlined in our [paper](https://arxiv.org/abs/2412.07670)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we create functions to build our CUDA-Q AIM circuits, both physical and logical versions. As we consider noisy simulation in this notebook, we will include some noisy gates. Here, for simplicity, we will just register a custom identity gate -- to be later used as a noisy operation to model readout error: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.register_operation(\"meas_id\", np.identity(2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def aim_physical_circuit(\n",
-    "    angles: list[float], basis: str, *, ignore_meas_id: bool = False\n",
-    ") -> cudaq.Kernel:\n",
-    "    kernel = cudaq.make_kernel()\n",
-    "    qubits = kernel.qalloc(2)\n",
-    "\n",
-    "    # Bell state prep\n",
-    "    kernel.h(qubits[0])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    # Rx Gate\n",
-    "    kernel.rx(angles[0], qubits[0])\n",
-    "\n",
-    "    # ZZ rotation\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.rz(angles[1], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    if basis == \"z_basis\":\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=2, function=lambda q_idx: getattr(kernel, \"meas_id\")(qubits[q_idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    elif basis == \"x_basis\":\n",
-    "        kernel.h(qubits)\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=2, function=lambda q_idx: getattr(kernel, \"meas_id\")(qubits[q_idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    else:\n",
-    "        raise ValueError(\"Unsupported basis provided:\", basis)\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def aim_logical_circuit(\n",
-    "    angles: list[float], basis: str, *, ignore_meas_id: bool = False\n",
-    ") -> cudaq.Kernel:\n",
-    "    kernel = cudaq.make_kernel()\n",
-    "    qubits = kernel.qalloc(6)\n",
-    "\n",
-    "    kernel.for_loop(start=0, stop=3, function=lambda idx: kernel.h(qubits[idx]))\n",
-    "    kernel.cx(qubits[1], qubits[4])\n",
-    "    kernel.cx(qubits[2], qubits[3])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[3])\n",
-    "\n",
-    "    # Rx teleportation\n",
-    "    kernel.rx(angles[0], qubits[0])\n",
-    "\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[3])\n",
-    "    kernel.h(qubits[0])\n",
-    "\n",
-    "    if basis == \"z_basis\":\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=5, function=lambda idx: getattr(kernel, \"meas_id\")(qubits[idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    elif basis == \"x_basis\":\n",
-    "        # ZZ rotation and teleportation\n",
-    "        kernel.cx(qubits[3], qubits[5])\n",
-    "        kernel.cx(qubits[2], qubits[5])\n",
-    "        kernel.rz(angles[1], qubits[5])\n",
-    "        kernel.cx(qubits[1], qubits[5])\n",
-    "        kernel.cx(qubits[4], qubits[5])\n",
-    "        kernel.for_loop(start=1, stop=5, function=lambda idx: kernel.h(qubits[idx]))\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=6, function=lambda idx: getattr(kernel, \"meas_id\")(qubits[idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    else:\n",
-    "        raise ValueError(\"Unsupported basis provided:\", basis)\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the circuit definitions above, we can now define a function that automatically runs the VQE and constructs a dictionary containing all the AIM circuits we want to submit to hardware (or noisily simulate):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_circuit_set(ignore_meas_id: bool = False) -> object:\n",
-    "    u_vals = [1, 5, 9]\n",
-    "    v_vals = [-9, -1, 7]\n",
-    "    circuit_dict = {}\n",
-    "    for u in u_vals:\n",
-    "        for v in v_vals:\n",
-    "            qubit_hamiltonian = (\n",
-    "                0.25 * u * cudaq.spin.z(0) * cudaq.spin.z(1)\n",
-    "                - 0.25 * u\n",
-    "                + v * cudaq.spin.x(0)\n",
-    "                + v * cudaq.spin.x(1)\n",
-    "            )\n",
-    "            _, opt_params = run_logical_vqe(qubit_hamiltonian)\n",
-    "            angles = [float(angle) for angle in opt_params]\n",
-    "            print(f\"Computed optimal angles={angles} for U={u}, V={v}\")\n",
-    "\n",
-    "            tmp_physical_dict = {}\n",
-    "            tmp_logical_dict = {}\n",
-    "            for basis in (\"z_basis\", \"x_basis\"):\n",
-    "                tmp_physical_dict[basis] = aim_physical_circuit(\n",
-    "                    angles, basis, ignore_meas_id=ignore_meas_id\n",
-    "                )\n",
-    "                tmp_logical_dict[basis] = aim_logical_circuit(\n",
-    "                    angles, basis, ignore_meas_id=ignore_meas_id\n",
-    "                )\n",
-    "\n",
-    "            circuit_dict[f\"{u}:{v}\"] = {\n",
-    "                \"physical\": tmp_physical_dict,\n",
-    "                \"logical\": tmp_logical_dict,\n",
-    "            }\n",
-    "    print(\"\\nFinished building optimized circuits!\")\n",
-    "    return circuit_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9\n",
-      "Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1\n",
-      "Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7\n",
-      "Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9\n",
-      "Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1\n",
-      "Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7\n",
-      "Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9\n",
-      "Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1\n",
-      "Computed optimal angles=[-1.7301462729177735, 1.570796033796985] for U=9, V=7\n",
-      "\n",
-      "Finished building optimized circuits!\n"
-     ]
-    }
-   ],
-   "source": [
-    "sim_circuit_dict = generate_circuit_set()\n",
-    "circuit_layers = sim_circuit_dict.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up submission and decoding workflow "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we define various helper functions that will play a role in generating the associated energies of the AIM circuits based on the circuit samples (in the different bases), as well as decode the logical circuits with post-selection informed by the `[[4,2,2]]` code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _num_qubits(counts: Mapping[str, float]) -> int:\n",
-    "    for key in counts:\n",
-    "        if key.isdecimal():\n",
-    "            return len(key)\n",
-    "    return 0\n",
-    "\n",
-    "\n",
-    "def process_counts(\n",
-    "    counts: Mapping[str, float],\n",
-    "    data_qubits: Sequence[int],\n",
-    "    flag_qubits: Sequence[int] = (),\n",
-    ") -> dict[str, float]:\n",
-    "    new_data: dict[str, float] = {}\n",
-    "    for key, val in counts.items():\n",
-    "        if not all(key[i] == \"0\" for i in flag_qubits):\n",
-    "            continue\n",
-    "\n",
-    "        new_key = \"\".join(key[i] for i in data_qubits)\n",
-    "\n",
-    "        if not set(\"01\").issuperset(new_key):\n",
-    "            continue\n",
-    "\n",
-    "        new_data.setdefault(new_key, 0)\n",
-    "        new_data[new_key] += val\n",
-    "\n",
-    "    return new_data\n",
-    "\n",
-    "\n",
-    "def decode(counts: Mapping[str, float]) -> dict[str, float]:\n",
-    "    \"\"\"Decode physical counts into logical counts. Should be called after `process_counts`.\"\"\"\n",
-    "\n",
-    "    if not counts:\n",
-    "        return {}\n",
-    "\n",
-    "    num_qubits = _num_qubits(counts)\n",
-    "    assert num_qubits % 4 == 0\n",
-    "\n",
-    "    physical_to_logical = {\n",
-    "        \"0000\": \"00\",\n",
-    "        \"1111\": \"00\",\n",
-    "        \"0011\": \"01\",\n",
-    "        \"1100\": \"01\",\n",
-    "        \"0101\": \"10\",\n",
-    "        \"1010\": \"10\",\n",
-    "        \"0110\": \"11\",\n",
-    "        \"1001\": \"11\",\n",
-    "    }\n",
-    "\n",
-    "    new_data: dict[str, float] = {}\n",
-    "    for key, val in counts.items():\n",
-    "        physical_keys = [key[i : i + 4] for i in range(0, num_qubits, 4)]\n",
-    "        logical_keys = [physical_to_logical.get(physical_key) for physical_key in physical_keys]\n",
-    "        if None not in logical_keys:\n",
-    "            new_key = \"\".join(logical_keys)\n",
-    "            new_data.setdefault(new_key, 0)\n",
-    "            new_data[new_key] += val\n",
-    "\n",
-    "    return new_data\n",
-    "\n",
-    "\n",
-    "def ev_x(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * ((-1) ** int(k[0]) + (-1) ** int(k[1]))\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def ev_xx(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * (-1) ** k.count(\"1\")\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def ev_zz(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * (-1) ** k.count(\"1\")\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def aim_logical_energies(\n",
-    "    data_ordering: object, counts_list: Sequence[dict[str, float]]\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    counts_data = {\n",
-    "        data_ordering[i]: decode(\n",
-    "            process_counts(\n",
-    "                counts,\n",
-    "                data_qubits=[1, 2, 3, 4],\n",
-    "                flag_qubits=[0, 5],\n",
-    "            )\n",
-    "        )\n",
-    "        for i, counts in enumerate(counts_list)\n",
-    "    }\n",
-    "    return _aim_energies(counts_data)\n",
-    "\n",
-    "\n",
-    "def aim_physical_energies(\n",
-    "    data_ordering: object, counts_list: Sequence[dict[str, float]]\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    counts_data = {\n",
-    "        data_ordering[i]: process_counts(\n",
-    "            counts,\n",
-    "            data_qubits=[0, 1],\n",
-    "        )\n",
-    "        for i, counts in enumerate(counts_list)\n",
-    "    }\n",
-    "    return _aim_energies(counts_data)\n",
-    "\n",
-    "\n",
-    "def _aim_energies(\n",
-    "    counts_data: Mapping[tuple[int, int, str], dict[str, float]],\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    evxs: dict[tuple[int, int], float] = {}\n",
-    "    evxxs: dict[tuple[int, int], float] = {}\n",
-    "    evzzs: dict[tuple[int, int], float] = {}\n",
-    "    totals: dict[tuple[int, int], float] = {}\n",
-    "\n",
-    "    for key, counts in counts_data.items():\n",
-    "        h_params, basis = key\n",
-    "        key_a, key_b = h_params.split(\":\")\n",
-    "        u, v = int(key_a), int(key_b)\n",
-    "        if basis.startswith(\"x\"):\n",
-    "            evxs[u, v] = ev_x(counts)\n",
-    "            evxxs[u, v] = ev_xx(counts)\n",
-    "        else:\n",
-    "            evzzs[u, v] = ev_zz(counts)\n",
-    "\n",
-    "        totals.setdefault((u, v), 0)\n",
-    "        totals[u, v] += sum(counts.values())\n",
-    "\n",
-    "    energies = {}\n",
-    "    uncertainties = {}\n",
-    "    for u, v in evxs.keys() & evzzs.keys():\n",
-    "        string_key = f\"{u}:{v}\"\n",
-    "        energies[string_key] = u * (evzzs[u, v] - 1) / 4 + v * evxs[u, v]\n",
-    "\n",
-    "        uncertainty_xx = 2 * v**2 * (1 + evxxs[u, v]) - u * v * evxs[u, v] / 2\n",
-    "        uncertainty_zz = u**2 * (1 - evzzs[u, v]) / 2\n",
-    "\n",
-    "        uncertainties[string_key] = np.sqrt(\n",
-    "            (uncertainty_zz + uncertainty_xx - energies[string_key] ** 2) / (totals[u, v] / 2)\n",
-    "        )\n",
-    "\n",
-    "    return energies, uncertainties\n",
-    "\n",
-    "\n",
-    "def _get_energy_diff(\n",
-    "    bf_energies: dict[str, float],\n",
-    "    physical_energies: dict[str, float],\n",
-    "    logical_energies: dict[str, float],\n",
-    ") -> tuple[list[float], list[float]]:\n",
-    "    physical_energy_diff = []\n",
-    "    logical_energy_diff = []\n",
-    "\n",
-    "    # Data ordering following `bf_energies` keys\n",
-    "    for layer in bf_energies.keys():\n",
-    "        physical_sim_energy = physical_energies[layer]\n",
-    "        logical_sim_energy = logical_energies[layer]\n",
-    "        true_energy = bf_energies[layer]\n",
-    "        u, v = layer.split(\":\")\n",
-    "        print(f\"Layer=({u}, {v}) has brute-force energy of: {true_energy}\")\n",
-    "        print(f\"Physical circuit of layer=({u}, {v}) got an energy of: {physical_sim_energy}\")\n",
-    "        print(f\"Logical circuit of layer=({u}, {v}) got an energy of: {logical_sim_energy}\")\n",
-    "        print(\"-\" * 72)\n",
-    "\n",
-    "        if logical_sim_energy < physical_sim_energy:\n",
-    "            print(\"Logical circuit achieved the lower energy!\")\n",
-    "        else:\n",
-    "            print(\"Physical circuit achieved the lower energy\")\n",
-    "        print(\"-\" * 72, \"\\n\")\n",
-    "\n",
-    "        physical_energy_diff.append(\n",
-    "            -1 * (true_energy - physical_sim_energy)\n",
-    "        )  # Multiply by -1 since negative energies\n",
-    "        logical_energy_diff.append(-1 * (true_energy - logical_sim_energy))\n",
-    "    return physical_energy_diff, logical_energy_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def submit_aim_circuits(\n",
-    "    circuit_dict: object,\n",
-    "    *,\n",
-    "    folder_path: str = \"future_aim_results\",\n",
-    "    shots_count: int = 1000,\n",
-    "    noise_model: cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel | None = None,\n",
-    "    run_async: bool = False,\n",
-    ") -> dict[str, list[dict[str, int]]] | None:\n",
-    "    if run_async:\n",
-    "        os.makedirs(folder_path, exist_ok=True)\n",
-    "    else:\n",
-    "        aim_results = {\"physical\": [], \"logical\": []}\n",
-    "\n",
-    "    for layer in circuit_dict.keys():\n",
-    "        if run_async:\n",
-    "            print(f\"Posting circuits associated with layer=('{layer}')\")\n",
-    "        else:\n",
-    "            print(f\"Running circuits associated with layer=('{layer}')\")\n",
-    "\n",
-    "        for basis in (\"z_basis\", \"x_basis\"):\n",
-    "            if run_async:\n",
-    "                u, v = layer.split(\":\")\n",
-    "\n",
-    "                tmp_physical_results = cudaq.sample_async(\n",
-    "                    circuit_dict[layer][\"physical\"][basis], shots_count=shots_count\n",
-    "                )\n",
-    "                file = open(f\"{folder_path}/physical_{basis}_job_u={u}_v={v}_result.txt\", \"w\")\n",
-    "                file.write(str(tmp_physical_results))\n",
-    "                file.close()\n",
-    "\n",
-    "                tmp_logical_results = cudaq.sample_async(\n",
-    "                    circuit_dict[layer][\"logical\"][basis], shots_count=shots_count\n",
-    "                )\n",
-    "                file = open(f\"{folder_path}/logical_{basis}_job_u={u}_v={v}_result.txt\", \"w\")\n",
-    "                file.write(str(tmp_logical_results))\n",
-    "                file.close()\n",
-    "            else:\n",
-    "                tmp_physical_results = cudaq.sample(\n",
-    "                    circuit_dict[layer][\"physical\"][basis],\n",
-    "                    shots_count=shots_count,\n",
-    "                    noise_model=noise_model,\n",
-    "                )\n",
-    "                tmp_logical_results = cudaq.sample(\n",
-    "                    circuit_dict[layer][\"logical\"][basis],\n",
-    "                    shots_count=shots_count,\n",
-    "                    noise_model=noise_model,\n",
-    "                )\n",
-    "                aim_results[\"physical\"].append({k: v for k, v in tmp_physical_results.items()})\n",
-    "                aim_results[\"logical\"].append({k: v for k, v in tmp_logical_results.items()})\n",
-    "    if not run_async:\n",
-    "        print(\"\\nCompleted all circuit sampling!\")\n",
-    "        return aim_results\n",
-    "    else:\n",
-    "        print(\"\\nAll circuits submitted for async sampling!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _get_async_results(\n",
-    "    layers: object, *, folder_path: str = \"future_aim_results\"\n",
-    ") -> dict[str, list[dict[str, int]]]:\n",
-    "    aim_results = {\"physical\": [], \"logical\": []}\n",
-    "    for layer in layers:\n",
-    "        print(f\"Retrieving all circuits counts associated with layer=('{layer}')\")\n",
-    "        u, v = layer.split(\":\")\n",
-    "        for basis in (\"z_basis\", \"x_basis\"):\n",
-    "            file = open(f\"{folder_path}/physical_{basis}_job_u={u}_v={v}_result.txt\", \"r\")\n",
-    "            tmp_physical_results = cudaq.AsyncSampleResult(str(file.read()))\n",
-    "            physical_counts = tmp_physical_results.get()\n",
-    "\n",
-    "            file = open(f\"{folder_path}/logical_{basis}_job_u={u}_v={v}_result.txt\", \"r\")\n",
-    "            tmp_logical_results = cudaq.AsyncSampleResult(str(file.read()))\n",
-    "            logical_counts = tmp_logical_results.get()\n",
-    "\n",
-    "            aim_results[\"physical\"].append({k: v for k, v in physical_counts.items()})\n",
-    "            aim_results[\"logical\"].append({k: v for k, v in logical_counts.items()})\n",
-    "\n",
-    "    print(\"\\nObtained all circuit samples!\")\n",
-    "    return aim_results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running a CUDA-Q noisy simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we will first explore the performance of the physical and logical circuits under the influence of a device noise model. This will help us predict experimental results, as well as understand the dominant error sources at play. Such a simulation can be achieved via CUDA-Q's density matrix simulator: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "cudaq.set_target(\"density-matrix-cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_device_noise(\n",
-    "    depolar_prob_1q: float,\n",
-    "    depolar_prob_2q: float,\n",
-    "    *,\n",
-    "    readout_error_prob: float | None = None,\n",
-    "    custom_gates: list[str] | None = None,\n",
-    ") -> cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel:\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "    depolar_noise = cudaq.DepolarizationChannel(depolar_prob_1q)\n",
-    "\n",
-    "    noisy_ops = [\"z\", \"s\", \"x\", \"h\", \"rx\", \"rz\"]\n",
-    "    for op in noisy_ops:\n",
-    "        noise.add_all_qubit_channel(op, depolar_noise)\n",
-    "\n",
-    "    if custom_gates:\n",
-    "        custom_depolar_channel = cudaq.DepolarizationChannel(depolar_prob_1q)\n",
-    "        for op in custom_gates:\n",
-    "            noise.add_all_qubit_channel(op, custom_depolar_channel)\n",
-    "\n",
-    "    # Two qubit depolarization error\n",
-    "    p_0 = 1 - depolar_prob_2q\n",
-    "    p_1 = np.sqrt((1 - p_0**2) / 3)\n",
-    "\n",
-    "    k0 = np.array(\n",
-    "        [[p_0, 0.0, 0.0, 0.0], [0.0, p_0, 0.0, 0.0], [0.0, 0.0, p_0, 0.0], [0.0, 0.0, 0.0, p_0]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k1 = np.array(\n",
-    "        [[0.0, 0.0, p_1, 0.0], [0.0, 0.0, 0.0, p_1], [p_1, 0.0, 0.0, 0.0], [0.0, p_1, 0.0, 0.0]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k2 = np.array(\n",
-    "        [\n",
-    "            [0.0, 0.0, -1j * p_1, 0.0],\n",
-    "            [0.0, 0.0, 0.0, -1j * p_1],\n",
-    "            [1j * p_1, 0.0, 0.0, 0.0],\n",
-    "            [0.0, 1j * p_1, 0.0, 0.0],\n",
-    "        ],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k3 = np.array(\n",
-    "        [[p_1, 0.0, 0.0, 0.0], [0.0, p_1, 0.0, 0.0], [0.0, 0.0, -p_1, 0.0], [0.0, 0.0, 0.0, -p_1]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    kraus_channel = cudaq.KrausChannel([k0, k1, k2, k3])\n",
-    "\n",
-    "    noise.add_all_qubit_channel(\"cz\", kraus_channel)\n",
-    "    noise.add_all_qubit_channel(\"cx\", kraus_channel)\n",
-    "\n",
-    "    if readout_error_prob is not None:\n",
-    "        # Readout error modeled with a Bit flip channel on identity before measurement\n",
-    "        bit_flip = cudaq.BitFlipChannel(readout_error_prob)\n",
-    "        noise.add_all_qubit_channel(\"meas_id\", bit_flip)\n",
-    "    return noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, with our example noise model defined above, we can synchronously & noisily sample all of our AIM circuits by passing `noise_model=cudaq_noise_model` to the workflow containing function `submit_aim_circuits()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example parameters that can model execution on hardware at the high, simulation, level:\n",
-    "# Take single-qubit gate depolarization rate: ~0.2% or better (fidelity ≥99.8%)\n",
-    "# Take two-qubit gate depolarization rate: ~1–2% (fidelity ~98–99%)\n",
-    "cudaq_noise_model = get_device_noise(0.002, 0.02, readout_error_prob=0.02)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running circuits associated with layer=('1:-9')\n",
-      "Running circuits associated with layer=('1:-1')\n",
-      "Running circuits associated with layer=('1:7')\n",
-      "Running circuits associated with layer=('5:-9')\n",
-      "Running circuits associated with layer=('5:-1')\n",
-      "Running circuits associated with layer=('5:7')\n",
-      "Running circuits associated with layer=('9:-9')\n",
-      "Running circuits associated with layer=('9:-1')\n",
-      "Running circuits associated with layer=('9:7')\n",
-      "\n",
-      "Completed all circuit sampling!\n"
-     ]
-    }
-   ],
-   "source": [
-    "aim_sim_data = submit_aim_circuits(sim_circuit_dict, noise_model=cudaq_noise_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_ordering = []\n",
-    "for key in circuit_layers:\n",
-    "    for basis in (\"z_basis\", \"x_basis\"):\n",
-    "        data_ordering.append((key, basis))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_physical_energies, sim_physical_uncertainties = aim_physical_energies(\n",
-    "    data_ordering, aim_sim_data[\"physical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_logical_energies, sim_logical_uncertainties = aim_logical_energies(\n",
-    "    data_ordering, aim_sim_data[\"logical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To analyze our simulated energy results in the above cells, we will compare them to the brute-force computed exact ground state energies for the AIM Hamiltonian. For simplicity, these are already stored in the dictionary `bf_energies` below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bf_energies = {\n",
-    "    \"1:-9\": -18.251736027394713,\n",
-    "    \"1:-1\": -2.265564437074638,\n",
-    "    \"1:7\": -14.252231964940428,\n",
-    "    \"5:-9\": -19.293350575766127,\n",
-    "    \"5:-1\": -3.608495283014149,\n",
-    "    \"5:7\": -15.305692796870582,\n",
-    "    \"9:-9\": -20.39007993367173,\n",
-    "    \"9:-1\": -5.260398644698076,\n",
-    "    \"9:7\": -16.429650912487233,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the above metric, we can assess the performance of the logical circuits against the physical circuits by considering how far away the respective energies are from the brute-force expected energies. The cell below computes these energy deviations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer=(1, -9) has brute-force energy of: -18.251736027394713\n",
-      "Physical circuit of layer=(1, -9) got an energy of: -15.929\n",
-      "Logical circuit of layer=(1, -9) got an energy of: -17.46016175277361\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, -1) has brute-force energy of: -2.265564437074638\n",
-      "Physical circuit of layer=(1, -1) got an energy of: -1.97\n",
-      "Logical circuit of layer=(1, -1) got an energy of: -2.176531948420889\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, 7) has brute-force energy of: -14.252231964940428\n",
-      "Physical circuit of layer=(1, 7) got an energy of: -12.268\n",
-      "Logical circuit of layer=(1, 7) got an energy of: -13.26321740664324\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -9) has brute-force energy of: -19.293350575766127\n",
-      "Physical circuit of layer=(5, -9) got an energy of: -16.8495\n",
-      "Logical circuit of layer=(5, -9) got an energy of: -18.46681284816878\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -1) has brute-force energy of: -3.608495283014149\n",
-      "Physical circuit of layer=(5, -1) got an energy of: -3.1965000000000003\n",
-      "Logical circuit of layer=(5, -1) got an energy of: -3.4531715120183297\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, 7) has brute-force energy of: -15.305692796870582\n",
-      "Physical circuit of layer=(5, 7) got an energy of: -13.336\n",
-      "Logical circuit of layer=(5, 7) got an energy of: -14.341784541550897\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -9) has brute-force energy of: -20.39007993367173\n",
-      "Physical circuit of layer=(9, -9) got an energy of: -17.802\n",
-      "Logical circuit of layer=(9, -9) got an energy of: -19.339249509416753\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -1) has brute-force energy of: -5.260398644698076\n",
-      "Physical circuit of layer=(9, -1) got an energy of: -4.8580000000000005\n",
-      "Logical circuit of layer=(9, -1) got an energy of: -5.1227150992242025\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, 7) has brute-force energy of: -16.429650912487233\n",
-      "Physical circuit of layer=(9, 7) got an energy of: -14.3635\n",
-      "Logical circuit of layer=(9, 7) got an energy of: -15.448422736181264\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "sim_physical_energy_diff, sim_logical_energy_diff = _get_energy_diff(\n",
-    "    bf_energies, sim_physical_energies, sim_logical_energies\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Both physical and logical circuits were subject to the same noise model, but the `[[4,2,2]]` provides additional information that can help overcome some errors. Visualizing the computed energy differences from the above the cell, our noisy simulation provides a preview of the benefits logical qubits can offer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2200x1400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(11, 7), dpi=200)\n",
-    "\n",
-    "layer_labels = [(int(key.split(\":\")[0]), int(key.split(\":\")[1])) for key in bf_energies.keys()]\n",
-    "plot_labels = [str(item) for item in layer_labels]\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    sim_physical_energy_diff,\n",
-    "    yerr=sim_physical_uncertainties.values(),\n",
-    "    ecolor=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    color=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Physical\",\n",
-    ")\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    sim_logical_energy_diff,\n",
-    "    yerr=sim_logical_uncertainties.values(),\n",
-    "    color=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    ecolor=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Logical\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Hamiltonian Parameters (U, V)\", fontsize=18)\n",
-    "ax.set_ylabel(\"Energy above true ground state (in eV)\", fontsize=18)\n",
-    "ax.set_title(\"CUDA-Q AIM Circuits Simulation (lower is better)\", fontsize=20)\n",
-    "ax.legend(loc=\"upper right\", fontsize=18.5)\n",
-    "plt.xticks(fontsize=16)\n",
-    "plt.yticks(fontsize=16)\n",
-    "\n",
-    "ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=2)\n",
-    "plt.ylim(\n",
-    "    top=max(sim_physical_energy_diff) + max(sim_physical_uncertainties.values()) + 0.2, bottom=-0.2\n",
-    ")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running logical AIM on Infleqtion's hardware "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The entire workflow we've seen thus far can be seamlessly executed on real quantum hardware as well. CUDA-Q has integration with Infleqtion's gate-based neutral atom quantum computer, [Sqale](https://arxiv.org/html/2408.08288v2), allowing execution of CUDA-Q kernels on neutral-atom hardware via Infleqtion’s cross-platform Superstaq compiler API that performs low-level compilation and optimization under the hood. Indeed, the AIM research results seen in [our paper](https://arxiv.org/abs/2412.07670) were obtained via this complete end-to-end workflow.\n",
-    "\n",
-    "To do so, users can obtain a Superstaq API key from [superstaq.infleqtion.com](https://superstaq.infleqtion.com/) to gain access to Infleqtion's neutral-atom simulator, with [pre-registration](https://www.infleqtion.com/sqale-preregistration) open for access to Infleqtion’s neutral atom QPU."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a tutorial, let us reproduce the workflow we've run so far but on Infleqtion's QPU. We begin with the same GPU-enhanced VQE to generate the AIM circuits:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "\n",
-    "if cudaq.num_available_gpus() == 0:\n",
-    "    cudaq.set_target(\"qpp-cpu\", option=\"fp64\")\n",
-    "else:\n",
-    "    cudaq.set_target(\"nvidia\", option=\"fp64\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9\n",
-      "Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1\n",
-      "Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7\n",
-      "Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9\n",
-      "Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1\n",
-      "Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7\n",
-      "Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9\n",
-      "Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1\n",
-      "Computed optimal angles=[-1.7301462945564499, 1.570796044872433] for U=9, V=7\n",
-      "\n",
-      "Finished building optimized circuits!\n"
-     ]
-    }
-   ],
-   "source": [
-    "device_circuit_dict = generate_circuit_set(\n",
-    "    ignore_meas_id=True\n",
-    ")  # Setting `ignore_meas_id=True` drops the noisy-identity gate from earlier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, we change backends! Before selecting an Infleqtion machine in CUDA-Q, we must first set our Superstaq API key, like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ['SUPERSTAQ_API_KEY'] = \"api_key\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we declare the type of execution we would like on Infleqtion's machine based on the keyword options specified:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "\n",
-    "# Set the following to run on Infleqtion's Sqale QPU:\n",
-    "cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\")\n",
-    "\n",
-    "# Set the following to run an ideal dry-run on Infleqtion's Sqale QPU:\n",
-    "# cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\", method=\"dry-run\")\n",
-    "\n",
-    "# Set the following to run a device-realistic noisy simulation of Infleqtion's Sqale QPU:\n",
-    "# cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\", method=\"noise-sim\")\n",
-    "\n",
-    "# Set the following to run a local, ideal emulation:\n",
-    "# cudaq.set_target(\"infleqtion\", emulate=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With that, we're all set! That simple change instructs our AIM circuits to execute on Infleqtion's QPU (or simulator). Due to the general queue wait time of running on hardware, we optionally recommend enabling the `run_async=True` flag to asynchronously sample the circuits. This will allow the cell to be executed and not wait synchronously until all the jobs are complete, allowing other classical code to be run in the meantime. When using `run_async`, an optional directory to store the job information can be specified with `folder_path` (this will be important to later retrieve the job results from the same directory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Posting circuits associated with layer=('1:-9')\n",
-      "Posting circuits associated with layer=('1:-1')\n",
-      "Posting circuits associated with layer=('1:7')\n",
-      "Posting circuits associated with layer=('5:-9')\n",
-      "Posting circuits associated with layer=('5:-1')\n",
-      "Posting circuits associated with layer=('5:7')\n",
-      "Posting circuits associated with layer=('9:-9')\n",
-      "Posting circuits associated with layer=('9:-1')\n",
-      "Posting circuits associated with layer=('9:7')\n",
-      "\n",
-      "All circuits submitted for async sampling!\n"
-     ]
-    }
-   ],
-   "source": [
-    "submit_aim_circuits(\n",
-    "    device_circuit_dict, folder_path=\"hardware_aim_future_results\", shots_count=1000, run_async=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the above cell execution, all the circuits will post to execute on QPU. We can then return at a later time to retrieve the job results with the cell below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Retrieving all circuits counts associated with layer=('1:-9')\n",
-      "Retrieving all circuits counts associated with layer=('1:-1')\n",
-      "Retrieving all circuits counts associated with layer=('1:7')\n",
-      "Retrieving all circuits counts associated with layer=('5:-9')\n",
-      "Retrieving all circuits counts associated with layer=('5:-1')\n",
-      "Retrieving all circuits counts associated with layer=('5:7')\n",
-      "Retrieving all circuits counts associated with layer=('9:-9')\n",
-      "Retrieving all circuits counts associated with layer=('9:-1')\n",
-      "Retrieving all circuits counts associated with layer=('9:7')\n",
-      "\n",
-      "Obtained all circuit samples!\n"
-     ]
-    }
-   ],
-   "source": [
-    "aim_device_data = _get_async_results(circuit_layers, folder_path=\"hardware_aim_future_results\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "physical_energies, physical_uncertainties = aim_physical_energies(\n",
-    "    data_ordering, aim_device_data[\"physical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logical_energies, logical_uncertainties = aim_logical_energies(\n",
-    "    data_ordering, aim_device_data[\"logical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer=(1, -9) has brute-force energy of: -18.251736027394713\n",
-      "Physical circuit of layer=(1, -9) got an energy of: -17.626499999999997\n",
-      "Logical circuit of layer=(1, -9) got an energy of: -17.69666562801761\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, -1) has brute-force energy of: -2.265564437074638\n",
-      "Physical circuit of layer=(1, -1) got an energy of: -2.1415\n",
-      "Logical circuit of layer=(1, -1) got an energy of: -2.2032104443266585\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, 7) has brute-force energy of: -14.252231964940428\n",
-      "Physical circuit of layer=(1, 7) got an energy of: -12.9955\n",
-      "Logical circuit of layer=(1, 7) got an energy of: -13.76919450035401\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -9) has brute-force energy of: -19.293350575766127\n",
-      "Physical circuit of layer=(5, -9) got an energy of: -18.331\n",
-      "Logical circuit of layer=(5, -9) got an energy of: -18.85730052910377\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -1) has brute-force energy of: -3.608495283014149\n",
-      "Physical circuit of layer=(5, -1) got an energy of: -3.476\n",
-      "Logical circuit of layer=(5, -1) got an energy of: -3.5425689231532203\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, 7) has brute-force energy of: -15.305692796870582\n",
-      "Physical circuit of layer=(5, 7) got an energy of: -14.043500000000002\n",
-      "Logical circuit of layer=(5, 7) got an energy of: -14.795918428433312\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -9) has brute-force energy of: -20.39007993367173\n",
-      "Physical circuit of layer=(9, -9) got an energy of: -19.4715\n",
-      "Logical circuit of layer=(9, -9) got an energy of: -19.96524696701215\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -1) has brute-force energy of: -5.260398644698076\n",
-      "Physical circuit of layer=(9, -1) got an energy of: -4.973\n",
-      "Logical circuit of layer=(9, -1) got an energy of: -5.207315773582224\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, 7) has brute-force energy of: -16.429650912487233\n",
-      "Physical circuit of layer=(9, 7) got an energy of: -15.182\n",
-      "Logical circuit of layer=(9, 7) got an energy of: -16.241375689575516\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "physical_energy_diff, logical_energy_diff = _get_energy_diff(\n",
-    "    bf_energies, physical_energies, logical_energies\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As before, we use the same metric of comparing against the true ground state energies; however, this time, both the physical and logical circuits are fully exposed to real hardware noise. Yet, we expect the use of logical qubits afforded to us by the `[[4,2,2]]` code to achieve energies closer to the true ground state than the bare physical circuits (up to a certain error threshold). And indeed they do! Visually, we can plot the energy deviations of both the physical and logical circuits from the cell above and observe that the logical circuits are able to outperform the physical circuits by obtaining much lower energies, demonstrating the power of error detection and the beginning possibilities of fault-tolerant quantum computation: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2200x1400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(11, 7), dpi=200)\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    physical_energy_diff,\n",
-    "    yerr=physical_uncertainties.values(),\n",
-    "    ecolor=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    color=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Physical\",\n",
-    ")\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    logical_energy_diff,\n",
-    "    yerr=logical_uncertainties.values(),\n",
-    "    color=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    ecolor=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Logical\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Hamiltonian Parameters (U, V)\", fontsize=18)\n",
-    "ax.set_ylabel(\"Energy above true ground state (in eV)\", fontsize=18)\n",
-    "ax.set_title(\"CUDA-Q AIM Infleqtion Hardware Execution (lower is better)\", fontsize=20)\n",
-    "ax.legend(loc=\"upper left\", fontsize=18.5)\n",
-    "plt.xticks(fontsize=16)\n",
-    "plt.yticks(fontsize=16)\n",
-    "\n",
-    "ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=2)\n",
-    "plt.ylim(top=max(physical_energy_diff) + max(physical_uncertainties.values()) + 0.2, bottom=-0.2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/qaoa.html b/pr-2308/applications/python/qaoa.html
deleted file mode 100644
index 26c965ac16..0000000000
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-  <section id="Max-Cut-with-QAOA">
-<h1>Max-Cut with QAOA<a class="headerlink" href="#Max-Cut-with-QAOA" title="Permalink to this heading">¶</a></h1>
-<p><a class="reference external" href="https://arxiv.org/abs/1411.4028">Farhi et al.</a> introduced the quantum approximation optimization algorithm (QAOA) to solve optimization problems like the max cut problem. Before diving into the details of QAOA, we’ll first define the max cut problem.</p>
-<p><a class="reference external" href="https://en.wikipedia.org/wiki/Maximum_cut">Max Cut</a> is the problem of finding a partition of a graph’s nodes into two sets which maximizes the edges between the two sets. Although this problem is relatively easy to solve for graphs with few vertices, this problem is <a class="reference external" href="https://en.wikipedia.org/wiki/NP-hardness#:~:text=7%20References-,Definition,complete%20problem%20G%20to%20H.">NP-hard</a>. The max cut problem has a wide range of applications including <a class="reference external" href="https://jmlr.org/papers/volume14/wang13a/wang13a.pdf">machine
-learning</a>, <a class="reference external" href="https://math.mit.edu/research/highschool/primes/materials/2020/Hong-Lee-Wei.pdf">circuit design</a> and <a class="reference external" href="https://www.researchgate.net/publication/262162554_An_Application_of_Combinatorial_Optimization_to_Statistical_Physics_and_Circuit_Layout_Design">statistical physics</a>, among others. Furthermore, the QAOA algorithm presented in this tutorial can be adapted to other related optimization problems with an even wider
-application field including <a class="reference external" href="https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.043204">portfolio optimization</a> and <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0377221723002072">job shop scheduling</a>, just to name a few.</p>
-<p>We take the convention that <span class="math notranslate nohighlight">\(G=(V,E)\)</span> represents a graph with vertex set <span class="math notranslate nohighlight">\(V\subseteq \mathbb{N}\)</span> and edge set <span class="math notranslate nohighlight">\(E\subseteq V\times V\)</span>. We use the terms vertex and node interchangeably. For this tutorial we assume that the graphs are undirected (that is, <span class="math notranslate nohighlight">\((u,v)\)</span> and <span class="math notranslate nohighlight">\((v,u)\)</span> represent the same edge). Our graphs contain no self loops (i.e., for every vertex <span class="math notranslate nohighlight">\(v\)</span>, there is no edge <span class="math notranslate nohighlight">\((v,v)\)</span>). A <em>cut</em> of the graph <span class="math notranslate nohighlight">\(G\)</span> is a partition, <span class="math notranslate nohighlight">\((V_0,V_1)\)</span>, of
-the vertex set such that every vertex of <span class="math notranslate nohighlight">\(V\)</span> is a member of exactly one of <span class="math notranslate nohighlight">\(V_0\)</span> or <span class="math notranslate nohighlight">\(V_1\)</span> (i.e., <span class="math notranslate nohighlight">\(V_0\bigcup V_1 = V\)</span> and <span class="math notranslate nohighlight">\(V_0\bigcap V_0=\emptyset\)</span>). The <em>cut value</em> for a partition is the sum of the edges with one node in <span class="math notranslate nohighlight">\(V_0\)</span> and one node in <span class="math notranslate nohighlight">\(V_1\)</span>.</p>
-<p>In the images below, we illustrate two cuts of a graph with the dotted lines. Each of these cuts partitions the graph into two disjoint sets. The cut on the left is not optimal, and the cut on the right is the max cut. The cut on the left divides the graph into disjoint sets <span class="math notranslate nohighlight">\(\{1,2\}\)</span> and <span class="math notranslate nohighlight">\(\{0,3,4\}\)</span>, and that cut contains 3 edges. To more easily visualize the cut, we have colored the vertices in one set of the partition green and the vertices in the other set of the partition gray.
-The cut depicted in the diagram on the right divides the graph vertices into two disjoint sets <span class="math notranslate nohighlight">\(V_0=\{0,2\}\)</span>, colored gray, and <span class="math notranslate nohighlight">\(V_1=\{1,3,4\}\)</span>, colored green. The number of edges connecting vertices in the distinct sets is computed by</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\sum_{\substack{u \in V_0; v\in V_1\\ (u,v) \in E}}1.\end{split}\]</div>
-<p>For the graph on the right, the number of edges in the cut (in this case there are <span class="math notranslate nohighlight">\(5\)</span> edges) is maximal, and this value is referred to as the <em>max cut value</em>. The partitioning <span class="math notranslate nohighlight">\((V_0,V_1)\)</span> — and sometimes the set of edges connecting vertices in <span class="math notranslate nohighlight">\(V_0\)</span> and <span class="math notranslate nohighlight">\(V_1\)</span> — is referred to as a <em>max cut of a graph</em>. Note that the max cut of a graph need not be unique; that is, two distinct partitions may produce the same cut value.</p>
-<p><img alt="image0" src="../../_images/max-cut-illustration.png" /></p>
-<p>We will use bitstrings to identify vertices in each of the two partitions. For example using the ordering of the vertices, the bitstring <code class="docutils literal notranslate"><span class="pre">01100</span></code> captures the partition in the image above on the left with vertices <span class="math notranslate nohighlight">\(1\)</span> and <span class="math notranslate nohighlight">\(2\)</span> in <span class="math notranslate nohighlight">\(V_1\)</span>, and the bitstring <code class="docutils literal notranslate"><span class="pre">01011</span></code> codes the partition in the image on the right with vertices <span class="math notranslate nohighlight">\(1\)</span>, <span class="math notranslate nohighlight">\(3\)</span>, and <span class="math notranslate nohighlight">\(4\)</span> in <span class="math notranslate nohighlight">\(V_1\)</span>.</p>
-<p>Let’s code our graph data as lists of integers so that we can call these variables when we create the <code class="docutils literal notranslate"><span class="pre">cudaq.kernel</span></code> for QAOA.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-
-<span class="c1"># We&#39;ll use the graph below to illustrate how QAOA can be used to</span>
-<span class="c1"># solve a max cut problem</span>
-
-<span class="c1">#       v1  0--------------0 v2</span>
-<span class="c1">#           |              | \</span>
-<span class="c1">#           |              |  \</span>
-<span class="c1">#           |              |   \</span>
-<span class="c1">#           |              |    \</span>
-<span class="c1">#       v0  0--------------0 v3-- 0 v4</span>
-<span class="c1"># The max cut solutions are 01011, 10100, 01010, 10101 .</span>
-
-<span class="c1"># First we define the graph nodes (i.e., vertices) and edges as lists of integers so that they can be broadcast into</span>
-<span class="c1"># a cudaq.kernel.</span>
-<span class="n">nodes</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
-<span class="n">edges</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
-<span class="n">edges_src</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">edges</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">edges</span><span class="p">))]</span>
-<span class="n">edges_tgt</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">edges</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">edges</span><span class="p">))]</span>
-</pre></div>
-</div>
-</div>
-<p>QAOA is a variational algortihm with a particular ansatz. QAOA is made up of a variational quantum circuit (i.e., a kernel that depends on a set of parameter values) and a classical optimizer. The aim of QAOA is to use the classical optimizer to identify parameter values that generate a quantum circuit whose expectation value for a given cost Hamilitonian is minimized.</p>
-<p>What distinguishes QAOA from other variational algorithms is the structure of the quantum circuit. For each vertex in the graph, there is an associated qubit in the circuit. The circuit is initialized in a superposition state. The remainder of the QAOA circuit is made up of blocks (referred to as layers). The more layers there are, the better the approximation the algorithm achieves.</p>
-<p><img alt="diagram of QAOA circuit layers" src="../../_images/qaoa-circuit-layers.png" /></p>
-<p>Each layer contains a problem kernel and a mixer kernel. The mixer kernel is composed of parameterized rotation gates applied to each qubit, depicted in green in the image above. The problem kernel encodes the graph edges. The image below shows an example of an graph edge encoded with controlled-X gates and a parameterized rotation gate.</p>
-<p><img alt="diagram of a QAOA problem kernel for a max cut problem" src="../../_images/qaoa-problem-kernel.png" /></p>
-<p>Let’s implement the QAOA circuit for our max cut problem in CUDA-Q.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Problem parameters</span>
-<span class="c1"># The number of qubits we&#39;ll need is the same as the number of vertices in our graph</span>
-<span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
-
-<span class="c1"># We can set the layer count to be any positive integer.  Larger values will create deeper circuits</span>
-<span class="n">layer_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="c1"># Each layer of the QAOA kernel contains 2 parameters</span>
-<span class="n">parameter_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">layer_count</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">qaoaProblem</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Build the QAOA gate sequence between two qubits that represent an edge of the graph</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    qubit_0: cudaq.qubit</span>
-<span class="sd">        Qubit representing the first vertex of an edge</span>
-<span class="sd">    qubit_1: cudaq.qubit</span>
-<span class="sd">        Qubit representing the second vertex of an edge</span>
-<span class="sd">    thetas: List[float]</span>
-<span class="sd">        Free variable</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    cudaq.Kernel</span>
-<span class="sd">        Subcircuit of the problem kernel for Max-Cut of the graph with a given edge</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">)</span>
-    <span class="n">rz</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">)</span>
-
-
-<span class="c1"># We now define the kernel_qaoa function which will be the QAOA circuit for our graph</span>
-<span class="c1"># Since the QAOA circuit for max cut depends on the structure of the graph,</span>
-<span class="c1"># we&#39;ll feed in global concrete variable values into the kernel_qaoa function for the qubit_count, layer_count, edges_src, edges_tgt.</span>
-<span class="c1"># The types for these variables are restricted to Quake Values (e.g. qubit, int, List[int], ...)</span>
-<span class="c1"># The thetas plaeholder will be our free parameters</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel_qaoa</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">layer_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">edges_src</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
-                <span class="n">edges_tgt</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">thetas</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Build the QAOA circuit for max cut of the graph with given edges and nodes</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    qubit_count: int</span>
-<span class="sd">        Number of qubits in the circuit, which is the same as the number of nodes in our graph</span>
-<span class="sd">    layer_count : int</span>
-<span class="sd">        Number of layers in the QAOA kernel</span>
-<span class="sd">    edges_src: List[int]</span>
-<span class="sd">        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes</span>
-<span class="sd">    edges_tgt: List[int]</span>
-<span class="sd">        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes</span>
-<span class="sd">    thetas: List[float]</span>
-<span class="sd">        Free variables to be optimized</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    cudaq.Kernel</span>
-<span class="sd">        QAOA circuit for Max-Cut for max cut of the graph with given edges and nodes</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Let&#39;s allocate the qubits</span>
-    <span class="n">qreg</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="c1"># And then place the qubits in superposition</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qreg</span><span class="p">)</span>
-
-    <span class="c1"># Each layer has two components: the problem kernel and the mixer</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layer_count</span><span class="p">):</span>
-        <span class="c1"># Add the problem kernel to each layer</span>
-        <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">edges_src</span><span class="p">)):</span>
-            <span class="n">qubitu</span> <span class="o">=</span> <span class="n">edges_src</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span>
-            <span class="n">qubitv</span> <span class="o">=</span> <span class="n">edges_tgt</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span>
-            <span class="n">qaoaProblem</span><span class="p">(</span><span class="n">qreg</span><span class="p">[</span><span class="n">qubitu</span><span class="p">],</span> <span class="n">qreg</span><span class="p">[</span><span class="n">qubitv</span><span class="p">],</span> <span class="n">thetas</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="c1"># Add the mixer kernel to each layer</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-            <span class="n">rx</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">thetas</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">layer_count</span><span class="p">],</span> <span class="n">qreg</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<p>As mentioned earlier, QAOA is a variational algorithm that aims to minimize a cost function. To apply QAOA to the max cut problem, we assign a binary variable <span class="math notranslate nohighlight">\(z_v\)</span> to each vertex <span class="math notranslate nohighlight">\(v\)</span>. These variables will take on the vales <span class="math notranslate nohighlight">\(-1\)</span> or <span class="math notranslate nohighlight">\(1\)</span>. Notice that an assignment of variable values corresponds to a partition of the graph. For example, <span class="math notranslate nohighlight">\(\bar z = [-1, 1, -1, 1, 1]\)</span> would partition the graph into two sets: <span class="math notranslate nohighlight">\(\{0,2\}\)</span> and <span class="math notranslate nohighlight">\(\{1,3,4\}.\)</span> We can compute the cut
-value of the graph for an arbirtrary assignment of variable values by the formula:</p>
-<div class="math notranslate nohighlight">
-\[C(\bar{z})= \frac{1}{2} \sum_{(u,v)\in E} (1-z_uz_v),\]</div>
-<p>where <span class="math notranslate nohighlight">\(E\)</span> is the set of edges of our graph. Our goal is to find a variable assignment that maximizes <span class="math notranslate nohighlight">\(C(\bar z)\)</span>. Equivalently, we want to find a variable assignment that minimizes <span class="math notranslate nohighlight">\(-C(\bar z)\)</span>. Since QAOA identifies minimum values, we have reframed the max cut optimization problem from one of maximization to minimization. Furthermore, we can promote this equation to a matrix equation by replacing <span class="math notranslate nohighlight">\(z_uz_v-1\)</span> with Pauli-Z operators acting on qubits associated with nodes
-<span class="math notranslate nohighlight">\(u\)</span> and <span class="math notranslate nohighlight">\(v\)</span>, respectively, and replacing 1 with the identity matrix. This leads to the reformulation of the problem from one of maximizing <span class="math notranslate nohighlight">\(C(\bar{z})\)</span> to one of minimizing the eigenvalues of</p>
-<div class="math notranslate nohighlight">
-\[H= \frac{1}{2}\sum_{(u,v)\in E} (Z_uZ_v-II).\]</div>
-<p>We can code this Hamiltonian using the <code class="docutils literal notranslate"><span class="pre">cudaq.spin</span></code> operators.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The problem Hamiltonian</span>
-<span class="c1"># Define a function to generate the Hamiltonian for a max cut problem using the graph</span>
-<span class="c1"># with the given edges</span>
-
-
-<span class="k">def</span> <span class="nf">hamiltonian_max_cut</span><span class="p">(</span><span class="n">edges_src</span><span class="p">,</span> <span class="n">edges_tgt</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Hamiltonian for finding the max cut for the graph with given edges and nodes</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    edges_src: List[int]</span>
-<span class="sd">        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes</span>
-<span class="sd">    edges_tgt: List[int]</span>
-<span class="sd">        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    cudaq.SpinOperator</span>
-<span class="sd">        Hamiltonian for finding the max cut of the graph with given edges</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">hamiltonian</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">edges_src</span><span class="p">)):</span>
-
-        <span class="n">qubitu</span> <span class="o">=</span> <span class="n">edges_src</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span>
-        <span class="n">qubitv</span> <span class="o">=</span> <span class="n">edges_tgt</span><span class="p">[</span><span class="n">edge</span><span class="p">]</span>
-        <span class="c1"># Add a term to the Hamiltonian for the edge (u,v)</span>
-        <span class="n">hamiltonian</span> <span class="o">+=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">qubitu</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">qubitv</span><span class="p">)</span> <span class="o">-</span>
-                              <span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">qubitu</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">i</span><span class="p">(</span><span class="n">qubitv</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">hamiltonian</span>
-</pre></div>
-</div>
-</div>
-<p>To inititiate the QAOA algorithm, we need to identify initial parameters and specify the classical optimization routine.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify the optimizer and its initial parameters.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_random_seed</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">NelderMead</span><span class="p">()</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
-<span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">8</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">8</span><span class="p">,</span>
-                                                 <span class="n">parameter_count</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Initial parameters = &quot;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">initial_parameters</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064]
-</pre></div></div>
-</div>
-<p>We can either use the <code class="docutils literal notranslate"><span class="pre">observe</span></code> primitive or the <code class="docutils literal notranslate"><span class="pre">vqe</span></code> primitive to code up the optimization loop of QAOA.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#cudaq.set_target(&#39;nvidia&#39;)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;qpp-cpu&#39;</span><span class="p">)</span>
-
-<span class="c1"># Generate the Hamiltonian for our graph</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">hamiltonian_max_cut</span><span class="p">(</span><span class="n">edges_src</span><span class="p">,</span> <span class="n">edges_tgt</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">hamiltonian</span><span class="p">)</span>
-
-<span class="c1"># Define the objective, return `&lt;state(params) | H | state(params)&gt;`</span>
-<span class="c1"># Note that in the `observe` call we list the kernel, the hamiltonian, and then the concrete global variable values of our kernel</span>
-<span class="c1"># followed by the parameters to be optimized.</span>
-
-
-<span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel_qaoa</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">layer_count</span><span class="p">,</span>
-                         <span class="n">edges_src</span><span class="p">,</span> <span class="n">edges_tgt</span><span class="p">,</span> <span class="n">parameters</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-
-<span class="c1"># Optimize!</span>
-<span class="n">optimal_expectation</span><span class="p">,</span> <span class="n">optimal_parameters</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span>
-    <span class="n">dimensions</span><span class="o">=</span><span class="n">parameter_count</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">objective</span><span class="p">)</span>
-
-<span class="c1"># Alternatively we can use the vqe call (just comment out the above code and uncomment the code below)</span>
-<span class="c1"># optimal_expectation, optimal_parameters = cudaq.vqe(</span>
-<span class="c1">#    kernel=kernel_qaoa,</span>
-<span class="c1">#    spin_operator=hamiltonian,</span>
-<span class="c1">#    argument_mapper=lambda parameter_vector: (qubit_count, layer_count, edges_src, edges_tgt, parameter_vector),</span>
-<span class="c1">#    optimizer=optimizer,</span>
-<span class="c1">#    parameter_count=parameter_count)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimal_expectation =&#39;</span><span class="p">,</span> <span class="n">optimal_expectation</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Therefore, the max cut value is at least &#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">optimal_expectation</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimal_parameters =&#39;</span><span class="p">,</span> <span class="n">optimal_parameters</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[0.5+0j] IIZIZ
-[0.5+0j] IZZII
-[-3+0j] IIIII
-[0.5+0j] ZZIII
-[0.5+0j] IIIZZ
-[0.5+0j] IIZZI
-[0.5+0j] ZIIZI
-
-optimal_expectation = -4.495973826282007
-Therefore, the max cut value is at least  4.495973826282007
-optimal_parameters = [0.51349181993727, -0.21299416361632417, 0.3250526425808945, 0.886630847343767]
-</pre></div></div>
-</div>
-<p>Now that we have identified the optimal parameters, we can read out the partitioning(s) that gives us a max cut of the graph using the <code class="docutils literal notranslate"><span class="pre">sample</span></code> primitive.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Sample the circuit using the optimized parameters</span>
-<span class="c1"># Since our kernel has more than one argument, we need to list the values for each of these variables in order in the `sample` call.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel_qaoa</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">layer_count</span><span class="p">,</span> <span class="n">edges_src</span><span class="p">,</span>
-                      <span class="n">edges_tgt</span><span class="p">,</span> <span class="n">optimal_parameters</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="c1"># Identify the most likely outcome from the sample</span>
-<span class="n">max_cut</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">counts</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The max cut is given by the partition: &#39;</span><span class="p">,</span>
-      <span class="nb">max</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">counts</span><span class="p">[</span><span class="n">x</span><span class="p">]))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{ 11111:4 11110:1 01101:2 01010:148 01110:47 01000:3 00110:54 10101:165 00101:2 01011:154 00100:3 01001:41 00000:1 00011:3 01100:8 10011:7 00010:2 01111:2 11011:3 00111:10 11100:7 10001:65 10010:7 10100:144 10110:40 10000:5 10111:3 11000:7 11101:1 11001:59 11010:2 }
-
-The max cut is given by the partition:  10101
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)
-</pre></div></div>
-</div>
-</section>
-
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diff --git a/pr-2308/applications/python/qaoa.ipynb b/pr-2308/applications/python/qaoa.ipynb
deleted file mode 100644
index b064182dd6..0000000000
--- a/pr-2308/applications/python/qaoa.ipynb
+++ /dev/null
@@ -1,376 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Max-Cut with QAOA\n",
-    "\n",
-    "[Farhi et al.](https://arxiv.org/abs/1411.4028) introduced the quantum approximation optimization algorithm (QAOA) to solve optimization problems like the max cut problem. Before diving into the details of QAOA, we'll first define the max cut problem. \n",
-    "\n",
-    "[Max Cut](https://en.wikipedia.org/wiki/Maximum_cut) is the problem of finding a partition of a graph's nodes into two sets which maximizes the edges between the two sets. Although this problem is relatively easy to solve for graphs with few vertices, this problem is [NP-hard](https://en.wikipedia.org/wiki/NP-hardness#:~:text=7%20References-,Definition,complete%20problem%20G%20to%20H.). The max cut problem has a wide range of applications including [machine learning](https://jmlr.org/papers/volume14/wang13a/wang13a.pdf), [circuit design](https://math.mit.edu/research/highschool/primes/materials/2020/Hong-Lee-Wei.pdf) and [statistical physics](https://www.researchgate.net/publication/262162554_An_Application_of_Combinatorial_Optimization_to_Statistical_Physics_and_Circuit_Layout_Design), among others. Furthermore, the QAOA algorithm presented in this tutorial can be adapted to other related optimization problems with an even wider application field including [portfolio optimization](https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.043204) and [job shop scheduling](https://www.sciencedirect.com/science/article/pii/S0377221723002072), just to name a few.\n",
-    "\n",
-    "We take the convention that $G=(V,E)$ represents a graph with vertex set $V\\subseteq \\mathbb{N}$ and edge set $E\\subseteq V\\times V$. We use the terms vertex and node interchangeably.  For this tutorial we assume that the graphs are undirected (that is, $(u,v)$ and $(v,u)$ represent the same edge). Our graphs contain no self loops (i.e., for every vertex $v$, there is no edge $(v,v)$). A *cut* of the graph $G$ is a partition, $(V_0,V_1)$, of the vertex set such that every vertex of $V$ is a member of exactly one of $V_0$ or $V_1$ (i.e., $V_0\\bigcup V_1 = V$ and $V_0\\bigcap V_0=\\emptyset$). The *cut value* for a partition is the sum of the edges with one node in $V_0$ and one node in $V_1$.\n",
-    "\n",
-    "In the images below, we illustrate two cuts of a graph with the dotted lines. Each of these cuts partitions the graph into two disjoint sets. The cut on the left is not optimal, and the cut on the right is the max cut. The cut on the left divides the graph into disjoint sets $\\{1,2\\}$ and $\\{0,3,4\\}$, and that cut contains 3 edges. To more easily visualize the cut, we have colored the vertices in one set of the partition green and the vertices in the other set of the partition gray.\n",
-    "The cut depicted in the diagram on the right divides the graph vertices into two disjoint sets $V_0=\\{0,2\\}$, colored gray, and $V_1=\\{1,3,4\\}$, colored green. The number of edges connecting vertices in the distinct sets is computed by $$\\sum_{\\substack{u \\in V_0; v\\in V_1\\\\ (u,v) \\in E}}1.$$ For the graph on the right, the number of edges in the cut (in this case there are $5$ edges) is maximal, and this value is referred to as the *max cut value*. The partitioning $(V_0,V_1)$  &mdash; and sometimes the set of edges connecting vertices in $V_0$ and $V_1$  &mdash; is referred to as a *max cut of a graph*. Note that the max cut of a graph need not be unique; that is, two distinct partitions may produce the same cut value.\n",
-    " \n",
-    "![](images/max-cut-illustration.png)\n",
-    " \n",
-    "We will use bitstrings to identify vertices in each of the two partitions. For example using the ordering of the vertices, the bitstring `01100` captures the partition in the image above on the left with vertices $1$ and $2$ in $V_1$, and the bitstring `01011` codes the partition in the image on the right with vertices $1$, $3$, and $4$ in $V_1$.\n",
-    "\n",
-    "Let's code our graph data as lists of integers so that we can call these variables when we create the `cudaq.kernel` for QAOA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "from typing import List\n",
-    "\n",
-    "# We'll use the graph below to illustrate how QAOA can be used to\n",
-    "# solve a max cut problem\n",
-    "\n",
-    "#       v1  0--------------0 v2\n",
-    "#           |              | \\\n",
-    "#           |              |  \\\n",
-    "#           |              |   \\\n",
-    "#           |              |    \\\n",
-    "#       v0  0--------------0 v3-- 0 v4\n",
-    "# The max cut solutions are 01011, 10100, 01010, 10101 .\n",
-    "\n",
-    "# First we define the graph nodes (i.e., vertices) and edges as lists of integers so that they can be broadcast into\n",
-    "# a cudaq.kernel.\n",
-    "nodes: List[int] = [0, 1, 2, 3, 4]\n",
-    "edges = [[0, 1], [1, 2], [2, 3], [3, 0], [2, 4], [3, 4]]\n",
-    "edges_src: List[int] = [edges[i][0] for i in range(len(edges))]\n",
-    "edges_tgt: List[int] = [edges[i][1] for i in range(len(edges))]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "QAOA is a variational algortihm with a particular ansatz. QAOA is made up of a variational quantum circuit (i.e., a kernel that depends on a set of parameter values) and a classical optimizer. The aim of QAOA is to use the classical optimizer to identify parameter values that generate a quantum circuit whose expectation value for a given cost Hamilitonian is minimized. \n",
-    "\n",
-    "What distinguishes QAOA from other variational algorithms is the structure of the quantum circuit. For each vertex in the graph, there is an associated qubit in the circuit. The circuit is initialized in a superposition state. The remainder of the QAOA circuit is made up of blocks (referred to as layers). The more layers there are, the better the approximation the algorithm achieves.\n",
-    "\n",
-    "![diagram of QAOA circuit layers](images/qaoa-circuit-layers.png)\n",
-    "\n",
-    "\n",
-    "\n",
-    "Each layer contains a problem kernel and a mixer kernel. The mixer kernel is composed of parameterized rotation gates applied to each qubit, depicted in green in the image above. The problem kernel encodes the graph edges. The image below shows an example of an graph edge encoded with controlled-X gates and a parameterized rotation gate.\n",
-    "\n",
-    "![diagram of a QAOA problem kernel for a max cut problem](images/qaoa-problem-kernel.png)\n",
-    "\n",
-    "Let's implement the QAOA circuit for our max cut problem in CUDA-Q."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Problem parameters\n",
-    "# The number of qubits we'll need is the same as the number of vertices in our graph\n",
-    "qubit_count: int = len(nodes)\n",
-    "\n",
-    "# We can set the layer count to be any positive integer.  Larger values will create deeper circuits\n",
-    "layer_count: int = 2\n",
-    "\n",
-    "# Each layer of the QAOA kernel contains 2 parameters\n",
-    "parameter_count: int = 2 * layer_count\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qaoaProblem(qubit_0: cudaq.qubit, qubit_1: cudaq.qubit, alpha: float):\n",
-    "    \"\"\"Build the QAOA gate sequence between two qubits that represent an edge of the graph\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    qubit_0: cudaq.qubit \n",
-    "        Qubit representing the first vertex of an edge\n",
-    "    qubit_1: cudaq.qubit \n",
-    "        Qubit representing the second vertex of an edge\n",
-    "    thetas: List[float]\n",
-    "        Free variable   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.Kernel\n",
-    "        Subcircuit of the problem kernel for Max-Cut of the graph with a given edge\n",
-    "    \"\"\"\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "    rz(2.0 * alpha, qubit_1)\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "\n",
-    "# We now define the kernel_qaoa function which will be the QAOA circuit for our graph\n",
-    "# Since the QAOA circuit for max cut depends on the structure of the graph,\n",
-    "# we'll feed in global concrete variable values into the kernel_qaoa function for the qubit_count, layer_count, edges_src, edges_tgt.\n",
-    "# The types for these variables are restricted to Quake Values (e.g. qubit, int, List[int], ...)\n",
-    "# The thetas plaeholder will be our free parameters\n",
-    "@cudaq.kernel\n",
-    "def kernel_qaoa(qubit_count: int, layer_count: int, edges_src: List[int],\n",
-    "                edges_tgt: List[int], thetas: List[float]):\n",
-    "    \"\"\"Build the QAOA circuit for max cut of the graph with given edges and nodes\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    qubit_count: int \n",
-    "        Number of qubits in the circuit, which is the same as the number of nodes in our graph\n",
-    "    layer_count : int \n",
-    "        Number of layers in the QAOA kernel\n",
-    "    edges_src: List[int]\n",
-    "        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    edges_tgt: List[int]\n",
-    "        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    thetas: List[float]\n",
-    "        Free variables to be optimized   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.Kernel\n",
-    "        QAOA circuit for Max-Cut for max cut of the graph with given edges and nodes\n",
-    "    \"\"\"\n",
-    "    # Let's allocate the qubits\n",
-    "    qreg = cudaq.qvector(qubit_count)\n",
-    "    # And then place the qubits in superposition\n",
-    "    h(qreg)\n",
-    "\n",
-    "    # Each layer has two components: the problem kernel and the mixer\n",
-    "    for i in range(layer_count):\n",
-    "        # Add the problem kernel to each layer\n",
-    "        for edge in range(len(edges_src)):\n",
-    "            qubitu = edges_src[edge]\n",
-    "            qubitv = edges_tgt[edge]\n",
-    "            qaoaProblem(qreg[qubitu], qreg[qubitv], thetas[i])\n",
-    "        # Add the mixer kernel to each layer\n",
-    "        for j in range(qubit_count):\n",
-    "            rx(2.0 * thetas[i + layer_count], qreg[j])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As mentioned earlier, QAOA is a variational algorithm that aims to minimize a cost function. To apply QAOA to the max cut problem, we assign a binary variable $z_v$ to each vertex $v$. These variables will take on the vales $-1$ or $1$. Notice that an assignment of variable values corresponds to a partition of the graph.  For example, $\\bar z = [-1, 1, -1, 1, 1]$ would partition the graph into two sets: $\\{0,2\\}$ and $\\{1,3,4\\}.$ We can compute the cut value of the graph for an arbirtrary assignment of variable values by the formula: $$C(\\bar{z})= \\frac{1}{2} \\sum_{(u,v)\\in E} (1-z_uz_v),$$ where $E$ is the set of edges of our graph.  Our goal is to find a variable assignment that maximizes $C(\\bar z)$.  Equivalently, we want to find a variable assignment that minimizes $-C(\\bar z)$. Since QAOA identifies minimum values, we have reframed the max cut optimization problem from one of maximization to minimization. Furthermore, we can promote this equation to a matrix equation by replacing $z_uz_v-1$ with Pauli-Z operators acting on qubits associated with nodes $u$ and $v$, respectively, and replacing 1 with the identity matrix. This leads to the reformulation of the problem from one of maximizing $C(\\bar{z})$ to one of minimizing the eigenvalues of $$H= \\frac{1}{2}\\sum_{(u,v)\\in E} (Z_uZ_v-II).$$  \n",
-    "\n",
-    "We can code this Hamiltonian using the `cudaq.spin` operators."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The problem Hamiltonian\n",
-    "# Define a function to generate the Hamiltonian for a max cut problem using the graph\n",
-    "# with the given edges\n",
-    "\n",
-    "\n",
-    "def hamiltonian_max_cut(edges_src, edges_tgt):\n",
-    "    \"\"\"Hamiltonian for finding the max cut for the graph with given edges and nodes\n",
-    "        \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    edges_src: List[int]\n",
-    "        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    edges_tgt: List[int]\n",
-    "        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.SpinOperator\n",
-    "        Hamiltonian for finding the max cut of the graph with given edges \n",
-    "    \"\"\"\n",
-    "\n",
-    "    hamiltonian = 0\n",
-    "\n",
-    "    for edge in range(len(edges_src)):\n",
-    "\n",
-    "        qubitu = edges_src[edge]\n",
-    "        qubitv = edges_tgt[edge]\n",
-    "        # Add a term to the Hamiltonian for the edge (u,v)\n",
-    "        hamiltonian += 0.5 * (spin.z(qubitu) * spin.z(qubitv) -\n",
-    "                              spin.i(qubitu) * spin.i(qubitv))\n",
-    "\n",
-    "    return hamiltonian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To inititiate the QAOA algorithm, we need to identify initial parameters and specify the classical optimization routine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Specify the optimizer and its initial parameters.\n",
-    "cudaq.set_random_seed(13)\n",
-    "optimizer = cudaq.optimizers.NelderMead()\n",
-    "np.random.seed(13)\n",
-    "optimizer.initial_parameters = np.random.uniform(-np.pi / 8, np.pi / 8,\n",
-    "                                                 parameter_count)\n",
-    "print(\"Initial parameters = \", optimizer.initial_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can either use the `observe` primitive or the `vqe` primitive to code up the optimization loop of QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.5+0j] IIZIZ\n",
-      "[0.5+0j] IZZII\n",
-      "[-3+0j] IIIII\n",
-      "[0.5+0j] ZZIII\n",
-      "[0.5+0j] IIIZZ\n",
-      "[0.5+0j] IIZZI\n",
-      "[0.5+0j] ZIIZI\n",
-      "\n",
-      "optimal_expectation = -4.495973826282007\n",
-      "Therefore, the max cut value is at least  4.495973826282007\n",
-      "optimal_parameters = [0.51349181993727, -0.21299416361632417, 0.3250526425808945, 0.886630847343767]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#cudaq.set_target('nvidia')\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "\n",
-    "# Generate the Hamiltonian for our graph\n",
-    "hamiltonian = hamiltonian_max_cut(edges_src, edges_tgt)\n",
-    "print(hamiltonian)\n",
-    "\n",
-    "# Define the objective, return `<state(params) | H | state(params)>`\n",
-    "# Note that in the `observe` call we list the kernel, the hamiltonian, and then the concrete global variable values of our kernel\n",
-    "# followed by the parameters to be optimized.\n",
-    "\n",
-    "\n",
-    "def objective(parameters):\n",
-    "    return cudaq.observe(kernel_qaoa, hamiltonian, qubit_count, layer_count,\n",
-    "                         edges_src, edges_tgt, parameters).expectation()\n",
-    "\n",
-    "\n",
-    "# Optimize!\n",
-    "optimal_expectation, optimal_parameters = optimizer.optimize(\n",
-    "    dimensions=parameter_count, function=objective)\n",
-    "\n",
-    "# Alternatively we can use the vqe call (just comment out the above code and uncomment the code below)\n",
-    "# optimal_expectation, optimal_parameters = cudaq.vqe(\n",
-    "#    kernel=kernel_qaoa,\n",
-    "#    spin_operator=hamiltonian,\n",
-    "#    argument_mapper=lambda parameter_vector: (qubit_count, layer_count, edges_src, edges_tgt, parameter_vector),\n",
-    "#    optimizer=optimizer,\n",
-    "#    parameter_count=parameter_count)\n",
-    "\n",
-    "print('optimal_expectation =', optimal_expectation)\n",
-    "print('Therefore, the max cut value is at least ', -1 * optimal_expectation)\n",
-    "print('optimal_parameters =', optimal_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have identified the optimal parameters, we can read out the partitioning(s) that gives us a max cut of the graph using the `sample` primitive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11111:4 11110:1 01101:2 01010:148 01110:47 01000:3 00110:54 10101:165 00101:2 01011:154 00100:3 01001:41 00000:1 00011:3 01100:8 10011:7 00010:2 01111:2 11011:3 00111:10 11100:7 10001:65 10010:7 10100:144 10110:40 10000:5 10111:3 11000:7 11101:1 11001:59 11010:2 }\n",
-      "\n",
-      "The max cut is given by the partition:  10101\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sample the circuit using the optimized parameters\n",
-    "# Since our kernel has more than one argument, we need to list the values for each of these variables in order in the `sample` call.\n",
-    "counts = cudaq.sample(kernel_qaoa, qubit_count, layer_count, edges_src,\n",
-    "                      edges_tgt, optimal_parameters)\n",
-    "print(counts)\n",
-    "\n",
-    "# Identify the most likely outcome from the sample\n",
-    "max_cut = max(counts, key=lambda x: counts[x])\n",
-    "print('The max cut is given by the partition: ',\n",
-    "      max(counts, key=lambda x: counts[x]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/quantum_fourier_transform.html b/pr-2308/applications/python/quantum_fourier_transform.html
deleted file mode 100644
index 7a53468462..0000000000
--- a/pr-2308/applications/python/quantum_fourier_transform.html
+++ /dev/null
@@ -1,1037 +0,0 @@
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="Quantum-Fourier-Transform">
-<h1>Quantum Fourier Transform<a class="headerlink" href="#Quantum-Fourier-Transform" title="Permalink to this heading">¶</a></h1>
-<p>Classical Fourier transforms aid many areas such as signal processing and data compression. Below, we quantize it to enable Quantum Fourier Transforms (QFT) in CUDA-Q.</p>
-<p>QFT is a key ingredient in Shor’s algorithm which is exponentially faster at factoring prime numbers and also plays a vital role in many other interesting quantum algorithms.</p>
-<p>The idea with classical Fourier transforms is to take the problem from the frequency domain, to the time domain where it is easier to solve. This is the same analogy we draw with QFT, where we take the basis state from the computational basis to the Fourier basis.</p>
-<p>The Quantum Fourier Transform is defined to be the linear operation which takes a basis state, <span class="math notranslate nohighlight">\(\ket{x}\)</span>,</p>
-<div class="math notranslate nohighlight">
-\[\begin{equation}
-\text{QFT}\ket{x}  =   \frac{1}{\sqrt{N}}\sum_{y=0}^{N-1} e^{\frac{2\pi ixy}{N}} \ket{y}
-\tag{1}
-\end{equation}\]</div>
-<p>where <span class="math notranslate nohighlight">\(n\)</span> is the number of qubits and <span class="math notranslate nohighlight">\(N= 2^n\)</span>.</p>
-<p>It is convenient to represent states like <span class="math notranslate nohighlight">\(\ket{x} = \ket{101}\)</span> in their binary representation which in this case would be <span class="math notranslate nohighlight">\(2^2 \times 1 + 2^1 \times 0 + 2^0 \times 1 = \ket{5}\)</span>.</p>
-<p>For the <span class="math notranslate nohighlight">\(n=1\)</span> qubit case, we have the following basis states: <span class="math notranslate nohighlight">\(\ket{0}, \ket{1}\)</span>. The QFT on these is as follows:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{equation}
-\begin{aligned}
-\text{QFT}|0\rangle &amp;= \frac{1}{\sqrt{2}}\sum_{y=0}^{2-1} e^{\frac{2\pi i(0)y}{2}}|y\rangle \\
-&amp;= \frac{1}{\sqrt{2}}\left(e^0|0\rangle + e^0|1\rangle\right) \\
-&amp;= \frac{1}{\sqrt{2}}(|0\rangle + |1\rangle) \\
-&amp;= \ket{+} \\
-\end{aligned}
-\tag{2}
-\end{equation}\end{split}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{equation}
-\begin{aligned}
-\text{QFT}|1\rangle &amp;= \frac{1}{\sqrt{2}}\sum_{y=0}^{2-1} e^{\frac{2\pi i(1)y}{2}}|y\rangle \\
-&amp;= \frac{1}{\sqrt{2}}\left(e^0|0\rangle + e^{\pi i}|1\rangle\right) \\
-&amp;= \frac{1}{\sqrt{2}}(|0\rangle - |1\rangle) \\
-&amp;= \ket{-} \\
-\end{aligned}
-\tag{3}
-\end{equation}\end{split}\]</div>
-<p>To summarize, <span class="math notranslate nohighlight">\(\text{QFT}|0\rangle = \ket{+}\)</span> and <span class="math notranslate nohighlight">\(\text{QFT}|1\rangle = \ket{-}\)</span> which can be achieved by applying Hadamard gates.</p>
-<p>Let us now extend this and see how we can apply this to <span class="math notranslate nohighlight">\(n=3\)</span> qubits.</p>
-<p>For <span class="math notranslate nohighlight">\(n=3, N = 2^3 = 8\)</span> and we would like to perform <span class="math notranslate nohighlight">\(\text{QFT}|101\rangle\)</span> which in binary is <span class="math notranslate nohighlight">\(2^2 \times 1 + 2^1 \times 0 + 2^0 \times 1 = 5\)</span>:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{equation}
-\begin{aligned}
-\text{QFT}|101\rangle = \text{QFT}|5\rangle &amp;= \frac{1}{\sqrt{8}}\sum_{y=0}^{8-1} e^{\frac{2\pi i(5)y}{8}}|y\rangle \\
-&amp;= \frac{1}{\sqrt{8}} [ e^{0}\ket{0} + e^{\frac{5i\pi (1)}{4}}\ket{1}  + e^{\frac{5i\pi (2)}{4}}\ket{2} +...++ e^{\frac{5i\pi (7)}{4}}\ket{7} ]   \\
-&amp;= \frac{1}{\sqrt{8}} [ \ket{000} + e^{\frac{5i\pi (1)}{4}}\ket{001}  + e^{\frac{5i\pi (2)}{4}}\ket{010} +...++ e^{\frac{5i\pi (7)}{4}}\ket{111} ]   \\
-&amp;=  (0.35+0i) \ket{000} + (-0.25-0.25i)\ket{001}  + (0+0.35i)\ket{010} +...+ (-0.25+0.25i)\ket{111}    \\
-\end{aligned}
-\tag{4}
-\end{equation}\end{split}\]</div>
-<p>where we have used the fact that <span class="math notranslate nohighlight">\(e^{i\theta} = \cos(\theta) + i\sin(\theta).\)</span></p>
-<p>Here, we see how in addition to the Hadamard gates, we need some phase gates to apply the appropriate phases to the basis states. It turns out that the gate we need is the controlled <span class="math notranslate nohighlight">\(R_k\)</span> rotation where the <span class="math notranslate nohighlight">\(R_k\)</span> gate is denoted by:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{equation}
-R_k = \begin{bmatrix}
-1 &amp; 0 \\
-0 &amp; e^{2\pi i/ 2^{k}}
-\end{bmatrix}
-\tag{5}
-\end{equation}\end{split}\]</div>
-<p>Here is a generalized ciruit for a <span class="math notranslate nohighlight">\(n\)</span>-qubit quantum Fourier transform:</p>
-<p><img alt="qft" class="no-scaled-link" src="../../_images/qft.png" style="width: 600px;" /></p>
-<p>Below we show the code for a QFT implementation in CUDA-Q based on the circuit diagram above:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">quantum_fourier_transform</span><span class="p">(</span><span class="n">input_state</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;Args:</span>
-<span class="sd">    input_state (list[int]): specifies the input state to be Fourier transformed.  &#39;&#39;&#39;</span>
-
-    <span class="n">qubit_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_state</span><span class="p">)</span>
-
-    <span class="c1"># Initialize qubits.</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-
-    <span class="c1"># Initialize the quantum circuit to the initial state.</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">input_state</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="c1"># Apply Hadamard gates and controlled rotation gates.</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-            <span class="n">angle</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">j</span> <span class="o">-</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="n">cr1</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="p">[</span><span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">]],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Can be changed to &#39;nvidia&#39; for single gpu, &#39;nvidia-mgpu&#39; for multi-GPU or quantum hardware.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;qpp-cpu&quot;</span><span class="p">)</span>
-
-<span class="c1"># The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.</span>
-<span class="n">input_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-
-<span class="c1"># Number of decimal points to round up the statevector to.</span>
-<span class="n">precision</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="c1"># Draw the quantum circuit.</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">quantum_fourier_transform</span><span class="p">,</span> <span class="n">input_state</span><span class="p">))</span>
-
-<span class="c1"># Print the statevector to the specified precision</span>
-<span class="n">statevector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">quantum_fourier_transform</span><span class="p">,</span> <span class="n">input_state</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">statevector</span><span class="p">,</span> <span class="n">precision</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮╭───╮╭───────────╮╭────────────╮
-q0 : ┤ x ├┤ h ├┤ r1(1.571) ├┤ r1(0.7854) ├───────────────────────
-     ╰───╯╰───╯╰─────┬─────╯╰─────┬──────╯╭───╮╭───────────╮
-q1 : ────────────────●────────────┼───────┤ h ├┤ r1(1.571) ├─────
-     ╭───╮                        │       ╰───╯╰─────┬─────╯╭───╮
-q2 : ┤ x ├────────────────────────●──────────────────●──────┤ h ├
-     ╰───╯                                                  ╰───╯
-
-[ 0.35+0.j   -0.25-0.25j  0.  +0.35j  0.25-0.25j -0.35+0.j    0.25+0.25j
- -0.  -0.35j -0.25+0.25j]
-</pre></div></div>
-</div>
-<p>We can verify that this is the correct answer as above we worked out that:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{equation}
-\begin{aligned}
-\text{QFT}|101\rangle = \text{QFT}|5\rangle &amp;= \frac{1}{\sqrt{8}}\sum_{y=0}^{8-1} e^{\frac{2\pi i(5)y}{8}}|y\rangle \\
-&amp;=  (0.35+0i) \ket{000} + (-0.25-0.25i)\ket{001}  + (0+0.35i)\ket{010} +...+ (-0.25+0.25i)\ket{111}.   \\
-\end{aligned}
-\tag{6}
-\end{equation}\end{split}\]</div>
-<p>Barring the gates used in the state preparation step, the circuit starts off by implementing a Hadamard gate and then <span class="math notranslate nohighlight">\(n-1\)</span> rotations on the zeroth qubit - a total of <span class="math notranslate nohighlight">\(n\)</span> gates. This is followed by a Hadamard and <span class="math notranslate nohighlight">\(n-2\)</span> conditional rotations on the first qubit which totals to <span class="math notranslate nohighlight">\(n -1\)</span> gates. If we continue the series, we get <span class="math notranslate nohighlight">\(n + (n-1) + (n-2)... = n(n+1)/2\)</span> gates. Hence, this circuit provides an algorithm with <span class="math notranslate nohighlight">\(O(n^2)\)</span> gates for computing the QFT.</p>
-<p>The best classical algorithms for computing fast Fourier transforms on <span class="math notranslate nohighlight">\(2^{n}\)</span> elements require <span class="math notranslate nohighlight">\(O(n2^{n})\)</span> gates. This means that it requires exponentially more operations to compute the fast Fourier transform on a classical computer than it does to implement the QFT on a quantum computer.</p>
-<p>Why are we not using QFT for real life applications then? The problem lies in quantum measurement. The quantum-Fourier-transformed amplitudes cannot be accessed by quantum measurement. However there are other quantum algorithms that do make use of QFT such as Shor’s algorithm.</p>
-<p>One can also invert the QFT where the circuit is executed in reverse with the inverse of each gate. The figure below shows the general <span class="math notranslate nohighlight">\(n\)</span>-qubit inverse QFT. Why don’t you try implementing this in CUDA-Q?</p>
-<p><img alt="qft" class="no-scaled-link" src="../../_images/inverse_qft.png" style="width: 600px;" /></p>
-<section id="Quantum-Fourier-Transform-revisited">
-<h2>Quantum Fourier Transform revisited<a class="headerlink" href="#Quantum-Fourier-Transform-revisited" title="Permalink to this heading">¶</a></h2>
-<p>We provided one implementation of the Quantum Fourier Transform above. To demonstrate some of the other features of CUDA-Q, let’s define a new kernel for the Quantum Fourier Transform, which we’ll call <code class="docutils literal notranslate"><span class="pre">quantum_fourier_transform2</span></code>. Pay attention to how the kernel definition differs from the example above.</p>
-<p>Since the Quantum Fourier Transform is a unitary operator, its inverse is its adjoint. Therefore, once we have implemented the Quantum Fourier Transform as a kernel in CUDA-Q, we can use the built-in <code class="docutils literal notranslate"><span class="pre">adjoint</span></code> operation to create the Inverse Quantum Fourier Transform.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define kernels for the Quantum Fourier Transform and the Inverse Quantum Fourier Transform</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">quantum_fourier_transform2</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;Args:</span>
-<span class="sd">    qubits (cudaq.qview): specifies the quantum register to which apply the QFT.&#39;&#39;&#39;</span>
-    <span class="n">qubit_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-    <span class="c1"># Apply Hadamard gates and controlled rotation gates.</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-            <span class="n">angle</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">j</span> <span class="o">-</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="n">cr1</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="p">[</span><span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">]],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">inverse_qft</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;Args:</span>
-<span class="sd">    qubits (cudaq.qview): specifies the quantum register to which apply the inverse QFT.&#39;&#39;&#39;</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">quantum_fourier_transform2</span><span class="p">,</span> <span class="n">qubits</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s see that this approach gives us the same results as the section above.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">verification_example</span><span class="p">(</span><span class="n">input_state</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;Args:</span>
-<span class="sd">    input_state (list[int]): specifies the input state to be transformed with QFT and the inverse QFT.  &#39;&#39;&#39;</span>
-    <span class="n">qubit_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_state</span><span class="p">)</span>
-    <span class="c1"># Initialize qubits.</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-
-    <span class="c1"># Initialize the quantum circuit to the initial state.</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">input_state</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="c1"># Apply the quantum Fourier Transform</span>
-    <span class="n">quantum_fourier_transform2</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-    <span class="c1"># Apply the inverse quantum Fourier Transform</span>
-    <span class="n">inverse_qft</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-
-<span class="c1"># The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.</span>
-<span class="n">input_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-
-<span class="c1"># Number of decimal points to round up the statevector to.</span>
-<span class="n">precision</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">verification_example</span><span class="p">,</span> <span class="n">input_state</span><span class="p">))</span>
-
-<span class="c1"># Print the statevector to the specified precision</span>
-<span class="n">statevector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">verification_example</span><span class="p">,</span> <span class="n">input_state</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">statevector</span><span class="p">,</span> <span class="n">precision</span><span class="p">))</span>  <span class="c1"># The result should be the input state</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮╭───╮╭─────────╮╭──────────╮                                      »
-q0 : ┤ x ├┤ h ├┤ r1(1.5) ├┤ r1(0.75) ├──────────────────────────────────────»
-     ╰───╯╰───╯╰────┬────╯╰────┬─────╯╭───╮╭─────────╮          ╭──────────╮»
-q1 : ───────────────●──────────┼──────┤ h ├┤ r1(1.5) ├──────────┤ r1(-1.5) ├»
-     ╭───╮                     │      ╰───╯╰────┬────╯╭───╮╭───╮╰────┬─────╯»
-q2 : ┤ x ├─────────────────────●────────────────●─────┤ h ├┤ h ├─────●──────»
-     ╰───╯                                            ╰───╯╰───╯            »
-
-################################################################################
-
-     ╭───────────╮╭──────────╮╭───╮
-─────┤ r1(-0.75) ├┤ r1(-1.5) ├┤ h ├
-╭───╮╰─────┬─────╯╰────┬─────╯╰───╯
-┤ h ├──────┼───────────●───────────
-╰───╯      │
-───────────●───────────────────────
-
-
-[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.-0.j 0.-0.j 0.+0.j]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)
-</pre></div></div>
-</div>
-</section>
-</section>
-
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diff --git a/pr-2308/applications/python/quantum_fourier_transform.ipynb b/pr-2308/applications/python/quantum_fourier_transform.ipynb
deleted file mode 100644
index cac053ce45..0000000000
--- a/pr-2308/applications/python/quantum_fourier_transform.ipynb
+++ /dev/null
@@ -1,412 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quantum Fourier Transform "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classical Fourier transforms aid many areas such as signal processing and data compression. Below, we quantize it to enable Quantum Fourier Transforms (QFT) in CUDA-Q.\n",
-    "\n",
-    "QFT is a key ingredient in Shor's algorithm which is exponentially faster at factoring prime numbers and also plays a vital role in many other interesting quantum algorithms. \n",
-    "\n",
-    "The idea with classical Fourier transforms is to take the problem from the frequency domain, to the time domain where it is easier to solve. This is the same analogy we draw with QFT, where we take the basis state from the computational basis to the Fourier basis. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Quantum Fourier Transform is defined to be the linear operation which takes a basis state, $\\ket{x}$, \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\text{QFT}\\ket{x}  =   \\frac{1}{\\sqrt{N}}\\sum_{y=0}^{N-1} e^{\\frac{2\\pi ixy}{N}} \\ket{y}\n",
-    "\\tag{1}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "where $n$ is the number of qubits and $N= 2^n$. \n",
-    "\n",
-    "It is convenient to represent states like $\\ket{x}  = \\ket{101}$ in their binary representation which in this case would be $2^2 \\times 1 + 2^1 \\times 0 + 2^0 \\times 1 = \\ket{5}$. \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the $n=1$ qubit case, we have the following basis states: $\\ket{0}, \\ket{1}$. The QFT on these is as follows: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|0\\rangle &= \\frac{1}{\\sqrt{2}}\\sum_{y=0}^{2-1} e^{\\frac{2\\pi i(0)y}{2}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}\\left(e^0|0\\rangle + e^0|1\\rangle\\right) \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}(|0\\rangle + |1\\rangle) \\\\\n",
-    "&= \\ket{+} \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{2}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|1\\rangle &= \\frac{1}{\\sqrt{2}}\\sum_{y=0}^{2-1} e^{\\frac{2\\pi i(1)y}{2}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}\\left(e^0|0\\rangle + e^{\\pi i}|1\\rangle\\right) \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}(|0\\rangle - |1\\rangle) \\\\\n",
-    "&= \\ket{-} \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{3}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "             \n",
-    "\n",
-    "To summarize, $\\text{QFT}|0\\rangle = \\ket{+}$ and $\\text{QFT}|1\\rangle = \\ket{-}$ which can be achieved by applying Hadamard gates. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Let us now extend this and see how we can apply this to $n=3$ qubits. \n",
-    "\n",
-    "For $n=3, N = 2^3 = 8$ and we would like to perform $\\text{QFT}|101\\rangle$ which in binary is $2^2 \\times 1 + 2^1 \\times 0 + 2^0 \\times 1 = 5$: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|101\\rangle = \\text{QFT}|5\\rangle &= \\frac{1}{\\sqrt{8}}\\sum_{y=0}^{8-1} e^{\\frac{2\\pi i(5)y}{8}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{8}} [ e^{0}\\ket{0} + e^{\\frac{5i\\pi (1)}{4}}\\ket{1}  + e^{\\frac{5i\\pi (2)}{4}}\\ket{2} +...++ e^{\\frac{5i\\pi (7)}{4}}\\ket{7} ]   \\\\\n",
-    "&= \\frac{1}{\\sqrt{8}} [ \\ket{000} + e^{\\frac{5i\\pi (1)}{4}}\\ket{001}  + e^{\\frac{5i\\pi (2)}{4}}\\ket{010} +...++ e^{\\frac{5i\\pi (7)}{4}}\\ket{111} ]   \\\\\n",
-    "&=  (0.35+0i) \\ket{000} + (-0.25-0.25i)\\ket{001}  + (0+0.35i)\\ket{010} +...+ (-0.25+0.25i)\\ket{111}    \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{4}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "where we have used the fact that $e^{i\\theta} = \\cos(\\theta) + i\\sin(\\theta).$\n",
-    "\n",
-    "Here, we see how in addition to the Hadamard gates, we need some phase gates to apply the appropriate phases to the basis states. It turns out that the gate we need is the controlled $R_k$ rotation where the $R_k$ gate is denoted by:\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "R_k = \\begin{bmatrix}\n",
-    "1 & 0 \\\\\n",
-    "0 & e^{2\\pi i/ 2^{k}}\n",
-    "\\end{bmatrix}\n",
-    "\\tag{5}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "Here is a generalized ciruit for a $n$-qubit quantum Fourier transform:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "<img src=\"images/qft.png\" alt=\"qft\" width=\"600\">\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we show the code for a QFT implementation in CUDA-Q based on the circuit diagram above: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "from typing import List\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform(input_state: List[int]):\n",
-    "    '''Args: \n",
-    "    input_state (list[int]): specifies the input state to be Fourier transformed.  '''\n",
-    "\n",
-    "    qubit_count = len(input_state)\n",
-    "\n",
-    "    # Initialize qubits.\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # Initialize the quantum circuit to the initial state.\n",
-    "    for i in range(qubit_count):\n",
-    "        if input_state[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮╭───────────╮╭────────────╮                       \n",
-      "q0 : ┤ x ├┤ h ├┤ r1(1.571) ├┤ r1(0.7854) ├───────────────────────\n",
-      "     ╰───╯╰───╯╰─────┬─────╯╰─────┬──────╯╭───╮╭───────────╮     \n",
-      "q1 : ────────────────●────────────┼───────┤ h ├┤ r1(1.571) ├─────\n",
-      "     ╭───╮                        │       ╰───╯╰─────┬─────╯╭───╮\n",
-      "q2 : ┤ x ├────────────────────────●──────────────────●──────┤ h ├\n",
-      "     ╰───╯                                                  ╰───╯\n",
-      "\n",
-      "[ 0.35+0.j   -0.25-0.25j  0.  +0.35j  0.25-0.25j -0.35+0.j    0.25+0.25j\n",
-      " -0.  -0.35j -0.25+0.25j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Can be changed to 'nvidia' for single gpu, 'nvidia-mgpu' for multi-GPU or quantum hardware.\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "# The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.\n",
-    "input_state = [1, 0, 1]\n",
-    "\n",
-    "# Number of decimal points to round up the statevector to.\n",
-    "precision = 2\n",
-    "\n",
-    "# Draw the quantum circuit.\n",
-    "print(cudaq.draw(quantum_fourier_transform, input_state))\n",
-    "\n",
-    "# Print the statevector to the specified precision\n",
-    "statevector = np.array(cudaq.get_state(quantum_fourier_transform, input_state))\n",
-    "print(np.round(statevector, precision))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can verify that this is the correct answer as above we worked out that: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|101\\rangle = \\text{QFT}|5\\rangle &= \\frac{1}{\\sqrt{8}}\\sum_{y=0}^{8-1} e^{\\frac{2\\pi i(5)y}{8}}|y\\rangle \\\\\n",
-    "&=  (0.35+0i) \\ket{000} + (-0.25-0.25i)\\ket{001}  + (0+0.35i)\\ket{010} +...+ (-0.25+0.25i)\\ket{111}.   \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{6}\n",
-    "\\end{equation}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Barring the gates used in the state preparation step, the circuit starts off by implementing a Hadamard gate and then $n-1$ rotations on the zeroth qubit - a total of $n$ gates. This is followed by a Hadamard and $n-2$ conditional rotations on the first qubit which totals to $n -1$ gates. If we continue the series, we get $n + (n-1) + (n-2)... = n(n+1)/2$ gates. Hence, this circuit provides an algorithm with $O(n^2)$ gates for computing the QFT. \n",
-    "\n",
-    "The best classical algorithms for computing fast Fourier transforms on $2^{n}$ elements require $O(n2^{n})$ gates. This means that it requires exponentially more operations to compute the fast Fourier transform on a classical computer than it does to implement the QFT on a quantum computer.\n",
-    "\n",
-    "Why are we not using QFT for real life applications then? The problem lies in quantum measurement. The quantum-Fourier-transformed amplitudes cannot be accessed by quantum measurement. However there are other quantum algorithms that do make use of QFT such as Shor's algorithm. \n",
-    "\n",
-    "One can also invert the QFT where the circuit is executed in reverse with the inverse of each gate. The figure below shows the general $n$-qubit inverse QFT. Why don't you try implementing this in CUDA-Q?\n",
-    "\n",
-    "<img src=\"images/inverse_qft.png\" alt=\"qft\" width=\"600\">\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Fourier Transform revisited\n",
-    "We provided one implementation of the Quantum Fourier Transform above.  To demonstrate some of the other features of CUDA-Q, let's define a new kernel for the Quantum Fourier Transform, which we'll call `quantum_fourier_transform2`.  Pay attention to how the kernel definition differs from the example above.\n",
-    "\n",
-    " Since the Quantum Fourier Transform is a unitary operator, its inverse is its adjoint.  Therefore, once we have implemented the Quantum Fourier Transform as a kernel in CUDA-Q, we can use the built-in `adjoint` operation to create the Inverse Quantum Fourier Transform."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define kernels for the Quantum Fourier Transform and the Inverse Quantum Fourier Transform\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform2(qubits: cudaq.qview):\n",
-    "    '''Args: \n",
-    "    qubits (cudaq.qview): specifies the quantum register to which apply the QFT.'''\n",
-    "    qubit_count = len(qubits)\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def inverse_qft(qubits: cudaq.qview):\n",
-    "    '''Args: \n",
-    "    qubits (cudaq.qview): specifies the quantum register to which apply the inverse QFT.'''\n",
-    "    cudaq.adjoint(quantum_fourier_transform2, qubits)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see that this approach gives us the same results as the section above. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮╭─────────╮╭──────────╮                                      »\n",
-      "q0 : ┤ x ├┤ h ├┤ r1(1.5) ├┤ r1(0.75) ├──────────────────────────────────────»\n",
-      "     ╰───╯╰───╯╰────┬────╯╰────┬─────╯╭───╮╭─────────╮          ╭──────────╮»\n",
-      "q1 : ───────────────●──────────┼──────┤ h ├┤ r1(1.5) ├──────────┤ r1(-1.5) ├»\n",
-      "     ╭───╮                     │      ╰───╯╰────┬────╯╭───╮╭───╮╰────┬─────╯»\n",
-      "q2 : ┤ x ├─────────────────────●────────────────●─────┤ h ├┤ h ├─────●──────»\n",
-      "     ╰───╯                                            ╰───╯╰───╯            »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "     ╭───────────╮╭──────────╮╭───╮\n",
-      "─────┤ r1(-0.75) ├┤ r1(-1.5) ├┤ h ├\n",
-      "╭───╮╰─────┬─────╯╰────┬─────╯╰───╯\n",
-      "┤ h ├──────┼───────────●───────────\n",
-      "╰───╯      │                       \n",
-      "───────────●───────────────────────\n",
-      "                                   \n",
-      "\n",
-      "[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.-0.j 0.-0.j 0.+0.j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def verification_example(input_state: List[int]):\n",
-    "    '''Args: \n",
-    "    input_state (list[int]): specifies the input state to be transformed with QFT and the inverse QFT.  '''\n",
-    "    qubit_count = len(input_state)\n",
-    "    # Initialize qubits.\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # Initialize the quantum circuit to the initial state.\n",
-    "    for i in range(qubit_count):\n",
-    "        if input_state[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "\n",
-    "    # Apply the quantum Fourier Transform\n",
-    "    quantum_fourier_transform2(qubits)\n",
-    "\n",
-    "    # Apply the inverse quantum Fourier Transform\n",
-    "    inverse_qft(qubits)\n",
-    "\n",
-    "\n",
-    "# The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.\n",
-    "input_state = [1, 0, 1]\n",
-    "\n",
-    "# Number of decimal points to round up the statevector to.\n",
-    "precision = 2\n",
-    "\n",
-    "print(cudaq.draw(verification_example, input_state))\n",
-    "\n",
-    "# Print the statevector to the specified precision\n",
-    "statevector = np.array(cudaq.get_state(verification_example, input_state))\n",
-    "print(np.round(statevector, precision))  # The result should be the input state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/quantum_teleportation.html b/pr-2308/applications/python/quantum_teleportation.html
deleted file mode 100644
index 95489f3edd..0000000000
--- a/pr-2308/applications/python/quantum_teleportation.html
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-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="Quantum-Teleporation">
-<h1>Quantum Teleporation<a class="headerlink" href="#Quantum-Teleporation" title="Permalink to this heading">¶</a></h1>
-<p>Below we shall study an interesting quantum phenomena - teleportation.</p>
-<p>Let us start by implementing the circuit shown below and discuss its implications later. We normally assume that the input qubits are initialised to the <span class="math notranslate nohighlight">\(\ket{0}\)</span> state unless specified otherwise. In this case however, the circuit diagram depicts the 0th qubit to be initialised in an arbitary quantum state, <span class="math notranslate nohighlight">\(\ket{\psi} = \alpha\ket{0} + \beta\ket{1}\)</span>, and the 1st and 2nd qubit to be in the state <span class="math notranslate nohighlight">\(\ket{\beta_{00}} = \tfrac{1}{\sqrt{2}} [\ket{00} + \ket{11}]\)</span>.</p>
-<p><img alt="tene.png" src="../../_images/teleportation.png" /></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;qpp-cpu&#39;</span><span class="p">)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_random_seed</span><span class="p">(</span><span class="mi">23</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We now replicate the circuit shown in the diagram above and step through each intermediate stage from psi0 to psi4.</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">teleportation</span><span class="p">():</span>
-
-    <span class="c1"># Initialize a 3 qubit quantum circuit</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-
-    <span class="c1"># Psi0 - random quantum state, psi, on qubit 0.</span>
-    <span class="n">rx</span><span class="p">(</span><span class="mf">3.14</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="mf">2.71</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">rz</span><span class="p">(</span><span class="mf">6.62</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># Psi0 - create a maximally entangled state on qubits 1 and 2.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-
-<span class="c1"># Let us save the statevector of the circuit so far for later use.</span>
-<span class="n">psi_0</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">teleportation</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now we continue with the rest of the circuit</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">teleportation</span><span class="p">():</span>
-
-    <span class="c1"># Initialize a 3 qubit quantum circuit</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-
-    <span class="c1"># Psi0 - random quantum state, psi, on qubit 0.</span>
-    <span class="n">rx</span><span class="p">(</span><span class="mf">3.14</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="mf">2.71</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">rz</span><span class="p">(</span><span class="mf">6.62</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># Psi0 - create a maximally entangled state on qubits 1 and 2.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-    <span class="c1"># Psi1</span>
-    <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="c1"># Psi2</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># Psi3 - measure qubits 0 and 1 and store it in variables m1 and m2</span>
-    <span class="n">m1</span> <span class="o">=</span> <span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">m2</span> <span class="o">=</span> <span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="c1"># Psi4 - apply conditioned pauli operators dependent on the measurement result of qubits 0 and 1.</span>
-    <span class="k">if</span> <span class="n">m1</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">z</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">m2</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-
-<span class="n">psi_4</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">teleportation</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The state of this 3 qubit system is described by a <span class="math notranslate nohighlight">\(2^3 = 8\)</span> dimensional vector. Sometimes we would like to extract the state of a single qubit from a multipartite state. We can use the partial trace operation defined in the function below which allows us to translate our statevectors to the density matrix representation to trace out subsystems.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Function to be used below to calculate the partial trace yielding a density matrix.</span>
-
-
-<span class="k">def</span> <span class="nf">partial_trace</span><span class="p">(</span><span class="n">state_vector</span><span class="p">,</span> <span class="n">trace_systems</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Partial trace of multi-particle quantum state.</span>
-
-<span class="sd">    Arguments:</span>
-<span class="sd">        state_vector: complex vector  of size 2**n</span>
-<span class="sd">        trace_systems (list(int)): a list of subsystems (starting from 0) to trace over.</span>
-<span class="sd">        dimensions (list(int)): a list of the dimensions of the subsystems.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        ndarray: A density matrix with the appropriate subsystems traced over.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">n_qubits</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">state_vector</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-    <span class="n">dimensions</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">)]</span>
-
-    <span class="n">trace_systems</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">trace_systems</span><span class="p">)</span>
-
-    <span class="n">rho</span> <span class="o">=</span> <span class="n">state_vector</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)</span>
-    <span class="n">rho</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tensordot</span><span class="p">(</span><span class="n">rho</span><span class="p">,</span> <span class="n">rho</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="n">trace_systems</span><span class="p">,</span> <span class="n">trace_systems</span><span class="p">))</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">rho</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
-
-    <span class="k">return</span> <span class="n">rho</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Trace out qubits 1 and 2 leaving us with qubit 0.</span>
-<span class="n">state_of_q0</span> <span class="o">=</span> <span class="n">partial_trace</span><span class="p">(</span><span class="n">state_vector</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psi_0</span><span class="p">),</span> <span class="n">trace_systems</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-<span class="c1"># Trace out qubits 0 and 1 leaving us with qubit 2.</span>
-<span class="n">state_of_q2</span> <span class="o">=</span> <span class="n">partial_trace</span><span class="p">(</span><span class="n">state_vector</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psi_4</span><span class="p">),</span> <span class="n">trace_systems</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|qubit_0&gt; == |qubit_2&gt;?  </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">state_of_q0</span><span class="p">,</span><span class="w"> </span><span class="n">state_of_q2</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-|qubit_0&gt; == |qubit_2&gt;?  True
-</pre></div></div>
-</div>
-<p>Let us discuss what has happened.</p>
-<p>We started with a 3 qubit quantum circuit with the 0th qubit in <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> and the 2nd qubit being a portion of the maximally entangled bell state <span class="math notranslate nohighlight">\(\ket{\beta_{00}}\)</span>.</p>
-<p>If we look closely at the code cell above, the output state on the 2nd qubit, <code class="docutils literal notranslate"><span class="pre">state_of_q2</span></code>, is the same as the random input state, <span class="math notranslate nohighlight">\(\ket{\psi}\)</span>, on the 0th qubit, <code class="docutils literal notranslate"><span class="pre">state_of_q0</span></code>.</p>
-<p>We have <strong>teleported</strong> a quantum state from one qubit to another. There is nothing to restrict how close qubits 0 and 2 have to be since they have no entangling operations between them. They can be placed in different labs or infinitely far apart.</p>
-<p>Moreover, we still obey the no-cloning theorem since the initial state <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> no longer exists on the 0th qubit and hence we only have 1 copy of it as shown below</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Using the final statevector, psi4, we show that psi has been teleported and only 1 copy of psi is preserved.</span>
-
-<span class="c1"># Trace out qubits 1 and 2 leaving us with qubit 0</span>
-<span class="n">state_of_q0</span> <span class="o">=</span> <span class="n">partial_trace</span><span class="p">(</span><span class="n">state_vector</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psi_4</span><span class="p">),</span> <span class="n">trace_systems</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-<span class="c1"># Trace out qubits 0 and 1 leaving us with qubit 2</span>
-<span class="n">state_of_q2</span> <span class="o">=</span> <span class="n">partial_trace</span><span class="p">(</span><span class="n">state_vector</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psi_4</span><span class="p">),</span> <span class="n">trace_systems</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
-<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">state_of_q0</span><span class="p">,</span> <span class="n">state_of_q2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-False
-</pre></div></div>
-</div>
-<p>None of this violates the universal limit of information travelling faster than the speed of light. Notice how we have conditioned operations meaning we have to communicate classical information via classical channels which adhere to this limit.</p>
-<section id="Teleportation-explained">
-<h2>Teleportation explained<a class="headerlink" href="#Teleportation-explained" title="Permalink to this heading">¶</a></h2>
-<p>We have stepped through the circuit above using code. We will now do the same using mathematics to enhance our understanding.</p>
-<p>We start by gathering experimentalists in a lab and performing the following</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-CX_{01}H_{0}\ket{00} &amp;= CX_{01}\tfrac{1}{\sqrt{2}}(\ket{0}+\ket{1})\ket{0} \\
-                        &amp;= CX_{01}\tfrac{1}{\sqrt{2}}(\ket{00}+\ket{10}) \\
-                        &amp;= \tfrac{1}{\sqrt{2}}(\ket{00}+\ket{11}) \\
-                        &amp;= \ket{\beta_{00}}
-\end{aligned}\end{split}\]</div>
-<p>to create an entangled bell pair. We then hand each qubit in <span class="math notranslate nohighlight">\(\ket{\beta_{00}}\)</span> to two parties, namely Alice and Bob for safekeeping. Many moons later, Alice comes in posession of an arbitary quantum state <span class="math notranslate nohighlight">\(\ket{\psi} = \alpha\ket{0} + \beta\ket{1}\)</span> which she would like to send to Bob who is many miles away. This entails communicating values of <span class="math notranslate nohighlight">\(\alpha\)</span> and <span class="math notranslate nohighlight">\(\beta\)</span>. However, measurement would lead to state collapse and only yield one bit of information, 0 or a 1.</p>
-<p>Referring to circuit in the figure above, the 0th and the 1st qubits are in posession of Alice and the 2nd qubit is with Bob. The 1st and 2nd qubits have been entangled but are now seperated in distance. Let us now step through the circuit to describe its evolution. Our input state is</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{0}} &amp;=  \ket{\psi} \ket{\beta_{00}}  = (\alpha\ket{0} + \beta\ket{1})(\tfrac{1}{\sqrt{2}} (\ket{00} + \ket{11}) \\
-&amp;= \tfrac{1}{\sqrt{2}}[\alpha\ket{0}(\ket{00} + \ket{11}) +\beta\ket{1}(\ket{00} + \ket{11})]
-\end{aligned}\end{split}\]</div>
-<p>Alice then performs a CX gate between the qubits she posses i.e. qubit 0 and 1</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{1}} &amp;= CX_{01}\ket{\psi_{0}} \\
-                &amp;= \tfrac{1}{\sqrt{2}}[\alpha\ket{0}(\ket{00} + \ket{11}) +\beta\ket{1}(\ket{10} + \ket{01})]
-\end{aligned}\end{split}\]</div>
-<p>which is then followed by Alice performing a Hadamard on the 0th qubit</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{2}} &amp;= H_{0}\ket{\psi_{1}} \\
-                &amp;= \tfrac{1}{{2}}[\alpha(\ket{0}+\ket{1})(\ket{00} + \ket{11}) +\beta(\ket{0}-\ket{1})(\ket{10} + \ket{01})].
-\end{aligned}\end{split}\]</div>
-<p>If we expand all the inner brackets</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-\ket{\psi_{2}} &amp;= \tfrac{1}{{2}}[(\alpha\ket{0} + \alpha\ket{1})(\ket{00} + \ket{11}) + (\beta\ket{0} - \beta\ket{1})(\ket{10} + \ket{01})] \\
-                &amp;= \tfrac{1}{{2}}[\alpha\ket{000}+\alpha\ket{011}  +\alpha\ket{100}  +\alpha\ket{111} +\beta\ket{010}+\beta\ket{001}-\beta\ket{110}-\beta\ket{101}]
-\end{aligned}\end{split}\]</div>
-<p>and then collect like terms whilst maintaining qubit ordering, we yield</p>
-<div class="math notranslate nohighlight">
-\[\begin{aligned}
-\ket{\psi_{2}} &amp;= \tfrac{1}{{2}}[  \ket{00}(\alpha\ket{0}+\beta\ket{1}) + \ket{01}(\alpha\ket{1}+\beta\ket{0}) +
- \ket{10}(\alpha\ket{0}-\beta\ket{1}) + \ket{11}(\alpha\ket{1}+\beta\ket{0})                              ]
-\end{aligned}\]</div>
-<p>where the qubit ordering notation states that the left most qubit is the 0th qubit. This expression naturally breaks down into 4 terms where each term has all 3 qubits and the 4 terms represent all the possibilities they can be in after they have been evolved.</p>
-<p>We now ask our friend Alice to measure her qubits. Quantum mechanically we know that upon measurement the possibilities of the 3 qubit system will collapse regardless of the distance between them and the unmeasured outcomes will therefore be deterministic. If her measurement result yields a 00, the first term in <span class="math notranslate nohighlight">\(\ket{\psi_{2}}\)</span> tells us that Bob’s qubit will be in the state <span class="math notranslate nohighlight">\(\alpha\ket{0}+\beta\ket{1}\)</span> which is the the original state, <span class="math notranslate nohighlight">\(\ket{\psi}\)</span>, we wanted to teleport. Alice
-has other potential measurement outcomes all of which are summarised in the figure below</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Alices’ measurement</p></th>
-<th class="head"><p>Bobs’ state</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><div class="math notranslate nohighlight">
-\[00\]</div>
-</td>
-<td><div class="math notranslate nohighlight">
-\[\ket{\psi_3(00)} = \alpha\ket{0} + \beta\ket{1}\]</div>
-</td>
-</tr>
-<tr class="row-odd"><td><div class="math notranslate nohighlight">
-\[01\]</div>
-</td>
-<td><div class="math notranslate nohighlight">
-\[\ket{\psi_3(01)} =  \alpha\ket{1} + \beta\ket{0}\]</div>
-</td>
-</tr>
-<tr class="row-even"><td><div class="math notranslate nohighlight">
-\[10\]</div>
-</td>
-<td><div class="math notranslate nohighlight">
-\[\ket{\psi_3(10)} = \alpha\ket{0} - \beta\ket{1}\]</div>
-</td>
-</tr>
-<tr class="row-odd"><td><div class="math notranslate nohighlight">
-\[11\]</div>
-</td>
-<td><div class="math notranslate nohighlight">
-\[\ket{\psi_3(11)} = \alpha\ket{1} - \beta\ket{0}\]</div>
-</td>
-</tr>
-</tbody>
-</table>
-<p>A quick glance at the figure above shows us that Bob’s qubit is nearly in the state <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> which is what we are after pending some minor corrections. The circuit diagram depicts conditioned gates that are applied depending on Alice’s measurement result fulfilling the minor corrections required to complete the teleportation protocol. The final gate operations are summarised below</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{aligned}
-M1M2 = 00, \ket{\psi_4} = Z^{0}X^{0}\ket{\psi_3(00)} = \alpha\ket{0} + \beta\ket{1} = \ket{\psi} \\
-M1M2 = 01, \ket{\psi_4} = Z^{0}X^{1}\ket{\psi_3(01)} = \alpha\ket{0} + \beta\ket{1} = \ket{\psi} \\
-M1M2 = 10, \ket{\psi_4} = Z^{1}X^{0}\ket{\psi_3(10)} = \alpha\ket{0} + \beta\ket{1} = \ket{\psi} \\
-M1M2 = 11, \ket{\psi_4} = Z^{1}X^{1}\ket{\psi_3(11)} = \alpha\ket{0} + \beta\ket{1} = \ket{\psi}
-\end{aligned}\end{split}\]</div>
-<p>It is clear to see that in all instances, we recover <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> on Bob’s qubit thus fulfilling our ambition of teleporting an unknown quantum state between 2 parties.</p>
-<p>It is important to note that quantum teleportation does not allow communication faster than the speed of light. The state <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> does not instantly appear with Bob. Alice has to use a classical communication channel which is bound by the speed of classical physics to communicate her measurement results to Bob so that he can make the minor corrections required.</p>
-<p>Moreover teleportation does not violate the no-cloning theorem. The protocol does not allow us to create a copy of <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> leaving us with <span class="math notranslate nohighlight">\(\ket{\psi\psi}\)</span> but rather transmits <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> from Alice to Bob.</p>
-<p>The images used in this notebook are courtesey of the Quantum Computation and Quantum Information textbook by Nielsen &amp; Chuang.</p>
-</section>
-</section>
-
-
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diff --git a/pr-2308/applications/python/quantum_teleportation.ipynb b/pr-2308/applications/python/quantum_teleportation.ipynb
deleted file mode 100644
index 244503021a..0000000000
--- a/pr-2308/applications/python/quantum_teleportation.ipynb
+++ /dev/null
@@ -1,395 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Teleporation\n",
-    "\n",
-    "\n",
-    "Below we shall study an interesting quantum phenomena - teleportation.\n",
-    "\n",
-    "Let us start by implementing the circuit shown below and discuss its implications later. We normally assume that the input qubits are initialised to the $\\ket{0}$ state unless specified otherwise. In this case however, the circuit diagram depicts the 0<sup>th</sup> qubit to be initialised in an arbitary quantum state, $\\ket{\\psi} = \\alpha\\ket{0} + \\beta\\ket{1}$, and the 1<sup>st</sup> and 2<sup>nd</sup> qubit to be in the state $\\ket{\\beta_{00}} = \\tfrac{1}{\\sqrt{2}} [\\ket{00} + \\ket{11}]$.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![tene.png](./images/teleportation.png)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "cudaq.set_random_seed(23)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We now replicate the circuit shown in the diagram above and step through each intermediate stage from psi0 to psi4.\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def teleportation():\n",
-    "\n",
-    "    # Initialize a 3 qubit quantum circuit\n",
-    "    qubits = cudaq.qvector(3)\n",
-    "\n",
-    "    # Psi0 - random quantum state, psi, on qubit 0.\n",
-    "    rx(3.14, qubits[0])\n",
-    "    ry(2.71, qubits[0])\n",
-    "    rz(6.62, qubits[0])\n",
-    "\n",
-    "    # Psi0 - create a maximally entangled state on qubits 1 and 2.\n",
-    "    h(qubits[1])\n",
-    "    cx(qubits[1], qubits[2])\n",
-    "\n",
-    "\n",
-    "# Let us save the statevector of the circuit so far for later use.\n",
-    "psi_0 = cudaq.get_state(teleportation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now we continue with the rest of the circuit\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def teleportation():\n",
-    "\n",
-    "    # Initialize a 3 qubit quantum circuit\n",
-    "    qubits = cudaq.qvector(3)\n",
-    "\n",
-    "    # Psi0 - random quantum state, psi, on qubit 0.\n",
-    "    rx(3.14, qubits[0])\n",
-    "    ry(2.71, qubits[0])\n",
-    "    rz(6.62, qubits[0])\n",
-    "\n",
-    "    # Psi0 - create a maximally entangled state on qubits 1 and 2.\n",
-    "    h(qubits[1])\n",
-    "    cx(qubits[1], qubits[2])\n",
-    "\n",
-    "    # Psi1\n",
-    "    cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    # Psi2\n",
-    "    h(qubits[0])\n",
-    "\n",
-    "    # Psi3 - measure qubits 0 and 1 and store it in variables m1 and m2\n",
-    "    m1 = mz(qubits[0])\n",
-    "    m2 = mz(qubits[1])\n",
-    "\n",
-    "    # Psi4 - apply conditioned pauli operators dependent on the measurement result of qubits 0 and 1.\n",
-    "    if m1 == 1:\n",
-    "        z(qubits[2])\n",
-    "\n",
-    "    if m2 == 1:\n",
-    "        x(qubits[2])\n",
-    "\n",
-    "\n",
-    "psi_4 = cudaq.get_state(teleportation)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The state of this 3 qubit system is described by a $2^3 = 8$ dimensional vector. Sometimes we would like to extract the state of a single qubit from a multipartite state. We can use the partial trace operation defined in the function below which allows us to translate our statevectors to the density matrix representation to trace out subsystems. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to be used below to calculate the partial trace yielding a density matrix.\n",
-    "\n",
-    "\n",
-    "def partial_trace(state_vector, trace_systems):\n",
-    "    \"\"\"\n",
-    "    Partial trace of multi-particle quantum state. \n",
-    "\n",
-    "    Arguments:\n",
-    "        state_vector: complex vector  of size 2**n\n",
-    "        trace_systems (list(int)): a list of subsystems (starting from 0) to trace over. \n",
-    "        dimensions (list(int)): a list of the dimensions of the subsystems.\n",
-    "  \n",
-    "    Returns:\n",
-    "        ndarray: A density matrix with the appropriate subsystems traced over.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    n_qubits = int(np.log2(state_vector.shape[0]))\n",
-    "\n",
-    "    dimensions = [2 for i in range(n_qubits)]\n",
-    "\n",
-    "    trace_systems = len(dimensions) - 1 - np.array(trace_systems)\n",
-    "\n",
-    "    rho = state_vector.reshape(dimensions)\n",
-    "    rho = np.tensordot(rho, rho.conj(), axes=(trace_systems, trace_systems))\n",
-    "    d = int(np.sqrt(np.prod(rho.shape)))\n",
-    "\n",
-    "    return rho.reshape(d, d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|qubit_0> == |qubit_2>?  True\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Trace out qubits 1 and 2 leaving us with qubit 0.\n",
-    "state_of_q0 = partial_trace(state_vector=np.array(psi_0), trace_systems=[1, 2])\n",
-    "\n",
-    "# Trace out qubits 0 and 1 leaving us with qubit 2.\n",
-    "state_of_q2 = partial_trace(state_vector=np.array(psi_4), trace_systems=[0, 1])\n",
-    "\n",
-    "print(f\"|qubit_0> == |qubit_2>?  {np.allclose(state_of_q0, state_of_q2)}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us discuss what has happened. \n",
-    "\n",
-    "We started with a 3 qubit quantum circuit with the 0<sup>th</sup> qubit in $\\ket{\\psi}$ and the 2<sup>nd</sup> qubit being a portion of the maximally entangled bell state $\\ket{\\beta_{00}}$.\n",
-    "\n",
-    "If we look closely at the code cell above, the output state on the 2<sup>nd</sup> qubit, `state_of_q2`, is the same as the random input state, $\\ket{\\psi}$, on the 0<sup>th</sup> qubit, `state_of_q0`. \n",
-    "\n",
-    "We have **teleported** a quantum state from one qubit to another. There is nothing to restrict how close qubits 0 and 2 have to be since they have no entangling operations between them. They can be placed in different labs or infinitely far apart. \n",
-    "\n",
-    "Moreover, we still obey the no-cloning theorem since the initial state $\\ket{\\psi}$ no longer exists on the 0<sup>th</sup> qubit and hence we only have 1 copy of it as shown below \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Using the final statevector, psi4, we show that psi has been teleported and only 1 copy of psi is preserved.\n",
-    "\n",
-    "# Trace out qubits 1 and 2 leaving us with qubit 0\n",
-    "state_of_q0 = partial_trace(state_vector=np.array(psi_4), trace_systems=[1, 2])\n",
-    "\n",
-    "# Trace out qubits 0 and 1 leaving us with qubit 2\n",
-    "state_of_q2 = partial_trace(state_vector=np.array(psi_4), trace_systems=[0, 1])\n",
-    "\n",
-    "np.allclose(state_of_q0, state_of_q2)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "None of this violates the universal limit of information travelling faster than the speed of light. Notice how we have conditioned operations meaning we have to communicate classical information via classical channels which adhere to this limit."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Teleportation explained\n",
-    "\n",
-    "We have stepped through the circuit above using code. We will now do the same using mathematics to enhance our understanding. \n",
-    "\n",
-    "\n",
-    "We start by gathering experimentalists in a lab and performing the following\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "CX_{01}H_{0}\\ket{00} &= CX_{01}\\tfrac{1}{\\sqrt{2}}(\\ket{0}+\\ket{1})\\ket{0} \\\\\n",
-    "                        &= CX_{01}\\tfrac{1}{\\sqrt{2}}(\\ket{00}+\\ket{10}) \\\\\n",
-    "                        &= \\tfrac{1}{\\sqrt{2}}(\\ket{00}+\\ket{11}) \\\\\n",
-    "                        &= \\ket{\\beta_{00}}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "to create an entangled bell pair. We then hand each qubit in $\\ket{\\beta_{00}}$ to two parties, namely Alice and Bob for safekeeping. Many moons later, Alice comes in posession of an arbitary quantum state $\\ket{\\psi} = \\alpha\\ket{0} + \\beta\\ket{1}$ which she would like to send to Bob who is many miles away. This entails communicating values of $\\alpha$ and $\\beta$. However, measurement would lead to state collapse and only yield one bit of information, 0 or a 1.\n",
-    "\n",
-    "Referring to circuit in the figure above, the 0<sup>th</sup> and the 1<sup>st</sup> qubits are in posession of Alice and the 2<sup>nd</sup> qubit is with Bob. The 1<sup>st</sup> and 2<sup>nd</sup> qubits have been entangled but are now seperated in distance. Let us now step through the circuit to describe its evolution. Our input state is \n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{0}} &=  \\ket{\\psi} \\ket{\\beta_{00}}  = (\\alpha\\ket{0} + \\beta\\ket{1})(\\tfrac{1}{\\sqrt{2}} (\\ket{00} + \\ket{11}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}[\\alpha\\ket{0}(\\ket{00} + \\ket{11}) +\\beta\\ket{1}(\\ket{00} + \\ket{11})]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "Alice then performs a CX gate between the qubits she posses i.e. qubit 0 and 1 \n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{1}} &= CX_{01}\\ket{\\psi_{0}} \\\\\n",
-    "                &= \\tfrac{1}{\\sqrt{2}}[\\alpha\\ket{0}(\\ket{00} + \\ket{11}) +\\beta\\ket{1}(\\ket{10} + \\ket{01})]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "which is then followed by Alice performing a Hadamard on the 0<sup>th</sup> qubit\n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= H_{0}\\ket{\\psi_{1}} \\\\\n",
-    "                &= \\tfrac{1}{{2}}[\\alpha(\\ket{0}+\\ket{1})(\\ket{00} + \\ket{11}) +\\beta(\\ket{0}-\\ket{1})(\\ket{10} + \\ket{01})].\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "If we expand all the inner brackets\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= \\tfrac{1}{{2}}[(\\alpha\\ket{0} + \\alpha\\ket{1})(\\ket{00} + \\ket{11}) + (\\beta\\ket{0} - \\beta\\ket{1})(\\ket{10} + \\ket{01})] \\\\\n",
-    "                &= \\tfrac{1}{{2}}[\\alpha\\ket{000}+\\alpha\\ket{011}  +\\alpha\\ket{100}  +\\alpha\\ket{111} +\\beta\\ket{010}+\\beta\\ket{001}-\\beta\\ket{110}-\\beta\\ket{101}]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "and then collect like terms whilst maintaining qubit ordering, we yield \n",
-    "\n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= \\tfrac{1}{{2}}[  \\ket{00}(\\alpha\\ket{0}+\\beta\\ket{1}) + \\ket{01}(\\alpha\\ket{1}+\\beta\\ket{0}) +\n",
-    " \\ket{10}(\\alpha\\ket{0}-\\beta\\ket{1}) + \\ket{11}(\\alpha\\ket{1}+\\beta\\ket{0})                              ]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "where the qubit ordering notation states that the left most qubit is the 0<sup>th</sup> qubit. This expression naturally breaks down into 4 terms where each term has all 3 qubits and the 4 terms represent all the possibilities they can be in after they have been evolved.  \n",
-    "\n",
-    "We now ask our friend Alice to measure her qubits. Quantum mechanically we know that upon measurement the possibilities of the 3 qubit system will collapse regardless of the distance between them and the unmeasured outcomes will therefore be deterministic. If her measurement result yields a 00, the first term in $\\ket{\\psi_{2}}$ tells us that Bob's qubit will be in the state $\\alpha\\ket{0}+\\beta\\ket{1}$ which is the the original state, $\\ket{\\psi}$, we wanted to teleport. Alice has other potential measurement outcomes all of which are summarised in the figure below \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "| Alices' measurement        | Bobs' state           | \n",
-    "| ---------------------------|:---------------------:| \n",
-    "|  $$00$$ | $$  \\ket{\\psi_3(00)} = \\alpha\\ket{0} + \\beta\\ket{1} $$ | \n",
-    "|  $$01$$ | $$ \\ket{\\psi_3(01)} =  \\alpha\\ket{1} + \\beta\\ket{0} $$ | \n",
-    "|  $$10$$ | $$ \\ket{\\psi_3(10)} = \\alpha\\ket{0} - \\beta\\ket{1} $$ | \n",
-    "|  $$11$$ | $$ \\ket{\\psi_3(11)} = \\alpha\\ket{1} - \\beta\\ket{0} $$ | \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A quick glance at the figure above shows us that Bob's qubit is nearly in the state $\\ket{\\psi}$ which is what we are after pending some minor corrections. The circuit diagram depicts conditioned gates that are applied depending on Alice's measurement result fulfilling the minor corrections required to complete the teleportation protocol. The final gate operations are summarised below "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "M1M2 = 00, \\ket{\\psi_4} = Z^{0}X^{0}\\ket{\\psi_3(00)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 01, \\ket{\\psi_4} = Z^{0}X^{1}\\ket{\\psi_3(01)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 10, \\ket{\\psi_4} = Z^{1}X^{0}\\ket{\\psi_3(10)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 11, \\ket{\\psi_4} = Z^{1}X^{1}\\ket{\\psi_3(11)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is clear to see that in all instances, we recover $\\ket{\\psi}$ on Bob's qubit thus fulfilling our ambition of teleporting an unknown quantum state between 2 parties. \n",
-    "\n",
-    "It is important to note that quantum teleportation does not allow communication faster than the speed of light. The state $\\ket{\\psi}$ does not instantly appear with Bob. Alice has to use a classical communication channel which is bound by the speed of classical physics to communicate her measurement results to Bob so that he can make the minor corrections required. \n",
-    "\n",
-    "Moreover teleportation does not violate the no-cloning theorem. The protocol does not allow us to create a copy of $\\ket{\\psi}$ leaving us with $\\ket{\\psi\\psi}$ but rather transmits $\\ket{\\psi}$ from Alice to Bob. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The images used in this notebook are courtesey of the Quantum Computation and Quantum Information textbook by Nielsen & Chuang.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/quantum_volume.html b/pr-2308/applications/python/quantum_volume.html
deleted file mode 100644
index c34b907fa4..0000000000
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-  <section id="Quantum-Volume">
-<h1>Quantum Volume<a class="headerlink" href="#Quantum-Volume" title="Permalink to this heading">¶</a></h1>
-<p><a class="reference external" href="https://en.wikipedia.org/wiki/Quantum_volume">Quantum volume</a> (QV) is a metric for determining the power of a noisy quantum device. The QV test is performed for a specified qubit number <span class="math notranslate nohighlight">\(N\)</span>. If the test is passed, then the device can claim a quantum volume of <span class="math notranslate nohighlight">\(2^N\)</span>. In practice, the procedure repeats, until the device reaches a qubit number for which the test fails and its greatest passing score is the device’s quantum volume. Though imperfect, the test is a reasonable
-approximation of the devices usable processing power. This tutorial will demonstrate how CUDA-Q can be used to perform the quantum volume test.</p>
-<p>The test consists of the following steps (see figure below): 1. A special random circuit is constructed (details below) 2. A simulation determines the exact probability distribution of every bitstring and the median probability is determined. 3. Every bitstring which has an associated probability greater than the median, is considered a heavy bitstring for that particular circuit. 4. The circuit is sampled on the noisy device, and the percent of shots resulting in heavy bitstring are 5. The
-process is repeated many times and the resulted averaged. The test is passed if the average is greater than 2/3.</p>
-<p><img alt="Drawing" src="../../_images/QVprocedure.png" /></p>
-<p>the circuits used are square, meaning they have the same number of layers as qubits. Each layer consists of a random permutation of qubits, followed by random SU4 operations applied to n/2 pairs of qubits. (See the first step in the figure above). For CUDA-Q implementation, the SU4 gates are decomposed using the KAK decomposition (figure from this <a class="reference external" href="https://arxiv.org/pdf/1811.12926">paper</a>).</p>
-<p><img alt="Drawing" src="../../_images/kakdecomp.png" /></p>
-<p>The cell below specifies a circuit size <code class="docutils literal notranslate"><span class="pre">n</span></code> and two CUDA-Q kernels, one performing an SU4 operation and another building the entire QV circuit. This example is constructed for an even number of qubits for simplicity.</p>
-<p>The QV kernel concludes with application of a bit flip operation on each qubit. This is not part of the QV circuit, but will be used later to introduce noise to the circuit. Otherwise the test would pass every time!</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Select an even number</span>
-<span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">su4_per_circuit</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n</span><span class="p">)</span>
-<span class="n">n_params_in_su4</span> <span class="o">=</span> <span class="mi">21</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">su4_gate</span><span class="p">(</span><span class="n">q0</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">,</span> <span class="n">q1</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">q0</span><span class="p">)</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">q1</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q0</span><span class="p">,</span> <span class="n">q1</span><span class="p">)</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">q0</span><span class="p">)</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">9</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">11</span><span class="p">],</span> <span class="n">q1</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q1</span><span class="p">,</span> <span class="n">q0</span><span class="p">)</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">13</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">14</span><span class="p">],</span> <span class="n">q0</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q0</span><span class="p">,</span> <span class="n">q1</span><span class="p">)</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">15</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">16</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="n">q0</span><span class="p">)</span>
-    <span class="n">u3</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">18</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">19</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">20</span><span class="p">],</span> <span class="n">q0</span><span class="p">)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">qv</span><span class="p">(</span><span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">permutations</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-
-    <span class="n">reg</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-    <span class="n">param_index</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">gate</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="mi">2</span><span class="p">):</span>
-            <span class="n">su4_gate</span><span class="p">(</span><span class="n">reg</span><span class="p">[</span><span class="n">permutations</span><span class="p">[</span><span class="n">layer</span> <span class="o">*</span> <span class="n">n</span> <span class="o">+</span> <span class="n">gate</span> <span class="o">*</span> <span class="mi">2</span><span class="p">]],</span>
-                     <span class="n">reg</span><span class="p">[</span><span class="n">permutations</span><span class="p">[</span><span class="n">layer</span> <span class="o">*</span> <span class="n">n</span> <span class="o">+</span> <span class="n">gate</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]],</span>
-                     <span class="n">params</span><span class="p">[</span><span class="n">param_index</span><span class="p">:</span><span class="n">param_index</span> <span class="o">+</span> <span class="mi">21</span><span class="p">])</span>
-            <span class="n">param_index</span> <span class="o">+=</span> <span class="mi">21</span>
-
-    <span class="n">x</span><span class="p">(</span><span class="n">reg</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Each circuit must be random. These function randomly choose parameters and permutations for each circuit.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">generate_random_params</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
-
-    <span class="n">params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">n_params_in_su4</span> <span class="o">*</span> <span class="n">su4_per_circuit</span><span class="p">)</span>
-
-    <span class="n">params_list</span> <span class="o">=</span> <span class="n">params</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">params_list</span>
-
-
-
-<span class="k">def</span> <span class="nf">generate_random_permutations</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]:</span>
-
-    <span class="n">circuit_permutations</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="n">circuit_permutations</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">n</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-
-    <span class="k">return</span> <span class="n">circuit_permutations</span>
-
-
-<span class="n">parameters</span> <span class="o">=</span> <span class="n">generate_random_params</span><span class="p">()</span>
-<span class="n">permutations</span> <span class="o">=</span> <span class="n">generate_random_permutations</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>This function is an auxillary function used later to convert an integer into a “big endian” bitstring. This is used to help determine the heavy bitstrings.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">make_bitstring</span><span class="p">(</span><span class="n">integer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-
-    <span class="k">return</span> <span class="nb">bin</span><span class="p">(</span><span class="n">integer</span><span class="p">)[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="n">n</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">percent_heavy_sampled</span></code> function takes the random circuit parameters and permutations and the error rate and returns the percent of heavy bitstrings produced by a noisy circuit sample.</p>
-<p>The function first sets up the noise model. It assumes that each <span class="math notranslate nohighlight">\(X\)</span> gate applied at the end of the circuit will fail with some probability denoted by the <code class="docutils literal notranslate"><span class="pre">error_rate</span></code>.</p>
-<p>Next, the noiseless simulation is performed on a GPU simulated with the <code class="docutils literal notranslate"><span class="pre">nvidia</span></code> backend to obtain the state vector. The <code class="docutils literal notranslate"><span class="pre">density-matrix-cpu</span></code> backend is used to sample the noisy circuit.</p>
-<p>The rest of function processes these results to determine the heavy bitstring sample probabilities.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">percent_heavy_sampled</span><span class="p">(</span><span class="n">circuit_params</span><span class="p">,</span>
-                          <span class="n">layer_permutations</span><span class="p">,</span>
-                          <span class="n">error_rate</span><span class="p">,</span>
-                          <span class="n">print_output</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-
-    <span class="c1"># Includes option to print results for a single circuit</span>
-
-    <span class="c1"># Define a bit flip error applied to all qubits</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">NoiseModel</span><span class="p">()</span>
-    <span class="n">bf</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">BitFlipChannel</span><span class="p">(</span><span class="n">error_rate</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="n">noise</span><span class="o">.</span><span class="n">add_channel</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">bf</span><span class="p">)</span>
-
-    <span class="c1"># Gets noiseless probability distribution</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-    <span class="n">clean_result</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">qv</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">circuit_params</span><span class="p">,</span> <span class="n">layer_permutations</span><span class="p">))</span>
-
-    <span class="c1"># Performs noisy sampling</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;density-matrix-cpu&quot;</span><span class="p">)</span>
-    <span class="n">noisy_result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">qv</span><span class="p">,</span>
-                                <span class="n">n</span><span class="p">,</span>
-                                <span class="n">circuit_params</span><span class="p">,</span>
-                                <span class="n">layer_permutations</span><span class="p">,</span>
-                                <span class="n">noise_model</span><span class="o">=</span><span class="n">noise</span><span class="p">,</span>
-                                <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-
-    <span class="c1"># Converts SV amplitudes to probabilities</span>
-    <span class="n">probs</span> <span class="o">=</span> <span class="n">clean_result</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(</span><span class="n">clean_result</span><span class="p">)</span>
-
-    <span class="c1"># Determines the median value</span>
-    <span class="n">cutoff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
-
-    <span class="k">if</span> <span class="n">print_output</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The Median for this circuit is:&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
-
-    <span class="c1"># Determines if a bitstring is heavy and saves the bitstring in a list if so.</span>
-    <span class="n">heavy</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">circuit_prob</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">for</span> <span class="n">outcome_prob</span> <span class="ow">in</span> <span class="n">probs</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">outcome_prob</span><span class="o">.</span><span class="n">real</span> <span class="o">&gt;</span> <span class="n">cutoff</span><span class="p">:</span>
-            <span class="n">heavy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">make_bitstring</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
-            <span class="n">circuit_prob</span> <span class="o">+=</span> <span class="n">outcome_prob</span><span class="o">.</span><span class="n">real</span>
-        <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="k">if</span> <span class="n">print_output</span><span class="p">:</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The heavy bitstrings for this circuit are&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">heavy</span><span class="p">)</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;This circuit has an ideal havy sampling probability of:&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">circuit_prob</span><span class="p">)</span>
-
-    <span class="c1"># Determines percent of noisy sample results that are heavy</span>
-    <span class="n">prob_heavy_in_noisy</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">heavy_bitstring</span> <span class="ow">in</span> <span class="n">heavy</span><span class="p">:</span>
-        <span class="n">prob_heavy_in_noisy</span> <span class="o">+=</span> <span class="n">noisy_result</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">heavy_bitstring</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">print_output</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Percent of time noisy sample returned heavy bitstring&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">prob_heavy_in_noisy</span><span class="p">)</span>
-
-    <span class="c1"># Returns this probability</span>
-    <span class="k">return</span> <span class="n">prob_heavy_in_noisy</span>
-</pre></div>
-</div>
-</div>
-<p>You can test a single circuit below to see if it passes.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">percent_heavy_sampled</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">permutations</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The Median for this circuit is:
-0.04363711
-The heavy bitstrings for this circuit are
-[&#39;0000&#39;, &#39;0100&#39;, &#39;0010&#39;, &#39;1010&#39;, &#39;0101&#39;, &#39;1101&#39;, &#39;0011&#39;, &#39;0111&#39;]
-This circuit has an ideal havy sampling probability of:
-0.8153219893574715
-Percent of time noisy sample returned heavy bitstring
-0.488
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-0.488
-</pre></div></div>
-</div>
-<p>The true quantum volume is detemined by repeating the process many times and averaging the results. This function repeatedly applies the <code class="docutils literal notranslate"><span class="pre">percent</span> <span class="pre">_heavy_sampled</span></code> function for <code class="docutils literal notranslate"><span class="pre">n_circuit</span></code>number of times and prints if the test is passed and returns the average.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">calc_qv</span><span class="p">(</span><span class="n">n_circuits</span><span class="p">,</span> <span class="n">circuit_size</span><span class="p">,</span> <span class="n">prob_of_error</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="n">circuit_size</span>
-    <span class="n">su4_per_circuit</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n</span><span class="p">)</span>
-    <span class="n">number_of_circuits</span> <span class="o">=</span> <span class="n">n_circuits</span>
-
-    <span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">circuit_results</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="c1"># Loop over n_circuits</span>
-    <span class="k">while</span> <span class="n">counter</span> <span class="o">&lt;</span> <span class="n">number_of_circuits</span><span class="p">:</span>
-        <span class="n">parameters</span> <span class="o">=</span> <span class="n">generate_random_params</span><span class="p">()</span>
-        <span class="n">permutations</span> <span class="o">=</span> <span class="n">generate_random_permutations</span><span class="p">()</span>
-        <span class="n">circuit_results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="n">percent_heavy_sampled</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span>
-                                  <span class="n">permutations</span><span class="p">,</span>
-                                  <span class="n">prob_of_error</span><span class="p">,</span>
-                                  <span class="n">print_output</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
-
-        <span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="c1"># Average the results</span>
-    <span class="n">score</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">circuit_results</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">circuit_results</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The score is:&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
-
-    <span class="c1"># Determined if QV test is passed</span>
-    <span class="k">if</span> <span class="n">score</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;passed!&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Quantum Volume&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">n</span><span class="p">)</span>
-
-    <span class="k">else</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;failed QV Test&#39;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">score</span>
-</pre></div>
-</div>
-</div>
-<p>Try running the QV procedure for 100 four qubit circuits with a 10% chance of error</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">calc_qv</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="mf">.1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The score is:
-0.7280300000000003
-passed!
-Quantum Volume
-16
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-0.7280300000000003
-</pre></div></div>
-</div>
-<p>an interesting benefit of simulation is the ability to explore how noise might affect the QV results. In this case, the noise model is trivial, but it is still possible to see a relationship between the probability of error in the <span class="math notranslate nohighlight">\(X\)</span> gates and the QV outcome.</p>
-<p><img alt="Drawing" src="../../_images/qvplot.png" /></p>
-</section>
-
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-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/pr-2308/applications/python/quantum_volume.ipynb b/pr-2308/applications/python/quantum_volume.ipynb
deleted file mode 100644
index a567ba857f..0000000000
--- a/pr-2308/applications/python/quantum_volume.ipynb
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0b2a1f13-07b1-4b72-9c81-4da1ba62aec6",
-   "metadata": {},
-   "source": [
-    "# Quantum Volume\n",
-    "\n",
-    "[Quantum volume](https://en.wikipedia.org/wiki/Quantum_volume) (QV) is a metric for determining the power of a noisy quantum device.  The QV test is performed for a specified qubit number $N$. If the test is passed, then the device can claim a quantum volume of $2^N$.  In practice, the procedure repeats, until the device reaches a qubit number for which the test fails and its greatest passing score is the device's quantum volume. Though imperfect, the test is a reasonable  approximation of the devices usable processing power. This tutorial will demonstrate how CUDA-Q can be used to perform the quantum volume test.\n",
-    "\n",
-    "\n",
-    "The test consists of the following steps (see figure below):\n",
-    "1. A special random circuit is constructed (details below)\n",
-    "2. A simulation determines the exact probability distribution of every bitstring and the median probability is determined.\n",
-    "3. Every bitstring which has an associated probability greater than the median, is considered a heavy bitstring for that particular circuit.\n",
-    "4. The circuit is sampled on the noisy device, and the percent of shots resulting in heavy bitstring are\n",
-    "5. The process is repeated many times and the resulted averaged. The test is passed if the average is greater than 2/3.\n",
-    "\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/QVprocedure.png\" alt=\"Drawing\" style=\"width: 700px;\"/>\n",
-    "\n",
-    "the circuits used are square, meaning they have the same number of layers as qubits. Each layer consists of a random permutation of qubits, followed by random SU4 operations applied to n/2 pairs of qubits.  (See the first step in the figure above).  For CUDA-Q implementation, the SU4 gates are decomposed using the KAK decomposition (figure from this [paper](https://arxiv.org/pdf/1811.12926)).\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/kakdecomp.png\" alt=\"Drawing\" style=\"width: 500px;\"/>\n",
-    "\n",
-    "The cell below specifies a circuit size `n` and two CUDA-Q kernels, one performing an SU4 operation and another building the entire QV circuit. This example is constructed for an even number of qubits for simplicity.\n",
-    "\n",
-    "The QV kernel concludes with application of a bit flip operation on each qubit. This is not part of the QV circuit, but will be used later to introduce noise to the circuit. Otherwise the test would pass every time!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b748ca48-8c65-4158-8c8a-126bbbb39afe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "# Select an even number\n",
-    "n = 4\n",
-    "su4_per_circuit = int(n / 2 * n)\n",
-    "n_params_in_su4 = 21\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def su4_gate(q0: cudaq.qubit, q1: cudaq.qubit, params: list[float]):\n",
-    "    u3(params[0], params[1], params[2], q0)\n",
-    "    u3(params[3], params[4], params[5], q1)\n",
-    "    x.ctrl(q0, q1)\n",
-    "    u3(params[6], params[7], params[8], q0)\n",
-    "    u3(params[9], params[10], params[11], q1)\n",
-    "    x.ctrl(q1, q0)\n",
-    "    u3(params[12], params[13], params[14], q0)\n",
-    "    x.ctrl(q0, q1)\n",
-    "    u3(params[15], params[16], params[17], q0)\n",
-    "    u3(params[18], params[19], params[20], q0)\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qv(n: int, params: list[float], permutations: list[int]):\n",
-    "\n",
-    "    reg = cudaq.qvector(n)\n",
-    "    param_index = 0\n",
-    "\n",
-    "    for layer in range(n):\n",
-    "        for gate in range(n / 2):\n",
-    "            su4_gate(reg[permutations[layer * n + gate * 2]],\n",
-    "                     reg[permutations[layer * n + gate * 2 + 1]],\n",
-    "                     params[param_index:param_index + 21])\n",
-    "            param_index += 21\n",
-    "\n",
-    "    x(reg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3d3fb4f-ca18-4eea-9b65-609fbc31317c",
-   "metadata": {},
-   "source": [
-    "Each circuit must be random. These function randomly choose parameters and permutations for each circuit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b139613f-f6ca-48de-a4c2-59aa7d705764",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_random_params() -> list[float]:\n",
-    "\n",
-    "    params = np.random.uniform(0, 2 * np.pi, n_params_in_su4 * su4_per_circuit)\n",
-    "\n",
-    "    params_list = params.tolist()\n",
-    "\n",
-    "    return params_list\n",
-    "\n",
-    "\n",
-    "\n",
-    "def generate_random_permutations() -> list[int]:\n",
-    "\n",
-    "    circuit_permutations = []\n",
-    "\n",
-    "    for i in range(n):\n",
-    "        circuit_permutations.extend(\n",
-    "            np.random.permutation(n).astype(np.int64).tolist())\n",
-    "\n",
-    "    return circuit_permutations\n",
-    "\n",
-    "\n",
-    "parameters = generate_random_params()\n",
-    "permutations = generate_random_permutations()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d11fd5a-da31-4645-976d-7d944ba4e18e",
-   "metadata": {},
-   "source": [
-    "This function is an auxillary function used later to convert an integer into a \"big endian\" bitstring. This is used to help determine the heavy bitstrings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ded42cc6-3bf7-4b56-90ca-0e69cbe85dad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_bitstring(integer) -> str:\n",
-    "\n",
-    "    return bin(integer)[2:].zfill(n)[::-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d631da6-97e3-473c-b59f-beb2302fa75e",
-   "metadata": {},
-   "source": [
-    "The `percent_heavy_sampled` function takes the random circuit parameters and permutations and the error rate and returns the percent of heavy bitstrings produced by a noisy circuit sample.  \n",
-    "\n",
-    "The function first sets up the noise model.  It assumes that each $X$ gate applied at the end of the circuit will fail with some probability denoted by the `error_rate`.\n",
-    "\n",
-    "Next, the noiseless simulation is performed on a GPU simulated with the `nvidia` backend to obtain the state vector.  The `density-matrix-cpu` backend is used to sample the noisy circuit. \n",
-    "\n",
-    "The rest of function processes these results to determine the heavy bitstring sample probabilities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e852e3c5-29a0-4588-ac56-a102463aa100",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def percent_heavy_sampled(circuit_params,\n",
-    "                          layer_permutations,\n",
-    "                          error_rate,\n",
-    "                          print_output=False) -> float:\n",
-    "\n",
-    "    # Includes option to print results for a single circuit\n",
-    "\n",
-    "    # Define a bit flip error applied to all qubits\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "    bf = cudaq.BitFlipChannel(error_rate)\n",
-    "    for i in range(n):\n",
-    "        noise.add_channel('x', [i], bf)\n",
-    "\n",
-    "    # Gets noiseless probability distribution\n",
-    "    cudaq.set_target(\"nvidia\")\n",
-    "    clean_result = np.array(\n",
-    "        cudaq.get_state(qv, n, circuit_params, layer_permutations))\n",
-    "\n",
-    "    # Performs noisy sampling\n",
-    "    cudaq.set_target(\"density-matrix-cpu\")\n",
-    "    noisy_result = cudaq.sample(qv,\n",
-    "                                n,\n",
-    "                                circuit_params,\n",
-    "                                layer_permutations,\n",
-    "                                noise_model=noise,\n",
-    "                                shots_count=1000)\n",
-    "\n",
-    "    # Converts SV amplitudes to probabilities\n",
-    "    probs = clean_result * np.conjugate(clean_result)\n",
-    "\n",
-    "    # Determines the median value\n",
-    "    cutoff = np.median(probs).real\n",
-    "\n",
-    "    if print_output:\n",
-    "        print('The Median for this circuit is:')\n",
-    "        print(np.median(probs).real)\n",
-    "\n",
-    "    # Determines if a bitstring is heavy and saves the bitstring in a list if so.\n",
-    "    heavy = []\n",
-    "    index = 0\n",
-    "    circuit_prob = 0\n",
-    "\n",
-    "    for outcome_prob in probs:\n",
-    "        if outcome_prob.real > cutoff:\n",
-    "            heavy.append(make_bitstring(index))\n",
-    "            circuit_prob += outcome_prob.real\n",
-    "        index += 1\n",
-    "\n",
-    "    if print_output:\n",
-    "\n",
-    "        print('The heavy bitstrings for this circuit are')\n",
-    "        print(heavy)\n",
-    "\n",
-    "        print('This circuit has an ideal havy sampling probability of:')\n",
-    "        print(circuit_prob)\n",
-    "\n",
-    "    # Determines percent of noisy sample results that are heavy\n",
-    "    prob_heavy_in_noisy = 0\n",
-    "    for heavy_bitstring in heavy:\n",
-    "        prob_heavy_in_noisy += noisy_result.probability(heavy_bitstring)\n",
-    "\n",
-    "    if print_output:\n",
-    "        print('Percent of time noisy sample returned heavy bitstring')\n",
-    "        print(prob_heavy_in_noisy)\n",
-    "\n",
-    "    # Returns this probability\n",
-    "    return prob_heavy_in_noisy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8eb4fff-c031-40d1-b521-7aea8bc1014f",
-   "metadata": {},
-   "source": [
-    "You can test a single circuit below to see if it passes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9cd7d79d-f489-426e-97a7-8664e62799a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The Median for this circuit is:\n",
-      "0.04363711\n",
-      "The heavy bitstrings for this circuit are\n",
-      "['0000', '0100', '0010', '1010', '0101', '1101', '0011', '0111']\n",
-      "This circuit has an ideal havy sampling probability of:\n",
-      "0.8153219893574715\n",
-      "Percent of time noisy sample returned heavy bitstring\n",
-      "0.488\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.488"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "percent_heavy_sampled(parameters, permutations, 1, True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecd2b646-df3e-4b4c-b9be-851f6bdb6116",
-   "metadata": {},
-   "source": [
-    "The true quantum volume is detemined by repeating the process many times and averaging the results. This function repeatedly applies the `percent _heavy_sampled` function for `n_circuit`number of times and prints if the test is passed and returns the average."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f94562c0-7ceb-4609-9e55-ae8791cd8dc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_qv(n_circuits, circuit_size, prob_of_error) -> float:\n",
-    "\n",
-    "    n = circuit_size\n",
-    "    su4_per_circuit = int(n / 2 * n)\n",
-    "    number_of_circuits = n_circuits\n",
-    "\n",
-    "    counter = 0\n",
-    "    circuit_results = []\n",
-    "\n",
-    "    # Loop over n_circuits\n",
-    "    while counter < number_of_circuits:\n",
-    "        parameters = generate_random_params()\n",
-    "        permutations = generate_random_permutations()\n",
-    "        circuit_results.append(\n",
-    "            percent_heavy_sampled(parameters,\n",
-    "                                  permutations,\n",
-    "                                  prob_of_error,\n",
-    "                                  print_output=False))\n",
-    "\n",
-    "        counter += 1\n",
-    "\n",
-    "    # Average the results\n",
-    "    score = sum(circuit_results) / len(circuit_results)\n",
-    "\n",
-    "    print('The score is:')\n",
-    "    print(score)\n",
-    "\n",
-    "    # Determined if QV test is passed\n",
-    "    if score > 2 / 3:\n",
-    "        print('passed!')\n",
-    "        print('Quantum Volume')\n",
-    "        print(2**n)\n",
-    "\n",
-    "    else:\n",
-    "        print('failed QV Test')\n",
-    "\n",
-    "    return score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2279de84-bb73-48a9-a33b-3aa2efa66573",
-   "metadata": {},
-   "source": [
-    "Try running the QV procedure for 100 four qubit circuits with a 10% chance of error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3cab12da-7cbc-4ff3-80d8-2a81007942ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The score is:\n",
-      "0.7280300000000003\n",
-      "passed!\n",
-      "Quantum Volume\n",
-      "16\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.7280300000000003"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n = 4\n",
-    "calc_qv(100, n, .1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afbc7d0a-2507-42e4-9946-3aa93384c645",
-   "metadata": {},
-   "source": [
-    "an interesting benefit of simulation is the ability to explore how noise might affect the QV results.  In this case, the noise model is trivial, but it is still possible to see a relationship between the probability of error in the $X$ gates and the QV outcome. \n",
-    "\n",
-    "\n",
-    "<img src=\"./images/qvplot.png\" alt=\"Drawing\" style=\"width: 900px;\"/>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/readout_error_mitigation.html b/pr-2308/applications/python/readout_error_mitigation.html
deleted file mode 100644
index 2f4c555af1..0000000000
--- a/pr-2308/applications/python/readout_error_mitigation.html
+++ /dev/null
@@ -1,1518 +0,0 @@
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-<html class="writer-html5" lang="en" >
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-<li class="toctree-l1 current"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
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-<h1>Readout Error Mitigation<a class="headerlink" href="#Readout-Error-Mitigation" title="Permalink to this heading">¶</a></h1>
-<p>Readout errors are caused by imperfect qubit measurement and are a common source of error in quantum computing. Properly modelling these errors in simulation can give the user tools to understand better what these errors are and how to mitigate them when running on actual quantum devices.</p>
-<p>Readout errors can be mitigated with a confusion matrix <span class="math notranslate nohighlight">\(A\)</span>. It is a square matrix of size <span class="math notranslate nohighlight">\(2^n \times 2^n\)</span> and tells about the probability of observing the state <span class="math notranslate nohighlight">\(|y\rangle\)</span> given the true state <span class="math notranslate nohighlight">\(|x\rangle\)</span>. The confusion matrix characterizes the readout error of the device and is circuit-independent. Once <span class="math notranslate nohighlight">\(A\)</span> is estimated, we can compute its pseudoinverse <span class="math notranslate nohighlight">\(A^+\)</span> which can be applied to the noisy probability distribution <span class="math notranslate nohighlight">\(p_{\text{noisy}}\)</span> to obtain an
-adjusted quasi-probability distribution</p>
-<div class="math notranslate nohighlight">
-\[p_{\text{mitigated}} = A^+ p_{\text{noisy}}\]</div>
-<p>In this tutorial, we show how to build a confusion matrix with the following approaches.</p>
-<ul class="simple">
-<li><p>Using a single qubit model</p></li>
-<li><p>Using <span class="math notranslate nohighlight">\(k\)</span> local confusion matrices</p></li>
-<li><p>A full confusion matrix for each <span class="math notranslate nohighlight">\(2^n\)</span> combination</p></li>
-</ul>
-<p>The last method works well for correcting correlated errors (which affect multiple qubits together). However, it becomes impractical for large numbers of qubits.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the relevant packages.</span>
-
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">matplotlib</span><span class="o">==</span><span class="m">3</span>.8.4<span class="w"> </span><span class="nv">pandas</span><span class="o">==</span><span class="m">2</span>.2.2<span class="w"> </span><span class="nv">scipy</span><span class="o">==</span><span class="m">1</span>.13.1<span class="w"> </span><span class="nv">seaborn</span><span class="o">==</span><span class="m">0</span>.13.2<span class="w"> </span>-q
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">itertools</span>
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
-<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
-
-<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;dark&quot;</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;density-matrix-cpu&quot;</span><span class="p">)</span>
-<span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_random_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>To model the readout error, we apply a bitflip channel on every qubit at the end of the circuit using an Identity gate. The probability of bitflip <code class="docutils literal notranslate"><span class="pre">probs</span></code> can be the same or different for all the qubits.</p>
-<div class="alert alert-block alert-success"><p>Note: In principle, readout error is applied to the measurement gate but we use the Identity gate because adding a quantum error on the measurement gate is not yet supported. Also, the Identity gate is not present in the add_channel method, so we model the Identity gate using a rotation gate with an angle of 0.0.</p>
-</div><div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_noise</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">probs</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]]):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates a noise model with specified probabilities of bit flip errors on each qubit.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        n_qubits (int): The number of qubits in the quantum circuit.</span>
-<span class="sd">        probs (Union[float, list[float]]): Probabilities of a bit flip error occurring on each qubit.</span>
-<span class="sd">        If a single probability value is provided, it will be applied to all qubits. If a list of</span>
-<span class="sd">        probabilities is provided, each qubit will have its own probability.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        cudaq noise model</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">NoiseModel</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-        <span class="n">probs</span> <span class="o">=</span> <span class="p">[</span><span class="n">probs</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_qubits</span>
-
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">probs</span><span class="p">):</span>
-        <span class="n">bit_flip</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">BitFlipChannel</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">noise</span><span class="o">.</span><span class="n">add_channel</span><span class="p">(</span><span class="s2">&quot;rx&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">bit_flip</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">noise</span>
-</pre></div>
-</div>
-</div>
-<p>We define a cudaq kernel which will help create a set of quantum circuits to take measurements of the basis states for <span class="math notranslate nohighlight">\(n\)</span> qubits. The kernel takes the number of qubits and the basis state as arguments.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">state_label</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">state_label</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">val</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="n">rx</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">qvector</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-          <span class="p">)</span>  <span class="c1"># Identity gate at the end on each qubit to model readout error</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>For the tutorial, we will use the GHZ state on three qubits to test readout error mitigation. The GHZ state on 3 qubits is described as:</p>
-<div class="math notranslate nohighlight">
-\[\frac{|000\rangle + |111\rangle}{\sqrt{2}}\]</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">ghz_kernel</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">)</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_qubits</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">cx</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-
-    <span class="c1"># Apply identity gates for readout error mitigation</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">):</span>
-        <span class="n">rx</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">qvector</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Function to draw the confusion matrix</span>
-<span class="k">def</span> <span class="nf">plot_cmat</span><span class="p">(</span><span class="n">mat</span><span class="p">):</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mat</span><span class="p">)</span>
-    <span class="n">im2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Reds</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="p">[</span><span class="s2">&quot;&quot;</span><span class="p">])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="p">[</span><span class="s2">&quot;&quot;</span><span class="p">])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;Confusion Matrix&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Prepared State&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_label_position</span><span class="p">(</span><span class="s2">&quot;top&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Measured State&quot;</span><span class="p">)</span>
-    <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im2</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>It is possible that the adjusted quasi-probability distribution <span class="math notranslate nohighlight">\(p_{\text{mitigated}}\)</span> obtained by application of <span class="math notranslate nohighlight">\(A^+\)</span> to <span class="math notranslate nohighlight">\(p_{\text{noisy}}\)</span> may be non-positive. We need to find the closest positive probability distribution in such a case.</p>
-<div class="math notranslate nohighlight">
-\[p'' = \min_{p_{\rm positive}} \|p_{\rm noisy} - p_{\rm positive}\|_1\]</div>
-<p>Then, we can draw samples from <span class="math notranslate nohighlight">\(p''\)</span>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">find_closest_distribution</span><span class="p">(</span><span class="n">empirical_dist</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Find the closest distribution to an empirical distribution by minimizing the L1 norm.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        empirical_dist: Empirical distribution that you want to find the closest distribution to.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        Closest distribution to `empirical_dist`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">empirical_dist</span> <span class="o">-</span> <span class="n">x</span><span class="p">,</span> <span class="nb">ord</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="c1"># Constraint: all elements of p must be positive, and the distribution must sum to 1</span>
-    <span class="n">cons</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;ineq&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;fun&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">p</span>
-        <span class="p">},</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;eq&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;fun&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
-        <span class="p">},</span>
-    <span class="p">)</span>
-    <span class="n">bnds</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">empirical_dist</span><span class="p">))]</span>
-    <span class="n">initial_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">empirical_dist</span><span class="p">))</span>
-
-    <span class="n">res</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span>
-        <span class="n">objective</span><span class="p">,</span>
-        <span class="n">initial_value</span><span class="p">,</span>
-        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;SLSQP&quot;</span><span class="p">,</span>
-        <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;maxiter&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">},</span>
-        <span class="n">bounds</span><span class="o">=</span><span class="n">bnds</span><span class="p">,</span>
-        <span class="n">constraints</span><span class="o">=</span><span class="n">cons</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">res</span><span class="o">.</span><span class="n">x</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_counts_from_distribution</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">dist</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generates samples based on a given distribution and returns the counts of each sample value.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        n_qubits: The number of qubits in the quantum circuit.</span>
-<span class="sd">        dist: The probability distribution from which samples are drawn.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        An array of counts for each possible value in the distribution. The array has a length of 2^n_qubits.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">n_qubits</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">dist</span><span class="p">)</span>
-    <span class="n">values</span><span class="p">,</span> <span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-    <span class="n">res</span><span class="p">[</span><span class="n">values</span><span class="p">]</span> <span class="o">=</span> <span class="n">counts</span>
-    <span class="k">return</span> <span class="n">res</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s run the <code class="docutils literal notranslate"><span class="pre">ghz_kernel</span></code> with the noise model. We will perform two experiments. First, we keep the bitflip probability the same for all the qubits. In the second experiment, we use different bitflip probabilities for qubits.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_qubits</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">shots</span> <span class="o">=</span> <span class="mi">1024</span>
-
-<span class="n">labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">repeat</span><span class="o">=</span><span class="n">n_qubits</span><span class="p">)))</span>
-<span class="n">states</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">label</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">label</span><span class="p">)),</span> <span class="n">labels</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="mf">0.1</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;bitflip probability: </span><span class="si">{</span><span class="n">p</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="n">noise_1</span> <span class="o">=</span> <span class="n">get_noise</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
-
-<span class="n">ghz_result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz_kernel</span><span class="p">,</span>
-                          <span class="n">n_qubits</span><span class="p">,</span>
-                          <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">,</span>
-                          <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_1</span><span class="p">)</span>
-<span class="n">noisy_dict_1</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ghz_result</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
-
-<span class="n">noisy_res_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-    <span class="p">[</span><span class="n">noisy_dict_1</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">states</span><span class="p">)])</span>
-
-<span class="n">noisy_dict_1</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-bitflip probability: 0.1
-
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;000&#39;: 352,
- &#39;010&#39;: 43,
- &#39;111&#39;: 367,
- &#39;001&#39;: 47,
- &#39;011&#39;: 47,
- &#39;100&#39;: 54,
- &#39;101&#39;: 61,
- &#39;110&#39;: 53}
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_qubits</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;bitflip probability: </span><span class="si">{</span><span class="n">p</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="n">noise_2</span> <span class="o">=</span> <span class="n">get_noise</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
-
-<span class="n">ghz_result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz_kernel</span><span class="p">,</span>
-                          <span class="n">n_qubits</span><span class="p">,</span>
-                          <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">,</span>
-                          <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_2</span><span class="p">)</span>
-<span class="n">noisy_dict_2</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ghz_result</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
-
-<span class="n">noisy_res_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-    <span class="p">[</span><span class="n">noisy_dict_2</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">states</span><span class="p">)])</span>
-
-<span class="n">noisy_dict_2</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-bitflip probability: [0.149816047538945, 0.3802857225639665, 0.292797576724562]
-
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;000&#39;: 212,
- &#39;010&#39;: 122,
- &#39;111&#39;: 189,
- &#39;001&#39;: 111,
- &#39;011&#39;: 87,
- &#39;100&#39;: 78,
- &#39;101&#39;: 131,
- &#39;110&#39;: 94}
-</pre></div></div>
-</div>
-<section id="Inverse-confusion-matrix-from-single-qubit-noise-model">
-<h2>Inverse confusion matrix from single-qubit noise model<a class="headerlink" href="#Inverse-confusion-matrix-from-single-qubit-noise-model" title="Permalink to this heading">¶</a></h2>
-<p>Here we assume that the readout error of each qubit is independent with the same confusion probabilities. Then, the confusion matrix <span class="math notranslate nohighlight">\(A_1\)</span> for a single qubit can be defined as</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}A_1 = \begin{bmatrix}
-P(0|0) &amp; P(0|1)\\
-P(1|0) &amp; P(1|1)
-\end{bmatrix}\end{split}\]</div>
-<p>where <span class="math notranslate nohighlight">\(P(y|x)\)</span> is the probability of observing state <span class="math notranslate nohighlight">\(|y\rangle\)</span> when measuring the true state <span class="math notranslate nohighlight">\(|x\rangle\)</span>. Using <span class="math notranslate nohighlight">\(A_1\)</span>, the full <span class="math notranslate nohighlight">\(2^n \times 2^n\)</span> confusion matrix <span class="math notranslate nohighlight">\(A\)</span> can be written as</p>
-<div class="math notranslate nohighlight">
-\[A = A_1 \otimes \dots \otimes A_1\]</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">single_qubit_labels</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-<span class="n">single_qubit_states</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">]</span>  <span class="c1"># Used for dictionary keys</span>
-
-<span class="c1"># we use noise_1 which has same bitflip probability for all qubits</span>
-<span class="n">results</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">,</span> <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_1</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">single_qubit_labels</span>
-<span class="p">]</span>
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">single_qubit_states</span><span class="p">):</span>
-    <span class="n">res</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">state</span><span class="si">}</span><span class="s2"> becomes </span><span class="si">{</span><span class="n">res</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-0 becomes {&#39;0&#39;: 924, &#39;1&#39;: 100}
-1 becomes {&#39;0&#39;: 104, &#39;1&#39;: 920}
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Construct confusion matrix for single qubit</span>
-
-<span class="n">A_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">single_qubit_states</span><span class="p">):</span>
-    <span class="n">true_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">observed_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">A_1</span><span class="p">[</span><span class="n">observed_state</span><span class="p">][</span><span class="n">true_state</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span> <span class="o">/</span> <span class="n">shots</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">A_1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[[0.90234375 0.1015625 ]
- [0.09765625 0.8984375 ]]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">,</span> <span class="p">[</span><span class="n">A_1</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_qubits</span><span class="p">)</span>  <span class="c1"># joint confusion matrix</span>
-<span class="n">A_pinv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>  <span class="c1"># Generate pseudoinverse confusion matrix.</span>
-<span class="n">mitigated</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A_pinv</span><span class="p">,</span> <span class="n">noisy_res_1</span><span class="p">),</span>
-                     <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>  <span class="c1"># Obtain mitigated counts</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mitigated counts:</span><span class="se">\n</span><span class="si">{</span><span class="n">mitigated</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">):</span>
-    <span class="n">positive_dist</span> <span class="o">=</span> <span class="n">find_closest_distribution</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">/</span> <span class="n">shots</span><span class="p">)</span>
-    <span class="n">mitigated</span> <span class="o">=</span> <span class="n">get_counts_from_distribution</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">shots</span><span class="p">,</span> <span class="n">positive_dist</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Corrected for negative counts:</span><span class="se">\n</span><span class="si">{</span><span class="n">mitigated</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Mitigated counts:
-[476   3   0   1  12  19   9 501]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s2">&quot;states&quot;</span><span class="p">:</span> <span class="n">states</span><span class="p">,</span>
-    <span class="s2">&quot;noisy&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">noisy_res_1</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">noisy_res_1</span><span class="p">),</span> <span class="mi">3</span><span class="p">),</span>
-    <span class="s2">&quot;mitigated_sg&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mitigated</span><span class="p">),</span> <span class="mi">3</span><span class="p">),</span>
-<span class="p">})</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;states&quot;</span><span class="p">,</span>
-             <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;noisy&quot;</span><span class="p">,</span> <span class="s2">&quot;mitigated_sg&quot;</span><span class="p">],</span>
-             <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span>
-             <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;noisy&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mitigated_sg&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;probabilities&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_readout_error_mitigation_21_0.png" src="../../_images/applications_python_readout_error_mitigation_21_0.png" />
-</div>
-</div>
-</section>
-<section id="Inverse-confusion-matrix-from-k-local-confusion-matrices">
-<h2>Inverse confusion matrix from k local confusion matrices<a class="headerlink" href="#Inverse-confusion-matrix-from-k-local-confusion-matrices" title="Permalink to this heading">¶</a></h2>
-<p>This method works under the assumption that the readout errors for different qubits are independent of each other and the confusion matrix <span class="math notranslate nohighlight">\(A\)</span> can be factorized into the product of <span class="math notranslate nohighlight">\(k\)</span> small local confusion matrices. We consider the special case when <span class="math notranslate nohighlight">\(k = n\)</span> for <span class="math notranslate nohighlight">\(n\)</span> qubits and different <span class="math notranslate nohighlight">\(2 \times 2\)</span> confusion matrices for each qubit</p>
-<div class="math notranslate nohighlight">
-\[A = A_1 \otimes \dots \otimes A_n\]</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">local_labels</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_qubits</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_qubits</span><span class="p">]</span>
-<span class="n">local_states</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;0&quot;</span> <span class="o">*</span> <span class="n">n_qubits</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span> <span class="o">*</span> <span class="n">n_qubits</span><span class="p">]</span>
-
-<span class="n">results</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                 <span class="n">n_qubits</span><span class="p">,</span>
-                 <span class="n">label</span><span class="p">,</span>
-                 <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">,</span>
-                 <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_2</span><span class="p">)</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">local_labels</span>
-<span class="p">]</span>
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">local_states</span><span class="p">):</span>
-    <span class="n">res</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">state</span><span class="si">}</span><span class="s2"> becomes </span><span class="si">{</span><span class="n">res</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-000 becomes {&#39;000&#39;: 388, &#39;010&#39;: 235, &#39;111&#39;: 18, &#39;001&#39;: 174, &#39;011&#39;: 79, &#39;100&#39;: 58, &#39;101&#39;: 28, &#39;110&#39;: 44}
-111 becomes {&#39;000&#39;: 23, &#39;010&#39;: 38, &#39;111&#39;: 370, &#39;001&#39;: 40, &#39;011&#39;: 60, &#39;100&#39;: 107, &#39;101&#39;: 224, &#39;110&#39;: 162}
-</pre></div></div>
-</div>
-<p>The local confusion matrices are generated using the marginal probability distribution for each qubit.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">counts</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">local_states</span><span class="p">)</span>
-<span class="p">]</span>
-<span class="n">matrices</span> <span class="o">=</span> <span class="p">[]</span>
-
-<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">):</span>
-    <span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">marginalized_counts</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">total_shots</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-        <span class="n">marginal_cts</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_marginal_counts</span><span class="p">([</span><span class="n">k</span><span class="p">])</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
-        <span class="n">marginalized_counts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">marginal_cts</span><span class="p">)</span>
-        <span class="n">total_shots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">marginal_cts</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-
-    <span class="c1"># matrix[i][j] is the probability of counting i for expected j</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-            <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">marginalized_counts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">),</span>
-                                                      <span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">total_shots</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-    <span class="n">matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">matrix</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">matrices</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
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-<div class="highlight"><pre>
-[[0.85546875 0.15722656]
- [0.14453125 0.84277344]]
-
-[[0.6328125  0.38476562]
- [0.3671875  0.61523438]]
-
-[[0.70800781 0.32226562]
- [0.29199219 0.67773438]]
-
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># To find the joint pseudoinverse confusion matrix, we can first perform pseudoinverse of the</span>
-<span class="c1"># local confusion matrices and then do the tensor product</span>
-
-<span class="n">pinv_confusion_matrices</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">matrices</span><span class="p">]</span>
-<span class="n">A_pinv</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">,</span> <span class="n">pinv_confusion_matrices</span><span class="p">)</span>
-
-<span class="n">mitigated</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A_pinv</span><span class="p">,</span> <span class="n">noisy_res_2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mitigated counts:</span><span class="se">\n</span><span class="si">{</span><span class="n">mitigated</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">):</span>
-    <span class="n">positive_dist</span> <span class="o">=</span> <span class="n">find_closest_distribution</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">/</span> <span class="n">shots</span><span class="p">)</span>
-    <span class="n">mitigated</span> <span class="o">=</span> <span class="n">get_counts_from_distribution</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">shots</span><span class="p">,</span> <span class="n">positive_dist</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Corrected for negative counts:</span><span class="se">\n</span><span class="si">{</span><span class="n">mitigated</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="n">A_joint</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">,</span> <span class="n">matrices</span><span class="p">)</span>
-<span class="n">plot_cmat</span><span class="p">(</span><span class="n">A_joint</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
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-<div class="highlight"><pre>
-Mitigated counts:
-[559  -1 -58  32 -28  26 -16 510]
-
-Corrected for negative counts:
-[521   0   0  35   0  22   0 446]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_readout_error_mitigation_27_1.png" src="../../_images/applications_python_readout_error_mitigation_27_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s2">&quot;states&quot;</span><span class="p">:</span> <span class="n">states</span><span class="p">,</span>
-    <span class="s2">&quot;noisy&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">noisy_res_2</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">noisy_res_2</span><span class="p">),</span> <span class="mi">3</span><span class="p">),</span>
-    <span class="s2">&quot;mitigated_k_local&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mitigated</span><span class="p">),</span> <span class="mi">3</span><span class="p">),</span>
-<span class="p">})</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;states&quot;</span><span class="p">,</span>
-             <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;noisy&quot;</span><span class="p">,</span> <span class="s2">&quot;mitigated_k_local&quot;</span><span class="p">],</span>
-             <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span>
-             <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;noisy&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mitigated_k_local&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;probabilities&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_readout_error_mitigation_28_0.png" src="../../_images/applications_python_readout_error_mitigation_28_0.png" />
-</div>
-</div>
-</section>
-<section id="Inverse-of-full-confusion-matrix">
-<h2>Inverse of full confusion matrix<a class="headerlink" href="#Inverse-of-full-confusion-matrix" title="Permalink to this heading">¶</a></h2>
-<p>Here we generate a quantum circuit for each of the basis states of <span class="math notranslate nohighlight">\(n\)</span> qubits (i.e., <span class="math notranslate nohighlight">\(2^n\)</span> combinations). This gives more accurate readout error mitigation. Since it scales exponentially with the number of qubits, it is only practical for small devices.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                 <span class="n">n_qubits</span><span class="p">,</span>
-                 <span class="n">label</span><span class="p">,</span>
-                 <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">,</span>
-                 <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_2</span><span class="p">)</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span>
-<span class="p">]</span>
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">states</span><span class="p">):</span>
-    <span class="n">res</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">state</span><span class="si">}</span><span class="s2"> becomes </span><span class="si">{</span><span class="n">res</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-000 becomes {&#39;000&#39;: 367, &#39;010&#39;: 248, &#39;111&#39;: 17, &#39;001&#39;: 159, &#39;011&#39;: 83, &#39;100&#39;: 68, &#39;101&#39;: 35, &#39;110&#39;: 47}
-001 becomes {&#39;000&#39;: 161, &#39;010&#39;: 93, &#39;111&#39;: 46, &#39;001&#39;: 363, &#39;011&#39;: 253, &#39;100&#39;: 17, &#39;101&#39;: 76, &#39;110&#39;: 15}
-010 becomes {&#39;000&#39;: 234, &#39;010&#39;: 378, &#39;111&#39;: 27, &#39;001&#39;: 100, &#39;011&#39;: 150, &#39;100&#39;: 36, &#39;101&#39;: 25, &#39;110&#39;: 74}
-011 becomes {&#39;000&#39;: 106, &#39;010&#39;: 145, &#39;111&#39;: 65, &#39;001&#39;: 251, &#39;011&#39;: 386, &#39;100&#39;: 11, &#39;101&#39;: 30, &#39;110&#39;: 30}
-100 becomes {&#39;000&#39;: 83, &#39;010&#39;: 34, &#39;111&#39;: 99, &#39;001&#39;: 29, &#39;011&#39;: 18, &#39;100&#39;: 388, &#39;101&#39;: 144, &#39;110&#39;: 229}
-101 becomes {&#39;000&#39;: 22, &#39;010&#39;: 13, &#39;111&#39;: 247, &#39;001&#39;: 70, &#39;011&#39;: 43, &#39;100&#39;: 163, &#39;101&#39;: 371, &#39;110&#39;: 95}
-110 becomes {&#39;000&#39;: 32, &#39;010&#39;: 77, &#39;111&#39;: 157, &#39;001&#39;: 14, &#39;011&#39;: 30, &#39;100&#39;: 233, &#39;101&#39;: 85, &#39;110&#39;: 396}
-111 becomes {&#39;000&#39;: 14, &#39;010&#39;: 31, &#39;111&#39;: 393, &#39;001&#39;: 38, &#39;011&#39;: 60, &#39;100&#39;: 95, &#39;101&#39;: 233, &#39;110&#39;: 160}
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># generate full confusion matrix</span>
-<span class="n">A_full</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="o">**</span><span class="n">n_qubits</span><span class="p">,</span> <span class="mi">2</span><span class="o">**</span><span class="n">n_qubits</span><span class="p">))</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">states</span><span class="p">):</span>
-    <span class="n">true_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">observed_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">A_full</span><span class="p">[</span><span class="n">observed_state</span><span class="p">][</span><span class="n">true_state</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span> <span class="o">/</span> <span class="n">shots</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_cmat</span><span class="p">(</span><span class="n">A_full</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_readout_error_mitigation_32_0.png" src="../../_images/applications_python_readout_error_mitigation_32_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Distance between full confusion matrix and tensored confusion matrix</span>
-<span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A_joint</span> <span class="o">-</span> <span class="n">A_full</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-0.09734490298929
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">A_pinv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">A_full</span><span class="p">)</span>
-<span class="n">mitigated</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A_pinv</span><span class="p">,</span> <span class="n">noisy_res_2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mitigated counts:</span><span class="se">\n</span><span class="si">{</span><span class="n">mitigated</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">):</span>
-    <span class="n">positive_dist</span> <span class="o">=</span> <span class="n">find_closest_distribution</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">/</span> <span class="n">shots</span><span class="p">)</span>
-    <span class="n">mitigated</span> <span class="o">=</span> <span class="n">get_counts_from_distribution</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">,</span> <span class="n">shots</span><span class="p">,</span> <span class="n">positive_dist</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Corrected for negative counts:</span><span class="se">\n</span><span class="si">{</span><span class="n">mitigated</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Mitigated counts:
-[ 714 -100 -211  152  -49   63   32  422]
-
-Corrected for negative counts:
-[683   0   0  39   0   0   0 302]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mitigated_full&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">mitigated</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mitigated</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;states&quot;</span><span class="p">,</span>
-    <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;noisy&quot;</span><span class="p">,</span> <span class="s2">&quot;mitigated_k_local&quot;</span><span class="p">,</span> <span class="s2">&quot;mitigated_full&quot;</span><span class="p">],</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span>
-    <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
-<span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;noisy&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mitigated_k_local&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">bar_label</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mitigated_full&quot;</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;probabilities&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-</div>
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-<img alt="../../_images/applications_python_readout_error_mitigation_35_0.png" src="../../_images/applications_python_readout_error_mitigation_35_0.png" />
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diff --git a/pr-2308/applications/python/readout_error_mitigation.ipynb b/pr-2308/applications/python/readout_error_mitigation.ipynb
deleted file mode 100644
index ec366366bd..0000000000
--- a/pr-2308/applications/python/readout_error_mitigation.ipynb
+++ /dev/null
@@ -1,922 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5b2ac00e-c7fc-45cc-888e-8c26dabf80d2",
-   "metadata": {},
-   "source": [
-    "# Readout Error Mitigation\n",
-    "\n",
-    "Readout errors are caused by imperfect qubit measurement and are a common source of error in quantum computing. Properly modelling these errors in simulation can give the user tools to understand better what these errors are and how to mitigate them when running on actual quantum devices.\n",
-    "\n",
-    "Readout errors can be mitigated with a confusion matrix $A$. It is a square matrix of size $2^n \\times 2^n$ and tells about the probability of observing the state $|y\\rangle$ given the true state $|x\\rangle$. The confusion matrix characterizes the readout error of the device and is circuit-independent. Once $A$ is estimated, we can compute its pseudoinverse $A^+$ which can be applied to the noisy probability distribution $p_{\\text{noisy}}$ to obtain an adjusted quasi-probability distribution \n",
-    "\n",
-    "$$\n",
-    "p_{\\text{mitigated}} = A^+ p_{\\text{noisy}}\n",
-    "$$\n",
-    "\n",
-    "In this tutorial, we show how to build a confusion matrix with the following approaches.\n",
-    "\n",
-    "- Using a  single qubit model\n",
-    "- Using $k$ local confusion matrices\n",
-    "- A full confusion matrix for each $2^n$ combination\n",
-    "\n",
-    "The last method works well for correcting correlated errors (which affect multiple qubits together). However, it becomes impractical for large numbers of qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "04b58854-7dab-4f1b-8c59-d8b9506bce44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "\n",
-    "!pip install matplotlib==3.8.4 pandas==2.2.2 scipy==1.13.1 seaborn==0.13.2 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "710a2a46-4e55-4508-a0b7-21969aac6a87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import itertools\n",
-    "from functools import reduce\n",
-    "from scipy.optimize import minimize\n",
-    "from typing import Union\n",
-    "\n",
-    "sns.set_style(\"dark\")\n",
-    "\n",
-    "cudaq.set_target(\"density-matrix-cpu\")\n",
-    "seed = 42\n",
-    "\n",
-    "cudaq.set_random_seed(seed)\n",
-    "np.random.seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8eab761a-0a82-4e20-8cf8-54602cd05d6a",
-   "metadata": {},
-   "source": [
-    "To model the readout error, we apply a bitflip channel on every qubit at the end of the circuit using an Identity gate. The probability of bitflip `probs` can be the same or different for all the qubits.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-success\"><b>Note: </b>\n",
-    "In principle, readout error is applied to the measurement gate but we use the Identity gate because adding a quantum error on the measurement gate is not yet supported. Also, the Identity gate is not present in the <kbd>add_channel</kbd> method, so we model the Identity gate using a rotation gate with an angle of 0.0.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d84bb420-4fab-4c26-919a-7efa8d3a219b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_noise(n_qubits: int, probs: Union[float, list[float]]):\n",
-    "    \"\"\"\n",
-    "    Creates a noise model with specified probabilities of bit flip errors on each qubit.\n",
-    "\n",
-    "    Args:\n",
-    "        n_qubits (int): The number of qubits in the quantum circuit.\n",
-    "        probs (Union[float, list[float]]): Probabilities of a bit flip error occurring on each qubit.\n",
-    "        If a single probability value is provided, it will be applied to all qubits. If a list of\n",
-    "        probabilities is provided, each qubit will have its own probability.\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq noise model\n",
-    "    \"\"\"\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "\n",
-    "    if not isinstance(probs, list):\n",
-    "        probs = [probs] * n_qubits\n",
-    "\n",
-    "    for i, p in enumerate(probs):\n",
-    "        bit_flip = cudaq.BitFlipChannel(p)\n",
-    "        noise.add_channel(\"rx\", [i], bit_flip)\n",
-    "\n",
-    "    return noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb4c7a14-1c0c-4fab-a6aa-7174c23baa7f",
-   "metadata": {},
-   "source": [
-    "We define a cudaq kernel which will help create a set of quantum circuits to take measurements of the basis states for $n$ qubits. The kernel takes the number of qubits and the basis state as arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "53e3f450-000f-49b8-ad5c-57aa74461694",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(n_qubits: int, state_label: list[int]):\n",
-    "    qvector = cudaq.qvector(n_qubits)\n",
-    "    for i, val in enumerate(state_label):\n",
-    "        if val == 1:\n",
-    "            x(qvector[i])\n",
-    "        rx(0.0, qvector[i]\n",
-    "          )  # Identity gate at the end on each qubit to model readout error\n",
-    "    mz(qvector)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95411463-cd1e-499f-baef-d624acc0122c",
-   "metadata": {},
-   "source": [
-    "For the tutorial, we will use the GHZ state on three qubits to test readout error mitigation. The GHZ state on 3 qubits is described as:\n",
-    "\n",
-    "$$\n",
-    "\\frac{|000\\rangle + |111\\rangle}{\\sqrt{2}}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c1476028-923d-4f2b-b5fc-39ebcc8cbab9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def ghz_kernel(n_qubits: int):\n",
-    "    qvector = cudaq.qvector(n_qubits)\n",
-    "\n",
-    "    h(qvector[0])\n",
-    "    for i in range(n_qubits - 1):\n",
-    "        cx(qvector[i], qvector[i + 1])\n",
-    "\n",
-    "    # Apply identity gates for readout error mitigation\n",
-    "    for i in range(n_qubits):\n",
-    "        rx(0.0, qvector[i])\n",
-    "\n",
-    "    mz(qvector)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "900d2110-d613-426a-8aa9-6c12b1238536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to draw the confusion matrix\n",
-    "def plot_cmat(mat):\n",
-    "    fig, ax = plt.subplots()\n",
-    "    n = len(mat)\n",
-    "    im2 = ax.matshow(mat, cmap=plt.cm.Reds, vmin=0, vmax=1.0)\n",
-    "    ax.set_yticks(np.arange(n))\n",
-    "    ax.set_xticks(np.arange(n))\n",
-    "    ax.set_yticklabels(n * [\"\"])\n",
-    "    ax.set_xticklabels(n * [\"\"])\n",
-    "    ax.set_title(r\"Confusion Matrix\", fontsize=16)\n",
-    "    ax.set_xlabel(\"Prepared State\")\n",
-    "    ax.xaxis.set_label_position(\"top\")\n",
-    "    ax.set_ylabel(\"Measured State\")\n",
-    "    fig.colorbar(im2, ax=ax)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "032f6898-c953-4d30-a2bf-e8b46ee77c62",
-   "metadata": {},
-   "source": [
-    "It is possible that the adjusted quasi-probability distribution $p_{\\text{mitigated}}$ obtained by application of $A^+$ to $p_{\\text{noisy}}$ may be non-positive. We need to find the closest positive probability distribution in such a case.\n",
-    "\n",
-    " $$ p'' = \\min_{p_{\\rm positive}} \\|p_{\\rm noisy} - p_{\\rm positive}\\|_1$$\n",
-    "\n",
-    "Then, we can draw samples from $p''$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9b355dcb-bf80-42a1-ae11-dcc9f11ba1e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_closest_distribution(empirical_dist):\n",
-    "    \"\"\"\n",
-    "    Find the closest distribution to an empirical distribution by minimizing the L1 norm.\n",
-    "\n",
-    "    Args:\n",
-    "        empirical_dist: Empirical distribution that you want to find the closest distribution to.\n",
-    "\n",
-    "    Returns:\n",
-    "        Closest distribution to `empirical_dist`\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def objective(x):\n",
-    "        return np.linalg.norm(empirical_dist - x, ord=1)\n",
-    "\n",
-    "    # Constraint: all elements of p must be positive, and the distribution must sum to 1\n",
-    "    cons = (\n",
-    "        {\n",
-    "            \"type\": \"ineq\",\n",
-    "            \"fun\": lambda p: p\n",
-    "        },\n",
-    "        {\n",
-    "            \"type\": \"eq\",\n",
-    "            \"fun\": lambda p: np.sum(p) - 1\n",
-    "        },\n",
-    "    )\n",
-    "    bnds = [(0, 1) for _ in range(len(empirical_dist))]\n",
-    "    initial_value = np.random.uniform(size=len(empirical_dist))\n",
-    "\n",
-    "    res = minimize(\n",
-    "        objective,\n",
-    "        initial_value,\n",
-    "        method=\"SLSQP\",\n",
-    "        options={\"maxiter\": 1000},\n",
-    "        bounds=bnds,\n",
-    "        constraints=cons,\n",
-    "    )\n",
-    "\n",
-    "    return res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0ceb5a15-cecf-47ed-b1cc-cea79fa936f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_counts_from_distribution(n_qubits, size, dist):\n",
-    "    \"\"\"\n",
-    "    Generates samples based on a given distribution and returns the counts of each sample value.\n",
-    "\n",
-    "    Args:\n",
-    "        n_qubits: The number of qubits in the quantum circuit.\n",
-    "        dist: The probability distribution from which samples are drawn.\n",
-    "\n",
-    "    Returns:\n",
-    "        An array of counts for each possible value in the distribution. The array has a length of 2^n_qubits.\n",
-    "    \"\"\"\n",
-    "    samples = np.random.choice(np.arange(2**n_qubits), size=size, p=dist)\n",
-    "    values, counts = np.unique(samples, return_counts=True)\n",
-    "    res = np.zeros(2**n_qubits, dtype=int)\n",
-    "    res[values] = counts\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1225d4de-fcc8-4a56-846b-44d6a52a272f",
-   "metadata": {},
-   "source": [
-    "Let's run the `ghz_kernel` with the noise model. We will perform two experiments. First, we keep the bitflip probability the same for all the qubits. In the second experiment, we use different bitflip probabilities for qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "53453484-a8d5-416c-8aad-7c982cad7191",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_qubits = 3\n",
-    "shots = 1024\n",
-    "\n",
-    "labels = list(map(list, itertools.product([0, 1], repeat=n_qubits)))\n",
-    "states = list(map(lambda label: \"\".join(map(str, label)), labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2d147fe7-ce5e-475c-9014-59d4ff63a644",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bitflip probability: 0.1\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'000': 352,\n",
-       " '010': 43,\n",
-       " '111': 367,\n",
-       " '001': 47,\n",
-       " '011': 47,\n",
-       " '100': 54,\n",
-       " '101': 61,\n",
-       " '110': 53}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = 0.1\n",
-    "print(f\"bitflip probability: {p}\\n\")\n",
-    "\n",
-    "noise_1 = get_noise(n_qubits, p)\n",
-    "\n",
-    "ghz_result = cudaq.sample(ghz_kernel,\n",
-    "                          n_qubits,\n",
-    "                          shots_count=shots,\n",
-    "                          noise_model=noise_1)\n",
-    "noisy_dict_1 = dict(list(ghz_result.items()))\n",
-    "\n",
-    "noisy_res_1 = np.array(\n",
-    "    [noisy_dict_1.get(state, 0) for i, state in enumerate(states)])\n",
-    "\n",
-    "noisy_dict_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "976f13da-277b-4754-9a58-277a5988f739",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bitflip probability: [0.149816047538945, 0.3802857225639665, 0.292797576724562]\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'000': 212,\n",
-       " '010': 122,\n",
-       " '111': 189,\n",
-       " '001': 111,\n",
-       " '011': 87,\n",
-       " '100': 78,\n",
-       " '101': 131,\n",
-       " '110': 94}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = list(np.random.uniform(low=0.0, high=0.4, size=n_qubits))\n",
-    "print(f\"bitflip probability: {p}\\n\")\n",
-    "\n",
-    "noise_2 = get_noise(n_qubits, p)\n",
-    "\n",
-    "ghz_result = cudaq.sample(ghz_kernel,\n",
-    "                          n_qubits,\n",
-    "                          shots_count=shots,\n",
-    "                          noise_model=noise_2)\n",
-    "noisy_dict_2 = dict(list(ghz_result.items()))\n",
-    "\n",
-    "noisy_res_2 = np.array(\n",
-    "    [noisy_dict_2.get(state, 0) for i, state in enumerate(states)])\n",
-    "\n",
-    "noisy_dict_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1889f50a-f051-4f0e-bace-18477122eafe",
-   "metadata": {},
-   "source": [
-    "## Inverse confusion matrix from single-qubit noise model\n",
-    "\n",
-    "Here we assume that the readout error of each qubit is independent with the same confusion probabilities. Then, the confusion matrix $A_1$ for a single qubit can be defined as\n",
-    "\n",
-    "$$\n",
-    "A_1 = \\begin{bmatrix}\n",
-    "P(0|0) & P(0|1)\\\\\n",
-    "P(1|0) & P(1|1)\n",
-    "\\end{bmatrix}\n",
-    "$$\n",
-    "\n",
-    "where $P(y|x)$ is the probability of observing state $|y\\rangle$ when measuring the true state $|x\\rangle$. Using $A_1$, the full $2^n \\times 2^n$ confusion matrix $A$ can be written as \n",
-    "\n",
-    "$$\n",
-    "A = A_1 \\otimes \\dots \\otimes A_1\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e6ceb0a9-c2ec-4fcd-bb23-457890a8b57e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 becomes {'0': 924, '1': 100}\n",
-      "1 becomes {'0': 104, '1': 920}\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_qubit_labels = [[0], [1]]\n",
-    "single_qubit_states = [\"0\", \"1\"]  # Used for dictionary keys\n",
-    "\n",
-    "# we use noise_1 which has same bitflip probability for all qubits\n",
-    "results = [\n",
-    "    cudaq.sample(kernel, 1, label, shots_count=shots, noise_model=noise_1)\n",
-    "    for label in single_qubit_labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(single_qubit_states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9f030f99-0ad9-4f89-8d99-a570b3c5e745",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.90234375 0.1015625 ]\n",
-      " [0.09765625 0.8984375 ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Construct confusion matrix for single qubit\n",
-    "\n",
-    "A_1 = np.zeros((2, 2))\n",
-    "for i, state in enumerate(single_qubit_states):\n",
-    "    true_state = int(state, 2)\n",
-    "    for key, val in results[i].items():\n",
-    "        observed_state = int(key, 2)\n",
-    "        A_1[observed_state][true_state] = val / shots\n",
-    "\n",
-    "print(A_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cbc4a228-935f-413c-9ad3-b6e26f22e802",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[476   3   0   1  12  19   9 501]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = reduce(np.kron, [A_1] * n_qubits)  # joint confusion matrix\n",
-    "A_pinv = np.linalg.pinv(A)  # Generate pseudoinverse confusion matrix.\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_1),\n",
-    "                     dtype=int)  # Obtain mitigated counts\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "b65c4657-87f7-44df-b205-7fc6195b95a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    \"states\": states,\n",
-    "    \"noisy\": np.around(noisy_res_1 / sum(noisy_res_1), 3),\n",
-    "    \"mitigated_sg\": np.around(mitigated / sum(mitigated), 3),\n",
-    "})\n",
-    "\n",
-    "ax = df.plot(x=\"states\",\n",
-    "             y=[\"noisy\", \"mitigated_sg\"],\n",
-    "             kind=\"bar\",\n",
-    "             figsize=(8, 5))\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_sg\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2927e72-98bb-41cf-8e77-35ea9572474a",
-   "metadata": {},
-   "source": [
-    "## Inverse confusion matrix from k local confusion matrices\n",
-    "\n",
-    "This method works under the assumption that the readout errors for different qubits are independent of each other and the confusion matrix $A$ can be factorized into the product of $k$ small local confusion matrices. We consider the special case when $k = n$ for $n$ qubits and different $2 \\times 2$ confusion matrices for each qubit\n",
-    "\n",
-    "$$\n",
-    "A = A_1 \\otimes \\dots \\otimes A_n\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "1e8fd13a-e0cf-4139-84ca-d81f7a71baa6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "000 becomes {'000': 388, '010': 235, '111': 18, '001': 174, '011': 79, '100': 58, '101': 28, '110': 44}\n",
-      "111 becomes {'000': 23, '010': 38, '111': 370, '001': 40, '011': 60, '100': 107, '101': 224, '110': 162}\n"
-     ]
-    }
-   ],
-   "source": [
-    "local_labels = [[0] * n_qubits, [1] * n_qubits]\n",
-    "local_states = [\"0\" * n_qubits, \"1\" * n_qubits]\n",
-    "\n",
-    "results = [\n",
-    "    cudaq.sample(kernel,\n",
-    "                 n_qubits,\n",
-    "                 label,\n",
-    "                 shots_count=shots,\n",
-    "                 noise_model=noise_2) for label in local_labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(local_states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f592fb8-2ded-498e-b4fe-eff6eac74750",
-   "metadata": {},
-   "source": [
-    "The local confusion matrices are generated using the marginal probability distribution for each qubit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "7fabcc1f-8d9c-42c2-b02a-3ce42c1e34c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counts = [\n",
-    "    dict(list(results[i].items())) for i, state in enumerate(local_states)\n",
-    "]\n",
-    "matrices = []\n",
-    "\n",
-    "for k in range(n_qubits):\n",
-    "    matrix = np.zeros([2, 2], dtype=float)\n",
-    "    marginalized_counts = []\n",
-    "    total_shots = []\n",
-    "    for i in range(2):\n",
-    "        marginal_cts = dict(results[i].get_marginal_counts([k]).items())\n",
-    "        marginalized_counts.append(marginal_cts)\n",
-    "        total_shots.append(sum(marginal_cts.values()))\n",
-    "\n",
-    "    # matrix[i][j] is the probability of counting i for expected j\n",
-    "    for i in range(2):\n",
-    "        for j in range(2):\n",
-    "            matrix[i][j] = marginalized_counts[j].get(str(i),\n",
-    "                                                      0) / total_shots[j]\n",
-    "    matrices.append(matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7f016f9b-3243-42b3-ab59-4d03f7765b82",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.85546875 0.15722656]\n",
-      " [0.14453125 0.84277344]]\n",
-      "\n",
-      "[[0.6328125  0.38476562]\n",
-      " [0.3671875  0.61523438]]\n",
-      "\n",
-      "[[0.70800781 0.32226562]\n",
-      " [0.29199219 0.67773438]]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for m in matrices:\n",
-    "    print(m)\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "51734299-307f-4081-b8cc-1968ebfc7d52",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[559  -1 -58  32 -28  26 -16 510]\n",
-      "\n",
-      "Corrected for negative counts:\n",
-      "[521   0   0  35   0  22   0 446]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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tW1f33Xef+vXrd8nVuX5//hUrVujNN9/UJ598os8//1xWq1U33HCD7rzzTg0ePNjtSVflrVKlSnr77bf1+uuva/369UpOTlZQUJBiYmI0e/Zs5ebmlpm0JemZZ55RcHCwNm3apPXr19va1e4m7ZLr2EVFRZoyZYoaN25s9369evWUmJiohx9+WI8//riaNGni8mpygLdZjLJ6hAAAoMLhmjYAACZB0gYAwCRI2gAAmARJGwAAkyBpAwBgEiRtAABMgqQNAIBJkLSBCurIkSOKiIhwaK1zAL6BpA3TSUhIUEREhCIiIhQZGamuXbtq7ty5l/VhFBVVenq6xo0bp/bt2ysqKkodO3bUyJEjdfDgQUnuJf6IiAh98sknng4ZgBtYxhSm1KFDByUmJqqgoEBffPGFnnnmGfn7++vBBx8stW9BQYECAgK8EOV5hYWFtqdcefq4w4YN0w033KC5c+fq6quv1rFjx7Rx40b9+uuvHj8fAO+j0oYpBQQE6Oqrr1ZYWJj69++vdu3a2Z4BnpCQoIcfflivvvqq2rdvrzvuuEOSlJGRoUcffVStW7dWmzZtNHLkSB05csR2zJJxc+fO1c0336yWLVtqypQpdk8I27hxo/7617+qdevWatu2rR588EGlpaXZ3i+pbNesWaOBAwcqKirK9rjKDz/8UHfeeaeioqJ0xx13aMmSJXafaffu3erdu7eioqIUHx9/yer4wIEDSktL01NPPaUWLVooLCxMrVq10tixY9WiRQtJUpcuXSRJvXv3VkREhAYNGmQ719ChQ9W2bVu1atVKAwcO1J49e2zHjo2NlSSNGjVKERERtp8l6ZNPPlFcXJyioqLUpUsXn+1yAN5ApY0rQuXKle0e+VjyIIuFCxdKOl+VDh8+XC1atNCSJUtUqVIlvfLKKxoxYoT+9a9/2Srx5ORkVa5cWW+//baOHj2qSZMmqWbNmho7dqwkKT8/X0OHDlVERITy8vI0a9YsjRo1SqtWrZLV+tt34BdeeEEJCQlq2rSpKleurH/961+aNWuWpkyZoqZNm2rfvn168sknVbVqVcXFxSk3N1cPPvig2rVrp3/+8586cuTIJR+kUatWLVmtVq1fv1733Xef/Pz8Su3z4Ycfqm/fvnrrrbfUuHFjW8Wfm5ur3r17a/LkyZKkN998Uw888IDWr1+voKAgLVu2TLfccosSExPVoUMH27G3bdumiRMnavLkyWrdurXS0tL05JNPSpJGjx7tyh8dAGcYgMlMnDjRGDlypGEYhlFcXGx8+eWXRmRkpPHcc8/Z3m/Xrp1x9uxZ25iVK1cat99+u1FcXGzbdvbsWSM6OtrYtGmTbVybNm2MvLw82z7vvvuu0aJFC6OoqKjMWLKysozw8HBj//79hmEYRnp6uhEeHm689dZbdvvddtttxkcffWS37eWXXzbuuecewzAMY+nSpUabNm2MM2fO2J07PDzc2Lt37wV/F++8845x4403GjExMcagQYOMuXPnGmlpabb3S+K52DEMwzCKioqMmJgYY8OGDbZt4eHhxscff2y333333We89tprdttWrlxp3HrrrRc9PgDPoNKGKX3++eeKiYlRYWGhDMNQjx49NGbMGNv74eHhdtexv/32W6Wlpally5Z2xzl79qxde7vkueElYmJilJeXp4yMDIWFhenHH3/U7NmztWvXLp08eVLG/x6Sl5GRofDwcNu4yMhI29/n5eUpLS1NTzzxhK0qlaRz586pevXqkqSDBw8qIiJClStXtjv3pQwYMEB33XWXvvrqK+3atUvr1q3TvHnz9Oqrr+rWW2+94LgTJ05o5syZ+uqrr5SVlaXi4mLl5+frp59+uuj5vv32W+3YsUOvvfaabVtRUZHOnj2r/Px8u98dAM8jacOU2rZtq6efflr+/v665pprVKmS/T/Kf0weeXl5at68uV544YVSx6pVq5bD533ooYcUFhamZ599Vtdcc42Ki4vVo0cP27OhS1StWtXu3JI0bdo03XjjjXb7/b6l7qqgoCDFxsYqNjZWjz32mIYPH37JpD1x4kSdOnVKTzzxhOrVq6eAgADdc889pT7HH+Xl5WnMmDH6y1/+Uuq933/hAHB5kLRhSlWqVNF1113n8P7NmzfX2rVrFRISoqCgoAvut3//fp05c0aBgYGSpJ07d6pq1aqqW7euTp48qUOHDunZZ59V69atJZ2/xnspoaGhuuaaa5Senq5evXqVuU+jRo20atUqnT171pb8du7c6fDnK2GxWNSwYUPt2LFDkmzXsIuKiuz227Fjh5566il16tRJ0vlOwcmTJ+328ff3LzWuWbNmOnTokFO/ewCew+xx+ISePXuqZs2aGjlypLZt26b09HSlpKTo2Wef1bFjx2z7FRQU6IknntCBAwf0xRdfaM6cORo4cKCsVqtq1Kih4OBgvf/++zp8+LCSk5P13HPPOXT+Rx55RK+//roWL16sQ4cOaf/+/Vq+fLltolyPHj1ksVg0efJk27nffPPNix5z3759GjlypNatW6cDBw7o8OHD+vDDD7V8+XLbrPGQkBAFBgZq06ZNOnHihO1WsOuvv17/+te/dPDgQe3atUvjx4+3fVEpERYWpuTkZGVmZur06dOSZJt0N3fuXH3//fc6ePCgVq9erZdeesmxPwgAbiFpwydUqVJF77zzjurVq6fRo0erW7dueuKJJ3T27Fm7yvuWW27RddddpwEDBuixxx5TbGys7Vq51WrVSy+9pD179qhHjx5KTEzUhAkTHDp/37599eyzz2rFihXq2bOnBg0apKSkJNWvX1+SVK1aNb322mv67rvv1Lt3b7300ksaP378RY9Zu3ZthYWF6eWXX1bfvn0VFxenxYsXa8yYMRo5cqQkqVKlSpo8ebLef/99dejQQQ8//LAkafr06Tp9+rTi4uI0YcIEDRo0SCEhIXbHnzhxojZv3qw///nPiouLk3T+/vjXXntN//3vf3X33XerX79+euuttxQWFubQ7wGAeyxGyUwawMclJCTol19+0SuvvOLtUACgTFTaAACYBEkbAACToD0OAIBJUGkDAGASJG0AAEyCpA0AgEmQtAEAMAmSNgAAJkHSBgDAJHwyaRcUFGjOnDkqKCjwdihOM2vsxF2+iLv8mTV2s8btq3w2ac+dO9eU/5CaNXbiLl/EXf7MGrtZ4/a2rVu36qGHHlL79u0VERGhTz755JJjUlJSFBcXp8jISHXt2lUrVqxw+rw+mbQBAHBHXl6eIiIi9NRTTzm0f3p6uh588EG1bdtWq1at0n333afJkydr06ZNTp2X52kDAOCkTp062Z5H74ilS5eqfv36SkhIkCQ1atRI27dv11tvvaUOHTo4fByfrbSrVavm7RBcZtbYibt8EXf5M2vsZo3b0woKCpSTk2P38tRlg507d+qWW26x29a+fXvt3LnTqeP4ZKUdFBSkHTt2eDsMl5g1duIuX8Rd/swau1nj/r3TGcdUo24dt49TWFioW265xS5Rjx49WmPGjHH72CdOnFBoaKjdttDQUOXk5OjMmTMKDAx06DgVPmmfe2OKVHjW22E4xdqxh7dDcIlx6mdvh+Ay6w2R3g7BNQGO/Yta4fhX8XYErjGKvR2B6yoFeDsCl1iq17rs56hRt44er99EZ3751eVjBF5VXX8/8q2Sk5PttgcEVKzfe4VP2io8KxWYK2mruMjbEbim6Jy3I3CdWf9jbNqH7BF3+TNj7JZyO9OZX37VmV9dT9olgoKCPBBNaaGhoTpx4oTdthMnTigoKMjhKlsyQ9IGAOASrBaLrBbXvyS4M9YRLVq00MaNG+22bd68WS1atHDqOD47EQ0AcOWweuDljNzcXO3bt0/79u2TJB05ckT79u3TTz/9JEmaMWOGJkyYYNv/3nvvVXp6uv7xj3/o4MGDWrJkidauXashQ4Y4dV4qbQAAnPTNN99o8ODBtp8TExMlSXFxcXruueeUmZmpjIwM2/sNGjTQvHnzlJiYqMWLF6tOnTp69tlnnbrdS5IshlGxL6qde2Wi6a5pW2N7ezsElxgnj3s7BJdZG0V7OwTXBJh0QpdZ4zbr3AfJpBPRLOUyEU2SJgbX11k3rmlXrl5dz5864sGILg8qbQCA6bnS4v7jeDMwS5wAAPg8Km0AgOlV9NnjnkLSBgCYnq+0x0naAADTs1rOv9wZbwZm+XIBAIDPo9IGAJge7XEAAEzCYrHI4sZkMnfGliezfLkAAMDnUWkDAEyP9jgAACbB7HEAAFChUGkDAEzPIveqUJMU2iRtAID5+coyprTHAQAwCSptAIDpMXscAACT8JXZ4yRtAIDp+UqlbZY4AQDweVTaAADTs8oiqxs3brkztjyRtAEApucr17RpjwMAYBJU2gAA0/OViWgkbQCA6dEeBwAAFQqVNgDA9M4/MMT1ctkkhTZJGwBgfrTHAQBAhUKlDQAwPWaPAwBgEr7SHidpAwBMz1eWMTVLRwAAAJ9HpQ0AMD3a4wAAmIRF7t1rbZKcTXscAACzoNIGAJge7XEAAEyC2eMAAKBCqfCVtrVLvFRc5O0wnGJ8ttLbIbjE2nuEt0NwWfHB3d4OwSXW6I7eDsE1Z/O9HYFrqgR5OwLXnSvwdgQuKL/qlfY4AAAmcf4pX+6NNwPa4wAAmASVNgDA9HzlPm2SNgDA9KwWi6wWN2aPuzG2PJG0AQCm5yuVNte0AQAwCSptAIDp+UqlTdIGAJieryRt2uMAAJgElTYAwPQsFossbswAd2dseSJpAwBMj/Y4AACoUKi0AQCmZ5V7VahZKliSNgDA9CyW8y93xpuBWb5cAADg86i0AQCmZ/nf/9wZbwYkbQCA6fnK7HGSNgDA9HwlaXNNGwAAk6DSBgCYnlWS1Y1y2SwVLEkbAGB6vjIRzSxfLgAA8HlU2gCAK4I5amX3kLQBAKbHimgAAOCilixZotjYWEVFRalv377avXv3Rfd/6623dPvttys6OlqdOnXS3//+d509e9bh85G0AQCmZ/HAy1lr1qxRYmKiRo0apaSkJDVp0kTDhw9XVlZWmft/9NFHmjFjhkaPHq01a9Zo+vTpWrNmjV588UWHz0nSBgCYnlUWt1/OWrhwofr166c+ffqocePGmjp1qgIDA7V8+fIy909NTVXLli3Vs2dP1a9fX+3bt1ePHj0uWZ3bf04AACBJysnJsXsVFBSUuV9BQYH27Nmjdu3a2bZZrVa1a9dOqampZY6JiYnRnj17bEk6PT1dX3zxhTp16uRwfExEAwCYnqeWMe3YsaNyc3Nt20ePHq0xY8aU2v/kyZMqKipSSEiI3faQkBD98MMPZZ6jZ8+eOnnypPr37y/DMHTu3Dnde++9euihhxyOk6QNADA9T80e37hxo932gIAAN6Kyl5KSonnz5umpp55SdHS00tLSNH36dL388ssaNWqUQ8cgaQMATM9TlXZQUJBD+9esWVN+fn6lJp1lZWUpNDS0zDGzZs1Sr1691LdvX0lSRESE8vLyNGXKFI0cOVJW66WvWHNNGwAAJwUEBKh58+ZKTk62bSsuLlZycrJiYmLKHHPmzJlSidnPz0+SZBiGQ+el0gYAmJ431h4fOnSoJk6cqMjISEVHR2vRokXKz89XfHy8JGnChAmqXbu2xo0bJ0nq3LmzFi5cqGbNmtna47NmzVLnzp1tyftSSNoAANOzWiTDnad8uTC2W7duys7O1uzZs5WZmammTZtq/vz5tvZ4RkaGXWU9cuRIWSwWzZw5U8ePH1etWrXUuXNnjR071uFzWgxHa3IvKd6fIhUXeTsMpxifrfR2CC6x9h7h7RBcVnzQ8fscKxJrdEdvh+Cas/nejsA1VRy7XlkhnSv71qOKzSJLzTrlcqYvGzVRUU6Oy+P9goJ068FvPRjR5UGlDQAwPU9NRKvoSNoAANPzlaTN7HEAAEyCShsAYHremD3uDSRtAIDp8TxtAABQoVBpAwBMzyrJnfuXzVLBkrQBAKbnK7PHSdoAAPOzWGTxgYvaZukIAADg8yp8pW1kH5OKznk7DKdY7xru7RBcUrx8nrdDcJmlcy9vh+Aaky3Ra2PW5UDzXV/m0usqV/F2BC4ov+qV9jgAACbhK0mb9jgAACZBpQ0AMD2LmxPR3JrEVo5I2gAA07O62R935Xna3kB7HAAAk6DSBgCYnsVqkcWNctmdseWJpA0AMD0eGAIAACoUKm0AgOn5SqVN0gYAmB63fAEAYBK+UmlzTRsAAJOg0gYAmB7tcQAATIL2OAAAqFCotAEApmd1s9S2mqTUJmkDAEyP9jgAAKhQqLQBAKZnkZuzx915rmc5ImkDAEzPYj3/cme8GZgkTAAAQKUNADA/NxdXMctMNJI2AMD0LBbJncvSJsnZJG0AgPmdT9ruLGPquVguJ65pAwBgElTaAADToz0OAIBJWC0WGT7wlC/a4wAAmASVNgDA9GiPAwBgEhY3nxhCexwAAHgUlTYAwPRojwMAYBK+krRpjwMAYBJU2gAA07NYLZLVjYlobowtTyRtAIDp+Up7nKQNADA9VkQDAAAVCpU2AMD0aI8DAGASrIh2EefOndPmzZu1dOlS5eTkSJKOHz+u3NxcjwYHAAB+43SlffToUY0YMUIZGRkqKCjQrbfeqqCgIL3xxhsqKCjQM888czniBADggiy2v7gx3gScrrSnT5+uyMhIffXVV6pcubJte9euXbVlyxaPBgcAgCMsFovbLzNwutLevn273nvvPQUEBNhtDwsL0/Hjxz0WGAAAsOd00i4uLlZxcXGp7ceOHVO1atU8EhQAAE5xbx6aafrjTrfHb731Vi1atMhuW25urubMmaNOnTp5LDAAABzlK+1xi2EYhjMDjh07puHDh8swDB0+fFiRkZH68ccfVbNmTS1ZskQhISEeDdDIOCgZpSv7iqz48B5vh+ASS8063g7BZcbnq7wdgkusA8Z7OwTXnCvwdgSusfp5OwLXFeR7OwLnWSyyhF5bLqc62fVmKc+NO5iqVlPNjyv+vCyn2+N16tTRqlWrtGbNGn377bfKy8vT3XffrZ49eyowMPByxAgAwEVZrHJvjU+TrA/qdNLeunWrYmJi1KtXL/Xq1cu2/dy5c9q6datuuukmjwYIAMCluLu4ilmWRHP6u8XgwYN1+vTpUtt//fVXDR482CNBAQDglJJHc7rzMgGnk7ZhGGVesD916pSqVKnikaAAAEBpDrfHR48eLel8CyIhIcHuPu2ioiLt379fMTExno8QAIBL8ZH2uMNJu3r16pLOV9rVqlWzm3Tm7++vFi1aqG/fvp6PEACAS/DWNe0lS5ZowYIFyszMVJMmTfTkk08qOjr6gvv/8ssveumll/Txxx/r1KlTCgsL0+OPP+7wLdMOJ+3ExERJ51c+GzZsmKpWreroUAAArjhr1qxRYmKipk6dqhtvvFGLFi3S8OHDtW7dujJvfy4oKNDQoUMVEhKiWbNmqXbt2vrpp5901VVXOXxOp2ePl7TJAQCoMNydTObC2IULF6pfv37q06ePJGnq1Kn6/PPPtXz5cj3wwAOl9l++fLlOnz6tpUuXyt/fX5JUv359p87p0vO0161bp7Vr1yojI0OFhYV27yUlJblySAAAXOeh9njJ46ZLBAQElHrWhnS+at6zZ48efPBB2zar1ap27dopNTW1zFNs2LBBLVq00DPPPKNPP/1UtWrVUo8ePXT//ffLz8+xhX+cnj2+ePFiTZo0SaGhodq7d6+ioqIUHBys9PR0dezY0dnDAQBQYXTs2FGtWrWyvebNm1fmfidPnlRRUVGpNnhISIhOnDhR5pj09HStX79eRUVFev311/Xwww9r4cKFevXVVx2Oz+lK+91339W0adPUo0cPrVixQvfff78aNGigWbNmlXn/NgAAl5vFQ+3xjRs32m0uq8p2lWEYCgkJ0bRp0+Tn56fIyEgdP35cCxYscPjSs9OVdkZGhu3WrsDAQOXmnl/r9a677tLq1audPRwAAO4raY+785IUFBRk97pQ0q5Zs6b8/PyUlZVltz0rK0uhoaFljrn66qt1/fXX27XCGzZsqMzMTBUUOLaev9NJOzQ01FZR161bVzt37pQkHTlyRE4+ewQAAI+wWCyyWN14OXk9PCAgQM2bN1dycrJtW3FxsZKTky+4ZknLli2VlpZm93jrH3/8UVdffbXDFb3TSfvmm2/Whg0bJEl9+vRRYmKihg4dqrFjx+q2225z9nAAAJjS0KFD9cEHHygpKUkHDx7U008/rfz8fMXHx0uSJkyYoBkzZtj2/+tf/6pTp05p+vTpOnTokD7//HPNmzdPAwYMcPicTl/TnjZtmu1bwoABAxQcHKzU1FTFxsbqnnvucfZwAAC4zwuLq3Tr1k3Z2dmaPXu2MjMz1bRpU82fP9/WHs/IyJDV+lttXLduXS1YsECJiYnq1auXateurcGDB+v+++93PExnn6f9008/qW7duqVaCYZhKCMjQ/Xq1XPmcJfE87TLD8/TLn88T7uc8Tzt8lWOz9PO6Rcr5ee5foAqVRX0wQbPBXSZON0e79Kli7Kzs0ttP3XqlLp06eKRoAAAQGlOt8cv9JSvvLw8Va5c2SNBAQDgDF95nrbTa49bLBbNnDnT7jGcRUVF2r17t5o0aeL5CAEAuBQvLGPqDQ4n7b1790o6X2l/9913tnVTpfNT35s0aaJhw4Z5PkIAACDJiaT99ttvS5ImTZqkJ554QkFBQZctKAAAnEJ7vGwlbfISR48eVX5+vho2bGg3tR0AgPJiscqFqdW/Y5L05XCYy5Yt08KFC+22Pfnkk7rtttvUs2dP9ejRQxkZGR4PEAAAnOdw0v7ggw/sHtS9ceNGrVixQs8//7yWLVumq666SnPnzr0sQQIAcFEeWnu8onM4aR8+fFiRkZG2nz/99FN16dJFvXr1UvPmzTV27Fi7NVgBACgvbq07/r+XGTictM+cOWM3+Sw1NVWtW7e2/dygQYMLPkMUAIDLikrbXr169bRnz/nlObOzs3XgwAG1bNnS9v6JEydUvXp1z0cIAAAkOTF7PC4uTs8884y+//57bdmyRQ0bNrRrl+/YsUN/+tOfLkuQAABcFIur2BsxYoTy8/P18ccfKzQ0VLNmzbJ7f8eOHerevbvHAwQA4FJYxvQPrFarHn30UT366KNlvj979myPBQUAAEpzenEVAAAqHNrjAACYhbszwM2RtE2ycBsAAKDSBgCYHhPRAAAwC65p/+aPT/a6mEmTJrkcDAAAuDCHkvbevXtL/VxUVKQbbrhBkvTjjz/KarWqefPmno8QAIBLoD3+O2+//bbt7xcuXKhq1arp+eefV40aNSRJp0+f1qRJk+zWIgcAoNz4SHvc6dnjb775psaNG2dL2JJUo0YNPfbYY3rzzTc9GhwAAA7hgSFly8nJUXZ2dqnt2dnZys3N9UhQAACgNKdnj3ft2lWTJk1SQkKCoqOjJUm7du3SP/7xD/3lL3/xeIAAAFyKxUfa404n7alTp+r555/XuHHjdO7cOUmSn5+f7r77bk2YMMHjAQIAcElMRCtblSpV9PTTT2vChAlKS0uTJF177bWqWrWqx4MDAAC/cXkZ08zMTGVmZur6669X1apVZRiGJ+MCAMBxVv02g9yll7c/gGOcrrRPnjypxx57TCkpKbJYLPrPf/6jBg0a6PHHH1eNGjWUkJBwOeIEAOCCuE/7AhITE1WpUiV9/vnnuvPOO23bu3Xrpueee87zSdvfXzJZFW9t1s7bIbimuMjbEbjMMmC8t0NwSdHC6d4OwSXW2Lu8HYJLjOzj3g7BZdaG0d4OwXkWk5SvJuJ00v7yyy+1YMEC1alTx2779ddfr59++sljgQEA4DBmj5ctLy9PgYGBpbafOnVKAQEBHgkKAACn+Eh73OneRevWrbVy5Uq7bcXFxZo/f77atm3rqbgAAMAfOF1p/+1vf9OQIUP0zTffqLCwUP/85z914MABnT59Wu+9997liBEAgIvzkUrb6aQdHh6u9evX65133lG1atWUl5enrl27asCAAbrmmmsuR4wAAFyCu+uHX4FJu7CwUCNGjNDUqVM1cuTIyxUTAADOsVrPv9wZbwJORenv76/9+/dfrlgAAMBFOP3VolevXlq2bNnliAUAANf4yKM5nb6mXVRUpPfee0+bN29WZGSkqlSpYvf+pEmTPBYcAAAOYSJa2b777js1a9ZMknTo0CG79ywm+dAAAJiR00n77bffvhxxAADgOiptAABMwkdmjzudtAcNGnTRNvjixYvdCggAAJTN6aTdtGlTu5/PnTunffv26fvvv1fv3r09FRcAAI6jPV62xx9/vMztc+bMUV5entsBAQDgNB9J2h5r4vfq1UvLly/31OEAAMAfeGwiWmpqKo/mBAB4h49U2k4n7dGjR9v9bBiGMjMz9c033+jhhx/2WGAAADiM2eNlq169ut3PFotFN9xwgx555BG1b9/eY4EBAOAwKu2yJSYmXo44AADAJTidtDMyMmSxWFSnTh1J0u7du/XRRx+pcePGuueeezweIAAAl2SRm5W2xyK5rJxu4o8bN05btmyRJGVmZmrIkCH6+uuv9dJLL2nu3LkeDxAAgEvykad8OZ20v//+e0VHR0uS1q5dq/DwcC1dulQvvPCCkpKSPB4gAAA4z+n2+Llz52y3dm3evFmxsbGSpIYNGyozM9Oz0QEA4ACLj8wedzrKxo0ba+nSpdq2bZs2b96sjh07SpJ+/vlnBQcHezo+AAAc4G5r/Aptj48fP17vv/++Bg0apO7du6tJkyaSpA0bNtja5gAAwPOcbo+3bdtWW7ZsUU5OjmrUqGHb3q9fP1WpUsWjwQEA4BDu074wPz8/u4QtSfXr1/dIQAAAOI2kfWHr1q3T2rVrlZGRocLCQrv3mEEOAMDl4fQ17cWLF2vSpEkKDQ3V3r17FRUVpeDgYKWnp9smpQEAUK5KZo+78zIBpyvtd999V9OmTVOPHj20YsUK3X///WrQoIFmzZql06dPX44YAQC4OB9pjzv91SIjI0MxMTGSpMDAQOXm5kqS7rrrLq1evdqz0QEA4AhWRCtbaGioraKuW7eudu7cKUk6cuSIDMPwaHAAAOA3Tiftm2++WRs2bJAk9enTR4mJiRo6dKjGjh2r2267zeMBAgBwSV6qtJcsWaLY2FhFRUWpb9++2r17t0PjVq9erYiICD388MNOnc/pa9rTpk1TcXGxJGnAgAEKDg5WamqqYmNjecoXAMA7vLCM6Zo1a5SYmKipU6fqxhtv1KJFizR8+HCtW7dOISEhFxx35MgRPf/882rdurXzYTo9wGpVpUq/5fru3btr8uTJGjRokG1NcgAArnQLFy5Uv3791KdPHzVu3FhTp05VYGCgli9ffsExRUVFGj9+vMaMGaMGDRo4fU6XvpZs27ZN48eP1z333KPjx49LklauXKlt27a5cjgAANzjofZ4Tk6O3augoKDM0xUUFGjPnj1q166dbZvValW7du2Umpp6wTBffvllhYSEqG/fvi59TKeT9vr16zV8+HAFBgZq7969tg+Uk5OjefPmuRQEAABu8VDS7tixo1q1amV7XSivnTx5UkVFRaXa4CEhITpx4kSZY7Zt26Zly5Zp2rRpLn9Mp69pv/rqq5o6dap69+5td4tXy5Yt9eqrr7ocCAAA3rZx40a7nz112TcnJ0cTJkzQtGnTVKtWLZeP43TSPnToUJkXz6tXr65ffvnF5UAAAHCZhyaiBQUFObR7zZo15efnp6ysLLvtWVlZCg0NLbV/enq6jh49qpEjR9q2lUzqbtasmdatW6drr732kud1OmmHhoYqLS2t1ANCtm/f7tJFdQAA3GaRmyuiObd7QECAmjdvruTkZNvtzsXFxUpOTtbAgQNL7d+wYUN99NFHdttmzpyp3NxcPfHEE6pTp45D53U6affr10/Tp0/X3//+d1ksFh0/flypqal6/vnnnb7fDAAAsxo6dKgmTpyoyMhIRUdHa9GiRcrPz1d8fLwkacKECapdu7bGjRunypUrKzw83G78VVddJUmltl+M00n7gQceUHFxsYYMGaL8/HwNHDhQAQEBGjZsmAYNGuTs4QAAcJ8X1h7v1q2bsrOzNXv2bGVmZqpp06aaP3++rT2ekZEhq4cfRGIxXFx7tKCgQGlpacrLy1OjRo1UrVo1jwZWwjiRJpltedSAKt6OwDXFRd6OwHWVzLlGQNHC6d4OwSXW2Lu8HYJLjOzj3g7BZdaG0d4OwXkWqyxhjleR7ih6NUEqOOv6AQIqy2/kc54L6DJx6Xna0vl+fuPGjT0ZCwAArrG4ORHNcoU9mnPSpEkO7ZeYmOhyMGUKqCrJZJV2Qb63I3BN5cvTLSkXRYXejsAl1r/083YILjE+W+ntEFxivWuYt0NwWfEPX3s7BOf5VZJfOVXavsLhpJ2UlKR69eqpWbNmPM0LAFCx+MjztB1O2n/961+1evVqHTlyRPHx8erVq5eCg4MvY2gAADjIR5K2w038p556Sv/97381YsQIffbZZ/rzn/+sRx99VJs2baLyBgCgHDg1ES0gIEA9evRQjx49dPToUSUlJWnq1KkqKirSv//978s2gxwAgIuyWN2bTHalTUT7o5J7zwzDUFGRiW8VAgCYn9Vy/uXOeBNwKmkXFBToP//5j5YvX67t27frz3/+s6ZMmaIOHTp4/AZyAABgz+Gk/fTTT2vNmjWqU6eO+vTpoxkzZrj1pBIAADyG9ri9pUuXql69emrQoIG2bt2qrVu3lrnf3LlzPRYcAAAO8ZHZ4w4n7d69e8tikg8FAMCVyOGk/dxzFX9NVgCAj/LQ87QrOpdnjwMAUGHQHgcAwCR8ZCKaOaIEAABU2gCAK4BFbrbHPRbJZUXSBgCYn49MRDNHlAAAgEobAHAFYPY4AAAmwexxAABQkVBpAwDMz+LmozlpjwMAUE5ojwMAgIqEShsAYH7MHgcAwCR8pD1O0gYAmJ/VzYlo7owtR+b4agEAAKi0AQBXAK5pAwBgEj5yTdscUQIAACptAMAVwEcmopG0AQDmZ7G42R43R9KmPQ4AgElQaQMAzI/Z4wAAmASzxwEAQEVCpQ0AMD9mjwMAYBI+0h4naQMAzM9HJqKZ46sFAACg0gYAXAGs1vMvd8abAEkbAHAFcLM9LtrjAADAg6i0AQDmx+xxAABMgtnjAACgIqn4lXZxkSTD21E4p3I1b0fgmjM53o7AdQFVvB2BS4wTR70dgkusvYZ6OwSXFK+c7+0QXGbp1NPbITjP6leO57JKBrPHAQCo+GiPAwCAioRKGwBgfhaLm7PHzVFpk7QBAObnI+1xkjYAwPx85D5tc0QJAACotAEAVwCrRTLcaHFbaY8DAFA+aI8DAICKhEobAGB+zB4HAMAkaI8DAICKhEobAGB6FtrjAACYBO1xAABQkZC0AQDmV1Jpu/NywZIlSxQbG6uoqCj17dtXu3fvvuC+H3zwgfr376+bbrpJN910k4YMGXLR/ctC0gYAmJ/Fcn5VM1dfLlzTXrNmjRITEzVq1CglJSWpSZMmGj58uLKyssrcPyUlRd27d9fixYu1dOlS1a1bV8OGDdPx48cdPidJGwBgfl6otBcuXKh+/fqpT58+aty4saZOnarAwEAtX768zP1nzJihAQMGqGnTpmrUqJGeffZZFRcXKzk52eFzkrQBAPifnJwcu1dBQUGZ+xUUFGjPnj1q166dbZvValW7du2Umprq0Lny8/N17tw51ahRw+H4mD0OADA/D93y1bFjR+Xm5to2jx49WmPGjCm1+8mTJ1VUVKSQkBC77SEhIfrhhx8cOuULL7yga665xi7xXwpJGwBgfhaLm7d8nU/aGzdutNscEBDgTlQX9Prrr2vNmjVavHixKleu7PA4kjYAAP8TFBTk0H41a9aUn59fqUlnWVlZCg0NvejYBQsW6PXXX9fChQvVpEkTp+LjmjYAwPxK2uPuvJwQEBCg5s2b200iK5lUFhMTc8Fxb7zxhl555RXNnz9fUVFRTn9MKm0AgPlZrJLFcG+8k4YOHaqJEycqMjJS0dHRWrRokfLz8xUfHy9JmjBhgmrXrq1x48ZJOt8Snz17tmbMmKGwsDBlZmZKkqpWrapq1ao5dE6SNgAALujWrZuys7M1e/ZsZWZmqmnTppo/f76tPZ6RkSGr9bcvA0uXLlVhYaEeeeQRu+NcaLJbWSyGYbjx1eTyM04dl1ShQyzNz9/bEbjmTI63I3BdQBVvR+CS4q83eTsEl1hviPR2CC4pXrXA2yG4zNKpp7dDcJ7VT9amjs+Mdkfx159LxUWuH8DqJ2vUnz0UzeVDpQ0AMD8vtMe9wRxRAgAAKm0AwBWA52kDAGASPtIeJ2kDAMzPRyptc3y1AAAAVNoAgCsA7XEAAEzCapVba3pYzZG0zRElAACg0gYAmJ/FRyaikbQBAObnoedpV3S0xwEAMAkqbQCA+dEeBwDALKySW3nXHI1nc0QJAACotAEAVwCLRW6V2rTHAQAoJ1arW2ursCIaAADlxUcqbXN8tQAAAFTaAIArgLvtbdrjHlLJ39sROO9cgbcjcE3lqt6OwHUF+d6OwCXWhtHeDsElxWn7vB2CSyxd+ng7BJcZGz/ydgjO868sNW1XPueiPQ4AACqSil9pAwBwSW5W2u6tzFJuSNoAAPOjPQ4AACoSKm0AgPn5SKVN0gYAXAF845o27XEAAEyCShsAYH60xwEAMAl3c645cjZJGwBwJeCaNgAAqECotAEA5sc1bQAATMJi+4sb4ys+2uMAAJgElTYA4ArgG9PHSdoAAPNz95q0Sa5p0x4HAMAkqLQBAFcA2uMAAJgD7XEAAFCRUGkDAMzPRyptkjYA4ArANW0AAEzB4iOVNte0AQAwCSptAID5+UilTdIGAFwBfOOaNu1xAABMgkobAHAFsLhZLJuj0iZpAwDMz+1r0uZI2rTHAQAwCSptAMAVwDcqbZI2AMD8aI8DAICKhEobAGB+5iiU3UbSBgBcAXyjPU7SBgCYH9e0AQBARUKlDQAwPx+ptEnaAIArgG8kbdrjAACYBJU2AMD8LLa/XNFI2gAA83P7mrY50B4HAMBFS5YsUWxsrKKiotS3b1/t3r37ovuvXbtWd9xxh6KiotSzZ0998cUXTp2PpA0AuAJYPPByzpo1a5SYmKhRo0YpKSlJTZo00fDhw5WVlVXm/jt27NC4ceN09913a+XKlerSpYtGjRql7777zuFzkrQBAOZnsbj/ctLChQvVr18/9enTR40bN9bUqVMVGBio5cuXl7n/4sWL1aFDB40YMUKNGjXSY489pmbNmumdd95x+JwV/pp29ll/GYa3o3CWv7cD8EGB3g7ANQHeDsBFjet4OwLfE9fM2xE4zWKRQsrzZO4ki/8l7ZycHLvNAQEBCggo/S9qQUGB9uzZowcffNC2zWq1ql27dkpNTS3zFDt37tSQIUPstrVv316ffPKJw2FW+KQdElLd2yEAACq6Kle5fYjc3FzdcsstKigosG0bPXq0xowZU2rfkydPqqioSCEh9l9LQkJC9MMPP5R5/BMnTig0NLTU/idOnHA4Rp9sj+fk5Khly5alvlGZgVljJ+7yRdzlz6yxmzXuy8Hf31/Jycnavn277fX7SroiqPCV9uWSm5vr7RBcZtbYibt8EXf5M2vsZo3b0y7UCi9LzZo15efnV2rSWVZWVqlqukRoaGipqvpi+5fFJyttAADcERAQoObNmys5Odm2rbi4WMnJyYqJiSlzTIsWLbRlyxa7bZs3b1aLFi0cPi9JGwAAFwwdOlQffPCBkpKSdPDgQT399NPKz89XfHy8JGnChAmaMWOGbf/Bgwdr06ZNevPNN3Xw4EHNmTNH33zzjQYOHOjwOX2yPR4QEKDRo0c73AapSMwaO3GXL+Iuf2aN3axxVwTdunVTdna2Zs+erczMTDVt2lTz58+3tbszMjJktf5WG7ds2VIvvPCCZs6cqRdffFHXX3+9Xn75ZYWHhzt8TothmO+GKgAAfBHtcQAATIKkDQCASZC0AQAwCZI2AAAmQdIGAMAkSNoAAJgESRsAAJMgaQMAYBIkbQAATIKkDQCASZC0AQAwif8HnTNd4kRIArQAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# To find the joint pseudoinverse confusion matrix, we can first perform pseudoinverse of the\n",
-    "# local confusion matrices and then do the tensor product\n",
-    "\n",
-    "pinv_confusion_matrices = [np.linalg.pinv(m) for m in matrices]\n",
-    "A_pinv = reduce(np.kron, pinv_confusion_matrices)\n",
-    "\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_2), dtype=int)\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")\n",
-    "\n",
-    "A_joint = reduce(np.kron, matrices)\n",
-    "plot_cmat(A_joint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "4261d8a3-b701-4939-8219-c0d823d42747",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    \"states\": states,\n",
-    "    \"noisy\": np.around(noisy_res_2 / sum(noisy_res_2), 3),\n",
-    "    \"mitigated_k_local\": np.around(mitigated / sum(mitigated), 3),\n",
-    "})\n",
-    "\n",
-    "ax = df.plot(x=\"states\",\n",
-    "             y=[\"noisy\", \"mitigated_k_local\"],\n",
-    "             kind=\"bar\",\n",
-    "             figsize=(8, 5))\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_k_local\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9329a30d-9224-42b1-9a66-1e87ba7c2674",
-   "metadata": {},
-   "source": [
-    "## Inverse of full confusion matrix\n",
-    "\n",
-    "Here we generate a quantum circuit for each of the basis states of $n$ qubits (i.e., $2^n$ combinations). This gives more accurate readout error mitigation. Since it scales exponentially with the number of qubits, it is only practical for small devices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "cf6025e8-1755-4c8f-96fa-1b67830d448d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "000 becomes {'000': 367, '010': 248, '111': 17, '001': 159, '011': 83, '100': 68, '101': 35, '110': 47}\n",
-      "001 becomes {'000': 161, '010': 93, '111': 46, '001': 363, '011': 253, '100': 17, '101': 76, '110': 15}\n",
-      "010 becomes {'000': 234, '010': 378, '111': 27, '001': 100, '011': 150, '100': 36, '101': 25, '110': 74}\n",
-      "011 becomes {'000': 106, '010': 145, '111': 65, '001': 251, '011': 386, '100': 11, '101': 30, '110': 30}\n",
-      "100 becomes {'000': 83, '010': 34, '111': 99, '001': 29, '011': 18, '100': 388, '101': 144, '110': 229}\n",
-      "101 becomes {'000': 22, '010': 13, '111': 247, '001': 70, '011': 43, '100': 163, '101': 371, '110': 95}\n",
-      "110 becomes {'000': 32, '010': 77, '111': 157, '001': 14, '011': 30, '100': 233, '101': 85, '110': 396}\n",
-      "111 becomes {'000': 14, '010': 31, '111': 393, '001': 38, '011': 60, '100': 95, '101': 233, '110': 160}\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = [\n",
-    "    cudaq.sample(kernel,\n",
-    "                 n_qubits,\n",
-    "                 label,\n",
-    "                 shots_count=shots,\n",
-    "                 noise_model=noise_2) for label in labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "28f2b2cb-13d3-4806-852d-06ea91e58c1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# generate full confusion matrix\n",
-    "A_full = np.zeros((2**n_qubits, 2**n_qubits))\n",
-    "for i, state in enumerate(states):\n",
-    "    true_state = int(state, 2)\n",
-    "    for key, val in results[i].items():\n",
-    "        observed_state = int(key, 2)\n",
-    "        A_full[observed_state][true_state] = val / shots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "c3b0b80b-63ac-438a-9f3d-a6a3687da167",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_cmat(A_full)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "507852a8-6715-4802-b6a0-10967d09ed94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.09734490298929"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Distance between full confusion matrix and tensored confusion matrix\n",
-    "np.linalg.norm(A_joint - A_full)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "9e6c3874-2905-4c25-9bf0-22ab27756fed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[ 714 -100 -211  152  -49   63   32  422]\n",
-      "\n",
-      "Corrected for negative counts:\n",
-      "[683   0   0  39   0   0   0 302]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A_pinv = np.linalg.pinv(A_full)\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_2), dtype=int)\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "cb109820-5478-4179-84b8-66e6af1008fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[\"mitigated_full\"] = np.around(mitigated / sum(mitigated), 3)\n",
-    "\n",
-    "ax = df.plot(\n",
-    "    x=\"states\",\n",
-    "    y=[\"noisy\", \"mitigated_k_local\", \"mitigated_full\"],\n",
-    "    kind=\"bar\",\n",
-    "    figsize=(12, 5),\n",
-    ")\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_k_local\"])\n",
-    "ax.bar_label(ax.containers[2], labels=df[\"mitigated_full\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
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-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/shors.html b/pr-2308/applications/python/shors.html
deleted file mode 100644
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
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-</li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-  <section id="Factoring-Integers-With-Shor's-Algorithm">
-<h1>Factoring Integers With Shor’s Algorithm<a class="headerlink" href="#Factoring-Integers-With-Shor's-Algorithm" title="Permalink to this heading">¶</a></h1>
-<p>The most famous application of quantum computers is factoring integers using Shor’s algorithm. This algorithm is particularly significant because an efficient factorization algorithm could potentially break modern asymmetric encryption schemes, such as RSA.</p>
-<p>For small integers, this quantum algorithm can be simulated on classical computers. The main challenge in classical implementation lies in the order-finding algorithm. We will first introduce the classical implementation of this algorithm as a preliminary step, and then proceed to explain the quantum order-finding algorithm.</p>
-<p>First let’s install some libraries.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">contfrac</span><span class="o">==</span><span class="m">1</span>.0.0<span class="w"> </span>-q
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[156]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">gcd</span><span class="p">,</span> <span class="n">log2</span><span class="p">,</span> <span class="n">ceil</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">random</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">import</span> <span class="nn">fractions</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">contfrac</span>
-</pre></div>
-</div>
-</div>
-<section id="Shor's-algorithm">
-<h2>Shor’s algorithm<a class="headerlink" href="#Shor's-algorithm" title="Permalink to this heading">¶</a></h2>
-<p>The factoring problem for an integer <span class="math notranslate nohighlight">\(N\)</span> aims to find integers <span class="math notranslate nohighlight">\(a_1\)</span>, <span class="math notranslate nohighlight">\(a_2\)</span> which are factors of <span class="math notranslate nohighlight">\(N\)</span>. This problem is interesting when <span class="math notranslate nohighlight">\(N\)</span> is not prime and the solutions <span class="math notranslate nohighlight">\(a_1\)</span> and <span class="math notranslate nohighlight">\(a_2\)</span> are non-trivial. In other words, we will attempt to find integers <span class="math notranslate nohighlight">\(a_1\)</span> and <span class="math notranslate nohighlight">\(a_2\)</span> satisfying <span class="math notranslate nohighlight">\(N=a_1\cdot a_2\)</span> with <span class="math notranslate nohighlight">\(a_1\neq 1\)</span> and <span class="math notranslate nohighlight">\(a_2\neq 1\)</span>.</p>
-<p>The algorithm consists of two ideas:</p>
-<ul class="simple">
-<li><p>Reduce the problem of factoring the integer <span class="math notranslate nohighlight">\(N\)</span> to the order-finding problem.</p></li>
-<li><p>Solve the order-finding problem: Given integers <span class="math notranslate nohighlight">\(a\)</span> and <span class="math notranslate nohighlight">\(N\)</span> so that <span class="math notranslate nohighlight">\(a\)</span> and <span class="math notranslate nohighlight">\(N\)</span> share no common factors (i.e., the greatest common divisor of <span class="math notranslate nohighlight">\(a\)</span> and <span class="math notranslate nohighlight">\(N\)</span> is 1), find the smallest positive integer which satisfies <span class="math notranslate nohighlight">\(a^r \equiv 1 \mod N\)</span>. This value <span class="math notranslate nohighlight">\(r\)</span> is refered to as the <em>order of</em> <span class="math notranslate nohighlight">\(a \mod N\)</span>.</p></li>
-</ul>
-<p>These two ideas are combined in the following steps in Shor’s algorithm:</p>
-<ol class="arabic simple" start="0">
-<li><p>Rule out the easy case that <span class="math notranslate nohighlight">\(N\)</span> is even</p></li>
-<li><p>Select a random integer <span class="math notranslate nohighlight">\(a\)</span> between <span class="math notranslate nohighlight">\(2\)</span> and <span class="math notranslate nohighlight">\(N-1\)</span></p></li>
-<li><p>Check to see if <span class="math notranslate nohighlight">\(a\)</span> is a factor of <span class="math notranslate nohighlight">\(N\)</span> (if so we’re done!)</p></li>
-<li><p>Find the order of <span class="math notranslate nohighlight">\(a \mod N\)</span>, (i.e., find <span class="math notranslate nohighlight">\(r\)</span> so that <span class="math notranslate nohighlight">\(a^r\equiv 1 \mod N\)</span>)</p></li>
-<li><p>Check to see if <span class="math notranslate nohighlight">\(a^{\frac{r}{2}}-1\)</span> or <span class="math notranslate nohighlight">\(a^{\frac{r}{2}}+1\)</span> are integers and share common, non-trivial, divisors with <span class="math notranslate nohighlight">\(N\)</span> (if so we’re done!)</p></li>
-<li><p>If no factor is found, repeat steps 1 through 4 above.</p></li>
-</ol>
-<p>The function <code class="docutils literal notranslate"><span class="pre">shors_algorithm</span></code> below outlines these steps for both the classical and the quantum implementation of Shor’s algorithm. For the purposes of demonstration, we will also control the initial random integer selected in step 1 so that we can investigate cases in which the selected integer produces common divisors of <span class="math notranslate nohighlight">\(N\)</span> in step 4 and others in which step 4 produces no common factors.</p>
-<p><strong>Note about terminology:</strong> Some literature refers to the “period-finding problem” in Shor’s algorithm. The order-finding problem, as we have described above, can be recast as finding the period of the function <span class="math notranslate nohighlight">\(f(x) = a^x\mod N\)</span>, by noticing that the period of <span class="math notranslate nohighlight">\(f(x)\)</span> is one more than the order of <span class="math notranslate nohighlight">\(a\mod N\)</span>. The period finding problem is more general than the order-finding problem since it aims to find the period of any periodic function, not just modular exponentiation.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">shors_algorithm</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">quantum</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Factor N using Shor&#39;s algorithm with initial starting value and choice of</span>
-<span class="sd">    using either classical or quantum approach for the period finding step</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    N: int</span>
-<span class="sd">        Integer to be factored (assumed to be non-prime and &gt;1)</span>
-<span class="sd">    initial: int</span>
-<span class="sd">        Initial choice of the random integer between 2 and N-1</span>
-<span class="sd">    quantum: boolean</span>
-<span class="sd">        True if we will call the quantum order-finding algorithm, and false if we call the classical one for step 3.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    a1, a2: int</span>
-<span class="sd">        Non-trivial factors of N</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># 0. Check to see if N is even.</span>
-    <span class="k">if</span> <span class="n">N</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">divisor1</span> <span class="o">=</span> <span class="mi">2</span>
-        <span class="n">divisor2</span> <span class="o">=</span> <span class="n">N</span> <span class="o">//</span> <span class="mi">2</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Found factors:&quot;</span><span class="p">,</span> <span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span><span class="p">)</span>
-
-    <span class="n">attempts</span> <span class="o">=</span> <span class="p">[</span><span class="n">initial</span><span class="p">]</span>
-    <span class="k">while</span> <span class="p">(</span><span class="kc">True</span><span class="p">):</span>
-        <span class="c1"># 1. Select a random integer between 2 and N-1</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attempts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="n">initial</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span>
-                <span class="p">[</span><span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">attempts</span> <span class="ow">and</span> <span class="n">n</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">])</span>
-
-        <span class="c1"># 2. See if the integer selected in step 1 happens to factor N</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Trying a =&quot;</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
-        <span class="n">divisor1</span> <span class="o">=</span> <span class="n">gcd</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">divisor1</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">divisor2</span> <span class="o">=</span> <span class="n">N</span> <span class="o">//</span> <span class="n">divisor1</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Found factors of N=</span><span class="si">{}</span><span class="s2"> by chance: </span><span class="si">{}</span><span class="s2"> and </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="n">N</span><span class="p">,</span> <span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span><span class="p">))</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span><span class="p">)</span>
-
-        <span class="c1"># 3. Find the order of a mod N (i.e., r, where a^r = 1 (mod N))</span>
-        <span class="k">if</span> <span class="n">quantum</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
-            <span class="n">r</span> <span class="o">=</span> <span class="n">find_order_quantum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">r</span> <span class="o">=</span> <span class="n">find_order_classical</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The order of a = </span><span class="si">{}</span><span class="s2"> is </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="p">))</span>
-
-        <span class="c1"># 4. If the order of a is found and it is</span>
-        <span class="c1"># * even and</span>
-        <span class="c1"># * not a^(r/2) = -1 (mod N),</span>
-        <span class="c1"># then test a^(r/2)-1 and a^(r/2)+1 to see if they share factors with N.</span>
-        <span class="c1"># We also want to rule out the case of finding the trivial factors: 1 and N.</span>
-        <span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span> <span class="o">=</span> <span class="n">test_order</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">divisor1</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>  <span class="c1"># test_order will return a 0 if no factor is found</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Found factors of N = </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2"> and </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="n">N</span><span class="p">,</span> <span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span><span class="p">))</span>
-            <span class="k">return</span> <span class="n">divisor1</span><span class="p">,</span> <span class="n">divisor2</span>
-
-        <span class="c1"># 5. Repeat</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;retrying...&quot;</span><span class="p">)</span>
-        <span class="n">attempts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s first explore the idea of reducing the factoring problem into the order-finding problem. This gets captured in step 4 described above. In this step, we have already established that <span class="math notranslate nohighlight">\(a\)</span> and <span class="math notranslate nohighlight">\(N\)</span> share no factors other than <span class="math notranslate nohighlight">\(1\)</span> (i.e., <span class="math notranslate nohighlight">\(\gcd(a,N)=1\)</span>) and we have found <span class="math notranslate nohighlight">\(r\)</span>, the order of <span class="math notranslate nohighlight">\(a\mod N.\)</span> With this information we know that <span class="math notranslate nohighlight">\(a^r \equiv 1 \mod N.\)</span> Rewritten in another way:</p>
-<div class="math notranslate nohighlight">
-\[a^r -1 \equiv 0\mod N
-\tag{1}.\]</div>
-<p>If <span class="math notranslate nohighlight">\(r\)</span> is even, we can rewrite <span class="math notranslate nohighlight">\(a^r\)</span> as <span class="math notranslate nohighlight">\(x^2\)</span> where <span class="math notranslate nohighlight">\(x=a^\frac{r}{2}\)</span>. Next, we can factor equation <span class="math notranslate nohighlight">\((1)\)</span> using the identity <span class="math notranslate nohighlight">\(x^2-1 = (x-1)(x+1)\)</span>:</p>
-<div class="math notranslate nohighlight">
-\[ (a^{\frac{r}{2}} - 1)(a^{\frac{r}{2}} + 1) \equiv 0\mod N
-\tag{2}.\]</div>
-<p>If, in addition, the equation</p>
-<div class="math notranslate nohighlight">
-\[a^{\frac{r}{2}}  \not\equiv -1\mod N
-\tag{3}\]</div>
-<p>is satisfied, then at least one of the terms <span class="math notranslate nohighlight">\(a^{\frac{r}{2}} - 1\)</span> or <span class="math notranslate nohighlight">\(a^{\frac{r}{2}} + 1\)</span> must share a common factor with <span class="math notranslate nohighlight">\(N\)</span>. <a class="reference external" href="https://arxiv.org/abs/quant-ph/9508027">Peter Shor</a> demonstrated that there is greater than a <span class="math notranslate nohighlight">\(50\%\)</span> chance of randomly selecting a value for <span class="math notranslate nohighlight">\(a\)</span> satisfying these properties.</p>
-<p>The code block below defines a function that tests whether <span class="math notranslate nohighlight">\(r\)</span> is even and whether equation <span class="math notranslate nohighlight">\((3)\)</span> is satisfied and searches for a non-trivial factor of <span class="math notranslate nohighlight">\(N\)</span>.</p>
-<div class="nbinput nblast docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">test_order</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Checks whether or not a^(r/2)+1 or a^(r/2)-1 share a non-trivial common factor with N</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a: int</span>
-<span class="sd">    r: int</span>
-<span class="sd">    N: int</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    int, int factors of N, if found; 0,0 otherwise</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">r</span> <span class="o">!=</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">r</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">a</span><span class="o">**</span><span class="n">r</span> <span class="o">%</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">a</span><span class="o">**</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">r</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)))</span> <span class="o">%</span> <span class="n">N</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-            <span class="n">possible_factors</span> <span class="o">=</span> <span class="p">[</span><span class="n">gcd</span><span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">gcd</span><span class="p">(</span><span class="n">r</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">N</span><span class="p">)]</span>
-            <span class="k">for</span> <span class="n">test_factor</span> <span class="ow">in</span> <span class="n">possible_factors</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">test_factor</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">test_factor</span> <span class="o">!=</span> <span class="n">N</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="n">test_factor</span><span class="p">,</span> <span class="n">N</span> <span class="o">//</span> <span class="n">test_factor</span>
-    <span class="c1"># period did not produce a factor</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;No non-trivial factor found&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
-</pre></div>
-</div>
-</div>
-<section id="Solving-the-order-finding-problem-classically">
-<h3>Solving the order-finding problem classically<a class="headerlink" href="#Solving-the-order-finding-problem-classically" title="Permalink to this heading">¶</a></h3>
-<p>The key component of Shor’s algorithm is an efficient quantum algorithm to find the order <span class="math notranslate nohighlight">\(r\)</span> of <span class="math notranslate nohighlight">\(a \mod N\)</span>. While a straightforward classical algorithm can solve this problem, it is notably inefficient:</p>
-<div class="nbinput nblast docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">find_order_classical</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;A naive classical method to find the order r of a (mod N).</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a: int</span>
-<span class="sd">        an integer in the interval [2,N-1]</span>
-<span class="sd">    N: int</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    r: int</span>
-<span class="sd">        Period of a^x (mod N)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="mi">1</span> <span class="o">&lt;</span> <span class="n">a</span> <span class="ow">and</span> <span class="n">a</span> <span class="o">&lt;</span> <span class="n">N</span>
-    <span class="n">r</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">a</span>
-    <span class="k">while</span> <span class="n">y</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="n">a</span> <span class="o">%</span> <span class="n">N</span>
-        <span class="n">r</span> <span class="o">+=</span> <span class="mi">1</span>
-    <span class="k">return</span> <span class="n">r</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s see how Shor’s algorithm works on a few examples using the classical order-finding problem. Notice how often our choice of value for the initial guess for <span class="math notranslate nohighlight">\(a\)</span> produces factors of <span class="math notranslate nohighlight">\(N\)</span>.</p>
-<div class="nbinput docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">my_integer</span> <span class="o">=</span> <span class="mi">123</span>  <span class="c1">#edit this value to try out a few examples</span>
-<span class="c1"># Edit the value in the line below to try out different initial guesses for a.</span>
-<span class="c1"># What happens when you choose a = 42 for the integer 123?</span>
-<span class="c1"># What happens when you choose a = 100 for the integer 123?</span>
-<span class="n">initial_value_to_start</span> <span class="o">=</span> <span class="mi">42</span>  <span class="c1"># edit this value; it should be less than my_integer</span>
-
-<span class="n">shors_algorithm</span><span class="p">(</span><span class="n">my_integer</span><span class="p">,</span> <span class="n">initial_value_to_start</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Trying a = 42
-Found factors of N=123 by chance: 3 and 41
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[159]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(3, 41)
-</pre></div></div>
-</div>
-</section>
-<section id="Solving-the-order-finding-problem-with-a-quantum-algorithm">
-<h3>Solving the order-finding problem with a quantum algorithm<a class="headerlink" href="#Solving-the-order-finding-problem-with-a-quantum-algorithm" title="Permalink to this heading">¶</a></h3>
-<p>The Fourier transform is a classical computation that provides a more efficient algorithm than the one encoded in <code class="docutils literal notranslate"><span class="pre">find_order_classical</span></code> for identifying the period of <span class="math notranslate nohighlight">\(f(x) = a^x\mod N\)</span>. The quantum version of the Fourier transform is the central idea of Shor’s Algorithm. This efficient quantum solution derives the period from a measurement of <span class="math notranslate nohighlight">\(n = \lceil log2(N) \rceil\)</span> qubits.</p>
-<p>The image below outlines the quantum kernel used to find the order of <span class="math notranslate nohighlight">\(a\)</span>. Notice the last step involves applying the Inverse Quantum Fourier Transform.</p>
-<p><img alt="image0" src="../../_images/shor_circuit.png" /></p>
-<p>Figure. Circuit diagram for finding the phase of the modular multiplication gate $U|x:nbsphinx-math:<cite>rangle `= |a^x:nbsphinx-math:</cite>mod <cite>N:nbsphinx-math:</cite>rangle <cite>$, which will be used to compute the order of :math:`a</cite> modulo <span class="math notranslate nohighlight">\(N\)</span>. The number of qubits in the control register determines the accuracy of estimating the phase of <span class="math notranslate nohighlight">\(U\)</span>. The size of the work register depends on <span class="math notranslate nohighlight">\(N\)</span>. The goal of this section is to code the diagram as a kernel named <code class="docutils literal notranslate"><span class="pre">phase_kernel</span></code>.</p>
-<p>We will need to create a quantum kernel for the Inverse Quantum Fourier Transform. Additionally we’ll need to create a kernel for modular multiplication: <span class="math notranslate nohighlight">\(g(y) = ay \mod N\)</span>, which can be repeatedly applied <span class="math notranslate nohighlight">\(x\)</span>-times to <span class="math notranslate nohighlight">\(y=1\)</span> to carry our modular exponentation <span class="math notranslate nohighlight">\(f(x)=a^x\mod N\)</span>.</p>
-<section id="Inverse-quantum-Fourier-transform">
-<h4>Inverse quantum Fourier transform<a class="headerlink" href="#Inverse-quantum-Fourier-transform" title="Permalink to this heading">¶</a></h4>
-<p>In the code block below we define a kernel for the quantum Fourier transform and then use <code class="docutils literal notranslate"><span class="pre">cudaq.adjoint</span></code> to create a kernel for the inverse quantum Fourier transform used in the quantum order-finding algorithm.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[160]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define kernels for the quantum Fourier transform and the inverse quantum Fourier transform</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">quantum_fourier_transform</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">qubit_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-    <span class="c1"># Apply Hadamard gates and controlled rotation gates.</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-            <span class="n">angle</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">j</span> <span class="o">-</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="n">cr1</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="p">[</span><span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">]],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">inverse_qft</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">quantum_fourier_transform</span><span class="p">,</span> <span class="n">qubits</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Quantum-kernels-for-modular-exponentiation">
-<h4>Quantum kernels for modular exponentiation<a class="headerlink" href="#Quantum-kernels-for-modular-exponentiation" title="Permalink to this heading">¶</a></h4>
-<p>While Shor’s algorithm provably can factor numbers faster than known classical techniques, the resources required for implementing Shor’s algorithm are hefty. A full-scale implementation to factor an <span class="math notranslate nohighlight">\(L\)</span>-bit number could require a quantum kernel with <span class="math notranslate nohighlight">\(5L+1\)</span> qubits to achieve accuracy for the continued fractions part of the algoirthm and as many as <span class="math notranslate nohighlight">\(72L^3\)</span> quantum gates for the modular exponentiaion <a class="reference external" href="https://arxiv.org/pdf/quant-ph/9602016">(Beckman, Chari, Devabhaktuni, &amp; Preskill,
-1996)</a>. Both of these are well beyond the capabilities of modern quantum hardware for numbers of interest. Advancements to reduce the number of gates and qubits required focus on optimizing the kernel for <span class="math notranslate nohighlight">\(f(x) = a\cdot x \mod N\)</span> for properties of the number to be factored. The difficulty is to create efficient quantum kernels (in terms of qubit and gate count) that compute <span class="math notranslate nohighlight">\(a\cdot x \mod{N}\)</span>.</p>
-<p>For the purposes of this demonstration, we will consider only the order-finding problem for the values of <span class="math notranslate nohighlight">\(a\)</span> to be either <span class="math notranslate nohighlight">\(4\)</span> or <span class="math notranslate nohighlight">\(5\)</span> with <span class="math notranslate nohighlight">\(N=21\)</span>. We’ll be using the quantum circuits depicted in <a class="reference external" href="https://arxiv.org/abs/2103.13855">this paper</a> and <a class="reference external" href="https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf">this report</a>, respectively.</p>
-<section id="The-case-N-=-21-and-a-=-5:">
-<h5>The case N = 21 and a = 5:<a class="headerlink" href="#The-case-N-=-21-and-a-=-5:" title="Permalink to this heading">¶</a></h5>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[161]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">modular_mult_5_21</span><span class="p">(</span><span class="n">work</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;&quot;Kernel for multiplying by 5 mod 21</span>
-<span class="sd">    based off of the circuit diagram in</span>
-<span class="sd">    https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf</span>
-<span class="sd">    Modifications were made to change the ordering of the qubits&quot;&quot;&quot;</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
-
-    <span class="n">swap</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
-    <span class="n">swap</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">modular_exp_5_21</span><span class="p">(</span><span class="n">exponent</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">work</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span>
-                     <span class="n">control_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Controlled modular exponentiation kernel used in Shor&#39;s algorithm</span>
-<span class="sd">    |x&gt; U^x |y&gt; = |x&gt; |(5^x)y mod 21&gt;</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">exp</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">control_size</span><span class="p">):</span>
-        <span class="n">ctrl_qubit</span> <span class="o">=</span> <span class="n">exponent</span><span class="p">[</span><span class="n">exp</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">exp</span><span class="p">)):</span>
-            <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">modular_mult_5_21</span><span class="p">,</span> <span class="n">ctrl_qubit</span><span class="p">,</span> <span class="n">work</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Verify in the code block below that the kernels defined above do in fact carry out multiplication and exponentiation with <span class="math notranslate nohighlight">\(a=5\)</span> and <span class="math notranslate nohighlight">\(N=21\)</span> by changing the <code class="docutils literal notranslate"><span class="pre">iterations</span></code> variable below.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[198]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Demonstrate iterated application of 5y mod 21 where y = 1</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">demonstrate_mod_exponentiation</span><span class="p">(</span><span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-     <span class="p">)</span>  <span class="c1"># initalizes the qubits in the state for y = 1 which is |10000&gt;</span>
-    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">iterations</span><span class="p">):</span>
-        <span class="n">modular_mult_5_21</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-
-<span class="n">shots</span> <span class="o">=</span> <span class="mi">200</span>
-
-<span class="c1"># The iterations variable determines the exponent in 5^x mod 21.</span>
-<span class="c1"># Change this value to verify that the demonstrate_mod_exponentiation</span>
-<span class="c1"># kernel carries out the desired calculation.</span>
-<span class="n">iterations</span> <span class="o">=</span> <span class="mi">1</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">demonstrate_mod_exponentiation</span><span class="p">,</span> <span class="n">iterations</span><span class="p">))</span>
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">demonstrate_mod_exponentiation</span><span class="p">,</span>
-                       <span class="n">iterations</span><span class="p">,</span>
-                       <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measurement results from sampling:&quot;</span><span class="p">,</span> <span class="n">results</span><span class="p">)</span>
-
-<span class="c1"># Reverse the order of the most probable measured bit string</span>
-<span class="c1"># and convert the binary string to an integer</span>
-<span class="n">integer_result</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">most_probable</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;For x = </span><span class="si">{}</span><span class="s2">, 5^x mod 21 = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iterations</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="o">**</span><span class="n">iterations</span><span class="p">)</span> <span class="o">%</span> <span class="mi">21</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;For x = </span><span class="si">{}</span><span class="s2">, the computed result of the circuit is </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-    <span class="n">iterations</span><span class="p">,</span> <span class="n">integer_result</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮╭───╮
-q0 : ┤ x ├┤ x ├─╳──╳─
-     ╰───╯╰───╯ │  │
-q1 : ───────────┼──┼─
-     ╭───╮      │  │
-q2 : ┤ x ├──────┼──╳─
-     ╰───╯      │
-q3 : ───────────┼────
-     ╭───╮      │
-q4 : ┤ x ├──────╳────
-     ╰───╯
-
-Measurement results from sampling: { 10100:200 }
-
-For x = 1, 5^x mod 21 = 5
-For x = 1, the computed result of the circuit is 5
-</pre></div></div>
-</div>
-</section>
-<section id="The-case-N-=-21-and-a-=-4:">
-<h5>The case N = 21 and a = 4:<a class="headerlink" href="#The-case-N-=-21-and-a-=-4:" title="Permalink to this heading">¶</a></h5>
-<p>The example below is one where the work register has been optimized to use fewer gates and qubits than would have been necessary through a straightforward implementation of modular multiplication as seen in the previous case.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[199]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">modular_exp_4_21</span><span class="p">(</span><span class="n">exponent</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">,</span> <span class="n">work</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Controlled modular exponentiation kernel used in Shor&#39;s algorithm</span>
-<span class="sd">     |x&gt; U^x |y&gt; = |x&gt; |(4^x)y mod 21&gt;</span>
-<span class="sd">     based off of the circuit diagram in https://arxiv.org/abs/2103.13855</span>
-<span class="sd">     Modifications were made to account for qubit ordering differences&quot;&quot;&quot;</span>
-    <span class="n">swap</span><span class="p">(</span><span class="n">exponent</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">exponent</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="c1"># x = 1</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">exponent</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="c1"># x = 2</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">exponent</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">exponent</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># x = 4</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">exponent</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">exponent</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">work</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">work</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">swap</span><span class="p">(</span><span class="n">exponent</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">exponent</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<p>Now we are ready to define the <code class="docutils literal notranslate"><span class="pre">phase_kernel</span></code> to carry out the instructions in the circuit diagram drawn at the beginning of this section.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[200]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">phase_kernel</span><span class="p">(</span><span class="n">control_register_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">work_register_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-                 <span class="n">N</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Kernel to estimate the phase of the modular multiplication gate |x&gt; U |y&gt; = |x&gt; |a*y mod 21&gt; for a = 4 or 5</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">control_register_size</span> <span class="o">+</span> <span class="n">work_register_size</span><span class="p">)</span>
-    <span class="n">control_register</span> <span class="o">=</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">control_register_size</span><span class="p">]</span>
-    <span class="n">work_register</span> <span class="o">=</span> <span class="n">qubits</span><span class="p">[</span><span class="n">control_register_size</span><span class="p">:</span><span class="n">control_register_size</span> <span class="o">+</span>
-                           <span class="n">work_register_size</span><span class="p">]</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">control_register</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">a</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">21</span><span class="p">:</span>
-        <span class="n">modular_exp_4_21</span><span class="p">(</span><span class="n">control_register</span><span class="p">,</span> <span class="n">work_register</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">a</span> <span class="o">==</span> <span class="mi">5</span> <span class="ow">and</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">21</span><span class="p">:</span>
-        <span class="n">modular_exp_5_21</span><span class="p">(</span><span class="n">control_register</span><span class="p">,</span> <span class="n">work_register</span><span class="p">,</span> <span class="n">control_register_size</span><span class="p">)</span>
-
-    <span class="n">inverse_qft</span><span class="p">(</span><span class="n">control_register</span><span class="p">)</span>
-
-    <span class="c1"># Measure only the control_register and not the work_register</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">control_register</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[201]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">control_register_size</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">work_register_size</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">values_for_a</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
-<span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># change to 1 to select 5</span>
-<span class="n">N</span> <span class="o">=</span> <span class="mi">21</span>
-<span class="n">shots</span> <span class="o">=</span> <span class="mi">15000</span>
-
-<span class="nb">print</span><span class="p">(</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">phase_kernel</span><span class="p">,</span> <span class="n">control_register_size</span><span class="p">,</span> <span class="n">work_register_size</span><span class="p">,</span>
-               <span class="n">values_for_a</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">N</span><span class="p">))</span>
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">phase_kernel</span><span class="p">,</span>
-                       <span class="n">control_register_size</span><span class="p">,</span>
-                       <span class="n">work_register_size</span><span class="p">,</span>
-                       <span class="n">values_for_a</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
-                       <span class="n">N</span><span class="p">,</span>
-                       <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="s2">&quot;Measurement results for a=</span><span class="si">{}</span><span class="s2"> and N=</span><span class="si">{}</span><span class="s2"> with </span><span class="si">{}</span><span class="s2"> qubits in the control register &quot;</span>
-    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">values_for_a</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">N</span><span class="p">,</span> <span class="n">control_register_size</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮                                                                    »
-q0 : ┤ h ├──●────●────●───●──●────────────────────────────────────────────────»
-     ├───┤  │    │    │   │  │                                                »
-q1 : ┤ h ├──┼────┼────┼───┼──┼───●────●────●───●──●───●────●────●───●──●──────»
-     ├───┤  │    │    │   │  │   │    │    │   │  │   │    │    │   │  │      »
-q2 : ┤ h ├──┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───●──»
-     ├───┤╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮»
-q3 : ┤ x ├┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├»
-     ╰───╯╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯»
-q4 : ────────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼──────»
-               ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      »
-q5 : ──────────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────»
-               ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         »
-q6 : ─────────────────┼───┼────────────────┼───┼────────────────┼───┼─────────»
-                    ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │         »
-q7 : ───────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─────────»
-                    ╰───╯                ╰───╯                ╰───╯           »
-
-################################################################################
-
-                                                                            »
-────────────────────────────────────────────────────────────────────────────»
-                                                                            »
-────────────────────────────────────────────────────────────────────────────»
-                                                                            »
-──●────●───●──●───●────●────●───●──●───●────●────●───●──●───●────●────●───●─»
-  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │ »
-──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳─»
-  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │ »
-──┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼─»
-╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │ »
-┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼─»
-╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │ »
-───────┼───┼────────────────┼───┼────────────────┼───┼────────────────┼───┼─»
-     ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │ »
-─────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─»
-     ╰───╯                ╰───╯                ╰───╯                ╰───╯   »
-
-################################################################################
-
-                           ╭─────────────╮╭────────────╮╭───╮
-───────────────────────────┤ r1(-0.7854) ├┤ r1(-1.571) ├┤ h ├
-        ╭────────────╮╭───╮╰──────┬──────╯╰─────┬──────╯╰───╯
-────────┤ r1(-1.571) ├┤ h ├───────┼─────────────●────────────
-   ╭───╮╰─────┬──────╯╰───╯       │
-─●─┤ h ├──────●───────────────────●──────────────────────────
- │ ╰───╯
-─╳───────────────────────────────────────────────────────────
- │
-─┼───────────────────────────────────────────────────────────
- │
-─╳───────────────────────────────────────────────────────────
-
-─────────────────────────────────────────────────────────────
-
-─────────────────────────────────────────────────────────────
-
-
-Measurement results for a=5 and N=21 with 3 qubits in the control register
-{ 000:2843 010:913 111:1850 001:1935 011:1830 100:2846 101:1861 110:922 }
-
-</pre></div></div>
-</div>
-</section>
-</section>
-</section>
-<section id="Determining-the-order-from-the-measurement-results-of-the-phase-kernel">
-<h3>Determining the order from the measurement results of the phase kernel<a class="headerlink" href="#Determining-the-order-from-the-measurement-results-of-the-phase-kernel" title="Permalink to this heading">¶</a></h3>
-<p>The measurement results from sampling the <code class="docutils literal notranslate"><span class="pre">phase_kernel</span></code> can be used to estimate the phase of the operator <span class="math notranslate nohighlight">\(U|x\rangle = ax\mod N\)</span>. We will only be interested in the most probable non-zero outcomes of the sampling. Therefore, we’ll create a function <code class="docutils literal notranslate"><span class="pre">top_results</span></code> to extract those values.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[202]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">top_results</span><span class="p">(</span><span class="n">sample_results</span><span class="p">,</span> <span class="n">zeros</span><span class="p">,</span> <span class="n">threshold</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to output the non-zero results whose counts are above the given threshold</span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">        dict[str, int]: keys are bit-strings and values are the respective counts</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">results_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">sample_results</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-    <span class="k">if</span> <span class="n">zeros</span> <span class="ow">in</span> <span class="n">results_dictionary</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="n">results_dictionary</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">zeros</span><span class="p">)</span>
-    <span class="n">sorted_results</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span>
-            <span class="n">results_dictionary</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">item</span><span class="p">:</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="p">}</span>
-    <span class="n">top_key</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">sorted_results</span><span class="p">))</span>
-    <span class="n">max_value</span> <span class="o">=</span> <span class="n">sorted_results</span><span class="p">[</span><span class="n">top_key</span><span class="p">]</span>
-    <span class="n">top_results_dictionary</span> <span class="o">=</span> <span class="p">{</span><span class="n">top_key</span><span class="p">:</span> <span class="n">max_value</span><span class="p">}</span>
-
-    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">sorted_results</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">results_dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">&gt;</span> <span class="nb">min</span><span class="p">(</span><span class="n">threshold</span><span class="p">,</span> <span class="n">max_value</span><span class="p">):</span>
-            <span class="n">top_results_dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">results_dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">top_results_dictionary</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s see how the <code class="docutils literal notranslate"><span class="pre">top_results</span></code> extracts the top results from the measurement results for a=4 and N=21 with 3 qubits in the control register that we computed above. One of these top result is likely to be an estimate for the phase of <span class="math notranslate nohighlight">\(U\)</span>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[203]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example</span>
-<span class="n">top_results</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="s1">&#39;000&#39;</span><span class="p">,</span> <span class="mi">750</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[203]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;100&#39;: 2846,
- &#39;001&#39;: 1935,
- &#39;101&#39;: 1861,
- &#39;111&#39;: 1850,
- &#39;011&#39;: 1830,
- &#39;110&#39;: 922,
- &#39;010&#39;: 913}
-</pre></div></div>
-</div>
-<p>The algorithm for translating the phase estimate of the operator <span class="math notranslate nohighlight">\(U|x\rangle = ax\mod N\)</span> into the order of <span class="math notranslate nohighlight">\(a \mod N\)</span> involves continued fractions. The function <code class="docutils literal notranslate"><span class="pre">find_order_quantum</span></code> below carries out this computation. To learn more about how the phase of <span class="math notranslate nohighlight">\(U\)</span> relates to the period of <span class="math notranslate nohighlight">\(a \mod N\)</span>, check out these three lectures by Scott Aaronson: lecture <a class="reference external" href="https://www.scottaaronson.com/qclec/19.pdf">19</a>, <a class="reference external" href="https://www.scottaaronson.com/qclec/20.pdf">20</a>,
-<a class="reference external" href="https://www.scottaaronson.com/qclec/21.pdf">21</a>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[204]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_order_from_phase</span><span class="p">(</span><span class="n">phase</span><span class="p">,</span> <span class="n">phase_nbits</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Uses continued fractions to find the order of a mod N</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    phase: int</span>
-<span class="sd">        Integer result from the phase estimate of U|x&gt; = ax mod N</span>
-<span class="sd">    phase_nbits: int</span>
-<span class="sd">        Number of qubits used to estimate the phase</span>
-<span class="sd">    a: int</span>
-<span class="sd">        For this demonstration a is either 4 or 5</span>
-<span class="sd">    N: int</span>
-<span class="sd">        For this demonstration N = 21</span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    int: period of a mod N if found, otherwise returns None</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">assert</span> <span class="n">phase_nbits</span> <span class="o">&gt;</span> <span class="mi">0</span>
-    <span class="k">assert</span> <span class="n">a</span> <span class="o">&gt;</span> <span class="mi">0</span>
-    <span class="k">assert</span> <span class="n">N</span> <span class="o">&gt;</span> <span class="mi">0</span>
-
-    <span class="n">eigenphase</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">phase</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">phase_nbits</span><span class="p">)</span>
-
-    <span class="n">f</span> <span class="o">=</span> <span class="n">fractions</span><span class="o">.</span><span class="n">Fraction</span><span class="o">.</span><span class="n">from_float</span><span class="p">(</span><span class="n">eigenphase</span><span class="p">)</span><span class="o">.</span><span class="n">limit_denominator</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">f</span><span class="o">.</span><span class="n">numerator</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">None</span>
-    <span class="n">eigenphase</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">numerator</span> <span class="o">/</span> <span class="n">f</span><span class="o">.</span><span class="n">denominator</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;eigenphase is &#39;</span><span class="p">,</span> <span class="n">eigenphase</span><span class="p">)</span>
-    <span class="n">coefficients_continued_fraction</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-        <span class="n">contfrac</span><span class="o">.</span><span class="n">continued_fraction</span><span class="p">(</span><span class="n">eigenphase</span><span class="p">))</span>
-
-    <span class="n">convergents_continued_fraction</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">contfrac</span><span class="o">.</span><span class="n">convergents</span><span class="p">(</span><span class="n">eigenphase</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;convergent sequence of fractions for this eigenphase is&#39;</span><span class="p">,</span>
-          <span class="n">convergents_continued_fraction</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">convergents_continued_fraction</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span>
-            <span class="s1">&#39;using the denominators of the fractions in the convergent sequence, testing order =&#39;</span><span class="p">,</span>
-            <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">a</span><span class="o">**</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">%</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Found order:&#39;</span><span class="p">,</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">return</span> <span class="kc">None</span>
-</pre></div>
-</div>
-</div>
-<p>We are now ready to combine all the elements above into a function to find the order of <span class="math notranslate nohighlight">\(a\)</span> <span class="math notranslate nohighlight">\(\mod N\)</span> and test it in the factoring algoithm.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[205]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">find_order_quantum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;The quantum algorithm to find the order of a mod N, when x = 4 or x =5 and N = 21</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a: int</span>
-<span class="sd">        For this demonstration a will be either 4 or 5</span>
-<span class="sd">    N: int</span>
-<span class="sd">        For this demonstration N will be 21</span>
-<span class="sd">    Returns</span>
-<span class="sd">    r: int the period if it is found, or None if no period is found</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="p">(</span><span class="n">a</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">21</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">a</span> <span class="o">==</span> <span class="mi">5</span> <span class="ow">and</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">21</span><span class="p">):</span>
-        <span class="n">shots</span> <span class="o">=</span> <span class="mi">15000</span>
-        <span class="k">if</span> <span class="n">a</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">21</span><span class="p">:</span>
-            <span class="n">control_register_size</span> <span class="o">=</span> <span class="mi">3</span>
-            <span class="n">work_register_size</span> <span class="o">=</span> <span class="mi">2</span>
-        <span class="k">if</span> <span class="n">a</span> <span class="o">==</span> <span class="mi">5</span> <span class="ow">and</span> <span class="n">N</span> <span class="o">==</span> <span class="mi">21</span><span class="p">:</span>
-            <span class="n">control_register_size</span> <span class="o">=</span> <span class="mi">5</span>
-            <span class="n">work_register_size</span> <span class="o">=</span> <span class="mi">5</span>
-
-        <span class="c1">#cudaq.set_random_seed(123)</span>
-        <span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">phase_kernel</span><span class="p">,</span>
-                               <span class="n">control_register_size</span><span class="p">,</span>
-                               <span class="n">work_register_size</span><span class="p">,</span>
-                               <span class="n">a</span><span class="p">,</span>
-                               <span class="n">N</span><span class="p">,</span>
-                               <span class="n">shots_count</span><span class="o">=</span><span class="n">shots</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measurement results:&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-
-        <span class="c1"># We will want to ignore the all zero result</span>
-        <span class="n">zero_result</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-            <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">elem</span><span class="p">)</span> <span class="k">for</span> <span class="n">elem</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">control_register_size</span><span class="p">])</span>
-        <span class="c1"># We&#39;ll only consider the top results from the sampling</span>
-        <span class="n">threshold</span> <span class="o">=</span> <span class="n">shots</span> <span class="o">*</span> <span class="p">(</span><span class="mf">.1</span><span class="p">)</span>
-        <span class="n">most_probable_bitpatterns</span> <span class="o">=</span> <span class="n">top_results</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">zero_result</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">most_probable_bitpatterns</span><span class="p">:</span>
-            <span class="c1"># Convert the key bit string into an integer</span>
-            <span class="c1"># This integer divided by 8 is an estimate for the phase</span>
-            <span class="n">reverse_result</span> <span class="o">=</span> <span class="n">key</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">phase</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">reverse_result</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Trying nonzero bitpattern from the phase estimation:&quot;</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span>
-                  <span class="s2">&quot;=&quot;</span><span class="p">,</span> <span class="n">phase</span><span class="p">)</span>
-            <span class="n">r</span> <span class="o">=</span> <span class="n">get_order_from_phase</span><span class="p">(</span><span class="n">phase</span><span class="p">,</span> <span class="n">control_register_size</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">r</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;No period found.&#39;</span><span class="p">)</span>
-
-                <span class="k">continue</span>
-
-            <span class="k">return</span> <span class="n">r</span>
-            <span class="k">break</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span>
-            <span class="s2">&quot;A different quantum kernel is required for this choice of a and N.&quot;</span>
-        <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[206]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">my_integer</span> <span class="o">=</span> <span class="mi">21</span>
-<span class="n">initial_value_to_start</span> <span class="o">=</span> <span class="mi">5</span>  <span class="c1"># Try replacing 5 with 4</span>
-<span class="n">quantum</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="n">shors_algorithm</span><span class="p">(</span><span class="n">my_integer</span><span class="p">,</span> <span class="n">initial_value_to_start</span><span class="p">,</span> <span class="n">quantum</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Trying a = 5
-Measurement results:
-{ 11010:452 11001:92 11101:47 00000:2485 01001:96 00100:131 01011:1747 00101:1654 10101:1736 00110:475 11111:23 01000:50 01101:38 01110:35 01010:420 11110:22 01111:26 00011:50 01100:120 10011:35 00010:25 00001:22 11011:1710 00111:79 11100:113 10001:14 10010:29 10100:129 10110:467 10000:2536 10111:81 11000:61 }
-
-Trying nonzero bitpattern from the phase estimation: 10000 = 1
-No period found.
-Trying nonzero bitpattern from the phase estimation: 01011 = 26
-eigenphase is  0.8125
-convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (4, 5), (13, 16)]
-using the denominators of the fractions in the convergent sequence, testing order = 1
-using the denominators of the fractions in the convergent sequence, testing order = 1
-using the denominators of the fractions in the convergent sequence, testing order = 5
-using the denominators of the fractions in the convergent sequence, testing order = 16
-No period found.
-Trying nonzero bitpattern from the phase estimation: 10101 = 21
-eigenphase is  0.65
-convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (1, 2), (2, 3), (13, 20)]
-using the denominators of the fractions in the convergent sequence, testing order = 1
-using the denominators of the fractions in the convergent sequence, testing order = 1
-using the denominators of the fractions in the convergent sequence, testing order = 2
-using the denominators of the fractions in the convergent sequence, testing order = 3
-using the denominators of the fractions in the convergent sequence, testing order = 20
-No period found.
-Trying nonzero bitpattern from the phase estimation: 11011 = 27
-eigenphase is  0.8421052631578947
-convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (5, 6), (16, 19)]
-using the denominators of the fractions in the convergent sequence, testing order = 1
-using the denominators of the fractions in the convergent sequence, testing order = 1
-using the denominators of the fractions in the convergent sequence, testing order = 6
-Found order: 6
-The order of a =5 is 6
-Found factors of N = 21: 7 and 3
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[206]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(7, 3)
-</pre></div></div>
-</div>
-</section>
-<section id="Postscript">
-<h3>Postscript<a class="headerlink" href="#Postscript" title="Permalink to this heading">¶</a></h3>
-<p>Recent <a class="reference external" href="https://arxiv.org/abs/2308.06572">work of Oded Regev</a> improves Shor’s Algorithm by reducing the number of gates needed. You can read more about it <a class="reference external" href="https://www.quantamagazine.org/thirty-years-later-a-speed-boost-for-quantum-factoring-20231017/">here</a>.</p>
-</section>
-</section>
-</section>
-
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diff --git a/pr-2308/applications/python/shors.ipynb b/pr-2308/applications/python/shors.ipynb
deleted file mode 100644
index 8063162ee4..0000000000
--- a/pr-2308/applications/python/shors.ipynb
+++ /dev/null
@@ -1,986 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "628c747b-084c-4fb6-843e-7c7f4cbf07a1",
-   "metadata": {},
-   "source": [
-    "# Factoring Integers With Shor's Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d0627c4-95bd-47b9-9ab5-e8374d25508e",
-   "metadata": {},
-   "source": [
-    "The most famous application of quantum computers is factoring integers using Shor's algorithm. This algorithm is particularly significant because an efficient factorization algorithm could potentially break modern asymmetric encryption schemes, such as RSA.\n",
-    "\n",
-    "For small integers, this quantum algorithm can be simulated on classical computers. The main challenge in classical implementation lies in the order-finding algorithm. We will first introduce the classical implementation of this algorithm as a preliminary step, and then proceed to explain the quantum order-finding algorithm.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "148568d8",
-   "metadata": {},
-   "source": [
-    "First let's install some libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "486fb3d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install contfrac==1.0.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "88619b35-624e-4853-8d2e-6d9952390e5e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from math import gcd, log2, ceil\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import cudaq\n",
-    "from cudaq import *\n",
-    "import fractions\n",
-    "import matplotlib.pyplot as plt\n",
-    "import contfrac"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8523c6f-d729-4d5e-8df9-1780c343a58e",
-   "metadata": {},
-   "source": [
-    "## Shor's algorithm\n",
-    "The factoring problem for an integer $N$ aims to find integers $a_1$, $a_2$ which are factors of $N$.  This problem is interesting when $N$ is not prime and the solutions $a_1$ and $a_2$ are non-trivial.  In other words, we will attempt to find integers $a_1$ and $a_2$ satisfying $N=a_1\\cdot a_2$ with $a_1\\neq 1$ and $a_2\\neq 1$.\n",
-    "\n",
-    "The algorithm consists of two ideas:\n",
-    "\n",
-    "* Reduce the problem of factoring the integer $N$ to the order-finding problem.\n",
-    "* Solve the order-finding problem: \n",
-    "Given integers $a$ and $N$ so that $a$ and $N$ share no common factors (i.e., the greatest common divisor of $a$ and $N$ is 1), find the smallest positive integer which satisfies $a^r \\equiv 1 \\mod N$.  This value $r$ is refered to as the *order of* $a \\mod N$.\n",
-    "\n",
-    "These two ideas are combined in the following steps in Shor's algorithm:\n",
-    "\n",
-    "0. Rule out the easy case that $N$ is even\n",
-    "1. Select a random integer $a$ between $2$ and $N-1$\n",
-    "2. Check to see if $a$ is a factor of $N$ (if so we're done!)\n",
-    "3. Find the order of $a \\mod N$, (i.e., find $r$ so that $a^r\\equiv 1 \\mod N$)\n",
-    "4. Check to see if $a^{\\frac{r}{2}}-1$ or $a^{\\frac{r}{2}}+1$ are integers and share common, non-trivial, divisors with $N$ (if so we're done!)\n",
-    "5. If no factor is found, repeat steps 1 through 4 above.\n",
-    "\n",
-    "The function `shors_algorithm` below outlines these steps for both the classical and the quantum implementation of Shor's algorithm. For the purposes of demonstration, we will also control the initial random integer selected in step 1 so that we can investigate cases in which the selected integer produces common divisors of $N$ in step 4 and others in which step 4 produces no common factors.\n",
-    "\n",
-    "\n",
-    "**Note about terminology:** Some literature refers to the \"period-finding problem\" in Shor's algorithm.  The order-finding problem, as we have described above, can be recast as finding the period of the function $f(x) = a^x\\mod N$, by noticing that the period of $f(x)$ is one more than the order of $a\\mod N$. The period finding problem is more general than the order-finding problem since it aims to find the period of any periodic function, not just modular exponentiation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7dd05729",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shors_algorithm(N, initial, quantum):\n",
-    "    \"\"\" Factor N using Shor's algorithm with initial starting value and choice of \n",
-    "    using either classical or quantum approach for the period finding step\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    N: int \n",
-    "        Integer to be factored (assumed to be non-prime and >1)\n",
-    "    initial: int \n",
-    "        Initial choice of the random integer between 2 and N-1\n",
-    "    quantum: boolean\n",
-    "        True if we will call the quantum order-finding algorithm, and false if we call the classical one for step 3.   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    a1, a2: int\n",
-    "        Non-trivial factors of N\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # 0. Check to see if N is even.\n",
-    "    if N % 2 == 0:\n",
-    "        divisor1 = 2\n",
-    "        divisor2 = N // 2\n",
-    "        print(\"Found factors:\", divisor1, divisor2)\n",
-    "        return (divisor1, divisor2)\n",
-    "\n",
-    "    attempts = [initial]\n",
-    "    while (True):\n",
-    "        # 1. Select a random integer between 2 and N-1\n",
-    "        if len(attempts) == 1:\n",
-    "            a = initial\n",
-    "        else:\n",
-    "            a = random.choice(\n",
-    "                [n for n in range(N - 1) if n not in attempts and n != 1])\n",
-    "\n",
-    "        # 2. See if the integer selected in step 1 happens to factor N\n",
-    "        print(\"Trying a =\", a)\n",
-    "        divisor1 = gcd(a, N)\n",
-    "        if divisor1 != 1:\n",
-    "            divisor2 = N // divisor1\n",
-    "            print(\"Found factors of N={} by chance: {} and {}\".format(\n",
-    "                N, divisor1, divisor2))\n",
-    "            return (divisor1, divisor2)\n",
-    "\n",
-    "        # 3. Find the order of a mod N (i.e., r, where a^r = 1 (mod N))\n",
-    "        if quantum == True:\n",
-    "            r = find_order_quantum(a, N)\n",
-    "        else:\n",
-    "            r = find_order_classical(a, N)\n",
-    "        print(\"The order of a = {} is {}\".format(a, r))\n",
-    "\n",
-    "        # 4. If the order of a is found and it is\n",
-    "        # * even and\n",
-    "        # * not a^(r/2) = -1 (mod N),\n",
-    "        # then test a^(r/2)-1 and a^(r/2)+1 to see if they share factors with N.\n",
-    "        # We also want to rule out the case of finding the trivial factors: 1 and N.\n",
-    "        divisor1, divisor2 = test_order(a, r, N)\n",
-    "        if (divisor1 != 0):  # test_order will return a 0 if no factor is found\n",
-    "            print(\"Found factors of N = {}: {} and {}\".format(\n",
-    "                N, divisor1, divisor2))\n",
-    "            return divisor1, divisor2\n",
-    "\n",
-    "        # 5. Repeat\n",
-    "        print(\"retrying...\")\n",
-    "        attempts.append(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed682e81",
-   "metadata": {},
-   "source": [
-    "Let's first explore the idea of reducing the factoring problem into the order-finding problem. This gets captured in step 4 described above.  In this step, we have already established that $a$ and $N$ share no factors other than $1$ (i.e., $\\gcd(a,N)=1$) and we have found $r$, the order of $a\\mod N.$  With this information we know that $a^r \\equiv 1 \\mod N.$  Rewritten in another way:\n",
-    "$$\n",
-    "a^r -1 \\equiv 0\\mod N\n",
-    "\\tag{1}.\n",
-    "$$ \n",
-    " \n",
-    " If $r$ is even, we can rewrite $a^r$ as $x^2$ where $x=a^\\frac{r}{2}$. Next, we can factor equation $(1)$ using the identity $x^2-1 = (x-1)(x+1)$:\n",
-    " $$ \n",
-    " (a^{\\frac{r}{2}} - 1)(a^{\\frac{r}{2}} + 1) \\equiv 0\\mod N\n",
-    "\\tag{2}.\n",
-    "$$  \n",
-    "\n",
-    "If, in addition, the equation\n",
-    "$$\n",
-    "a^{\\frac{r}{2}}  \\not\\equiv -1\\mod N\n",
-    "\\tag{3}\n",
-    "$$\n",
-    "is satisfied, then  at least one of the terms $a^{\\frac{r}{2}} - 1$ or $a^{\\frac{r}{2}} + 1$ must share a common factor with $N$. [Peter Shor](https://arxiv.org/abs/quant-ph/9508027) demonstrated that there is greater than a $50\\%$ chance of randomly selecting a value for $a$ satisfying these properties.\n",
-    "\n",
-    "The code block below defines a function that tests whether $r$ is even and whether equation $(3)$ is satisfied and searches for a non-trivial factor of $N$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "6c8eed12",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_order(a, r, N):\n",
-    "    \"\"\"Checks whether or not a^(r/2)+1 or a^(r/2)-1 share a non-trivial common factor with N\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "    r: int\n",
-    "    N: int\n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    int, int factors of N, if found; 0,0 otherwise  \n",
-    "    \"\"\"\n",
-    "\n",
-    "    if r != None and (r % 2 == 0) and a**r % N == 1:\n",
-    "        if (a**(int(r / 2))) % N != -1:\n",
-    "            possible_factors = [gcd(r - 1, N), gcd(r + 1, N)]\n",
-    "            for test_factor in possible_factors:\n",
-    "                if test_factor != 1 and test_factor != N:\n",
-    "                    return test_factor, N // test_factor\n",
-    "    # period did not produce a factor\n",
-    "    else:\n",
-    "        print('No non-trivial factor found')\n",
-    "        return 0, 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a43af433-905c-4070-94b3-0f6be6f67f6b",
-   "metadata": {},
-   "source": [
-    "### Solving the order-finding problem classically\n",
-    "\n",
-    "The key component of Shor's algorithm is an efficient quantum algorithm to find the order $r$ of $a \\mod N$. While a straightforward classical algorithm can solve this problem, it is notably inefficient:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "14f4b0d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_order_classical(a, N):\n",
-    "    \"\"\"A naive classical method to find the order r of a (mod N).\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "        an integer in the interval [2,N-1]\n",
-    "    N: int\n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    r: int \n",
-    "        Period of a^x (mod N)\n",
-    "    \"\"\"\n",
-    "    assert 1 < a and a < N\n",
-    "    r = 1\n",
-    "    y = a\n",
-    "    while y != 1:\n",
-    "        y = y * a % N\n",
-    "        r += 1\n",
-    "    return r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aae6b36d",
-   "metadata": {},
-   "source": [
-    "Let's see how Shor's algorithm works on a few examples using the classical order-finding problem.  Notice how often our choice of value for the initial guess for $a$ produces factors of $N$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "3f75b314-9f0c-40c5-a7bc-58ae235fc183",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trying a = 42\n",
-      "Found factors of N=123 by chance: 3 and 41\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(3, 41)"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_integer = 123  #edit this value to try out a few examples\n",
-    "# Edit the value in the line below to try out different initial guesses for a.\n",
-    "# What happens when you choose a = 42 for the integer 123?\n",
-    "# What happens when you choose a = 100 for the integer 123?\n",
-    "initial_value_to_start = 42  # edit this value; it should be less than my_integer\n",
-    "\n",
-    "shors_algorithm(my_integer, initial_value_to_start, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "275efced-b10e-4438-9b5e-f3534079304b",
-   "metadata": {},
-   "source": [
-    "### Solving the order-finding problem with a quantum algorithm\n",
-    "\n",
-    "\n",
-    "The Fourier transform is a classical computation that provides a more efficient algorithm than the one encoded in `find_order_classical` for identifying the period of $f(x) = a^x\\mod N$. The quantum version of the Fourier transform is the central idea of Shor's Algorithm. This efficient quantum solution derives the period from a measurement of $n = \\lceil log2(N) \\rceil$ qubits. \n",
-    "\n",
-    "The image below outlines the quantum kernel used to find the order of $a$. Notice the last step involves applying the Inverse Quantum Fourier Transform."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aba9fbd7",
-   "metadata": {},
-   "source": [
-    "![](images/shor_circuit.png)\n",
-    "\n",
-    "Figure. Circuit diagram for finding the phase of the modular multiplication gate $U|x\\rangle = |a^x\\mod N\\rangle $, which will be used to compute the order of $a$ modulo $N$.  The number of qubits in the control register determines the accuracy of estimating the phase of $U$. The size of the work register depends on $N$. The goal of this section is to code the  diagram as a kernel named `phase_kernel`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7eff3b6",
-   "metadata": {},
-   "source": [
-    "We will need to create a quantum kernel for the Inverse Quantum Fourier Transform.  Additionally we'll need to create a kernel for modular multiplication: $g(y) = ay \\mod N$, which can be repeatedly applied $x$-times to $y=1$ to carry our modular exponentation $f(x)=a^x\\mod N$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "567e104e",
-   "metadata": {},
-   "source": [
-    "#### Inverse quantum Fourier transform\n",
-    "In the code block below we define a kernel for the quantum Fourier transform and then use `cudaq.adjoint` to create a kernel for the inverse quantum Fourier transform used in the quantum order-finding algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "0e04a0a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define kernels for the quantum Fourier transform and the inverse quantum Fourier transform\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform(qubits: cudaq.qview):\n",
-    "    qubit_count = len(qubits)\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def inverse_qft(qubits: cudaq.qview):\n",
-    "    cudaq.adjoint(quantum_fourier_transform, qubits)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39db9e6a",
-   "metadata": {},
-   "source": [
-    "#### Quantum kernels for modular exponentiation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b8b8884",
-   "metadata": {},
-   "source": [
-    "\n",
-    "While Shor's algorithm provably can factor numbers faster than known classical techniques, the resources required for implementing Shor's algorithm are hefty. A full-scale implementation to factor an $L$-bit number could require a quantum kernel with $5L+1$ qubits to achieve accuracy for the continued fractions part of the algoirthm and as many as $72L^3$ quantum gates for the modular exponentiaion [(Beckman, Chari, Devabhaktuni, & Preskill, 1996)](https://arxiv.org/pdf/quant-ph/9602016).  Both of these are well beyond the capabilities of modern quantum hardware for numbers of interest. Advancements to reduce the number of gates and qubits required focus on optimizing the kernel for $f(x) = a\\cdot x \\mod N$ for properties of the number to be factored. The difficulty is to create efficient quantum kernels (in terms of qubit and gate count) that compute $a\\cdot x \\mod{N}$.  \n",
-    "\n",
-    "For the purposes of this demonstration, we will consider only the order-finding problem for the values of $a$ to be either $4$ or $5$ with $N=21$. We'll be using the quantum circuits depicted in [this paper](https://arxiv.org/abs/2103.13855) and [this report](https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf), respectively. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a48d6fa1",
-   "metadata": {},
-   "source": [
-    "##### The case N = 21 and a = 5:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "c4b8e2e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def modular_mult_5_21(work: cudaq.qview):\n",
-    "    \"\"\"\"Kernel for multiplying by 5 mod 21\n",
-    "    based off of the circuit diagram in \n",
-    "    https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf\n",
-    "    Modifications were made to change the ordering of the qubits\"\"\"\n",
-    "    x(work[0])\n",
-    "    x(work[2])\n",
-    "    x(work[4])\n",
-    "\n",
-    "    swap(work[0], work[4])\n",
-    "    swap(work[0], work[2])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def modular_exp_5_21(exponent: cudaq.qview, work: cudaq.qview,\n",
-    "                     control_size: int):\n",
-    "    \"\"\" Controlled modular exponentiation kernel used in Shor's algorithm\n",
-    "    |x> U^x |y> = |x> |(5^x)y mod 21>\n",
-    "    \"\"\"\n",
-    "    x(work[0])\n",
-    "    for exp in range(control_size):\n",
-    "        ctrl_qubit = exponent[exp]\n",
-    "        for _ in range(2**(exp)):\n",
-    "            cudaq.control(modular_mult_5_21, ctrl_qubit, work)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c644786",
-   "metadata": {},
-   "source": [
-    "Verify in the code block below that the kernels defined above do in fact carry out multiplication and exponentiation with $a=5$ and $N=21$ by changing the `iterations` variable below. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "e93fcf39",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮      \n",
-      "q0 : ┤ x ├┤ x ├─╳──╳─\n",
-      "     ╰───╯╰───╯ │  │ \n",
-      "q1 : ───────────┼──┼─\n",
-      "     ╭───╮      │  │ \n",
-      "q2 : ┤ x ├──────┼──╳─\n",
-      "     ╰───╯      │    \n",
-      "q3 : ───────────┼────\n",
-      "     ╭───╮      │    \n",
-      "q4 : ┤ x ├──────╳────\n",
-      "     ╰───╯           \n",
-      "\n",
-      "Measurement results from sampling: { 10100:200 }\n",
-      "\n",
-      "For x = 1, 5^x mod 21 = 5\n",
-      "For x = 1, the computed result of the circuit is 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Demonstrate iterated application of 5y mod 21 where y = 1\n",
-    "@cudaq.kernel\n",
-    "def demonstrate_mod_exponentiation(iterations: int):\n",
-    "    qubits = cudaq.qvector(5)\n",
-    "    x(qubits[0]\n",
-    "     )  # initalizes the qubits in the state for y = 1 which is |10000>\n",
-    "    for _ in range(iterations):\n",
-    "        modular_mult_5_21(qubits)\n",
-    "\n",
-    "\n",
-    "shots = 200\n",
-    "\n",
-    "# The iterations variable determines the exponent in 5^x mod 21.\n",
-    "# Change this value to verify that the demonstrate_mod_exponentiation\n",
-    "# kernel carries out the desired calculation.\n",
-    "iterations = 1\n",
-    "\n",
-    "print(cudaq.draw(demonstrate_mod_exponentiation, iterations))\n",
-    "\n",
-    "results = cudaq.sample(demonstrate_mod_exponentiation,\n",
-    "                       iterations,\n",
-    "                       shots_count=shots)\n",
-    "\n",
-    "print(\"Measurement results from sampling:\", results)\n",
-    "\n",
-    "# Reverse the order of the most probable measured bit string\n",
-    "# and convert the binary string to an integer\n",
-    "integer_result = int(results.most_probable()[::-1], 2)\n",
-    "\n",
-    "print(\"For x = {}, 5^x mod 21 = {}\".format(iterations, (5**iterations) % 21))\n",
-    "print(\"For x = {}, the computed result of the circuit is {}\".format(\n",
-    "    iterations, integer_result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff07579c",
-   "metadata": {},
-   "source": [
-    "##### The case N = 21 and a = 4:\n",
-    "\n",
-    "The example below is one where the work register has been optimized to use fewer gates and qubits than would have been necessary through a straightforward implementation of modular multiplication as seen in the previous case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "69963075",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def modular_exp_4_21(exponent: cudaq.qview, work: cudaq.qview):\n",
-    "    \"\"\" Controlled modular exponentiation kernel used in Shor's algorithm\n",
-    "     |x> U^x |y> = |x> |(4^x)y mod 21>\n",
-    "     based off of the circuit diagram in https://arxiv.org/abs/2103.13855\n",
-    "     Modifications were made to account for qubit ordering differences\"\"\"\n",
-    "    swap(exponent[0], exponent[2])\n",
-    "    # x = 1\n",
-    "    x.ctrl(exponent[2], work[1])\n",
-    "\n",
-    "    # x = 2\n",
-    "    x.ctrl(exponent[1], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    x.ctrl([exponent[1], work[0]], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "\n",
-    "    # x = 4\n",
-    "    x(work[1])\n",
-    "    x.ctrl([exponent[0], work[1]], work[0])\n",
-    "    x(work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    x.ctrl([exponent[0], work[0]], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    swap(exponent[0], exponent[2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84a197e2",
-   "metadata": {},
-   "source": [
-    "Now we are ready to define the `phase_kernel` to carry out the instructions in the circuit diagram drawn at the beginning of this section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "206bcac3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def phase_kernel(control_register_size: int, work_register_size: int, a: int,\n",
-    "                 N: int):\n",
-    "    \"\"\" \n",
-    "    Kernel to estimate the phase of the modular multiplication gate |x> U |y> = |x> |a*y mod 21> for a = 4 or 5\n",
-    "    \"\"\"\n",
-    "\n",
-    "    qubits = cudaq.qvector(control_register_size + work_register_size)\n",
-    "    control_register = qubits[0:control_register_size]\n",
-    "    work_register = qubits[control_register_size:control_register_size +\n",
-    "                           work_register_size]\n",
-    "\n",
-    "    h(control_register)\n",
-    "\n",
-    "    if a == 4 and N == 21:\n",
-    "        modular_exp_4_21(control_register, work_register)\n",
-    "    if a == 5 and N == 21:\n",
-    "        modular_exp_5_21(control_register, work_register, control_register_size)\n",
-    "\n",
-    "    inverse_qft(control_register)\n",
-    "\n",
-    "    # Measure only the control_register and not the work_register\n",
-    "    mz(control_register)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "id": "ca9b570a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮                                                                    »\n",
-      "q0 : ┤ h ├──●────●────●───●──●────────────────────────────────────────────────»\n",
-      "     ├───┤  │    │    │   │  │                                                »\n",
-      "q1 : ┤ h ├──┼────┼────┼───┼──┼───●────●────●───●──●───●────●────●───●──●──────»\n",
-      "     ├───┤  │    │    │   │  │   │    │    │   │  │   │    │    │   │  │      »\n",
-      "q2 : ┤ h ├──┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───●──»\n",
-      "     ├───┤╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮»\n",
-      "q3 : ┤ x ├┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├»\n",
-      "     ╰───╯╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯»\n",
-      "q4 : ────────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼──────»\n",
-      "               ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      »\n",
-      "q5 : ──────────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────»\n",
-      "               ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         »\n",
-      "q6 : ─────────────────┼───┼────────────────┼───┼────────────────┼───┼─────────»\n",
-      "                    ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │         »\n",
-      "q7 : ───────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─────────»\n",
-      "                    ╰───╯                ╰───╯                ╰───╯           »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                                                                            »\n",
-      "────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                            »\n",
-      "────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                            »\n",
-      "──●────●───●──●───●────●────●───●──●───●────●────●───●──●───●────●────●───●─»\n",
-      "  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │ »\n",
-      "──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳─»\n",
-      "  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │ »\n",
-      "──┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼─»\n",
-      "╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │ »\n",
-      "┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼─»\n",
-      "╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │ »\n",
-      "───────┼───┼────────────────┼───┼────────────────┼───┼────────────────┼───┼─»\n",
-      "     ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │ »\n",
-      "─────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─»\n",
-      "     ╰───╯                ╰───╯                ╰───╯                ╰───╯   »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                           ╭─────────────╮╭────────────╮╭───╮\n",
-      "───────────────────────────┤ r1(-0.7854) ├┤ r1(-1.571) ├┤ h ├\n",
-      "        ╭────────────╮╭───╮╰──────┬──────╯╰─────┬──────╯╰───╯\n",
-      "────────┤ r1(-1.571) ├┤ h ├───────┼─────────────●────────────\n",
-      "   ╭───╮╰─────┬──────╯╰───╯       │                          \n",
-      "─●─┤ h ├──────●───────────────────●──────────────────────────\n",
-      " │ ╰───╯                                                     \n",
-      "─╳───────────────────────────────────────────────────────────\n",
-      " │                                                           \n",
-      "─┼───────────────────────────────────────────────────────────\n",
-      " │                                                           \n",
-      "─╳───────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "─────────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "─────────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "\n",
-      "Measurement results for a=5 and N=21 with 3 qubits in the control register \n",
-      "{ 000:2843 010:913 111:1850 001:1935 011:1830 100:2846 101:1861 110:922 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "control_register_size = 3\n",
-    "work_register_size = 5\n",
-    "values_for_a = [4, 5]\n",
-    "idx = 1  # change to 1 to select 5\n",
-    "N = 21\n",
-    "shots = 15000\n",
-    "\n",
-    "print(\n",
-    "    cudaq.draw(phase_kernel, control_register_size, work_register_size,\n",
-    "               values_for_a[idx], N))\n",
-    "\n",
-    "results = cudaq.sample(phase_kernel,\n",
-    "                       control_register_size,\n",
-    "                       work_register_size,\n",
-    "                       values_for_a[idx],\n",
-    "                       N,\n",
-    "                       shots_count=shots)\n",
-    "print(\n",
-    "    \"Measurement results for a={} and N={} with {} qubits in the control register \"\n",
-    "    .format(values_for_a[idx], N, control_register_size))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4718d04f",
-   "metadata": {},
-   "source": [
-    "### Determining the order from the measurement results of the phase kernel\n",
-    "\n",
-    "The measurement results from sampling the `phase_kernel` can be used to estimate the phase of the operator $U|x\\rangle = ax\\mod N$. We will only be interested in the most probable non-zero outcomes of the sampling.  Therefore, we'll create a function `top_results` to extract those values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "id": "9e96846f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def top_results(sample_results, zeros, threshold):\n",
-    "    \"\"\"Function to output the non-zero results whose counts are above the given threshold\n",
-    "    Returns\n",
-    "    -------\n",
-    "        dict[str, int]: keys are bit-strings and values are the respective counts  \n",
-    "    \"\"\"\n",
-    "    results_dictionary = {k: v for k, v in sample_results.items()}\n",
-    "    if zeros in results_dictionary.keys():\n",
-    "        results_dictionary.pop(zeros)\n",
-    "    sorted_results = {\n",
-    "        k: v for k, v in sorted(\n",
-    "            results_dictionary.items(), key=lambda item: item[1], reverse=True)\n",
-    "    }\n",
-    "    top_key = next(iter(sorted_results))\n",
-    "    max_value = sorted_results[top_key]\n",
-    "    top_results_dictionary = {top_key: max_value}\n",
-    "\n",
-    "    for key in sorted_results:\n",
-    "        if results_dictionary[key] > min(threshold, max_value):\n",
-    "            top_results_dictionary[key] = results_dictionary[key]\n",
-    "    return top_results_dictionary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b55ab5a",
-   "metadata": {},
-   "source": [
-    "Let's see how the `top_results` extracts the top results from the measurement results for a=4 and N=21 with 3 qubits in the control register that we computed above.  One of these top result is likely to be an estimate for the phase of $U$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "id": "cb717231",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'100': 2846,\n",
-       " '001': 1935,\n",
-       " '101': 1861,\n",
-       " '111': 1850,\n",
-       " '011': 1830,\n",
-       " '110': 922,\n",
-       " '010': 913}"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Example\n",
-    "top_results(results, '000', 750)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cda8137a",
-   "metadata": {},
-   "source": [
-    "The algorithm for translating the phase estimate of the operator $U|x\\rangle = ax\\mod N$ into the order of $a \\mod N$ involves continued fractions. The function `find_order_quantum` below carries out this computation.  To learn more about how the phase of $U$ relates to the period of $a \\mod N$, check out these three lectures by Scott Aaronson: lecture [19](https://www.scottaaronson.com/qclec/19.pdf), [20](https://www.scottaaronson.com/qclec/20.pdf), [21](https://www.scottaaronson.com/qclec/21.pdf)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "id": "a562fde9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_order_from_phase(phase, phase_nbits, a, N):\n",
-    "    \"\"\"Uses continued fractions to find the order of a mod N  \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    phase: int\n",
-    "        Integer result from the phase estimate of U|x> = ax mod N\n",
-    "    phase_nbits: int\n",
-    "        Number of qubits used to estimate the phase\n",
-    "    a: int\n",
-    "        For this demonstration a is either 4 or 5\n",
-    "    N: int\n",
-    "        For this demonstration N = 21\n",
-    "    Returns\n",
-    "    -------\n",
-    "    int: period of a mod N if found, otherwise returns None\n",
-    "    \"\"\"\n",
-    "\n",
-    "    assert phase_nbits > 0\n",
-    "    assert a > 0\n",
-    "    assert N > 0\n",
-    "\n",
-    "    eigenphase = float(phase) / 2**(phase_nbits)\n",
-    "\n",
-    "    f = fractions.Fraction.from_float(eigenphase).limit_denominator(N)\n",
-    "\n",
-    "    if f.numerator == 1:\n",
-    "        return None\n",
-    "    eigenphase = float(f.numerator / f.denominator)\n",
-    "    print('eigenphase is ', eigenphase)\n",
-    "    coefficients_continued_fraction = list(\n",
-    "        contfrac.continued_fraction(eigenphase))\n",
-    "\n",
-    "    convergents_continued_fraction = list(contfrac.convergents(eigenphase))\n",
-    "    print('convergent sequence of fractions for this eigenphase is',\n",
-    "          convergents_continued_fraction)\n",
-    "    for r in convergents_continued_fraction:\n",
-    "        print(\n",
-    "            'using the denominators of the fractions in the convergent sequence, testing order =',\n",
-    "            r[1])\n",
-    "        if a**r[1] % N == 1:\n",
-    "            print('Found order:', r[1])\n",
-    "            return (r[1])\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47511fbe",
-   "metadata": {},
-   "source": [
-    "We are now ready to combine all the elements above into a function to find the order of $a$ $\\mod N$ and test it in the factoring algoithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "7ad0abcf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_order_quantum(a, N):\n",
-    "    \"\"\"The quantum algorithm to find the order of a mod N, when x = 4 or x =5 and N = 21\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "        For this demonstration a will be either 4 or 5\n",
-    "    N: int\n",
-    "        For this demonstration N will be 21\n",
-    "    Returns\n",
-    "    r: int the period if it is found, or None if no period is found\n",
-    "    -------\n",
-    "    \n",
-    "    \"\"\"\n",
-    "\n",
-    "    if (a == 4 and N == 21) or (a == 5 and N == 21):\n",
-    "        shots = 15000\n",
-    "        if a == 4 and N == 21:\n",
-    "            control_register_size = 3\n",
-    "            work_register_size = 2\n",
-    "        if a == 5 and N == 21:\n",
-    "            control_register_size = 5\n",
-    "            work_register_size = 5\n",
-    "\n",
-    "        #cudaq.set_random_seed(123)\n",
-    "        results = cudaq.sample(phase_kernel,\n",
-    "                               control_register_size,\n",
-    "                               work_register_size,\n",
-    "                               a,\n",
-    "                               N,\n",
-    "                               shots_count=shots)\n",
-    "        print(\"Measurement results:\")\n",
-    "        print(results)\n",
-    "\n",
-    "        # We will want to ignore the all zero result\n",
-    "        zero_result = ''.join(\n",
-    "            [str(elem) for elem in [0] * control_register_size])\n",
-    "        # We'll only consider the top results from the sampling\n",
-    "        threshold = shots * (.1)\n",
-    "        most_probable_bitpatterns = top_results(results, zero_result, threshold)\n",
-    "\n",
-    "        for key in most_probable_bitpatterns:\n",
-    "            # Convert the key bit string into an integer\n",
-    "            # This integer divided by 8 is an estimate for the phase\n",
-    "            reverse_result = key[::-1]\n",
-    "            phase = int(reverse_result, 2)\n",
-    "\n",
-    "            print(\"Trying nonzero bitpattern from the phase estimation:\", key,\n",
-    "                  \"=\", phase)\n",
-    "            r = get_order_from_phase(phase, control_register_size, a, N)\n",
-    "            if r == None:\n",
-    "                print('No period found.')\n",
-    "\n",
-    "                continue\n",
-    "\n",
-    "            return r\n",
-    "            break\n",
-    "    else:\n",
-    "        print(\n",
-    "            \"A different quantum kernel is required for this choice of a and N.\"\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "id": "79927898",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trying a = 5\n",
-      "Measurement results:\n",
-      "{ 11010:452 11001:92 11101:47 00000:2485 01001:96 00100:131 01011:1747 00101:1654 10101:1736 00110:475 11111:23 01000:50 01101:38 01110:35 01010:420 11110:22 01111:26 00011:50 01100:120 10011:35 00010:25 00001:22 11011:1710 00111:79 11100:113 10001:14 10010:29 10100:129 10110:467 10000:2536 10111:81 11000:61 }\n",
-      "\n",
-      "Trying nonzero bitpattern from the phase estimation: 10000 = 1\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 01011 = 26\n",
-      "eigenphase is  0.8125\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (4, 5), (13, 16)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 5\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 16\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 10101 = 21\n",
-      "eigenphase is  0.65\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (1, 2), (2, 3), (13, 20)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 2\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 3\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 20\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 11011 = 27\n",
-      "eigenphase is  0.8421052631578947\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (5, 6), (16, 19)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 6\n",
-      "Found order: 6\n",
-      "The order of a =5 is 6\n",
-      "Found factors of N = 21: 7 and 3\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(7, 3)"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_integer = 21\n",
-    "initial_value_to_start = 5  # Try replacing 5 with 4\n",
-    "quantum = True\n",
-    "shors_algorithm(my_integer, initial_value_to_start, quantum)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c243a5ee",
-   "metadata": {},
-   "source": [
-    "### Postscript\n",
-    "Recent [work of Oded Regev](https://arxiv.org/abs/2308.06572) improves Shor's Algorithm by reducing the number of gates needed.  You can read more about it [here](https://www.quantamagazine.org/thirty-years-later-a-speed-boost-for-quantum-factoring-20231017/)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/trotter.html b/pr-2308/applications/python/trotter.html
deleted file mode 100644
index 32d61703e1..0000000000
--- a/pr-2308/applications/python/trotter.html
+++ /dev/null
@@ -1,954 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
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-  <section id="Computing-Magnetization-With-The-Suzuki-Trotter-Approximation">
-<h1>Computing Magnetization With The Suzuki-Trotter Approximation<a class="headerlink" href="#Computing-Magnetization-With-The-Suzuki-Trotter-Approximation" title="Permalink to this heading">¶</a></h1>
-<p>A key application for quantum computers is the simulation of quantum systems. This tutorial will demonstrate a CUDA-Q implementation of a Suzuki-Trotter simulation of a Heisenberg Model Hamiltonian to compute the magnetization of a spin chain.</p>
-<p>This example takes advantage of CUDA-Q’s state handling abilities to perform the recursive Trotter simulations.</p>
-<section id="Problem-Setup">
-<h2>Problem Setup<a class="headerlink" href="#Problem-Setup" title="Permalink to this heading">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-</pre></div>
-</div>
-</div>
-<p>The goal of this problem is to estimate the time evolution of an initial quantum state, governed by a Hamiltonian, and then compute the average magnetization fo the final state. The time evolution can be approximated using the Trotter method:</p>
-<div class="math notranslate nohighlight">
-\[e^{-iHt} \approx \prod_{n=0}^N e^{\frac{-iHt}{n}}\]</div>
-<p>The Heisenberg Hamiltonian is defined below, parameterized by these predefined constants: <span class="math notranslate nohighlight">\(g\)</span>, <span class="math notranslate nohighlight">\(Jx\)</span>, <span class="math notranslate nohighlight">\(Jy\)</span>, and <span class="math notranslate nohighlight">\(\omega = 2\pi\)</span>. The time step <span class="math notranslate nohighlight">\(dt\)</span> is selected for this problem as well as <code class="docutils literal notranslate"><span class="pre">n_steps</span></code> and <code class="docutils literal notranslate"><span class="pre">n_spins</span></code> (<span class="math notranslate nohighlight">\(N\)</span>), determining the size of the simulation. The default setup considers 11 spins and 10 steps, which can easily be simulated on a CPU. If you have access to at least one GPU, the problem can be increased to around 25 steps and 100 time steps.</p>
-<div class="math notranslate nohighlight">
-\[H = \sum_{j=1}^{N}(J_x\sigma^x_j\sigma^x_{j+1} + J_x\sigma^y_j\sigma^y_{j+1} + J_x\sigma^z_j\sigma^z_{j+1} + \cos(\omega * t)\sigma^x_j   )\]</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">g</span> <span class="o">=</span> <span class="mf">1.0</span>
-<span class="n">Jx</span> <span class="o">=</span> <span class="mf">1.0</span>
-<span class="n">Jy</span> <span class="o">=</span> <span class="mf">1.0</span>
-<span class="n">Jz</span> <span class="o">=</span> <span class="n">g</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="mf">0.05</span>
-<span class="n">n_steps</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">n_spins</span> <span class="o">=</span> <span class="mi">11</span>
-<span class="n">omega</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-
-
-<span class="k">def</span> <span class="nf">heisenbergModelHam</span><span class="p">(</span><span class="n">t</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">:</span>
-    <span class="n">tdOp</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">(</span><span class="n">num_qubits</span><span class="o">=</span><span class="n">n_spins</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_spins</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">tdOp</span> <span class="o">+=</span> <span class="p">(</span><span class="n">Jx</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">tdOp</span> <span class="o">+=</span> <span class="p">(</span><span class="n">Jy</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">tdOp</span> <span class="o">+=</span> <span class="p">(</span><span class="n">Jz</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_spins</span><span class="p">):</span>
-        <span class="n">tdOp</span> <span class="o">+=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">omega</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">tdOp</span>
-</pre></div>
-</div>
-</div>
-<p>Next, two CUDA-Q kernels are defined. The first, <code class="docutils literal notranslate"><span class="pre">getInitState</span></code>, prepares an initial state where an <span class="math notranslate nohighlight">\(X\)</span> gate is applied to the first and then every other qubit to initialize a chain of alternating spins.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">getInitState</span><span class="p">(</span><span class="n">numSpins</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">numSpins</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">qId</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numSpins</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
-        <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">qId</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<p>The second, <code class="docutils literal notranslate"><span class="pre">trotter</span></code>, performs a single Trotter step given some provided initial state and then returns the resulting state. Two notes should be made about this kernel. 1. It takes advantage of CUDA-Q’s state handling abilities, allowing each step to proceed from the previous, still stored in GPU memory during the simulation. This provides a significant speedup which is demonstrated in the plot at the end of this tutorial. 2. This kernel takes advantage of the <code class="docutils literal notranslate"><span class="pre">pauli_word</span></code> object which
-allows a list of Pauli words and their coefficients to be passed into the kernel and applied as an exponentiated matrix operation using <code class="docutils literal notranslate"><span class="pre">exp_pauli</span></code>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">trotter</span><span class="p">(</span><span class="n">state</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">State</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">complex</span><span class="p">],</span>
-            <span class="n">words</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">pauli_word</span><span class="p">],</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)):</span>
-        <span class="n">exp_pauli</span><span class="p">(</span><span class="n">coefficients</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">*</span> <span class="n">dt</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">words</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<p>The functions below are used to strip the Hamiltonian spin operator into a list of coefficients and Pauli words.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">termCoefficients</span><span class="p">(</span><span class="n">op</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">complex</span><span class="p">]:</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ham</span><span class="o">.</span><span class="n">for_each_term</span><span class="p">(</span><span class="k">lambda</span> <span class="n">term</span><span class="p">:</span> <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">term</span><span class="o">.</span><span class="n">get_coefficient</span><span class="p">()))</span>
-    <span class="k">return</span> <span class="n">result</span>
-
-
-<span class="k">def</span> <span class="nf">termWords</span><span class="p">(</span><span class="n">op</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ham</span><span class="o">.</span><span class="n">for_each_term</span><span class="p">(</span><span class="k">lambda</span> <span class="n">term</span><span class="p">:</span> <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">term</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="kc">False</span><span class="p">)))</span>
-    <span class="k">return</span> <span class="n">result</span>
-</pre></div>
-</div>
-</div>
-<p>Finally, a second spin operator is defined which will be used to compute the expectation value corresponding to the average magnetization.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">average_magnetization</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">(</span><span class="n">num_qubits</span><span class="o">=</span><span class="n">n_spins</span><span class="p">)</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_spins</span><span class="p">):</span>
-    <span class="n">average_magnetization</span> <span class="o">+=</span> <span class="p">((</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">n_spins</span><span class="p">)</span> <span class="o">*</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
-<span class="n">average_magnetization</span> <span class="o">-=</span> <span class="mf">1.0</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Running-the-Simulation">
-<h2>Running the Simulation<a class="headerlink" href="#Running-the-Simulation" title="Permalink to this heading">¶</a></h2>
-<p>Before looping through the Trotter steps, the initial state is constructed.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">getInitState</span><span class="p">,</span> <span class="n">n_spins</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, the time steps are looped through. At each step, the time dependent Hamiltonian is defined. This Hamiltonian is used to construct the Trotter kernel for that time step, which is then used to compute the average magnetization expectation value. The state is saved, and used as the initial state for the next time step.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_steps</span><span class="p">):</span>
-    <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-    <span class="n">ham</span> <span class="o">=</span> <span class="n">heisenbergModelHam</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">dt</span><span class="p">)</span>
-    <span class="n">coefficients</span> <span class="o">=</span> <span class="n">termCoefficients</span><span class="p">(</span><span class="n">ham</span><span class="p">)</span>
-    <span class="n">words</span> <span class="o">=</span> <span class="n">termWords</span><span class="p">(</span><span class="n">ham</span><span class="p">)</span>
-    <span class="n">magnetization_exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">trotter</span><span class="p">,</span> <span class="n">average_magnetization</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span>
-                                          <span class="n">coefficients</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="n">magnetization_exp_val</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-    <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-    <span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">trotter</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">coefficients</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-    <span class="n">stepTime</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span>
-    <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">stepTime</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: time [s]: </span><span class="si">{</span><span class="n">stepTime</span><span class="si">}</span><span class="s2">, result: </span><span class="si">{</span><span class="n">result</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step times: </span><span class="si">{</span><span class="n">times</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Results: </span><span class="si">{</span><span class="n">results</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Step 0: time [s]: 0.03444695472717285, result: -0.09042024163828166
-Step 1: time [s]: 0.0026793479919433594, result: -0.08898564687193886
-Step 2: time [s]: 0.002758026123046875, result: -0.08698024360923415
-Step 3: time [s]: 0.002524852752685547, result: -0.08507694741170907
-Step 4: time [s]: 0.0026259422302246094, result: -0.08394118068746997
-Step 5: time [s]: 0.002542734146118164, result: -0.08394076573115139
-Step 6: time [s]: 0.0027430057525634766, result: -0.08502222139504187
-Step 7: time [s]: 0.0025305747985839844, result: -0.08677832064885871
-Step 8: time [s]: 0.003045797348022461, result: -0.08863390649349775
-Step 9: time [s]: 0.0025949478149414062, result: -0.09005513983609514
-Step times: [0.03444695472717285, 0.0026793479919433594, 0.002758026123046875, 0.002524852752685547, 0.0026259422302246094, 0.002542734146118164, 0.0027430057525634766, 0.0025305747985839844, 0.003045797348022461, 0.0025949478149414062]
-Results: [-0.09042024163828166, -0.08898564687193886, -0.08698024360923415, -0.08507694741170907, -0.08394118068746997, -0.08394076573115139, -0.08502222139504187, -0.08677832064885871, -0.08863390649349775, -0.09005513983609514]
-</pre></div></div>
-</div>
-<p>CUDA-Q’s state handling capabilities provide a massive performance boost for this algorithm. Rather than resimulate all previous operation at any given time step, saving the previous state in GPU memory allows completion of the simulation with fewer operations. The figure below demonstrates the 24X speedup realized by a 100 step Trotter simulation.</p>
-<p><img alt="Htest" src="../../_images/statehandle.png" /></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-
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diff --git a/pr-2308/applications/python/trotter.ipynb b/pr-2308/applications/python/trotter.ipynb
deleted file mode 100644
index b508dd155c..0000000000
--- a/pr-2308/applications/python/trotter.ipynb
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "231113c7-9432-497b-91f1-c4d068dd61b8",
-   "metadata": {},
-   "source": [
-    "# Computing Magnetization With The Suzuki-Trotter Approximation\n",
-    "\n",
-    "A key application for quantum computers is the simulation of quantum systems. This tutorial will demonstrate a CUDA-Q implementation of a Suzuki-Trotter simulation of a Heisenberg Model Hamiltonian to compute the magnetization of a spin chain.\n",
-    "\n",
-    "This example takes advantage of CUDA-Q's state handling abilities to perform the recursive Trotter simulations. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ccd64e3-9e36-470b-9b15-2a7de6bf6ee3",
-   "metadata": {},
-   "source": [
-    "### Problem Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5c2633a0-979b-46b8-a7a6-44fa61bc62c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import time\n",
-    "import numpy as np\n",
-    "from typing import List"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbbf2909-2607-48d1-aa72-6594f09bbd88",
-   "metadata": {},
-   "source": [
-    "The goal of this problem is to estimate the time evolution of an initial quantum state, governed by a Hamiltonian, and then compute the average magnetization fo the final state.  The time evolution can be approximated using the Trotter method:\n",
-    "\n",
-    "$$  e^{-iHt} \\approx \\prod_{n=0}^N e^{\\frac{-iHt}{n}}$$\n",
-    "\n",
-    "The Heisenberg Hamiltonian is defined below, parameterized by these predefined constants: $g$, $Jx$, $Jy$, and $\\omega = 2\\pi$.   The time step $dt$ is selected for this problem as well as `n_steps` and `n_spins` ($N$), determining the size of the simulation.  The default setup considers 11 spins and 10 steps, which can easily be simulated on a CPU.  If you have access to at least one GPU, the problem can be increased to around 25 steps and 100 time steps.\n",
-    "\n",
-    "\n",
-    "$$ H = \\sum_{j=1}^{N}(J_x\\sigma^x_j\\sigma^x_{j+1} + J_x\\sigma^y_j\\sigma^y_{j+1} + J_x\\sigma^z_j\\sigma^z_{j+1} + \\cos(\\omega * t)\\sigma^x_j   )$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e409539e-e97b-4815-8021-477308726155",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 1.0\n",
-    "Jx = 1.0\n",
-    "Jy = 1.0\n",
-    "Jz = g\n",
-    "dt = 0.05\n",
-    "n_steps = 10\n",
-    "n_spins = 11\n",
-    "omega = 2 * np.pi\n",
-    "\n",
-    "\n",
-    "def heisenbergModelHam(t: float) -> cudaq.SpinOperator:\n",
-    "    tdOp = cudaq.SpinOperator(num_qubits=n_spins)\n",
-    "    for i in range(0, n_spins - 1):\n",
-    "        tdOp += (Jx * cudaq.spin.x(i) * cudaq.spin.x(i + 1))\n",
-    "        tdOp += (Jy * cudaq.spin.y(i) * cudaq.spin.y(i + 1))\n",
-    "        tdOp += (Jz * cudaq.spin.z(i) * cudaq.spin.z(i + 1))\n",
-    "    for i in range(0, n_spins):\n",
-    "        tdOp += (np.cos(omega * t) * cudaq.spin.x(i))\n",
-    "    return tdOp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "067ae183-8e36-4171-adaf-c8506ac2ac48",
-   "metadata": {},
-   "source": [
-    "Next, two CUDA-Q kernels are defined. The first, `getInitState`, prepares an initial state where an $X$ gate is applied to the first and then every other qubit to initialize a chain of alternating spins. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1b64301b-f967-4e69-95d7-64a0091054cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def getInitState(numSpins: int):\n",
-    "    q = cudaq.qvector(numSpins)\n",
-    "    for qId in range(0, numSpins, 2):\n",
-    "        x(q[qId])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0077726b-7c07-4699-8f05-b685d8a4d3a7",
-   "metadata": {},
-   "source": [
-    "The second, `trotter`, performs a single Trotter step given some provided initial state and then returns the resulting state.  Two notes should be made about this kernel. \n",
-    "1. It takes advantage of CUDA-Q's state handling abilities, allowing each step to proceed from the previous, still stored in GPU memory during the simulation.  This provides a significant speedup which is demonstrated in the plot at the end of this tutorial.\n",
-    "2. This kernel takes advantage of the `pauli_word` object which allows a list of Pauli words and their coefficients to be passed into the kernel and applied as an exponentiated matrix operation using `exp_pauli`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4bd5de1b-76c5-4dd7-844a-dfc598a78e6a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def trotter(state: cudaq.State, coefficients: List[complex],\n",
-    "            words: List[cudaq.pauli_word], dt: float):\n",
-    "    q = cudaq.qvector(state)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(coefficients[i].real * dt, q, words[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74593ace-c2d5-4b4b-931f-54fc4f6ee855",
-   "metadata": {},
-   "source": [
-    "The functions below are used to strip the Hamiltonian spin operator into a list of coefficients and Pauli words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0aeba077-3a12-4c1d-919e-efc521f531fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def termCoefficients(op: cudaq.SpinOperator) -> List[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "def termWords(op: cudaq.SpinOperator) -> List[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02aa14dc-bf7a-40e2-b55c-77e4e1d13d33",
-   "metadata": {},
-   "source": [
-    "Finally, a second spin operator is defined which will be used to compute the expectation value corresponding to the average magnetization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e0be9949-0d1a-44f5-bdeb-6be751dfe136",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "average_magnetization = cudaq.SpinOperator(num_qubits=n_spins)\n",
-    "for i in range(0, n_spins):\n",
-    "    average_magnetization += ((1.0 / n_spins) * cudaq.spin.z(i))\n",
-    "average_magnetization -= 1.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5527c9b-86e0-4f3d-b5cf-db887d07b375",
-   "metadata": {},
-   "source": [
-    "### Running the Simulation\n",
-    "\n",
-    "Before looping through the Trotter steps, the initial state is constructed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "78348fa1-6f14-4238-a6ec-b0c8449892d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = cudaq.get_state(getInitState, n_spins)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "812b8f44-0a96-4d43-a622-8a4b4a59241d",
-   "metadata": {},
-   "source": [
-    "Next, the time steps are looped through. At each step, the time dependent Hamiltonian is defined. This Hamiltonian is used to construct the Trotter kernel for that time step, which is then used to compute the average magnetization expectation value.  The state is saved, and used as the initial state for the next time step. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "57e4929d-9b79-4d21-b48a-27d47d7e65a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 0: time [s]: 0.03444695472717285, result: -0.09042024163828166\n",
-      "Step 1: time [s]: 0.0026793479919433594, result: -0.08898564687193886\n",
-      "Step 2: time [s]: 0.002758026123046875, result: -0.08698024360923415\n",
-      "Step 3: time [s]: 0.002524852752685547, result: -0.08507694741170907\n",
-      "Step 4: time [s]: 0.0026259422302246094, result: -0.08394118068746997\n",
-      "Step 5: time [s]: 0.002542734146118164, result: -0.08394076573115139\n",
-      "Step 6: time [s]: 0.0027430057525634766, result: -0.08502222139504187\n",
-      "Step 7: time [s]: 0.0025305747985839844, result: -0.08677832064885871\n",
-      "Step 8: time [s]: 0.003045797348022461, result: -0.08863390649349775\n",
-      "Step 9: time [s]: 0.0025949478149414062, result: -0.09005513983609514\n",
-      "Step times: [0.03444695472717285, 0.0026793479919433594, 0.002758026123046875, 0.002524852752685547, 0.0026259422302246094, 0.002542734146118164, 0.0027430057525634766, 0.0025305747985839844, 0.003045797348022461, 0.0025949478149414062]\n",
-      "Results: [-0.09042024163828166, -0.08898564687193886, -0.08698024360923415, -0.08507694741170907, -0.08394118068746997, -0.08394076573115139, -0.08502222139504187, -0.08677832064885871, -0.08863390649349775, -0.09005513983609514]\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "times = []\n",
-    "for i in range(0, n_steps):\n",
-    "    start_time = time.time()\n",
-    "    ham = heisenbergModelHam(i * dt)\n",
-    "    coefficients = termCoefficients(ham)\n",
-    "    words = termWords(ham)\n",
-    "    magnetization_exp_val = cudaq.observe(trotter, average_magnetization, state,\n",
-    "                                          coefficients, words, dt)\n",
-    "    result = magnetization_exp_val.expectation()\n",
-    "    results.append(result)\n",
-    "    state = cudaq.get_state(trotter, state, coefficients, words, dt)\n",
-    "    stepTime = time.time() - start_time\n",
-    "    times.append(stepTime)\n",
-    "    print(f\"Step {i}: time [s]: {stepTime}, result: {result}\")\n",
-    "\n",
-    "print(f\"Step times: {times}\")\n",
-    "print(f\"Results: {results}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b46a3b14-31c0-4711-9064-51ca1f48e7d4",
-   "metadata": {},
-   "source": [
-    "CUDA-Q's state handling capabilities provide a massive performance boost for this algorithm. Rather than resimulate all previous operation at any given time step, saving the previous state in GPU memory allows completion of the simulation with fewer operations. The figure below demonstrates the 24X speedup realized by a 100 step Trotter simulation.\n",
-    "\n",
-    "![Htest](./images/statehandle.png)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f59cf33e-e939-4abe-ae48-2a77f25be5a0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/unitary_compilation_diffusion_models.html b/pr-2308/applications/python/unitary_compilation_diffusion_models.html
deleted file mode 100644
index 8844e5e3e3..0000000000
--- a/pr-2308/applications/python/unitary_compilation_diffusion_models.html
+++ /dev/null
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="Compiling-Unitaries-Using-Diffusion-Models">
-<h1>Compiling Unitaries Using Diffusion Models<a class="headerlink" href="#Compiling-Unitaries-Using-Diffusion-Models" title="Permalink to this heading">¶</a></h1>
-<p>This notebook is based on the work presented in <a class="reference external" href="https://doi.org/10.1038/s42256-024-00831-9">Quantum circuit synthesis with diffusion models, Florian Fürrutter, Gorka Muñoz-Gil &amp; Hans J. Briegel , Nat. Mach. Intell. 6, 515–524 (2024)</a>.</p>
-<p>Quantum computing relies on efficiently translating quantum operations into viable physical realizations on existing quantum hardware. Recently, diffusion models — a powerful class of generative models in machine learning — have demonstrated exceptional performance on this task.</p>
-<p>In this tutorial notebook, we will demonstrate how to use this method to synthesize arbitrary unitaries into a <code class="docutils literal notranslate"><span class="pre">cudaq.kernel</span></code>, effectively decomposing them into sequences of quantum gates, a process commonly known as unitary compilation.</p>
-<p>Our focus will be on generating circuits using a pre-trained model. Specifically, we will illustrate how to compile 3-qubit unitaries.</p>
-<section id="Diffusion-model-pipeline">
-<h2>Diffusion model pipeline<a class="headerlink" href="#Diffusion-model-pipeline" title="Permalink to this heading">¶</a></h2>
-<p>Generative diffusion models (DMs) have recently delivered remarkable results across a wide range of applications, from image generation to protein folding. In this work, we leverage DMs to generate quantum circuits based on a user specified unitary matrix and a text prompt that defines the allowed gate set, effectively using DMs as unitary compilers. The method is thoroughly explained in Ref. <a class="reference external" href="https://doi.org/10.1038/s42256-024-00831-9">(Fürrutter et al., 2024)</a>. The following figure provides
-an overview of the proposed circuit generation pipeline:</p>
-<figure><p><img alt="Quantum circuit generation pipeline overview" class="no-scaled-link" src="../../_images/pipeline_simplified.png" style="width: 70%;" /></p>
-<figcaption><p>Quantum circuit generation pipeline. Figure adapted from (Fürrutter et al., 2024)</p>
-</figcaption></figure><p>The pipeline consists on 3 main components:</p>
-<p><strong>1) Circuit encoding:</strong> Like any neural network, diffusion models operate with continuous inputs and outputs. However, since the circuits we consider are composed of discrete gates (i.e., with no continuous parameters), we develop a mapping that transforms each gate into a continuous vector. This allows us to represent a given circuit as a three-dimensional tensor, as illustrated. Crucially, this mapping is invertible: when the DM generates continuous tensors, we can apply the inverse map to
-convert them back into the circuit form. An overview of these steps is provided in the figure below:</p>
-<figure><p><img alt="Quantum circuit encoding" class="no-scaled-link" src="../../_images/circuit_encoding.png" style="width: 60%;" /></p>
-<figcaption><p>Quantum circuit encoding. Figure adapted from (Fürrutter et al., 2024)</p>
-</figcaption></figure><p><strong>2) Conditioning:</strong> The user’s input (the set of available gates and the unitary to compile) is also transformed into a continuous tensor by two neural networks. For the gate set description, where the input is a text prompt (e.g., “Compile using [‘x’, ‘h’]”), we utilize a pre-trained language model. For the unitary, we employ a neural network that is training jointly with the diffusion model.</p>
-<p><strong>3) Unitary compilation:</strong> The generation procedure follows the typical DM process: the model is given a fully noisy tensor which is iteratively de-noised until reaching a clean sample based on the given conditioning (the desired unitary and gate set). The tensors generated by the DM are then mapped to circuits via the inverse encoding procedure. To learn more about the practical implementation of diffusion models we recommend <a class="reference external" href="https://course.fast.ai/Lessons/lesson9.html">this tutorial</a>.</p>
-<p>In the following we will use <code class="docutils literal notranslate"><span class="pre">CUDA-Q</span></code> and <code class="docutils literal notranslate"><span class="pre">genQC</span></code> to perform all these steps and go from a desired unitary matrix, <span class="math notranslate nohighlight">\(U\)</span>, to a quantum circuit that we can execute using CUDA-Q.</p>
-</section>
-<section id="Setup-and-compilation">
-<h2>Setup and compilation<a class="headerlink" href="#Setup-and-compilation" title="Permalink to this heading">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">genQC</span><span class="o">==</span><span class="m">0</span>.1.0<span class="w"> </span>-q
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">genQC</span>
-<span class="kn">from</span> <span class="nn">genQC.imports</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">genQC.pipeline.diffusion_pipeline</span> <span class="kn">import</span> <span class="n">DiffusionPipeline</span>
-<span class="kn">from</span> <span class="nn">genQC.inference.export_cudaq</span> <span class="kn">import</span> <span class="n">genqc_to_cudaq</span>
-<span class="kn">import</span> <span class="nn">genQC.inference.infer_compilation</span> <span class="k">as</span> <span class="nn">infer_comp</span>
-<span class="kn">import</span> <span class="nn">genQC.util</span> <span class="k">as</span> <span class="nn">util</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="c1"># Fixed seed for reproducibility</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">infer_torch_device</span><span class="p">()</span>  <span class="c1"># Use CUDA if we can</span>
-<span class="n">util</span><span class="o">.</span><span class="n">MemoryCleaner</span><span class="o">.</span><span class="n">purge_mem</span><span class="p">()</span>  <span class="c1"># Clean existing memory allocation</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[INFO]: Cuda device has a capability of 8.6 (&gt;= 8), allowing tf32 matmul.
-cuda
-</pre></div></div>
-</div>
-</section>
-<section id="Load-model">
-<h2>Load model<a class="headerlink" href="#Load-model" title="Permalink to this heading">¶</a></h2>
-<p>First, we load the pre-trained model weights and setup the DM pipeline.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pipeline</span> <span class="o">=</span> <span class="n">DiffusionPipeline</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
-    <span class="s2">&quot;Floki00/qc_unitary_3qubit&quot;</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>  <span class="c1"># Download from Hugging Face</span>
-<span class="n">pipeline</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">set_timesteps</span><span class="p">(</span><span class="mi">40</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Set parameters the model was trained on. Note that these are fixed and depend on the pre-trained model.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vocab</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span> <span class="n">gate</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">gate</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pipeline</span><span class="o">.</span><span class="n">gate_pool</span><span class="p">)</span>
-<span class="p">}</span>  <span class="c1"># Gateset used during training, used for decoding</span>
-<span class="n">num_of_qubits</span> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Number of qubits</span>
-<span class="n">max_gates</span> <span class="o">=</span> <span class="mi">12</span>  <span class="c1"># Maximum number of gates</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Unitary-compilation">
-<h2>Unitary compilation<a class="headerlink" href="#Unitary-compilation" title="Permalink to this heading">¶</a></h2>
-<p>Let’s start by defining the unitary we want to compile. Note that this model has been trained to compile unitaries that arise from circuits composed of the gates contain in <code class="docutils literal notranslate"><span class="pre">vocab</span></code>. While this is a universal gate set (i.e. can perform universal computation), it can only do so with an infinite number of gates. Because the number of gates in the model is restricted to 12, we can only expect the model to generate unitaries under this constraints. We will consider here the compilation of one of
-such unitary. Nonetheless, stay tuned for bigger and better models!</p>
-<p>We start by defining our unitary as a <code class="docutils literal notranslate"><span class="pre">numpy.matrix</span></code>:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">U</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matrix</span><span class="p">([[</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
-               <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
-               <span class="p">[</span><span class="o">-</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
-               <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
-               <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">],</span>
-               <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
-               <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">],</span>
-               <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]],</span>
-              <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>and check that the matrix is indeed unitary, i.e. <span class="math notranslate nohighlight">\(U^{\dagger} U = U U^{\dagger} = I\)</span></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">U</span><span class="o">.</span><span class="n">H</span> <span class="o">@</span> <span class="n">U</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">num_of_qubits</span><span class="p">))</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span>
-    <span class="n">U</span> <span class="o">@</span> <span class="n">U</span><span class="o">.</span><span class="n">H</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">num_of_qubits</span><span class="p">))</span>  <span class="c1">#check if unitary</span>
-</pre></div>
-</div>
-</div>
-<p>The loaded model was trained on the gate set <code class="docutils literal notranslate"><span class="pre">['h',</span> <span class="pre">'cx',</span> <span class="pre">'z',</span> <span class="pre">'x',</span> <span class="pre">'ccx',</span> <span class="pre">'swap']</span></code>. Specifically, it was trained to generate circuits using any arbitrary subset of this gate set. Therefore, during inference, we can instruct the model to compile the unitary using any of these subsets. However, it is crucial to follow the prompt structure <code class="docutils literal notranslate"><span class="pre">Compile</span> <span class="pre">using</span> <span class="pre">[...]</span></code>, as the model was trained with this specific format. For example, let’s consider a scenario where we compile the unitary without using
-the <code class="docutils literal notranslate"><span class="pre">x</span></code> gate:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Notice how the x gate missing from the prompt since this is a restriction we set</span>
-<span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;Compile using: [&#39;h&#39;, &#39;cx&#39;, &#39;z&#39;, &#39;ccx&#39;, &#39;swap&#39;]&quot;</span>
-</pre></div>
-</div>
-</div>
-<p>Now, we call the diffusion model pipeline to generate encoded circuits based on the specified conditions: <code class="docutils literal notranslate"><span class="pre">prompt</span></code> and <code class="docutils literal notranslate"><span class="pre">U</span></code>. One of the key advantages of this method is that, once the model is trained, sampling new circuits is very fast. Therefore, under the same conditions defined above, we will generate 128 circuits and analyze them in the following sections.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Number of circuits to sample from the trained DM.</span>
-<span class="n">samples</span> <span class="o">=</span> <span class="mi">128</span>
-
-<span class="c1"># As the neural network works only with real numbers, we first separate</span>
-<span class="c1"># the two components and create a 2 dimensional tensor for the magnitude</span>
-<span class="c1"># of each component:</span>
-<span class="n">U_r</span><span class="p">,</span> <span class="n">U_i</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">U</span><span class="p">)),</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">U</span><span class="p">))</span>
-<span class="n">U_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">U_r</span><span class="p">,</span> <span class="n">U_i</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="c1"># Now we generate a tensor representation of the desired quantum circuit using the DM based on the prompt and U. This is also known as inference.</span>
-<span class="n">out_tensors</span> <span class="o">=</span> <span class="n">infer_comp</span><span class="o">.</span><span class="n">generate_comp_tensors</span><span class="p">(</span>
-    <span class="n">pipeline</span><span class="o">=</span><span class="n">pipeline</span><span class="p">,</span>
-    <span class="n">prompt</span><span class="o">=</span><span class="n">prompt</span><span class="p">,</span>
-    <span class="n">U</span><span class="o">=</span><span class="n">U_tensor</span><span class="p">,</span>
-    <span class="n">samples</span><span class="o">=</span><span class="n">samples</span><span class="p">,</span>
-    <span class="n">system_size</span><span class="o">=</span>
-    <span class="n">num_of_qubits</span><span class="p">,</span>  <span class="c1"># Max qubit number allowed by the model (this model is only trained with 3 qubits)</span>
-    <span class="n">num_of_qubits</span><span class="o">=</span><span class="n">num_of_qubits</span><span class="p">,</span>
-    <span class="n">max_gates</span><span class="o">=</span><span class="n">max_gates</span><span class="p">,</span>
-    <span class="n">g</span><span class="o">=</span><span class="mi">10</span>  <span class="c1"># classifier-free-guidance (CFG) scale</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Below is an example of a generated tensor which represents a quantum circuit:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">out_tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],
-        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],
-        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])
-</pre></div></div>
-</div>
-</section>
-<section id="Convert-tensors-to-CUDA-Q">
-<h2>Convert tensors to CUDA-Q<a class="headerlink" href="#Convert-tensors-to-CUDA-Q" title="Permalink to this heading">¶</a></h2>
-<p>Next, we convert the generated tensors into <code class="docutils literal notranslate"><span class="pre">cudaq.kernels</span></code>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;qpp-cpu&#39;</span><span class="p">)</span>  <span class="c1"># Note that cpu is faster for 3 qubit kernels</span>
-
-<span class="c1"># cudaq.set_target(&#39;nvidia&#39;) # Set to GPU for larger circuits</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">kernel_list</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">valid_tensors</span> <span class="o">=</span> <span class="p">[]</span>
-
-<span class="n">invalid_tensors</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="k">for</span> <span class="n">out_tensors_i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">out_tensors</span><span class="p">):</span>
-
-    <span class="c1"># Use a try-except to catch invalid tensors (if any)</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">kernel</span> <span class="o">=</span> <span class="n">genqc_to_cudaq</span><span class="p">(</span><span class="n">out_tensors_i</span><span class="p">,</span>
-                                <span class="n">vocab</span><span class="p">)</span>  <span class="c1"># Convert out_tensors to CUDA-Q kernels</span>
-    <span class="k">except</span><span class="p">:</span>
-        <span class="n">kernel</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">if</span> <span class="n">kernel</span><span class="p">:</span>
-        <span class="n">kernel_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-        <span class="n">valid_tensors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_tensors_i</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">invalid_tensors</span> <span class="o">+=</span> <span class="mi">1</span>
-
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;The model generated </span><span class="si">{</span><span class="n">invalid_tensors</span><span class="si">}</span><span class="s2"> invalid tensors that does not correspond to circuits.&quot;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"model_id": "f7cdb66833df4aee8a75c3c7a3558ebc", "version_major": 2, "version_minor": 0}</script></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The model generated 0 invalid tensors that does not correspond to circuits.
-</pre></div></div>
-</div>
-<p>For example, the following tensor encoding</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">valid_tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],
-        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],
-        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])
-</pre></div></div>
-</div>
-<p>corresponds to the following <code class="docutils literal notranslate"><span class="pre">cudaq.kernel</span></code></p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Arbitrary input state to the circuit for plotting</span>
-
-<span class="n">input_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">num_of_qubits</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">input_state</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮╭───╮        ╭───╮╭───╮
-q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──
-     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮
-q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├
-       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯
-q2 : ──●──┤ h ├─╳───●──────────────●──
-          ╰───╯
-
-</pre></div></div>
-</div>
-</section>
-<section id="Evaluate-generated-circuits">
-<h2>Evaluate generated circuits<a class="headerlink" href="#Evaluate-generated-circuits" title="Permalink to this heading">¶</a></h2>
-<p>As mentioned earlier, one of the key advantages of using diffusion models (DMs) as a unitary compiler is the ability to rapidly sample many circuits. However, as is common in machine learning, the model has a certain accuracy, meaning not all generated circuits are expected to exactly compile the specified unitary. In this section, we will evaluate how many of the generated circuits are indeed correct and then perform post-selection to identify (at least) one circuit that successfully performs
-the desired unitary operation.</p>
-</section>
-<section id="Simulate-kernels">
-<h2>Simulate kernels<a class="headerlink" href="#Simulate-kernels" title="Permalink to this heading">¶</a></h2>
-<p>First, we calculate the unitary matrix <span class="math notranslate nohighlight">\(U\)</span> implemented by each of the kernels. The elements of this matrix are defined by the transition amplitudes between the basis states, which can be expressed as:</p>
-<div class="math notranslate nohighlight">
-\[\begin{equation}
-   \langle i|kernel|j\rangle = U_{ij},
-\end{equation}\]</div>
-<p>where <span class="math notranslate nohighlight">\(|i\rangle\)</span> and <span class="math notranslate nohighlight">\(|j\rangle\)</span> are computational basis states (typically in the <span class="math notranslate nohighlight">\(z\)</span>-basis), with <span class="math notranslate nohighlight">\(|n\rangle\)</span> representing the standard basis vector that has a 1 in the <span class="math notranslate nohighlight">\(n\)</span>-th position and 0 elsewhere.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">N</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="n">num_of_qubits</span>
-
-<span class="n">got_unitaries</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_list</span><span class="p">),</span> <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">kernel_list</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="n">got_unitaries</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
-        <span class="n">basis_state_j</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
-        <span class="n">basis_state_j</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-
-        <span class="n">got_unitaries</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span>
-                      <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">basis_state_j</span><span class="p">),</span>
-                                    <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"model_id": "2f3961ae5e254402a4dcd5124de3a5d6", "version_major": 2, "version_minor": 0}</script></div>
-</div>
-<p>For example, the circuit printed above corresponds to the following unitary:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">got_unitaries</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">4</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]
- [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]
- [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]
- [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]
- [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]
-</pre></div></div>
-</div>
-</section>
-<section id="Compare-unitaries">
-<h2>Compare unitaries<a class="headerlink" href="#Compare-unitaries" title="Permalink to this heading">¶</a></h2>
-<p>Now that we have the unitaries for each of the kernels, we compare them to the user provided unitary matrix, <code class="docutils literal notranslate"><span class="pre">U</span></code>. To do so, we compute the infidelity between the exact unitary and the generated ones. The infidelity is a value between 0 and 1, where 0 indicates that the unitaries are identical (up to a global phase).</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">infidelity</span><span class="p">(</span><span class="n">want_unitary</span><span class="p">,</span> <span class="n">got_unitary</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">want_unitary</span><span class="p">)</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">got_unitary</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="o">**</span><span class="n">num_of_qubits</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-
-
-<span class="n">infidelities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-    <span class="p">[</span><span class="n">infidelity</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">got_unitary</span><span class="p">)</span> <span class="k">for</span> <span class="n">got_unitary</span> <span class="ow">in</span> <span class="n">got_unitaries</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s now plot an histogram of the infidelities for our generated circuits:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Distribution of infidelities for </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">got_unitaries</span><span class="p">)</span><span class="si">}</span><span class="s2"> generated circuits&quot;</span><span class="p">,</span>
-    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Number of circuits&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Unitary infidelity&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">infidelities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_unitary_compilation_diffusion_models_41_0.png" src="../../_images/applications_python_unitary_compilation_diffusion_models_41_0.png" />
-</div>
-</div>
-<p>As we can see, the model generated almost 30 circuits that compile the unitary with an infidelity close or equal to zero! The kernel with the lowest infidelity is:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">min_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">infidelities</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The best kernel has an infidelity of </span><span class="si">{</span><span class="n">infidelities</span><span class="p">[</span><span class="n">min_index</span><span class="p">]</span><span class="si">:</span><span class="s2">0.2</span><span class="si">}</span><span class="s2">,&quot;</span><span class="p">)</span>
-
-<span class="n">input_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">num_of_qubits</span><span class="p">)</span>
-<span class="n">input_state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel_list</span><span class="p">[</span><span class="n">min_index</span><span class="p">],</span> <span class="n">input_state</span><span class="p">))</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;with the unitary:&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">got_unitaries</span><span class="p">[</span><span class="n">min_index</span><span class="p">],</span> <span class="mi">4</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The best kernel has an infidelity of 3.4e-09,
-     ╭───╮╭───╮        ╭───╮╭───╮
-q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──
-     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮
-q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├
-       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯
-q2 : ──●──┤ h ├─╳───●──────────────●──
-          ╰───╯
-
-with the unitary:
-[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]
- [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]
- [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]
- [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]
- [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]
-</pre></div></div>
-</div>
-<p>which, as we can see, exactly compiled our wanted unitary:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]
- [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]
- [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]
- [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]
- [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]
- [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]
-</pre></div></div>
-</div>
-</section>
-<section id="Choosing-the-circuit-you-need">
-<h2>Choosing the circuit you need<a class="headerlink" href="#Choosing-the-circuit-you-need" title="Permalink to this heading">¶</a></h2>
-<p>As we have seen above, we now have almost 30 kernels that compile the desired unitary! This is particularly valuable when dealing with hardware constraints, where, for instance, we might want to avoid using certain qubits or specific gates. One practical example is finding the circuit with the fewest Toffoli gates (also known as <code class="docutils literal notranslate"><span class="pre">ccx</span></code>). In our <code class="docutils literal notranslate"><span class="pre">vocab</span></code> definition above, we identified that <code class="docutils literal notranslate"><span class="pre">ccx</span></code> corresponds to the label 5 in our tokenized tensors. Let’s use this information to search for the
-circuit that minimizes the number of <code class="docutils literal notranslate"><span class="pre">ccx</span></code> gates:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># First, we remove possible duplicates and only pick distinct circuits</span>
-<span class="n">_</span><span class="p">,</span> <span class="n">idx_unique</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">return_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">unique_tensors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">)[</span><span class="n">idx_unique</span><span class="p">]</span>
-<span class="n">unique_infidelities</span> <span class="o">=</span> <span class="n">infidelities</span><span class="p">[</span><span class="n">idx_unique</span><span class="p">]</span>
-<span class="n">unique_kernels</span> <span class="o">=</span> <span class="p">[</span><span class="n">kernel_list</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">idx_unique</span><span class="p">]</span>
-
-<span class="c1"># Then, find the correct circuits</span>
-<span class="n">idx_correct</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">unique_infidelities</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-<span class="n">correct_tensors</span> <span class="o">=</span> <span class="n">unique_tensors</span><span class="p">[</span><span class="n">idx_correct</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;The model generated </span><span class="si">{</span><span class="n">correct_tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> distinct correct circuits.&quot;</span>
-<span class="p">)</span>
-
-<span class="c1"># Now let&#39;s flatten the last two dimensions (related to the actual circuit) and find out how many 5&#39;s (i.e. ccx) gates each circuit has:</span>
-<span class="n">num_ccx</span> <span class="o">=</span> <span class="p">(</span><span class="n">correct_tensors</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">==</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;These circuits have this number of ccx gates:&quot;</span><span class="p">,</span> <span class="n">num_ccx</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The model generated 16 distinct correct circuits.
-These circuits have this number of ccx gates: tensor([1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 1, 1, 1, 1, 1])
-</pre></div></div>
-</div>
-<p>As we can see, it appears that the diffusion model (DM) requires at least one Toffoli gate to compile the unitary. We can now print a few of these circuits to select the one that best suits our needs, or to study whether there are any interesting patterns the model employs for this specific unitary.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get the correct kernels</span>
-<span class="n">correct_kernels</span> <span class="o">=</span> <span class="p">[</span><span class="n">unique_kernels</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">idx_correct</span><span class="p">]</span>
-
-<span class="c1"># Get the ones with only one ccx</span>
-<span class="n">correct_kernels_ccx1</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">correct_kernels</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">num_ccx</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-<span class="p">]</span>
-
-<span class="c1"># Draw a few of this circuits</span>
-<span class="k">for</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">correct_kernels_ccx1</span><span class="p">[:</span><span class="mi">3</span><span class="p">]:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">input_state</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-        ╭───╮╭───╮
-q0 : ───┤ x ├┤ z ├───────●───────
-        ╰─┬─╯├───┤╭───╮  │
-q1 : ─╳───●──┤ h ├┤ z ├──┼───────
-      │   │  ├───┤╰───╯╭─┴─╮╭───╮
-q2 : ─╳───●──┤ h ├─────┤ x ├┤ h ├
-             ╰───╯     ╰───╯╰───╯
-
-     ╭───╮╭───╮
-q0 : ┤ x ├┤ z ├──●──────────
-     ╰─┬─╯├───┤╭─┴─╮
-q1 : ──●──┤ h ├┤ x ├─╳──────
-       │  ├───┤├───┤ │ ╭───╮
-q2 : ──●──┤ h ├┤ z ├─╳─┤ h ├
-          ╰───╯╰───╯   ╰───╯
-
-     ╭───╮╭───╮
-q0 : ┤ x ├┤ z ├─────●───────
-     ╰─┬─╯├───┤     │  ╭───╮
-q1 : ──●──┤ h ├─╳───┼──┤ z ├
-       │  ├───┤ │ ╭─┴─╮├───┤
-q2 : ──●──┤ h ├─╳─┤ x ├┤ h ├
-          ╰───╯   ╰───╯╰───╯
-
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;genQC Version&quot;</span><span class="p">,</span> <span class="n">genQC</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)
-genQC Version 0.1.0
-</pre></div></div>
-</div>
-<script type="application/vnd.jupyter.widget-state+json">
-{"state": {}, "version_major": 2, "version_minor": 0}
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-</section>
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diff --git a/pr-2308/applications/python/unitary_compilation_diffusion_models.ipynb b/pr-2308/applications/python/unitary_compilation_diffusion_models.ipynb
deleted file mode 100644
index 0e5d788ac2..0000000000
--- a/pr-2308/applications/python/unitary_compilation_diffusion_models.ipynb
+++ /dev/null
@@ -1,1062 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d60f1e23-2541-4b01-84c8-0aff758061b4",
-   "metadata": {},
-   "source": [
-    "# Compiling Unitaries Using Diffusion Models\n",
-    "\n",
-    "This notebook is based on the work presented in [Quantum circuit synthesis with diffusion models, Florian Fürrutter, Gorka Muñoz-Gil & Hans J. Briegel , Nat. Mach. Intell. **6**, 515–524 (2024)](https://doi.org/10.1038/s42256-024-00831-9)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b6a7722-f302-458b-9f90-8493e46d786d",
-   "metadata": {},
-   "source": [
-    "Quantum computing relies on efficiently translating quantum operations into viable physical realizations on existing quantum hardware. Recently, diffusion models — a powerful class of generative models in machine learning — have demonstrated exceptional performance on this task. \n",
-    "\n",
-    "In this tutorial notebook, we will demonstrate how to use this method to synthesize arbitrary unitaries into a `cudaq.kernel`, effectively decomposing them into sequences of quantum gates, a process commonly known as unitary compilation.\n",
-    "\n",
-    "Our focus will be on generating circuits using a pre-trained model. Specifically, we will illustrate how to compile 3-qubit unitaries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fef3bf4a-1008-4df8-b6e8-d5357db1f6d5",
-   "metadata": {},
-   "source": [
-    "### Diffusion model pipeline\n",
-    "\n",
-    "Generative diffusion models (DMs) have recently delivered remarkable results across a wide range of applications, from image generation to protein folding. In this work, we leverage DMs to generate quantum circuits based on a user specified unitary matrix and a text prompt that defines the allowed gate set, effectively using DMs as unitary compilers. The method is thoroughly explained in Ref. [(Fürrutter et al., 2024)](https://doi.org/10.1038/s42256-024-00831-9). The following figure  provides an overview of the proposed circuit generation pipeline:\n",
-    "\n",
-    "<figure>\n",
-    "  <img src=\"images/pipeline_simplified.png\" alt=\"Quantum circuit generation pipeline overview\" width=\"70%\"/>\n",
-    "  <figcaption><b>Quantum circuit generation pipeline</b>. Figure adapted from <a href=\"https://doi.org/10.1038/s42256-024-00831-9\">(Fürrutter et al., 2024)</a></figcaption>\n",
-    "</figure>\n",
-    "\n",
-    "The pipeline consists on 3 main components:\n",
-    "\n",
-    "**1) Circuit encoding:** Like any neural network, diffusion models operate with continuous inputs and outputs. However, since the circuits we consider are composed of discrete gates (i.e., with no continuous parameters), we develop a mapping that transforms each gate into a continuous vector. This allows us to represent a given circuit as a three-dimensional tensor, as illustrated. Crucially, this mapping is invertible: when the DM generates continuous tensors, we can apply the inverse map to convert them back into the circuit form. An overview of these steps is provided in the figure below:\n",
-    "\n",
-    "<figure>\n",
-    "  <img src=\"images/circuit_encoding.png\" alt=\"Quantum circuit encoding\" width=\"60%\"/>\n",
-    "  <figcaption><b>Quantum circuit encoding</b>. Figure adapted from <a href=\"https://doi.org/10.1038/s42256-024-00831-9\">(Fürrutter et al., 2024)</a></figcaption>\n",
-    "</figure>\n",
-    "\n",
-    "**2) Conditioning:** The user's input (the set of available gates and the unitary to compile) is also transformed into a continuous tensor by two neural networks. For the gate set description, where the input is a text prompt (e.g., \"Compile using ['x', 'h']\"), we utilize a pre-trained language model. For the unitary, we employ a neural network that is training jointly with the diffusion model.\n",
-    "\n",
-    "**3) Unitary compilation:** The generation procedure follows the typical DM process: the model is given a fully noisy tensor which is iteratively de-noised until reaching a clean sample based on the given conditioning (the desired unitary and gate set). The tensors generated by the DM are then mapped to circuits via the inverse encoding procedure. To learn more about the practical implementation of diffusion models we recommend [this tutorial](https://course.fast.ai/Lessons/lesson9.html). \n",
-    "\n",
-    "In the following we will use `CUDA-Q` and `genQC` to perform all these steps and go from a desired unitary matrix, $U$, to a quantum circuit that we can execute using CUDA-Q. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1453ad1-e4d4-4e38-9684-fd967bc1a8de",
-   "metadata": {},
-   "source": [
-    "### Setup and compilation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f1b7d96a-92d4-46f3-95a6-1d1e46d63240",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.037362Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.036980Z",
-     "iopub.status.idle": "2024-08-27T18:11:38.867336Z",
-     "shell.execute_reply": "2024-08-27T18:11:38.866445Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.037346Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install genQC==0.1.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "07d177c6-60fd-497d-ae2b-41e225b56a2d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.868456Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.868206Z",
-     "iopub.status.idle": "2024-08-27T18:11:38.872654Z",
-     "shell.execute_reply": "2024-08-27T18:11:38.871931Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.868440Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import genQC\n",
-    "from genQC.imports import *\n",
-    "from genQC.pipeline.diffusion_pipeline import DiffusionPipeline\n",
-    "from genQC.inference.export_cudaq import genqc_to_cudaq\n",
-    "import genQC.inference.infer_compilation as infer_comp\n",
-    "import genQC.util as util\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "\n",
-    "# Fixed seed for reproducibility\n",
-    "torch.manual_seed(0)\n",
-    "np.random.seed(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ccb97524-0f13-49ff-893d-983c572e66c6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.873619Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.873403Z",
-     "iopub.status.idle": "2024-08-27T18:11:39.025224Z",
-     "shell.execute_reply": "2024-08-27T18:11:39.024476Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.873603Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\n",
-      "cuda\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = util.infer_torch_device()  # Use CUDA if we can\n",
-    "util.MemoryCleaner.purge_mem()  # Clean existing memory allocation\n",
-    "print(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acf7656c-be92-4b36-8c38-d3ff6c1912e3",
-   "metadata": {},
-   "source": [
-    "### Load model"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "0047cbe9-a21c-4c72-a144-7a2929752329",
-   "metadata": {},
-   "source": [
-    "First, we load the pre-trained model weights and setup the DM pipeline.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a6b18d03-6976-4eec-9ab0-7b763d73ad17",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:39.026541Z",
-     "iopub.status.busy": "2024-08-27T18:11:39.025956Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.879166Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.878426Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:39.026521Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = DiffusionPipeline.from_pretrained(\n",
-    "    \"Floki00/qc_unitary_3qubit\", device)  # Download from Hugging Face\n",
-    "pipeline.scheduler.set_timesteps(40)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc112d57-71a8-403c-a0a9-d2f7d0eb7492",
-   "metadata": {},
-   "source": [
-    "Set parameters the model was trained on. Note that these are fixed and depend on the pre-trained model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3037d98c-16fb-4a7f-bf37-7e9991b6603e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.880376Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.879966Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.883994Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.883336Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.880352Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "vocab = {\n",
-    "    i + 1: gate for i, gate in enumerate(pipeline.gate_pool)\n",
-    "}  # Gateset used during training, used for decoding\n",
-    "num_of_qubits = 3  # Number of qubits\n",
-    "max_gates = 12  # Maximum number of gates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d7cc1c9-7ddd-4464-98b8-8dbcaa02a53e",
-   "metadata": {},
-   "source": [
-    "### Unitary compilation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b962aea8-9c46-4795-8b74-bca35900f1ed",
-   "metadata": {},
-   "source": [
-    "Let's start by defining the unitary we want to compile. Note that this model has been trained to compile unitaries that arise from circuits composed of the gates contain in `vocab`. While this is a universal gate set (i.e. can perform universal computation), it can only do so with an infinite number of gates. Because the number of gates in the model is restricted to 12, we can only expect the model to generate unitaries under this constraints. We will consider here the compilation of one of such unitary. Nonetheless, stay tuned for bigger and better models!\n",
-    "\n",
-    "We start by defining our unitary as a `numpy.matrix`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5107c719-4220-4209-a657-4a9c0766b80a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.884880Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.884694Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.896385Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.895667Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.884863Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "U = np.matrix([[0.70710678, 0., 0., 0., 0.70710678, 0., 0., 0.],\n",
-    "               [0., -0.70710678, 0., 0., 0., -0.70710678, 0., 0.],\n",
-    "               [-0.70710678, 0., 0., 0., 0.70710678, 0., 0., 0.],\n",
-    "               [0., 0.70710678, 0., 0., 0., -0.70710678, 0., 0.],\n",
-    "               [0., 0., 0.70710678, 0., 0., 0., 0., 0.70710678],\n",
-    "               [0., 0., 0., 0.70710678, 0., 0., 0.70710678, 0.],\n",
-    "               [0., 0., -0.70710678, 0., 0., 0., 0., 0.70710678],\n",
-    "               [0., 0., 0., -0.70710678, 0., 0., 0.70710678, 0.]],\n",
-    "              dtype=np.complex128)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14954138-f16b-48f6-b35d-5b1ac00cff1e",
-   "metadata": {},
-   "source": [
-    "and check that the matrix is indeed unitary, i.e. $U^{\\dagger} U = U U^{\\dagger} = I$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "57164622-8359-4c58-b4d9-1b64ae603b43",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.897692Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.897368Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.904008Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.903243Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.897677Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "assert np.allclose(U.H @ U, np.identity(2**num_of_qubits)) and np.allclose(\n",
-    "    U @ U.H, np.identity(2**num_of_qubits))  #check if unitary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0ba07c5-075a-46ca-9a22-cc758ce33cb9",
-   "metadata": {},
-   "source": [
-    "The loaded model was trained on the gate set `['h', 'cx', 'z', 'x', 'ccx', 'swap']`. Specifically, it was trained to generate circuits using any arbitrary subset of this gate set. Therefore, during inference, we can instruct the model to compile the unitary using any of these subsets. However, it is crucial to follow the prompt structure `Compile using [...]`, as the model was trained with this specific format. For example, let's consider a scenario where we compile the unitary without using the `x` gate:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0f0d3e85-a22e-4a1b-ad17-b02417e56734",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.905214Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.904884Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.912812Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.911995Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.905195Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Notice how the x gate missing from the prompt since this is a restriction we set\n",
-    "prompt = \"Compile using: ['h', 'cx', 'z', 'ccx', 'swap']\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cab3a1e8-a404-4ebb-9a58-251d47eebb4a",
-   "metadata": {},
-   "source": [
-    "Now, we call the diffusion model pipeline to generate encoded circuits based on the specified conditions: `prompt` and `U`. One of the key advantages of this method is that, once the model is trained, sampling new circuits is very fast. Therefore, under the same conditions defined above, we will generate 128 circuits and analyze them in the following sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "56edb114-3ef7-455b-b4b8-0e262e7b6316",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.913747Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.913584Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.824624Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.823799Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.913732Z"
-    }
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-   "outputs": [],
-   "source": [
-    "# Number of circuits to sample from the trained DM.\n",
-    "samples = 128\n",
-    "\n",
-    "# As the neural network works only with real numbers, we first separate\n",
-    "# the two components and create a 2 dimensional tensor for the magnitude\n",
-    "# of each component:\n",
-    "U_r, U_i = torch.Tensor(np.real(U)), torch.Tensor(np.imag(U))\n",
-    "U_tensor = torch.stack([U_r, U_i], dim=0)\n",
-    "\n",
-    "# Now we generate a tensor representation of the desired quantum circuit using the DM based on the prompt and U. This is also known as inference.\n",
-    "out_tensors = infer_comp.generate_comp_tensors(\n",
-    "    pipeline=pipeline,\n",
-    "    prompt=prompt,\n",
-    "    U=U_tensor,\n",
-    "    samples=samples,\n",
-    "    system_size=\n",
-    "    num_of_qubits,  # Max qubit number allowed by the model (this model is only trained with 3 qubits)\n",
-    "    num_of_qubits=num_of_qubits,\n",
-    "    max_gates=max_gates,\n",
-    "    g=10  # classifier-free-guidance (CFG) scale\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81f09d75-a960-4456-bdb9-57ef7668864e",
-   "metadata": {},
-   "source": [
-    "Below is an example of a generated tensor which represents a quantum circuit: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "13c1af73-11bc-46ca-99dd-bccea29f95c1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:41.825914Z",
-     "iopub.status.busy": "2024-08-27T18:11:41.825524Z",
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-     "shell.execute_reply.started": "2024-08-27T18:11:41.825898Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],\n",
-       "        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],\n",
-       "        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out_tensors[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5605d0a1-626d-4aa5-8721-0416470215dc",
-   "metadata": {},
-   "source": [
-    "### Convert tensors to CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b43d006-1e6a-4911-9283-b5a96eb5cf06",
-   "metadata": {},
-   "source": [
-    "Next, we convert the generated tensors into `cudaq.kernels`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "01efc6f7-8f0f-425f-be58-7fcaf880d0fd",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply": "2024-08-27T18:11:41.839940Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:41.832289Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')  # Note that cpu is faster for 3 qubit kernels\n",
-    "\n",
-    "# cudaq.set_target('nvidia') # Set to GPU for larger circuits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9d5b9ae5-c11e-413b-af00-4412dc86b48d",
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-     "shell.execute_reply.started": "2024-08-27T18:11:41.841726Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7cdb66833df4aee8a75c3c7a3558ebc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model generated 0 invalid tensors that does not correspond to circuits.\n"
-     ]
-    }
-   ],
-   "source": [
-    "kernel_list = []\n",
-    "valid_tensors = []\n",
-    "\n",
-    "invalid_tensors = 0\n",
-    "for out_tensors_i in tqdm(out_tensors):\n",
-    "\n",
-    "    # Use a try-except to catch invalid tensors (if any)\n",
-    "    try:\n",
-    "        kernel = genqc_to_cudaq(out_tensors_i,\n",
-    "                                vocab)  # Convert out_tensors to CUDA-Q kernels\n",
-    "    except:\n",
-    "        kernel = None\n",
-    "\n",
-    "    if kernel:\n",
-    "        kernel_list.append(kernel)\n",
-    "        valid_tensors.append(out_tensors_i)\n",
-    "    else:\n",
-    "        invalid_tensors += 1\n",
-    "\n",
-    "print(\n",
-    "    f\"The model generated {invalid_tensors} invalid tensors that does not correspond to circuits.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a5823e6-9189-46b7-974d-43a2a0cad531",
-   "metadata": {},
-   "source": [
-    "For example, the following tensor encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dc7d35c7-a7cb-4663-8220-b1030e534deb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:43.439383Z",
-     "iopub.status.busy": "2024-08-27T18:11:43.438787Z",
-     "iopub.status.idle": "2024-08-27T18:11:43.443973Z",
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-     "shell.execute_reply.started": "2024-08-27T18:11:43.439367Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],\n",
-       "        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],\n",
-       "        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "valid_tensors[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47116105-b1e1-454e-80fc-6a03061045f5",
-   "metadata": {},
-   "source": [
-    "corresponds to the following `cudaq.kernel`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4835c288-b018-49d7-b307-3ff05c2c8c56",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:43.444990Z",
-     "iopub.status.busy": "2024-08-27T18:11:43.444651Z",
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-     "shell.execute_reply": "2024-08-27T18:11:43.568320Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:43.444975Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮        ╭───╮╭───╮     \n",
-      "q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──\n",
-      "     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮\n",
-      "q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├\n",
-      "       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯\n",
-      "q2 : ──●──┤ h ├─╳───●──────────────●──\n",
-      "          ╰───╯                       \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Arbitrary input state to the circuit for plotting\n",
-    "\n",
-    "input_state = [0] * (2**num_of_qubits)\n",
-    "\n",
-    "print(cudaq.draw(kernel_list[0], input_state))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59809685-5d70-4612-8642-c4e924a1ab6c",
-   "metadata": {},
-   "source": [
-    "### Evaluate generated circuits\n",
-    "\n",
-    "As mentioned earlier, one of the key advantages of using diffusion models (DMs) as a unitary compiler is the ability to rapidly sample many circuits. However, as is common in machine learning, the model has a certain accuracy, meaning not all generated circuits are expected to exactly compile the specified unitary. In this section, we will evaluate how many of the generated circuits are indeed correct and then perform post-selection to identify (at least) one circuit that successfully performs the desired unitary operation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2685d88b-aceb-4a8c-8404-f744196d34c5",
-   "metadata": {},
-   "source": [
-    "### Simulate kernels"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f5d1e8ca-3abf-47b4-bbca-932bdff86f94",
-   "metadata": {},
-   "source": [
-    "First, we calculate the unitary matrix $U$ implemented by each of the kernels. The elements of this matrix are defined by the transition amplitudes between the basis states, which can be expressed as:\n",
-    "$$\n",
-    "\\begin{equation}\n",
-    "   \\langle i|kernel|j\\rangle = U_{ij},\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "where $|i\\rangle$ and $|j\\rangle$ are computational basis states (typically in the $z$-basis), with $|n\\rangle$ representing the standard basis vector that has a 1 in the $n$-th position and 0 elsewhere."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "11143924-c33f-4fd5-8f04-422eba9ed5a0",
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-    "execution": {
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-     "shell.execute_reply.started": "2024-08-27T18:11:43.570011Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f3961ae5e254402a4dcd5124de3a5d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "N = 2**num_of_qubits\n",
-    "\n",
-    "got_unitaries = np.zeros((len(kernel_list), N, N), dtype=np.complex128)\n",
-    "\n",
-    "for i, kernel in tqdm(enumerate(kernel_list), total=got_unitaries.shape[0]):\n",
-    "    for j in range(N):\n",
-    "        basis_state_j = np.zeros((N), dtype=np.complex128)\n",
-    "        basis_state_j[j] = 1\n",
-    "\n",
-    "        got_unitaries[i, :,\n",
-    "                      j] = np.array(cudaq.get_state(kernel, basis_state_j),\n",
-    "                                    copy=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5eb62ba-40a6-486c-b307-47b11b9b3226",
-   "metadata": {},
-   "source": [
-    "For example, the circuit printed above corresponds to the following unitary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4a471612-a3e4-4bea-9f61-dc823283af95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.875219Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.874792Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.879735Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.878882Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.875200Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.set_printoptions(linewidth=1000)\n",
-    "print(np.round(got_unitaries[0], 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "203def54-bb97-4344-8544-6b1374b3f9a3",
-   "metadata": {},
-   "source": [
-    "### Compare unitaries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5241775-2692-4b10-b9ef-a75a3ddb0eb8",
-   "metadata": {},
-   "source": [
-    "Now that we have the unitaries for each of the kernels, we compare them to the user provided unitary matrix, `U`. To do so, we compute the infidelity between the exact unitary and the generated ones. The infidelity is a value between 0 and 1, where 0 indicates that the unitaries are identical (up to a global phase)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "bb4915e8-5a92-4038-ab49-fa67a0a5b668",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.880925Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.880574Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.891293Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.890501Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.880909Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def infidelity(want_unitary, got_unitary):\n",
-    "    return 1 - np.abs(\n",
-    "        np.trace(np.conj(want_unitary).T @ got_unitary) / 2**num_of_qubits)**2\n",
-    "\n",
-    "\n",
-    "infidelities = np.array(\n",
-    "    [infidelity(U, got_unitary) for got_unitary in got_unitaries])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2ae9d89-3aab-43db-85cb-2aa6fc494d87",
-   "metadata": {},
-   "source": [
-    "Let's now plot an histogram of the infidelities for our generated circuits:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c8313728-3a69-475b-bf3c-f0a8b8e8324a",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply.started": "2024-08-27T18:11:55.892404Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "plt.title(\n",
-    "    f\"Distribution of infidelities for {len(got_unitaries)} generated circuits\",\n",
-    "    fontsize=12)\n",
-    "plt.ylabel(\"Number of circuits\", fontsize=14)\n",
-    "plt.xlabel(\"Unitary infidelity\", fontsize=14)\n",
-    "plt.hist(infidelities, bins=30)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1912f6c-e685-4994-bf3a-fe9530dbd513",
-   "metadata": {},
-   "source": [
-    "As we can see, the model generated almost 30 circuits that compile the unitary with an infidelity close or equal to zero! The kernel with the lowest infidelity is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "656baf92-5a1e-4227-ac1f-67b5f9394310",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.983493Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.983309Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.988444Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.987556Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.983478Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The best kernel has an infidelity of 3.4e-09,\n",
-      "     ╭───╮╭───╮        ╭───╮╭───╮     \n",
-      "q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──\n",
-      "     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮\n",
-      "q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├\n",
-      "       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯\n",
-      "q2 : ──●──┤ h ├─╳───●──────────────●──\n",
-      "          ╰───╯                       \n",
-      "\n",
-      "with the unitary:\n",
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "min_index = np.argmin(infidelities)\n",
-    "\n",
-    "print(f\"The best kernel has an infidelity of {infidelities[min_index]:0.2},\")\n",
-    "\n",
-    "input_state = [0] * (2**num_of_qubits)\n",
-    "input_state[0] = 1\n",
-    "print(cudaq.draw(kernel_list[min_index], input_state))\n",
-    "\n",
-    "print(f\"with the unitary:\")\n",
-    "print(np.round(got_unitaries[min_index], 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e724b39-cc63-40c8-8ad0-9d90d94e23bd",
-   "metadata": {},
-   "source": [
-    "which, as we can see, exactly compiled our wanted unitary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "46d190d6-7bed-4490-a343-bf856190a101",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.989293Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.989114Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.996387Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.995546Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.989278Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.round(U, 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8bf2fe6-07ca-4bfa-a46b-96a91daeb185",
-   "metadata": {},
-   "source": [
-    "### Choosing the circuit you need"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "983dc8c1-f3ae-4d06-ad7a-e57addfc6e74",
-   "metadata": {},
-   "source": [
-    "As we have seen above, we now have almost 30 kernels that compile the desired unitary! This is particularly valuable when dealing with hardware constraints, where, for instance, we might want to avoid using certain qubits or specific gates. One practical example is finding the circuit with the fewest Toffoli gates (also known as `ccx`). In our `vocab` definition above, we identified that `ccx` corresponds to the label 5 in our tokenized tensors. Let's use this information to search for the circuit that minimizes the number of `ccx` gates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "acb0f4d8-2e8a-4b32-9e37-006571ca2aa5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.997443Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.997033Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.005395Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.004630Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.997427Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model generated 16 distinct correct circuits.\n",
-      "These circuits have this number of ccx gates: tensor([1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 1, 1, 1, 1, 1])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, we remove possible duplicates and only pick distinct circuits\n",
-    "_, idx_unique = np.unique(np.array(valid_tensors), axis=0, return_index=True)\n",
-    "unique_tensors = torch.stack(valid_tensors)[idx_unique]\n",
-    "unique_infidelities = infidelities[idx_unique]\n",
-    "unique_kernels = [kernel_list[idx] for idx in idx_unique]\n",
-    "\n",
-    "# Then, find the correct circuits\n",
-    "idx_correct = torch.argwhere(torch.tensor(unique_infidelities) < 0.01).flatten()\n",
-    "correct_tensors = unique_tensors[idx_correct]\n",
-    "print(\n",
-    "    f\"The model generated {correct_tensors.shape[0]} distinct correct circuits.\"\n",
-    ")\n",
-    "\n",
-    "# Now let's flatten the last two dimensions (related to the actual circuit) and find out how many 5's (i.e. ccx) gates each circuit has:\n",
-    "num_ccx = (correct_tensors.flatten(1, 2) == 5).sum(1)\n",
-    "print(\"These circuits have this number of ccx gates:\", num_ccx)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7625aac1-78d8-4ce3-9765-88ae48c92dd3",
-   "metadata": {},
-   "source": [
-    "As we can see, it appears that the diffusion model (DM) requires at least one Toffoli gate to compile the unitary. We can now print a few of these circuits to select the one that best suits our needs, or to study whether there are any interesting patterns the model employs for this specific unitary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "535501a6-cafb-48e6-b53c-636d7ac5a815",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:56.006584Z",
-     "iopub.status.busy": "2024-08-27T18:11:56.006136Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.013882Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.013117Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:56.006569Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        ╭───╮╭───╮               \n",
-      "q0 : ───┤ x ├┤ z ├───────●───────\n",
-      "        ╰─┬─╯├───┤╭───╮  │       \n",
-      "q1 : ─╳───●──┤ h ├┤ z ├──┼───────\n",
-      "      │   │  ├───┤╰───╯╭─┴─╮╭───╮\n",
-      "q2 : ─╳───●──┤ h ├─────┤ x ├┤ h ├\n",
-      "             ╰───╯     ╰───╯╰───╯\n",
-      "\n",
-      "     ╭───╮╭───╮             \n",
-      "q0 : ┤ x ├┤ z ├──●──────────\n",
-      "     ╰─┬─╯├───┤╭─┴─╮        \n",
-      "q1 : ──●──┤ h ├┤ x ├─╳──────\n",
-      "       │  ├───┤├───┤ │ ╭───╮\n",
-      "q2 : ──●──┤ h ├┤ z ├─╳─┤ h ├\n",
-      "          ╰───╯╰───╯   ╰───╯\n",
-      "\n",
-      "     ╭───╮╭───╮             \n",
-      "q0 : ┤ x ├┤ z ├─────●───────\n",
-      "     ╰─┬─╯├───┤     │  ╭───╮\n",
-      "q1 : ──●──┤ h ├─╳───┼──┤ z ├\n",
-      "       │  ├───┤ │ ╭─┴─╮├───┤\n",
-      "q2 : ──●──┤ h ├─╳─┤ x ├┤ h ├\n",
-      "          ╰───╯   ╰───╯╰───╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the correct kernels\n",
-    "correct_kernels = [unique_kernels[idx] for idx in idx_correct]\n",
-    "\n",
-    "# Get the ones with only one ccx\n",
-    "correct_kernels_ccx1 = [\n",
-    "    correct_kernels[idx] for idx in torch.argwhere(num_ccx == 1).flatten()\n",
-    "]\n",
-    "\n",
-    "# Draw a few of this circuits\n",
-    "for kernel in correct_kernels_ccx1[:3]:\n",
-    "    print(cudaq.draw(kernel, input_state))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "dd32819f-8bb6-4af8-bf69-84b600ba7ad5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:56.020863Z",
-     "iopub.status.busy": "2024-08-27T18:11:56.020639Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.027045Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.026361Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:56.020849Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n",
-      "genQC Version 0.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)\n",
-    "print(\"genQC Version\", genQC.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/applications/python/vqe.html b/pr-2308/applications/python/vqe.html
deleted file mode 100644
index d09408d074..0000000000
--- a/pr-2308/applications/python/vqe.html
+++ /dev/null
@@ -1,928 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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-    <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="VQE with gradients, active spaces, and gate fusion" href="vqe_advanced.html" />
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-</head>
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-  <style>
-    /* Sidebar header (and topbar for mobile) */
-    .wy-side-nav-search, .wy-nav-top {
-      background: #76b900;
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#measurements">Measurements</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="Variational-Quantum-Eigensolver">
-<h1>Variational Quantum Eigensolver<a class="headerlink" href="#Variational-Quantum-Eigensolver" title="Permalink to this heading">¶</a></h1>
-<p>A common application of the Variational Quantum Eigensolver (VQE) algorithm is to compute the ground state energy of a molecular system. The code below demonstrates how to perform classical preprocessing for a <span class="math notranslate nohighlight">\(H_2\)</span> molecule (i.e. obtain the integrals from a Hartree-Fock computation to build the molecular Hamiltonian), prepare the initial Hartree-Fock state on the quantum register, add the parameterized UCCSD ansatz to the kernel, and select the COBYLA optimizer. We are then ready to call
-<code class="docutils literal notranslate"><span class="pre">cudaq:vqe</span></code> to estimate the minimum energy of the system.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">openfermionpyscf</span><span class="o">==</span><span class="m">0</span>.5<span class="w"> </span><span class="nv">matplotlib</span><span class="o">==</span><span class="m">3</span>.8.4<span class="w"> </span><span class="nv">scipy</span><span class="o">==</span><span class="m">1</span>.13.0<span class="w"> </span>-q
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Single precision</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-<span class="c1"># Double precision</span>
-<span class="c1">#cudaq.set_target(&quot;nvidia-fp64&quot;)</span>
-</pre></div>
-</div>
-</div>
-<p>The problem of interest here is a chain of hydrogen atoms seperated along the z-axis at a fixed interval called the bond distance.</p>
-<p>The interatomic electrostatic forces due to the electrons and protons and the shielding by the neutrons creates a chemical system whose energy can be minimised to find a stable configuration.</p>
-<p>Let us first begin by defining the molecule and other metadata about the problem.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Number of hydrogen atoms.</span>
-<span class="n">hydrogen_count</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="c1"># Distance between the atoms in Angstroms.</span>
-<span class="n">bond_distance</span> <span class="o">=</span> <span class="mf">0.7474</span>
-
-<span class="c1"># Define a linear chain of Hydrogen atoms</span>
-<span class="n">geometry</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="n">bond_distance</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">hydrogen_count</span><span class="p">)]</span>
-
-<span class="n">hamiltonian</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">chemistry</span><span class="o">.</span><span class="n">create_molecular_hamiltonian</span><span class="p">(</span>
-    <span class="n">geometry</span><span class="p">,</span> <span class="s1">&#39;sto-3g&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-<span class="n">electron_count</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_electrons</span>
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">n_orbitals</span>
-</pre></div>
-</div>
-</div>
-<p>We now generate a Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz from the template provided by CUDA-Q.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">electron_count</span><span class="p">):</span>
-        <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">uccsd</span><span class="p">(</span><span class="n">qubits</span><span class="p">,</span> <span class="n">thetas</span><span class="p">,</span> <span class="n">electron_count</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">)</span>
-
-
-<span class="n">parameter_count</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">uccsd_num_parameters</span><span class="p">(</span><span class="n">electron_count</span><span class="p">,</span>
-                                                     <span class="n">qubit_count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Using-CUDA-Q-Optimizers">
-<h2>Using CUDA-Q Optimizers<a class="headerlink" href="#Using-CUDA-Q-Optimizers" title="Permalink to this heading">¶</a></h2>
-<p>We use the builtin optimizers within CUDA-Q for the minimization procedure.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">COBYLA</span><span class="p">()</span>
-
-<span class="n">energy</span><span class="p">,</span> <span class="n">parameters</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">vqe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                               <span class="n">hamiltonian</span><span class="p">,</span>
-                               <span class="n">optimizer</span><span class="p">,</span>
-                               <span class="n">parameter_count</span><span class="o">=</span><span class="n">parameter_count</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">energy</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
--1.1371744305855906
-</pre></div></div>
-</div>
-</section>
-<section id="Integration-with-Third-Party-Optimizers">
-<h2>Integration with Third-Party Optimizers<a class="headerlink" href="#Integration-with-Third-Party-Optimizers" title="Permalink to this heading">¶</a></h2>
-<p>We can also integrate popular libraries like scipy with CUDA-Q.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define a function to minimize</span>
-<span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
-
-    <span class="n">exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">exp_val</span>
-
-
-<span class="n">exp_vals</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">xk</span><span class="p">):</span>
-    <span class="n">exp_vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cost</span><span class="p">(</span><span class="n">xk</span><span class="p">))</span>
-
-
-<span class="c1"># Initial variational parameters.</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-
-<span class="c1"># Use the scipy optimizer to minimize the function of interest</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span>
-                  <span class="n">x0</span><span class="p">,</span>
-                  <span class="n">method</span><span class="o">=</span><span class="s1">&#39;COBYLA&#39;</span><span class="p">,</span>
-                  <span class="n">callback</span><span class="o">=</span><span class="n">callback</span><span class="p">,</span>
-                  <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">40</span><span class="p">})</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">exp_vals</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Energy&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;VQE&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
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diff --git a/pr-2308/applications/python/vqe.ipynb b/pr-2308/applications/python/vqe.ipynb
deleted file mode 100644
index bed99f59db..0000000000
--- a/pr-2308/applications/python/vqe.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Variational Quantum Eigensolver"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A common application of the Variational Quantum Eigensolver (VQE) algorithm is to compute the ground state energy of a molecular system. The code below demonstrates how to perform classical preprocessing for a $H_2$ molecule (i.e. obtain the integrals from a Hartree-Fock computation to build the molecular Hamiltonian), prepare the initial Hartree-Fock state on the quantum register, add the parameterized UCCSD ansatz to the kernel, and select the COBYLA optimizer.  We are then ready to call `cudaq:vqe` to estimate the minimum energy of the system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install openfermionpyscf==0.5 matplotlib==3.8.4 scipy==1.13.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import numpy as np\n",
-    "\n",
-    "# Single precision\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "# Double precision\n",
-    "#cudaq.set_target(\"nvidia-fp64\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The problem of interest here is a chain of hydrogen atoms seperated along the z-axis at a fixed interval called the bond distance. \n",
-    "\n",
-    "The interatomic electrostatic forces due to the electrons and protons and the shielding by the neutrons creates a chemical system whose energy can be minimised to find a stable configuration. \n",
-    "\n",
-    "Let us first begin by defining the molecule and other metadata about the problem.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of hydrogen atoms.\n",
-    "hydrogen_count = 2\n",
-    "\n",
-    "# Distance between the atoms in Angstroms.\n",
-    "bond_distance = 0.7474\n",
-    "\n",
-    "# Define a linear chain of Hydrogen atoms\n",
-    "geometry = [('H', (0, 0, i * bond_distance)) for i in range(hydrogen_count)]\n",
-    "\n",
-    "hamiltonian, data = cudaq.chemistry.create_molecular_hamiltonian(\n",
-    "    geometry, 'sto-3g', 1, 0)\n",
-    "\n",
-    "electron_count = data.n_electrons\n",
-    "qubit_count = 2 * data.n_orbitals"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now generate a Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz from the template provided by CUDA-Q.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(thetas: list[float]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    for i in range(electron_count):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_count, qubit_count)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using CUDA-Q Optimizers\n",
-    "\n",
-    "We use the builtin optimizers within CUDA-Q for the minimization procedure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-1.1371744305855906\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "energy, parameters = cudaq.vqe(kernel,\n",
-    "                               hamiltonian,\n",
-    "                               optimizer,\n",
-    "                               parameter_count=parameter_count)\n",
-    "\n",
-    "print(energy)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Integration with Third-Party Optimizers\n",
-    "\n",
-    "We can also integrate popular libraries like scipy with CUDA-Q. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Define a function to minimize\n",
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, np.pi, parameter_count)\n",
-    "\n",
-    "# Use the scipy optimizer to minimize the function of interest\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 40})\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
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-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/applications/python/vqe_advanced.html b/pr-2308/applications/python/vqe_advanced.html
deleted file mode 100644
index 9899847bc0..0000000000
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+++ /dev/null
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
-</ul>
-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-  <section id="VQE-with-gradients,-active-spaces,-and-gate-fusion">
-<h1>VQE with gradients, active spaces, and gate fusion<a class="headerlink" href="#VQE-with-gradients,-active-spaces,-and-gate-fusion" title="Permalink to this heading">¶</a></h1>
-<p>This tutorial will explore the Variational Quantum Eigensolver, a hybrid quantum classical algorithm for determining the ground state energy of molecules. The first part of this tutorial will walk through the key aspects of the VQE algorithm and how to implement it with CUDA-Q. The following sections explore advanced topics: parallel gradients, active spaces, and gate fusion</p>
-<section id="The-Basics-of-VQE">
-<h2>The Basics of VQE<a class="headerlink" href="#The-Basics-of-VQE" title="Permalink to this heading">¶</a></h2>
-<p>The VQE algorithm is hybrid quantum-classical algorithm, meaning some subroutines run on a quantum computer or quantum simulator and others run on a traditional (super)computer.</p>
-<p>The goal is to take a parameterized quantum circuit and a qubit form of the molecular Hamiltonian, measure an expectation value that corresponds to the ground state energy of the molecule, and then repeat the process to variationally minimize the energy with respect to the parameters in the quantum circuit. The optimization is performed on a classical device while the expectation values are determined on a quantum device. See the figure below.</p>
-<p><img alt="VQE.png" src="../../_images/VQE.png" /></p>
-<p>The next few cells will elaborate on each part of the VQE procedure and show you how to build a VQE simulation to compute the ground state energy of the water molecule.</p>
-</section>
-<section id="Installing/Loading-Relevant-Packages">
-<h2>Installing/Loading Relevant Packages<a class="headerlink" href="#Installing/Loading-Relevant-Packages" title="Permalink to this heading">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the relevant packages.</span>
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pyscf</span><span class="o">==</span><span class="m">2</span>.6.2<span class="w"> </span><span class="nv">openfermionpyscf</span><span class="o">==</span><span class="m">0</span>.5<span class="w"> </span><span class="nv">matplotlib</span><span class="o">==</span><span class="m">3</span>.8.4<span class="w"> </span><span class="nv">openfermion</span><span class="o">==</span><span class="m">1</span>.6.1<span class="w"> </span>-q
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openfermion</span>
-<span class="kn">import</span> <span class="nn">openfermionpyscf</span>
-<span class="kn">from</span> <span class="nn">openfermion.transforms</span> <span class="kn">import</span> <span class="n">jordan_wigner</span><span class="p">,</span> <span class="n">get_fermion_operator</span>
-
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Implementing-VQE-in-CUDA-Q">
-<h2>Implementing VQE in CUDA-Q<a class="headerlink" href="#Implementing-VQE-in-CUDA-Q" title="Permalink to this heading">¶</a></h2>
-<p>Like most quantum chemistry programs, the first step is to specify a molecular geometry, basis set, charge, and multiplicity.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">geometry</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;O&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.1173</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)),</span> <span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.4691</span><span class="p">,</span> <span class="mf">0.7570</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)),</span>
-            <span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.4691</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.7570</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))]</span>
-<span class="n">basis</span> <span class="o">=</span> <span class="s1">&#39;sto3g&#39;</span>
-<span class="n">multiplicity</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">charge</span> <span class="o">=</span> <span class="mi">0</span>
-</pre></div>
-</div>
-</div>
-<p>The VQE procedure requires some classical preprocessing. The code below uses the PySCF package and OpenFermion to compute the Hartree Fock reference state and compute the integrals required for the Hamiltonian.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">molecule</span> <span class="o">=</span> <span class="n">openfermionpyscf</span><span class="o">.</span><span class="n">run_pyscf</span><span class="p">(</span>
-    <span class="n">openfermion</span><span class="o">.</span><span class="n">MolecularData</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">basis</span><span class="p">,</span> <span class="n">multiplicity</span><span class="p">,</span> <span class="n">charge</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<p>Next, the Hamiltonian is built using <code class="docutils literal notranslate"><span class="pre">get_molecular_hamiltonian</span></code>. The Hamiltonian must then be converted to a qubit Hamiltonian consisting of qubit operators. The standard Jordan-Wigner transformation is used to perform this mapping.</p>
-<p>Finally, the Jordan-Wigner qubit Hamiltonian is converted into a CUDA-Q spin operator which can be used to evaluate an expectation value given a quantum circuit.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">molecular_hamiltonian</span> <span class="o">=</span> <span class="n">molecule</span><span class="o">.</span><span class="n">get_molecular_hamiltonian</span><span class="p">()</span>
-
-<span class="n">fermion_hamiltonian</span> <span class="o">=</span> <span class="n">get_fermion_operator</span><span class="p">(</span><span class="n">molecular_hamiltonian</span><span class="p">)</span>
-
-<span class="n">qubit_hamiltonian</span> <span class="o">=</span> <span class="n">jordan_wigner</span><span class="p">(</span><span class="n">fermion_hamiltonian</span><span class="p">)</span>
-
-<span class="n">spin_ham</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">(</span><span class="n">qubit_hamiltonian</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, the quantum circuit needs to be defined, which models the wavefunction. This is done in CUDA-Q by specifying a CUDA-Q kernel. The kernel takes as an input the number of qubits, the number of electrons, and the parameters of the circuit ansatz (form of the wavefunction) yet to be defined.</p>
-<p>The number of qubits corresponds to the potential positions of electrons and is therefore twice the number of spatial orbitals constructed with the chosen basis set, as each can be occupied by two electrons.</p>
-<p>The Hartree-Fock reference is constructed by applying <span class="math notranslate nohighlight">\(X\)</span> bitflip operations to each of the first <span class="math notranslate nohighlight">\(N\)</span> qubits where <span class="math notranslate nohighlight">\(N\)</span> is the number of electrons. Next, a parameterized ansatz is chosen. Theoretically, any set of operations can work as an ansatz, however, it is good practice to use an ansatz that captures the underlying physics of the problem. The most common choice for chemistry is the Unitary Coupled Cluster Ansatz with Single and Double excitations (UCCSD). This UCCSD hate
-operations are automatically added to the kernel with the <code class="docutils literal notranslate"><span class="pre">cudaq.kernels.uccsd(qubits,</span> <span class="pre">thetas,</span> <span class="pre">electron_num,</span> <span class="pre">qubit_num)</span></code> function.</p>
-<p>The STO-3G water molecuule UCCSD ansatz requires optimization of 140 parameters.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">electron_count</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="mi">7</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">electron_num</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">thetas</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">electron_num</span><span class="p">):</span>
-        <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">uccsd</span><span class="p">(</span><span class="n">qubits</span><span class="p">,</span> <span class="n">thetas</span><span class="p">,</span> <span class="n">electron_num</span><span class="p">,</span> <span class="n">qubit_num</span><span class="p">)</span>
-
-
-<span class="n">parameter_count</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">uccsd_num_parameters</span><span class="p">(</span><span class="n">electron_count</span><span class="p">,</span>
-                                                     <span class="n">qubit_count</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">parameter_count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-140
-</pre></div></div>
-</div>
-<p>The classical optimizer requires a custom cost function which is defined below. The <code class="docutils literal notranslate"><span class="pre">cudaq.observe()</span></code> function computes an expectation given the Hamiltonian and the kernel defined above.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
-
-    <span class="n">exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">spin_ham</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">electron_count</span><span class="p">,</span>
-                            <span class="n">theta</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">exp_val</span>
-
-
-<span class="n">exp_vals</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">xk</span><span class="p">):</span>
-    <span class="n">exp_vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cost</span><span class="p">(</span><span class="n">xk</span><span class="p">))</span>
-
-
-<span class="c1"># Initial variational parameters.</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The final step is to run the optimization using the scipy minimize function and a selected optimizer, in this case COBYLA.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">)</span>
-<span class="n">start_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span>
-                  <span class="n">x0</span><span class="p">,</span>
-                  <span class="n">method</span><span class="o">=</span><span class="s1">&#39;COBYLA&#39;</span><span class="p">,</span>
-                  <span class="n">callback</span><span class="o">=</span><span class="n">callback</span><span class="p">,</span>
-                  <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">50</span><span class="p">})</span>
-<span class="n">end_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;UCCSD-VQE energy =  &#39;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of qubits = &#39;</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of parameters = &#39;</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of terms in the spin hamiltonian = &#39;</span><span class="p">,</span>
-      <span class="n">spin_ham</span><span class="o">.</span><span class="n">get_term_count</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total elapsed time (s) = &#39;</span><span class="p">,</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">exp_vals</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Energy&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;VQE&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-UCCSD-VQE energy =   -70.21455023422772
-Total number of qubits =  14
-Total number of parameters =  140
-Total number of terms in the spin hamiltonian =  1086
-Total elapsed time (s) =  3.9171073289999185
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_vqe_advanced_21_1.png" src="../../_images/applications_python_vqe_advanced_21_1.png" />
-</div>
-</div>
-<p>The result of this procedure is an estimate of the ground state energy of water. However, the convergence behavior is not perfect, more iterations would greatly improve the result, but would take a few minutes to run.</p>
-</section>
-<section id="Parallel-Parameter-Shift-Gradients">
-<h2>Parallel Parameter Shift Gradients<a class="headerlink" href="#Parallel-Parameter-Shift-Gradients" title="Permalink to this heading">¶</a></h2>
-<p>One way to accelerate VQE is to use an optimizer that accepts a gradient. This can drastically lower the number of VQE iterations required at the cost of computing the gradient on the quantum side of the algorithm.</p>
-<p>The parameter shift rule is a common technique to compute the gradient for parameterized circuits. It is obtained by computing two expectation values for each parameter corresponding to a small forward and backward shift in the ith parameter. These results are used to estimate finite difference contribution to the gradient.</p>
-<p><img alt="parametershift.png" src="../../_images/parametershift.png" /></p>
-<p>This procedure can become cost prohibitive as the number of parameters becomes large.</p>
-<p>Each of the expectation values needed to evaluate a parameter shift gradient can be computed independently. The CUDA-Q <code class="docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> backend is designed for parallel computations across multiple simulated QPUs. The function below uses <code class="docutils literal notranslate"><span class="pre">cudaq.observe_asynch</span></code> to distribute all of the expectation values evaluations across as many GPUs that are available. First, try it with <code class="docutils literal notranslate"><span class="pre">num_qpus</span></code> set to 1.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia-mqpu&quot;</span><span class="p">)</span>
-
-<span class="n">num_qpus</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">()</span>
-
-<span class="n">epsilon</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">4</span>
-
-
-<span class="k">def</span> <span class="nf">batched_gradient_function</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">):</span>
-
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
-
-    <span class="n">xplus</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">epsilon</span><span class="p">)</span>
-
-    <span class="n">xminus</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">epsilon</span><span class="p">)</span>
-
-    <span class="n">g_plus</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">g_minus</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">gradients</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="n">qpu_counter</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># Iterate over the number of GPU resources available</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-
-        <span class="n">g_plus</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                <span class="n">hamiltonian</span><span class="p">,</span>
-                                <span class="n">qubit_count</span><span class="p">,</span>
-                                <span class="n">electron_count</span><span class="p">,</span>
-                                <span class="n">xplus</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
-                                <span class="n">qpu_id</span><span class="o">=</span><span class="n">qpu_counter</span><span class="o">%</span><span class="k">num_qpus</span>))
-        <span class="n">qpu_counter</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="n">g_minus</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                <span class="n">hamiltonian</span><span class="p">,</span>
-                                <span class="n">qubit_count</span><span class="p">,</span>
-                                <span class="n">electron_count</span><span class="p">,</span>
-                                <span class="n">xminus</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
-                                <span class="n">qpu_id</span><span class="o">=</span><span class="n">qpu_counter</span><span class="o">%</span><span class="k">num_qpus</span>))
-        <span class="n">qpu_counter</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="n">gradients</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">(</span><span class="n">g_plus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="o">-</span> <span class="n">g_minus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">())</span> <span class="o">/</span>
-        <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">epsilon</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">g_minus</span><span class="p">))</span>
-    <span class="p">]</span>
-
-    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">gradients</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
-        <span class="n">parameters</span><span class="p">)</span> <span class="o">==</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">xplus</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">xminus</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="k">return</span> <span class="n">gradients</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[<span style="color: rgb(255,0,0)">warning</span>] Target <span style="color: rgb(0,0,255)">nvidia-mqpu</span>: <span style="color: rgb(0,0,255)">This target is deprecating. Please use the &#39;nvidia&#39; target with option &#39;mqpu,fp32&#39; or &#39;mqpu&#39; (fp32 is the default precision option) by adding the command line option &#39;--target-option mqpu,fp32&#39; or passing it as cudaq.set_target(&#39;nvidia&#39;, option=&#39;mqpu,fp32&#39;) in Python. Please refer to CUDA-Q ]8;;https://nvidia.github.io/cuda-quantum/latest/using/backends/platform\documentation]8;;\ for more information.</span>
-</pre></div></div>
-</div>
-<p>The cost function needs to be slightly updated to make use of the gradient in the optimization procedure and allow for a gradient based optimizer like L-BFGS-B to be used.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gradient</span> <span class="o">=</span> <span class="n">batched_gradient_function</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">spin_ham</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">)</span>
-
-<span class="n">exp_vals</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">def</span> <span class="nf">objective_function</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> \
-                       <span class="n">gradient</span><span class="o">=</span><span class="n">gradient</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="o">=</span><span class="n">spin_ham</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">):</span>
-
-    <span class="n">get_result</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">parameter_vector</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span>\
-        <span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">electron_count</span><span class="p">,</span> <span class="n">parameter_vector</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-    <span class="n">cost</span> <span class="o">=</span> <span class="n">get_result</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">)</span>
-    <span class="n">exp_vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
-    <span class="n">gradient_vector</span> <span class="o">=</span> <span class="n">batched_gradient_function</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">parameter_vector</span><span class="p">,</span>
-                                                <span class="n">spin_ham</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">cost</span><span class="p">,</span> <span class="n">gradient_vector</span>
-</pre></div>
-</div>
-</div>
-<p>Run the code below. Notice how the result is converged to a lower energy using only 10% of the steps as optimization above without a gradient.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">init_params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-
-<span class="n">start_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
-<span class="n">result_vqe</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">objective_function</span><span class="p">,</span>
-                      <span class="n">init_params</span><span class="p">,</span>
-                      <span class="n">method</span><span class="o">=</span><span class="s1">&#39;L-BFGS-B&#39;</span><span class="p">,</span>
-                      <span class="n">jac</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                      <span class="n">tol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span>
-                      <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">5</span><span class="p">})</span>
-<span class="n">end_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;VQE-UCCSD energy= &#39;</span><span class="p">,</span> <span class="n">result_vqe</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total elapsed time (s) = &#39;</span><span class="p">,</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">exp_vals</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Energy&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;VQE&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-VQE-UCCSD energy=  -73.19471262288755
-Total elapsed time (s) =  57.27010986900132
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_vqe_advanced_30_1.png" src="../../_images/applications_python_vqe_advanced_30_1.png" />
-</div>
-</div>
-<p>Now, run the code again (the three previous cells) and specify <code class="docutils literal notranslate"><span class="pre">num_qpus</span></code> to be more than one if you have access to multiple GPUs and notice resulting speedup. Thanks to CUDA-Q, this code could be used without modification in a setting where multiple physical QPUs were availible.</p>
-</section>
-<section id="Using-an-Active-Space">
-<h2>Using an Active Space<a class="headerlink" href="#Using-an-Active-Space" title="Permalink to this heading">¶</a></h2>
-<p>Performing electronic structure computations with all electrons and orbitals is often prohibitively expensive and unnecessary. Most of the interesting chemistry can be modeled by restricting simulations to the highest energy occupied molecular orbitals and lowest energy unoccupied molecular orbitals. This is known as the active space approximation.</p>
-<p>Below is an example of STO-3G water modeled with a 4 electron 3 orbital active space simulated with UCCSD-VQE. Using an active space means you can run VQE for the same molecule using fewer qubits and a more shallow circuit.</p>
-<p><img alt="cas.png" src="../../_images/cas.png" /></p>
-<p>The molecule is defined the same way, expect for you now include variables <code class="docutils literal notranslate"><span class="pre">nele_cas</span></code> and <code class="docutils literal notranslate"><span class="pre">norb_cas</span></code> to define the active space. The <code class="docutils literal notranslate"><span class="pre">ncore</span></code></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">geometry</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;O&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.1173</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)),</span> <span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.4691</span><span class="p">,</span> <span class="mf">0.7570</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)),</span>
-            <span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.4691</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.7570</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))]</span>
-<span class="n">basis</span> <span class="o">=</span> <span class="s1">&#39;sto3g&#39;</span>
-<span class="n">multiplicity</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">charge</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">ncore</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">nele_cas</span><span class="p">,</span> <span class="n">norb_cas</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-
-<span class="n">molecule</span> <span class="o">=</span> <span class="n">openfermionpyscf</span><span class="o">.</span><span class="n">run_pyscf</span><span class="p">(</span>
-    <span class="n">openfermion</span><span class="o">.</span><span class="n">MolecularData</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">basis</span><span class="p">,</span> <span class="n">multiplicity</span><span class="p">,</span> <span class="n">charge</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<p>The Hamiltonian is now constrcuted with the same steps, but only models the active space.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">molecular_hamiltonian</span> <span class="o">=</span> <span class="n">molecule</span><span class="o">.</span><span class="n">get_molecular_hamiltonian</span><span class="p">(</span>
-    <span class="n">occupied_indices</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">ncore</span><span class="p">),</span>
-    <span class="n">active_indices</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">ncore</span><span class="p">,</span> <span class="n">ncore</span> <span class="o">+</span> <span class="n">norb_cas</span><span class="p">))</span>
-
-<span class="n">fermion_hamiltonian</span> <span class="o">=</span> <span class="n">get_fermion_operator</span><span class="p">(</span><span class="n">molecular_hamiltonian</span><span class="p">)</span>
-
-<span class="n">qubit_hamiltonian</span> <span class="o">=</span> <span class="n">jordan_wigner</span><span class="p">(</span><span class="n">fermion_hamiltonian</span><span class="p">)</span>
-
-<span class="n">spin_ham</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="p">(</span><span class="n">qubit_hamiltonian</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Similarly, the kernel is defined only by the orbitals and electrons in the active space. Notice how this means you only need to optimize 8 parameters now.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">electron_count</span> <span class="o">=</span> <span class="n">nele_cas</span>
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">norb_cas</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">electron_num</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">thetas</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_num</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">electron_num</span><span class="p">):</span>
-        <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">uccsd</span><span class="p">(</span><span class="n">qubits</span><span class="p">,</span> <span class="n">thetas</span><span class="p">,</span> <span class="n">electron_num</span><span class="p">,</span> <span class="n">qubit_num</span><span class="p">)</span>
-
-
-<span class="n">parameter_count</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">uccsd_num_parameters</span><span class="p">(</span><span class="n">electron_count</span><span class="p">,</span>
-                                                     <span class="n">qubit_count</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">parameter_count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-8
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
-
-    <span class="n">exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">spin_ham</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">electron_count</span><span class="p">,</span>
-                            <span class="n">theta</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-    <span class="n">thetas</span> <span class="o">=</span> <span class="n">theta</span>
-    <span class="k">return</span> <span class="n">exp_val</span>
-
-
-<span class="n">exp_vals</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">xk</span><span class="p">):</span>
-    <span class="n">exp_vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cost</span><span class="p">(</span><span class="n">xk</span><span class="p">))</span>
-
-
-<span class="c1"># Initial variational parameters.</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The VQE procedure below is much faster using an active space compared to inclusion of all orbitals and electrons.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-
-<span class="n">start_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span>
-                  <span class="n">x0</span><span class="p">,</span>
-                  <span class="n">method</span><span class="o">=</span><span class="s1">&#39;COBYLA&#39;</span><span class="p">,</span>
-                  <span class="n">callback</span><span class="o">=</span><span class="n">callback</span><span class="p">,</span>
-                  <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">500</span><span class="p">})</span>
-<span class="n">end_time</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;UCCSD-VQE energy =  &#39;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of qubits = &#39;</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of parameters = &#39;</span><span class="p">,</span> <span class="n">parameter_count</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total number of terms in the spin hamiltonian = &#39;</span><span class="p">,</span>
-      <span class="n">spin_ham</span><span class="o">.</span><span class="n">get_term_count</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total elapsed time (s) = &#39;</span><span class="p">,</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">exp_vals</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Energy&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;VQE&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-UCCSD-VQE energy =   -74.96341992791962
-Total number of qubits =  6
-Total number of parameters =  8
-Total number of terms in the spin hamiltonian =  62
-Total elapsed time (s) =  1.754178541001238
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/applications_python_vqe_advanced_43_1.png" src="../../_images/applications_python_vqe_advanced_43_1.png" />
-</div>
-</div>
-</section>
-<section id="Gate-Fusion-for-Larger-Circuits">
-<h2>Gate Fusion for Larger Circuits<a class="headerlink" href="#Gate-Fusion-for-Larger-Circuits" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q simulations take advantage of a technique called gate fusion. Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below). By targeting the <code class="docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend and setting the <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code> environment variable, you can select the degree of fusion that takes place.</p>
-<p><img alt="gate-fuse.png" src="../../_images/gate-fuse.png" /></p>
-<p>This is particularly important for larger circuits and can have a significant impact on the performance of the simulation. Each system is different, so you should test different gate fusion levels to find out what is best for your system. You can find more information <a class="reference external" href="https://developer.nvidia.com/blog/new-nvidia-cuda-q-features-boost-quantum-application-performance/">here</a>.</p>
-</section>
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diff --git a/pr-2308/applications/python/vqe_advanced.ipynb b/pr-2308/applications/python/vqe_advanced.ipynb
deleted file mode 100644
index 3135b93829..0000000000
--- a/pr-2308/applications/python/vqe_advanced.ipynb
+++ /dev/null
@@ -1,729 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# VQE with gradients, active spaces, and gate fusion\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial will explore the Variational Quantum Eigensolver, a hybrid quantum classical algorithm for determining the ground state energy of molecules. The first part of this tutorial will walk through the key aspects of the VQE algorithm and how to implement it with CUDA-Q.  The following sections explore advanced topics: parallel gradients, active spaces, and gate fusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The Basics of VQE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE algorithm is hybrid quantum-classical algorithm, meaning some subroutines run on a quantum computer or quantum simulator and others run on a traditional (super)computer.  \n",
-    "\n",
-    "The goal is to take a parameterized quantum circuit and a qubit form of the molecular Hamiltonian, measure an expectation value that corresponds to the ground state energy of the molecule, and then repeat the process to variationally minimize the energy with respect to the parameters in the quantum circuit. The optimization is performed on a classical device while the expectation values are determined on a quantum device.  See the figure below.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![VQE.png](./images/VQE.png)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next few cells will elaborate on each part of the VQE procedure and show you how to build a VQE simulation to compute the ground state energy of the water molecule."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installing/Loading Relevant Packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "!pip install pyscf==2.6.2 openfermionpyscf==0.5 matplotlib==3.8.4 openfermion==1.6.1 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openfermion\n",
-    "import openfermionpyscf\n",
-    "from openfermion.transforms import jordan_wigner, get_fermion_operator\n",
-    "\n",
-    "import os\n",
-    "import timeit\n",
-    "\n",
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementing VQE in CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Like most quantum chemistry programs, the first step is to specify a molecular geometry, basis set, charge, and multiplicity. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = [('O', (0.1173, 0.0, 0.0)), ('H', (-0.4691, 0.7570, 0.0)),\n",
-    "            ('H', (-0.4691, -0.7570, 0.0))]\n",
-    "basis = 'sto3g'\n",
-    "multiplicity = 1\n",
-    "charge = 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE procedure requires some classical preprocessing.  The code below uses the PySCF package and OpenFermion to compute the Hartree Fock reference state and compute the integrals required for the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecule = openfermionpyscf.run_pyscf(\n",
-    "    openfermion.MolecularData(geometry, basis, multiplicity, charge))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the Hamiltonian is built using `get_molecular_hamiltonian`. The Hamiltonian must then be converted to a qubit Hamiltonian consisting of qubit operators. The standard Jordan-Wigner transformation is used to perform this mapping. \n",
-    "\n",
-    "Finally, the Jordan-Wigner qubit Hamiltonian is converted into a CUDA-Q spin operator which can be used to evaluate an expectation value given a quantum circuit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecular_hamiltonian = molecule.get_molecular_hamiltonian()\n",
-    "\n",
-    "fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)\n",
-    "\n",
-    "qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)\n",
-    "\n",
-    "spin_ham = cudaq.SpinOperator(qubit_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the quantum circuit needs to be defined, which models the wavefunction. This is done in CUDA-Q by specifying a CUDA-Q kernel. The kernel takes as an input the number of qubits, the number of electrons, and the parameters of the circuit ansatz (form of the wavefunction) yet to be defined. \n",
-    "\n",
-    "The number of qubits corresponds to the potential positions of electrons and is therefore twice the number of spatial orbitals constructed with the chosen basis set, as each can be occupied by two electrons. \n",
-    "\n",
-    "The Hartree-Fock reference is constructed by applying $X$ bitflip operations to each of the first $N$ qubits where $N$ is the number of electrons. Next, a parameterized ansatz is chosen. Theoretically, any set of operations can work as an ansatz, however, it is good practice to use an ansatz that captures the underlying physics of the problem. The most common choice for chemistry is the Unitary Coupled Cluster Ansatz with Single and Double excitations (UCCSD).  This UCCSD hate operations are automatically added to the kernel with the `cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)` function. \n",
-    "\n",
-    "The STO-3G water molecuule UCCSD ansatz requires optimization of 140 parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "140\n"
-     ]
-    }
-   ],
-   "source": [
-    "electron_count = 10\n",
-    "qubit_count = 2 * 7\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_num: int, electron_num: int, thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    for i in range(electron_num):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)\n",
-    "\n",
-    "print(parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The classical optimizer requires a custom cost function which is defined below. The `cudaq.observe()` function computes an expectation given the Hamiltonian and the kernel defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, spin_ham, qubit_count, electron_count,\n",
-    "                            theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final step is to run the optimization using the scipy minimize function and a selected optimizer, in this case COBYLA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "UCCSD-VQE energy =   -70.21455023422772\n",
-      "Total number of qubits =  14\n",
-      "Total number of parameters =  140\n",
-      "Total number of terms in the spin hamiltonian =  1086\n",
-      "Total elapsed time (s) =  3.9171073289999185\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.set_target('nvidia')\n",
-    "start_time = timeit.default_timer()\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 50})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('UCCSD-VQE energy =  ', result.fun)\n",
-    "print('Total number of qubits = ', qubit_count)\n",
-    "print('Total number of parameters = ', parameter_count)\n",
-    "print('Total number of terms in the spin hamiltonian = ',\n",
-    "      spin_ham.get_term_count())\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result of this procedure is an estimate of the ground state energy of water. However, the convergence behavior is not perfect, more iterations would greatly improve the result, but would take a few minutes to run."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parallel Parameter Shift Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One way to accelerate VQE is to use an optimizer that accepts a gradient. This can drastically lower the number of VQE iterations required at the cost of computing the gradient on the quantum side of the algorithm.\n",
-    "\n",
-    "The parameter shift rule is a common technique to compute the gradient for parameterized circuits. It is obtained by computing two expectation values for each parameter corresponding to a small forward and backward shift in the ith parameter. These results are used to estimate  finite difference contribution to the gradient.\n",
-    "\n",
-    "![parametershift.png](./images/parametershift.png)\n",
-    "\n",
-    "This procedure can become cost prohibitive as the number of parameters becomes large."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each of the expectation values needed to evaluate a parameter shift gradient can be computed independently.  The CUDA-Q `nvidia-mqpu` backend is designed for parallel computations across multiple simulated QPUs. The function below uses `cudaq.observe_asynch` to distribute all of the expectation values evaluations across as many GPUs that are available. First, try it with `num_qpus` set to 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\u001b[38;2;255;000;000mwarning\u001b[0m] Target \u001b[38;2;000;000;255mnvidia-mqpu\u001b[0m: \u001b[38;2;000;000;255mThis target is deprecating. Please use the 'nvidia' target with option 'mqpu,fp32' or 'mqpu' (fp32 is the default precision option) by adding the command line option '--target-option mqpu,fp32' or passing it as cudaq.set_target('nvidia', option='mqpu,fp32') in Python. Please refer to CUDA-Q \u001b]8;;https://nvidia.github.io/cuda-quantum/latest/using/backends/platform\u001b\\documentation\u001b]8;;\u001b\\ for more information.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)\n",
-    "\n",
-    "cudaq.set_target(\"nvidia-mqpu\")\n",
-    "\n",
-    "num_qpus = cudaq.get_target().num_qpus()\n",
-    "\n",
-    "epsilon = np.pi / 4\n",
-    "\n",
-    "\n",
-    "def batched_gradient_function(kernel, parameters, hamiltonian, epsilon):\n",
-    "\n",
-    "    x = np.tile(parameters, (len(parameters), 1))\n",
-    "\n",
-    "    xplus = x + (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    xminus = x - (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    g_plus = []\n",
-    "    g_minus = []\n",
-    "    gradients = []\n",
-    "\n",
-    "    qpu_counter = 0  # Iterate over the number of GPU resources available\n",
-    "    for i in range(x.shape[0]):\n",
-    "\n",
-    "        g_plus.append(\n",
-    "            cudaq.observe_async(kernel,\n",
-    "                                hamiltonian,\n",
-    "                                qubit_count,\n",
-    "                                electron_count,\n",
-    "                                xplus[i],\n",
-    "                                qpu_id=qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1\n",
-    "\n",
-    "        g_minus.append(\n",
-    "            cudaq.observe_async(kernel,\n",
-    "                                hamiltonian,\n",
-    "                                qubit_count,\n",
-    "                                electron_count,\n",
-    "                                xminus[i],\n",
-    "                                qpu_id=qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1\n",
-    "\n",
-    "    gradients = [\n",
-    "        (g_plus[i].get().expectation() - g_minus[i].get().expectation()) /\n",
-    "        (2 * epsilon) for i in range(len(g_minus))\n",
-    "    ]\n",
-    "\n",
-    "    assert len(gradients) == len(\n",
-    "        parameters) == x.shape[0] == xplus.shape[0] == xminus.shape[0]\n",
-    "\n",
-    "    return gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The cost function needs to be slightly updated to make use of the gradient in the optimization procedure and allow for a gradient based optimizer like L-BFGS-B to be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gradient = batched_gradient_function(kernel, x0, spin_ham, epsilon)\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector: list[float], \\\n",
-    "                       gradient=gradient, hamiltonian=spin_ham, kernel=kernel):\n",
-    "\n",
-    "    get_result = lambda parameter_vector: cudaq.observe\\\n",
-    "        (kernel, hamiltonian, qubit_count, electron_count, parameter_vector).expectation()\n",
-    "\n",
-    "    cost = get_result(parameter_vector)\n",
-    "    exp_vals.append(cost)\n",
-    "    gradient_vector = batched_gradient_function(kernel, parameter_vector,\n",
-    "                                                spin_ham, epsilon)\n",
-    "\n",
-    "    return cost, gradient_vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the code below. Notice how the result is converged to a lower energy using only 10% of the steps as optimization above without a gradient. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "VQE-UCCSD energy=  -73.19471262288755\n",
-      "Total elapsed time (s) =  57.27010986900132\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "init_params = np.random.normal(0, 1, parameter_count)\n",
-    "\n",
-    "start_time = timeit.default_timer()\n",
-    "result_vqe = minimize(objective_function,\n",
-    "                      init_params,\n",
-    "                      method='L-BFGS-B',\n",
-    "                      jac=True,\n",
-    "                      tol=1e-8,\n",
-    "                      options={'maxiter': 5})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('VQE-UCCSD energy= ', result_vqe.fun)\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, run the code again (the three previous cells) and specify `num_qpus` to be more than one if you have access to multiple GPUs and notice resulting speedup.  Thanks to CUDA-Q, this code could be used without modification in a setting where multiple physical QPUs were availible."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using an Active Space"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Performing electronic structure computations with all electrons and orbitals is often prohibitively expensive and unnecessary.  Most of the interesting chemistry can be modeled by restricting simulations to the highest energy occupied molecular orbitals and lowest energy unoccupied molecular orbitals. This is known as the active space approximation. \n",
-    "\n",
-    "Below is an example of STO-3G water modeled with a 4 electron 3 orbital active space simulated with UCCSD-VQE. Using an active space means you can run VQE for the same molecule using fewer qubits and a more shallow circuit.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![cas.png](./images/cas.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The molecule is defined the same way, expect for you now include variables `nele_cas` and `norb_cas` to define the active space. The `ncore` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = [('O', (0.1173, 0.0, 0.0)), ('H', (-0.4691, 0.7570, 0.0)),\n",
-    "            ('H', (-0.4691, -0.7570, 0.0))]\n",
-    "basis = 'sto3g'\n",
-    "multiplicity = 1\n",
-    "charge = 0\n",
-    "ncore = 3\n",
-    "nele_cas, norb_cas = (4, 3)\n",
-    "\n",
-    "molecule = openfermionpyscf.run_pyscf(\n",
-    "    openfermion.MolecularData(geometry, basis, multiplicity, charge))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian is now constrcuted with the same steps, but only models the active space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecular_hamiltonian = molecule.get_molecular_hamiltonian(\n",
-    "    occupied_indices=range(ncore),\n",
-    "    active_indices=range(ncore, ncore + norb_cas))\n",
-    "\n",
-    "fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)\n",
-    "\n",
-    "qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)\n",
-    "\n",
-    "spin_ham = cudaq.SpinOperator(qubit_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, the kernel is defined only by the orbitals and electrons in the active space. Notice how this means you only need to optimize 8 parameters now. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8\n"
-     ]
-    }
-   ],
-   "source": [
-    "electron_count = nele_cas\n",
-    "qubit_count = 2 * norb_cas\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_num: int, electron_num: int, thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    for i in range(electron_num):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)\n",
-    "\n",
-    "print(parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, spin_ham, qubit_count, electron_count,\n",
-    "                            theta).expectation()\n",
-    "    thetas = theta\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE procedure below is much faster using an active space compared to inclusion of all orbitals and electrons."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "UCCSD-VQE energy =   -74.96341992791962\n",
-      "Total number of qubits =  6\n",
-      "Total number of parameters =  8\n",
-      "Total number of terms in the spin hamiltonian =  62\n",
-      "Total elapsed time (s) =  1.754178541001238\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.set_target(\"nvidia\")\n",
-    "\n",
-    "start_time = timeit.default_timer()\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 500})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('UCCSD-VQE energy =  ', result.fun)\n",
-    "print('Total number of qubits = ', qubit_count)\n",
-    "print('Total number of parameters = ', parameter_count)\n",
-    "print('Total number of terms in the spin hamiltonian = ',\n",
-    "      spin_ham.get_term_count())\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gate Fusion for Larger Circuits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CUDA-Q simulations take advantage of a technique called gate fusion. Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. \n",
-    "\n",
-    "![gate-fuse.png](./images/gate-fuse.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is particularly important for larger circuits and can have a significant impact on the performance of the simulation. Each system is different, so you should test different gate fusion levels to find out what is best for your system. You can find more information [here](https://developer.nvidia.com/blog/new-nvidia-cuda-q-features-boost-quantum-application-performance/)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/examples/python/executing_kernels.html b/pr-2308/examples/python/executing_kernels.html
deleted file mode 100644
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-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Executing Kernels</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="#Get-state">Get state</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#Sample-Async">Sample Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-  <section id="Executing-Quantum-Circuits">
-<h1>Executing Quantum Circuits<a class="headerlink" href="#Executing-Quantum-Circuits" title="Permalink to this heading">¶</a></h1>
-<p>In CUDA-Q, there are 3 ways in which one can execute quantum kernels:</p>
-<ol class="arabic simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">sample</span></code>: yields measurement counts</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">observe</span></code>: yields expectation values</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">get_state</span></code>: yields the quantum statevector of the computation</p></li>
-</ol>
-<section id="Sample">
-<h2>Sample<a class="headerlink" href="#Sample" title="Permalink to this heading">¶</a></h2>
-<p>Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q <code class="docutils literal notranslate"><span class="pre">sample</span></code> call enables this:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="c1"># Define the simulation target.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;qpp-cpu&quot;</span><span class="p">)</span>
-
-<span class="c1"># Define a quantum kernel function.</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-
-    <span class="c1"># 2-qubit GHZ state.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="c1"># If we dont specify measurements, all qubits are measured in</span>
-    <span class="c1"># the Z-basis by default or we can manually specify it also</span>
-    <span class="c1"># mz(qvector)</span>
-
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">))</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮
-q0 : ┤ h ├──●──
-     ╰───╯╭─┴─╮
-q1 : ─────┤ x ├
-          ╰───╯
-
-{ 11:506 00:494 }
-
-</pre></div></div>
-</div>
-<p>Note that there is a subtle difference between how <code class="docutils literal notranslate"><span class="pre">sample</span></code> is executed with the target device set to a simulator or with the target device set to a QPU. In simulation mode, the quantum state is built once and then sampled <span class="math notranslate nohighlight">\(s\)</span> times where <span class="math notranslate nohighlight">\(s\)</span> equals the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code>. In hardware execution mode, the quantum state collapses upon measurement and hence needs to be rebuilt over and over again.</p>
-<p>There are a number of helpful tools that can be found in the <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api">API docs</a> to process the <code class="docutils literal notranslate"><span class="pre">Sample_Result</span></code> object produced by <code class="docutils literal notranslate"><span class="pre">sample</span></code>.</p>
-</section>
-<section id="Observe">
-<h2>Observe<a class="headerlink" href="#Observe" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">observe</span></code> function allows us to calculate expectation values. We must supply a spin operator in the form of a Hamiltonian, <span class="math notranslate nohighlight">\(H\)</span>, from which we would like to calculate <span class="math notranslate nohighlight">\(\bra{\psi}H\ket{\psi}\)</span>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="c1"># Define a Hamiltonian in terms of Pauli Spin operators.</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="c1"># Compute the expectation value given the state prepared by the kernel.</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&lt;H&gt; =&#39;</span><span class="p">,</span> <span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;H&gt; = 0.0
-</pre></div></div>
-</div>
-</section>
-<section id="Get-state">
-<h2>Get state<a class="headerlink" href="#Get-state" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">get_state</span></code> function gives us access to the quantum statevector of the computation. Remember, that this is only feasible in simulation mode.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute the statevector of the kernel</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[0.70710678+0.j 0.        +0.j 0.        +0.j 0.70710678+0.j]
-</pre></div></div>
-</div>
-<p>The statevector generated by the <code class="docutils literal notranslate"><span class="pre">get_state</span></code> command follows Big-endian convention for associating numbers with their binary representations, which places the least significant bit on the left. That is, for the example of a 2-bit system, we have the following translation between integers and bits:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{matrix} \text{Integer} &amp; \text{Binary representation}\\
-&amp; \text{least signinificant bit on left}\\
-0 =\textcolor{red}{0}*2^0+\textcolor{blue}{0}*2^1 &amp; \textcolor{red}{0}\textcolor{blue}{0} \\
-1 = \textcolor{red}{1}*2^0 + \textcolor{blue}{0} *2^1 &amp; \textcolor{red}{1}\textcolor{blue}{0}\\
-2 = \textcolor{red}{0}*2^0 + \textcolor{blue}{1}*2^1 &amp; \textcolor{red}{0}\textcolor{blue}{1} \\
-3 = \textcolor{red}{1}*2^0 + \textcolor{blue}{1}*2^1 &amp; \textcolor{red}{1}\textcolor{blue}{1} \end{matrix}\end{split}\]</div>
-</section>
-<section id="Parallelization-Techniques">
-<h2>Parallelization Techniques<a class="headerlink" href="#Parallelization-Techniques" title="Permalink to this heading">¶</a></h2>
-<p>The most intensive task in the computation is the execution of the quantum kernel hence each execution function: <code class="docutils literal notranslate"><span class="pre">sample</span></code>, <code class="docutils literal notranslate"><span class="pre">observe</span></code> and <code class="docutils literal notranslate"><span class="pre">get_state</span></code> can be parallelized given access to multiple quantum processing units (multi-QPU).</p>
-<p>Since multi-QPU platforms are not yet feasible, we emulate each QPU with a GPU.</p>
-<section id="Observe-Async">
-<h3>Observe Async<a class="headerlink" href="#Observe-Async" title="Permalink to this heading">¶</a></h3>
-<p>Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result.</p>
-<p><code class="docutils literal notranslate"><span class="pre">observe</span></code> can be a time intensive task. We can parallelize the execution of <code class="docutils literal notranslate"><span class="pre">observe</span></code> via the arguments it accepts.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the simulation target to a multi-QPU platform</span>
-<span class="c1"># cudaq.set_target(&quot;nvidia&quot;, option = &#39;mqpu&#39;)</span>
-
-<span class="c1"># Measuring the expectation value of 2 different hamiltonians in parallel</span>
-<span class="n">hamiltonian_1</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="c1"># hamiltonian_2 = spin.z(1) + spin.y(0) + spin.x(1)*spin.x(0)</span>
-
-<span class="c1"># Asynchronous execution on multiple qpus via nvidia gpus.</span>
-<span class="n">result_1</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian_1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">qpu_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="c1"># result_2 = cudaq.observe_async(kernel, hamiltonian_2, qubit_count, qpu_id=1)</span>
-
-<span class="c1"># Retrieve results</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result_1</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">())</span>
-<span class="c1"># print(result_2.get().expectation())</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-2.220446049250313e-16
-</pre></div></div>
-</div>
-<p>Above we parallelized the <code class="docutils literal notranslate"><span class="pre">observe</span></code> call over the <code class="docutils literal notranslate"><span class="pre">hamiltonian</span></code> parameter however we can parallelize over any of the argument it accepts by just iterating obver the <code class="docutils literal notranslate"><span class="pre">qpu_id</span></code>.</p>
-</section>
-<section id="Sample-Async">
-<h3>Sample Async<a class="headerlink" href="#Sample-Async" title="Permalink to this heading">¶</a></h3>
-<p>Similar to <code class="docutils literal notranslate"><span class="pre">observe_async</span></code> above, <code class="docutils literal notranslate"><span class="pre">sample</span></code> also supports asynchronous execution for the <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.sample_async(kernel%3A%20object%2C%20%5C*args%2C%20shots_count%3A%20int%20%3D%201000%2C%20qpu_id%3A%20int%20%3D%200)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult">arguments it accepts</a>. One can parallelize over various kernels, variational parameters or even
-distribute shots counts over multiple QPUs.</p>
-</section>
-<section id="Get-State-Async">
-<h3>Get State Async<a class="headerlink" href="#Get-State-Async" title="Permalink to this heading">¶</a></h3>
-<p>Similar to <code class="docutils literal notranslate"><span class="pre">sample_async</span></code> above, <code class="docutils literal notranslate"><span class="pre">get_state</span></code> also supports asynchronous execution for the <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.get_state(arg0%3A%20object%2C%20%5C*args)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State">arguments it accepts</a></p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-CUDA-Q Version  (https://github.com/NVIDIA/cuda-quantum 0eb6b444eb5b3a687e6fd64529ee9223aaa2870e)
-</pre></div></div>
-</div>
-</section>
-</section>
-</section>
-
-
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diff --git a/pr-2308/examples/python/executing_kernels.ipynb b/pr-2308/examples/python/executing_kernels.ipynb
deleted file mode 100644
index 0d3250c8c1..0000000000
--- a/pr-2308/examples/python/executing_kernels.ipynb
+++ /dev/null
@@ -1,284 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Executing Quantum Circuits \n",
-    "\n",
-    "In CUDA-Q, there are 3 ways in which one can execute quantum kernels: \n",
-    "\n",
-    "1. `sample`: yields measurement counts \n",
-    "2. `observe`: yields expectation values \n",
-    "3. `get_state`: yields the quantum statevector of the computation \n",
-    "\n",
-    "## Sample\n",
-    "\n",
-    "Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q `sample` call enables this: \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮     \n",
-      "q0 : ┤ h ├──●──\n",
-      "     ╰───╯╭─┴─╮\n",
-      "q1 : ─────┤ x ├\n",
-      "          ╰───╯\n",
-      "\n",
-      "{ 11:506 00:494 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np \n",
-    "\n",
-    "qubit_count = 2\n",
-    "\n",
-    "# Define the simulation target.\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "# Define a quantum kernel function.\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_count: int):\n",
-    "    qvector = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # 2-qubit GHZ state.\n",
-    "    h(qvector[0])\n",
-    "    for i in range(1, qubit_count):\n",
-    "        x.ctrl(qvector[0], qvector[i])\n",
-    "\n",
-    "    # If we dont specify measurements, all qubits are measured in\n",
-    "    # the Z-basis by default or we can manually specify it also \n",
-    "    # mz(qvector)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, qubit_count))\n",
-    "\n",
-    "result = cudaq.sample(kernel, qubit_count, shots_count=1000)\n",
-    "\n",
-    "print(result)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that there is a subtle difference between how `sample` is executed with the target device set to a simulator or with the target device set to a QPU. In simulation mode, the quantum state is built once and then sampled $s$ times where $s$ equals the `shots_count`. In hardware execution mode, the quantum state collapses upon measurement and hence needs to be rebuilt over and over again.\n",
-    "\n",
-    "There are a number of helpful tools that can be found in the [API docs](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api) to process the `Sample_Result` object produced by `sample`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Observe\n",
-    "\n",
-    "The `observe` function allows us to calculate expectation values. We must supply a spin operator in the form of a Hamiltonian, $H$,  from which we would like to calculate $\\bra{\\psi}H\\ket{\\psi}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<H> = 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from cudaq import spin\n",
-    "\n",
-    "# Define a Hamiltonian in terms of Pauli Spin operators.\n",
-    "hamiltonian = spin.z(0) + spin.y(1) + spin.x(0) * spin.z(0)\n",
-    "\n",
-    "# Compute the expectation value given the state prepared by the kernel.\n",
-    "result = cudaq.observe(kernel, hamiltonian, qubit_count).expectation()\n",
-    "\n",
-    "print('<H> =', result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Get state\n",
-    "\n",
-    "The `get_state` function gives us access to the quantum statevector of the computation. Remember, that this is only feasible in simulation mode. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.70710678+0.j 0.        +0.j 0.        +0.j 0.70710678+0.j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute the statevector of the kernel\n",
-    "result = cudaq.get_state(kernel, qubit_count)\n",
-    "\n",
-    "print(np.array(result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statevector generated by the `get_state` command follows Big-endian convention for associating numbers with their binary representations, which places the least significant bit on the left.  That is, for the example of a 2-bit system, we have the following translation between integers and bits:\n",
-    "$$\\begin{matrix} \\text{Integer} & \\text{Binary representation}\\\\\n",
-    "& \\text{least signinificant bit on left}\\\\\n",
-    "0 =\\textcolor{red}{0}*2^0+\\textcolor{blue}{0}*2^1 & \\textcolor{red}{0}\\textcolor{blue}{0} \\\\\n",
-    "1 = \\textcolor{red}{1}*2^0 + \\textcolor{blue}{0} *2^1 & \\textcolor{red}{1}\\textcolor{blue}{0}\\\\\n",
-    "2 = \\textcolor{red}{0}*2^0 + \\textcolor{blue}{1}*2^1 & \\textcolor{red}{0}\\textcolor{blue}{1} \\\\\n",
-    "3 = \\textcolor{red}{1}*2^0 + \\textcolor{blue}{1}*2^1 & \\textcolor{red}{1}\\textcolor{blue}{1} \\end{matrix}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Parallelization Techniques\n",
-    "\n",
-    "The most intensive task in the computation is the execution of the quantum kernel hence each execution function: `sample`, `observe` and `get_state` can be parallelized given access to multiple quantum processing units (multi-QPU). \n",
-    "\n",
-    "Since multi-QPU platforms are not yet feasible, we emulate each QPU with a GPU.\n",
-    "\n",
-    "\n",
-    "### Observe Async\n",
-    "\n",
-    "Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result. \n",
-    "\n",
-    "`observe` can be a time intensive task. We can parallelize the execution of `observe` via the arguments it accepts. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.220446049250313e-16\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set the simulation target to a multi-QPU platform \n",
-    "# cudaq.set_target(\"nvidia\", option = 'mqpu')\n",
-    "\n",
-    "# Measuring the expectation value of 2 different hamiltonians in parallel\n",
-    "hamiltonian_1 = spin.x(0) + spin.y(1) + spin.z(0)*spin.y(1)\n",
-    "# hamiltonian_2 = spin.z(1) + spin.y(0) + spin.x(1)*spin.x(0)\n",
-    "\n",
-    "# Asynchronous execution on multiple qpus via nvidia gpus.\n",
-    "result_1 = cudaq.observe_async(kernel, hamiltonian_1, qubit_count, qpu_id=0)\n",
-    "# result_2 = cudaq.observe_async(kernel, hamiltonian_2, qubit_count, qpu_id=1)\n",
-    "\n",
-    "# Retrieve results \n",
-    "print(result_1.get().expectation())\n",
-    "# print(result_2.get().expectation())"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Above we parallelized the `observe` call over the `hamiltonian` parameter however we can parallelize over any of the argument it accepts by just iterating obver the `qpu_id`."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sample Async\n",
-    "\n",
-    "Similar to `observe_async` above, `sample` also supports asynchronous execution for the [arguments it accepts](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.sample_async(kernel%3A%20object%2C%20%5C*args%2C%20shots_count%3A%20int%20%3D%201000%2C%20qpu_id%3A%20int%20%3D%200)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult). One can parallelize over various kernels, variational parameters or even distribute shots counts over multiple QPUs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get State Async\n",
-    "\n",
-    "Similar to `sample_async` above, `get_state` also supports asynchronous execution for the [arguments it accepts](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.get_state(arg0%3A%20object%2C%20%5C*args)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version  (https://github.com/NVIDIA/cuda-quantum 0eb6b444eb5b3a687e6fd64529ee9223aaa2870e)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/examples/python/measuring_kernels.html b/pr-2308/examples/python/measuring_kernels.html
deleted file mode 100644
index abb7e7bef5..0000000000
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="Measurements">
-<h1>Measurements<a class="headerlink" href="#Measurements" title="Permalink to this heading">¶</a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-</pre></div>
-</div>
-</div>
-<p>Kernel measurement can be specified in the Z, X, or Y basis using <code class="docutils literal notranslate"><span class="pre">mz</span></code>, <code class="docutils literal notranslate"><span class="pre">mx</span></code>, and <code class="docutils literal notranslate"><span class="pre">my</span></code>. If a measurement is specified with no argument, the entire kernel is measured in that basis. Measurement occurs in the Z basis by default.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">mz</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>Specific qubits or registers can be measured rather than the entire kernel.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qubits_a</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">qubit_b</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubits_a</span><span class="p">)</span>
-    <span class="n">mx</span><span class="p">(</span><span class="n">qubit_b</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Midcircuit-Measurement-and-Conditional-Logic">
-<h2>Midcircuit Measurement and Conditional Logic<a class="headerlink" href="#Midcircuit-Measurement-and-Conditional-Logic" title="Permalink to this heading">¶</a></h2>
-<p>In certain cases, it it is helpful for some operations in a quantum kernel to depend on measurement results following previous operations. This is accomplished in the following example by performing a Hadamard on qubit 0, then measuring qubit 0 and savig the result as <code class="docutils literal notranslate"><span class="pre">b0</span></code>. Then, an if statement performs a Hadamard on qubit 1 only if <code class="docutils literal notranslate"><span class="pre">b0</span></code> is 1. Measuring this qubit 1 verifies this process as a 1 is the result 25% of the time.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">b0</span> <span class="o">=</span> <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">if</span> <span class="n">b0</span><span class="p">:</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
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diff --git a/pr-2308/examples/python/measuring_kernels.ipynb b/pr-2308/examples/python/measuring_kernels.ipynb
deleted file mode 100644
index 40fa54bff8..0000000000
--- a/pr-2308/examples/python/measuring_kernels.ipynb
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13858fc6-edc7-49cb-8b56-152ebec2f729",
-   "metadata": {},
-   "source": [
-    "# Measurements\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ec9af8fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa410efc-fcb1-43e1-ab99-25a7fcbab868",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Kernel measurement can be specified in the Z, X, or Y basis using `mz`, `mx`, and `my`. If a measurement is specified with no argument, the entire kernel is measured in that basis. Measurement occurs in the Z basis by default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7f45a62e-48cb-4705-8081-e59782c91b5f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    mz()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39efaac5-5400-4df6-ac5d-80637d9d3082",
-   "metadata": {},
-   "source": [
-    "Specific qubits or registers can be measured rather than the entire kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "16a14177-fa79-4eb3-8fc8-f3e937bc21b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits_a = cudaq.qvector(2)\n",
-    "    qubit_b = cudaq.qubit()\n",
-    "    mz(qubits_a)\n",
-    "    mx(qubit_b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb5dd767-5db7-4847-b04e-ae5695066800",
-   "metadata": {},
-   "source": [
-    "### Midcircuit Measurement and Conditional Logic\n",
-    "\n",
-    "In certain cases, it it is helpful for some operations in a quantum kernel to depend on measurement results following previous operations. This is accomplished in the following example by performing a Hadamard on qubit 0, then measuring qubit 0 and savig the result as `b0`. Then, an if statement performs a Hadamard on qubit 1 only if `b0` is 1. Measuring this qubit 1 verifies this process as a 1 is the result 25% of the time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "44001a51-3733-472c-8bc1-ee694e957708",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    q = cudaq.qvector(2)\n",
-    "    h(q[0])\n",
-    "    b0 = mz(q[0])\n",
-    "    if b0:\n",
-    "        h(q[1])\n",
-    "        mz(q[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/examples/python/noisy_simulations.html b/pr-2308/examples/python/noisy_simulations.html
deleted file mode 100644
index 0f3d54ac60..0000000000
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@@ -1,944 +0,0 @@
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
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-  <section id="Noisy-Simulation">
-<h1>Noisy Simulation<a class="headerlink" href="#Noisy-Simulation" title="Permalink to this heading">¶</a></h1>
-<p>Quantum noise can be characterized into coherent and incoherent sources of errors that arise during a computation. Coherent noise is commonly due to systematic errors originating from device miscalibrations, for example, gates implementing a rotation <span class="math notranslate nohighlight">\(\theta + \epsilon\)</span> instead of <span class="math notranslate nohighlight">\(\theta\)</span>.</p>
-<p>Incoherent noise has its origins in quantum states being entangled with the environment due to decoherence. This leads to mixed states which are probability distributions over pure states and are described by employing the density matrix formalism.</p>
-<p>We can model incoherent noise via quantum channels which are linear, completely positive, and trace preserving maps. These maps are called Kraus operators, <span class="math notranslate nohighlight">\(\{ K_i \}\)</span>, which satisfy the condition <span class="math notranslate nohighlight">\(\sum_{i} K_i^\dagger K_i = \mathbb{I}\)</span>.</p>
-<p>The bit-flip channel flips the qubit with probability <span class="math notranslate nohighlight">\(p\)</span> and leaves it unchanged with probability <span class="math notranslate nohighlight">\(1-p\)</span>. This can be represented by employing Kraus operators:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}K_0 = \sqrt{1-p} \begin{pmatrix}
-    1 &amp; 0 \\
-    0 &amp; 1
-\end{pmatrix}\end{split}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}K_1 = \sqrt{p} \begin{pmatrix}
-  0 &amp; 1 \\
-  1 &amp; 0
-\end{pmatrix}\end{split}\]</div>
-<p>Let’s implement the bit-flip channel using CUDA-Q:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># To model quantum noise, we need to utilize the density matrix simulator target.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;density-matrix-cpu&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Let&#39;s define a simple kernel that we will add noise to.</span>
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-     ╭───╮
-q0 : ┤ x ├
-     ├───┤
-q1 : ┤ x ├
-     ╰───╯
-
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># In the ideal noiseless case, we get |11&gt; 100% of the time.</span>
-
-<span class="n">ideal_counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-<span class="n">ideal_counts</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{ 11:1000 }
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># First, we will define an out of the box noise channel. In this case,</span>
-<span class="c1"># we choose depolarization noise. This depolarization will result in</span>
-<span class="c1"># the qubit state decaying into a mix of the basis states, |0&gt; and |1&gt;,</span>
-<span class="c1"># with our provided probability.</span>
-<span class="n">error_probability</span> <span class="o">=</span> <span class="mf">0.1</span>
-<span class="n">depolarization_channel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">DepolarizationChannel</span><span class="p">(</span><span class="n">error_probability</span><span class="p">)</span>
-
-<span class="c1"># Other built in noise models</span>
-<span class="n">bit_flip</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">BitFlipChannel</span><span class="p">(</span><span class="n">error_probability</span><span class="p">)</span>
-<span class="n">phase_flip</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">PhaseFlipChannel</span><span class="p">(</span><span class="n">error_probability</span><span class="p">)</span>
-<span class="n">amplitude_damping</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AmplitudeDampingChannel</span><span class="p">(</span><span class="n">error_probability</span><span class="p">)</span>
-
-<span class="c1"># We can also define our own, custom noise channels through</span>
-<span class="c1"># Kraus operators. Here we will define two operators representing</span>
-<span class="c1"># bit flip errors.</span>
-
-<span class="c1"># Define the Kraus Error Operator as a complex ndarray.</span>
-<span class="n">kraus_0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">error_probability</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span>
-                                                     <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span>
-                                                    <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
-
-<span class="n">kraus_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">error_probability</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
-                                                 <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span>
-                                                <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
-
-<span class="c1"># Add the Kraus Operator to create a quantum channel.</span>
-<span class="n">bitflip_channel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">KrausChannel</span><span class="p">([</span><span class="n">kraus_0</span><span class="p">,</span> <span class="n">kraus_1</span><span class="p">])</span>
-
-<span class="c1"># Add noise channels to our noise model.</span>
-<span class="n">noise_model</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">NoiseModel</span><span class="p">()</span>
-
-<span class="c1"># Apply the depolarization channel to any X-gate on the 0th qubit.</span>
-<span class="n">noise_model</span><span class="o">.</span><span class="n">add_channel</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">depolarization_channel</span><span class="p">)</span>
-<span class="c1"># Apply the bitflip channel to any X-gate on the 1st qubit.</span>
-<span class="n">noise_model</span><span class="o">.</span><span class="n">add_channel</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">bitflip_channel</span><span class="p">)</span>
-
-<span class="c1"># Due to the impact of noise, our measurements will no longer be uniformly</span>
-<span class="c1"># in the |11&gt; state.</span>
-<span class="n">noisy_counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                            <span class="n">qubit_count</span><span class="p">,</span>
-                            <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_model</span><span class="p">,</span>
-                            <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-<span class="n">noisy_counts</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{ 11:844 10:88 01:65 00:3 }
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We can also use noise models with the observe function</span>
-
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="n">noisy_result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                             <span class="n">hamiltonian</span><span class="p">,</span>
-                             <span class="n">qubit_count</span><span class="p">,</span>
-                             <span class="n">noise_model</span><span class="o">=</span><span class="n">noise_model</span><span class="p">)</span>
-
-<span class="n">noisy_result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
--0.8666666666666666
-</pre></div></div>
-</div>
-</section>
-
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diff --git a/pr-2308/examples/python/noisy_simulations.ipynb b/pr-2308/examples/python/noisy_simulations.ipynb
deleted file mode 100644
index 73861d9246..0000000000
--- a/pr-2308/examples/python/noisy_simulations.ipynb
+++ /dev/null
@@ -1,213 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Noisy Simulation \n",
-    "\n",
-    "Quantum noise can be characterized into coherent and incoherent sources of errors that arise during a computation. Coherent noise is commonly due to systematic errors originating from device miscalibrations, for example, gates implementing a rotation $\\theta + \\epsilon$ instead of $\\theta$.\n",
-    "\n",
-    "Incoherent noise has its origins in quantum states being entangled with the environment due to decoherence. This leads to mixed states which are probability distributions over pure states and are described by employing the density matrix formalism. \n",
-    "\n",
-    "We can model incoherent noise via quantum channels which are linear, completely positive, and trace preserving maps. These maps are called Kraus operators, $\\{ K_i \\}$, which satisfy the condition $\\sum_{i} K_i^\\dagger K_i = \\mathbb{I}$. \n",
-    "\n",
-    "The bit-flip channel flips the qubit with probability $p$ and leaves it unchanged with probability $1-p$. This can be represented by employing Kraus operators: \n",
-    "\n",
-    "$$K_0 = \\sqrt{1-p} \\begin{pmatrix}\n",
-    "    1 & 0 \\\\\n",
-    "    0 & 1\n",
-    "\\end{pmatrix}$$\n",
-    "\n",
-    "$$K_1 = \\sqrt{p} \\begin{pmatrix}\n",
-    "  0 & 1 \\\\\n",
-    "  1 & 0\n",
-    "\\end{pmatrix}$$\n",
-    "\n",
-    "Let's implement the bit-flip channel using CUDA-Q:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "# To model quantum noise, we need to utilize the density matrix simulator target.\n",
-    "cudaq.set_target(\"density-matrix-cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮\n",
-      "q0 : ┤ x ├\n",
-      "     ├───┤\n",
-      "q1 : ┤ x ├\n",
-      "     ╰───╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's define a simple kernel that we will add noise to.\n",
-    "qubit_count = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_count: int):\n",
-    "    qvector = cudaq.qvector(qubit_count)\n",
-    "    x(qvector)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, qubit_count))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11:1000 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "# In the ideal noiseless case, we get |11> 100% of the time.\n",
-    "\n",
-    "ideal_counts = cudaq.sample(kernel, qubit_count, shots_count=1000)\n",
-    "ideal_counts.dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11:844 10:88 01:65 00:3 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, we will define an out of the box noise channel. In this case,\n",
-    "# we choose depolarization noise. This depolarization will result in\n",
-    "# the qubit state decaying into a mix of the basis states, |0> and |1>,\n",
-    "# with our provided probability.\n",
-    "error_probability = 0.1\n",
-    "depolarization_channel = cudaq.DepolarizationChannel(error_probability)\n",
-    "\n",
-    "# Other built in noise models \n",
-    "bit_flip = cudaq.BitFlipChannel(error_probability) \n",
-    "phase_flip = cudaq.PhaseFlipChannel(error_probability)\n",
-    "amplitude_damping = cudaq.AmplitudeDampingChannel(error_probability)\n",
-    "\n",
-    "# We can also define our own, custom noise channels through\n",
-    "# Kraus operators. Here we will define two operators representing\n",
-    "# bit flip errors.\n",
-    "\n",
-    "# Define the Kraus Error Operator as a complex ndarray.\n",
-    "kraus_0 = np.sqrt(1 - error_probability) * np.array([[1.0, 0.0], \n",
-    "                                                     [0.0, 1.0]],\n",
-    "                                                    dtype=np.complex128)\n",
-    "\n",
-    "kraus_1 = np.sqrt(error_probability) * np.array([[0.0, 1.0], \n",
-    "                                                 [1.0, 0.0]],\n",
-    "                                                dtype=np.complex128)\n",
-    "\n",
-    "# Add the Kraus Operator to create a quantum channel.\n",
-    "bitflip_channel = cudaq.KrausChannel([kraus_0, kraus_1])\n",
-    "\n",
-    "# Add noise channels to our noise model.\n",
-    "noise_model = cudaq.NoiseModel()\n",
-    "\n",
-    "# Apply the depolarization channel to any X-gate on the 0th qubit.\n",
-    "noise_model.add_channel(\"x\", [0], depolarization_channel)\n",
-    "# Apply the bitflip channel to any X-gate on the 1st qubit.\n",
-    "noise_model.add_channel(\"x\", [1], bitflip_channel)\n",
-    "\n",
-    "# Due to the impact of noise, our measurements will no longer be uniformly\n",
-    "# in the |11> state.\n",
-    "noisy_counts = cudaq.sample(kernel,\n",
-    "                            qubit_count,\n",
-    "                            noise_model=noise_model,\n",
-    "                            shots_count=1000)\n",
-    "noisy_counts.dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.8666666666666666"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can also use noise models with the observe function\n",
-    "\n",
-    "hamiltonian = spin.z(0)\n",
-    "\n",
-    "noisy_result = cudaq.observe(kernel,\n",
-    "                             hamiltonian,\n",
-    "                             qubit_count,\n",
-    "                             noise_model=noise_model)\n",
-    "\n",
-    "noisy_result.expectation()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/examples/python/operators.html b/pr-2308/examples/python/operators.html
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-  <section id="Operators">
-<h1>Operators<a class="headerlink" href="#Operators" title="Permalink to this heading">¶</a></h1>
-<p>Operators are important constructs for many quantum applications. This section covers how to define and use spin operators as well as additional tools for defining more sophisticated operators.</p>
-<section id="Constructing-Spin-Operators">
-<h2>Constructing Spin Operators<a class="headerlink" href="#Constructing-Spin-Operators" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">spin_op</span></code> type provides an abstraction for a general tensor product of Pauli spin operators, and their sums.</p>
-<p>Spin operators are constructed using the <code class="docutils literal notranslate"><span class="pre">spin.z()</span></code>, <code class="docutils literal notranslate"><span class="pre">spin.y()</span></code>, <code class="docutils literal notranslate"><span class="pre">spin.x()</span></code>, and <code class="docutils literal notranslate"><span class="pre">spin.i()</span></code> functions, corresponding to the <span class="math notranslate nohighlight">\(Z\)</span>, <span class="math notranslate nohighlight">\(Y\)</span>, <span class="math notranslate nohighlight">\(X\)</span>, and <span class="math notranslate nohighlight">\(I\)</span> Pauli operators. For example, <code class="docutils literal notranslate"><span class="pre">spin.z(0)</span></code> corresponds to a Pauli <span class="math notranslate nohighlight">\(Z\)</span> operation acting on qubit 0. The example below demonstrates how to construct the following operator <span class="math notranslate nohighlight">\(2XYX - 3ZZY\)</span>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="n">operator</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>There are a number of convenient methods for combining, comparing, iterating through, and extracting information from spin operators and can be referenced <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.SpinOperator">here</a> in the API.</p>
-</section>
-<section id="Pauli-Words-and-Exponentiating-Pauli-Words">
-<h2>Pauli Words and Exponentiating Pauli Words<a class="headerlink" href="#Pauli-Words-and-Exponentiating-Pauli-Words" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">pauli_word</span></code> type specifies a string of Pauli operations (e.g. ‘XYXZ’) and is convenient for applying operations based on exponentiated Pauli words. The code below demonstrates how a list of Pauli words, along with their coefficients, are provided as kernel inputs and converted into operators by the <code class="docutils literal notranslate"><span class="pre">exp_pauli</span></code> function.</p>
-<p>The cell below applies the following operation: <span class="math notranslate nohighlight">\(e^{i(0.432XZY +0.324IXX)}\)</span></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">words</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;XYZ&#39;</span><span class="p">,</span> <span class="s1">&#39;IXX&#39;</span><span class="p">]</span>
-<span class="n">coefficients</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.432</span><span class="p">,</span> <span class="mf">0.324</span><span class="p">]</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">coefficients</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">words</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">pauli_word</span><span class="p">]):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)):</span>
-        <span class="n">exp_pauli</span><span class="p">(</span><span class="n">coefficients</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">q</span><span class="p">,</span> <span class="n">words</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-</pre></div>
-</div>
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diff --git a/pr-2308/examples/python/operators.ipynb b/pr-2308/examples/python/operators.ipynb
deleted file mode 100644
index 801b2357d2..0000000000
--- a/pr-2308/examples/python/operators.ipynb
+++ /dev/null
@@ -1,99 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9a37411d-09c5-42f4-9d62-132715cead3e",
-   "metadata": {},
-   "source": [
-    "# Operators\n",
-    "\n",
-    "Operators are important constructs for many quantum applications.  This section covers how to define and use spin operators as well as additional tools for defining more sophisticated operators."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3695762-03b5-46d6-9aac-853d1ebca9d1",
-   "metadata": {},
-   "source": [
-    "### Constructing Spin Operators\n",
-    "\n",
-    "The `spin_op`  type provides an abstraction for a general tensor product of Pauli spin operators, and their sums.\n",
-    "\n",
-    "Spin operators are constructed using the `spin.z()`, `spin.y()`, `spin.x()`, and `spin.i()` functions, corresponding to the $Z$, $Y$, $X$, and $I$ Pauli operators. For example,  `spin.z(0)` corresponds to a Pauli $Z$ operation acting on qubit 0.  The example below demonstrates how to construct the following operator $2XYX - 3ZZY$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "29ca747b-ee90-4a83-a41b-6480e0c7b6c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "operator = 2 * spin.x(0) * spin.y(1) * spin.x(2) - 3 * spin.z(0) * spin.z( 1) * spin.y(2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "222ea088-ea18-46cf-872d-5e76d25e5e93",
-   "metadata": {},
-   "source": [
-    "There are a number of convenient methods for combining, comparing, iterating through, and extracting information from spin operators and can be referenced [here](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.SpinOperator) in the API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "970e6f09-f0db-4aa3-b611-c33e8436aa55",
-   "metadata": {},
-   "source": [
-    "### Pauli Words and Exponentiating Pauli Words\n",
-    "\n",
-    "The `pauli_word` type specifies a string of Pauli operations (e.g. ‘XYXZ’) and is convenient for applying operations based on exponentiated Pauli words.  The code below demonstrates how a list of Pauli words, along with their coefficients, are provided as kernel inputs and converted into operators by the `exp_pauli` function.\n",
-    "\n",
-    "The cell below applies the following operation: $e^{i(0.432XZY +0.324IXX)}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3fe49bcb-0fe1-48e3-aee2-bff882f03fe6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words = ['XYZ', 'IXX']\n",
-    "coefficients = [0.432, 0.324]\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(coefficients: list[float], words: list[cudaq.pauli_word]):\n",
-    "    q = cudaq.qvector(3)\n",
-    "\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(coefficients[i], q, words[i])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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deleted file mode 100644
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
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-  <section id="Optimizers-and-Gradients">
-<h1>Optimizers and Gradients<a class="headerlink" href="#Optimizers-and-Gradients" title="Permalink to this heading">¶</a></h1>
-<p>Many quantum algorithms require the optimization of quantum circuit parameters with respect to an expectation value. CUDA-Q has a number of tools available for optimization techniques. This example will demonstrate how to optimize the variational parameters of a circuit using:</p>
-<ol class="arabic simple">
-<li><p>Built in CUDA-Q optimizers and gradients</p></li>
-<li><p>A Third-Party Optimizer</p></li>
-<li><p>A Parallel parameter shift gradient.</p></li>
-</ol>
-<p>First, the kernel and Hamiltonian and specified below.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span>
-    <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">.21829</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">6.125</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">angles</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="n">initial_params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Built-in-CUDA-Q-Optimizers-and-Gradients">
-<h2>Built in CUDA-Q Optimizers and Gradients<a class="headerlink" href="#Built-in-CUDA-Q-Optimizers-and-Gradients" title="Permalink to this heading">¶</a></h2>
-<p>The optimizer and gradient are specified first from a built in CUDA-Q optimizer and gradient technique. An objective function is defined next which uses a lambda expression to evaluate the cost (a CUDA-Q <code class="docutils literal notranslate"><span class="pre">observe</span></code> expectation value). The gradient is calculated using the <code class="docutils literal notranslate"><span class="pre">compute</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">()</span>
-<span class="n">gradient</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">gradients</span><span class="o">.</span><span class="n">CentralDifference</span><span class="p">()</span>
-
-
-<span class="k">def</span> <span class="nf">objective_function</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
-                       <span class="n">hamiltonian</span><span class="o">=</span><span class="n">hamiltonian</span><span class="p">,</span>
-                       <span class="n">gradient_strategy</span><span class="o">=</span><span class="n">gradient</span><span class="p">,</span>
-                       <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]]:</span>
-
-        <span class="n">get_result</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">parameter_vector</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">parameter_vector</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-        <span class="n">cost</span> <span class="o">=</span> <span class="n">get_result</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">)</span>
-
-        <span class="n">gradient_vector</span> <span class="o">=</span> <span class="n">gradient_strategy</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">,</span> <span class="n">get_result</span><span class="p">,</span> <span class="n">cost</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">cost</span><span class="p">,</span> <span class="n">gradient_vector</span>
-</pre></div>
-</div>
-</div>
-<p>Finally, the optimizer is called to return the optimal cost and parameters.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">energy</span><span class="p">,</span> <span class="n">parameter</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">dimensions</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">function</span><span class="o">=</span><span class="n">objective_function</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">minimized &lt;H&gt; = </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">energy</span><span class="p">,</span><span class="mi">16</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;optimal theta 0 = </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">parameter</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="mi">16</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-
-minimized &lt;H&gt; = -1.748382901613712
-optimal theta 0 = 0.58409164053813
-</pre></div></div>
-</div>
-</section>
-<section id="Third-Party-Optimizers">
-<h2>Third-Party Optimizers<a class="headerlink" href="#Third-Party-Optimizers" title="Permalink to this heading">¶</a></h2>
-<p>The same procedure can be accomplised using any third-party such as SciPy. In this case, a simple cost fucntion is defined and provided as an input for the standard SciPy <code class="docutils literal notranslate"><span class="pre">minimize</span></code> function.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
-
-<span class="k">def</span> <span class="nf">cost</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
-
-    <span class="n">exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">exp_val</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">initial_params</span> <span class="p">,</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;COBYLA&#39;</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">40</span><span class="p">})</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
- message: Optimization terminated successfully.
- success: True
-  status: 1
-     fun: -1.7488646919931474
-       x: [ 5.944e-01  1.288e+00]
-    nfev: 33
-   maxcv: 0.0
-</pre></div></div>
-</div>
-</section>
-<section id="Parallel-Parameter-Shift-Gradients">
-<h2>Parallel Parameter Shift Gradients<a class="headerlink" href="#Parallel-Parameter-Shift-Gradients" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q’s <code class="docutils literal notranslate"><span class="pre">mqpu</span></code> backend allows for parallel computation of parameter shift gradients using multiple simulated QPUs. Gradients computed this way can be used in any of the previously discussed optimization procedures. Below is an example demonstrating how parallel gradient evaluation can be used for a VQE procedure.</p>
-<p>The parameter shift procedure computes two expectations values for each parameter shifted forwards and backwards. These are used to estimate the gradient contribution for that parameter.</p>
-<p>The following code defines a function that takes a kernel, a Hamiltonian (spin operator), and the circuit parameters and produces a parameter shift gradient with shift <code class="docutils literal notranslate"><span class="pre">epsilon</span></code>. The first step of the function builds <code class="docutils literal notranslate"><span class="pre">xplus</span></code> and <code class="docutils literal notranslate"><span class="pre">xminus</span></code> , arrays consisting of the shifted parameters.</p>
-<p>Next, a for loop iterates over all of the parameters and uses the <code class="docutils literal notranslate"><span class="pre">cudaq.observe_async</span></code> to compute the expectation value. This command also takes <code class="docutils literal notranslate"><span class="pre">qpu_id</span></code> as an in out which specifies the GPU that will be used to simulate the ith QPU. In the example below, four GPUs (simulated QPUs) are available so the gradient is batched over four devices.</p>
-<p>The results are saved in the <code class="docutils literal notranslate"><span class="pre">g_plus</span></code> and <code class="docutils literal notranslate"><span class="pre">g_minus</span></code> lists, the elements of which are accessed with commands like <code class="docutils literal notranslate"><span class="pre">g_plus[1].expectation()</span></code> to compute the finite differences and construct the final gradient.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span>  <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="c1"># cudaq.set_target(&#39;nvidia&#39;, option = &#39;mqpu&#39;)</span>
-
-<span class="n">num_qpus</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">epsilon</span> <span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span>
-
-
-<span class="k">def</span> <span class="nf">batched_gradient_function</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">):</span>
-
-    <span class="c1">#Prepare an array of parameters corresponding to the plus and minus shifts</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters</span><span class="p">),</span><span class="mi">1</span><span class="p">))</span>
-    <span class="n">xplus</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">epsilon</span><span class="p">)</span>
-    <span class="n">xminus</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">epsilon</span><span class="p">)</span>
-
-    <span class="n">g_plus</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">g_minus</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">gradient</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="n">qpu_counter</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># Iterate over the number of GPU resources available</span>
-
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-
-        <span class="n">g_plus</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="n">hamiltonian</span><span class="p">,</span> <span class="n">xplus</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">qpu_id</span> <span class="o">=</span> <span class="n">qpu_counter</span><span class="o">%</span><span class="k">num_qpus</span>))
-        <span class="n">qpu_counter</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="n">g_minus</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">xminus</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">qpu_id</span> <span class="o">=</span> <span class="n">qpu_counter</span><span class="o">%</span><span class="k">num_qpus</span>))
-        <span class="n">qpu_counter</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="c1">#use the expectation values to compute the gradient</span>
-    <span class="n">gradient</span> <span class="o">=</span> <span class="p">[(</span><span class="n">g_plus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="o">-</span> <span class="n">g_minus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">())</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">epsilon</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">g_minus</span><span class="p">))]</span>
-
-    <span class="k">return</span> <span class="n">gradient</span>
-<br/></pre></div>
-</div>
-</div>
-<p>This function can be used in a VQE procedure as presented below. First, the gradient is computed using the initial parameters, then the standard VQE construction is used, but the <code class="docutils literal notranslate"><span class="pre">batched_gradient_function</span></code> is used to evaluate the gradient at each step. This objective function will return the cost and gradient at each step and can be used with any SciPy optimizer that uses a gradient.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gradient</span> <span class="o">=</span> <span class="n">batched_gradient_function</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">initial_params</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">objective_function</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">,</span>
-                       <span class="n">hamiltonian</span><span class="o">=</span><span class="n">hamiltonian</span><span class="p">,</span>
-                       <span class="n">gradient</span><span class="o">=</span><span class="n">gradient</span><span class="p">,</span>
-                       <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">):</span>
-
-    <span class="n">cost</span><span class="o">=</span><span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="n">hamiltonian</span><span class="p">,</span><span class="n">parameter_vector</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-
-    <span class="n">gradient_vector</span> <span class="o">=</span> <span class="n">batched_gradient_function</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">initial_params</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">cost</span><span class="p">,</span> <span class="n">gradient_vector</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result_vqe</span><span class="o">=</span><span class="n">minimize</span><span class="p">(</span><span class="n">objective_function</span><span class="p">,</span><span class="n">initial_params</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;L-BFGS-B&#39;</span><span class="p">,</span> <span class="n">jac</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="mi">5</span><span class="p">})</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
- message: Optimization terminated successfully.
- success: True
-  status: 1
-     fun: -1.7488646919931474
-       x: [ 5.944e-01  1.288e+00]
-    nfev: 33
-   maxcv: 0.0
-</pre></div></div>
-</div>
-</section>
-</section>
-
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diff --git a/pr-2308/examples/python/optimizers_gradients.ipynb b/pr-2308/examples/python/optimizers_gradients.ipynb
deleted file mode 100644
index 35b7442af7..0000000000
--- a/pr-2308/examples/python/optimizers_gradients.ipynb
+++ /dev/null
@@ -1,303 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e466cb3d-c036-45f7-af9f-b18b9aa22e8a",
-   "metadata": {},
-   "source": [
-    "# Optimizers and Gradients\n",
-    "\n",
-    "Many quantum algorithms require the optimization of quantum circuit parameters with respect to an expectation value.  CUDA-Q has a number of tools available for optimization techniques.  This example will demonstrate how to optimize the variational parameters of a circuit using:\n",
-    "\n",
-    "1. Built in CUDA-Q optimizers and gradients\n",
-    "2. A Third-Party Optimizer\n",
-    "3. A Parallel parameter shift gradient.\n",
-    "\n",
-    "First, the kernel and Hamiltonian and specified below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a77a963a-6f5c-4751-93c8-b3ccbb5921f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "import numpy as np\n",
-    "\n",
-    "hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(\n",
-    "    0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: list[float]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    x(qubits[0])\n",
-    "    ry(angles[0], qubits[1])\n",
-    "    x.ctrl(qubits[1], qubits[0])  \n",
-    "\n",
-    "initial_params = np.random.normal(0, np.pi, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c3de68e-754a-4412-8063-78ee9646b4b0",
-   "metadata": {},
-   "source": [
-    "### Built in CUDA-Q Optimizers and Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eea5d9f2-ade2-4411-88a7-b8c5505c29c3",
-   "metadata": {},
-   "source": [
-    "The optimizer and gradient are specified first from a built in CUDA-Q optimizer and gradient technique. An objective function is defined next which uses a lambda expression to evaluate the cost (a CUDA-Q `observe` expectation value). The gradient is calculated using the `compute` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "87387c0a-86cf-4dfb-9130-b1c3054e43d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = cudaq.optimizers.Adam()\n",
-    "gradient = cudaq.gradients.CentralDifference()\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector: list[float],\n",
-    "                       hamiltonian=hamiltonian,\n",
-    "                       gradient_strategy=gradient,\n",
-    "                       kernel=kernel) -> tuple[float, list[float]]:\n",
-    "\n",
-    "        get_result = lambda parameter_vector: cudaq.observe(kernel, hamiltonian, parameter_vector).expectation()\n",
-    "\n",
-    "        cost = get_result(parameter_vector)\n",
-    "    \n",
-    "        gradient_vector = gradient_strategy.compute(parameter_vector, get_result, cost)\n",
-    "\n",
-    "        return cost, gradient_vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b98f7129-d360-4e9c-9ea5-d4173ecb5ef1",
-   "metadata": {},
-   "source": [
-    "Finally, the optimizer is called to return the optimal cost and parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "713d7619-7b62-42b7-bb46-601ed9883e6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "minimized <H> = -1.748382901613712\n",
-      "optimal theta 0 = 0.58409164053813\n"
-     ]
-    }
-   ],
-   "source": [
-    "energy, parameter = optimizer.optimize(dimensions=1,function=objective_function)\n",
-    "\n",
-    "print(f\"\\nminimized <H> = {round(energy,16)}\")\n",
-    "print(f\"optimal theta 0 = {round(parameter[0],16)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a36a46fc-b04b-43e3-ac10-3b2ee59c3418",
-   "metadata": {},
-   "source": [
-    "### Third-Party Optimizers\n",
-    "\n",
-    "The same procedure can be accomplised using any third-party such as SciPy. In this case, a simple cost fucntion is defined and provided as an input for the standard SciPy `minimize` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0541fa5c-3c4e-44e7-bd13-52d3b07af50e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " message: Optimization terminated successfully.\n",
-      " success: True\n",
-      "  status: 1\n",
-      "     fun: -1.7488646919931474\n",
-      "       x: [ 5.944e-01  1.288e+00]\n",
-      "    nfev: 33\n",
-      "   maxcv: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.optimize import minimize\n",
-    "\n",
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "result = minimize(cost, initial_params ,method='COBYLA', options={'maxiter': 40})\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d73755a8-2c45-41c8-8d9b-d88a0b411869",
-   "metadata": {},
-   "source": [
-    "### Parallel Parameter Shift Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b01d93b-0953-4139-8a90-44414327f726",
-   "metadata": {},
-   "source": [
-    "CUDA-Q's `mqpu` backend allows for parallel computation of parameter shift gradients using multiple simulated QPUs. Gradients computed this way can be used in any of the previously discussed optimization procedures.  Below is an example demonstrating how parallel gradient evaluation can be used for a VQE procedure. \n",
-    "\n",
-    "The parameter shift procedure computes two expectations values for each parameter shifted forwards and backwards. These are used to estimate the gradient contribution for that parameter.\n",
-    "\n",
-    "The following code defines a function that takes a kernel, a Hamiltonian (spin operator), and the circuit parameters and produces a parameter shift gradient with shift `epsilon`. The first step of the function builds `xplus` and `xminus` , arrays consisting of the shifted parameters. \n",
-    "\n",
-    "Next, a for loop iterates over all of the parameters and uses the `cudaq.observe_async` to compute the expectation value.  This command also takes `qpu_id` as an in out which specifies the GPU that will be used to simulate the ith QPU.  In the example below, four GPUs (simulated QPUs) are available so the gradient is batched over four devices. \n",
-    "\n",
-    "The results are saved in the `g_plus` and `g_minus` lists, the elements of which are accessed with commands like  `g_plus[1].expectation()` to compute the finite differences and construct the final gradient. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7508c92a-ff80-4a92-9396-691825b1cabb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import  numpy as np\n",
-    "# cudaq.set_target('nvidia', option = 'mqpu')\n",
-    "\n",
-    "num_qpus = 1\n",
-    "epsilon =np.pi/4\n",
-    "\n",
-    "\n",
-    "def batched_gradient_function(kernel, parameters, hamiltonian, epsilon): \n",
-    "\n",
-    "    #Prepare an array of parameters corresponding to the plus and minus shifts\n",
-    "    x = np.tile(parameters, (len(parameters),1))\n",
-    "    xplus = x + (np.eye(x.shape[0]) * epsilon)\n",
-    "    xminus = x - (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    g_plus = []\n",
-    "    g_minus = []\n",
-    "    gradient = []\n",
-    "\n",
-    "    qpu_counter = 0 # Iterate over the number of GPU resources available\n",
-    "    \n",
-    "    \n",
-    "    for i in range(x.shape[0]): \n",
-    "\n",
-    "        g_plus.append(cudaq.observe_async(kernel,hamiltonian, xplus[i], qpu_id = qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1 \n",
-    "\n",
-    "        g_minus.append(cudaq.observe_async(kernel, hamiltonian, xminus[i], qpu_id = qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1 \n",
-    "        \n",
-    "    #use the expectation values to compute the gradient    \n",
-    "    gradient = [(g_plus[i].get().expectation() - g_minus[i].get().expectation()) / (2*epsilon) for i in range(len(g_minus))]\n",
-    "\n",
-    "    return gradient\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a317073-0e29-40d1-bf09-d168e7227f14",
-   "metadata": {},
-   "source": [
-    "This function can be used in a VQE procedure as presented below. First, the gradient is computed using the initial parameters, then the standard VQE construction is used, but the `batched_gradient_function` is used to evaluate the gradient at each step. This objective function will return the cost and gradient at each step and can be used with any SciPy optimizer that uses a gradient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6313f4ed-a620-4982-b587-42e19267ef84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gradient = batched_gradient_function(kernel, initial_params, hamiltonian, epsilon)\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector,\n",
-    "                       hamiltonian=hamiltonian,\n",
-    "                       gradient=gradient,\n",
-    "                       kernel=kernel):\n",
-    "\n",
-    "    cost=cudaq.observe(kernel,hamiltonian,parameter_vector).expectation()\n",
-    "   \n",
-    " \n",
-    "    gradient_vector = batched_gradient_function(kernel, initial_params, hamiltonian, epsilon)\n",
-    "\n",
-    "    return cost, gradient_vector\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "edd702a6-213d-469e-861b-9beee6207284",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " message: Optimization terminated successfully.\n",
-      " success: True\n",
-      "  status: 1\n",
-      "     fun: -1.7488646919931474\n",
-      "       x: [ 5.944e-01  1.288e+00]\n",
-      "    nfev: 33\n",
-      "   maxcv: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "result_vqe=minimize(objective_function,initial_params, method='L-BFGS-B', jac=True, tol=1e-8, options={'maxiter': 5})\n",
-    "print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/examples/python/performance_optimizations.html b/pr-2308/examples/python/performance_optimizations.html
deleted file mode 100644
index 71621e6a4d..0000000000
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Get-state">Get state</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Observe-Async">Observe Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Performance Optimizations</a><ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="Optimizing-Performance">
-<h1>Optimizing Performance<a class="headerlink" href="#Optimizing-Performance" title="Permalink to this heading">¶</a></h1>
-<p>Performance is a key focus for the CUDA-Q design. This section highlights some features that advanced users can take advantage of to increase performance in certain situations.</p>
-<section id="Gate-Fusion">
-<h2>Gate Fusion<a class="headerlink" href="#Gate-Fusion" title="Permalink to this heading">¶</a></h2>
-<p>Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below). By targeting the <code class="docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend and setting the <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code> environment variable, you can select the degree of fusion that takes place. A full command line example would look like <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE=4</span> <span class="pre">python</span> <span class="pre">c2h2VQE.py</span> <span class="pre">--target</span> <span class="pre">nvidia</span> <span class="pre">--target-option</span> <span class="pre">fp64,mgpu</span></code></p>
-<p><img alt="dc6d7fdc12d84131a42d258ddac20384" src="../../_images/gate-fuse1.png" /></p>
-<p>The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation. See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.</p>
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diff --git a/pr-2308/examples/python/performance_optimizations.ipynb b/pr-2308/examples/python/performance_optimizations.ipynb
deleted file mode 100644
index db8a563f80..0000000000
--- a/pr-2308/examples/python/performance_optimizations.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf94a9f4-6be0-4eda-b441-ecc85f89e0c7",
-   "metadata": {},
-   "source": [
-    "# Optimizing Performance\n",
-    "\n",
-    "Performance is a key focus for the CUDA-Q design.  This section highlights some features that advanced users can take advantage of to increase performance in certain situations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dcc9c85-780b-495e-aebe-8cd9c0012792",
-   "metadata": {},
-   "source": [
-    "### Gate Fusion\n",
-    "\n",
-    "Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. A full command line example would look like `CUDAQ_MGPU_FUSE=4 python c2h2VQE.py --target nvidia --target-option fp64,mgpu`\n",
-    "\n",
-    "<img src=\"../images/gate-fuse.png\" style=\"width:400px;\">\n",
-    "\n",
-    "The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation.  See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.\n",
-    "\n",
-    "<img src=\"../images/gatefusion.png\" style=\"width:300px;\">\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
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-   "version": "3.10.12"
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../../using/examples/examples.html">   Examples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
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-  <section id="Visualization">
-<h1>Visualization<a class="headerlink" href="#Visualization" title="Permalink to this heading">¶</a></h1>
-<section id="Qubit-Visualization">
-<h2>Qubit Visualization<a class="headerlink" href="#Qubit-Visualization" title="Permalink to this heading">¶</a></h2>
-<p>What are the possible states a qubit can be in and how can we build up a visual cue to help us make sense of quantum states and their evolution?</p>
-<p>We know our qubit can have two distinct states: <span class="math notranslate nohighlight">\(\ket{0}\)</span> and <span class="math notranslate nohighlight">\(\ket{1}\)</span>. If these were the only two states, we could represent them as two vectors on a one-dimensional line (i.e., the z-axis in the image below). We also know that qubits can be in an equal superposition of states: <span class="math notranslate nohighlight">\(\ket{+}\)</span> and <span class="math notranslate nohighlight">\(\ket{-}\)</span>. In order to capture all of the states in equal superposition, we will need a 2D plane (i.e., the <span class="math notranslate nohighlight">\(xy\)</span>-plane in the image below). If you dive deeper you will learn
-about the existence of other states that will call for a 3D extension.</p>
-<p>In general, a quantum state can be written in the form <span class="math notranslate nohighlight">\(\ket{\psi} = \cos(\frac{\theta}{2})\ket{0}+e^{i\varphi}\sin(\frac{\theta}{2})\ket{1}\)</span> where <span class="math notranslate nohighlight">\(\theta\)</span> is a real number between <span class="math notranslate nohighlight">\(0\)</span> and <span class="math notranslate nohighlight">\(\pi\)</span> and <span class="math notranslate nohighlight">\(\varphi\)</span> is a real value between <span class="math notranslate nohighlight">\(0\)</span> and <span class="math notranslate nohighlight">\(2\pi\)</span>. For example, the minus state, <span class="math notranslate nohighlight">\(\ket{-} = \frac{1}{\sqrt{2}}\ket{0}- \frac{1}{\sqrt{2}}\ket{1}\)</span>, can be rewritten as</p>
-<div class="math notranslate nohighlight">
-\[\ket{-}  = \cos(\frac{\theta}{2})\ket{0}+e^{i\varphi}\sin(\frac{\theta}{2})\ket{1}\text{ with }\theta = \frac{\pi}{2}\text{ and }\varphi = \pi.\]</div>
-<p>This can be visualized in the image below as a unit vector pointing in the direction of the negative <span class="math notranslate nohighlight">\(x\)</span>-axis.</p>
-<p>Using spherical coordinates, it is possible to depict all the possible states of a single qubit on a sphere. This is called a Bloch sphere.</p>
-<p><img alt="Bloch Sphere" class="no-scaled-link" src="../../_images/Bloch_sphere.png" style="width: 300px; height: 300px;" /></p>
-<p>Let us try to showcase the functionality to render such a 3D representation with CUDA-Q. First, let us define a single-qubit kernel that returns a different state each time. This kernel uses random rotations.</p>
-<p>Note: CUDA-Q uses the <a class="reference external" href="https://qutip.org">QuTiP</a> library to render Bloch spheres. The following code will throw an error if QuTiP is not installed.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install `qutip` in the current Python kernel. Skip this if `qutip` is already installed.</span>
-<span class="c1"># `matplotlib` is required for all visualization tasks.</span>
-<span class="c1"># Make sure to restart your kernel if you execute this!</span>
-<span class="c1"># In a Jupyter notebook, go to the menu bar &gt; Kernel &gt; Restart Kernel.</span>
-<span class="c1"># In VSCode, click on the Restart button in the Jupyter toolbar.</span>
-
-<span class="c1"># The &#39;\&#39; before the &#39;&gt;&#39; operator is so that the shell does not misunderstand</span>
-<span class="c1"># the &#39;&gt;&#39; qualifier for the bash pipe operation.</span>
-
-<span class="kn">import</span> <span class="nn">sys</span>
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-    <span class="kn">import</span> <span class="nn">qutip</span>
-
-<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Tools not found, installing. Please restart your kernel after this is done.&quot;</span><span class="p">)</span>
-    <span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>qutip<span class="se">\&gt;</span><span class="o">=</span><span class="m">5</span><span class="w"> </span>matplotlib<span class="se">\&gt;</span><span class="o">=</span><span class="m">3</span>.5
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">New libraries have been installed. Please restart your kernel!&quot;</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1">## Retry the subsequent cells by setting the target to density matrix simulator.</span>
-<span class="c1"># cudaq.set_target(&quot;density-matrix-cpu&quot;)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">angles</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-    <span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">rz</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubit</span><span class="p">)</span>
-    <span class="n">rx</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubit</span><span class="p">)</span>
-    <span class="n">rz</span><span class="p">(</span><span class="n">angles</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">qubit</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<p>Next, we instantiate a random number generator, so we can get random outputs. We then create 4 random single-qubit states by using <code class="docutils literal notranslate"><span class="pre">cudaq.add_to_bloch_sphere()</span></code> on the output state obtained from the random kernel.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
-<span class="n">blochSphereList</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-    <span class="n">angleList</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-    <span class="n">sph</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">add_to_bloch_sphere</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">angleList</span><span class="p">))</span>
-    <span class="n">blochSphereList</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sph</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<p>We can display the spheres with <code class="docutils literal notranslate"><span class="pre">cudaq.show()</span></code>. Show the first sphere:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">blochSphereList</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/examples_python_visualization_7_0.png" src="../../_images/examples_python_visualization_7_0.png" />
-</div>
-</div>
-<p>We can also show multiple Bloch spheres side by side - simply set the <code class="docutils literal notranslate"><span class="pre">nrows</span></code> and <code class="docutils literal notranslate"><span class="pre">ncols</span></code> in the call to <code class="docutils literal notranslate"><span class="pre">cudaq.show()</span></code> accordingly. Make sure to have more spaces than spheres in your list, else it will throw an error! Let us show two spheres in a row:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">blochSphereList</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/examples_python_visualization_9_0.png" src="../../_images/examples_python_visualization_9_0.png" />
-</div>
-</div>
-<p>We can show them in a column too, if we want! Simply set the <code class="docutils literal notranslate"><span class="pre">nrows</span> <span class="pre">=</span> <span class="pre">2</span></code> and <code class="docutils literal notranslate"><span class="pre">ncols</span> <span class="pre">=</span> <span class="pre">1</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">blochSphereList</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/examples_python_visualization_11_0.png" src="../../_images/examples_python_visualization_11_0.png" />
-</div>
-</div>
-<p>Can we show the entire list of 4 Bloch spheres we created? Absolutely!</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">blochSphereList</span><span class="p">[:],</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/examples_python_visualization_13_0.png" src="../../_images/examples_python_visualization_13_0.png" />
-</div>
-</div>
-<p>What if we had to add multiple vectors to a single Bloch sphere? CUDA-Q uses the <a class="reference external" href="https://www.qutip.org">QuTiP</a> toolbox to construct Bloch spheres. We can then add multiple states to the same Bloch sphere by passing the sphere object as an argument to <code class="docutils literal notranslate"><span class="pre">cudaq.add_to_bloch_sphere()</span></code>.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">qutip</span>
-
-<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">47</span><span class="p">)</span>
-<span class="n">blochSphere</span> <span class="o">=</span> <span class="n">qutip</span><span class="o">.</span><span class="n">Bloch</span><span class="p">()</span>
-<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
-    <span class="n">angleList</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-    <span class="n">sph</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">add_to_bloch_sphere</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">angleList</span><span class="p">),</span> <span class="n">blochSphere</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<p>This created a single Bloch sphere with 10 random vectors. Let us see how it looks.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blochSphere</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../../_images/examples_python_visualization_17_0.png" src="../../_images/examples_python_visualization_17_0.png" />
-</div>
-</div>
-<p>Unfortunately, there is no such handy visualization for multi-qubit states. In particular, a multi-qubit state cannot be visualized as multiple Bloch spheres due to the nature of entanglement that makes quantum computing so powerful.</p>
-</section>
-<section id="Kernel-Visualization">
-<h2>Kernel Visualization<a class="headerlink" href="#Kernel-Visualization" title="Permalink to this heading">¶</a></h2>
-<p>A CUDA-Q kernel can be visualized using the <code class="docutils literal notranslate"><span class="pre">cudaq.draw</span></code> API which returns a string representing the drawing of the execution path, in the specified format. ASCII (default) and LaTeX formats are supported.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel_to_draw</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">y</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="n">z</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-    <span class="n">swap</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">swap</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-    <span class="n">swap</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-    <span class="n">r1</span><span class="p">(</span><span class="mf">3.14159</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">tdg</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">s</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel_to_draw</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-     ╭───╮                  ╭───────────╮
-q0 : ┤ h ├──●────●────╳───╳─┤ r1(3.142) ├───────
-     ├───┤╭─┴─╮  │    │   │ ╰───────────╯╭─────╮
-q1 : ┤ h ├┤ x ├──●────╳───┼───────╳──────┤ tdg ├
-     ├───┤╰───╯╭─┴─╮╭───╮ │       │      ╰┬───┬╯
-q2 : ┤ h ├─────┤ y ├┤ z ├─┼───────╳───────┤ s ├─
-     ├───┤     ╰───╯╰───╯ │               ╰───╯
-q3 : ┤ h ├────────────────╳─────────────────────
-     ╰───╯
-
-</pre></div></div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s1">&#39;latex&#39;</span><span class="p">,</span> <span class="n">kernel_to_draw</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
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-\documentclass{minimal}
-\usepackage{quantikz}
-\begin{document}
-\begin{quantikz}
-  \lstick{$q_0$} &amp; \gate{H} &amp; \ctrl{1} &amp; \ctrl{2} &amp; \swap{1} &amp; \swap{3} &amp; \gate{R_1(3.142)} &amp; \qw &amp; \qw \\
-  \lstick{$q_1$} &amp; \gate{H} &amp; \gate{X} &amp; \ctrl{1} &amp; \targX{} &amp; \qw &amp; \swap{1} &amp; \gate{T^\dag} &amp; \qw \\
-  \lstick{$q_2$} &amp; \gate{H} &amp; \qw &amp; \gate{Y} &amp; \gate{Z} &amp; \qw &amp; \targX{} &amp; \gate{S} &amp; \qw \\
-  \lstick{$q_3$} &amp; \gate{H} &amp; \qw &amp; \qw &amp; \qw &amp; \targX{} &amp; \qw &amp; \qw &amp; \qw \\
-\end{quantikz}
-\end{document}
-
-</pre></div></div>
-</div>
-<p>Copy this output string into any LaTeX editor and export it to PDF.</p>
-<p><img alt="Circuit in PDF format" class="no-scaled-link" src="../../_images/circuit_pdf.png" style="width: 500px; height: 250px;" /></p>
-</section>
-</section>
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diff --git a/pr-2308/examples/python/visualization.ipynb b/pr-2308/examples/python/visualization.ipynb
deleted file mode 100644
index 76347fe487..0000000000
--- a/pr-2308/examples/python/visualization.ipynb
+++ /dev/null
@@ -1,394 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization\n",
-    "\n",
-    "## Qubit Visualization\n",
-    "\n",
-    "What are the possible states a qubit can be in and how can we build up a visual cue to help us make sense of quantum states and their evolution?\n",
-    "\n",
-    "We know our qubit can have two distinct states: $\\ket{0}$ and $\\ket{1}$. If these were the only two states, we could represent them as two vectors on a one-dimensional line (i.e., the z-axis in the image below). We also know that qubits can be in an equal superposition of states: $\\ket{+}$ and $\\ket{-}$. In order to capture all of the states in equal superposition, we will need a 2D plane (i.e., the $xy$-plane in the image below). If you dive deeper you will learn about the existence of other states that will call for a 3D extension. \n",
-    "\n",
-    "In general, a quantum state can be written in the form $\\ket{\\psi} = \\cos(\\frac{\\theta}{2})\\ket{0}+e^{i\\varphi}\\sin(\\frac{\\theta}{2})\\ket{1}$ where $\\theta$ is a real number between $0$ and $\\pi$ and $\\varphi$ is a real value between $0$ and $2\\pi$.  For example, the minus state, $\\ket{-} = \\frac{1}{\\sqrt{2}}\\ket{0}- \\frac{1}{\\sqrt{2}}\\ket{1}$, can be rewritten as\n",
-    "$$\\ket{-}  = \\cos(\\frac{\\theta}{2})\\ket{0}+e^{i\\varphi}\\sin(\\frac{\\theta}{2})\\ket{1}\\text{ with }\\theta = \\frac{\\pi}{2}\\text{ and }\\varphi = \\pi.$$ \n",
-    "This can be visualized in the image below as a unit vector pointing in the direction of the negative $x$-axis.\n",
-    "\n",
-    "Using spherical coordinates, it is possible to depict all the possible states of a single qubit on a sphere. This is called a Bloch sphere. \n",
-    "\n",
-    "<img src=\"../images/Bloch_sphere.png\" alt=\"Bloch Sphere\" width=\"300\" height=\"300\">\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us try to showcase the functionality to render such a 3D representation with CUDA-Q. \n",
-    "First, let us define a single-qubit kernel that returns a different state each time. This kernel uses random rotations.\n",
-    "\n",
-    "Note: CUDA-Q uses the [QuTiP](https://qutip.org) library to render Bloch spheres. The following code will throw an error if QuTiP is not installed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# install `qutip` in the current Python kernel. Skip this if `qutip` is already installed.\n",
-    "# `matplotlib` is required for all visualization tasks.\n",
-    "# Make sure to restart your kernel if you execute this!\n",
-    "# In a Jupyter notebook, go to the menu bar > Kernel > Restart Kernel.\n",
-    "# In VSCode, click on the Restart button in the Jupyter toolbar.\n",
-    "\n",
-    "# The '\\' before the '>' operator is so that the shell does not misunderstand\n",
-    "# the '>' qualifier for the bash pipe operation.\n",
-    "\n",
-    "import sys\n",
-    "\n",
-    "try:\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    import qutip\n",
-    "\n",
-    "except ImportError:\n",
-    "    print(\"Tools not found, installing. Please restart your kernel after this is done.\")\n",
-    "    !{sys.executable} -m pip install qutip\\>=5 matplotlib\\>=3.5\n",
-    "    print(\"\\nNew libraries have been installed. Please restart your kernel!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "## Retry the subsequent cells by setting the target to density matrix simulator.\n",
-    "# cudaq.set_target(\"density-matrix-cpu\")\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: np.ndarray):\n",
-    "    qubit = cudaq.qubit()\n",
-    "    rz(angles[0], qubit)\n",
-    "    rx(angles[1], qubit)\n",
-    "    rz(angles[2], qubit)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we instantiate a random number generator, so we can get random outputs. We then create 4 random single-qubit states by using `cudaq.add_to_bloch_sphere()` on the output state obtained from the random kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.default_rng(seed=11)\n",
-    "blochSphereList = []\n",
-    "for _ in range(4):\n",
-    "    angleList = rng.random(3) * 2 * np.pi\n",
-    "    sph = cudaq.add_to_bloch_sphere(cudaq.get_state(kernel, angleList))\n",
-    "    blochSphereList.append(sph)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can display the spheres with `cudaq.show()`. Show the first sphere:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jcIdEx3Xheh50XUe5WGxKKXC4rouvv/4aa+vrOLq8jBfOn8c0ixLUGw14vp86HmraHhFIkrS8kldzZKoxNJaO0VUVCtNzkE5RDkYSYu6nMAKCMMSjR4/w+eefA4qCM2fPCvdHXn6aLxRQmZrCzNwcLMsS/yQkDjskOZB4ZkEphe/7qFeraFSr8Fy3aUHhvgCu76eLk6ZhqlzuKfDreV7sCQ0pWwizyxgPpw+cTtj3g/YfbdfzYDuOOFc+l0MuS3ZaTYqwRw6EX0GhkLoh8tez16ysrODbb76Bqml47Qc/wNEjRwB2vbxiod5oQNc0FPL5pnOJPgtMnKl1S8twDwX2dR8R4MSrnxvXA7bj4KOPPoJt23jt9deRy+WE7oE7TyqqilK5jMr0NKamp1EqlZDL5aSgUeLQQpIDiWcOQRCgXquhXq3Cs+2mhVDTNBi6DtMwEEYR6rYNyioQKqVS18melyuGUSRC5BH7Pg7DNCUBpItZxpiImxrxnXP257z7ocpsi8Vr+1x0OPngOXqOOI5hOw6CMASlFJqqolAoCEOlVh0BBRD4PnbrdQDA9NRU0xgajQa++vJLPNrcxPMnTuDll16CoeupGyRLy4A5RTZcF7quY2ZqKiUCjBQMs9NvV8VB2f3l0YVsembYWEIQhvjkk0+wubmJH/7whzh69GhqWMVSEfz9DoMAmmmiNDWFmZkZTDOiIPtBSBw2SHIg8UwgjmO4rovdnR249Toi3xeGQoauwzAMGIYBlS14NnPbo0kCXdeRt6x0Acgs+nH2/3G8Zw2ccSMUUYFMCV62t4FYrHiYnFcpsL8RoHs2yMIAqcVmWNM0mIYB0zDENe39OW06Dz+v7/vpdbI0Ro4ZDWW1CHys9Xodnu/D0HWUikVxrOvXr+PSpUvI5XJ47bXXMDMzk+buw7DJG0FhxMt2HKiKso9gdERm7DxKwMfG0y8kc12UO0GynwuL6cxxhFEU+3k/UZo4SfDll1/izp07ePHFF3Hu3Dlx3iiOEYYhgiCA63kIggCEEOQKBVRmZjAzM4OpqSnkZP8HiUMCSQ4knlpwG2PbttHY3UXgukjCEIQQmEy1nxWTBVGERqOBre1tOK6LOI6hahoMvutjC7+CPeGfIAEMfFevsdy4ytoUE9Y/AEBzLwRKQVlpXcJC1FyRny1l5N/z6wLalx7yUDqlaX8GI2OKZDIClN2hc1JgOw68IAAohaooqRbBMISZEQWwtb2NJElSu2LDQLVaxZdffYXdnR2cOn0ap06fFqWC4v4wMyjTMMTueYf5QpRKJZitTodtUhq90LFSIQN+D7uZUHFip7RLq2SIxeVLl3Dp8mWcPHECr/7gB/sITpIk8IMAjuPAcV1EUQQzl0OpUsHU9DRmZ2dR6hGFkpB43JDkQOKpQxiGqR9/vQ7XcRB5XhoB0DRYliWcC6MoguO6cFwXtusi8Dz4YYiEhapN00zz6iw0z4VxKgvz83C4+HmmSRIHF8f1+zHbV6HQ+vuMViH7lac0/DBEGAR7boyKkhKR9I+BTKSERxi4UVIQBGg4ThrhoGkzpXw+D4WkDZWqu7sghKBUKuHa1au4euUKCuUyLly4gEI+v1cpQYggIrqmNXVxBFJRoh8E+2yXh0U/5CB7/zoZIzWBa0FarahZdOLO3bv44osvMDc/j7feeqtjY6coiuC6LhrMslnTdRRKJZSnpjA9PY2ZmZmmCI+ExJMCSQ4kngokSQLHceCyZj2h4yAKAhAAlmUJwZ3jurDZjs73fRBCRCrB8zwRKZiqVGDlck1WxoOCInVSHOQD1steeZDjBEGQ/mPh7pARBpG+wF7kgGstDF1HxMiBpmkAISjk82mfAteFbdu4dOkSbNvG8ydO4MTzz4sdsJ6JTvDywnbggkjDMFAek2fAIAQhZk6Ww4Iw/cfDjQ387ne/Qz6fx9vvvLOXMujgrRAEAeq2Ddu2kQAoFIsolEqoVCqYmZlBpVIZekwSEuOGJAcShxpxHIuGOhFrgZxEUboz1nXElArS4HleWifPc/VsF22ZZipsY+VppWJxLAKyaICIQfZ6RG+CPgV6Is+e0SW0hud5Pj6IItE+OQgChMxlkL+SpyXiKIIXhlCQagVcx8HDhw+xvbWFSqWCcy+8gEIuJ3QOhq53DJO36gCiOE77LDCHxXbXyd8f/l4B2KeByF5v3Kb8shNEtGUMU1+tVsOHH34IwzTxwfvv7yOR7XpXJEkC27axW6/DDwLkWHfJYrmMo0ePolAoSN8EiccOSQ4kDiUipg9wXTe1NW40oCLd1YXMHdBxXSE244uNqqp73f1yOei6nu6IWWlfuVQauVQRgOikmBUcAi2LW8v3nfonTAJZ8WPIejP4vi9MgPwgSEWcvo/q7i7ura2BAliYncXRI0dQKpVQzOdF1GXQc/Oqh3Kx2DP3zttT846L2f9z8kC51oKTpMz9bhV38v9HUdQ3AeuGarWKX/3ylzh15gxeeemltPNm9v3OlKtyPQhf/D3Pw26tBttxkM+kG+bm5pBjwlAJiccBWV8jcagQRRHq9Tpc1wUAOLUanFpNmPzwjogKIdAUBYqqImeaMBkhMLmJTeZ4juOAAiiwNMLAaF34+1DAkzbfj2sZ4OWLQvTIwuj8K7dlphkLYm7wpLNOiFEYomHbaTlnrYbpfB5hkmC6UoGmKHAcB4HvI29ZaTvllvuaHQtfrHnkAAAMXRfCRa3NPc82WOKaik7gZAEs0tBKILjQkINrIzRmwgT033yqHaampvDSyy/jm2++weLCAhYWFprG1gqSIQqWZcE0TYRhiM3tbWxtbAidQrlcRrlcRj6flyRB4sAhyYHEoUAYhiJSAKQufNsbGwhcF4qmpVEBQqAZRlqJYFnI5XKwDCPdybUBTRI0Gg1QALqm7SMObdFCBNphGN1APwG8VsOkJLPIx8zWOc5UQvTjsEiAvYoKACAEYRCAKAqCIMD9u3cxOzWF50+cwBdffYXFubmUQFEKVddTrUejAd/3UcjnUSgUkGf3sVvPBU3TEARBauPcwVFQEBneZTHToZL3RuD3oZPTJADhE8EjDdnvE2Cv+gT7Izr94uyZM9h4+BCff/45/n9/5+/AYPcg+ywIcWOGkIGdzzQMLC0soOE4qO/sIMqkfizLQrFYlCRB4kAhyYHEE40gCNBoNOB5HgDAcRxUd3bgNxowNA2GacLUdeSYy98grnS26yJiu9diobD/BX0QgVZ08vfv+XedFjakoXJk2hGHzGeh34ygmtlNK9nwPC/dU5Q0hcDr8xUF6w8e4Mb16zj+3HM4c/o06o0GVEJQLpVgWdaeNiGO4XkeEkrRaDRQq9WgKIogCjnLgqooYqHl2geejkh42WO7cRMCMMfE1ivl4XmaIQu8H0O2zTXYa6IOhI1Xh/BGWZqui7ENRBgIwRs//CH++he/wOdffIF33nln30toC2FTmLCRH1dRFJSLRViGgVq9jka1ijiOkc/nRcSsWCxKTYLEgUCSA4knElEUYXd3F77vI2E7fC461ADkDAM5y0KlUklV4gPWx/u+L/on8GZAwOih/UGjBsLBEHv5cf5VOCwy18V2UAkBUVXxNduUiIfaWxeS7K41SRK4jgPX98W137p1C3fu3MGF8+dx5tw5OLad7rYJEakZl5G1fC6H+dlZ2KxKxPW8VHDnOKg3GlAUBfl8HsV8HnlG3CjSVtYgJI10cHOjLvep9XeCNGXKR7nFczaFwIlCu8hDEscglKZdGVllBwDhU9FKFrLjaKcZsSwLb/zwh/joww9x8+ZNnD59ussV7T0rlL2PvAzUMAxMTU2hYdtwazWoioIkSZDP51Gr1dBoNCRJkJg4JDmQeKJA2Q600WgIguC6LhRKQaIIlqqikM+jVC7vefunf9j3Obh1MADkLAtGhxr1YcffC2JCzyzSEaUiIsAdF9v9He/+yP0Vsk6MvcaVNW7iBlGu54lwt6oouHz5MtZWV/H6a6/h1MmT4j6prHqDMIJAmO6Ak7dSoYBSoZCSDd+HY9twPQ9xksCxbTQaDRBFQd6yUCwUUvMppKkdBRACwsyAe9/Hdj9jCzm/XoURJqqqMNrcoyRJ4AUBQlbqGbNyz6CFLOi6nt5zRhbaaUYApC2ez5zBt999h7m5OZQrlZ6Ekzs9ih4TjNhVSiU4joPa9jaK5bI4v67rqNVqcF0XU1NTHT0WJCRGgSQHEk8MgiBAtVqF53nY2dmB67owNA16koBQivLUFMql0n5jmn6Q2aHbrJ+CpmnI5/NjvYYsOciSgNbFO9uHIWCVAa1QMmRAZ0ZLTcK6fggBabZxTijdi5qwY+isv8Fnn32GarWKH7/9No4sL4voBYDm0k5CYLF0QcO2EYYh6o0GioUCVEbe8rmcICCO66ZVJczC2nZdkQbI53IoFQr7S0dJptsjK1nsZ5EVNsrZlBBbzLPlj1wcqSgKTF2HzqIPCaWI2fuSJQu+78Pn7wsnC+y9aU1DvPzyy9jc3MSnn32Gn3/wgSBW7bpdtgOPKBCkkRlN01KCVaulvgjMOClkXSOLxSJK7HMhITEuyFJGiceOJElQq9Wwvb2NnZ0d2LaNvK5DUxRoqpqqtplZzqCPK/fP5xMnt7RNKMVUpTIWC1uKVNQG5iTYbpKOeWMeFhlIMmSAaxRUFs4Wiw7LtQs73wF8D/i4st/7LFLA76GmachZFoIowke//S2CMMRP3nkH09PTIj++W6uJv//lL36Bn3/wAaampsTCzUtKE96ZsFhMNQYZ22U+BjdDFMIwRK3RQELTrpCFfB6lYhHFfL7te5J917stsgNrPihFzAhBOySs5DHKkIVWZMmCzjpJ1up1/PKXv8Tx48fxg9deE+fi72G/RCE7Rt/3RTqnUCxiem4OOoueqaqKSqUi20VLjA0yciDxWOG6LlZXV7G1tQXXdWGqKsrMmKhcLqNQKIidY7/EQJQG8sWUfQ3DUHgfFAuF0YkB351mdQOKArBdcVYz0E6LoGoadFYpoLGvYtfL6vVbr6EdOpVN8t1ywBaVhJUuqowUmIaBnWoVH334ITRNwwfvv5/eb6T3LgjDdJyquj+ywd4TTdNQKhbRsG3EcZyK5rKRAF5WSdOmTjnLAqan4QcBlI0NuJ4HTVEQ+D4eeh62VBVT5fK+DphNV9diZ9w0rI53qQOY2HOfLgMs5cGMsnjqSZCFMEzJAhNAco8IYI8sXLx4Ed98+y0WFxawfPTo/nLKAcaoMC2Coihp9KVeh12vp1bM09Owcjlsb28jl8uhPIa24hISkhxIPBaEYYi7d+/i4cOHCIIAWpKgZJooFYtCES9K9tCDGGQW1GyUIIskSdCwbVBAdGAcCoTsn9jZ4ucHgdgVtxtvVuSmqSoI22FzvwF+7H4IAbCXNujkpxCw8cQ8MsEMoEzWfGmN2f9WymW88847QsPBd/sRIwd6xg9g75L3BKCqpqFUKqU6EUYQCoVCc5Mn9np+bNMwMD01hVIUQdd1RGGIOiMYWzs72KlWUWEkoTXl0EoUWishktbX9EC796pJn5G+CAAGIguzs7OYn5vDZ198gXdzOZRKpT19QLv3rMszzktOQSkKhUJaWeL7aNTrCFwXVj4Pg6VyfN8X/ggSEsNCkgOJA8fdu3dx7949xEEAkiTIGwZmZmcxMz0tJkCOpsUzi2zNf1aD0GGhdFmXRUKI2B33AqVU+Oh38ssPM3bEwJ5lMiEkJQKZf+2MePg59n4xQOqgHVHBnuCSixoVQtJIgWmK+3R7ZQVffvkljiwv40c/+lHTTpMfm1dIaLoO+H7rafYqRFg5ZLlUQsO2RflpvlAQ5kitokmaJFBVFVEUiehDZWoK9Xod9UYDYRhip1bDTrWKUqmE6XK5I6HLRop4ioenRfrJw3csI22tImHHz6YEepGFFy5cwKe/+x2++/57vPTSS6nGgTX02he56lFxQ5ASMRpFyDPDLod10ySqCpokqFeryJdKovnYNP9MSUgMCEkOJA4Mruvi2rVr2N3ZAcIQlmFgbmEBM9PTwiUvG37nbXab8rQZItCvAIsr6BMApT52U1miQfZ+KH4fRRF8pmbPLiyqqsIyTRCmlWhaDNmC0q2ssCdaCEq7v/I8D7brioiCZZowTVMsRBTA5UuXcPnKFZw+dQqv/uAHTccRVs4ZHwW9Q58JgpYqA0JQLBRgEyJaFidJ0tR5Ubya+S0Ae++5Qggq5TJKpRIc1nsgCEPYtp1GI/J5TPfIq2fvsdBbdFl0+05VZY6Zfb8SQLS15teQJQslSnHh/Hl8/c03cB0HuXw+bQ7muqJRVbZteGvKpDVywSMIcRyLjqFcxwEAlmnCazSwu72N8tQUwjDE7Oys7PwoMTAkOZA4EDx8+BA3btxA5LpQAcwvLmJpYWFv0eIRAgYuLMsq7puqFAZQZnu+nzr6sVxwR7QsItkziLxyEOxzveM7Qb5DyzYy4q/pRAD6IgbZtEkHcG+BMIpAABiahnyh0FSjTwF89eWXuL2ygpdfeglnz51r6x/QFDVoiXi0Gxu7EPG1wKoWHMcRBknFFlJGsGct3CryUwhBsVhEsViE47qo1eui7NK2bViWhempKRQ6ED1esYAsSWh5b/l3o3bBVNASVWjRLyiE4NSpU7h27RrWVlfx8iuvwGcttcMwRBiGUBRFtNBu2uVnKzYy16AQAigK4iSBaZqglAqxJ2HkRNd11KtV2I0GfM/D4tLSXtdICYk+IMmBxEQRBAHu3LmDjbU10CCApet47vhxFLOteluJAScFwF5Yf0gkSZIq9AHkW3ecHfQD2XHwevdWEyI+mXOvgezfKKq6V43Qz8La9leZcrweBCIIAtiOI3bBhUw4P4vvvvsOt+/cwZtvvIHnnnuu6zF5CWOvGvpsx8QsLMuCQkiaZvB9uITsW5xUVd3bGXdAPpdDPpeDHwTY3d2FwzQdaxsb0HUd01NT+yocSKf7mo26sKhUFMdde2D0A9JCQrgeg49CVRScO3sW33z7LV68eBHlUglxHMNnDa6SJIHnefA8Dzpz/TSy0YTs2DlBUBRBni3LQkIpAt+H4zgg+TwMw0Ahn4fjebi/sgK7VsOxEydkW2iJviHJgcTEsL29jTt37qBRrYIkCWbKZRw5ckSUXwFIy7SYFgBAxzz6sPB9P93NAc0CuQ6gLGfsZ0xwOHRNE2LGbpEAAgBs8u51rnY/410keyGhaTtqPwhASFqFUCwUhN0wD/sTSnH95k1cvXoVr776Kp577rmOCzLfdYdhiCRJ0mOxRYi3Oc5el9ilI0Ni2K7dME3kkfpKOJ4HoigwLUu4IRJFASUkPSa/dx3umWkYWJifT0sg63XUbRtJkuDh5mbbCofsuNqBIiWOwtp5AI1CN7QKMPmifuLkSVy5ehXXrl/Hqz/4AVRVTTuDWhbCMITP2mdzfwWH+S8Yprm/MRU7pqqqqa6ElYOCiWJtx0k1L7qOfC4H1/OwvbUF23Hw3PPPY3F5eeTrlHj6IcmBxNgRRRHu37+PRw8fwqvXYWgaFhYXMTs9ndrmctC0854o3WM/jgepAe8CHm6lSHeg3SZEsZPz/X3+9yZr5jRI6aOiKE1eBm3H1zLW1soDHq4GpXutiNnvQuYvwMdqsWZThC224viU4v6DB/j9V1/h3Asv4MyZM22JQfaaY9a9kbCulkDn6EX275oWY/a9aRhIWN8G27ZT0qDre/4HvNUyrzRoIR5ZEAC6YWB2ZgaVSiXtP9BotK1wUDWta0SCZho1ibQDae4cOSqy4zd0HWfOnMHlK1dw/oUXYLIoCu+GaRgG4iRBkEldeb4Pz/ehaRrMNqSUu1ry7pU5Vq0QsI6apWIxNfrKiBdvXr2K2u4uzrzwwlg8PiSeXkhyIDFWVKtVPHjwQHSWqxSLmJudRblU2ie2itmOnkyAGPBdVMJ24u06LiZJIiIE2VI9QoiYjPc596F/EaGqqmk+vd1rM776/B6IkryWc/Hr4Yu947rwPQ8gRDgSthsnAGw+eoTPPvsMzz3/PF5+6aWu4xVWziylwMP+/aLTfcnlckKzYTcaUFh5IiEkjR7w9tEtaYbWRbqppFBRUKlUUCmX04ZPmQqH7WoVZVYSa3R43+M2WoN94tMxkQSO06dP49q1a7h56xZefPFFQfo4VEVJO4laVuqc6fsIMl4Zjus2pbOAVNMgrkRRUCgUQJn1eL3RQLlUgqqqojrCtm2sr66iXqvhhYsXUSyVxnqNEk8PJDmQGAuiKMLq6ip2trfh7O6CUIrFuTlUKpW29dYxM5hpVcq3CroGQVNLY0JEY6VcJmrAQ+a+7+/TEei6DtM0obfoCLLja1dx0Am89EwQD17nTwgoD3u3LBDdlqMoimDbtmguZDFToU7jqdVq+OjjjzE3N4cf/vCHfYs4eQnkoCVw3e5LPp/ft6vlnSFjsBA/j1LsHTCNYHQ6H3tNqVRKxYuOg916HWEQoGHb2KlWUSgWUSmV0m6dhCBmBlWtzpn7jsuODTSTklFgGgZOnz6NWzdv4twLL+w9Z5mIUHravYoHTmD9IEASx8JsSVVVmKYpjJESRrS5IJSLUxu2LWyt9Yxhld1o4Jsvv8Tpc+ewuLw88rVJPH2Q5EBiZNRqNdy/fx+h48B3HOSZu2G5WExzzBnwBVFtt/gOmfNt8iNgf+/7vlgETNNEGIYI2CSbhcZ0BGYvPUKXfDj/fbsFhCBt/tOkq0gHvfd3vS8wVeu7buopwKIF3cSCjuvitx9+iEKhgLd//OPuIeSW+86rB/blukcA95dIGg1EnCBkokmd7qzSQzvQenxuEFSr1US1hO04aQfPclncs2xEplVQ2OnnrR4Hw+DMmTO4ceMGbjOCkC1RBPbfB0VRhLMk1yYEYYg4juE4DlzXTc21WFMm/jfFQgG1ej31PmARBN6qu1Qspj4YUYRrly+jsbuL46dOyXJHiSZIciAxNJIkwYMHD7C9uQlEEWgUYapSgWkYKBQKzcSAT7ad6s3Z8QYOY2erGTJ/67puqupWVdTq9aa0AdcRGKbZVOq379hdxpLtGcD+oONrFQBQ1bT/APtZvwV0cRTB4YZGLD+dz+e7KuyDMMRHv/0tFELw7rvvikWj47VkIjiUKfgBdExVDAtCUh+Eer2OmLXh5uftVlLYznyoG/hi6noedut1uLYN13VRZ7vo6Uql6dqyfSCaSlkzkSge8clGoIYhCTnLwonnn8eNGzdw+syZfdEZIaTMiBk5dFaimCQJAhb9iuM4Jb5hCOJ5yOVyIiJRKhZRbzSQxDEato1CPp9Gaxh5qDO9xsbGBlzXxZHjxzEzNzfEVUk8jZCKFImh0Gg0cPXqVWw/fAj4PvKmKYiBZVl7RjV0z7ioq3p8kKgB323xhb3l78IggOt5wmmPEwPTMFAqFjFVqSCXy7UlBpQdb99YWsbe78LA/0pBmlMWYeQ+dsO+56FWr4sISLFQSHtCdLlPSZLgk48/hud5ePfdd3s24mklOXHGVXESznqKoqDImjPFUSQEo7QLOQAb46Alh5ZlYW5mBvMLC2mkRVHgOg4erK2hVq/3/HtOSHjJYJIkSLg+Aimp4q8ZBGfPnUMQhlhZWWlz0kxrbfb/ViiKAss0USmXUS6VUrEs0veu0WiIPheKooj0TZIkcBxnj2QRgkI+D6IoafOseh33V1awcv06gnZumBLPHCQ5kBgIPFpw88YN+I0G1CTB4sJC6gzIyqe4OQ0hpMnkaJS8Lc3mZRWlYzg+DAJsPHoEh4VbeVh2emoKhUJhfyieqdSbJvkx5JebxstPhXSBI13GDyDd6dXrcJjrnaZpqHSxD86e87PPP8fO9jbe+clP0rB9D7SOQugNhowatLvuVqiqikKxmGoKWI3/IHec7+R7IUkShMyeeW5uDgsLC0KYt1WtYnV9HR7rcjgI+LPCKx4S9rOEXXuvsRULBTx3/DiuXbvWkVjw90VEKzq8TtM0FPJ5VBgxByDKPR3HEecjhKSpCFYxAqQko5DPAyRtsuX5Puq1Gm5cvozq9vZA90Ti6YMkBxJ9I0kS3Lp1C1sbG4DnoZzL4djRo6LroKqqKBaLovogG6ruteB28gQQIkO+6+5CCnZ3d7FdrQpdQblUwvTUVJMgsfXYTYZLZDweCyL6gPYRBl6CpqSDaPpdEASo1etCLJnP50W+uPtJKb759ls8ePAAP3rrLczOzvYeZ5sIRpypVBgG/d5DjeW+CSEIwxAuW8j6OX7byE4G3NwobklTWZaFxcVFTJXL0FUVETNT2tzaQjSCU2K2/JFHGGLmCcHJQuvTfe6FF+B7Hu7du9f7BJnoW6er5qmCcqa5k8+epSAMUcjnQRkJ4A21QNL+HzkWXfI8T1Tu3Lt9G3du3hSVKxLPHqTmQKIvRFGE27dvw9ndhZIkWFhYQLFYTPPHLOxdLpX2QucZcBOiTtiXv0eGFPQIJYdBAMd1xSQW+D5MwxD2u9lz7DvfmMhA0zl6CReBprSICgjjJdd1U+MlQsSOsN9F+tqNG7h54wZef+01HDlypO8xt97faExixH7SRLquo1gowGfWyJ7vt3V2bAcC7FV5ZBbmmGbcNtvk7nkJZLFQwHa1CtdxYLsuHNdNu0RmnTuHRFPvhWzJKiFiN1Yul7F85AiuXL2K48eP95dS489NNjWV+TvCUkHFQkGUPsZxDM/zoLBeFjSO4bguypmKHMs0QZmvgu26UBQFmqahVq3CdV2cOH0alrRefuYgIwcSPRFFEW7fvAmPEYPlpSUUi0WxmHFi0M5KOOlBDPjrmtBHpCAKQ9RqtTQnz7wEDNOElculor1cruOujWT+jQPZUHpPYoA29fs0dToM2I7OsiyRK+4H9+7dw3fffosL58/j5KlTA409i2z9/6hixH71I6ZpwrSs1LAqcw/6Ogf2UgxxHKcdMVv6XnQah6ppmJ+bw/zCAkxmVby5s4PV9fV9FS2joCmKxvQLCftcnDlzBo1aDZubm4MdNEMSSMv/OTRNQ7lUQoFZSydJIsyoQqb1yCKXy4mIQyNjxR36Pm5duYJarTbE1UscZkhyINEVURTh5vXr8Gs1gBEDy7JAkwSu64omOe3y4f3Wh/PwK7qQATEeRgp2azWEYQjC6v2nKhWh0tZ0vcmJMbsYj5MUAL1TCL0QRVFaTZEk6YReqaRCsT4X142HD/H5F1/gxIkTuHjx4gAD37+z52JENdMxcdIQlSPs+bGZiLQXeI4/imMkcTyQZiGLnGVhaXERU6WSSDWsrq9ja3t75KZMAh1SAjOzs8jl87j/4EEqdBxS69JaRZGFYRgol0rIWRYU1njMcRxUd3fhtwgPC/l8aifN+pFwxEmCO9evY+vhw6HGJ3E4IcmBREdwYhDV66CUCmIAmtoSc5OfdqHg1i6LnUD7yCED7UmBaZqoVCqiCyBvoWy0iA5bNRDjAI9IDHNcvuhEYYh6vY4kjqEqCsrFomi4oyiK6LHQacmoVqv43SefYHFhAa+99lrfJkcAmlstM4iUwphLGLuBv++GrqcEgRDYTG3fDpSmzppRFKVmUBmfgGHNsxRFQWVqCsuLi6KqoWHbeLC2hkajMdQx24I/5xn/hCPLy1hbXRVpgmxFxEBUIVsVtO+0KYEul0pClBtFER6xfgv8eSSECP1Ba/dRAHhw9y5W+9FISDwVkORAoi2CIMDNa9dSYkAIji4vi7I4Smlq34u0Z0E79KoDz+64u5WohVGEeoYUIEMKipnQe5IkCMMQFKn/ftOiMdil9wTNTO5DgRD4vo96vQ4KQNN1lEql5r4T7HVCvNhCoBzbxm8//BClUglvvfXWQDv9dhoPIBM5eAzkgJC0a6OmaaCUotFoiN10wghBGMeIWeqgrdCTH68lD98vdF3H/Pw85ubmYBgGKKV4tL2NtY2NfU24RgEfJwWwfOQIXNdFdXe36TW8vFOQhT6jCiJC0aZ0WFEU5HM5zM/OCr+EeqOBWr2elpRSCl3XU3JIadrqHHvRCEIIth4+xO3r1/e12ZZ4+iDJgcQ+BEGAW9euIWw0QAnBkaUl0UmRACLkqPHdXgu6VSdkSQFHu+mbh9t3d3fhZ0jBVAspEGNmxEBli+m4xYZ87BSprmCUY7uum7ZYJkR4L/Rc3Bk54PnjDz/+GLqm4Z133hl4p99p7MPaJndCP2WNvL8CB8+RR3GcWiGzCAFNkr70HATppDbK+5PP57G8uJg6KioKwjDEg/V1bO3sjC/VgJQUz8/NQTcMrK2tdX1t31EFXnnDIgntXqfrOmZnZlDI5xFFkRAt1up1+EEgIoE+q1xoLaWs12q4cfXqvrSExNMFSQ4kmuB5HlauXkXkOAAjBtxJUGF16SJq0KZnAhddtQNXbANo2t1lXx1FEer1Oqq7u0LsyA1f2pECjiAIRNTgSUohtB6n3mik5IpSWJaFAqtBHwTfffstXNvGO++8I9I8tOVfxzF0+H0cx2LhG5dtcj9ljdl20GEUgVKaPleEIGK190Ofm5CuRLUbFEXB9NQUlpeXkTPNNNXQaODB2hrsPssu+zqPqmJ5aQmrq6t9/002qhBn9Bat7ysnSu1+xzuNcg8ETjodxxEpw6boAfY+pwSA77q4efky7D7MpCQOJyQ5kBBwHAd3r11D4LogioJjR47ANM2mCZ4bq+iGAb3NjjXBfsW+mFTaLYJsV5IlBVwtniUF3XbHvJkSKBVGMOPGqGQjoTTdmfk+QCkKhULHlEw3PHjwADdv3sSrr76KcrnMBkf26uDbkC5h2tNG1c6RjRoMm7vvhCxp4RURvLqAV0ckXKNCWO8I5k3hue6+Bln9gmD0KIiu61hcXMTs7CwMlvJ4uLmJ9YcPEYwptH70yBHU63W4rjuwsJIQ0kwU2hAh4eHR8r7mcznxO9M0hR8If29cz4PnecKhU/iWsL+J4xi3rl1DrVod/KIlnnhIciABII0Y3LtxA4HnQVHVNJXAxHF8UomiSORe2y1slFLRipj/n7aIsFoRxzHqtt1ECrLpg66kgP0Lw1DU1Y9LTEcxBm0BQ5QkqHEhJYASs7wdFI5t44svvsDRY8dw4sSJjq8jLePOLg7CCpgv2GClgOwe8qhBUyQCzfX6wlegzev4sbMle5wECGOglnQDH2824qSznS1vMxwPq+QH9ms5hkChUMDy8jIqpRJ0RUHg+1hdW8POGFINi0tLUFUVa6ur6YQ8JDnj72/WgEn8DhApBw5VVWFxEub7MHRd2DFz3YntONipVjtGoyiluHv7NlzbHmrMEk8uJDmQgOM4WLl+HaHnQdU0LC8twWjTpZDnGA3T3LcI81Bn9v+9wsqe72N3dxdhCyko9REp4AY3BBC18foYogZN4dMhF6QsIiaojFhFQjHjYDfQuJIEn33+OXRNG7gyYe8gVIg/SeZnoLQpcrBvIWD/5/luvuhz+2Dxjwvnsv+AnnoKUYbX8vNcLifG44yw+HRS8Q8KRVEwPT2NxaUl5CwLGiHYrdexur6+zzdgEGiahvn5eayurTVpZdpVlPSLJqfGFvLCBYtAml5QVVVUIHHRYrlYRKlQAJBqZHYzzctaR5XEMVZu3hSfY4mnA5IcPONwHCf9YLsuVFXF0aUlUU7XCh7ebS0VBPaIgRAcdosWJAlq9Toato2EUmi63hcpyB4/W/4Y8hLGEclBNqc6jsC6zzzu4ySBzkxphg1zX758GZubm3jzrbeGvs5uiyMP70+i2VJ68s5nF4tha86cEKHJiMJwqD4IwB7JG7RxUyeYhoHFxUXMzMzAZKr/9Y0N7LRUHAyCI0eOYGtrS0TmuJviICmeblofHrlpJb6EEBEFDIJAeEyoqopSqYRioQAQgjAIUG809lVt8A1AFIZYuXmzYwmqxOGDJAfPMBzHwc3r1xFzYnDkCLQOxIBSKsqXWrUGPOxMWwlBm8mKRwsC5lVQyOXSUG2vtsIdShPDKEoXNkLaaiD6gUh/YDykAEjTNI16HQnzXSgVi02q/EHw6NEjXLl8GS9evIjZmZmhx9QpNJxkdpeTMD/q5UEgftdmfKqqIs9MoRyW/x4U2R34ONUUxWIRS4uLKObz0FUV1VoN6w8fDtWnYXl5GaAU6+vr+37XT+SDu0T2QjaawJ93TddTbREhcFy36TmxTBNFJhCllMJ2nLS7Y+Y1nLC7to37t2/3e8kSTzgkOXhG0Wg0cPP6dVDfh65pOHbkSNddO48aEEXZ5z6YxHFqd4yWUHxmUo6TBPVstIB1GuQiKKEsb4FojNRhceE7GUPXh5r4RaRjDCkEDpuVKgJscmXEYJjxBb6Pzz77DHMLCzh37txI4+p0D0Wb5gN0RmwCTyt0eA9M04TB2hLz52egw7eca1wNtoDUE2Jubg7T09PQCUHg+1hbWxs4ymFZFqZnZrpWLXSLfAzU8hx70YSIGUnlLAuEVSxkUyQqcx3lrdiBtMSx3mjspRmYqJgQgmq1irX79/seh8STC0kOnkEEQYDbN28iCQIYhpFGDHrsunknt+wOP+F5/+xk22aC8ljHRL8lWpAtmWvdXTb1K+gy6XEx4lB5/My5xwFeqsgXhlwu11SqODD9oBRffPkl4jjGG2+8MXTkIT1U57NPPKWA/iIH3caYz+WgqCooK7cbw4A6EtJhUCqVsLi4CN0wkFCKtYcPsTtgP4KjR4/i4cOHXSMAnfpFDCuKJKw8mVKKHBPJer6/r6w1TpKU6BYKIIqSCok7pBk2Nzaw9ejRUOOReHIgycEzhiRJcOfOHdAggKVpOLa83NeiEGZSCsJuFb3z2PV6PXW7axMtaEXrItorHB3HMaIoSv0NBiQH4yYGvFQxCAKAUhSLReRG7GR38+ZNrK6u4o033hjpWL3MiLI9FR4H+iFPRFEE0QrCcGzNkRRFGRtBME0TS/PzKOZy0BUF29UqHj561PfCfeTIEcRxjId99DAQmp4+vC36hXBHREoQQClUVU2jSZQijmPouo4y0wa1SzPwz9PqvXuoy2ZNhxqSHDxj2NjYgF+rQaEUi/PzfYWReUthinR3ya18u+1kW6MF+TbRgk7n6kUKOPiuRdf1vsVm4xYdAqlHQNbeuVQqtRcNDjCBV6tVfPvttzhz+jSWlpZGHmO3a+VakknbJnd6T4UgscciqmmaKL2zbbtv/QEv4+sEvviNAyqrPKhUKtAVBa7nYXV9vS/75WKxiHK53NMtkYMgJU3jIjcAxHPr+z5C5pvANw+894aiKCgWCh3TDAAASnHv1i24YzSMkjhYSHLwDKHRaGB7fR00jjE3Owujz1r7KIoAJmAyDEOQgnY7ok7RgnyHaAEHL30cJOfNd+ntqic6nWPcCJl5UxzHUFU1tdwdIsWRRRxF+OzTT1Eql/HSyy+PaaRdzjdmZ8RO6PTuD5Irt0xTdN90HKfv97TXOYiijPX5qFQqWFhYgKFpSOIYqxsbqPfRxGl5eRlra2t9L/jcV0IhZCwER9c0KCzdEjHHSpXdmzCz+PMmTd3SDNwkaSxpIIkDhyQHzwiiKMLaygpoHKOQz++563UBn6AoALQRIrZORsNEC3jIm+/kWxsMdQK320361BuMy9AoiyAMUa/VRKliZYRSxSy+/v3v4TgO3nzzzZEFgr1CznGmqc9BiBE7umSid+SA/32hUICiqojjGK7r9nvini8ZV6kjh8XaQecsCyoh2NzexqOtra5phuXlZQRhiGofroO8QyUftaIoI7tbcrdEENJUVgnmY5F9XgC0TzNkKh6iKMKNK1fgjLO7pcSBQJKDZwRrKyuIfR8qSZu9dEOrEJDnopvKlzLfjxItyJq+cPQzwWUV9t1y5aIvAh2tWVIrwihKSxWRRlNKpVJPwWA/u8F79+5hZWUFr772Gkql0ljG2lW3wVMKE7BN7hf8vvW771UUBQVW3uh5Xl82xv1e27jvAE8zlFkTJ9u2u1ovT09NQWGq/17o1Ddi1PfR0HWhMeDRg+xxuQMjx740g++LNINCUnvn61eujLf9tcTEIcnBUw5KKbbu34fXaCCMYywuLHTc3Wbz8dkJoVVNTjPfD60tyJyrFTyC0A0839zLXrnTOUYB7wPBiUGxz+ZJvciBbdv48ssv8dzx43j++PExjbY7OMmadEqBo9P7DQyW9tF1HRavzR/BXnn/YMbfzZM3cZqbm4OhaWkUr0MDJ0VVUSoWe5ID7lLZCh6BG6WyRVEUmMwh1WcNzYA9wyO+4PNmXbyMslOagTdsu3n5MhqyUdOhgSQHTzGSJIGzs4OdzU34YYiZqSnB7rPIWgYDezXTwqO/tcSQhReHiha0pBE6oVctetwHOUCPcwyDKI5RY9ds6HrfxACUdg1tJ0mC3/3udzBNEz/4wQ+G9tdvOiV6ExKuNxhH/4F+0Xq/+illbAcrl4OuaaBI+050rXYYbIATiaLk83ksLi7CYgvvw83Ntm2gp2dmupID7i3SDYTpEIa9Ct77I2Btm/efYK8UVJhosRRfuzSDoiiIkwQ3r1xBfQQnSYmDgyQHTymSJEHUaODR2hqiOIZpGJienm56TXYy5ou16ImQmRyzuegkSYTL4VDRggEmrG67H6Gcbjlnkz9Cn+fpFxEjRAnTGBSLxb4XkV7L3qVLl1Dd3cWP3nwT2oiCxix6je9xlTG2PnvshwMRBEII8oWCsO/1uxgPDRNXGEc/hlbouo6FhQWUCgXoioJ6vZ66KmbSDJVKBbVarWuzo37RzWCsG1RVTVNNiiLKRrPPEv+O3yOuf0iSBISQfWmGRqORujNGEW5evYqaJAhPPCQ5eAqRJAli28b2o0cp86cUC/PzTa+hmZ1sdnLuVEbIzVJq9TrqA0YLgOFC/ATtRXKU5UMTSpsIyaDkYxDESSLEh5qmpXbIA+wuuzkwbmxs4NrVq3jx4kVMj2CPvA89FgR+H4HJlzFm0fqMNZHAIRaxQtZeeUxtlDmUIcbU85iKgtnZWczMzEDTNERh2NS8aWpqCgkjoq1o10ipFwiGI3/Z6EE3B8YsieKRBAD7TJNcx0EQRWmK4epV7MpWz080Dm5GkDgQcGJg12qo1+sI4hgLs7NC0Z/9kItcL08j9NipN2wb+VwOqqYhZ1mp5WovUsBIyLALNicI2QmR961XCNkTS3IL1yHP0w0JpWm5Iqv5HqZPQqflJfB9fP7ZZ1hYWMC5s2dHHqvQhSQJkr0f7kVUMrvzKIrSHhdInSYDJnBrqlJhf+O4LijSDn16pmMnAfbe3zapqCwREM8K/8qfjRZDn0HfQ8M0EUYRAt9Hw3FQLpX2aVaGfi74zpv17xgnisUidMNIGy6FIdYfPsRUpYKpSgWEEOxWq01VRTHrhDksBk3fiLLGJEEURV19KlqjE8JhUdNQLhZhOw6iKILv+4iTBDnTxK1r13Dy7FlMtUQ0JZ4MSHLwFCFJEsSNBkLPw/b2NsIoQrlYRLFYFK9pnahb9QXt4Lgu3Ex5UqVU6pnrB8a3k+cEgbKuctn2wvw84+yNkAUnBrzlcrlUGq7kr0Md+jfffouEUrzxxhttFx8u+BJfWb5ZNLtiyvHWksWmBaBV78D+H7BOh6qqdq1Fp9jrfOmzCX7f5aFF0Jr5HmgmRwRAgpTA8Ocuq2TXNC3t88AIK/e/IIqSdipsU7KXz+UQRRHiOIbneaLT4LhACEGCEUhGB5iGgaXFRWxtb8N1HFRrNfi+j5xlobq7Cy5LTUYkBkBm7CwK2NP7gaQ9Feq2LRql9fiDJjEzkBIaXl3i+z5c34fveSBIIwucIIw1YiYxFkhy8JSAE4MkDEXrV03TMDMzIwSAraBA14ZAlFLYtg3P99MJW1VhWVb/xKDDeYcBAQBFAVjeEkj1Bp2ubRzgvRKiKIKiKCgNSwyAJmLAF/pHjx7hzsoKXn7lFcRJAttxmhb8bLSk3SLLr11MxhkSIHbgfIHl32e1JGEIVVVh6npavpaJAIhzsr/hhMDSdZiWtXc92UhDRu+RHROPWLS+hi9SYLvOmBnv9IMsUeBEQlEUuL6PgL1fuq6npIJfSw9RaPcTpi2U+1lUB4WiKJifm0OtVkO1WoXv+9BzOWzv7ABo7po5DvA0Qz8NrFTWz4Jm/BS6HRdgRIqnKLHnX2FZFhRVRb1eh+d5aT8Hy8Lt69dBz5zBzOzsCFclMW5IcvAUgIsPEUWo1evwfB8xpVheWOis+qe08+/YMeuNhujvXq5U4LquMB7qVGo4ahqhGzhBiFhaQdO0yRMDdv2lYrFvgyNezcEFWDELy2bzxTRJ8NXXX6NULmNmdjb1skebBR8QKR8l+5UthkpmZ81fCzSnjzqhTghUTUMhnxf55U4wma2uYZrItal4GQacBAFpeVwUxyjkclA0TSxIPCqS8O+5CQ+l6U6+ZdGMowhhGCL0fSFWFKI8pIuioqriq8JTGn1C4QvfBKoZyuUyTNPE5uYmSoUC7j94gN3dXRQKhbGfC4BwQuxGEaI4hsb6qQRh2Lf7J08zZO8SF/Lm8vlUf8CEjjnLwsqNG6BJgtkWbZTE44MkB4ccSZIgqteBOE6rCKpV+FGEuZkZGIYhdmYc/egL4jgWoXQQglKxCF3XRVlTEASw2iwmkyQGTedJEiBJJlabTwHYGWJU6pBGSVitdxzH6cIfRYg6hH/jKBK6CBCCe/fuodFo4O0f/xiGYYgdbuuC3yvl02n8/fyFMJI6wDLGJrBnhV+3yghfrwWIR1W4riJLJAqsAoA/p5qm7YW7kwT74hKKApWQlDCwJkOif0i7zwghom5/EgTBNE0sLS6iVqth5c4drG5sYHF+HuUxGWK1gm8Q4g7XE4Zh2myNUoRBgMSy+oqetdMhAOkcYRoGaJKkBlYZgnDn1i1QSjG3sDCOS5MYEZIcHGJkiUGcJNje2kIYxyjmcqhUKiI6wEEp7ZpGAFKRWo2V6yksx65pGmJWDum4bltyMMlKgSxi1gBK4R3kJqA1sG0bfhgClIpyxSAIRBQgZqSg7bm5jwMh0FRV7FKBvRC453m4ffs2zp45gyNHjox9/P3ck2zaYpKtmrtBPJtZrUQff6ewxRwdxm0aBhzHSUvqmN4mYe9bFEVI4hgx9wqIY8SEIE6SlAxmIzbsvdsXbWD6BzohgkAUBcePHcM333wDz3Wxs7sLSikqfVieD4t23SkjNq9wDUicJOlnf5DIURsdAgBoug4TgOe6CFjULGdZuMsIwvzi4ohXJDEqJDk4pMgSAwDY2dmBFwQgJLVH5qIvjn6Ige/7qNs2wEoES6xXAP9gG4wcZFMLk/IUaAeK5iZBfELrZCM7CPii32g04DgOkiiClct1tHzlERhu36xmiEA7K+I4E0349ttvoes6Lly4MNKYR0GS8TcYd0+BYTDOEZiGAZ+ROd/3kc/n07p9SqFnIkBc1JkkCZI4RsS+8p+ROAaN4zTakL1HhEDlKR72HGrs+1HIAn+WkySBmcvBMk0kUQRVUVCt1ZBQiulKZejjd4PY6WNvIQ8zXU91TYPtuvCDACZzpuz3uOk3zWkGwo5LKYXneSKtlrMs3Lt9G0mSYHF5eVyXJzEEJDk4hEjiONUYsAk+DMO0hW2S4Mji4l7NekYU1stx0PU82LYNIP3QlopFET7kk4WmqmldNpt0+Q7ioIgBsGd+xHe7rZGRfo8VRxEi9i9ktde+78Njtea5XE6Qj9YogNoSERgE6+vruH//Pn705psjd29sh37vAY8aHESzpW4QJJZrJcZxTEKQtyzUmZjWMIy2aSEuzFMVBdA0ZGNhvCokzpAHoRlJEsSEAEkCGoZNVuOqpkFVVfFZ6ff+ZitS+NgqU1PwPA+Vchm7rDQ5SRLMTrD0j4tQKaUIWFqNkwPF80TDs347obY7LgFE5MUwDFAAvucJ0WvOsvDgzh2AUixOIrIm0RckOThkEOLDjKVpdXcXcZIgn8shn88D2JtseAi0E7jFKV8ULdNEIWMJ3FoiZ+p6Ezk4SGIAsLQCbTY/aqqTb5fvTxJErIkML3fj0QbKJvkgCNIctaqiUCymu80OUYChroG9H7//+msszM/j6LFjIx9zFLSWgz42EJI2xWqTYhgFuq6LCILjuqk3BfonHwohUDRt3wTZLtoQZ8SmNAxTt0b2PCqKIogCJw6tz1OnaoSpqSncvXsXlXIZhBBUd3dhOw5okmBmenpixI4AouESAUSLbF3X4fs+oiHIAT8u1yEo2ItsmoYBJAm8IGgmCHfvAoAkCI8JkhwcMsS2LYgBRWob6zoOoiTB0tSUeB2lNA1zdjkWZTX8fIeQz+f314e3TGQ8tRDHcUoQeqjcR0HWxZGDVyq0c/TjJCGMIkRhiCiOEYbh3sTLd3tId8yarkNTVeELb+g68vn8YDnVPkEIwbWrV2HbNt5+552J5KoHWVZF5OBxkwO0RH/GeFwrl0MYhoijCEEQQGcVF6OgU7SBAqKLYRxFKRlNElCWpuCfsWx0gZPPTqhMTcG9ehV+EAhjp+1qFY7nIdnextzMzEQIAqWp6RUBoGdSCJqmwQ+C0V0oucg2Q4hMZqjmtkYQ7t1DrlBAeULpFInOkOTgECF2XVA2yXBFeq1eR5QkyFvW3qJGe7vM7atIKBTalrNld+KccOTzeTisoYrBasnHjXbEgJezARkDJBbm5FGBKI4FAciGaVVVha5p0Ng//ve+76cGQITAyt7DMaPRaODKlSs498ILY2vFvA8D7LqflLTCPoxRYKqy99RxXbie15c/x7DgpEExDICREJoke5GFOEbE9QxBgAB77wHvYyBSEYzUT01NAYSgtruL+fn5VByrKKlhku/j0dYW5mdnx/4eeix9oCgK8mzRphm9BtdlDHteHsFpjeQYGQ1CliDcuXEDL7z8MowxkDuJ/iHJwSFBEoaIXRfA3gcqDEPYjUYaNeB5SF5O2KaMiCNbkUBYRYLeYeLMGtxw5bFlWQjYDqLhOChnHBjHAV750IqY7cpiZs7EledZ90EuvNSzodw2jnoAEDCtBgiBaZpjd9XL4uuvv4aVy+H8+fMTO8cgOOhWzV0xQUGkaRjiWfU8b2LkD9hfukcUBbqiiM8WZSZPIYtqAdgrhY1jBJnj8MiCpqrY2dnBPKv/L+TzIAC2t7fhBwEebm5iYW5ubAQhYiXRQOo6yUXHInqgqoiQziEjLdZ8Psn6LChK+v4Q0iRSBICVGzdw7uLF4c8nMTAkOTgESJIEERMLchBC9kcNMgt4Ry//IEC90UhD86qKcrHYsemOUC6zcH1WaVwoFLBbqyEMglTBPCZWv2/cTBgVhCHq9Tocx0l3/5nSK5URAZHbbbfgUdrkCBdGERr1OijSHUuBaTUmgQcPHmB9YwPvvP32xHL82VbYvcAjKjwH/CRg3JqD7HHzuRxqjQb8IICqaR2J8LjOB+wXhvKuhYqiwDRNkY7IemVwXQxNEkSUIkL6Odvc2sKRRgOarkNnqS9FUbC5uYkwDLHx6BEW5+dHJgiUUmGjbRiGEMxmKw40TUvTdaOSg8xz10SqKIXJzssJAr+Ta/fvY/kxa3WeJUhycAgQ27YIlQPphykMguaoQR8GRJ7nodGhIqEjSMZKNwNNVZHL5eA6DmzHSa1qR1xo+PUlTCAYhiHCMNxXqWAYhrBx1lhb2Z4gqf0t7xPQYJ0lTcNo6j0xbkRxjK9+/3scWVrC8oRLs/q9+1nzoyeGHPDKmAkcW9M0mKYJz/Pgui60UmliQtpWYp7tUtjunNz0Klu5EsWx0C3k83k0HCclDqxvhEIINF3H9PQ0dqpVRKxp08Lc3EipE8/3EccxiKK0dcHk5YceEyWOgqyFNq+mokwgzHs6AGhKMaw9eIBCqST1BweEJyzhKNGK2HWRBIH40PDJvClqYJr7iUHLzqVh24IYmKbZVwOhbpMakOYDufe63RLZGBRhFMFxHOzWatipVmE7DgJWJqawsH8hn0e5VEK5VEIul4Ou64N1R2QTkeO6SCiFoWkTJQYAcOXyZQRBgFdefXWi5xkkNC9y3U+Q3oATt0mYWgHps6oQgiSOhenOpMAjMjHLzQ8KTVXF814sFhEx0zFVUURJZRAECKMIlmkKcfDGo0dDL9pxNp3QxQVRZ+6RlBuBDYmmpzVDEACkzwAj7pZlCR2C47q4c+OGcFWUmCxk5OAJRhKGiFx33+4uDALha7DIREv7lgZe2kcpGiykCqCp3LEb+ETdbckhAIqFAnbrdQQDphcoUs1EyEoI46zNM6tGMHUdumGIvHiVCbuGjlBQioZtC9e3UqmU1luPwUSpHer1Oq5fv47z589PzB9fYIDxc0OmJ6FSQYDs9T+YBBRCkGOmVo7rQtP1sZMjnobjIXKFEMR9iIO7wbIs+MyrwTCMPd0Cq8hRNA3lchk71Srq7NqWFhbS1ur99gKhFLbrApRC1/Wu6QLu+RGycYwtTZaJUPKNELdaBliKgXVzvHPzJs4+RgOxZwWSHDyhSJIEoW03dcnjqDHv+LxlIZfLdW2eVKvXxW6iVCikHfV6oF8jHSAN2VqWBc910bDtNETaIbTJS7pCpiFAZiIFITA0DTrLdbabuCkrORwoWpCB63lp61mgKaUi8sSA6CY3Dnz51VcoFAo4d/bsmI44HvAd35MUOQAmb6ZlGgY8XUfIWlWPS2fCScG+fgs8lTUC8bQsS1TiaBm/AV3XQXO5tNFUFIEQIuzTV9fXMV2piNSboesd/Tq4z0nMjtGPKFdnRmhRHMOgwzWh6nlPOGFvSTG4rouHGxsolkpSfzBhSHLwhCK27bTjXIeoQZgkWJie7jihxlGEXUYiCCFpRUIfxiXDTGT5XA5xHCMMAtTq9abqhyiOU0LQpj6aECJ2RNxopRsSJrobRngVBgFcxwEFUGzXYZFFX1Se+xwxknDv3j1sbm7ip+++C1VVm+yTHzeeyDJG9t6PszVxu3PkcjlEzPeAP3fDgEfWsiLXds9vJ4Fiv7BMEwRpyW2rnoAAojQ3Z1koFgpY39iAFwTYqlZTgmAYaRqFkXadNbbiO3PbcdKeEoSgwISOvaDpOojnpZ9n7no4Lu1KVvicESq2EoTbN29K/cGEIcnBE4jYdZGEYdsPXDZqkO8QBYjjWBCDXhUJWWQFj+2cBjuB78TrjQbCIEC1WoVhmqIeOgtVVaEbxt5ups9zJJnQ/6BphThJ0GDEwLKsnirrrLaDsgVgkDOGYYhvvvkGx48dw/zCwsTy6ByDHJ1Suhc5eILSCjxCNkmBJK/QsUwTLsthlwcUJ7amDiYN07JAWWlfr9SUrus4sryMza0t+L6PBqvsUZnRFw0ChEy/pKnqnguioqCYz/dt560qCqAoAHOJ1FjXxrG/c5noAdBMEBzXxfXLl/Hy669L/4MJ4QnaOkgAzM/A89pOklmtwUwHf/U4SbBbr6d5e1VFpVzujxiwDyI/bzLE5GfoOjwWPdja2hLCIV5+NVWpoFKpCCHjIJOJEEcqymB18ZTCbjSQJAk0XUdhQC8DQtImO8oAC9f333+PKIrw8iuvDHSuYTCoiE/cR0KeqMhBtlxu0ucwmbiPJgl8ZhveCSJlwEoRs8ZafZ93BNLDLcq9PkWUqqpibnY2JcG6joZtp/qaQiFdRBUFSZKg3mig1mig4TipedMAzwLXHRBCRDQw24StH/S8h4QAbfxJTLaxAFLb+FvXrg1wVolB8OTMDhJt/Qyy4FGDnGW1LTVKmB0yZfXUU5VKXx96HhYcJgQaxTFsx0krDGwbJmulzLs5lkollEolWJYFJdPhcdCpkmZsjweB7TgiJ1ssFIZbfLjPA1tQFT5ptTnWTrWKm7du4eKLLwrDnYnuMgc8Nk9vPElRgyZM8F4RropXFKHVcT1vX8onSwi4K+eo7yF/dgY9js4cSHuRmCxUVcX83BxyjARtMcOkXC6HUrGYpid4ySCrdqjV66gz4XI/Y9Q1DQRplRFH302m0H/6iL9nWVismoJSivX1dTy4d6+vY0kMBplWeIIQ23bHHvE8ahAnCRbbRA0oUnU8r1Mul8t9s/lBdzUUaQ7Ub6l3JooCyzAwVanAdhxEUYR6o4FyqQRN0/YqIIZYoLnqe5CUQrbLYl+eDgMg2+WSZtIwX3/9NabKZZw5fXps5+oxkMEiB0+oGHGSEYN259ENA1oYIgoCuK4rQvaTThkorPS3XxBCYOVy4jke5O/mZmdTq2XPw9bOjrA8juIYlmkiNzUFhRD4rCySCx/djBaok4OmpmkpgWK9TgTxIqRniqGdNXrH15K0AVaSKZvkwskGc0lduXULpXJZ6g/GjCdshnh2ETlOR50BANRqNSRJAqtN1IACqDcaQiBULhY7WgY3/2H7SbDT5BVGERq2LaIEURSJ8qdisYipSiXtZsjKBDVNAyhFrV5Pe8N3ID79gEcO+q1UiKIIjm2DAsjl89Am0B4ZgBBQKYqC+/fvY2t7G6++9tqBLHbDLGTxkyhGxOQrFfZOtFcua5mmILqe7w+VMhj49Nkx9AnLsgYmB+lpCGZnZtKyRgCr6+uo7u6CIG2yZrLKoGKhgEqpBNM0QRQFMaXwfR/1eh21eh2+7++7L6qqimeoVWjci8APeof5fEQyY1BVVcyDruvi+pUr0v9gzHiyZohnFHEXnQHAlPauiyiO22oNbNtOF19gb1Fm6Pgx7cDeuQ0vR0IpXM9DdXcXtVotnSjYDiSXy2FqagqlUgmGYTSNnxAixkIpRa3R6Dtv2g68c2I/kQOaJGjYdmp0ZBhtUzDjBqUUly9dwrEjRzA/N5dqFICm9MMkFp5BF9UnUYzYiol0rOTPNU3beieUQlUU8dwOErYfFYMKai3THPqzQwjB9NRUmgpDmpoEsK/lssL6GpRLJRQZmeadJh3XxS5rF50lAry0sp3xEiEkbYfeAorOm48uF5ESkRZCm7V4rlarUn8wZsi0wmNGHMdp2WKHCYMAontiu6iB47rCXrRYLMLoIT4UIcAeE1TIGtVk2TilNPWFN82+lM2EEBSLRdiNBoIoguO6CMMQhUJh4LB20m/kgBsdxTEUVU11BgeAO3fuoG7beOvHPxY/ayVLIn/K0j20R/i1JwZMKQBPaBljK3h53AiHoGgj1mTEANgjVdxkKGbtvftV7I+KQQySLMvC5tbWUOcJwxAOS5skSYKI6QusXA5WB5U/91HgviRBEAhL8yAMBanin+EwitBO5itcLzNo3Xz0i6xgOkuyc5YlnCjX19ZQqlRw9PjxIc4g0YoneIZ4NpA4TtcJPqGp3W8Ux5iemmr6nef7cFmnxgILE7Yiu0D1mnBpksB1XREl4MRAVRTk83nMTE+jWCz2PYHybm6lUkm4MoZRlB6btZ7uF6JVc49FrdXo6CB6ByRJgstXruDY0aOo9Mp7kj0nuGwFRDbc3HeEYVBiQPd8/p8od8Q26Pdd4yQgAbs+VlXQ0fWy5XlQCEm9BBRlqND90GAVMP3AsqyBIxsUabjddhxQSqGpKpaXllAsFKASgq2trZ5Wy4qiwGJW68VCARojDFEcw3XdtBW268Jn5KH9ZbZsRIaNnhEiNgZZkWLWuCmKY6zcvIlatTrcOSSaIMnBY0TkOKCdPqBsIXddV9QSZ93LfCZQBJDuArqEzgm6RwxoksCxbWzv7IhdN5CWDZXLZVSmptKSqgF2m4Lps//nTDMtq1SUtKqi0YDtun0vhNkSvE4QRkeUIl8oHFjo/M7KChzHwYUhW8ry94WnIQRpaCmBoy274EHBRV2KoozcJGvsaB1Py/959QDXWSQt/8/ej0EJocnKBeM4PtC8db8ljqZppum8Po8bx3GTZbplmiiwKoWZ6enUXTFJ8HBzs++qAU3TUGDlyDk2F1BCkCQJPM9DtVZLzZTaXSe7Vu5AOiyyf0kURbznTfoDz8O1y5dHbgwlIcnBY0Mcx0i65BH54uA4DmJKm4gBFwYC6cTRq3a/k4NZlhS4ngeK9IOWz+cxPTUlJpRB0alcUWO+C1kzk916vS/3QG6d3Ckc3mp0NK4W0r0gogbHjqFcKo3noNloAvYWEV5CKXoQ8EgDex1PVXQbK/DkpRSaFviMyDJm2oA405FUpAvGiKbowYSbMg0D3nyon4ZRAes4GscxFEVBsVAQXglA+vmenZ2FpmmI4xiPtrYGcqXk5Y/lUgmlYhGGaYJSmnY6dZzUCK1LVHCk966VBGae46zb5W6thvsrK6OcSQKSHDw2UJYOaAceYuYmLXEci1KrKIpSURGrEujZ0IctqNmFuh0pUJiTYoV5sg/bv6DpGtr9nPkNFAsFsVvb3d0Vu5z2l0DFwtd2xzui0dEouHX7NlzXxcUDbgRDgL3QauZnbUkg+xpnIgetvxf/BtzZtS7s2QU+W02RXdzb/b51rMhcy0Gkhsxs18YnLHrAu676XcgBt0J2XBcUqQ9BqQO51zUNczMzUAlBEATY2d0dauyaqqKYz6ebCFUFkgRRFHUkCTRJRtbYtM5L2fkgl8sJ/4O7d+6IlKvEcJDk4DEgjiLEHSagbO7ZY8SAK4njJEGt0QAo3esq2OtkPJXAyIbjOPtIQYmVIY5DjNWvkIynLFRVBUXaUrreaLSNIvDwMWkzOQBjMjoaAnEc4+qVK3j++ecn3vo5C0opBtF787uRsHvYmm4h2X8t0YjsOff9y7xXtOVf698iQyL6pR8HQQo4eFtwQlKr4oOyRwZ6GySZlgVQ2nGxC8JQLMYEqUivUCh0vX+GYWBmZgYq0mqn3VptqLHzFJVhGCiXSjB1PSUJzByt3mggZDbNXIM0TmSPmdUfBGEoqxdGhCQHjwFJh7LF1hpo13WRUCrEfLVaTZQR9kUM2PGSJIHDhIYu21lkScG4vMkHVZhrqooKc08E0g90lZVMZXOTfMfRjhgIoyNKUSoUDjRkfuvmTXi+j/Pnzx/YOYFmA6ZBwMPH/VSKtB5faXfOJ0230Ae6jdg0TSjMXrhbaHxS6CQStSwrLbdsiRxwkzHHcZAkCVRFQbFYhGmafZ0vn8uhUqlAUxTs1mqwHWfwMbPcf5wpby6XSjA0DZSRhEajgd1arclqeegnp53QtMX/gM8na6ur2NneHvZMzzwkOThgxGGIpM+wJfc2yOdyqNfraUvYjPthLyQsUrBTrcLrgxSMslfqqA7vAUIICrkcKhl/Bs/3Ua1W0+gGpYjZcVuv+cCMjtogiiJcvXoVJ0+c6J3aGTOGfZ+SDmmFJwHtnubWsrVxoNvxuPZAeQzRA6CzQZKqqjAMQ1RTxHEM27aFeJgQAsuyUCyVBhbhlkolUVq8tb0Nb8CUCn+WaMZASlEU5DMkIc6QBO5q2M4WuR/QdtHDlsoP0zCERfS1K1cO/H18WvDkzRJPOWLX3R81aFNJwFMKAOu50OJ+2A2CFOzupjlISlNSUChgempqnwHKqOAfvVFChpqmocJETqqigCL1cKjWavBZCiQ7KTwOo6Msbt68iTAM8cILLxzoeYEhTGSAJqX4k0gOOmHsqYUexzNNE4R1MXws0YMO4zNZJ0nXdUWonrCfl0sl0dp5GExVKrBMs+8Sx6bxZpxYW4WNPB1aKhRS/xVKEYWhIAnDtudut9S3pixyLNpS393FXSlOHAqHZ5Z4ChAHAZDxCOdoNwG6rpuG6rBnT1pucT9sBV9Qd3Z34Xie8FIvFQpppIDlVDuJDYdh2K0li6PC0HVUymXk8/lUIMZIQKPR2PNXf0xGRxxRGOLq1as4cfKkSPl0xRh3LkObyGQm4ieaHLTcq4PUHfDzWSy98DiiB8D+DoeUaYy4pwCQfk5KpZJYBEcBIWSvxDGOBypxBCDcC1v/hiKNVvF0Q6lUSjcmGZLgcBv2QdBlbPxO8OgFCMFdVmosMRie4Fni6UKSJIjbCIo6hRJdx4HreaLdcrFYFKU6raBI63u3d3ZS0xNOCnj6oM8c5KAYtsNiLxBCkDPNvZpqStPabSZarDvOgRsdZXH9+nUkcXzgWgNgeL3Bk9pT4aDRl6+AYQjtwaBmXWMBC5NTmvY4qNVqopJEZWQ4n8+P9b0UJY66PnCJI9cdtL4+SZKmuS1LEnROEqIItm2nTeXabJw6nLDjr7LpCk3TYDGPiJXbt6X3wYB4tmeKAwRljYf6QcCcD33fh5nLdXQ/BNKowu7uLhwWplNVVTRB4urrVnQqnxpmlzTJZZmrj4uVikiFuK6Lzc1NOK4LwzQPvEdAEAS4dv06Tp4+3dV4alIYdif7JOsNuoEbeB3oOVnlAm+VfNDn51bFjUYjFRCzEl6V6YWG8R7pB8OWOPI0Z5YcdGsKxnf1rSShUa/3Ha3pRPJa04+maUJTVTxcX8fW1pbUHwyAwzVTHFIkSYK41f6Uh+LbPOS7tRpcz4NpmsLEpBUUaQlftVZDFMeghIhIQSdSkEXbHegAO/CR+wIMAJUQ5HI5FAoFRGEo2lrzXZUfBGMN3XfD9evXkQA4d+5c33/TrgHNsBhajMjd5A4ZOWh1iRwZfT4npmGk7ZUpPTDfgziO4TgOavV6WqmUJICiwMrlJkYIWjFMiaOiKKIqCkif0biP+9xEEti99n1fiBa7odfRm/QHuRziKML66ipqQ5ZsPouQjZcOAEmbxauTlXEURahWq4iSBHPT020NfcIoSl3QmGugbhgo5vMD++VzC9JBKw0OkhiIcxKCOIpgWBYM04RpGAjDEFEcI7JtOCxXzHd8k4AfBLh+4wbOnDqVGtMcMPhubKgyRhY5GNXc6nFgElUL/ZzTNE24cQzf9/d1HR0XKKUIWXOj7IKoKApMy4LOupoSRRlKiDoM8rkc4koFu7Uadms1YZ3cCfzzxntaxAPOJ6qioFAoQA/DtIyZpRoMwxBlnKOApzO2Hz3C/OIi8vn8gTXYOsyQ5OAAQFudzSjt2OOgtruLKIqgKAoW5uaafp9QCsdxREkTYR8q0zBGqjkn6H9HKojEAeb5ubAp8H2YpinCkZRSeL4P3/eRJEmq5vY86LoOq8/OkYPg2tWrAICzZ88OeAHjW9iGvesicvCEN1x6koK+pq7DV1XEUYQgCPr2D+gHosthS9MiXddhmGaTvojStL30sOr+YVAqldLyQ9vG1vY2VFZ50A5ZcjDKGA1dh1Yqpd1b2T2Pogi5DpGTXqRRIUQ897qmIVQUVLe3YVkWZmZmhh7nswJJDiaM2PeRsFpkAGk6ocPujXc6o0mCSrnc9IEIwhAN2xa7B9M0kc/lRu6uJ3z6+1jAxlGyOAy4s6OqKE3togkhyFkWcqYpWkyHUYQwDBGGIVRVhWkYfaVZesHzPNy8eRNnz54deJEYhHx1wyjHENbJh9S4iLs7jn6w/uvreeWCE0XwxhQ9iNiiF4Thnv21osDQ9e5Rr8xCd1CYqlQQsc/V1s4OFufn2y7SWUEiN0MaFoqioJDPIwhDuKziyrZt6KxcuWMjsjagaP7smaaJRw8fojI9jSAIxmb+9rRCkoMJglcoZB/oTpNLGATwgwC+50EzDFGel1AK27YRBIHwKyjyuuExgRCS2vF2iQhMqjKhH7i+jziORVOofSBE9KCPWRjYD4I0h8vayhqmCVPXhTnKoLh27RoURRk8avAEINsz4UmPHLTFuHUHA8DQdXiahjiK4Pv+UCLUJEnSnTB7jjlUVRVkt9v1EUKgqSrCA+z5wM87Mz2NR5ubCMIQDzc3sbSwsG/x5yOngCifHvW8hq5DU1V4vo8gCBDyKIJl7UUEBzRSUlUVJAiwtbkJ0zQx1xKZlWiGJAcTBG3RGnQSIPKdMWXd5wghKBaLaVtm/nNKYVkW8vn8RCZK7qtP2xCEx0kMojiGz0pA+7l2TiByuVxKttiE7LP0A594RBe3Pu6l67q4dfMmXjh/fqhUxViiBiPoDbK56n6eHcr+Jk4SJHGc7t5YnbjPJmtVVVMb3HFpGPoQ0I5FdzDgMXh0ym40hPagn8WPmygFjKRmDiiev0FEhq3eBwcFXuL4aHMTYRTh0dYW5mdn9+4BpSKqwyM84wJRlJQMaFrqlsrmSV3XYVmWaCjX9YxsXPzZMQ0Dmw8fYmZ2Fp7nPZaKo8MCSQ4mhCRJkLRWKHSAw3oJ+EEATdNAkYaxgyBILY+ZkdEkRTRZh7F2H7bHQQwoMztKAGi6PpCzIw8JW6aJKAzhs4masvvsBwEIWI7XMLru3q5cvQpV03DmzJnxXNiQyO7QAtZTgveWiKIoFYQlCeI4TluCs+/5rjVhuWu+6Iuvmdfy71sRxzESSvHb3/626VkgigJVUaBoGlT+vaqm5EFRoKqq+Bn/v8J+pqqq+HsgFdpapokgDNP3zrJEpGNs5GAI215d06BqGiL2DHVaUOI4RsBSWklLzT63QNZ1faidNVEUEEYOD5ok8BLHh48eiRLH2elpACkZyEYLxqWLEK6LzACqWCikz3oQpELkKIJlWX3PCfz5UVQ1dYJ8+BAme8Yk2kOSgwmB+n7Tbq9TdULg+6mhD/ud7/tQVRU+W8jyuRxyuVyTZmGSYkBBENgE+jgqEzg830/FmWCd6Ya8bk3XRSvnKIoQhCGCMBQmN9xQiROQbG7ZcRzcvn0bL168OHw5WT/vGaVCN+F5HnzPgx8E4nvbcVLxJfs90JxT5aFnQsjeYqyq0FiZGUW6QOmaBt0woBDStEgrmUV8388UBbZt49PPPsMrr76KXC63n1QwMVrCSAonE0kcizbIraSE56h5gx6xoLIFnIAZ2VgWTMuCwfQjlmWJn1mmCdOyYJrmxMo0ee8CO1O5wBfDKKNxaVoYCYGmaWm6S9NGD7UzS/GDrFrIgpc4bm9twW40oDHPBX7NreWM4wABBJnl74Gu60KX5bouwjBEjkUReh6PEQTTNLH56BFm5ufhui5yB9ji/TBBkoMJIEkSJKxCQfgJtEsnxLFow6prGh41GrBdF/Nzc6LDWtOCNCFBUuuOjI+3SUh5wIhYzTcohZnLjW3XyIlCHnsTexCG6Q6bTfK246SLqK7j0qVLMHQdp06dGuqUvCOm2OWz73mVBScDHhNfZRd8LsC0TBOGaWJ6ampvYWSLpMkWR62LlsBxXfieB8M0u5akdcPOzg4AYG52FlNTU0MdoxuCMITdaIidos/vESdLvi/q7jlxAtBEJPi9EsShhUDwSNIwDboMXYenquL5UBUFURTtIwT8udHGQAiyIEifJW5g9jjMfLIljjvValMFAxe6TqKiQskQIu4QyVOGURShbtvI9RNFYM+JqijCGIl/jh7XPPckQ5KDCYC2phM6PHi8NbGiKHBsG4HvQyEEU1NT+z3T+WRwQA+xiHY8JjiOk3rK63raJa+1HHQM0DQNmqalJilsd8u9E8IoQq1ex8qdOzh79iw8zxOvbxdB4O1z67Ua6vU6arVa2mCm0UDAmuRwZBf8YrGIubm55h0x+17TNLEQ8IVhGAzSqvmxguXjS8UiUC7v+zVl+W0gJdZ+EMDj6ZUWMmE3Gtja2oLP3lN+fBrH0A0D5XIZJdboq1QqoVQuI5+N0DEklCKOIkRxjJjV3ydJgkKxmGoumBiWRwkm9ZnJEoJBKozGjWKpBM/3EcYxtqtVIVDsZKE8DhAAyBAEnjLkWoSERRGSJOnuP5J5bwzDwPbWFuYWFuA4zoF3Vj0MkORgzEiSZK8lc5fyK9/3EfKdB6XwWWphemoqbRiSxYSJwb4phtKm1qiU0tT6+YDIguf7YkIv5PMiujLJs6uqihxL4fAa9Js3bkDTNCwvLwudQsBC/a7rwnUcOLaNWqORhvpZUyTu+jY3P4+Tp07ByuXErpU3vxoII5bxNYV+DzMyegGiqul97SMkHMdxU3qmxgjc9vY27ty500SeiqUSisUiCvk8LPY85PJ5qIwIKJoGGkVQCEGhUBAEbtJQFKWJHCgYr/ivH3ByNjU1lc5fcSz0B0JzwFOpY74nPJqZjZioqopCPi8qGrjfSa9mVDz1Zug6Nh8+nKjQ+zBDkoMxg7JFTaj+2zxwCUsnRKzWWdF10CSBYVntJ7uD9hVoOR8hBJTtDCYNbvSUICUGTeHyA7oPCkmtmVdXVzEzPY2V27fTBYW1yk2HwiydczkszM+jWCyiMjWFcrm8Lwca9dtQpgNGveuCHDzBZYx9X+MQmhtVVVEoFFAoFDA1PY0jR46I38VxjEa9jt1aDbVaDTbrAPro4UOhRQF7r4ulEor5PAzTRJIkB7ugELKv6uQg0wuUUpH7VxQF09PT2Hz0CK7rwmbpKhHlohTqJCqqWvRQ/GecDPBNRZIkqQdMJzLM/t7QddR2dxHHMWzbRrFYHPuYDzMkORgzaKYWue3EwXwLfN9HHIbI5fPQDQOarsMLw8djzNFno5ODCGXatp3mnVV1n5J4EtNwo9HAzs4OGvU66o2GSAdwsdzW1hZK5TLK5TKOHDmCYqmEQqEAwzCQJAmiKGqarHjJpKIo0DMpCMJDrwcMSqlYVJ5oA6Q+ymVFue1Qh0/vQxRFCKMIcRQJ0SQAlNl7DKRkQtU0UOaTYdu2SBWtr63B8TxhXW7lcigVi3tpilIJ06z98TjRrpSRKAroiMSzF3hZa7aaCQAsy0KxVEK90UB1d7e5Mdwk54gO1SamaUJRVbiOgxhpyjafz7dPpfGoaJJAVRQ06vW0z8OYO10edkhyMEYkSZJ+WLso/F3XRaPRQBRFKLCmSjnLQrVaBShtFtUcsM4gPWXnnRmvuJhUmiFgpWKgFMV8vql0bxxIkgQ7OzvY3trC1tYWtra34TMtg2lZKBWLmJ2dxfPPP49r165hanoaP37rrZ67wziOEUWRWHgSptznqYiE+VQQknbWayrvY993gqh4GfJe81bNj4ucDIwR/Q64PoNXRcSsCiJmCxySpCkcTwCojMTx9yZ7nwrFIubn55vOYTca2KpW4ToOwiBAvV7Ho81N3L59W2hDKpUK5ubmMDMzg9nZ2fbmXQNAaVMJQDDZ6gVuatQJ5XI5JcNhiJ1qNXUOnfDmoVV/IH7OxKBKoQDHdZGwaEC+k/UyIAh8o15HsVRCo9EQBFFCkoOxgmYV1G0QhiF2dnYQhaHoMmiapijtArA/cnAQxICxcdrn+SaRZuBOkBTo3IVuwHvhB8EeEdjaws7OjmhrPTM9jZMnT2JmZgbTMzMwM6RsbX0druvizR/9qK9FmS8q3FY5juO0IRTbnXKrXEqpIBHNl9W+rFBV1X07tkHBJ/enJp/Kn9WMSRMvo+SllZ2eSgIAqgqDkzRGCAa9N7lcDqUoQiGfR6lYbHKdbDQa2NzcxPb2NtbX13Hjxg2ApK3HZ2dnMTs7i5mZGVQqlYHIGsloDrJQCEEygfRCNo3QCYqiYHpqCg8fPYLPhLy5A/ANIGCpzzbXzHUIDmt1bdt2+0ZLmb8PMq8tFAqH00V0ApDkYIyg3C+9g85g89EjRGEIXdcxlQk9hmGYNldp2bUcWMSAE4MBMO40g+O6wpd9nyCTn7PHMer1Ora2trC9vY3NzU00Gg0AQM6yMDM3h5eOHsXs7Gw6MXe5tzdv3sT01BRmh2zOIsgCI3pxZuHKGg1FcSzcL+Mowr4AMYvUcFU8V4Xz/xP2fbdFRojtnvQJjz9HLGdNuW8C/z7zNQzDrmI8fk9azZn4AjvqQqqoKkxdh8cEqtk6+WKxiGKxiBMnTgAAgiAQ5HRrexsPHjwATRKomiaiCrOzs5iZnobeJaXYLWJCSNp3YRyzRac0QicYpolyuYzdWg12oyEaok0aCoAY7ecE3p/BcV3EUQTHcUQpqwCbpxV2X33XhZXPo9FooFKpTHz8hwGSHIwJIqXQRigURRG2trYQMAfEuYWFpq5rIXOvE+z2oNMJdLhOi01phhEmhDAM4TO1f7FQ2PeBbzfZxHGMnWo1JQObm9ja3k5TEgAqU1OYX1jAhfPnMTs315FstEO90cD6+jp++MMfjnW3zRf21ogIZUZBURtDIXAToV4HbyEP/HtCCHzPQxTH0BgZ4QI7nr+fVBtiYC8dlH0+kpbFn2sBPN+H67pQVVWIPnsdnxs5aS2mT90snccVgjeYkyPvt9DpPhqGgeXlZSwvLwPIpLa2t7G5tYVbN2/i8pUrAKWYqlQww8gCT0Xw4/YiB8qY0gvDlCKWy+X0/fM81Ot1lA6iLJAv7Hy8rV4tLFrTsZKBv1/s73zPg5XPi7LGoQ3PniLIOzAmUN9vu8CGvo/dWg2e60LVNMzNzTURAyC1jaVZcgA8MTqDftAkEhtQi0ApRcNxQJGKilrvDUcQBKjXatjd3RUpAh5tmZmZwalTpzA7N5fuwEb4YN+4eROmaeLYsWNDH2MQEELS8HYb0hBHEUKmYRG5dPaV7+4oIxE8DNwafXBcV6Q3WtMZmUE0LW5ZR09OJnhvBYc9x00EgC3+/PthaGLMiIIgLlmSoyhQAPE9gPH2dRgCXKPAbc+71tdnoCiKWPx5E68G82TY3t7Go0ePcOvWLYCktfxzc3OYnZ2F4zjNPRpa0C3U3gvZaMGwmJ6aQq1WQ8hMiWYPQFidvWbKnrvWJne8csjzPFHJUMjomfjz1qjVMLe4iDAMUa/XMc3soZ9lSHIwJvASxuzD6XkeXNuG67rQNA2Vqam27X7DKEKCtLRm0vbIbTEG/YAoM1LVlCD0CcfzEMcxFEL2ibZ2qlWsra3h7t27qNZqIJQin89jbnYWx597Ls3dlstjU+EHUYSV27dx9uzZxx6G56ShV/khJw1Z8pDdlSusckLVtL28det73RJqb/ck8IWpnWai63Ug85nIpElEhIMt8noYQmP9B0o9Ssp6CeUOCqZhIGLmWf2Sg3bgqYjnn38eQEqEt7e3RTriu+++S7UVlOLjTz7B0SNHsLS01KRPGsb7gGLv+Rk1gqTrOkqlEqrVKuqNBkqsomfSUAhBTPcaP7W7epPZXXPH1YZto9ASTYxYBQuQisaLxeJEe9kcBkhyMAYkcSxSChye48Bl7m2ariOfz6PYQbEchWHqc3DQZYwTyA0KNXEfqYYoiuC5LkAp8mxBePjoEVZXV7G2ugrbdWHoOqZnZnD02DEsLy9jaoJq4jsrK0iSBCeHtEoeN/p5d3gFRCdwXUOpVGoKlWZz7zS728/+LPM9T9nkLAsFnvrJpCfA/8/D4Jnx9QOaJAgPoKJinE+8bhhQWdomZFqiccAwDCwtLWFpaQlRFOHTTz/FxsYGFELgOA4++/xzKIRgbm4Oy8vLOHLkiEhB9ON9wCM8nEiMK7WUZ2F5CmC7WsXC3NyBVMj0k1LRWfMm23GQxDHqrJKBj09RFNSrVcwtLcF1XdTrdcwMqTl6WiDJwRhAg6BpYuQd86IogqJpMHQ99bRv8yHkYjUgZd8HHjUAJkMSeugReDohjCLUdndx6+ZNrK2vIwxD5HM5LLFJb3Z2VvQGaBd1GRcSSnHj5k0cO3Zs7Irroc1qRnxfsgt86wIwqN6AE1d9wO6YA6OfaplRDo/xEQRCCAzTRMx6P4x7p+l5Hj766CPYto2jR49ip1rFBx98ANd1sba2hrXVVXzz7bf45ptvUKlUcOTIESwvL6eRlw73cZJRFwKgUiqhYduI4xi79TqmD0Lcx9NRhHQlCbwvg+04oEnahrxYKKSfA0qxs7OD4ydPil4nQRA8Ht+ZJwSSHIwBWeOjiKljufDKYL3HOwlceKWCQsjBimAOQFEMtNcjuK6LO3fv4sH9+9hm/g5TU1M4c+YMlpeXUalU9uXAJz3mjY0N2I0G3njjjbEfe1j1NsVoC2GSIWaHwuNgAAy7yI/7CTIMAy6LHsRxPLZ01O7uLj766CMoioL3338fN27eFFqaXC6HU6dO4dSpUwjDEBsbG1hdXcW169dx6fJl5CwrFUEeOYK52VnxWUro6JUa3UCQVnKUy2XU6nU0Gg3kDqAtskhp9kF6eCVDg/XIcFxX9NRImFtmLpeD4zio1+uYnZ2d6NifZEhyMCISJiYkhCCJItFZLo5jGLoOTVW77kR5WZb2OFIK2VzwhCeNeq2G1dVV3F9dxfb2NhIAU+UyXrx4EcePH+/a+ER8+Cc2wlSIODU9PXT5YjcMEzkYR6tsETVg+f0nGfzu9DvKx9WZsBWKonQsaxwWDx8+xO9+9zsUi0W8/fbbsCwLURS17Sap6zqOHTuGY8eOIUkSbD56hLX1ddy/fx83bt6EpqppimJ5GQuLiyOJdfuFZVlpkyrXxU61ikXWnGmS4Gmufp4J7oZYr9XS1Kbvp1UMALYePcKZF16A4zipi+0YCd9hgyQHI4JHDZIkScNp7OHkUYBcodA1VBoxcpA7KHJwQGWSNEmwvb0t9AMN24bKJqqjR4+iWC6jkM+jXCr1PhgnBxNaDGr1OjbW1ycSNRgW47hSbqz1tEUNAEyc0A4CwzRFh8hR2/+urKzg66+/xuLSEn705ptiYQqDoOfCrigKFhYXsbC4iFdeegmbW1t4sLaGtbU1rNy9C1VRsDA/j2WWfhj7jj5z3eVKBT5rgc6bM00ahBCQJNnXG6YdVEVBLpeD67rwgyBtxGQYqFWroJTCMAwEQSDEic8iJDkYEQlTuDq2jThJ0oYjhCBmAkOtB+vk3QcnmsfthTFNsjRJsL6xgbXVVayurSHwfZimieXlZbz86qtYWFhAEseo1WpIKEUhnxfiqK5ObFy/MKFc6c2bN2FaFo5OqnxxiPtLhiwJzCI5TCmFYUrwejw37V4/CfA23jSKBiprbMV3332H69ev49SpU3j11VebfhdFUX9RCfbcJACmZ2ZQmZ7GxYsX4dg21tbWsLq6iq+++gpfffUVZqansby8jOPHjyM/Bm+CrN25qqqYqlSwubWVdi81zZEtpPsag6r2NU9wXxluc+56XupDoqrY3tpCoVgUHVglOZAYGEkYAqyXOF/kTZav4l37uoLStIyx1eNgUugwOY46ZbqOg5WVFaysrMB1XZRKJTz//PM4cuQIZqanm3YUjUYjtUjO6DCyZjntJnsuGJrE1B5EEVZWVnD23LmuPQ5GwhC73LFEDg5DwyWGgR06kaZLBrmvk7wLo5Q1JkmCzz//HA8ePMArr7yC06dP73tNGIYodajUaVt1wj0q2O/yhQJOnzmD02fOIPB9bKyv48HaGq5cuYLvL13C0uIiTpw8iaXl5dGfF3ZOi/UrqTcaqNZq6WZpwmkNAmYp3eu5YL/n9vVRFAmB4tajR5idm8Pu7i6CIEhTOs+gKdKzd8VjBA0CBL4P3/NAkdYre54HgNXW9viQUSCtraV0okr89GTjXVppkmDj4UPcvn0b62trUBQFzz33HE6cPImpqam2fxMGgSBRWR1GKyFoJQnC0WwCO787rFnOqZMnx37sUTBKsyUOQQ4OQ+SAYZArHlSUOEmNwrBljUEQ4JNPPkG1WsWPf/xj4aTYinYLVGtJ4j4QApX5AGRhmCaOP/88jj//POIowr3793H79m188vHHsPJ5nDxxAidOnBhcP9HmeS2XSqlLIUsvzE9Y4Ef6IAatv81ZVmoyxQSK2NqC67owTRM+c+4s9ZP+fMogycGQoFGE0HXhui4oIOqMoygCISTN5/VY0IIgAEU6eR8W0YvneSJKYNs2pioVvPrqqzh+/HhbwVQWDiNRlmW1NfdptYoV7nsZtfU4kVCKG7du4fjx45NVVA+4yI+DGAB76u0nXYzYhAHGOnDFwgR1CsOUNTYaDXz88ceIogg/+9nPOpJqII0cCM0BTx3040aqKCBx3PE+qZqGE4wMVKtVrNy6hWvXruHylStYXlrCyZMnsbC4OFA0IXsuwpozPXr0CL7vo9FoTDRMn3VJ7EQGW3/Oo7w2Iwie7+P+ygqeO3VKkgOJwRE6DmzbRkIpLMOAaZqo1+sA0qhBV2tX9nDylMLEQ1ajToiU4uHGBm7fvo1VFiU4duwYfvTmm6nNaB8Thx8EiMJQWJr2QtbxTMlYpI4T6xsbaDQaeHPCQkTyGFIKwOGMHAwCMuBiP2mKNEhZ49bWFj755BOYpon33nuvZz4+DEMoqtrcdbLPBVtRFNG6uxumpqbwg9dfx0svv4z79+7h9soKPvzwQxQKBUEgupLoDhE+wzBQLpWwW6+n6QXLgjHp9EIHnZKwHG8Bb/hm2zaiKML6+jqWjh4VG75xmlwdFkhyMATiIECjWkXMFvZcPo8wDJujBt3APtQh0ywYk9y19jN5drBs9j0Pd+7cwcrt22jYNkrlMl555RUcP368awe5dscXERbmdd4vREMhQtIGRGPc/d28eRPT09MTd0J7XJr6wyRIHLSUcahzTLi6IVvW6AdBx4Zf9+/fxxdffIGZmRn8+Mc/7rno8BbgGmvhPTD6dE7k0HQdJ06dwomTJ1Hd2cGt27dx5coVXL58GUeWl3Hy5EnMLyzsN9YCOn42i8UiXNYEqbqzg7nZ2Yk8l9z4ixACQikSND9T3Z4vVVWRy+fhOg78IMCtGzdw4vTp1AbfdSU5kOgOSinqW1uIoih13GJuZEJrYJrNUYMui1nEeiocRO1xV2Q/5JTi0eYmVm7dwv3VVRAAx44dww/ffHOfuLBfeEGAOIqgKMpQ4XvRBIjV/tOW8Q4DXr745ptvDvX3g2DQsP44PA54V0fgcJCDYd/HgfwOFAXo0rxoHOBljT6vnW95769du4bvv/8ezz33HF5//fWOzwbve0ApRcB0Or3Sdt3AexAMBEIwNTOD12dm8Morr+Du3bu4vbKC33z4IYqFAk6eOIHnn38eJv9Md3nOiaJgZmoKG5ubaXMj20ZlAqF67nfAx6O0RA96pSZ1TUNiWfB9H1ubm5hdWICu63BdF+UJWrc/iZDkYEA4tRoi3wchBIViEYSQ5qjBAMJCnlefmN6gj8mAvyLwfdy5excrt26ljVNKJbz00kt4/rnnBosStBmD57qglCI/ZA14tkQq+39geKJw88YNGKaJI0ePDjyeQTHIjlXYTY8qRjxEBkhZDDrWgXQHB+CLoGkaNF0HDcOmskZKKb7++musrKzgwoULOH/+/L6x8Q6DSYYcEqReKABG27kOGD1ohabrOHX6NE6dOoXt7W3cunUL31+6hO8vXcLRI0dw8tQpsVHqdJ81XUelXE6bMzEnwkmkF7LPhNhMZCo6esE0DCRJgjAMcW9lBafOnkXMKlGeJTtlSQ4GQJIk8Go1IUDki7rnugDaRA36ON6o7ZI7os9JYHt7G9evX8f9Bw9AKcWxo0fxg9dfx9zs7FjG5bEcrKqqQ1dkEEUR+cNWS+G2RKHHAuuHIVbu3MG5F16YXPniCHgmxYgHsHAf1J0wDQNRFImyRt486eHDh/jhD3+I5557DgATz7HQN6VUaFNax8krfEbVJg0VPWgFIZiZncXM7CxeffVV3L17F7du3cK93/wGxUIBR44cwXPPPYdO8cFioZA2pQsC1Go1zE0gpdd6hU2ljX1eP3/fHNvGzs4Opqen4bquJAcS7dGoVoEkEW5aQPrBjeK4t9agy2L1OCbwnWoVly9dwur6OnKWhYsXLuD5EyfG+vDTJIHreQClyDH/8mGQrWJI2P1v+7q9P+hKFO6srCChFCdPnBhqPINiIKOeMZ0zPmRixEFFdgIDaFAOSvuh6zpURUEURajVavjss8/gOA7effddzM3NiZRB1ruj21XzFtmjpBXSk4wWPWiFbhipd8Lp09jc3MSNmzdx/cYN3F5Zwflz53Dy1Km2UdFypQLv0SO4rpu6So65jLtdNKlfUaY4BosCu56HjdVVVCoVeJ6Hcrl8eAj3iJDkoE/EcYzAtgGkjU/4JNYUNej00HTKK07KpKbLh79Wr+Py5cu4f/8+isUifvTmm1haWhoo4tEvXM9DkiTQNG1k0sEbOPU7sXUiCkmS4OatWzh+7NjEG8IMhXG5VXK9wSGZyMT7OkRa4UkDIQSGYaC6u4tvfv97EEXBe++/j2KhMFRHRB45GIc2SVGU1FtlnM8FIZibn0exWMTuyZO4e+cOvv3+e1y/fh3nz5/HiRMnmuYXwzDSygDXRa1eHzs56EQYB733uq6nFtBBgJ2dHczOziIIgsl70jwhkOSgT9R3d4EkgaaqgsH3HTUA0ge2n7rkCcF2HFy+fBl379yBlcvh9ddfx3PPPQeFEEQTEGklSZKaQ40YNeDgpY1DWRHvHQSPHj1Co9HAG2++OTY/gXGiNW0yLA5rX4Vhrn1gv4MJg1KKne1tfPnFFzBzObzz9tsdKxf6QRRFoBg9rcChKMrYPUOAVOdimiZefvllXLhwAZcvXcJXX3+Na9ev48KFCzh+/Lj4vJVLJTiuiyAIRGfEsaHDtfXlnNgCyzDgOA52d3YwNTUlzJGeBUhy0AeiKIJn29CRRg34BNZX1CCLFrtX0TVvnAtUSxjddV1cuXIFKysr0E0TL7/6Kk6cOAGttaJizHA9T3g4jCNVwXfAo05p9+/fR7lUatsIZiLvxwAYR5VC9lgAJhIRmgTEeIf428feoTEjJqSU4vbt2/j973+P+fl5vPDCCyNHb6IoAsH4hMtkQhsVUUKoKCgUCnjjzTfxwvnzuHTpEj7//HNcvXoVFy5cwLFjx6DpurBW5tGDSRJZkVIYsBRa03WomgaX+drouv5EbiomAUkO+kCtVoNC0/4HmqYhoVREDXqW52Xz3S0Ppcg3jvtDQQi8IMC1K1dw89YtqKqKCy++iNOnTrVtBDXunVecJPB5hcK4mq30cD3rBxGzij137lyHUzR/4LlD48hRjz5fN+73ADh8kYOhFqwJOh+2A21D8Pn5v/32W9y4cQNnzpzBCy+8ANt1EYThSCmskLdrHteCxLxDxh094NVX2VGWSiW89dZbqO7s4PvLl/HZp5/i6rVrePHCBcwtLMBxHIRxDMdxJuacKCqAMET0gFJYpgnbcbC7u4tcLgefdd982iHJQQ8EQQDPtmGx8Dh/rPqOGmR/14EkjI2Dspro69ev4/qNGwCAc+fO4ezZs93zlWOeXF3HSf0bDGNsxiH8Ho8yoa2trSGOIhw/fry/c6YnbvrZJHcN4+jEyCE0B4eEHIxqgjTJ96UtGWhBHMf44osvmpsnMfOviDX2GTYtEEXR2L1QBnWX7AeUeWu0e+ampqfxk3fewdbWFr6/dAkfffwxZmZmcOLkSWiahlqjgXw+P57nNXttlCJOkubnahARK7seXdPgseiB67qSHEhkogaGAZWx7TAIRNSga/6ph8hqnF3zojjG9evXcePaNURxjDNnzuDs2bMwD7j0JopjBL4PUDrWPKKY+Edo23z3/n1Mz86iMEJ7WuHbDhZxGWMqYpzEYGJi10lhhPtIgP46NPazIGQIGo8c9UIQBPj444+xu7vb3DyJEOiGgYQ1HhqWHIQj/G1HMIOgcUYPkh7zHQDMzs7iZ+++i41Hj3D50iV8+cUXmJqawtKRIyjk86iMwWgoGwnNekZwDHLd/Hk0TRO1eh12sYharYapqamnPrUgyUEXuK6LwPNgUgqLLXQUaHZD7Ddq0PpzHuoa0ecgjuPU3vTyZURRhBMnT+L8uXMDMdtxphV41MAwzbFOaApGG6fn+1hfXcUrP/jBWMYjTGo6VaK0LHb95MXHtftNMvnVwxI5GHUXO+izse+9GDJq06jX8dHHHyOOIvzspz/FVIuWxdA0+KwbKR3SBGxSLYO5xfBYPvuZ+9nzGgnB4sICFufnsba+ju+++w5Xr1zB6toafvDyyyPbmYsW1mjzPmOIOYR9jizTxM7WFvL5PFzXHV/K9AmFJAcdQClFvV6HGkWwcjmorJRuXFGDLIaZMJIkwcqdO7hy+TJcz8Nzzz2HixcuDLdbH1OIMWJlP+OOGgBIxZwYPq1w/8EDUEJw7AAcEYH97ylB89h7takeBY9bWDkMhvY54H+G9pM+v6+Ups6Dw5QSdsLW1hY+/vhjWJaFn37wQdsWx5qmQVUUhHGcpgeGSLOFQTAZX/8x+h5kj9F3tIoQLC8vY3lpCd9fuYK7d+7g13/7tzi6vIwLFy+iPKS9MvdDaddgCWDEXlE6/n4f2LUZhoFGtYpGvY6dnR1JDp5VOI6DKAhgECK8wweKGgDdJzpKh26Ms7u7i88/+wy7u7s4duwYzl+8iNIIofJxwWHNlSzTHLsltMJsgPv+QLfg3t27WFxcfHwOZ2wi3ge2c0syoqlRF/WYlTE+ie6PHTHCAiVEcNldcEYcJ3aSYyQG9+/dwxdffonZ2Vn8+K23OhsUEQJd01KflCE7+03S139c2gMRvh9SUHr65ElUymVsPHqE1dVVrP71X+OFF17A+fPnB54facvXtqcEmmyVe42PaynyuRwePXqEYqmEpaWlyVnfPwGQ5KADbNuGFsfI53Lgj+bAUYNuGDJacPXqVVy5cgXFUgnvf/ABprv0gO8X49hfhix0CqDtDmpU8N3IIC5nHA3bxtbWFt44gCZLA4P5N2Tfg2xZH22NQGRd9XqlNA4ROWi9puwnKLvAt/s7HjVozS+33p1xpc6uXr2KS5cu9WyexGEYBnzfRxSGSCgdWAdiOw4WlpZGGXJnEAKVjG6rLCoVhiS2lmXBsiwsLCzg6NGj2Hz4EFeuXcPa2hreeOMNVCqV/seC/gyP+o6aZMo+c5aFWqOBWrWKarWK2dnZvsd12CDJQRtEUZRGDZipB5A+cO4gvgY9fs8niX7DybXdXXz++eeo7u42M+rHWd+dgeN5adTAsqBMgE1zhj7M7u/evXtQVRVHuFDsMaDne9wh5bDvr7gZFNC2LwfhoXNKBTnYJ6zL7JgmkXpoGhf7nnT4OX+92N3T/W12e+0AxfcTLmmklOKrr77CnTt32jdP6gBVVaGqqiiBHkQkHMcxfN8ff5ouizFoDyirCBjleSqVSvB8H4Hv4+Tp0zhy5Ai++OIL/PJXv8L5F15IPSP6ILz9zhEE2PestX9h5vNICHKmiXqjAdu2JTl41uD7PhSaGvjwxyKMovFFDQDRRIirhjshSRJcu3YNly9fRqlYxPvvv49pLnp6QoiBHwSIwjD94EyoxEchBOC5RAzmHXD37l0cPXr08YYAJyBGbPt6do/4jhBoX5KZ/X+n3VN2Z0VZk7CmhRztF3Ee4s+coCncj5bvaQsxGnZ56SZKHPWTEoUhfvfpp3j06BHeeOONvsthOXRdRxTHA5MDviGZaH6bCe6Gicpx8MjBKNUxpmEgl8vBdRzUajUszM3hg/ffx5UrV3D5yhWsrq72jCJQDLaBUPrVHmSIp65paLCSxqcZhyfueIAIggCE0qY8YhAEANIQYc8KhQE+IEqnXDTSaMGvf/1rXPr+e5w7exbv//zne8SAn+txg9Vyi6jBhELZ2WjEIKKynZ0d1Or1gSfzsaPLezVKOLbt8QaMChD2DLb+a/od64zZ9Hetx2nzs0Exyt9PSoDpui7+5m//Fjs7O3j33XeHepb4vBGy1EK/cNgCNIlUXRO6zEP9gKd0Rn0PysViep+YrTJRFFy4eBHvv/cekiTBL3/1K1y5cqXtHEApTfU2E54XDcNAFMewbVukUp9GSHLQAkopfN8HSRLRa5xXKQCA0U1QlBGV9XMegTbK9StXruAXv/wl4jjG+x98gIsvvrhfYDamyMG+nd4A8IIAcRRBmWDUgINf/yDk4N69e7BME/Pz85MaVl/oOl2NOQIk/DMOi+bgCa6uqFar+PWvfpV+Dt97D3Nzc0MdR1EUaKoKRVHEXNIPXMcBxQGQA4zoiTGGyAGQRliKhQIUQlCr18WzPDU9jQ9+/nOcO3sWly5fxq9//Wvs7u7unR7Yb3bUBwgG1+aoqgpNUdCo1+E4zoBnPDw4JLPHwSGKIiRRBJUQqIwchGGYNj5RVfGzthggatBaqcDzpbVaTUQLzp49iw9aowUc404pDPOhphQes0keR3OlXlAGJAcJpbh37x6OHTv22Beeg0wAHbZSxlH6KjRhzNe7vr6O3/zt3yKXz+O9995DccjSOg4ePQgGIAeO40xMx7MPI0QPkjE+c8ViESrrHulkQvdKJooQxzF+9atf4crVq6JEdZQz9/x8tqS+VF2HY9tPNTmQmoMWCL2BrouHLQhDUEqhdcsVDmNmlMkzU0px7fp1fPf99ygWCqm2oJMZyBOiNQjCEHEcQ1HVA+lUpihKKiLqkxw8fPgQrufh+HPPTXZgI2KcTYOymoBDEzngGHFhGWd05tatW/jmm2+wvLyMN954Yyx6FZ3NKVGSIE6SvkpNHdedeEQui2F9D8ZZIaOqatr+uVZDvV5PK8Yyx+VRhCtXruD777/H3Xv3Ui3CsL4I6EN70DK/G7oOx3HgOM5ErbsfJyQ5aAFPKeiZKoUoozfoiAEfjuxuqVav44vPP8fOzg7Onj2LC+fPd5+MJqDK7ibm6gTh+dBLhzEmDBo5uH/vHkrF4ljKPSeJcU4uWQvbw0IOxhY5QJfFrd/7Sym+/e470Tzp5ZdeGltEghACXdeRBAHCIIDax6LvOs5kKxVaMOxzyJsbjWsWKBSLsFlTpnqjsc9WWVEUXLx4EYtLS/j8s8/wq1/+EucvXMC5s2eHSm30rFxoOaahaWgkCeq1GoIgeCrbOB+O2eOAQClFEARQAWFWwlMKKiGdF+xhmDbbPayuruIXf/3XCIMA7733Hl5+6aW0c2KnYw6gaxgIA36goihCxCIq1gF9MFRmhNQPOYiiCPcfPMCxxy1EZOh0d8fdapjvfg7lPmYMi/AoO9c4jvG7Tz/FzZs38eqrr+Lll18ee6rCMAwQRUHQp5DNcZwDd+IbZnHl1VfjIqQKISiVSiCUot5oIIqifa9JkgRTlQp+/vOf4/Tp07j0/ff49a9/jXqtNtw5Bxi7qqpQVFWQg6cRkhxkEAQBaBxDIUSE/Pgbr4/ZWa9h2/jiyy9x5+5dLCws4N2f/nTPU7xT7u8JSScAaYSF91A4kHwoAMJIUz8lV2tra4jC8PFXKfTAOBTeWQzruvk4cRBPdS8S5vs+fvOb3+DhxgZ+/NZbOHXq1ETGoWsaFKQLW7sFrxWO6yJ3wORgGL+IfpouDYpCPg9d16EQgnq93nw+VloLpM/6iy++iJ+99x6iKMJf//VfY2VlZeDzDeJ3wAWmjXpdRFCfNhyeGeQA4Ps+lCQRUYMEELvjruRgUMcz28bnn38OgpR0bKyv4//+3/+Lv/n1r3H1yhVUd3fb1/JPMHQ/yJFpkojOiwcVNQDS6A1IfxbKd+/fx9TsLIpPgK10N4x7YRSRg17kIDP5txsD1y4k7Gscx8LrIPt3/YyfP8vtSh+bxvyYnu9GvY6/+Zu/ges4+OnPfoalSZplkbRTo9JH9MAPAsTMpfVAMWhKij8TE8i9V8plEKQmayEjU1ldTRYz09P44IMP8NyJE/jyq69w6fLlgSNzXT83mWNxcuC6LmzbHmvPjicFUnOQARcjipRCEKShMuZw1hYDChF3trfx0ccfQ1VVvPzSS1A0DUuLi1hfX8fGxoawZrVyubRz2dIS5ufnx97PfR8G2C34QYCEmURNpCFMB/AIRcIWrU7hT8/3sba+jldefvnAxjYsOtoC9zHR8n4M2ddlTaJ6Tow9CAKwt6gSXolDyL7X9vPUtJs6acvX7Pck8/9BlhuunWn9m05j3NzcxCeffIJcLoef/vSnB1IyaGgaAt/v2anRZUr4g44cAGyRZD06ekHs4nsYug0Dbqvseh5qtRpmZma6LsSqquK1H/wAhXwe333/PRzbxuuvvz6eSFrLXK9pGgghqO3uIgiCgTrhHgZIcsCQJAlC34dJaVMJI9BDiDgA1tfW8Lvf/Q6VqSn8+K23sPHwIYIggGEYOHnyJE6ePIk4jrG1tYWN9XWsb2zgzp07ACGYnp7G8vIylpaW0lzcWEY0BCiF53kHqjXgIGxHw0uXOqUz7j94ACTJgXVg7BuENPm0t/O3aPd9x8O1OYYoKTtEaYV2pLQdYWiHVjIgDKX6sAO+f+8ePv/iC8zPz+OtH/2oc/OkMUPTtNSRMIq6dmo8MAOkDui3cmHcJl6t4LbKnufBc92+5uOz587ByuXw5RdfwPM8vPXWW31tZAgAqijp57QVLXb1qqJAVRTs7u7C931JDp5WxHGc7sJUFSohIqUAdDE+GiBqcPvWLXz91VdYPnoUb7zxhjBEASGIo0hEJlRVxcLCAhYWFvAy0hTE+sYGVldXcen77/Hdd98hn8thcXERi0tLWJifn0iv904Q5YuKAuMxKHQVtqNJkgToQA7u3b2LxaWlJ0ZBLBYvPuFkwrCtzYI4hlaNcwOkQ1RaNYovQ7voQ+v/Ka+Dz0Q9rly+jMuXLuH5Eyfw2muvHWwpGqtaiJOka6dG13FACHlsz7FC+mvINI6+Ct1gGgYsw4DNQvg9yQEjh8ePH4dlWfjdJ5/gb3/zG/zk7bdh9UG0FLSPdDURezBRoqLArtdFJ9SnCZIcZKBkFnueUsg2TRkKlOLSpUu4cuUKTp0+jVdeeUVM3Fx9H8UxOj3uhUIBp0+exKmTJxFHER5tbmJ9YwPr6+u4vbICRVEwNzeHJUYWSsXicOPsE77vA0iFiI+jtpf08Dp43B0YKdqnBCiwL3UzaMi8H4zTjOagIFIJ4xpzy33mFtCcJHz19de4u7KCCxcv4vwLL4gdYdZzZNLQdb1np0ZRqfCEv5dRHIu5ctzgn6dCsQjbdeH6PkpR1H1DlHn/5ufn8bOf/Qwfffwxfv3rX+Odd99FuR8/hE7Rg6aXpNG5p1WQKMlBB4gqhRGiBjRJ0oqEO3fwyssv48yZM00TIA+L92Kd/FFXNQ1LS0tYWlwEffllNGw7TT+sr+O777/HN99+i0KxiMXFRSwtLmJubi4ti+wDCtjC0uWa4jhOSROlyD2m3UwvC+V79+5BVRQsT6rFLUMnEgB0XuSG8ZIYFIfOOhnj9Tngx9kXSaBpR8RPfvc7bG1t4c0339wrc2XnbyIF7L2lJNMIqo9URb/QNK1np0bX8x6L3kCAp/F6LJL89/2YOg0Cmjm2YRgwdB1+FMH1vIE2QeVKBe+99x4++vBD/M2vf4233367pw122+hBy+eaf8biOIbjOHvVZk8JJDnIQEkSQFGQAIiZMrZjCKsHMYjYRLT56BF+9KMf4fixY/teo7K0Qj8lTa3nJgBKxSJKZ87gzJkziKIIDx89wsb6OtYePMCtmzehKArmFxYEoSj0mGiIonTdNXm+D4qDLV9shdLF64Ai7cB45OjRsaRaRMh6QBLQEa2RgwnsUA+lO+KYy+DaHcVxHHz00UdwPQ/v/uQnvXsk8BREO0JAWjpHDkkaenVqdBwHhcdcbUOAnhuhhKVkx/nMZYkBRz6fR7C7C9txupKDdu9FLpfDz957D5988gl++9vf4s033sDRNnNy63Fa3+fsfeAaKABoDOmt8CRDkgMGSmmaVkBLSkFR2n/wu3xgPNfFRx99BNu28e6773aciFRNS/N6XSIHHRcQvtCwr5qm4cjyMo4sL4MCqNVqWN/YwMb6Or75/e/xe0pRKhaxuLSEpeVlzM7Oipa+/YCXLz4OIWIW3VwSq9Uq6vV6al4zIPhC0O49nVSIfuxljNkSryc8FJ3FRNIKGexsb+O3H34ITdPwwRh6JHQjA4SFo/t5bw3DgMeqFtqlFhzHGbrR09iQEQG3BfMd4VVdo4KXx7YrV87n86jV64iTBJ7nDSwA1HUd7/7kJ/j8iy/wu88+w8uui7Nnz3Z8vdIiyGyXBhTkoNEYaCyHAZIccGQexqCXXXIXYlCv1fDhhx8CAN57//2u+S3eKyDqRA64G2KnSbOFIIgfI60PrpTLeOHsWQRRhIcbG1jf2MC9+/dx48YNaJqGhfl5LC4tYXFpCXlWUtWJjPhBkPrBq+qBli+2gos425GDu3fvwjRNLCws9HWspDUiMOEFNSuIAzB2U6vsBH4oBYljPCZ/ltfW1vDpp5+iWCzi7bffnri4j2ZEp9mKkXafK14rnyQJwhYL3oSmTc0ea1qBoZspUvZzOHJagZGuTnOQoijI5XKwbRs2a0g1KIii4M033kA+l8O3334Lx3XxyssvtyembebVfWNihKguycFTDPaQU0pFmJ8vgu3Ciu2wubmJjz/6CLl8Hj95552eJUiaqqbiug7kQIjYuiFbFtfhQ2VoGo4dPYpjR4+CAtjd3cU60yp8/dVXoEjzcosLC5hfWMDszMy+UjiPmx495nIdlYnHEjaRZEvYVldXceTIkbYf9Ha+6Y9btDf2hi0ZMeLjvrZBMKkukjdv3MDvv/kGR44cweuvvz4RwVxHtBJtTuJbPqe6riOMIoRR1EQOPM8DBQ7eAKkdWBql3ezCo57j0Bv0UxlRLBTgOA4830fUTpjYj18LIXjxpZeQz+fx+2++gee6/TXXavN88uirY9s9x37YIMkBA2HkIIwi6Ews1PGBb7ObX33wAJ9+9hnmZmf7rqlVVTUtZRyXu1YfHwwCYKpSwVSlgvMvvAA/DPGQpR/u3LmDK1euQNd1LCwupuWSi4tQVRVxFIEoymMvDyQscgCmPOfvUa1WQ9228QoTIrbehSdtqZxEJze+iztMxKAJYxo3TRJ88803uH79Os6ePYuXXnrp8TvY8WvLlLFCUdLUguftSy1wA6SD7qvQCaRD10I+d42SUqBISUY/z62u66kwMQxhO86+hkyDiH5PnjoFy7Lw2Wef4be//S3eeeedpnmbAKA95lROKHzfF541TwskOeBgD3kUhtA1rXtKoQXb29v49LPPcGR5GT98442+WbSiqiCECFvQXueZBExdx/Fjx3D82DFQAJvcgGl9HV9+8QUAoFQuo1KpYHl5GVOVymNdfIQRUpKkRkhME7K2vg6FEMzNzR2IV/9Q6MORcBQcxkoFYLyRgyiK8Plnn2F1dRU/ePVVnDp9+kBKEwcGW3T486woCqIogmkYoJTCdhxQPD4DpFYQtF94kxEiB1l9wSDvfaFQgF+tiqqFUZ735SNH8NOf/hQffvghPvv8c/z4xz9uSskphHQtYxfOoQB2q1XM95nSPAyQ5IAjSZBQKqoUOvZSaHmIPdfFJx9/jOnp6YGIAQAREqOUph+QbI5ywOE3jW/IyZAAmJ6ZwfT0NM5fuADf97G2toa79+7h/r17uHPnDkzDSEWNS0tYWFg40EhC1qKVItVqcDfLjfV1zM/Pi/8/kcg+O5OsVDhskYMxiSg9z8PHH3+Meq2Gt99+e2+ifsLvh6ZpCOMYURQJw7VGowHTMPbmiMc5QCBNLbTRJCVJklYqDBg5oNiz/x4UlmVBZUJuz/NGjq5Mz8zgR2++iY8++QSXL13Ciy++2DTO5oE3R40pUmKUUCrJwdMI/oBzYaCmaX0p+eM4xicffwxFUfDWW28NzJ5VRRETeRT//9n70xg5sjU7EDzXdl/DY/OIIGPhElySTDKTmUkmmZWZzHyqBtTAtICSBKEHI2h+CChhGpA0rVKVBOmHRgJUgy4JKEFQT6GAgiCgJXT/6HkaVAElQa/qvcyqVyQzk/uWXINkcHOPPcI32+/8sHstzD18MXc39wiScQCCZIS7mbn5tXvP/b7znc+BzN/f7cLRxSIUnAC4uC+VSkEURdiW5WkV8nk8f/4chBAMZjJeqeTEBAZ6EFXg10IB36FMFEUQ5tQIeDbXi4uLHVUp9BNckBgmXNkJ3kjrZEQjSORCYJdSfHn+PDKZDFxmztPKzGa7IUsSDMOoiiCur60hnU5XVZ8E9TXbAUJItUsgr1Roo4yRX3vYNEI9CIKAeDyOQrGIcqUSSeolOz6O48eP4/bt2xhIp30PjC3Pap1r5tGF0lsmStwlB4DfYIT3UmhqfAT4u/NrV69ibWMDX331Vcflfdzxz3acxgLIblCb62z18uD5KYXByhdjsRiUgQEMj4zg+Pvvo1KpIM+aRT14+BB3f/wRmqZ5OoVsFtmxsY7zbz4hqL1m9lnEGvOoxYUFOJRirMfGR1HA9/2n9W2Tu8GbaJ0MBEoZO3z/4sICLn33HeKxGD777LNNhT+/1z0gYlGCN/ChjgPHcSCKItY3NjAeHM/B8slAJURfP1VNWaOvlWI9Bloh6F3Q7SYiHo+jWCrBsCyYlrVpcd/F93xodhbra2u4cvUqkqkUMpmMf61NU1Pss5TeMlHiLjkAANeF47pwbBuCIGxNKQQHBhsIDx8+xPz8PM6cOYPMwEDHp5a4KJHvGlqVL/YYwbMapumJ/kRxy0Ifi8Wwb/9+7Nu/H67jYHllxessyYSNhBAMDw/7bo3pEFGFRvXNtRBZlQcnB7lcDqlEAskeW0dHhXr10lHgTbROBrrTHMw/fYqr165hdHQUZ2qEwJzohhlT2wpCIEkSHNeFZduglKJYKiFdI7YLvp7WzEn9iioEyxq5+VErlX83KYRGkGUZmqqiousolctQ2Bzc1RkIwalTp1AoFnHx4kV8/fXX0DStpciR6zEqrFHW24JdcgCAOg7guhAI2VTlBga8v/Ngk1c+n8etW7dw5OhRTLZw2WqFoAWnj15M7h2kGrgjotqifFEQRYyOjmJ0dBQnTpxAqVRCnvkq3Lt3D3fu3EGMNYviWgVJkjYd6NqcNLgxleu6cB0HuVwOe3ZaB8ZG6OEu1t+VvUFpheB33w45oJTi3r17+PHHH7FvZganPvwQpHaR4qI/7ICcfQvIsgzTsmBbFsqsUmEg7KYjGFUgpHHjoCgQKGt0mJCwUUqBCw7dLlIIzRBPJLxOjboON5WKRIgrShLOnj2Lb37xC3x36RI+/+ILv6qsnqeMw1JXhImk/X4YbwF2yQHgRQ44A2aq4S09CdiAKBaL+P677zAxMYFj773X9alFSQJh5wT6NIk1WaD4I2yziYoQUtfatRkSiQQOHDiAAwcOwHUcr1kUS0E8efIEhDWLGmPWzqlksi1CRATB96VfXV1FuVLB+NhYW9e4XSBA5028WoELEt8gclAvKtfyLY6DK1evYv75cxw/dgyHjxypb2/NXx/BZfYaPLVg2TbW19YASpHqxMmRUo8YsFLJXtwD3q3Rr1SoEzmgzIsE6F0kK6ZpECUJlm2jXKkgmUhE8jljsRjOnj2LP/vzP8f169fx0UcfbVYtBCuOAp443H+lWCjskoO3Ba7rAizXJ8kyKDztQZWGgA0Iy7Jw4eJFaJqGTz75JJJBL7IH2GGiyL6h2Q6WED9qoClKV4sNEQRkx8Ywms3ihOuiWC57WoVcDnfu3sXtW7cQTyQwPj6OsbExr+IghPJZFATYhOBVLgdREDA8PNzxNb4teFPTChxhrtsyTVz67jusLC/jzJkzzSN3wQl9h98TQRAgCQJcQcDa2hoSiUR3pk1cbxH4P8FmxU9XIASEiRFdSrfoDZyIUwjNEI/FfGFiMsI+FINDQzj14Ye4cvUqMpkMDh486P0iMG9y22iuxaCAJ0p8QzYqrfDOkwOuZHZsG6IogrJyxtq8MAXw/Q8/wNB1fP3115FZCIui6D1oXFndTzQSK7I+CqC041LFKlFhQMiZTCSQPHgQBw8ehOM4frOoXC7nNYsSRYyOjHhkYXy8YeMZSRRhWhby+TyyY2M7u4QxCEJ68j0HTX7eJEFiO5UKpWIRFy5ehKnr+PzzzzEcou+AQAicN+R+8JLG9Y0NpLvQMdUFJwrMW6Hb3Hyw+Zkgil7EgpC+6zsSiQQKxSJs24YRsQnR9MwM1tfXcePmTaRSKYyOjlbdN64T86PMb1nFwhsyo/YQTFTjOM5mxz9KYdk2FL7gUIrbt28jn8vhVz7/PFLhG08rOLa9fTucGpLAowayLLfd3ZDySShY8tQAoihiYnzca69MKQqFAnJMq3Dz1i3QGzeQ5C2oJyYwMjLiRzFESYJt21hdXcWpU6fa/MDbB757i7xSIRA1eJMiB2EbRa2urODixYuQJAnnv/469DP45twJQJJlwDRRLBaxf9++3pwkKGbsQsjIy7+5GK+f0YIgRFFETNNQrlRQLpUidyh8/8QJbGxs4Pvvv8dXX32FeGCzYrNNpCSKsNnm7m2yUX7nyQFllQqAFwJXZBmGYcC2LJ8cPJufx8MHD3Dy5EmMRWxyIXKXRMfZ/tAnY/6GrsOltKUQkcMXFrK/+bHaPXcqnUYqncahQ4dgWRaWFheRy+Xw8tUrPH78GKIoYnRsDOPZLEZGR7GyvAyXUoy9SWG8HoW5e9G8qB/wyxib3I9XL1/i8g8/YCCTwdmzZ0OPS3bg7i6wj5BEEY5lwTQMJPpReRNIO/D7H3Z5dxj5py0cBPuBZDLpkQNdR7Jev4UuQAjBmTNn8Itf/AIXLl7EV+fPe2JqSmG7LqjrQlJV3yPHNM23xkb5nScHXG8AeAu1LEkwdN03JFlZW8PlK1cwMzOzmXeKEDxf59j2FpfE7YDtOF4ekRAoTIPRaHr1SUGIKEG7kGUZE3v2YGLPHoBSrBcKyL9+7TWLunED1HUhSBJkWUahUICmqpG0jO01KHqjCXgTKxWA5mWMlFI8fvwYN2/exOTevfj444/fnPRRJyAEZVYO129RW200oeFyz17nC/F2APlSFAWKLMO0bd9SOUrIioJz587hm2++wQ8//ICzZ8/6n59bXwOb97BUKEB5CzRQb/GTFhLM3wDwmLvEtASubcNxXVy5cgWDAwM4depUTyZ1SZIgEM8SWDdNxLa566FpmgAhkFV1czcRmMD9SaRG7NRTEOK3oD585Ags00R+YQHfff89qOvil7/8JRTeLIpVQGg7xJO+X2jlFUBZyWgwuhMsJa1XVsp/TilFRdcBeA1mdPZvwsPSgb/5v/lVVKU56qU8GkSaqOvi1s2beDQ3hyOHD+PY8eNvVLqkU5TLZYjNerv0GjXRBF/AyMeM64ISz7qYYOdUxsTjcViFAsrlcuTkAPD6y3xy+jQuXriAly9f+noX7lMTRKFQwOAuOXizUWubzEP8kizDtm08ffYMa6urOP/115G0JG0EWVGgWxZ0w9gZ5IDSqvLFoKufLzjaxolaVhQk4nE4joMPP/wQsVgMa6urXrOoa9cAeDXi4+PjGB8bw+DQ0I6ZxLq5b5R6nShr/3ZdFxVW7y1J0qZtMOq4THYI7h5qmqZPDroBX+htNu5lZllOBAGu4+D6jRtYWFjABydP4sCBAx0TgzeNUBSLRSSTSU+gvM2RRE4oCdhcycgfAH+MRRnC7wbxeBwbhQJs9iz0Yh6dmJjA+Pg47ty9i3Nnz4JSCklVNyOr7FnjPhVvOnbGN7tdYKSAaw546ZDEDEnu37uHPXv3YnhwsHfXwBbicrkMQ9eBqFXKbcCyLN/YhKcU+E6Cd06rUjpvY4lYLp+HLEkYyGSgyDLGx8f9ZlH5fN7zVJibw/3796HIsmfrPD6OsWy2vZx11Kij5q636Dssn+kGftZsobdZpEsM1Jc3QtVun/87GAWo/R3g29PKsuztaoMRB2xGIRCINlRFl+p8ZsATsrlM92NaFkxdx41bt1CpVHDyxAkMj4xgfWMDgLdLJawfCTcsC/7Nf19zoqb3YqehsLGBVCIBQRA8UfR2RhCC3yUDF9TyngqSKO4IHwlBEBCPxVAslVAul3u2yXr//ffxJz/7GV68eoWJiQnPb4U90/w+lN+SioV3mxywXRdf+Hh0QJYkvH71CqVyGZ+dO9dTVzsKQGEhfNM0e3KOsDAty3NEZMQgWIYY3IFVKZy3iSDkcjlks1mvVC3gLqmqKqanpzE9PQ3Xdb2IQj6PXC6H55cvAwCGBgc9p8axMQwODvZ8d0mZYYzturBME5ZlhV7068FfEFl1DY+KCKKIuKZ5lq/B7ysQzu/0s/JogaZpHeXDg2Mp+Jl1XYdAPPtgy7Jw+do1gFKcPn0ayUTCu0fsGK7remnAFufi9yRIcrhx1o6OJFCKjY0NjM7Oev1WLKu/5ICTAfbvRr+3bNtLHbH7bPfIAbEdEEL8fgu6rntGdj2IagwMDGDP5CSezs1hz5491Wkzds8Mw4j8vNuBd5scYNO2WBQEf5GjAJ48eYLx8XHE+6AaVhk5sEzTb7yyHTBN09sNyLIXIWhRsxycEPq5ezAMAyvLy/jwo48AwN/F1E5QgiBgaHgYQ8PDOHbsGHRd9zwV8nk8ZM2iVFX1+z9ks1koXbSgDpIAx3G8fztOlQcBDxfX3q/gZBtc9Ov9rB64W10n5af9QLDvffBbEkQRkiShUCzi+rVrSMTjOPfZZ4gFNCM8neXyv4P/ZkSDEy3+ev5cu9xDhP2ORxhEUfT/3imkoVgqwXEcZDIZEEI80Vs/yDe7N2GrDvi9FSQJRBQhotpnY7vAo1qGZaFULmOgUW+KDsHnmIOzs3idy+Hl8+c4GnDJjTqVt93YebNIP+G6m3qDwIQ69/gxLMvC/v37YRrGZpe3iMGHkCgI3s7JtqEbBhLbYL9p2bY/GXVi8LSlXrqHk1o+lwMFMDE+7qm72aLcSsmuaRpm9u3DzL59cB0HK6uryL1+jVw+j/n5eRBCMDQ05PeAaNSCOgwJqAVhoXApsBCFWfTDYrvLyToGpXj1+jUe3LuHbDaL02fObBl/hBAQUUSrO7SFRDC/Etu24bC0i8u/O6c6/rATSEOhUAAFkMlkYLOW5Lbj9I7stUkKOLiAW5YkP03GTZG2i2TxTxCPxWCaJoweRGEF9hk1VcX4+DgePnqEgwcPQgyMV05U3wa82+QAm4yXl8GZpon79+9j//79iKkqDMOAoqq9ESQGHkpFUVAxDBh9Jgc8hOgbHylKVQi6XRbsk4QeThL5hQUMZjLQNM3rHMlEpe2UuQmiiJGREYyMjOD9EydQLpc9rUIuh/v37+Pu3bvQVBXZbBaj2awnaiQkFAkQBcHbEbMFhi82voAwKOyMCP443inCyxCglOL+gweYm5vD1NQUPvn4464EeFtIBKWQJclP4YDlyl1G6hz2Xe4U0rC2tgZFlhGLxVBhEY+ehMdZeqCTxZz3E3Ap9a+L8NQejw5tB1Fl59Q0DVhfh2WaPbl3Omthf2h2Fq9evsSDhw9x7NixLa/bzghwVHjnyQFnwfyLvP/gAVxKcfS99zxDEstCJWLfbh+BB0lRFIhAJErwMOCkwGVqZItXKQRb3nZADvz38vPUlEJ1G02grotXuRwO7t8PwFNL27Zd3dWyA8TjcczMzGDv3r0wTRNLi4tYWFzE4tISns7PQyAEA5kMhoeGMDwygkQiAUkU65KAhtfO/m5aR94puDbkDSEHLmue9Pz5c8zOzuLI4cM9u/aguJIv7lXRiVrSwLp9NiUNguB/75IkVaUlO0WhUEAqlQKBN64NtsBFAb/qICgu7OB6/ZRCQKPFSxqrNEq8u22fwOcp/t1SFj2IkhzYtg3LskBdFwPpNGZnZ/H48WMcYHMRAN8XhjpOVWffNxHvNDkI5iYlUUSlUsHjx49xeHYWmqrCkWVYjIUastx2d8IWJ6/6r6ooEESx56LEqp4H8AZycHGVA5+RixK72SUFa9yDHgn8Z+1iZXUVlmFgbHwcwCapczqYiHjY1rYsX+3Pkc5kkM5kcOjwYRi6jtWVFSwtL+Ppkyd4/PgxEvG4Vyo5Po6R0dFQkxAnBb2IqbxJDomWaeLipUtYXVnByZMnq2yxowQNjL2m6IQ0uC5M1wVYiSdhKSNRkjy/lDr1762wvr6OEV4/L0n+s9lxSWNN2iAKQsrJSu14r91I0EBUotc5+Npzq4oCy7Iij8LyVIUsyxBFEYcPH/Yqoh48wAFukMc2Q7brvvGL65t+/V3BZg+2wMKE9378EaIo4tChQwA8LYAai0GvVKDrOhQm1IsCtCb8pqiqn9Pqhf1mM7EMr1KQaz4fz4VH9XDXJQreRYWeSHO5HFRFwWAmA6DaYbIVeJiW56HrpQcEpv+QJcnbETLyMc7IiGPbWFxa8oWNc0+eQCAEI6OjGGdahWSDVrs0wkm69rhB45qdjFKxiAsXLsA0DHz++edQVHUzJB01goSpk3B3E9Jgc2LJiDVl+gburEqATaLA/m72GR3XRaFQ8BcZPg5d14VlWeGFsnXKD6ME7yFQm8JrpM3hv2tYAdEDqJqGUqnkpRxdNxLiaTENiOu6UGMxzyhOknD4yBHcvXsXe/bsqTZee1M1QAG80+SALyiCIKBQLOLJ06c4ceKE75IIAJqqwjQMOI4DwzSrWzl3g5rBIxACWZZhMMYbFTmojRTUA69SqHvOHuUQG1Y6tCAKuVwOY+Pjmw2YApGD2onAYSTAJwN1PgcPC3My0GoiESXJjxicpF4XthzrKnn79m3cvHULyUTC91UYHRnxJ9Lg2buNyARR1ZFxB6cVVpeXceHiRciKgq++/hqJZBIb6+sAek9qIk3lsNJLSZIAVfUjDLZtw3YcOGys2Vzky0rbgkRBlKSqz8zFiAMBYinJsj92W5GDehUwUYPrDWhAb8DBq23qzTN1HVYjvq4gVKabopTCtKyu52zKNFkupX4Le37OgwcO4PHjx5ibm8Ox48c3/Tu6THPuBLzT5MAOpBRu3bqFmKZh/4EDVa8hhEDTNL9+VlGUnrXElRUFgq5DNwykGuw+20GtXW49cGFW0Oim3whLFCqVClbX1jA7O+v/jCv+HRZxAeBPyvXIgBTczYUgA62uO5lKYTaVwuyhQ7BtG4sLC8jl83j1+jUez81BEASMZbMYGxvD6Ogo4olEdW10BOCfcie3an754gWuXL6MTCaDs+fO+YtdT9MhNfn1noW3AxEGvgw5zJadEwaXVUYFtQuSIHjRBUnC+toaQClSARM0WZKgw6sk2kKaA892v/aoNpsniCDUFWgTQfBy7Q3gOy5GPE5rhZWEECiqClfXYRhG1+TANE0vTRLYQHEyJIoi3jt6FDdv3cL0zIyfxtglB284eOSgWCzi+fPnXmOXOoNeUVVIug7bcWDoelUNdido9DCrigIB3Zto+JNGiMnQYCkFSZbr5jW5iKlf4epmRCGfzwN0swujy3ZrhmGgrOte+LXG9pk30+JkoJefQ5Ikv1kUpZ6hTT6XQ35hATdu3IDtOEin0z5RGBoejqQKZidYWjcCpRSPHj7Erdu3MTU5iY8//tivDAqmQ3ZyxKMTiIIAUVH88cjHKk9tOa4L23VhM+Hc4vIyYrEYHNv2xH4BkSsnFlKd+9ZP8BbFcgN9DQkx3wR1CFGkLBsRbU1Voet61yWNruvCYBUKsTrmYpRSTM/M4MGjR3gyN4cs69r7NngdvNPkwGZOX69fvYKmaZienvZ+UcPSCYBYPI5CoeBFD7otbWwwcBRVBREETwPQgQiJD0hegRAGFhv4DaMGEe9y20EtUcjn8xjIZOBSikKx6CmHA2VqrutCluUqvcB2XvvAwAAGBgZw+MgRmIax6dT4/Dnu378PURSRZY2ixsbGOiadQYOfnQTqurhx4wbmnjyp3zwp+Bz04NqrbH+3+d4IglBFXHmInnswFNbXkUgmYbByZiII3iLM8vWWaULY5r4rvhixgQqfO1KGWRarIjk9SF1GVdLIyYXANAZB+H4lhGBmehr3HzyAbZqQYjHflOxNxjtLDrjhDyEES0tLGB0drRLM1UKWZciyDKuHpY2KLEMUBBBK2+7QGBQhhZ0GuVqfAE0rMfpdllQLl1KYhoGFxUWMZrMolUr+7wRBQCIe97QDstyTjmxRQFIU7N27F3v37gWlFKurq3jNDJiuXbsGSikGBgY8A6aJCQwNDobeTfvf+w7afdu2je+//x75fB6nTp3C/kC5F0evF++qEPYO28kRpjGSZRmu62J9fR2zhw75ZmguT0lYFnTThClJfgpiO0BZxQZF82ZLPMXX7rE7jSI0ekcUJY2O48BiPiqJOsQ9KPIeHBwEKMXKygr27N37VhghvdvkAF7YaHVtDft5KUoTxGIxWJYF0zRhq2pnD2qLB0BW1bY6NIYRHDaC2SKlwFHVbKlPcJhK2zRNWLYNg3UdHEinPQEhKy3ljU8sFrLl+cft3iluQc1CmBkcxMDAAI4ePQrTNLGwsIBcLoenT5/iwYMHkBUFY9kssszaWWsyFtw2SWGvoVcquHDhAoqlEj47d84vO61FqzbT3WBLbpvtVHfcuACwurYGy7YxOjqKWCwGjUUVeE09KIWu616qQRAgMVLRz8gY30RwU6iG6OB6glGEtrUhTV7bbUmjXiMkrT0XCcy9mqZB1TQsLS3tkoM3HZwcrK2tAYCfK2oEAo+NqooCwzRRqVQ6Eg3WljDWQpVllAkJ1aGxVee7VvCrFEIIEfsxsTqsMZHBfAeCKBQKAKWYmpzc0vhHYCFYi+VzFUXZIsTcaYtCMPyqKAomJycxOTnpRxXyLAVx9coVAMDg4KBfATGYyVRN0DspcrCxtoYLFy8CAL768kukWclpPfS0/LLmmHxh2wnxgyChJ4RgeWkJoiB4u09URxVisRgogLKue5bQxGvQZpqm9zpJgqQoVbbcvUAjf4NadFw2yuC6LgRRDB2pbEYkeEmj2UFJI0/3uK6LeIt0HyfnQ4ODWFhc9H62Sw7eXPBBtby8jEQy2XIA8LC9pmn+blbvRAnb4qFRNc3THbQQ0nTSzS8IXnoVtkqBCEJPQrO268IyDJi87CsASZKgyDJkRcHz58+RTCYbdgSUJMmvO1cUZXOSYqi6V4T0fZcd9rvi/R2GhobwHmtBnWOixsdzc7h37x4URfF1Ctls1p9It1tzsJDP49J33yGZSGxpnlQPvYwc1EWPynJboZYMbF6O9++l5WVkMpm63x8hxGvMRilkTYMoCLAtCxYrKTSZiysBa7rFGm9FfU8b+RvUg0BIx70+eMO3MBGEVvoGXtLoUM+DIqyJHWWRGgqPuDcjFUQQQJlfzvDwMF6+eAG9UtmtVnjTQeCRg1ZRA/5aEAJBFKHFYqhUKqiUy57QKMISQL6wcdONWjvebtIIQVim6ecPhQYCoyCiLAVzXBemYcBgnzEISZKgMkIQFH2uLC1heHi44TElWQYMo6HdbO1k2W+yQNFZ2F9VVczMzGBmZgau6/rNovILC36zqPTAAIaGhrB3zx6/w2e/8fTpU1y7dg1jY2M4ffp0qOZd/SYHPS1nDKAVGah97fLSEqZnZhoeTxRFQBDg2ja0RAKyLEOjnrurZVmwWQtwThQAr6JAVpSOmqhtuUbeoI7ShmLEIKL4Nvnmp1kUodU3GSxp1HU9FDmglKJcqfgiZ19E2oBYBi2ph4aHQQEsLi7uViu8yaCUwjAMlEoljI6OhnoPH/SapsFlpkjlYhFCOh3qoQkzXERB8ISPtg1d15EICB+7jRYEYTClfz/7xdss2mIyYgLA95FX2ERWrwrEdhysra9jkleT1AEPd9rMra7VgtOQLHDFdYQLF/e133L+2tr1FhAEASPDwxgZHsb78Hwf8vk8Xrx4gWfPnmFubg4xTfPTD9lstufeFZRS3L1zB/cfPMD+/fvx4QcfhE5v9MvjgCPS87Dvrsr1MgQZqEWpVIJhGM2JryiCoLp2ngSMmKimbSEK3K2RV0koLXRFzWDbNgTAa2oV4hhc8xNFmWK3Quh2Sxp1XffTCYl4PFQ0jl9jTFWRTqWwuLS0Gzl4k0GZshQARoORgyYTdjCMFYvHvYfQslAsFpFKpVqXN4Z8WBRFQYWZISUSicj7hDuuC5uFIsMuHoQQuGh/gqWUeq6PzCeCQ5IkqKwOvNUDuLa6Csd1MdJkAhWZGRJvktOoFrsRaifyWl/42skuCtJAuqzzjsVi2LdvHwaHhjwnPdPE0tIScrkcnj17BkEQMDQ05Kcg0ul0pLt013Fw5coVPH/xAifefx+zhw61dfxtSSt0CE7wuMcAqdlJ1qaxwmJ5eRmUUgwPDTV8TdAFtF6Jc5AoIBaDzVT2Fsu162znrLDnrd1ugTyl0M4zFXWUpp4vQpjjt1PSqBsGLMvydQZV96kBSQlGDoggYDSbxevXr3c1B28yKKVYW1lBOp2uDjc1ecCDYkJCCBLxOIrFImzHQalUQjKZjCTvqygKREI88w0AiNga1WIsWmRdBcNCaOOBd5h5iMFsRwHvQVIUBVqblR4rKysQBaGlAFRmXvS2bbdNDhqh0U6QMhIZjDLUvqbqtT0EpdQTtI2OYnx8HO+//z5KpZLXgjqfx48//ojbt28jFo9jjPkqZLPZrsriTMPApUuXsLa6ik/PnMHeycmOrhvoETnosCwu+H3WRqAoEHkKanl5Gal0umn4nxACMTi2W0T7JFGEFItB0zRf4Os6ji9k5JG6sNoEbn4UJjoavOYowclGu6QjbEmjaVkwDQOu60LTtK2va5BWoECV5md0dBSPHz/2HC/fcLyz5MC1baysrWHPxETo99QKYIggIJFIoFAswrZtlEslJJg97ha0MaBVZobEW7ZG4aIXhJ9SaFdMGULQZbEyTJ77BFgDK0WBqmkdkafllRUMDQ+H6nuACNvcNkM90lDVACnw736QA3Yh/s8SiQQOHDiAAwcOwHEcP6KQX1jA06dPvRTFyIjnqzA+jmQyGXpCLxWL+IsLF2CZJj7/4gsMNYnotLhw7+9edGRskFbwF3xKfbIf1INUPd99iGgsLy83TSlwSKIIm3jd/sIminjOXVFV2LYN0zD8Xg02c2JUVLVpyoF3oQTQVsSBoLVgsBPwZ0xo49itShptlsJ1WZq1bjS1wTNMAPAYAREEjI6MgAB4/uIFPvjoo5BXuDPxzpKD1dVVGLrut0jtFIIoIplMorCx4amGK5W6A7BVCWMQvDsiDwlG2XbUpRQ28zdQOgi91/sElFIYpunl6wLhNFmSoGoalC7U02EEW8HzAegLOaiH2rr6qgWn3mLFVN0k8Jp271NV06UG7xVF0dMhMNvpYqnkEYVcDnfu3MGtW7eQYM2ixsfHMTo62nAhWF9bw+3bt6EoCr766iskujCdilRzwBZ7QqnXhCgYfkb1QuX/rpeahxAwTBMbGxt+F9hmEJlbYpjuo/XA0w6u43i7ZGbu46ccZNlzfq353rm/AU/btYNemaf5C3LIOVVlvXHqlTQ6joNyueylTUSxbvVZs/FBKfU3AgIhIJKEzOAgXrx40ean2nl4Z8nBixcvQIC2dz31xG6iKCKRSKDIxEUCIVvLuEISA/4qLRaDadsolUqRkgMuBuSd4dpCzWewHcdPHQQFhoqqeqmDNnOb9VAql6HreqjviZvCuKzpTdufr0cgYOSwFnSrT0VtFYX//sDfQRIRXGDDEotkIoHZgwcxe/AgHMfBwuKi14I6l8Pc3BwEUcToyIjffTIoir127RqGhoZw9uzZ9iNPNeDppqbRpMCiD6BqnAXFgCQQBYAgAEHxXs3fOwUrTG8QZmxLoggB4QW3jSCIIjRRhKqq3m7aNDcJg2X5Xi4S26BwMtJJCqpX97u2C2mryJzKdE12TUmj67ooVyr+wt7QaKwJCXFd198I8O8km83i5atXffGG6SV2xuy5DXj56hWGM5m2Sn2aTcCyoiBOKcrlMiq6DoE9ZO2Ch83i8TjKxaJXVkNpZDXsFi916qRKgamQeZTACuxiREGAxkKYUdbbrywvgwJNBVubl+cJs1xmorRTyEHHodWaBbEeiXBZBQsRhKqUBniUJ5hyqCOuFEQR48yF8YMPPkChUEAun8dCPo9bt27hxo0bSCaTPlEZHRnBuc8+26JV8Ssy6oTqt7gTsgWfV99Q1JCiRot+8Hz8XKizCDVZLPpVzhgGy8vLUDWtoXdHEIIggIgiiOvCcd2uiTchxBco2o7jpRxYaXG5UoGg65AVxXdR7eRZ6tXCKAqCTyy5ULGZALBeSSMvWaSu6/XOicU6ul6f4AaiEaOjo3g8N4d8Po/xBu6gbwJ2xuy5DVhbXcX+0dH22W0TFqmqqp8KKJdKXrMOWQ61OFBsslDAK8ERRBHUcaDrekuTprDgPSU6EewZhoFypVIVtld46qBHJXMrKytIJpOhSy4lSYLFTGK629dGiDZLFts7dEDUxwkDPycCC2etyjsQeQheWYK1oD40OwvLtrGQy+HO3btYWVmBSykWFhdx6dIljI+NYWxiwo+QCYHj+eepPXdwkad0U6hae28aLfph0eRe7zRyMDw8HPpzSqIIhzn3RRGVCx5XisdBXReGZcFiIuJKpYJSuQxRkqqiR+2g1cLdCWpTtC6r4GiWwlBZSSN3ha1woyJKm5Ys+pG6RtfCyEVwDA9mMqCUYmFhYZccvImgHU7YrUQ2sVgMruvCNE2vgiFEiSMFY6BBkZsgVKUWoiAHjuv6D2o7YULLslAul/3GKwReHk9VVUg9tuwNK9jiCPod7AS0GmdVO+xOwL5PEowU1Crs619Y3dfU6iSePnuGb0p/huvqTWi2hv/7xN+EWbFw/cYN0Bs3kE6lMD4xgbGxMQwPDTWtpd+y+2fagHqeD1XPGQ2UD4bATln8m8F2HKysrODY8eOh3yOK4qbuoMuUTj0QFv1TFQU2K9EmAOC6KJZK0HjpcRvPfNSU2A2M9yBauSrGNA3r6+swTXNzLuNeBk0+T6tP6rKxKdYIlN/cZMIm3llywCekTr7EVrmkeDzulx0Vi0Ukk8mGBKE2YlB7nFKxiFK5jOEIUgu8JClswxbbtr1IAa88IAQxTYPWJ+Mk27axur6OmX37Qr+H76hcx6nrMLmjUGcSaaScbxR9cGonw4giFLx50svSK1wmV+FSBzrR8RfmJfyLL/9fsEzT1yo8Y82iJEnCWDaLMear0KxZFFDdMKpWtNlI+Mr1G0EdQUPNxg7G2toaHNdti/iKdcyQegFCCGRFgaQo0AJznc5KIRVGILYjnx5MKdSiWamjKIqQJQlmpYJSqQRFURCr9TKoB0Fo6HEAbEYOtpSEv8FaA453lhwALDzV5pcYRvhFCEEykUChUIDjOCgWCnUjCBTebr7R8TSmHrYiSi349cotogYuyzsG+zuoqop4LOYb9/SjhfPK6mpowRaHIAh+asHeAeSgKrReEw6laLAI1jtQo7Bno9B8F1gPNE8aPzYB3N783YvycwCeZoW3oAalWFtbQy6fRz6Xw9WrVwEAmUzG91UYHBra6hPRwbVzElFPh+G/ho/NwP3mpGunCMSWl5chCgIyLZqrBSGKIoggwGG+Be14lLQL13G8c0gS0skkHMeBbhhwXNd3OdVUta8Oq0Drqq9mBIEQ4pUtEoJ0KhWJFwonKlGXm+8EvNPkwBdQtf3G1iU0RBCQTCaxUSjAcV0UCgUkEgl/QPJUQrPJKurUQiu9AXVdz5lR1/2fyYqCuKZVCZL61cJ5ZXkZsiQh3Wb3S0mS/FruTkSh3aB2saaUbppY1Rkz3TSpCSKqRS+fy+H7779HMpnEuc8+w7XVa1W/r9sDlHgtqDODg14LasPwDJgWFvDkyRPcf/AAiiz77afHxsY2hcCE9KSbZL373WkqsRdYXl7eQphagRCvXTIhBE6PyYHJqxSYZTIn3SZzO3UpRZnl8OuaBgWuOSqdR6OUQi2qvmcmetV1HZRSv6V7GFJDvZM2jS7717RLDt4+dDKpttIdcAiiiFQqhRJzUSwWi4jFYlBUNfTDwlML3VYtUDRuu8r7TJQrFX9ClSQJ8Xi8cZQhZI1xN1hZWcHg4GDb35EsSdCBjmvCQyNQCVDbECv4d9O71OV99KsOIlj0njx5guvXr2N8bAynz5yp+92HuVJFVTE1PY2p6WlQSrG6suIbMF1m9d+ZTAaDg4MYGRlBJp2ObtFudr/ZOeqR8i3pnR6WoXHvjv3797f9Xt8MyXFCmyF1Aot7oQSExoQQz/JclmGYJgzDgO26KJbLkETRIwl1CEtU5KBZSmELWGSKwiuHdmwbhDXJE1i5cyvtRKvlPtjrZrs7ovYCu+Sgw/eFnTwEZvtbKpe9Rk/MTTEWi4WaEHnVgmUYXaUWeEpBEISqHQcnBb4FqCAgHo+3ZNa9Vn1TSrG0vIwDBw60/V5flMja2nY7ydeW4W2ZoFqEOZuh2ymlKoTe8UEobt+5g4cPHuDAgQM4efKkvxMSSE0qrM3vnBCCoeFhDA0P49jx4zAMA/lcDq9ev8b88+d48uQJbt644RkwTUwgOzraWZnt5glbki2hTuRryztqRJL891EsAcViEYZptqU34OCWx70U3DpMr0MprUsQCSFeSkGWPZ8TlsIrlkp+9VIvwuztGMkBXsq2XC77osFUKuXZuTNhdkthZSu9QeBagvNh1yLjHYJ3nhx0grZzl4T4qthKuQyDOQkm4vGW4SgiCIjFYrC6TC1YNVGDYAWCfx5WgRDms/nufj1izMViEYZhdGTNKwgCRFH03CBtu+22tb7wLfD/jolQj6Mr3boM8uZJL168wPsnTuDQ7GxN1UCNKrzLhJKqqpiemcHExASKpRKKxSI2NjaQY2SBwDMmG2edJQeijCpwhFxk6hlXRTHxLy8vgwAYDOHdUQteseB2aYbUDBZryibJcnMlP5ubFFYmaNk2TNuGWSxWVTZEcY1uE21Wo8/AWy8TQvy+N5IowmTzQje9RYDqFKIgCJtzKS/FfcOjCe8kOQh+qR1/gW2yWApvYhQEAeVSCZZpouA4SCSTLUVz8VgMpUKhq9QC1xsQABuFQlUFgqZpiGla2/cijDtZp1heXQUoxeDgYEfvlyQJjuOEIgfBSZaLLes6GnaAlotJl/fP39F2cL2mYeDipUtYW1vDp59+ij179255zZZ6ioi+b37Ph4aGMDU1hePHj6NSLvvNou7dv487d+9CYy2oJ8bGMDo21vWEHsm1V/2n/QV6aXkZ6XS6I0GcIAheeJ3t7ntxP3hKIez1iYKARDwOmwmnbcfZWtmAcCmpRgg71/AUqcHsoWVJghaL+b0eREkCWDvrZrU07egNejkPbie2/0nbBtT9ItsUK4UZ7EEPd/5vWZaRTKdRKhbhOA4KGxueULFJKFXTNAiS1FVqwbIsVCoV2I7j5wWDFQgdocamNkqsLC0hPTDQsbmSJIow0KTPQlDxXhsZiIoYcEV+s+MRUtWkqaNzoH1yUCwWcfHCBZimiS+++AJDjXaxtdygk4usg2AZI0csHse+/fuxb/9+uI6D5eVlX6vw7NkzEEI2m0WNjXldOmvr3UOcO9KUWG2KIkg0UZ8YLi0tITs62vEpRVGEZds9IQdOoNFSuxE3SRSRTCT85mu8ssEyTa/pWo9Fe9zcyLQsuJRCU5QtkVBZln0C0wycTDRDI/vvNz1iwLFLDro8TsuBQCkcWu2nwNsPl0olWJaFUrEILRaD1mDR7za1YJimX1apaVrdCoSOwBbVXjwMKysrjResEAiaIfH8ol8G1y+W301kKiQ6IQfLS0u4eOkSVFXFV181b55UGzlwaTQlrP51N1gwBFHEaDaL0WwWJwCUWbOoXD6PH+/exe3btxGPxTDG+j+Mjox443m7d3A1ZKH2+dB1HaViEcNHj3Z8ClEUIZDemCGZLKUgdtEsTZblqsoGh1KUymXIstyRP0KY59V1XZTKZS/d4rqebqqG3ATLuFuJlYOOo43A0whbSM92j8GI8E6SAx8BM5Ve+B0A8LvubT21lwcrVyowdB0Vlh+Lx+P1DZECqYV2oOs6VtfXvV0Gq56IqjaZEOJZPEfseWBaFtbW1nDg4MGOj8GNnnhJ43aEo0OTkC4qFuq1a26GFy9e4PLlyxhmzZNaif+i1hz4x2mT1MQTCRw4eBAHWLOopaUlv1nUkydPIBCC0dFRjI6Oes2imhGefu7sasjC0tISXAAjIyMd6xd4GrIXokTbNOECiHVph84rG2RJgq7rMFjLZNu2EY/F2ooitLJg5mZt/DXJJqlaiXW3bHnvQvgpcHIg1RCp3cjBG4zIQshNBhCvqW2FOHPpqpRKMA0DjuMgmUiA1AzuYGqhrOuIt3CfcxiTtizL71A4mMlEb1rSgwdhdXUVFOgochBMEUiSBMd1YW0TOQibLhAI2ep0GBKhS6koxYMHD3Dnzh1MTU3ho48+ClUnX1utEBW6MW8KtqA+efIkiqUS8rkcXudyuHX7Nm7evIlEMonx8XGMjY9jZHi4erHYxp3d61wOA6kUtFhsM6rASuvC3gluhkRtO1IzJNu2/c1MVM8Lr36STBOFUskzhSsWoWlaJHORaZrQKxU4lEIURcSb9EkA2Odiz2WzTYPruk1LGd2A9bcgCFVj6u2IG7zD5IACvvVlp19m0/e2MQGpigJJEFAsleDYNjaKRSQTiWrjoWBqoVhsSg4My0KpVKrSOWii2BM3M0IpXERburOyvAxFlr1ugCHQqJpAliSYpgnLshBrQaZ6gbAjoOPJpKaUqhFc18WN69fx9OlTHD16FO+99154UrdFcxDN1BeqXXMYsAhccnYWB2dnq2ydX718icePHkGUJIyOjPgpiFiELdDbAqXI5/OYnppil17TOZP9rJUynxACURDgRGyGFBQiRr37VRQFaUJQYg2PKroO27a9TU+zfhwNnm1ubGRYFiilkGU5lKjar1hgYuVGpZqkxfzNIw88Qhl8tes4fUkp9hrvJDkQBAGZwUFvh9qmELEW9XLuLY1v6kCUJF+H4Ng2CoUCYrEYVFX1r69VaoHCy83qzPZYEkXE4nEUNja8B6gXu2dCvLrxCHdjyysrGBoebvhw1RoNNYIsy14I0bY9IrhDXcy68drwj9HgXtmWhe+++w6Li4v46OOPMTMz0+a19Sat4C+IEX8noiRhYmICExMTAKXYKBQ8UWM+j5s3b+LG9etIpdMYHhnB+NgYhoeHe+o0GMTa2hr0SgVjdTr1BUWMwf83IgqiJIHYdmRmSJTS7tq5h4AoSUjE495u3zBg2TbsYhGxeLzh3FQvpeC6rt8dllLqNYtqQ3shShIEx2mcWgiR5vMN5fjYCXxHK6urECXpje7ICLyj5EAURezZswdPfvwROvMI7wT1dAfd9B0QAkJF0zRRKZdhmibi8ThESWqaWrBt24s8sHNrmoZ4LOb3YxcEoWeLY9SGSCsrK1vMjzoREwqCAEkUvYnPtvvvAx/2WsNUNbQ6fp336pUK/uIv/gLlSgW/8iu/gtFstq3jA3VMkKLSHPAysF7urghBOp1GOp3G4cOHYVkWFhYWkMvl8OL5czx6+BCSJCHLmkWNj401FAVHgVwuB0mWMdwiXUa4eNb/GFsjDFLETZhsx9lMKfSILPH5UlVVSJLkGxSVy2UoigKthcdKsEyRsv8nmrm4NoAsSTAMw3NNrDd3tXhug3qDLaJuQrCQzyOTyXRkcrWT8E6SAwCYnp7G7atXsZjPY2p6uqPJuRY+MeDH6XDBTCQSkGQZOmPHhY0NqKoKLRbbklqgACpM0Ah4k20ymfSZOGe4PYkacLDJK4pp3jBNGKaJVDodSXWBLMuwbRuWZfWdHIQGyzu3C78csM57efMkAuD8l18i3UaDn2aIigRy+hwlNQgTSeLNoj788EOsrq35osbr166BUoqBgQGvVHJ8HEMtWlC3i1wuh2w229YxtxAFlt8WBME3Q4oCNos2yrLcu3A4W4gJIRBFEclkErquw7QsmKbpixW5PiSYUrAsCzrr6eC6rudf0KLdciOIAVEipdS7x4ENnUtpU71B0OyIO0GSwLO4tLyME6dO7aYV3lRkMhlAlrGwuOiRgw6/SM48a2vau51EuYd5pVKBaRheJzSWIxMBlFnurlgu+wRAlWXEE4mq3RgPvfVakBdVaqFQKMB1XSQSiUiqIGRJQoUQL5/ao7LLRmiHMHWlfan5TLlcDj8Emie1ap3c/Lqiv19VHRm3K9VDCDKZDDKZDI6wZlELCwvI5fN4ylpQy7KMLOsqOTY2BrWL+2joOpZXVvDxRx91dc2+AJU1Q3LCWgE3AU8pULTvbdAOCDbJDeCN21gsBkmS/GqtYqmEGBMr8gZTuq771wd46dVurlOSpKrdv+u6/vzVjt6g3pxqGgaKpVJHfTN2Gt5ZciCKIsbGx5HL57teNIKe2lGCEIJ4PA5VUfwcGyiFYZqwbBv5xUXf5CPBXld1XWjcbClydGuIxISNa2trIEBoMWIrSJLkhV/ZZNCvqgVuwRwWnaRm6in+n8zN4fqNGxgfH8fp06e7/ry90BwE+0FEStbauH+191tRVUxOTWFyagrUdb0W1EyrcOXKFQDA4OCgXyUxODjYFrFZWFgAAIyNjYV+T9PrZ7bEBN5OV+yCnPt9V4CetzivN85lWYYoir4Fc4V1exRFEYZhgBIC6rpQmbag2zEjszmhygMlWBIcUm8QvFf8HQuLixAlqaOeMDsN7zQ52Lt3L368dg3lSgWJDhXMfJj20liHixWDfghlpi/Yu2cPUqlU3UYnDrdMJqR7w6MW6GTnW49UFYtFxBOJSBu3yLIMl1KYlrUj7HejQhU5oBS3b9/Gw4cPceDgQZw8cSKSXflWTU330Rz+fUcZM2hXBNxseSGCgMGhIQwODeG9Y8dg6Dry+TxyuRweP36Me/fuQVEUjLGIQjabbSmIe/36NTIDA11FH2ohiCKIZXnGP5LkLfB19Amt4AsRe5lSYBAIQb0tBC95NE0TxWIRJcPwCYGiKIilUpE5LHITKRfeQs/TjWGMj3haA6ivzVhYWMA4m5PfdLw9M2Wb4KLE7y9cwOLiIhJtqrg5+lnTqmoaTNuGqqooFYt+OZBt2xDr5NMttiPoqd4ggNrcXUOwibxeCWShUIj8wVJkGaZlef0keig4C6LdcRFmYtpyDnavXcfB999/j5cvX+LEyZOYnZ1t8+xNrmtL5KB78Mk1ypRCL7uEqpqG6ZkZTM/MgLouVngL6nwez+fnQQjB4OCg76uQGRioSlNS10U+n498NykQArDUAhAQLiJQ/RBCXOenFLZZk+O6LmwuEgRbiCn1e9JECUmS4FhWFTkIE0EOuiLWu6al5WWc+/zzSK91u/BOkwNVVRFLJrGQz2Nfh+SAox/57FKxCMe2Pb0EgGKphEKhAEIIDEVBIiDmAfqYUmCorfetBf8d7+ZY725tbGxgYs+eSK+L5yeDYcReo+1+CR0sbBSeCczt27dRKBRw9uzZyO9dvWqcrhEM4W4XOtUYCQKGR0YwPDKC4++/7zWLWlhAPpfDgwcPcPfuXaiahjGmVchms9jY2IBpWZGXtomsYqGeKLG22qFRSSRvwEZY/X+v0eium6bpCw4pIUgnk3Bd12+7HO+gKqEZJEmCwUpB/WsLEMxG17mlhJG/F958bJjmW5FSAN5xcgAA4xMTePH6NU6jfeU0xWYdci87c1FKUSqV/IGZSCR8y07dMLyQuWnCMk3E4nHPDAT9EyMGLrTutfP2zhyNSJTDekdEHTngE5/L3BJrtRm9QNsjgRCvd3wbi1ahWMSVK1fgOA6+/OKLjloAt7ysHmgOIjNACqCTZy8KQh+Lx7Fv3z7s27dvs1lUPo98Lod5FlXQNA0i22lGuYng/gxNnVh5JZEgbKb+AveKRw3UHgoRaxH0LnAcx28IRyn1Ozzye8W1VuVyGVos1nEjtlpIsgzCdVw1IILgPYt10LCEEV5KQZZl7Nu3L5Jr3G688+Rg7969uDI/j431dQx0UO5V90GPkCRQSlFkHRwJIT4xkCQJ6+vrkFzXq2AQBJiW5XkjGAZUVfUtWftGDpjALJj/dYGW6l+OQrEISmlP8nVKoKSxH+SgI7SxaCwtLeHSxYuQZRnnzp3rCTGoe0kRjO1urJOjAkG0ZZRATbOoEydQLpWQz+dx+/ZtOK6Ln//854jxZlFjYxjNZrt6NkV2/xzXDWXytcVgyXFg8g1EH8kBt4s2DAMm6+UASj1LZVn205OEEMRjMVRYtQIv146CIPil3jxyEIgaUNf1BZpBuKwyBKivN1hcXMTM/v1tGTLtZLyz5IDX2o6Pj6Nimsjn822Rg2DUgB/PH1wd5I/rwXVdFItF/zzBhiL8//bqKorFIib37oVpmiiXy7AdB+X1ddiWhXg83t9JmCmLm6UOGqFQKABAT8iB33ClTyWNnXz7YUWdz58/x5UrV5DJZHD8+HEkEokOzhb2mmpNkLpHT8hBJ89bjSd+1IgnEpiYmMDVa9fw8UcfQYvFvM6Sr1/j6ZMnEATBa0HNyEIimWzrnhBBgMh24Y7rQgqZLuOf2OKqe2YW1g9wjUOpVPLFfYosV9koB3VLhBDf+pwTBC5U7AZ8HnVYqlEUxaqxLdSZw2stk6s+FzxnxI8//bSr69pJeGfJAeB9yaIoYnBkBAsLCzh8+HBb768dIFGKohzXRbFQ8BeyZJ2KhGQqhfWNDe+hKZd9G9Iyq2owDQMAfN/xXkcQuAkURWcTf6FQ8NTJPdjFSJIEgRDYlMK27Z7WcwPoaNFpOX4oxf3793H37l1MT0/j0JEjcFlEqVeoPbaLCKoVAgK6qNBLMtYN8vk8AC99qSgKstms1yyqUPC0Cvk87ty+jVs3byKRSPgGTCOjo6HKCgVRBNgC1y4M0wQB+mIORlm1kMGay9muC5FFQoOC6XrfCY8g6ITAYNbLlEUaOgWvzKCuC7uODqme9sCpU8LIsb62Btd13xq9AbBLDgAAU1NTmH/8GI7rhiqhq40aVP0uAnLAO5dR5oSWTCbriuhEQUAikUBhYwPrGxuIxeMgLGdnmSZsx4EgCF74zjAgKwpimhb9wsh2AISlFQSgSmMQFsVCAal0OtprC0DmXRotq+fkoCPHyCYRJ9d1cf36dTx7+hRH33sP7x096kWV0GML4l6YILG/e3vdIRBRhK8ZXudyGBoc3LIAJ1MpJFMpHGQtqBeZAVMul8Pc3BxEUcTI6CjGGVmIN4gOCUxL0C45sFlHR0qpT8br2TR3C9d1PQdEpo3i4zXGShSDcyhl1QmNSKPGGivp3EKZEYROSaYkSX7XWjRowBS8F775UR1ykFtYgBaPY3JysqNr2YnYJQcADhw4gIe3b+Ppkyc4ePBgy/c1CksT4jUh6mRh5HBYjwRKvRakiUSiqbo+nUp5ZY2VCkzT3JyECEEyHkcsFoPjODBN0xctSpLkkYSah7MtMF0BN/upF2Zr98jrGxsdtWkOC1lRYDCb1p4j4JYZFo1ebbHmSUuLi/j4448xzSpr+pG7F7ZcVTTpMiDCUsYOn7deRw5cx8FCPo9Dhw41fZ0oihifmMD4xISnMQo2i7p1Czdu3EAqlUJ2bAwT4+NVzaKEJhULzcCjiqqi+N9DlIJqx3Vh1mgKBEFATFGgKArqtbQPM45VVQURBG++Y2LKMN0Y60GSJJi27UcEahGMHjiMSAFbIwe26+LJ3BzeP3Wq5yZS/cQuOQAwMDCAg7Oz+PHHHzEzM9My/N6rnLVt2ygWi961SRKSiUTL88iyjFgsBttxsL6+jtHRUQCbk4WiKBCZUr+i6zCYN0KhWIQoitA0rX3XMUrrht04CCEQ2S4hLFzXRaFQaLtrYDuQue4gAsvZZuh4fNSZnCvlMi5cuOA1T/r8c//75ecB0NOSwFrVSDfElyNyUtOHCEAnWFlZgWXbdbswNgIhBKl0Gql0GocOH4ZtWVhYXEQul8PLFy+8FtSi6DeLGhkeBgjxvQ7CwHUcmKyEUWmSuyeCAMraD4eF7TgeKWALN6UUkiBAYRFL3zSu5n3tEBMe6dArFc/AiVLEYrG2x5MkSRAohdWEWPHKhWZ6g7m5Oei6jl/91V9t6/w7HbvkAB4rPHP2LP7bH/4hHjx8iGPvvdfwPS5aTGodTlSmZaFcKgHwBm079sHpVAoVXUepWMRgJuN9LrZz5WkSgaUb4rEYdF2HrutwHAelUgnlSgUxTfO6ojVbMHkVQpjPx/J5YR/YcrkMx3V76ixGCIEsiqAstbDjVMU1Y2dtbQ0XL1yAIAj46vz5LSmXVjXZ0VzSzi9l7PSKei1KfZ3LQVXVjqqgOCRZxp49e7Bnzx5QSlHY2PDSD69f4/r16wClSCaTyGQymJyexsjQEEiL3SvXGoii2JQgB22uw1gKc1t3sPSAxLxk6pmw1d75dvVaiixDIATlctkze2Oaq3bGlECIb83cCPx3vISxduNoWBbu3buHQ4cPv/FdGGuxSw7gffGpwUEcPHgQDx48wMH9+xvanIZZ8NoNVzq27RMDWZbbVp+rmgaVleptFApIs46GhJAtrJ83O4lpGnTDQEXX/f7olUoFmqp6yuGaCYaLDcNOqO16P/SyUiEIWZZh8ZLGHpGDbpZPPnZe53L44bvvkEqnce7cubriq9rStF4gap8D6rr+QrPdaYVeI5fLYXxsLLLvhxCC9MAA0gMDXgtq08TCwgKev3iBl7kcnj57BkmWPQOmsTGMjY9vmccopbBYy+NQJb1BYlCHJFhMZGiz79WlFLIsI64ozSsgIrgnkiQhHo97XgjML6FtG/wwZmWENDQ/enD/PlzHwUfdNNTaodglB/BC2pQQHDlyBE+fPsWP9+7hww8/3PL6llEDwK/1573RW4FSimIXxIAjmUpBNwxsFAqIx+OgaNGXnZmzaKoKw7K89tCOg4phQNd1KKqKmKb5KQkuNmwH7ewGCoUCZFFErMf2xrIsg7AGL71KD3WTyyaE4PHjx7hx4wYmWPOkln0xelqtsPVn3dy3YCoksnu/A8lBuVRCYWMD7x092rNzyIqCvZOTSGcyOMRC+ctLS16zqKtXAXjdZ8fGxjA+NobBoSG/O6lASPveBrxUG4Blml7lAatOoq4LRVWhKkpHfVE61TtIkoREPO6bxBnM4yUM+Aao1blt2/bGK6r1BuVKBY8eP8ahQ4cwwFxr3ya80+SA+2NzcwtFVXHkyBHcvnMHs7OzVaF9/gC0nNBYOF8IaZ9bYuJDgVUedApuL2qbpmeOJEnhcuqEQFUUqIrilUTqOmxWLlTRdaiKAk3TOurP0K7HQbKHlQocwZxhz0oaO5zoKKW4efMmHj58iIMHD+JEs+ZJQcfJjs4WFvWO3lEtBoAeuCOGfM76jVwuBwBVGpFeQRIEWIKAdCqF0dFRHH3vPZiGgXw+j3w+jydzc7h//z4UWcbwyAgGBwexZ2Ki7fPQOpUHgOeuqKhq599pl+ROFEVosRgqlQp0w4AoiuHLtkOmTPh5grj744+QJAlHDh/uqqxyp+KdJgcA/J2x4ziQBAEHZ2fx+PFj3Ll7F5+eOVP12rYMSkLsmnnjJABIdNmimBCCVCoFe3UVG4UCBgcH22bwsix7YXdGEkxWMmQGKxza7NwWtnpjo1BAKqI2za0gy3JPSxo7meocx8EPly/j1cuXOHnyJA62aJ4UHFs9rVYgW8eQFzno7HiRexzsUDFiLp/H0PBwX5oZ+RULgdy5oqqYmp7G1PQ0qOtidWUFL1+/Rj6fx6tcDnfu3MHg0JBXKjk25vVrafCduK7rVztx91MCjxSozKq9XZDA3907Z1Q7oJYrFSRbVHn51xAQV9dDVWOqwFyxXihgfn4eJ06cgMTuw9uGXXIgil6tq+NAYnnyY8eO4fKVKzg0O+uX1kU9/ViWBUPXASCyFsWJZBIbGxu+F3knbah5L4ZUMglH07wKB8Pw8vTFIkRBgMLKkcJGE1rtM7nQaiybbft6O4Eiy5viqR0AwzBw8eJFrG9s4Oy5cxgbG2v5nqoJrc8+By5ox+2W/chBH5pftUKv7ppj21jI5/FeE2FzlBAEAYRV4dQDEQQMjYxAjcUwMzUFx3Wxtr6OhXweDx8+xI8//giVt6AeH8dYNgtJFH1tDt/AuNTrfaCy598/fhtp1KrrQnRusoBX0ug6jtesiekPGpHQYJq02dl5+lEQhKpoxO3btxGLxbB/3z6A0l1y8DYiKEokLOQ8NTWFhw8f4vbt2/jiyy99htkWWI6qHivlncYAj+FH5QgoMsOk9UIBxVIJ42EX20Ausep4rGoiFotBZzXLjuOgwqodBD5RqGpDfQM3RWrGzrnHer96oMuyDFAKh0WMtrM2uVAo4C8uXIDLmycNDoYqS+tVk69aNNIcdIqoyxh3XszAa8DjOE7kXRgbwddONSnJcx0HtmXBpRTpdBqZTAb7Zmbg8hbUrAKCN4saGBjA8PAwBoeG/H4uCVWtG66nHRADAH5FU1QghCAej6NYKsFxHOi6XlfD1E4JsGWaAJhWiZU1Li0vI5fL4ZNPPvH0QLvk4O0Ef7Bs2wa4U5gg4Pj77+PChQvIv36NMWZO0rYgD/UnrzI3OZIkxCMW4CWTSUiEoGiaMC2rtegoRM5WFEW/DNKyLC/EaFmwHQeOrqOi6xAlyc891kZBmrWMBVilAu1Nw6VGkGUZ1DRhmGbk30HYBWtpaQmXLl2Cpmk498UXfqQnjKCxH5UKQD0TpODZ20fkHgc7MKXw/PlzpJlXQT/g74Iphes4WyqNAK98EfDGfTBqIxCCzMAA4vE4pqemUKlUsLS8jOWlJcw9fQrn0SOomoYJFlHItmoWxdKpYb7dYHfGqCAIAmKahjIzSRIlacvmi1dRCbyaq8EYclgUAtjcUFAAt27d8kpHuRsiE3e/bXjnyQEPj5mmCTBBICHE65o2Oorbd+4gOz7e2WRWZ+CVy+XNDosdhP1bQRAEKKoK2bJQYJULdcGEk83sSmtBCPFTCpRSnySYhgGbhR/LlQokSfKUy2wiImA1xQ2OWygUAEHoWnfRDlRV9cOmiLpCIsSCNT8/j6tXr2JkZASffvppVT6znSqPXhsQ12ud5XZBDvhiEFVaodsISpT9UAAvpfD69WscPnIksmO2Ai8b5mZIW8qQmVthsHzRtm3YlgWTRRO8F1JoLFR++NAhgBAsLy8jn8shl8/j6ZMnIIRUNYvaQug5MQiRLuhV9EuWZaiO4/VhqFQgNfJz4ISqwXHMIKFi89fLV6+wsrqKX/mVX/H0F29p1ADYJQc+k3ZdF5bjQAT8hfP948fxi2++wfyzZ75lbUvUPBRBQR4X9wFoaYvcKVzXRTyRQEXXvfrfetGDgJlRpzs4QghUVfVaQ8fjm/bMluXnKcvw7q+iKE2Ff4VCwbsfPd4FB6EwtzbHcWDbdqRNqZppLCjdbJ40MzODU6dObR0HYfKwUe/AGyLa4/MFIZLvegdGDV6/fg3bcTDVZ499URQh2HbdnTifcwCPFFQqFa98G5tlqYosQ5LlLenB7OgosqOjOHHiBErlMnK5HBbyedy9exe3b91CPB73m0WNBptFUeq3Xt4OqKrqPduOg1K5XDW/hDEPcyn1NUk88uC6Lu7cvo1sNovs6KiXZqB055mpRYR3nhzwRa7CrDj9R4NSDA4NYXLvXty5cwd7JyfD5aabhKgqTGeg9bBDosNboKoqXABrGxuexWrg+iht3/e/GQRB8DwTNM3r48C7r9m2F1mwLK+uWhQhM6IQXNQ2CgWk+5hS4JBlGRTwqzEiQ4P767ourl27hmfPnuHY8eM4cvhw134BPU8r1Dl+V5oDvlhEQYwjELNFHTl4/vw5BjOZho2SegUuSqwlBw6zSrcMA5Ise5+VjU9ZkiDLcuixn4jHcfDAARw8cMBrFrW05JOFJ0+egAgCsqOjGGMGTMlEwvuea66pH5SOEIJYPO63vNd1HfFYrG4qo973bzF7ad65FwCePX2KQrGI06yKjaf/mllQv8l458kBAJ8cGIYBTVGqJvZjx4/jv/23/4YrV67g9OnT7U/GbPIpcaMjSeppfoq6LiilSKXTKBWLKJZKVZbKlF1TryCKImKiiJimwbFtGCzt4DgODEo961ZBgMIiCpIkobC+jr1TUz27pkZQFcUnL7GIDJEaaVP85klLS/jkk08wPT3d8BgkxGLlv2IbIgftdc2oRuQ+BzsIpmEgl8/j+PHjfT83jz65juP7EfCKKF3XAUqhMMW90gYhaARRFP0ySAAoFovIsfTDrdu3cfPmTSSSSYyPj2NsbAyjIyP+NfJFtdcjQGCp21Kp5GmlRLGqyqJR5Ja7SAKb5Yvr6+u4eesWpqenkWF22PwZ3I0cvMXgX65p26CKUpUzSyYSOP3JJ/j+hx+QSqU6Kk/iYTxCSM93FJwVJxMJz9aUuSZmuvB37xSiJCHORJe2bXutVg0DruPAcF3v36xyoxf6i1bgjWAcSntniASgVC7j4oUL0HUdn3/+OUZGRpq/YRvztVsvJbrIAc/RAtGQgyjuQZQL1KvXr0EpxeTevREeNTwsRnSDJbqGYUAQBMTj8VCN3DpFMpnE7OwsZmdnYds2FpeWkM/l8PLlSzx+9AgCbxY1NoaxbNabB1kev5ckgTeXq+i6b5BULwIcbMRmO46vxZIlCbqu4+LFi0gmEpvOuYFndJccvMUQRdE3/7FtG7IkgbBdNgUwOTmJQqGAu3fuIJVKtdWz27Ztn4Umk8meh4G5upYIAlKpFAzTxNraGlKJRGsb3h5CkiQkGVHwhYym6ZUduS5ACFbX1rzUA9vZ9Cr1EoSiKKBMK9ELcrC6uoqLFy9CEEWcP38+dEVGq3B3v9IKpJ4JUqcH65s3w/bgxfPnGBkehtZjC3DAW8xs1m7Ytm3YjGxbtg1JliFLkudJoKqe2LdJzX/UkCQJE+PjmBgfx4fw0oY8/XDjxg24rot0Ou1FFEZHMTQ83NNIkqIosJm2SNd134m2kftoUIjoui4uXroEx3XxxblzEEURFKjy+VD78H1vB3bJAYOqqh7zdhy/tS8CTWKOvvceisUiLv/wAxLxOAaZOVIrVCoV//j9qKd3Wd9xkU0I6+vrcBwHq+vr1dqDbYJAiO/EmIjFvBbVhECLxbxwaEAIRAiBJEmQGVHoBVlQFcUXinbS9rUZXr9+je9/+AEDrHlSOzuMsP0ZtkOO2GnbZjdAaLq9z7wRWNckI6LvW69UkF9YwKlTpyI5Xi0oIwN8kXMCngZcXCyIIhRBQDKRgCzLqJTLEJiV8HaaTqVTKaRTKRw+dAiWbSPPPBWeP3+Oe/fuQZQkjLGowvj4eE/SrpqqosjKr7kA2SckrGJBAHzvE8AjB5cvX8b62hq++PJL3zPBJxVsPGu7kYO3G6qqolgsekIU/mUHanYJgI8++gjFUgkXLlzAT37yE8RahMJtxuoJq4ONWvxUD36NMQvZZTIZLC0uYmNjA+l0OjLDpY7Byq5cNrE7jgMBniqaUuqXRFq2XZU79d66SRZkWY6EbPFJwgZgW1b3drfs+330+DFu3byJiT17cPqTT9q/1laphX5FDuqfvKNjRVnG6Lvr7RC8fPkSAiHYu2dPJMejrusvZFvIAItqcsIscQIgijAty5tn2LNDKd3+Zz4AWZIwuXcv9u7dC1CKldVVvHr1CgsLC7h27Zo/Z41lsxgfH8fg0FDXUQVKqddvgUWHjYAAmfASa9cFBMGP8kqShAcPHuDlixc48+mnGBwcDB6wKh2yW8r4lkNRFBBWJ+y75tWYeYiiiHNnz+IX33yDCxcu4PxXXzXdzfKogaIo/vFc9NC2lYkRg3KxWCwGVdPgVCpYWVnxBUQ7BYauQw4YJwVLqfjEaLG/q8hCpeLlBFkKQpakjsmCoihwdB1GBOSAArh58yYePXqEQ4cO4f333+/JAt6/aoU6C3mnBDfKa95hPRWeP3+O0Wy24/FDme7FCRACvnBRVnbISYDExnptWJxwV1bXhek4nr9Ije3vTgHfwAwNDSGdTuPo0aMwLQsL+TxyuRyePn2K+w8eQJFlT6fA/nSS3xdEEdR1oamqv/moKl/mDrGBqOXC4iJ+vHsX7x07hj01hM83UIKnq9qJ9zcKvJ2fqgPwkkad2YyKoljX7UvTNJw7dw5/9u23+OGHH3D27Nm6k13wAVcDkYhWVsLdgLouXOYDHsTQ4CAM5ntQLpcbGyP1CSTgTKYbRsO+8nwi1ODdMz5xWkx0RV3XTwkQeOE+WZL8nGvYHaoiy749NO0iN+s4Dr777ju8evUKH3z4IQ4eONDRcYDNpjCNwt7tWMB2gyhNkNw+EZp2wC3Ou7mmUrGIlZUVfPzJJ6HfExzPfmQgoHOilEJiCzsnBQ07dDIILF3jUArLMEAp3TnOfQ3IHL/3PMIxOTmJyclJuJRibW3NM2DK5fD8xQsAwGAm41VAjI9jMJNp+b1RSv1SSkEQtkQPePqOd+allKJQLOL6tWuYnJrC4cOHtxxTYP4GwNsrRgR2yUEVVFWFXi7Dsm3wr7ze0MsMDOD0mTO4eOECbt++jRMnTmx5zZaoQeB4vdrv8N21WBNGlGQZyVQK6xsbWFldhaZp2974hptDGboeKizHUwoSKwWllHphV0YUbNsGdRzojgPCyAKfDLhmodFnliQJIiFwCYFlWVXlTmFhGAYuXLiA1bU1nDt3LhJffT/kWQf92zPXK2Xc/rRClAS7W6ry4sULCIKAiSZtkCllvTy4gJCVHAZ/LzByKzJCUM8GuRk4ebBM0yNiLLK2I9Dg+2q0uAuEYGhwEEODg3jvvfeg6zoWFhaQy+fx+PFj/HjvntcsiukUsmNjddMn3CqZozZ6ECTXpmVBNwxcu3YNAwMD+OijjxpW6/Cf7hjy1QPskoMAOAu0bbtqMNcrt5kYH8eJEydw69YtpFIp7Nu3z/9d3agBB4sedCrqagSX7USA+pPvwMAASqUSTNPcttLGIHj0QDeMjh4wXmYkSxJi2AzL8qiCY9veZKzrMFi4FcyISWQTL/83IQSKqsKuVGCaZtvkYGNjAxcvXoTjODh//nx097ZJ6DyMy1s0lxAdOehXKqRtBHaCneD58+cYn5iAJEl+uSYff7wdPHckBOBHCARCfCLANQPdgPdX0A0Diiy/cbvaZposTdMwPT2N6elpuJRidWUFuVwO+YUFzD9/DgJgeHjYJwvpdLru8WqjBzzaYjkOLMvCzZs3IQoCzp49W3cerR27yi45eDcgSRJEWQZlC4wkSf4DVw+zhw6hUCzi6tWrSCQSGB0dBQDPdARbowa9Ap94eFisnoBHEAQMpNNYWV3F+vr6tpc2At7kqFcqSEbQU4HrD/hOiZd6WcGwLauGIJbl7xh4OoKAdYdkhM7/7ltgcXERly5dQjwexxdffBHphBwmytQq1OyjwzREvbQCwES6gchGbfqt3nVH5nEQVaUCQ6fRPNd1sba6io2NDRw8eBAF5saHABEI3h9BEDxCykhtt2SgFgIhHjF2HEBR3ghywEk7AfMaYPqAZhAIwfDwMIaHh3H8+HFUKhXk83nk83ncf/AAd+7ehaZpvknTaDZbFUEJRg94GbVpGLhz5w7KxSLOnz9f997xFEhwTXgT7nGn2CUHNVBVFSazUg4KTepFDwiADz74AMVCAZcuXcLXX38NVVFgMcVww4FDGrdzbhcu3dzH8bBtI0KSSqVQLJWgG8aOKG0khMAwzZ6UAgmC4DeJAjZzvP4fRh6o64I6DihLKTjMAEVVFIiC4Jum8D/B3QRvnjQ6OoozZ85AluUqVXnXaCa6q40ctNAnVL2uS/AxVzt+Wx2Zj88tpYz82mm4mESvXT63nC8wdngkgI+TJ8+eQZAkJNNp2KyqBoR440UQIAgCROY5EJrIdQjCxjB13Tdq0eLkTGC9GNqt6orFYti3bx/27dsHl1IsLy3hNfNVePbsGQgjE8FmUVLA1waE4NHjx1haWsLpTz5p2E1TYKLyIPrZLK7f2CUHNdA0DbogeA86r2ttMlhFQcCnZ8/im2++wcULF/AJEyW1ihrwqa2bqboqVIkQgi9CqkobU8nktk4ivA9DP0qBgpqFIFzX9XPBoBSlchmOZYGKIixWTuYL1tjuTxAEPH36FI8ePcL0zAxOffih/11H6fjWcAdFKVz2h/+/6u8adHM99Vo2d1yswDUHorj1eaolGjTQUhfwjWd8UtKEDLULvjhRSqvSAEFCUPuR+TXkcjmMZbOIxWI+meQdEvsNTgwANBT59hNhF/ng6/i/O23nLBCC0dFRZLNZUEpRLpc9X4VcDvd+/BF3bt9GPBbD6NiYl/4jBMsrK3jx8iWOHD6MiSalqNwYj49VURQxkMm0fY1vCnbJQQ0URQEVRTim6YW5ePi5yS5OVRR89tln+PnPf46r167h5MmTrRfdLqMHTrP3NZmYYrEYtFgMTrmM1dXVSIRzncI0TQjYXlEPX+y5dwLgEYZUKlUdbQgsFrfv3EHu9WscOHgQ+2ZmsL6+DpH5JVDA3yUKzNPBb6kbEr7NMKtXrxvYp5uh/Z6iruag/Uk7WGIbJq3gpywC49wN/G7LdfEFpkUEhZcG8rJf7idgWRZcx6n6ZPz83FRMYNEATgA21tdRLpXw0YcfbrswjZf5AujeqyMihJ7bal7HCUKnvjDBeTUej2P//v3Yv38/XNfFEmsWlcvnMf/0KQRJAoXns3KgSXURCQob2VqQGhjYdmF3L7FLDmogCAJkVYVrmrADyvVWeclUMolTp07h8pUruHrlCj7/4ovWJYMd1mq7fBKsQVjB12AmA71SQVnXvXam21TaqOs6KDzC0m05WRQQRRGyJMFkTmqaqlblKk3TxKVLl7CysoIPT53CWDbric6YrsEN5sIDEwkBKzNjREFkO0tOTEiARPj3oAUp7V8pYx10QmiDY7bLa663YPilaK4LF8wbwHXhUArqOHD4z+q816EULksH8SoXngrg/65HaF68fAlN03yt0XbCCFToiGwh2+7nKSxoqzEeEQRB8NotZ7M4QSnu3LmDx3NzIIKAmZmZppUd9eb/sC65byp2yUEdqKrqdfKy7dDKdZdSpFIpnP7kE9y6dQs//8Uv8Nm5cxhqMYDaDZ/Vy/X6vwv5MPHSxo2NDayuriK2TaWNhmEAlEJTVb83+nZDURRPuVyjhSiXy/iLCxdgGsaW5klVHgy2XVUzTbliHd7iRCiFxSfDOpM3YQtSMOrAJ/1gNKJfqGeC1Em1QrAbYzuLln8P2d88ghP8uRtc9APH5tGVKtGkIEAA0z2w+8ndREVRDH19lFI8f/HCa7K0zbtH6rowGNHm7YO3lRy0uenxw/VbDtN+5CDMe1xKceP6dTx58gQj2SwGBwehqmrD+8WNpWqf16EdYEffS+ySgzrQNA1FQfAEazUPWaOcss2ctVKpFH7y9de4cPEivv32W5w+fbplo6Z21NKu47TceYWZFAYGBlAul2FsY2kjr+rQNM1TK++A3Y4sy0Cl4nVmY53aePMkSZLw5ZdfbmmexPUMoiRB5gtX4PdugCTwvHbwZ3yBA9vluoxE8O/ZJ49c90AICqwFOGHit+B944SCh9+DinD/9zW/A1CVpuCvqWuC5EscqB92BzaJq58O4D+nnvOcaZoQRBEV1kJ4y3sDx+S6iuBEzxd7J7jos/MRQvyFPUiigpEZTgT4Z/SfZUp9bUnYqMbS0hJ0XW+rCVuvYBgGAEASBAiyDJMR1Dcm5N2ETPjfVQiSUFsxUw+8dfri4iKOHz8Ol333UrOoQY1XAo92RtVga3JyEi9fvsTXX3+Nn//8501fe/v2bXz44YdwHAf/+l//a/zGb/xGJNdQD7vkoA5kWQYRRVCmXg/6cAOoO5A5OZAlCaqm4csvvsCVq1fx3XffoVgs4uiRIw0nHkI8Q6BWA9upw16rwNMKLY4DeDvRdDqN1ZWVbStt1HUdsixv9nnfAdEDURShSJLvvLi6uooffvgBAwMDOHv27KaWJLCQ8ImjSiQYgEAI0KBVbBDB3TEnEdRxYPNweaBk1bFtUEJ8wVxXqDM587FuOiaGMQQHLigoCAiKhSLWrM0dqv/6wO49OJkSQmBYFkxdhyjLvlV2U1Dqh5v5gs939hLgL/YkQADaJZZbRMEBUsXvc6NjPn/+vK0GbL2C6zjQGTlQNc1r0YzuhM5doZNUaRgNCqtkaH6YxqZhAFAulfAXFy6gous4d+4cZFnG0tISJEVp6pRZOw4IEKkQ8dNPP8VPf/pTXL16tWXE5x/8g38Ax3Fw8OBB/N2/+3cju4Z62CUHDaDGYjANY0tJI1A/esBL2PgCK0oSTp8+jWQyidu3b6NYLOLUqVP1FwhCIDQIrXG0XAA6WFRTySSKxSJ0Xcfq2lpVqLwfMGoMkAjQ094TYSErCkzbxsNHj/DowQPsnZzExx9/vFmRwO911DnRBiSiSmNCPac9m0Ua4tzumYvt+HUFdvDBSIa/Uw+8hvKFOPCZ/ImYUu9HLgV1vZSC67hbF05OlvgCy7wjeBTDdhyIsgxFlqGoalU0oyp6EYhqCNhMs1Tdj27JUAP4ZKFOCoQy4kcIges4ePniBfbv378tVQlBGIbh61hkSfLaDTMh67agUz1Kq5e0KHFstaguLy/j0qVLECUJX50/D1GSUCwWIcoyJDa+ghtBjkbHjJIUnj17Fj/96U+xvr6OBw8e4MiRI3Vf90d/9Ef42c9+BgD4nd/5nY6cXNvBLjloAFVVoQsCTMvyW3UC9RcuCnilcEDV4CKE4NixY0imUrhy+TJKpRLOnTtX90ttOvDRo50ACZQ2FgpIpVJ9LW00DKPqfIT0tvdEWEiiiAf372P++XMcmp317bG367qq0k4sfM4bVKmKEv0CFQjnG46JNazBJRQu8QhwLKVhIDHALifkjr1chigI0DStK2V/r4hBIwS/cy5KzC8swLQsTE1N9fVaauE6DgzWRZDfU36N/b5PADoWWIc7dPNj19okBzH//DmuXrmCTCaDs2fP+sTAdd2qnhW15CDY4bb2WgaCXRq7xNmzZ/1/X758uS45sCwL//Af/kMAwJdffom/+lf/amTnb4Q3JCnVf2iaBogiXFbm1Ay2bft5z3qRgempKXzxxRdY39jAz3/+cxQKha0HaTLJciV1UwR3l22A585EllvvJ/SQfRX6BZ4bv/Tdd3jx4gWOHDmCA7Oz4d/fo+vq+96UheuJIEASRDhw4MBmfzsgBFX5/FAIGCB1ilotR2Ro85rmnjzB4OAg0gMD3mK8TaRR13UIgN+cCdiM2GzHFfWcPLNxWe88jX5298cfcfmHHzA5OYkvvvgCiqp6mhfAJ9kyIwR2zTwrsOZ7tUil05F2YvzkbXe3gQAAjCVJREFUk0/84/3www91X/Pv/t2/w4MHD0AIwe/+7u9Gdu5m2CUHDSAIAuLJJCg2hXMctbktOxA1CNbYBjEyMoKvv/oKgiDgF7/4BRYWFra8pt4U1VJnwFB1xjYnu8FMBqIg+KWN/YKu61vcEevWsPcQQb1ApVLBN998g8WFBZz99FPs3bMHpmGE74PRq8mx2f3o8b0idasV2odfrdCFSI6nKKJGO8cslctYyOX8mngezhbQ38nUsW2YlgUXqHqG+PPT97RCN99NO2OYbu0620gn8MPly7j34484duyY10RJEGCZJhzbBoE3X1Ng09+Ei1ID56qHTIRRA8DboJ08eRJAfXKwvLyMf/Ev/gUA4G/9rb+Fjz76KNLzN8IuOWiCZCoFiKLfRS2I4HC0a1MKDQZ7MpnE+fPnMTg4iF/+8pd48vRp9QtqogcdhQY7WCx4aaMkCFhZWelbSFLX9boh5lbCom7BTYZ4HtlxXayvr+MX33wDwzDw5fnz2LN3L0RG9iwWum2FnlVa1ExS/fI4AOovnJ00DfM7Mm5zjr4e2tnxPn3yBBJrLVx1DGxqkQT0Ptqj6zoIPAG0FIhWbktaodvoSZvvrdUX1H5WQ9fxZ3/2Z3j18iXOnDmDw0wMTl3X3+ipmuZpSCj1+lywOYdrxxo9y5RSZHogQuWphevXr2+xYP9n/+yfYW1tDYlEAr/9278d+bkbYZccNIEoilCYQRAvF+LgC1hQbyCHCDUpzE1x3759uHr5Mm7dvFnF8rlAq90QKn9tpwvUwMAAJEmCbdvY2Njo6BjtwHEcmKZZV+NA0LtFhKvQg5UFCwsL+Pbbb6HIMr766itkmBJZVVWAEOghyUG/Igf9DBlH0ZXRd3wEet5foBOEJaOO6+LJ06eYmZ5uWnniP4vBPxGODd71lWKruyi/v31NK3T52dquMgmQkVrNy8bGBn7xzTcolkr48osvsGfvXv93BmtlLQoCVEXxKn248yX7PluRA1VVt5QyRwFODsrlMu7cueP//O7du/j93/99AMBv/dZvYU8Te+eosfOe1B2GGBsIpml6If4ACDxiwHcM/oTR4mERBAEffvghTn7wAR48fIhL333nRx8QKKPqJ3hpoyQIWFtf9wlPr2CyBbepADKCCdUT2nvRAd5CN7jYzs/P48KFCxgeGsKXX35ZJT5VWbiRMoOjMOfqFepNVv3Yg9cNFrf5vQTHcjdphV7mtMPcy1cvX8IwDOxrYrPb+ATRpR783W+dslCeeulXWiGKaFlH3ypLnwTHVj6fxzfffANRkvD1V19V7fBdx4HJNniapvnvJYws+OSgRVVElELEIIKixGBq4Td+4zdg2zYmJyd9QWK/sEsOWiAWi0FUFIBSf3AFYTWoUmgFQghmZ2dx7tw55PN5fPvNN1hbW9v8fZuTaFjr5GZIJZO+L/vi0lLHxwkDwzDq7nw4eG17J+BRl6BTYb3X3L17F5cvX8bM9DTOnTu3xQiFCIJPXvQ6330/sW3B+Hr53A4iB0D3UYNtUeAHMPfkCUZGRpDqsBNfFKkH27Lgsg1JPWLNjZ5qDaR6hSjO0cl9cFnPEUEQ4FKKe/fu4S/+4i8wPDyM819+iViNJTy3apckCZIse34ijuM19JKkLZGDRhjsETk4dOiQ76Z7+fJlAMAf//Ef47/+1/8KAPjt3/7t1nb8EWOXHIRALJEA4C1owXwrIcQfTJ2qVycmJnD+/Hk4rouf/+mf4tbNm7CZYKYtRDEREK+1qSQIqOg61tbXuz9mA+jMJS/S0knqNffxLXUbvMx1XVy5cgX37t3D+8eP48NTpxouXLy7nWVZHeXae4EoiGA72BI96DBy0G2qqKeft8Wx1zc2sLS0hAP790dyOn4HhTaFfLquw4U3LutFYYLumNtdEhwWnVwnX8yXl5fxp3/6p7h79y4OHz6Mc2fPbiH53NacAIixzYifPoDnLMldRrnZWCP00vTq008/BeBFDmzb9t0PP/nkE/zNv/k3e3beRtglByGgJZN+m1mzJv9ss1a/3ZS2ZDIZ/OQnP8F7x4/j3oMH+NnPfoZ8Pt9RmVS306eiKBjIZCCJIlZXV7doLaJC0Dq5EcLW0AeFha3CqZZp4pe//CVeBsRKzc7B2zwToG7kqOZCWl5rx9hGIV/tot6J5qDecd4kPJmbg6ppked8/XvD/jQbQ5Zp+otas5bMpE7IPXJE+F22TfoIgWkYuH79updGEAT85Cc/wbHjx7f2uaDUn2sUVfXKEwHPRIwQ//+ARzgIs82vh2Qq1dOOlzy1cOvWLfybf/NvcO/ePQDA7/7u7/ZtIxDErglSCBBJgqooqFQqXm2+olR/WV2EwDlEUfT6iY+P49q1a/jzX/4SMzMzOHHiRCgnLB5Kj2IQpdNpv2vj4tIS9kxMRO7TbhhGlXVyI7TaAXFxYZgSSG6fapomPv/8cwyHbJyiaRqKpRJ004TK+kDUQ6PucpGDRw56fyaGGnJA21t03AjSCr3eBTcbZ5Zt49n8PGYPHuyZoDIYSQCwtSk2pX5qS1PVps+NwJpL9eyOsXu1HVSPUopXr17h+vXrsC0LJ0+exIGDBxu+3mCESiCkilC5jAAE76MAeA3S+lTCWAtODkzTxD/5J/8EAPDX//pfx+eff97T8zbCLjkIAUEQoCWTXt7KdWEFWjlHCdd1kUwm8fnnn+P58+e4efMmcq9f4/0TJzAzPd3X3ePw8DCM169h2TZWV1dDL6Rhoet66JRC0FedN8epUsCHuC8rKyu4ePEiZFnG+fPnkWwjbyzLsh9ytGwbSqMoUb8XsH6lFbqslvDTCjuwUsFHk+/u+fPncGwb+yJKKTS9DPY3r3LgJME0Td8MTWnSJAjog9dBxMQg7HiqlMu4fv06XuVyGB8bw4cffripLahT3UVd1498qppWRewcx/FE5IGfEVEEmmgOet1H49NPP/WfccuyoKoqfud3fqen52yGXXIQEoIkeZbKug7DMKrIAc/xBf/fbofB2kY109PTyI6N4datW7h8+TLm5+dx6tSptha1biBKEoaHhrC8vIz1jQ3E4nHEI+pCBngPZ5jSTwC+4xtFdVlcWLx69QqXf/jBs09tYF/d9Pxs16HrOkxdh9LoO+hx5IB0QIqiwNa0Qnvgi1TH19sPcV2D745Sirm5OYxNTFRVsvQDXLwIoGqRaxltA3p3z3owxluVebquiydPnuD27dsQJQlnTp/Gnr17t0Rva69LNwxQSr2S9Jpn3nFdUBJwtA2SqTrjVBTFSJst1cPAwACOHj2KH3/8EQDw9/7e3/P6d2wTdjCV31kgsuxZ/RLi1xlXoZa5tjkR1hO7aaqK0598gi+++AKlchk/+5M/wf179+ruCHoxEcQTCcQTCUiiiKWlpUjLG8OSJ787IaW+ujgsKKV49PAhLl26hPGJCXz+xRcdR3y454Fl21tKWgMn7OjYobFNArPab8ndGvRuim7dEWkfSFCjM6ysrmJ9fR0Ht3GSrug6KHMGbKY18NErl9FeiRybXOv6+jq+/fZbXLt2DZNTU/jvfvVXsXdycms0q2Y+4T4qAOqSOsdxQNg9pZR6C2GT6+h1SoEjm80CAEZHR/FP/+k/7cs5G2GXHISEKMsQAgyU+3MHhxPf4QJoayKnlDYNAY5ls/jvfvVXcejAAdy5cwd/+qd/ipWVlbqvjXo3OTg4CEWW4ToOlhqcsyO4bsv8rVuzS27V8jgI6rq4ceMGbt66hSNHjuD06dNdhbVF5sFOCPGb3Ww5Z8dHbw/9pwhbJ+J2QLusVuhHfKTRJ5p7/BiJeBzZsbE+XMVWOI4Dk5GDWCzm97xo9h3w+xXlOKFA5OmEZnAcB3fu3MHPf/5zmKaJ8+fP49SpUw0FgbXzHp+fFVmuO2+4rkdxRVGEKAib3UwboB+tuX/44Qd8++23AIB//s//OQYGBnp+zmbYTSu0ASLL0FQVpmHAtqwtNbGtOoc1Qqt2o4DXKfDEiROYnJrClatX8c3Pf44Ds7N4//hxr243eA0RQhAEDA0NYWFhAaVyGQXWvbFbuJRWKYWD6CR1EIRt2/j++++Rz+dx6tSpyEJzqqrCsm2YhoFYHWFizydO/t320T4ZAMiWPUSbxHcHuyNyVBF7BsM08fLlS7x37Nh2XBIogHKlAhDi2SRzUy54+XECz9yn4TMf4S6/07mtEywsLODatWsol8s4cvQojrSoKOLgIkkz0D+hUTUUjxz4xCCA2jOJgoDh0dFOPkpb+K3f+i0AwPvvv49f//Vf7/n5WmGXHLQBQdMgGAYkWYZtWVU7yC0DLOTEzcPmoV5PvBbLX3/1Febm5nDn7l28evUKpz74AMMjI218kvagahpS6TTW19extLwMTdP8ZiWdoi4hYj4FjZTQvulJkyiLruu4cOECisUiPvvsM4xFuOPj1RW8U2dQGNbP3XxQtNYP1Fbit0PconBH7Ee9fr17+ezZM1AAMzMzPT9/PZiG4Rse1QuNU3idA3vuhtgnYmCYJm7fuoWnT59ieHgYZ8+dQzqVCu0vwjU5wf4J9Qipb4BESKgS9OzERNfzXSv8wR/8Ab755hsAXgfGdqKkvcIuOWgDoijCVRRoqoqiZdX1APB92kMO6HaFi4QQCIKAg7OzmNizBzdu3MCFS5cwMT6OA/v392wQZwYGYOg6KoaBhaUlTIyNdRWmd13XDzMHOyNylXYjEHjh6XoTxsbGBi5cuABKKc6fPx95WI4LEytclNrjCaMetmPvvYXDtfHerqMG/XL6q5PDfjI3h7179/akMqkVXNYkiBODRvePAgBv00ypN+9EHVHqg5Zmfn4eN2/dgus4+OijjzA9M+Mv9u3Mj7quV/VPqAfHccCb3LVahAVCsHdqqq2PEwblchmvXr1CoVDAH/7hH+Jf/st/CQD4O3/n7+D8+fORn68T7JKDNiFqGiTThChJftvU2kWiXoiyHiilXp/7Dh/meDyOc2fP4uXLl7h+7RryuRz2HTiA9957L/oJjbknvn79GqZhYH19vSsrUf7QV4WdQ1/K1p3MwsICvvvuOyTicZz77LOeKct5xYpt23AcJ3Q/jSjR98hBTdvmTiIHtT0A2jh5X+5t7b1cWFhAqVTCx5980vNz10NF1wG2eCkhSn4p4M8jkY6LHt5/CmBxYQF3f/wR+YUF7N2zByc/+MBPBRC0J0a1LAumaUIgxO+fUA+26wKsiqH2emoxNDra1KitU/yn//SftqQOzpw5g9/93d+N/FydYpcctAlBkgBJgqaqKNk2TNNsuFsPxXo7IAbcIpU3Edo7OYlMJoM7d+7gydwcnj59igMHDuDQoUORDmxJljE4NISVlRWsrq0hFot1fHyXsfdOd4XB9MLTp09x/fp1ZEdHcebMmS32qVFCEATIsgzTNGGY5mZ5Zz8dzPquOag5fRvVCm63ZYx9Qu0onJubQzqd9v3u+wnLtmFblidCbNdPn1UqCBFUFvTKgpkCyL1+jXv372NleRkDAwP47Nw5jI2Pb54b7UWoXMdBpVIB4DkhNpsDLMMAEQSIjVIKnGQRgskeRA0A4OrVqwC8zca+ffvwN/7G38Bv/uZv9r1cthl2yUEHkGMxUMvyQn3MsKKWXQuEoHkLj+5zqcFWs4qq4tChQ9h/4AByuRzmHj/G48ePsW/fPhw5fLj9SaYBkskk9EoFxUoFi0tL2LtnT9vpBUopbNftynKah1F/vHsX9+7fx/79+/HhBx/0RfSmqipM04Rpml7IF/3JiwOoIgTbpzkI/17fAKnDHGq/4jHBT1iuVJDL5XDygw/6dPZNUEr9RU7VtMYLWDMEFrdudv48zRcVXErx6uVL3Lt3D2vr6xgZHsZnv/IryGazVeXBtE3ySylFuVLxPQ1alXsapgm4bsPX8bOmMxkke9CeGQB+7/d+D7/3e7/Xk2NHhV1y0AEEWQYRRaiqigKwpd8CByGkadqg24kvyOx5/l6SJBw/fhyHDx/G3OPHePjoER7PzWHfzAwOHz4cSaXB0PAwzNevYVgWlldWMBpSDMl7IICVbnbjtW87Dq5cuYL5+Xm8//77OHToUN92p7Isb6aVTDNc7XmU6PMufKtDYhvVCl2WMfLue/3E06dPQUQRUz3aNTaDbhigrMxX67ApWXDXzb+7tskrIS3NicLCcV08n5/Hg/v3sVEsIpvN4ssvv8QImzdqr00IbHrCQNd13yI5EY83LfWkrutFZQjZqhmqcR+dnJxs4yrePuySgw4hxmJQbBtEEOA4DnTDQLKG5RMAYOK5ugtXBA8fnwgIEyVxoZ4syzhy9ChmZ2fx9OlTPHjwAM+ePsXk1BQOHz6MTBduX4IgYHBoCEuLiygUi4hpWkvnxiAxALvOTnf5hmHg0qVLWFldxdmzZyNvhhMGqqKg7DgwDKP/5IChb10Zt5CD8GkFp0sDpH4TA5dSPHnyBNNTU11FtjqBzcpkKaWIN8mZtw2WZgjdiCkinYHtOHj29CkePHyIUqmEiYkJfPzJJ1s9A2qiYe2c2WIRPADQGDEAGouWeXdVQRC26LKCr07E4xiM2DL+TcMuOegQkqrCLpUQj8dRKBRQKpUQi8WqhVdNbG6jCkMTzvB5CNFx/J0H4NkgH5ydxf79+zE/P4/7Dx7g53/6pxifmMDRo0c7zqnGYjEkUylsbGxgeWUFqqo21V7UTkw0bPlmDYrFIi5cvAjTMPDFF19geHgYtEl75l6BN+JyHAe2bXccNu8IfdYcbDFBauOdlHmB7PSOjPzqXr9+DV3XI2vNHBYUmyJEWZYj0c3Qmn+3KgMGvPmk23SCZdt4MjeHhw8fQjcM7N27F+fOnUM6nW5woZtX2s7Ycmx7MwWjqlV27I2Oo7N7vIUYMAdWwKtK60WFwpuGXXLQBaRYDDFdR6lUgmVZqFQqSCYS/u9JgL3WihMjXcxqcoz1fAIEUcS+/fsxMzODFy9e4N79+/jFL36BbDaLo0ePeqmBNifwwUzG7zWxtLyMiYCgCIC/aNcjQm4HaYWl5WVcungRsqLgq6++2oxWBBoz9QtEEKCoKgzDgG4YiEek6diJEGoLKMOW6QZIW1/JUweg7Ll5+PAhhoeHke6zO51pGL5IN9atiJjUqkQ88AgjDUTwtrymC2JgmiYeP36MR48ewbJtzExP49Dhw62jitg6P7YCdV1PZwBAlqQtwuh6c47A3U0p3RLtcwIkNh6LYYTZGL/L2CUHXUBgu+WYpqFcLvsNmYK5LH+416mjjvRasBmhcF234WRMBAFT09OYmprCq1evcO/ePfzZn/0ZhoaHcfTIEW+BD/uQsvLGfC4HXdextrbmpyv8NEIDtJtWePHiBX64fBlDg4M4e/ZsVUdHgt4pq5tBU1UYug7LsjxHtj45AAbL1vqB2jOFjdMEGy51EiUKa34TFZaWl7G8vIxz58719bzc0wDwxlS344inMxv+vlMdQgPouo5HTNvkuC4O7N+PQ7Oz4UXQlLatM6hUKt48Jwh1Ff618wEhXk8cy7JAgS2RA5tHDSQJE5OTO7uDaJ+wSw66gCAIkBMJSIUCZEWB4zgol8uQ0unNXTHL323JpUU98bGaaJ5TC/P6PXv3Ys+ePcgvLOD+vXu4eOECBgYGcOToUUzu3RtqAVIUBZlMBqtra355o6woLXfyYQWJlFI8ePgQd27fxuTUFD766CNIdYgP11v0s2SOd3szLQsV0/TyxP0A391tVyljGwZfQBetmvtMDu7fu4eBgQGM10TAeo0Ks0gWWDQqErS6d4ywdRNxK5XLePjgAZ48eQJBFHHgwAHMzs6GbsXuXyrai6Qaug6LNYGLNzGI8gkCmxe4NkGUpC16Ese2vYiCqmJsYqKt639bsUsOuoTIyo1URYFl26Cui0qlgkRNn3E+wfKJtd0wWhgIrClLW5MqIRgbG8PY2BiWl5Zw7/59fP/dd7ibSuHw4cOYnp5uObmn0mlUdB2lchn5hQWMj421dB5z0VpQ57LmSU+ePMHhw4dx/Pjxhu/hbmehRVcRQdM0mKYJyzThKspbuePYaoIU7h67XVYq9BOrKyvILyzgzOnTfT2vZVmwbdsTIQZSkl2BRwZCvbT9KEKhWMSD+/fxbH4ekiThyNGjOHjgQMOmSC3Rxrlt24bOnGljLUo9g2JtCkA3TYAQqDV6DpdSOEyntXdysu9C1J2K3bvQJQRBgJpIVJkhmcxaV5blqvBWy+qFLkEEoUrn0C6GR0bwKyMjWFtdxb3793Hl8mXcvnULeycnMTMzg6HBwYa71aHBQRiGAdOysLi0hLFstvlnbCFItCwL3//wAxbyeZz66CPs37ev5fVz6+V+7jclSYIsy3BN09Me9NHEpG9LbhdliABAdrjeAPCiBslkEnv27u3bOSmlfvdAVdMi02X4UaUQ84D/ihavN00TL16+xPyzZ1hm/VWOHz+O/fv3d72Y0pCWz9R1USmXAXjdFltFWWo3Yiazo64lMbxJkyiKmNqmPho7EbvkIAJo6TQ2WDtjWVFgWxbKlQrSrMXvlmHfo3BpsFdBNzn4DMvrFwoFzD97hvn5eczNzSGZTGJ6ehrT09NIBHc5lEKQJIyMjCCfz0NnAsVm/gc8X1gP5UoFFy9eRKmD5kmENUbqZ3qBRw8MXYemaX3bKfetlLHDagXfOrmD6+ynfmRjYwOvcjmcPHmyr+NGZ2WLQheeBt2A8lw/tzCvIQiO4yCXz2N+fh6516/hUopsNotPTp/2zM+6IDNVkdOQ6cVyueyXIWohSDjF5jPi2DZsx/H0BjX32nYcuADGx8a2pY/GTsUuOYgAkixD0jTYug5RFOE4DlzHga7rW8Qy/mMQki23A+51EBSCdTPJplIpHH//fRw/fhyLS0teKeT9+7h79y6Gh4cxMz2NvXv3QlIUP183PDyMpeVllEolyLKMTAPVd6NOlGvr67hw4QIIgC/Pn2/4/kYg8Hz8+ylm802RmLCsX9GDfi1jtWSnXc1BJwK70J1KI8C9+/cRj8Uw3cfyNdu2YbCdbDyZjF4/QltbVQUtloNzxfLyMuafP8eL589hmCYGMxkcf/99TE5ORmLHTl3Xiya18YwahgHbcUDgeRCEGRsCS7FS9n4QAkUUIdWUm/P2zfv6XL6607FLDiKCkkjA1nXYloVYLIZyqQTdMCArCiRRrKodJoT4pVNRToACK2Xk5+LMuetdGCEYHR3F6OgoPvzwQ7x+/Rrz8/O4ev06rly7honxcUxPTWF8YgKJRAK2bWNtfR2r6+uQJKmqvJOj3uSfy+Xw/fffI5FM4rPPPuu8pCsiE5d2ENuO6ME2+Ry4IU2QfM1BB+SgX8SgVCrh5YsXeP/Eib5Wm1R0HYS59EWd4w5z72rFiMViEc+fP8f8/DyKxSJUVcXMvn2YmZ5GqpE/QQegvEopEPJvBSvQATcWi4WKWBDv4D5B4joFuSZqQCmF6zhIZzKRd3F907FLDiJCLJFAeXUVtuMgLkmQZNlLL5TLSNWr821Qi9wN+OTmRw7Yz2mEi6UoipicnMTevXtRqVTw4sULPJ+fx8VLl6AoCiYnJzE9PY1kIoFioYClpSVIori1DhnVte9PnjzxmieNjeHTM2e67rsgEuK78/UDMpvkbcuCUSdi1BPwevUeL6R1LLxavocyi2ygi2qFPuD+/fuQFQX79u3rWyrDNAy/aVovOv5xNPo0BB5xswI6gpWVFUiShD179uDURx9hZGSkJ/dji2C6lSg52FBJUUKJHv2NUeBnhmmCAlvEiLzqYe/U1I4ep9uBXXIQERRFAVEUuLoO13G8ro2s/l03DG8SqH3YIt7hCswtsXZfJwBtGN6GA3VdaKqK2YMHMXvwIArFIubn5zE/P48nc3OIxeMYGRnBwMAAFhYXMTE+XuWgSJlnPqUUt+/cwcMHD7B//3588MEH0TykEZRqtQtN01ByHBim6QnMer37JaSpj3xUEGqrFUKQg2DVSLtRgH4t0pVKBc+ePcOx48chiiJstlD0Er6nAaVVdr9RI9iUrfb8r1+98nQEuRwAIJvN4vTp05iYmKiuMorYO6RuFLPJ8WsbKoUlUrXEwLIs32CKawp46bNj24gnEn6fh11sYpccRARCCJR4HIZpwrIsaJoGNRaDXqlA13UvL8139j28BhACuO6WlEWUJkGus7XfZCqZxPFjx3Ds2DEsLS35Icr5+XnEEwnkFxZw/L33fCdBno/+/ocf8PLFC5w4cQKzs7ORhpM5+ehX+F2RZVRYrw2TE8Ieox/mT1tMkMKo4AN6g7a+U2a13Y+0wsOHDyFKEg6EqISJAhSeNwD3JOmZ+I0Tg0Dofnl5Gc/n5/HixQuYloXBTAYnTpzA5ORkQ18CAibwjYJgNxinzUZSsKFSPBZrOSa4uNP/3Oznvt5Alv2NB0+92o6DPWNjbXszvAvYJQcRQlVVGLLsdepTVSiKAss04TgOKuUyEsnk1nI7Hj2IYBELOtE5rltlFsTLm2iTLpFhUI8YVF0DgNGREYyOjODkBx/g1cuXePToEZ49fYqnT59iz8QEZmZmQF0Xj+fmoFcqOHPmTE86oBF4qQunX9ULxLO+LTG3TFVVe3Lenppp1UUH7oYdphQ6dVNsF4Zh4MmTJzh06BAkWe5L+avOooogpPd225SiVC7j+fPneM50BPFYDPsPHMD01FT47qxswe2aIDQapw1+XtVQKaTOQKiNxLI51WDloj4ZY8TAsiyosRiGR0Ya9oV5l7FLDiJEPB5HsViEw2w6FUVBLBZDsVj01MmGUb9kqaYEsRsIogjbdes+dP5OoMMFpRUxqIUkCJiemsLY2BhevnyJhcVFrK6u4rtLl+C6LsqlEg6xDpG9MIUC+l+9oCgKKmzHw0nim46tJkjh0wrtplb6pRJ59PgxAODAwYN9OZ/FOi66lCLZo3SCY9tYXl5GPp/H69evsb6+DlGSsHfPHnz88ccYGh7uTOfUZWl0s2e73hG3NFQKsXDXPTrbDJmWBUppld6AUgrTsnDwyBEkEom+lrC+KdglBxFCEAQkk0ls2DYqLJUgiCJUVYVuGH56oe5A5DumLhcxHploxvQ7edC7CV2rioLx8XGIkoTs6ChkWcbFixchyzLu37/vG9CMZrPIZrMYHRmJPCTft94LTGRWLpehs14bPZt4WBrJT5/0ApRCoAQEBAIEUNBQLZs7jRz0gxxYloXHjx9j//79mw14ejg2OBEGvN4JUXRcZAfG2vo6FhYWsLCwgKWlJbiOA0XTMJjJYHp6Gvv274ckin3vOwLA37k3Hf811+UyC3oKQGJzZ6hToT5BsCzLS1MJgnff2fksy8JINotkMtmyMdS7il1yEDESiQRKpRIc1/UiBZoGRVVhMlFMmaUXemaExHNqzcgB0J7YKKA87xTxWAyZgQGsra/DtG0oqor9rEHL0vKyN8Hl85ibmwMBMDg46BGF0VGMjIx0V73ASFdPp8cAsVNZ9MANRJB6emo0XlSp68Jl358b/De/HyylRQP/973u2edJ0zSGMeyXMFolG2tra5tOfIygkMC/K5WKV5dOiCeUFQTP3psQ/+96cJlQtZd4PDcHx3FwaHbW/1mvCBwFUGaufkIborr6B6MolUpYXFhAfmEBi4uLME0ToihidGQEx48fRzab9YSx5TIIIZERA/79uiHTkrUliw1fF/i36zgolUqe0ZEoIh7Cz4AbOTW6Zq43UBWlyiROkCSM79mDVCq1GzVogF1yEDEIIUilUlhzHL9LoyAIiMfjKBQKsCwLpmE03j10GT3g5CDMhBDWajiqfgUD6TRs20ahUAAhBJVKBbIsY2J83G/3XNF1bxe0uIhn8/O4/+ABBEHA8PAwsqOjGBsbQyaT6SiXLcDTYvRkMqgpz9JUFRVmhNUwWhTBuVzXhe04cFhfD4eJUX0iEAgJV0Wm+PUESJNf+srPwV7fyCGRAiCcXLD/8x2caVmgrgvbslAJeHz452IkoZY48B4ZvZqwbdvGo4cPMTMzE8plr1twUR2l1DPHavNzGbqOxaUlLObzyC8soMwW/aGhIRw4cADZbBaDg4NVz4NpmpuLOe28BXM9hKmO2TLWmr/Y82apIQZhUi/NiAHgjTXf3yCwubBsG9PM9vltbrXeLXbJQQ/g6wxcF4Zpeg1CWIisousol8tIp1JAs8HfoUAx2La56evg+R+QZjtq2rztcicYHByE7ThQZBmFQgGmZVW1uI5pGmampzEzPQ1KKQrFIhYXFrCwuIj79+/jzp07kBUF2dFRZMfGkM1mkQyZMySE9E1/oCqKL0CzbLvqM3YCl5m1OI6Diq57zZ5sG7IkeWkk9joaXIgDY0jkC3BwIQaqdv1+tQs2d4oAUBALWMISXLggIBBjQDqVqlok/KgDG088z6tpmh+lcl3Xq6Rhr3WZba3/GfnPKYUkCBAkCaIgQBRFiKIYSYnr06dPYZomjhw+vOV3jULTncKybd/PP5FIhBLV2baN5aUlLC4uIp/PY319HaAUqYEBTExMIMsjaU3GE6XUe7YRvYtmqw0FrxgIHa3gxCBgjRxWkyG0iEzYlgWT+Rtwy2RKKQYGBxFPJJBOp3ejBk2wSw56AEII0uk0VlZWYDgOVNZHgHvw25aFYqmEZCPFcGBX1y5B8C2UQ0YOmqUX+GQdJQRCPE2BqsKwLM8DoUEXR0II0qkU0qkUDh48CJdSrK6uYmFhAYsLC7h+/Tqo6yIejyM7NoYxloZoFboNGzHpBoT55VdYKWs75CAYDXBcF67j+NaxgLcztB3HCxljc+EXCIHAdt1iiBB+6M8SeD/vMdqs66bruv7nrSf2clmKg1JaHelwXa91LivFs20bDjaJD2G7SkkQIEoSBEYcwn4+13Xx4MEDTE1N9XzHSClFpVwGZSHthou563pjenERC/k8lpaXQV0XaiyGsWwWs4cOITsy0laUg1IKuC6EHinwm1UvtEUMGEEslct+r5VkIhGKGBD2/mYolsuglEJRFCgsckBYOkGW5b6UGr/J2CUHPYKmaVAUxbPUtW3EmDAtkUhgY2MDhmlCLJcRazRJdVjBIIoiQEhbhi4C2drJkTvc9YJXC4QgkUyivLQEx3GwsLiIsbGxlsp2gRAMDw1heGgI7x09Ctu2sbS05OkVFhfx5MkTEEIwkE5jbGwMo9kshgYHq0RNfHcctTlSvWmKR4oc294SIQnCpRS2bcNm7XuDRMA/NsvhiqIIVZYhM1tqjdV/BzUCUaN2/LVSb/jdGBsQE4GRGQDgd8TXPGCTPDgsUuI4DhyW63ZdF5brwrRt//OKkgRJkiCxCEOj5+XZs2fQdR1Hjhyp/zmbfqrwoPBsmSmw1bzHdVEoFLC4tISFfB6LS0uwLAuSJGF0eBgnT57E6OgokslkR6SO8mhfj6p/2Enqzhn8/GHhuG7nxCDMsYtFgBAk2BxLAeyZmPBTv7tRg+bYJQc9RCqVwvLyMgxK/egB7zWwsbHh5Q9ZRKEuSPsWy3zHyCfXZju8IKoe9h4SAw6FCYREQmCYJpZbdHGsB0mSMD4+jnGmV9B1HYuLi1hYXMTz589x/8EDEEIQY2LI9MCA93c6jVQ63XvzIBY90HW9KnrgkwH2xwkQOX5Foij6IXVBFCGJYtXEadl2T3PzVZ9jS2+F5vfN4d0Y20kDBHLUnDwERag8usDJAo8y8HSL6TiosEWLEwVJkvzxTynFwwcPsIeJ0HoJg5eyWhZc20Yul8PG+jrWVlexUSj4Jk9DQ0M4NDuLUaYb6Pa79DcSNfbpvUIwgtBuNRBlizcnBomQqQQe9Wv62QhBqVTyzKYC82tmeBixRAKKouxGDUJglxz0EKqqesZIhgEDAA8MKqqKWDyOcqWCUrEIkZAtDUF8tClQ5Aplk4Wmw5IDgBEEJmTrNRRFgWXbGBkZweLSEkqlEiRZxmAXzU80TcPU1BSmpqZAqWcCs7a2hvX1dayvr2P+2TPcZ/XTgiginUphgJGGAfYn6soClZEDyzBQAPxwea34jy9mkixvIQKhEBAfRo+aY7YSpAUiB+2codlRuVCxdjw7juOTLNg2XLD2vLYNV9chMrKQz+VQLJVw+syZ0NcUFpRSFItFrK+vY3V1FasrKygUi75NsiAI3hjLZDA9M4OBdBqZwcHIGy4JggDK0jQUvavA4KjSnLRJDIqlEqwAMWin/XMrYuA6DorlMsBFoAAUTcPQ8DAAIB1hI6m3GbvkoMdIpVIwDAO6bXt++2zi1GIx2Mwop1AsIiUIkbl0SZIEk4Wo1TYXO8pC7r3edciyDMs0EY/H/RLHtfV1P1zeLQghSCYSSCYSmNy71/+5aVlYX1vDxsaG1zlydRXzz575TZpisRgGBgaQYV3a0uk0UslkR6Y1tm3DMk0Ypun5XJimP1nxHS5v2NStKU4vFwJhS7VCi8gBM8tqJ3LQKR3lhEFVVU/DwPQZPCLDxZEPHj7E0PAwJFmGYZqeB0kH98yyLI9srq1hfWPDH0sOMx5TNQ2JRAJ79+zB0PAwBgYGOk4RtAOCTVLmt8ruwzn5+cKeixMDh80xoYlBq3PwcmVKPSGwbYMQ4leITOzdC0KIn+7dRWvskoMeg7skVioVVFwXCTaIuV84dV1Yto1ioYBUKlVfuMQfipA1xpIkgRBSFa4OjWDpW/vvDg1FUXzh3cDAgFfiWCxicWnJK/3sUZmZIst++2nAW5Rc10WxWMTa+jo22KT/7NkzlCsV32ExNTCAwUBqYmBgoG6HOJcRPsM04ToOKCMBhNVbS5KEeEg72JaoGQu9MnraojlocQ6+OIWtLqAhjhkGhKcUJAkIkIW5uTmUKhUcO3bMb3xUKZchKQoUpt+o9xmLpZK3+DPiur6+jgrzLCCCgIF0GulMBlNTUxgYGPBFkoSQvhCC4OcO+kP4JaW97jLI0p5iA/1BLajrosirEghBPGQFh3eq5sQgOH6KLKWgxWIQBAHZsTGILErT65TS24RdctAHpFIp6Kz8TIvHITJWSwQBiUQChWIRjuOgWCwilU57O656DwNPMbSYdCRJAgkowUOzemxO0lG4NTYDZ++WaUKKxTA4OAjHcVCuVLCwsIBsNtszghAEgbeIpVIpb+II9HgwDMOPMGywKMOzZ898A6FEPO5HF1RWripLEhRV9SdqWZYhx+PQYjGYuu67tb1JqB09rUYFjxyEJQe9qh7h4/7hw4eYZm3GTcuCZZqw4aUfNsplGKYJ27ZRKhZRKBSwsbGB9fX1zWhALIZMOo2pyUkMZDIYSKe9xT/w+QzmgEqB0KW1UWGLcRS77l5ege+hEHKeoEHxIbwW96HP1fxCqoiBaRhe+SIhSMRiUGMxDAwNwXEcxGKx3R4KbWCXHPQBkiQhkUigWCyiUKkgraoQmCiJsvB3oViEw3awyWSyvlYgZAUDIQSiJPm517APxBb230OCwK/JsizEGMPn+oNKpYL84iKyw8NIRJBiaIVGi5OqqlVRBsCb5Aosv7yysoLVtTXMzc15Oc7A8VRVhaZpSMTjiMVi0DTNq7dmjbkGBgb8CE+7aPqNdOiP0RRt9FagTCkPtBc5iBo8vHz37l1YloVEIoG7d+96UYNKxbO31nWvVJQtrpbrIplIYCCdxp49e5DJZJDOZFqm5mzb9ogBpf5Y7gdqIwYctKf6E/jVMf7xW8wTnBg4jBgkmF1xmGhDs09Qz7GxUCyCEAKV2VSP7dnjdXYUhF2tQZvYJQd9QiqVgmVZMAwDBcNASpYhgBkRiSKSySSKhQJs20a5XEY8kYBIyNaJPmQFgyhJIG2QAwrUL+/jE0HEJIFHDkzL8n8mCAJGR0awvLyMUrmMheVljAKRaBBaIXRInng94VOpFOLJJCaZ+NFhboC248BgCxBfiJaWl1GpVLzGO4FDyaKIGCMPMU2DFot5/+b/j8ehKUp7kYYQlrXtYqtDYuMyUB418C4lJDlo83pt1piH31/+b71SQalSQYV1xeTHppTi3r17iMViiMfj0DQNAwMDiMdi0NhuUpIkz3shkMKTFKWlbsJ1Xd8eWVaUvuaz62mDeFSrF90tuSVyvW+rkfdBLTGIJxIQBAF2iCZuAjyPi0afwq0hwg5zJAWARCyGzNCQL/ocHBxsS5y9i11y0DcQQjA4OIilpSUvhGmaSEiSr1YXRdGLLpRKME0TAsuZNYwg8B1agwlAYs55ToiHUDcM/P2///exsbGB0dFR/Kt/9a+qzstdFE3Lwv/7t38bDx89gixJ+Ef/6B/hvaNHO7of3FLYYm1ZObhVMgCUymUsLi2BUopUj5ujtDSEYr0ydMPwPSAgCFA1zd9ZttoJ8a555XIZtuOAMrfDSqWCQrGIhcVFVHTddxHkLoVaPI6YpvmLGWFlfvFYzEtnBFwEeXmjGHAX7HYn247mwG+41GAipsCWckQ76GcQ+MOJMl/4y4wI2La96f5IqWdoE4shHothIJ3G+NgYNE3D8+fPsb6+jq+//jpUBMpiIl6DuU867DkVZRmqotQl2ZVKxb+WfpXHkWY+HYHvJkpy4FIKsQEx8E5bx/OgDjEQRTEUeRVa6J7qHaHIfA1kWUYsmcTA4CAAYGBg4K3ojtpv7JKDPkIQBAwNDXkEwXVh2DZUSfL3YZIsIxaL+R39BFH0/ABEcetD0kp3wMyQLKbYbvZqVVXxV/7KX8F//I//EYuLi/jzP/9zfPXVV9XnohT/n//1f8XDR48gEIL/x//0P3VMDABvlwVK/Z7tQXCCQAQBxWIRSysroJR6ltM9RD2CwHcjBrNhhet6DXQSCSiBnglh+k+IkoTBoSEoigLqukgPDGzZmVJKveoGthj6u2P29/r6OkqVSrXYlBA/ssOrTfj4IJSCBDwT6v2RmJdC0KY4+DuT7cY4cvk87pbv+pUBvHcAN3IyWZ8HyjwIbEYEbNuGwwyefOvuwE7XN3vC5uTPUzKxWAyjo6P+/zlR0jTNG+s1WF1bw7Vr1/Dhhx+GIgZ8gZdkGZIsw2G9UUzT9LQwpZL3PKqq/70bhuGVTFKKVCLRUfVDJ2jWnKpKMxQhmhEDfj4/zM+uo1Sp+FUJPjFA6zRSU4fXBgSeuq5vPKVpGkZGRyGy5k39SE2+jdglB32GJEnIZDJYWVmB7roAK6viUFUV1HX9xUAgBDKbOIXaNEOTCIIoihAIgcOMY+pNoD4oxV/6S38J/+W//BcsLy/jD//wD/HFF19URQ/+4//2v+Hq1auA6+L/9rf+Fj49fbq7+8C884NphSAEQcAIjyAUi1heXQV1XQx04YMQBvwuWiyPbJim76goSRK0WAxSnUZKYYVZMlt8LMtCpVLZkjIhxGvapKkqMplM3WMUi0XopgmNRQ38nTjfcfMFOWDBXOs26Nh2lamQZRj+e93AaxzXRcEtwqEOXJeCgOJ1/jWeLD2pMmgSRdFLZQmCZxcsSVAUxScYYg0B4SSEezrU+7nIol/tggK4desWUqkU9u3b18ER4C8smqr61Sdcx2DoOkRJgmUYfrlcP0LWPGLQ7J74aYWodA9c5xTipVxvQuF1ouREMBEgBv7rGkBosPj7pKDBe0vMKlkQBAwODSGRTEJRlJ7PF28zdsnBNkDTNKTTaWxsbEC3bQi27ZfaAJ4HguO6ME0TpXIZKTZpOpRCQM2uoMkOQZIkLxdu203JgcMWvl/7tV/DH/zBH2BxcRF/9u23+PonPwEA/Jc//mP8t5/9DADwf/kf/gf85b/8lyPJaytskWyGkeFhCISgUChgZX0dFECmhw+8y0KhhmH41RuyJEFLJJqa1rSTN4/FYt4Om4Wx2zbDIZ7zmyxJdcVyUZUGcvz5L3+JV8uv4S03BCfeP4H/ft9/X/e1xVIJtmUhHo+3zL/7UYPIrtTD69evsbS0hM/Onet6By2IIrRYzDMzYx1VXdfF+toaKLxW5P3QGQR9DJohyshBu98NIQQQBJSKRd8CvJYYAPXTb5SnLRqM21bjucCiBjFNw8TevRBFMRLXyXcZb1ZN1VuEZDKJWCwGSBKKlQrcGm1APB6HLEm++5rLw7G0TqdE/gDU/Jw/lM38DoLv+PLLL30r4j/8oz+C4zj47rvv8L//H/8HAODcuXP4v/6P/6N/zm6XH0VRYDLhWDMMDQ156n5B8I2LogalFJVKBWvr67AsC5QQyIqCNCtba7mAtzEJiTxdRAjKzLGxo2uO4FrCoB3NQVvuiEKtvVL3cCnF7du3MTo6ijE2lqMAt8JOJZN+OSr/hEW2GPYS7TQzIuiecHUiaHRdF+VSCRZzAI3H46EiKtR16xMDNse0+uSVSgUuS1nN7NsHSZIwNDS0K0DsErvkYBuRyWQ8YZ4so1gqVT0cvEmTKIq+SY+/c2DmLlUPU53KBm7u0qwJUzBXTgjBX/vrfx0AsLS0hP/wH/4Dfv/3fx+UUhx77z38+q//etU5SJcEgVsoh0GG1ZdLgoD1jQ2sREgQTMvCxsaG1ySJWboOpFJI1dn1RAXeZY978HeERguG26r7QbsIV63AqzaAcO6IvdjTzc3NoVAs4sT770d/cEpRrlQ88XA8jszQEARRhEspSoUCypVK5NbjhJ03/CVGUMYYtnInAMe2PZJk2xAJQTwer0uoa4/bsMVzG9fAyVkilcLQyIg/r+6iO+ySg20Eb74iaRpceJ3cqn7POpUJguB1GQsQCF56VyWEq9UdMDMk3uWuFvXCz2fPnMH09DQA4JtvvoFlWZiamsL/83/+n+s+7N1MQoqitEwrBJHJZHwR38bGBpZWVjo+N+ClUwrFIorFIiy2oCYSCaRSKZ8UCAi3a2u31FMQBL+joq94D4sW95yELHcNf7pw9sl+TjjkrjNqN0fLtnHvxx8xMz2NgQZ6jW4QrJRIJBJedUQq5QkURdFrxV4oeCY8EXy2Thwv/fmhQ82Br51p47k2TdPb3LiuX5bdKNIW/DScGNRcQFtpMdMwvO/FsrD/wIHNiOwuusYuOdhm8NyYqGmwTdO3Z+UQ2MNGWOVBueb3AKqjCIEIAiEEoiyDtogeVIEQfP311/5/BwYG8Ju/+ZvNH7gOIwhyyLRCEJmBAe9+sdzm0vJy2+elbAe4vr7udc6jFKqiYCCd3prDZ/n9luiAJGmq6tWNswqFKBFlrnVrb4X64FEDbiHcDLwyIUrcu3cPtuvi+PHjkR4X8Mp9DcvynTH54kcEAfF4HMlEwq8Qqui6p73oxL6cIazGoBZd9VXgz3HI93KBZoU1OeIdZ8Vm2pzAZxKCIl42b7VLhgosWjEwOIg9e/bsGh1FiF1ysAOgKAoyw8MgigLdMLYsmKIo+pasZh0CURVFCD5s2PQ7CJsTzefz+OlPf+r/3zCMUIK5TiYjRZY7mkDT6TSGhoYgCAJKpRIWFhdDh3Nt2/aEoLoOh/lLpFMpxNkuvhF6ImsiBDFWG19pIyTtX0vEi2vrE/LTNkgrBMhB62NGe0dL5TIePXqEw4cORe43YJmmb64T07S6PTUkSUIymURc0/znsVwsth0V4rX9HUceWPSm3bvbqb7AYPdF1TTf4Kj55dW5Pna/OjHDWltfh0MpDh0+3LC6ZxedYZcc7BDE43EkBwchACiXSlsIAvdAALxdTKlGo8DhuK43SfO67SZmSLXvLxQK+J3/5X9BoVBAkpkO6bqOP/qjPwr3IdqcYBRZ9p3s2kUqlcLw0BBEQUBF17G4tNRycTUMAxuFAix2L5LxOFKNrKprEHWonoP7WIAQfwEKi6Z1591dVs2xQkYO2ujGGLXj5u3bt6GqKg4fOtTR+xtdjW3bKDHRqKoozc10CIGiqhhIpaDKMiCKsGwbpVIplA8GpbRxKV9ItNuumUcK2jmjY9ubkRFCEGOuk8Fz1vUiqI1qEM8+vpPPS10Xi4uLgOticHAQs4cO9c22+l3B7t3cQRgYHISSSACEoMSc4YJQVRXxeByAl+crBUWKDFwk6DCGLvPIATNDCiL4UBqGgX/9r/4VFhYXoWka/vE//sf4+OOPAQB/8id/Er5CoA2CEE8kYBhGW7qDIJLJJIaGhyGyvP1igwgCpRSlUslr/EI9R71UKtV2CVoUKvCtB/Xq5Am87yBUhKdBdcrWl0VztULtNNHgvH5IO8QkHSU1WF5ZwcuXL3HsvfeahrSbod6dchzH6/AHT9wbCxmRIIKAWDyOJHtWXUpbpxkiIAYA2xwgXPSmHRLBYVmWpy9wHF8TVe85akQQ+fn8tEkHn9d1HKytrXnznyDg1Mcft18OvIuW2CUHOwiEEAxPTEBlXf0q5fKWCIGqqn6KwWJtjmvLIAHv4XOYU57ALEuDkxPFZhjYdV3823/7bzH35AkEQcDf+7t/FzMzM/hrf+2veeexLPz//vN/bvfDtHwJzw8WCoX2jh1AMpHAyMgIJBZBqE0xOK6LjUIBhmnCcV3ENM0XebYNFj2o98m6WYa5Kx8JGT0Ie66oyEFt5KBRLYTbRuQgqpQIBXDz1i2k02lMz8x0dZyq/zMBMIHnNpqIx9uOjEmyjHQyCYndj1KpVDdSxqsLuiUGLkstUlZx0wydEANd11EulTb1BclkYzJWxyQsGKVw67wmDHj32tW1NRBRxL59+zAZ6KS6i+iwSw52GERJwsDICGJsN2nxCEFg4pBZ4x+BuePxls+14BOOIAighMCpjR6wh/Pf//t/j5s3bwIA/vbf/ts4cfIkAGBqagqffPIJAOCbb7/FwsJCuA8RDC82eVkqmQQBsNEFOQC8lMzI6CgkQYCh68jn83AphWlZWF9f9/oYsPN1nY+uUzIKYLNhT4fgKSPLsjqOpNSCh5i7xhbNQf2j+mWMrdI0EeoNXr58idWVFZw4caI7MhT8TCzS5LLdfCfEgIMLihVZBmHjs8wiWN6pookYAIG+FoLQlBy062HgMqLE9QWKqrbUFwSP7hMfSjsSWXJYto1yqYSNYtEzoEok8BGbn3YRPXbJwQ6EHI8jFo8jnkwCLEJQrIkQiKLo5ctZN7RCobCliRGHJMsApdW7FjYZ/eef/hTffvstAOCv/tqv4csvv6x676/92q/5nun/3//z/2zvg7BJqNG0J0kSEskkChsb7R23DuKxGLLZLERBgGlZmJ+fx/r6ujfBCwJSqVRktc8E2Oqj3+XkLjLffsLU7q3OD4TzqI9iGQ5TyugGdC4tKxW6WCCCcFwXt2/dwtjYGLLZbCTHBKUolcuweXvhRKJ7K2JW9x/XNIB1JOSeAFFEDDhcxwGYyLbxpZC2vBgcx0GJuV4C8JqAtRDvAltdEF1u3tYhyTINAxUWebEMA4qq4vDhw7t9E3qIXXKwAyGIImRmy8pD4LZloVgTIRBE0Vv0mJNiKdCqNgiFtZ61HQeWacKl///23uzJjuu+8/zmvtylVqA2VGEjKa4iCFISRYUkOqy2FG63NdZY7nl02BF+8UxEd8fMy8S8+E+YDj9MTzvGdtvR7QjLbluOaDvstmW3SYoSTQKkSVGUAJAACBBLrXfJPfPMQ55zKvPuW4EE+PsoSgXWcjPz3lt5vue3fH8MSZriH/7hH/CnPF3wla98Bb/0rW91/e7m5iY+x+covPrqq7h+/frY1zPoRjI3N4fGDMQBkNtSHz9+HEkco91u4+7ODlRFkSJq1hR3fLMI4Tu2DSj5PIxwRtGDWZxXVylmj26FYjHioGPO0tvgypUr8HwfTz311Mwe0/d9aczlVip9p0tOgskdFoWw9KZsd+xkUL2BSCOMk06I+T0nE/UF1WpPu+5+iONM+5oHQQA/CPKZDb4P07ZRr9Xw6GOPTfW4xGBIHHxC0V03H0BjGHluT9OQZhlazWbphqKoqhwywhiD53k9Wx1FTltMQXzzzTfx//7H/4g0y/DZp57Cr//6r/c9l29961tQ+Y7jO3/8x5NdkOhj7vhyrVabmTgA8kVKpFyE3fI0FsXDKN2Ip7wJKqqaV31jSGujuOlOdbQxzmuEboVho5rlY81IpAVBgB+/+y5Onz6N2gymdTLkXUBRHAOMlbwMZonGBb2u6/kcgnZ7pLHqo5Bxv5NexkIKxnu/FOsLtGH1BQVkLVNBOHeZtY0IYwy+5yEKQyjIowe6rkNVVTz2+ON5RJQ4MqjE8xOKqqqw5+fh7+3JG4qoeG61WvnAF95WJa2WeVFeEIbIGIPrulK9W5YlR9A6rotz587hD//gD+Qf86Cb9sbGBn7/P/2nmVyX3L3wG0e9XpfOc9PejD3e4aFrGjY3N9FoNBBGEe5ubyOq17FwBH3QCgqh2hns0m3LksN9fN/P8909jglgJDEyagpi4GN0pRW6Kea7BzGLyAEDcOHixXyRmNHuMSp6GTjO0dnv8oW66rr53zNyj4bKiHMIBiEiB8XRyCJtMWq0QLzvRBrBsCw4HW2KvRCvKsuyQ5fFKWBZBo9HcTQAqq4j5sOVTp46hZOnT0/1+MRwKHLwCUYzDFhiV8TD44Zh5JXUnicLhAS246DCBUEURaV5DLquy8U34mOIAZ4/V1UwbrF8L3ajxduM6FiYNnrg+X4eemQsr9dwXaysrOSObYqCxsEBbt+5M7NdWiczcyRUFLg8jzpNm2f5Iac7t+5uhQFphWEL3AzEwY0bN/DRzZt4+ty5wb4DI5IkCXzPgwLAHuZlMCligS6YlFVcN+9k4K3Lk+yuBb06FWTb4Iivf1d9geMMNQcDNy8SbYm9fnbceRNZmqLleUiSJN8kuS5arRagKJibm8O5Z54Z6/GIySBx8AnHcByYIgKgKKhWKvLm5fk+PD7HXGDyVkdVyS2Tm82mvHFblgUoSk+rXvH4UiQctfseP94sOhZ830fg+2BZBoeP180PoWBpaSm3W9Y0hEGAW7duTWy8NIhZeiDoug7TsqCqKrxeDnv3Oq0wQvHlSAOXZiCgwijCxYsXsba+jo2NjakfTwwMgqLko7mPwpefMaDHcCFFVVGpVKRAaI1oltSLYuRmkghEZ31BpVodLJL4tQy0axYdChMUQKZpmtdUuS58z0OaptB1Hc9/6UvkaXCPIHFwH2BWq9BFIRDfWbquKzsQvA4vBN0wUK3VoPGBTaJOweR1B2mS9BzjLELkwGGe8KhFgm4YU3UsBEEA3/dzDwPHgdWjVbFWq+HYsWPQdR1pmuLW7dto95hRMS2zdFF0bFvOXejbvXCP7JO7aw7Kx5U7RwxOK8zivfTWW2+BZRmeOXdu6sfKsgwt/j7QNE0ajM2cAWF2IRA0IRAmjCCIegNN08aajwAgb6/sqC8YNDiJIRcFog2zL2N2YsRxjHarhYwxmLqOquvmnR3tNqCqOHf+/EzqS4jRIHFwH6AoCqy5OaiaJv/YLMvKBzIhHzlcjBAA5cKnjLF88mAcwzTNUmFiv+MJGG9BkhP3joC5uTk0m82xF48wivKe8QHCQGDbNlZWVnKDKUXB3e1t7O3vT3nmZYriaurHUlVZbxCE4dRV7dOcV1daoeN1Eu87ZUCnAsP04uDWrVu4fv06nvrsZwe+1qMgevfBGFQhDGbovwBg5McTToMa//m2540dFZKdCmNEDdI0RbvVkrUWhmmi0se/QLx+Mn0gig0HPD5jLHdqHYIY4OTx6zYMQ9ZL7e3tAYqS1xmcOjXytRHTQ+LgPkFVVdhcIAgM00S1VoOqKLkZUrMJv5BmEO1HpmHIVkdRSTzKWNmSeckRioR6vY5Go5EXRY742GmaSjMZy7ZHMjfSdR0rKyt5a6OioNFo4Pbt2zOvQ1CAmTxHumHAME2oioJ2Mb0w60VsTDqvLBsxpTDNWcdJggsXLuD48eM4OYUTIgDZ1SPOu+K6g3fAkzBmUZ6IIIj22HHmbCiqKj1QRrJN5o8vO58UJa8vKBQwF364FBmS359R14mIDIRhCIZ801PhnVrNRgNJmmJ+fp7qDD4GSBzcR2iGAatjJKmu66jX67KVMQjD/I+K7zQVnoawef4wThIpDAYVu8lipo6bQEkkzIhix0I/B8JOPN9HmqbQNG1kz3sgv67FxUUs8qFNQRjOrg6hUACmqupMhgu5jiNv/mLBGKdbQTLFuajK4NkKoxQjTvtcvP3224iTBM9MuUiIiEHKDYOqlcphKH4WdBYejoGqaXAdJxfvI0SLGCBrGVL+Nzms3kB0O4liZmGDXKwvEBMSxd86MFnkaVByRKTL2rzOQVVVVF1XFkCGYYhmqwXLtnGOZid8LJA4uM8wLAtmpVJanMWuQxQiptwxURQrKooiK/gFfo/BTr2Qt4TOdjZekzALoSDyiK1m8/A4A5wVRUsmgInDwdVqFcePH4fB6xA+un07DzPPkHEiIYMeQ9ww/SDIBzNNcL3jWuaWfrer5qB82x+ljXGaZ+Hu9jbef/99PPHEE1PVBRSFAWMMlULv/izErgKMnWfvRDcMWIYBRdPgceOfnscSryVfwPt6HHAy3hooFmPw4VCV4lRSxmRNQa/jjEs/J0whUMTkWdM08wgnr6vKsgx7e3vQDQOfefxxLC0tjX1sYnpIHNyHWNUq9B67ZcM0UeNRBICPKG40ZGuSGNpk6no+kOjgYKQdsyi061eRzBjLc54TCoVarZZ3LHQUJfY6XsLTCYwx2LY9VW+4ZVlYXV2FbVlQkS9Ce3t7E1eM91p6ZzFG1jDNPL2gqvA8T96oj7yjhNPlc9Bx2GHigPX6pRFJkgSvv/46lpaWcObMmYkeAzgUBuJca4Wiu5kIgxnaINu2nac5ehSjMuSCseh0mIkBa31mKohOhJj/rRumiZowTkOhngDIIx+9L3Csa+g5spl7KIjXQVUUVKtVuK5bSkk1Gw2kjGHj5EmcJj+Djw0SB/cpdr0OtUeoTeWFbNVqFaqYu9Bq5dMdswyGaWJufh6WaSLlCn2sHus+NwkFh+FI2eUw4s1S13VUKpXeXgeixZI/ls/PVdO06YcoIQ+FH19ZQb1eh6aqaDSbuHv37kzrENQJw8xFXMcB+KAtIegmecRJfmfQbAUhDIERPA4m4Efvvgs/CHD+/PmJH0OMXhaLaLVSmXi0cy+kMJhhMaqIiEVhKO2cpTDoeC+Jv7fOmg/RFujxv31F01CpVuE4Tu5tUigw7AXrJxRGoHNSrJgPIyJ+pq5L6/fiMUQ64fjqKs6cOUPphI8REgf3KaJAsecNifds12s1mUuMoggNPpxJ0zQsLi3Btm3EcYwwCNDk3QzDEEcb6pgmwp0jtkPWecdC3+OqKoIgQBzH+US2GbadKYqChYWFUh3CRzP2Q5jWNlhRVbjcqS7i46fHXehn5cVQTCsUBy7NIkpSZGd3F5cuXcITjz+OarU60WOIBZIdhTA4wgiOruuwTBOqpsH3/VwYFI5ZJE0S2XUhzicMw3y4k4ga2raMlhQjfIP+jtUBqb2BFIQSyzL4ngdPTLnkmxeXb146r2Nvbw/zy8vYOHECi4uLkxydmBEkDu5jNF2HMz/fVyCIHUi1ML2x1W6j3W7D0HW4fMIaA3cl4xGGYVGEcZ3XGGN5nnfAgiY6FgY8CEJuC+1MmU7oR7EOIeN1CK0x6hAGPR9yYZ5iITEtS87ICIJgoumGk9QdDJqtMKxTYdLuljTL8Mbrr2Nhfh4PPfTQ2L8PHEYMRN2NKD6cCSMWzk6DY9uyvTEMw77HE86mqqoiTRK0220Evi99Cyq1mhyYNI6V8qTv1DRNkWWZTGdEXKCI2gLDNLtEapZl2N7ZgVOtzszgipgOEgf3Obppwp6fH3gDNngIzy5EEZrNJjRNg6ZpsExThujF9wb5IBQZ1fhHzFRgWSbrE4rU63V43DK1F0IYaJo2dY/7IDrrELa3t7E7RR1CEVEUOM2SItrN0jTt6XQ5FNZr4PJgOrsViq/dsE6FSSMmP/7xj9FqtXD+2WcnEjTS+ZAx2dLb7xwn2vnPaOx0X/gibvNJnVEc9z3PlBcGi8mtne2JwmBplGPOgizL4HmebDVWecF0hZ9LT2GwvQ3TtrG2sYFTp07Juini44PEwQOAYZqw5uf73+T4ouS4Lmq1GjRNQ8YY4iRB6PuIogiWZR1+jxdvjRJFKD7+qIj6hKwwz6FWq4GBdyz0IIoiZGkKi+86RGX4UdBZh9BsNnHn7l3EM6hDGFjcOQKqqsKtVGT0IB1zkVIUZeqe/qK8GFqMOMGCs7+/j/feew+PPvaYnL0xDmmSoNlqyfOq9jH2mQT5uh1h1EBamSM3BBLeB3EP4ZwxhjiK0G61EPKUm2hPFIZnIx0zP/DU5x6FIZrttjxXUQRt9RlklWUZtnd3oRkG1k6cwOnTp4/OqZIYCxIHDwimbcOemxsoEMAYdB5FcGwbuqYBioJ2u41mqyVdFR3eOieiCKPuUCe9wbAsk3bQu7u7XTuYhNs9M0BOogSm750fRGcdQhRFuHnjxsAZECNXrE8pECzThKZpUPhrNwmdLWusx0eG3kNzZPEdj2AwRZFeAUzkqcXnMV+jjDG8fuEC6vU6HnnkkbGvK0kStNrtXARpWl/Hv7G5hx0ipWMoyqGraUcNTMat0YWzoMbTiJNc87RXJdIZLV7foWoaqtWqTAH2enzRsqiqKtY3N3H69OmJa0uI2UPi4AHCdBzY9Xr/3b6oFQAvUKrV4LguGGNoHBzg4OCgZxTBa7fzoSzDdqli0Ssca1QMw8Dy8jI+unWrVMgoiqtSxmBbVldIcpYDj3oh6hBMw4ACYHd3F7du3+5bvDnOuSgTVoOLcLOCvAo8GFA4WTK0AZAW6z9E0WjhZ4ofYkFUO24TDIeueYmw0xWLUeciqii5m96Ioe2f/OQnONjfx7PPPjv2Aie8+Rny6I9w2hvKoAV/jBz9xAw5hmWaAH/dkjQFQ24d3mg288FcyNsfq3xq66jM4qqkKOAFzYwxmLYt6zsGXdf+/j4yRcEGFwaTRImIo4P6RB4wTNdFlmUIm83+xjd8EddUFYsLC9KKOOAjj4MwhMWLh8Sc+ziO0Wg0SlMP+yLy6qJiecTd1uraGt55553cMY3PkWBZlofP0xQG78vudbspfm3WeztRh9BoNNBoNBCGIW589BEW5uZQq9VKC5CorRgJRRnJo77X72mals+JQD6V0jAMaKI9DfnzkaHjuS8u2OMcrrMgkT9OWuhUGGid3LET7vf9RrOJd3/8Yzz88MOYn58f6xzjOJZRFJ1HDEZd1AdJ3lm3KfY4wGDjJC68TMNAGEXwfR8AZG0OY7l9+NipE8bAprimlIvSpCCSVU2TKa9hqau9/X0kaYqNkydx8tQpzM3NTXwuxNFAkYMHELtahVWr5TvCQT/I28/m5+dlKJKx3G3N933ZWigGOAlP+larNVaue9Sb9NraGrIsw93tbfm1kFs964Yhz08UYA0SHbO+lStKPkt+dXUVjmVBA7B/cIDbd+6UWh7HFiZcwI1dB6AouTESb8trtdtIsgwpjwSkw56fcWpEunwOckQkSR+14K3/AcAAvP5P/wTXcfDYo4+O9etRFElhYBjGWMIAQHfkoNiKN9aZjI6oK0BnN0fh30LoMd51EEUR9vf3EQuvANPM2x0VZbgfQGeUasLXqxgpEMLA4BuJCi+WHfZePuDma+ubm9ja2sLCwsJE50IcLSQOHlDsahVWpSJtVQeh6zosy4JhGNB1HW6lApWnFHzfR6vVgmEYshYhjmM0Gw0ZaRiVYTfsGr/B3L51S34t5uLAKlQvK4Ubd6eXQldkYcY7PsMwsLK6igVeixDHMW7duoW9/X1kWTZxCHqcFrPCL8HSdaS8WC3gu8pRjzdqPUDnWcnIAd+99jLjGpe33noLB40GPve5z+WtbiPWb4RhmLtGKkppmt9UiEX7KChGCnqIAulWyP87yzL4fCy56EJQFCWPWPGaIV3Xh0cNJvUs4CR9REGtVpMFhAwYKgwazSZ838fG1hY2t7awvLw8xVkRRwmJgwcYp16H6bo9WwclPHrg8PysaE2r12pdIiEMQximKaMIQjiM4iYovRHQXyQoioLV1VV8dOuWvEGKKMHAnRG/8XXWKgBHV7RYq9WwtrYGl7vNNRoNfHT7thxoMy4jdTHwBSVLU7A0RcYYFF3PRzvzdFAygpFV8Zgj/mD5NHggvq8z4hjCAwA+vH4dVy5fxtNPP43FxUX52qm87a3fWYZhiMD3oQAwpxAGYqGedGDSiAc5fHwRdSvWeAClv1HxN9dsNuXEQsMw4PAJpJqqIkmSPKo24G9jWmksREG7IArMgihQNQ1pmkJR1YHCgDGGFi9YXN/awsaJEzh+/PiUZ0ccJSQOHnDc+XlYjpMbk/S58Sl89+HwiXBi7LNlml0iIQrD3BedLwhJkqDZbI4eRRAioU+udW1tTe5QsizLK5+V0d33pJ8CDq19j2LMNJBHXI4dO4alpSXomoY0SXDrzh3s7u5ONZ+h5wKXZUjTVDojFgsATdPMizUVBS3P67Ku7X+wUdvcOsVBoeYAPQyQRMh8BJrNJt64cAEnNjdx+tSp3ueodPtDhHw3nSEfRjaNMFB4WuSoagsUQE5PLAqBnhX8aQpPiIIoKnUh1Ot1GIYh39NJkoBl2UBxMOm7PkkStFutnqLA4aJAXJsw9xr09+/5PhqNBtY3N7G+sYG1tbUJz4y4V1BB4qcAh3sgRL4P9BnOoiLvYIiiCHEcIwgCecO1TBOmYSDiXy8uPhljUHgUIYwi2JY1cn+1AsjJheK2sry8DF3TcPv2bZzc2gIwnWd/MQUhbmCqopSK0KZpKwSASqUC27axu7eHdquFVrsNz/exuLiYz0QY95z5+Qhr4pRb/xYXrs5pebbjIElTWVxarVaHLnSjpoS6TJD472Z9DJBGTQkkcYxXX30Vruvmo5iHvQZ85x0GgZwoatv2WCO7S9fBxcBM2xN5hIDJ/8xrQIa9v7I0RRCG0k0QyJ9Xm6f7gMMixDRNZdRAUdVDcdBx7ElIkgRhEJTMyEzThGVZUhAIFEU5fG8OEgaeh/2DA6xvbmJtbY3cD+8TKHLwKUBRFDjz8zBsu7ybLv+Q3KGIeerFdIEQCfVaLS9e5DcKlfe6R3GMNEngeR4aIhQ66k230FWh6zqOr6zg1q1bedsWGz6jfhyKN89OM6bOlr5x0DQNx5aXsXzsGHRNA8sy3L1zB9vb2zI3PyrFuRSJuPkO+FlxXaIgLOG7z5GYaLedyfeG2tmuxjtMRngQvHHhAgLfx/Nf+MJIA3YYY2h7Xu67wds5JxUGxR3vVIhagUJUQBQCjzK/IONirlGwGdZ1HdVKBbWO1sRiwXCcJECWQTeMUhfKTCIF/P3aK1IA5IuGgkPBDfSOUCiKgsD3sX9wgLWNDayuruLEiRMTniFxr6HIwacEVVXhLiygvbODNI6RZlme0y0szAoOixODIIDneajVanJ3JXa0pmnCMAzEcQw/CKCDh/DFYs4YvDRFEASweCRhnDar1dVVXLx4EVEhrDprxO26lN7oDPWKCu9CSHvYDtB1XRiGgf39/XwinufBDwIsLiygUqkMPa+MMTDeyy6O16tYUUZECoubaCVr8+l3uqaVTKN6MsLi2OuapW1yr5TCCFy6fBk3PvwQX/jCF1Ct1Yb+fJqmaHuenM9RqVRgGkZXrn5UhFHTuOJA/B10vk/E98QCPuxx0zRFGASICsJR13XYltVXKKmqCk1VkTImxbdpGJOnQnhqIgjDkoDtFykAcmEgWiDTNB1Y0xAEAXb39nB8bQ0rq6vY2tqa+XAu4uggcfApQlVVVJaWpEAQux0hEsDbkBzHQRhFeYgxiqRlcZFeIkEsZEmSIObiQ4wYHkUkiEVobXUVFxjDnTt38q6AIxzbKq+rx2ILFNIR8guHggGFRbsosDRVxdLiIqqVCnZ2dxEnCbZ3dtD2PCwsLMDocT3SqKhj1y0EWedSU0qXFDB0HbZt5+IuCKDp+tDIS9di10GnG76IPgGTpXx2dnbw9ttv46GHH8b6CCHmOI7R9ry8TY779GvFnDd/300kEjp/p7gLLyz6RYHUeRQR3heRqEH0EgUGF+SjRE9UTUOcJEiSBKqm5S6nY5ImCeI4zuc1FN5vA0VBoZYHOBRJAE8toiCOsgxRFGFnZwfLq6tYWVvDqVOnSBjcZ5A4+JTRKRCA/IalapqsAVAZg+s4aHseAt/P3QEHdBiYpgnTNBFFEaIoKokEz/dly5+ISliWNfBGUalUUK/Xsb29jfmFhelb00ZgmEgoUgqnFuoYRIRB5JhNy8La2hoO9vfRaDYR+D4+CgIszM/nERn5gGy0jg+glBLpd562bSPhC0i73UatWh08AGnYDrrj+c/AkPHFrSQORijoC4IAP/zBD7C4uIgnn3xy4M+CMfhBgDAM86JZ7nrY61qEuM3StOv90tneKgsPC6kAIQCVwx8qP3Yv+Os9SqQgSRKEYViaj2DqOqwxJ4xqfPJilqb5cKIR/zZYliGKY8Q8/ScvgRssmXw8dCeiw6Lz6ooiVil8Fl0JjUYDSysrWF1bw5kzZ0gY3IeQOPgUIgRC0Ggg4jsyEa5VecGiZVmyONH3/dIwFFnh3XFDFCIh4zuHMIqg6zpSXmyFIEAQhjANAzZvyep301hfW8OVK1egPPLIkdojdzKOSDj8UaVrgSn6LtTn5mA7Th5FiCLs7O6i3W5jaXExf35GyNGXbsCF8+u3KFVcF03e8eH5/sCURuc13vFu47Z3O7dJBnDXu1v6/l3vLt7afjtvMfU1LDtLODt3tud7ovM4r/3wh2AAPv/5zw8UfYwxtNttGbq2bFt2ZPRDAR9bnGWynkARYX7+3yINIR4nKyzsI7/PCs89w+BUkwjbJ1OKguIinXAhOYrxUZIk8u+4iG4YsHhbci+BUYpYdXy/+H4V3h5CJO/v7iKKY6xxDwOKGNy/kDj4lKKqKtz5eeimCb/RkOFFGUVQFLiuK+2CLcs6DOUWblS9bouqqsrFX6QmRPohjKLcuMb3YZkmXNeF7ThduevV1VW89957aLVaqPfKSY+wS52GTpHQa/fU/5eVrgXHNE2srqyg2WjggD+n12/eRMVxpAPl4a8r8nGkT0NnzQH/3O+cFFVFxXXRaLVyg6QgkGO5e/48f6w/eO8P8Uc/+aOBl/fP2/+MD7c/BAPDDnYAAC+e+Cr+j2f+94G/984772B7extf/vKXB55LwgtbxfvLrVRgCBMs8ToUd+2FKIBw6BNtu+J9XXyeOgsoR6IgiEfqxOglCgyj9Hc0FEWBioK9MxfxSZpC07Se6Smgf9pA03UYhpFHAvss2DJlht7vrWLUrFhIHEcRdvb2YNs2tjY3sbq2hpWVFRIG9zEkDj7lmK4LzTTh7e0hjePDKAJjULl/f6k4scAoS7Ou67lpkuPkNyw+qyHgAsH3fZimCcd1Ua1WpUhYWlqCruvY3d7G2upq9wPfg1QDUBYJYgFlQzoI+uXwFUVBfW4Ojuvi7vY2Em5F3Wq3UeWpFI3PlABQEiRM7IaVw7kVojJeCCXWIUhUTYNr2/ncDN+Xr0UvGIAoDfGdn/7J0OdEQ764pThMhfz9h/+AX3vs17BkL/b8nY9u3sRPfvITPPnkk1g+dqzvY4dhCC8I8op4UV+gqjKvfXjC5ToQoFD3IQTCCKmaUbwNpHfGEFHAGJMCuJgmMg0Ddp9cfo+D5UV/4jFR/jsTw410VS093rC0gWEYg2t3ehS99iLjxbKiS0lRFLRaLTQaDSweO4ZjKys4ceIEDVF6ACBxQEDT9TzN0Gwi4h71QiRYto2Q74BEcWIvZKi2z822WJvgui6iKJKV/CFPQTQbDbiui1qtBsMwsLS8jO3d3b7mTfKxAVksddSSQQHyCYMYsFgMyeFrmobjx47B930cNBqIwlC6x1VrNdSr1Z7eAQKZuii0YBaPVzyyYZowkgRRHKPZaslpefJawKvPswyaYsDUTIRRCAYGBQo/FkPGMvk6qFCRsgyhEiFBHvY3NRNVvVJKpygsH+7Tajbx2muvYW1tDQ899FDP540xBs/3kfCaFc0wpPtk8VxHReHPsxwO1YsBwqAo8AbWn/B6ERG6L9aEGKYJu08uv+cxB+zYBRH3NzB4C+OkaYPScQH5nh6EEFIKv+YsTbF/cIAkSbC+tYXlY8dw8uTJkQoriU8+9CoSAHiaYW4OumXB39/PnQlVFeACwff9gcWJxVA4gIE33s60g+d5aLfbcgFrtlqwbRsL8/O4efMmwiDIW7b60FWkh6MVCV11CR2RhEHHFgs6ADiOA8dx4LXbOGg0EEcRWs0m2q0WatVqHknps7DI51vkfAdEM1zukJkmCXxukFR8DRVx01cU/KtTv4D/8pM/kq6Ih/9/uHPXYUBRgBSJ7GT4xubXYWqF+Rf8+cnSFD/4wQ9gWRaeffbZvm2Rnuflz4uiyPfGSLDBxj+igK/Xc9Or60C0Ig5LHBQFQVG8qko+EMsasX133GLbJI7l6OZGszl22qATdcBz14kUoYzJNILjuljjrocrKytjXQvxyYYSQkQJ07ZRW16GxhdjVVXz4inu5d5sNkdrGRvxpqfrOur1OlZXV3H82DFpahMEQb4wKgquXb0qbZtHoVSZPtJvTIYiPkQr6BCKLYBF3EoFa2trWOLukFmW4YDPajgo1IMUEQuPuGEP60aoVCqAkjva+QPmP/zSmW+iort9vw8AekdawVAMfPuhX+55vRcuXEC71cIXnn++ZOgjiJNEWmWL8xxFGLA+kZJeDGuFFVGvUvFiD4SLYbPZRLPVQhhFMt1hGoZMDTkDCm0BXjipdJpSDz6/hD9PbT5aXViLK7x4uMrFpGVZQ4WBwlMXI7sp8hSCSDc2Wi3c3d3FwvIyNk+dwsOPPELC4AGEIgdEFypPM4TNJsJ2G7qioFKtotlsIoqiPDxdrY49ZriXmY9AURS5k47jGM1mE2mSwHUcfHjzJupzczJUajsOTD5Bchii6nqaiYmjHENcA5AXkPW61mHiplKpwHHd3DGPz7tvNBp5USa/+YsbvwxBF4b4iK/3WuBUVUW1UkGr1UIUhtB1vTsawxiqRhW/dPZ/wh++9597nqPK/wcACfLc9r/Y+hdYdrqn6129ehXXrl3Dc88+i7m5ua7vh2EI3/fzxYq3KY5arNcVqRr287z/vggrREz6iYIsy5DEMSLeGlrE1HXopglD14e+t6SfCHAYaRjwO8IiOYlj6RTq+35ejMhTdNIwadTnoJDuYqP8juhC4PUTWZZhb3cXaZZhY2sLx48fx+bmJqURHlDoVSV6oqoqnLk5aDzNYAK5QGg0coHQbKJWq6FzIM4gRC/6sApxwzDyuQSui2aziXd//GN4vg+HhzPbnpcvbroO07bzm7Rh9F1Y5M4QRxtJEKhKbkVdLF4cZXS2+N1qpQLXdfN0w8EB0jTFfqOBZquFWq2GaqVSyk+LHaRs3+uTZtB5C6koMFUrle4bu6LgF0//Iv7synfRiltd56cjFxQpUjAw6IqOX+kRNdjZ2cHFixdx+vRpbJ08WfoeYwye58k8+SijlgcJy1EQ8zSKrXmlyvuOY8VJgiSKSmZFAOT7TjeMkdIGoniv+Hr1uo6MdyEkcYwkSXoKyTiOoWsa6vPz+eyMoQdn3SJqDFEgfSBY7si4s7MDt1rF+toa1tfXaariAw6JA2Igpm1DX16Gt78PmzEwvvuMowi+58HiIdSRRUJx1zRksbQsC5ubm7h69Sr2dnaw+fTT8D1P7uLiKIIXBKUbtmEY+eceu7lSOxvyHf5R1SbIXRqv2xhWVNmJqiioVqtwKxW0ms08kpJl2D84QLPZzKvBRZcCOq5D9Pf3EAi2bSPJMiRRlBdAVqtllz3GUDEq+KWzv4Q/+PEfdJ2Xzm8ZMfKF/WubX8Nxt7xI7O/v45WXX8bS0hI++9nPlr5XtEEGjxbZAyyepbCbQdRH7ShQ7BRrxeK+4nc0VYVhmjB1feTiQtFVMuis0ySRboedUQngsNNH13VEUQRN02CZZj49dRCidmLc54yLAoUdOh6yLEOz2cRBo4FjKytY5tGCkcQJcV9D4oAYiqrrqC4vI2g0wHjNQZt3GiiqCss0kQGTiQTxn+i+kSqKAsuycPLUKbz77rv47Gc/i6XlZaRJgjCOEQYB4ig6HDRUEAqqpkHnBVqGrnflncXxip0Oo7ZzjYJ4XAVAiu7d4qhpDlVRUK/XUa3VDkVCmmJvbw9hHMN1HFRdF2pnF0m/glBFQdV10eJ57DYXCEWfCUVR8M3T/wp/duXP0IyapV83eOQgQQJN0btqDZrNJl566SVUazV88fnnS9GcMAzz9ww/j1qvyEXX6U73ehSnV8oOCv7fAOTQsJjXDwhURcmL+0xz/FRHH7IsQ8wjA2KqYhGVT1jUDQO6ph3WlXBTsTiOc8vyAc9Zp83xiCdeEgXAYVvm7u4uMgCbJ0/KNkVKI3w6oFeZGBm7XodumsDeHjLG4PMuA1VVYeh6XomPMUUCR5qvdNwwdcPA+vo6Ll++jJ9euoSnn34amq7D1XW4jpPv9uIYEW+3TLjhD0O+8yoKA4OLhWJImHUenzN1xwO/ll7zEgAcpgGA7h7+HhRFQpOnGLIgQKPRQJqmWFxcRMUtFxKK3V9XFEHJJzi2uAOh8FkQAoFlGVzdwS+f/RZ+993fLz3mYeQgwc9u/gzWKoceFJ7n4aWXXoJt23jhhRfyiYHg3Qi+jzRJ8jZFXUfFdXuG5adNH+SXp8jHKr6fFOTRA2Fl7AdBqUBUAUqCYJQ0R68BWMWfEe/JmFsed56nrmm5GBgwAyPmwkDhEYxOoVtMmfVyNOxLD1Eg8DwPu7u7cKtVHOdjlo8N8KcgHjxIHBBjods26seOQVVVZHyoUou33um6fth/jwlEQo8brMFvmlsnTuDy++/jsUcfLU0aFGFXKRT4jVS0e4VxDMYHEKW6nnvb+36+S+M7NI1/FOk850kXrb4OhoV/i8LOfjlwdPzs3NwcatUqbt+5g0ajgSSOsbuzg0ajgbl6vWR1LdIbiqLkYWZRuMgLFIXFsoggFPPjv3DqX+JPLv9XNKIGAECDXliEGH7loV+RxwnDEC+99BIURcGXvvQlWJYFxhiCMMxnI/BzsW07r6jvTPlMmT4oRgg63SkZY3k+ny/UostAiDYhGo0BM0S6jtPj31mWj7JOC8fqROOCVYiBUQoZgzBEFMcybVY6fsFXYmQGiAI/CLC3v484inBsZQXHVlawtbVVek8Rnw5IHBBjo+k6aseOAaqKve1tRFGULy61mtx9TiUSisfi6YGNzU28f/WqFAi9KDoAxtwtTjjKiT7/KEmgIo9IZFmGqONYGp90p2matJEGUDr/bAShIP3mR2y/BMrRC+HI1+8oqqahXq/DMk254CVxjJ2dHewfHKAiHCcLoqcUUuete9VqVbYStlstVEQXiqLA0R388kP/M/6/H/0uAMDgt4sECX7mxItYr6wBAKIowksvvYQkjvHVF1/MO064p4JYhHTDgOM4XSJsUlFQivR0mkAVduxCFJR+V1Ggqyp00xxeWKiUHQsFRSGQcSHaK3qg8PSEeG+OayccRVFerJgksG07FzAd5zcyA0RBGIZoNBp5sa+mYX1zExsnTmBtbY3SCJ9S6FUnJkJVVcwdOwZN17H90UeI4jiPINRqpRbHaUWCmFkfmyZOnDiBS5cu4ZGHHhrau24UdlkxzylHvDo+40ZEURznOVrG5KKVpmlJMKiqCk1V5ehjrZALlueI3imIQS1ywxA7bXmMPguPoqqo1etYNk0c8LbHlBvkNBoNWLaNaqUCx7YPUxkFh0cxArnVbiMVEYRKRV7PL5z6l/jP7/0XtJN2qRjxf3no2/L5euX734fvefjyV74Ch0/zjLnTIZR8RofZw1mzM5UzynNSSj3xz2LxFEKg14RLITJ1TZN+Bj2LREUxn/xPHnUQQiDLkCZJ3526qqpSYA7qoBmVUEQNuLAYp3VRMoIoCMIQLMtgGAYeffJJnDp1qudrRnx6IHFATEV1YQGqpuHWtWt5/poLhK7iQkBOfizuFEfZMVqWhTAMZefCB1ev4qGzZ0e2TBZCwQUQxXHeotZhNytu+vykSueVZVlp1K6iKLlQEIKBLwhdKKO7z41CZ35bKdQIqJqGhYUFzM3Pw+NWzFEUIQwChEEARdNQcRxUKhV50xcpAk3T8hQDL3Zse54cjWxrFr668RX8t6t/KYsR16tr2KidQJqm+P6rr+Jgfx9f/vKX4TgOms2mfGzTNEuTNztTM6M8N70iBDJsz6NBvYyldCEG+Efp9QRKr7/4XsqNhaIoQsIjA/3mMyjIC3XFay8+ZlnQmvBrC8IQruP0TMcM+n02SBREERoHBwiDIH+fZhkWFhfx3PPPUycCAQBQ2KRbG4Io4DWb+OjqVaRpCkPXUalWhy7aDJDGMMNuemEYot1u4+2338bu/j6+8Y1vyHCvzC2Pec6RqE1IktKwGoHISXeeW79z1ToWC6YoSJLkyNolwyhCu9WCYZqo9hjJHMUx2q0WPM/Ld9P8vA3DQKVSyWcXaJosXEy4uBM/4zoOFABhFuN//d7/hnbTQ0Wv4v/6yv+JNXcVr732Gj766CN88YtfRKVaRcIHd6mqCtd1y5MmMfrr0ymCsjRFzF+jJEm6dvwK8udeioEBi7TwhQiiCKkQAEmChAuDfudTFAAiejQrIcDArWo7CnKbrRba7TaSNIVl26h12F73OdmBdSudogAAHNvG+uYmHn3iCUohEBISB8TM8Npt3LxyBVmWweQL0DgoA4QCYwzNZhO7e3t49dVX8bnPfQ6bJ070faxJ3tTFYjLx717n0VmINtBdL03lQiJ2loqqQuWL6DRFeDFP5ei6jlqt1jWlUJ4HYwh8H612G0GHdbLDowmObYMh9+5v8eFbpmnC5T31d7a38b2//x6+8LnPY2NjA6+/8QauX72K888+i7n5ebn4O6LgMH9yRr8YUXHPmBQBIjrQ+fyK57IUGSh8X9hUCwOfjP87yzJkaYo0yxBGEXrRmUZS+0WFZoAyYCFPkgQHzSYaBwdwKhVU+qRmxOOIa+33XgqjKB+/zh0pgfy1r9VqWNvYwNaZMzMTO8SDAclEYma4lQrWz5zBRx98gCiKoKgqHL77HIVBaQdR6T5Xq2F5eRk/ee89nDhxou9ji8VKhFdlMd4AxK5Q3ISLhYxCNHTuLcXCJYr4Ct9AhsP6BgBdk/PkufIFCcgXp86PfgJC2jV3VOd3LsoqY3BdF67rIkvTwx0pLxr0PS9vD3VdVPhHu91GxLsMHMdBliRQkC/K77z9Nq5dvYonnngiFwa84NC27ZF3nuJ5yXgeX3ykfCRw53WKiIAQWCICkCSJLDoVjzGs60OkIUTRqc7FgHi+j4quV3DAOYouD4N31PSaS1EUBeK/OxGiIPB92YliOw7m5+ag6zrWNjawtrk5zWURDygkDoiZ4lYqWDl5ErevXUMYhkjTFK7rHprsDAl7ApA3vKL1rKLkfvKhYeDUyZP4p9dfx507d7AywMJV3CqVHmHokfLdYlEqLHid7Wop39l2jk1WlHw6n/AwKB5PXh9fyFiWIRnS1SCeA01V88gDfz7F8dM0lQWAndGX4iAeTdcxNz+Pubk5BGEIr9XKPQjSFM1GIy9itCyYlgXGW1UVVZU78Rs3buDatWt45JFHsLqaexy4rltqLwUgOwSyNM3bBjs+F5Fjngu7X1VEV0ShKBcC8v0j8ul9ni9VRGgKAkvjn9Mhu+xZUEoVFK5xFBL+vAdBkDtI2vbhuRbbF4ekD5rNJgLPy5/PgigweFHj1pkzWCLvAqIPlFYgjgTP83Dnww8R+T4U5KJBDPqZ1GBI4eN3m80mfvDDH8IwDHzly1+e+BxlZGHiR8gpVsuLSIMg6nDeEygAFE07dDLsaMXj/xh4bowxNBoNQFG6ikBLXSEdoqH0PVUFGIPn+/DabSRxLG130zSFqmlwLAstz8OFCxcAAFtbW/nAHb6jFeIoS9N84S2kAsQuXn4uXGdx4Zafi/n8PiJg0MJf/Fo/oj6zC6al9HxPcVtt+z729/aQZhmq1SoqfJbGKGZZYRii2Woh8Dz5mktRYBi554Wm4czDD6M+Pz/xORIPPhQ5II4E13WxefYsbt+4gfb+PlqtFmzb7plmGFUsiAFDhmni1KlTePPiRezs7WFpYWGic1Q6Pkt3ucJNeBQBoaoqTNNEMSMsUhG6rudGTHy+AuNhcwbIaXeTIM5VhJYzETkoiozSL7CSTXTXnoAx2JaFWNMQBgF8PhY4ShJsxzG2d3bAAMzVaqjVagh8H5quH45/FiKHo6Cw+Kuq9IwQEQEZzeDnJaMdfJcr0yoF4SBrNqbY8Yt6hEkfoev3ekUwphAGaZahxaefWrYt5070i5JkWSanW/phCCQJGH9uHcfBXK0mozqMMTiVCk6fPQubTI2IIVDkgDhSGGPY393F7u3bSOMYmqahUrDqnegxATQODvDSK6+AMYavfe1rMI6oaKx4zH7CYdAAJ8YYoo7itwy8voILhmLBXPGzSDsMot1qIWUMruNA1/WuxV9O1+s4J/G5M0pRTIP4QYBWs4lbH32E3f19AMDK6irm6nUYhgHLsmDZNhzTzBdyXYfGd/9iJy8EQ3HxL/679L17QMZYVxtrJ70EwDQL/iiIR9/b25PRoMWlJVg9ihCzLIMfBAh8X/oTFN+bdocoAGOAqmJ1YwOr6+tUeEiMBIkD4p4QhiFu37iBsNkEA/J++x5FVp2tif1uY2mW4ebNm3j11VdxYnMTz54/LyvklcKicy8o/gHJQkh+s86QpxYmWVzETl+Me5aige9+kWVotVpIkgSWbeeFlB3XLiIL0phKpBTE88SYNAUShX4Zd1yM4xhXrlzB7du3Ua/XUavVcpc+04RpmoePraowLUt2KliF+oPi7YWNOGzqKIl56ufjEACddEal2u02dnZ3kaQp5ufmSn4DaZrm0QEuCISgEh4Xjm3DdRzYjlN+jhmDU63i1JkzFC0gxoLEAXHPSNMUO9vbaO7sIOUT5lzX7b/rxuAFPkkS/Ojdd/Gjd9/F5557DpuFqmtRzCh3qrO7jLFggKxFKKUnCjn14mfRXdFZi9CzZZIxeEGAKAhg2zZsxyn/jjheR8i/8zGEUIiTBIHv510ZaSo9JT7zyCNYX1+HoqqIguDwehSlq6ZCuE0KkeBwy98ShesrRl5k6Lx4/f3Ov/A1hbFyR0qPnxVnGERRV4HqvUKIxs6BUEDuSXHn7l3EcQy3UsH83FzeTeL78IMAURRBxtoYg6brcBwHjuvC4kKtE1VVscKjBQQxLiQOiHsKYwytVgu7d+4gbLehKko+EXCEtIB4oxZvg2EY4pXvfx+7e3t48cUXURvg7lZqkZziGsYl5X4HR/GnFoYhfM+DaVkjD8fptGVO4hgBX/ShKAiDAP/81lvwwxBf+PznYVsWGIBqrYY0SdBqtXJnRVWF47pIkyQPc/MR2qLTRAgTXddhWhZs/tGrLe9ekPKRyfcSKVoKtRW9zuvOnTsIowgMeb1OGIZIeDuwEE06FwSu43R1h5QPyuDW6zh55gxs2z6aCyMeeEgcEB8LYRhid3sb3v4+0iSBy3dAo1K80e7u7+Mf/8f/gFup4EsvvDC0116MMe6cAXFUiDRAMkUBYj9i7oKo6zqqtVrfn1N4V0LxnGIuCooFeoHv44033oCu6/jiCy/Atix4ngeVT74E8khIu9WSEYMqn8gJQE7qDMMwnyYYRaVx0YxxTwQhFmz7yEyGijDGEA+wQ57ZcXAYsRnUailI0hS37tzJXQuj6FAo8zSPaZq5IKhUoI/wPKmqirUTJ3B8bW0GV0N8miFxQHxsZFmG/f19NPf2ELbbMHUdjuuWFu1RYIzhg6tX8cPXXsPDDz+MRz/zmbFtYGV7X7HNb4akWYa0YIg0s8fl/gSKqmJubg4ADkPzPSrpRYGk8KAQXQWmZWF7extvvP465ufn8fwXvwjLNOUQJcuyYBTEW5ZlaHue9DPo2s1yQcAYQxAE0tQnCsOu4ki9w/pYjDM2ZjC4SD5PInoz4+dfRrN4/r8XGRdicRwfOj9y86a250l761qtBovPo7B5hGCc66/W69g8dSpPLxHElFArI/GxoaoqFhcXYZom9vf2EDSbaDYasG0bprDgHQFFUXDy5EncvXsXly5fxly9jpXV1bzgkeevmaJAKexgOxFh32KOX5lhZEFBXgMhhuHMAtHfD0UpLVKSYmcFtwwOwxAZLwwUtQGmYeCnly7hnbffxubWFp49f16aD4nFvygMGCDHPXvtNqI4hud5yBg7DGMXRnc7jgOHL1hpmuZCIQgQhiHiOEbKfQfCMCycOuMPk7dBFoWD+DBGHIEsTI+mFQadxbLFNkwxpyGOY8RCAHAxIA2qOCKy4HkeAt+HwhiWl5awvLTUXVA4AqqqYn1rC8dWVqa6PoIoQpED4hNBFEXY29uD324j9jwoWQaTF7WNGklIkgR/+7d/iyTLcP78eVRctyvn2pX3FTu+ESIGvSZJjnobFy2LAHI3xGF/djz1Ic95QDX9wcEBsixDvV7v2mkWRYG4TlVVcwFmmmBZhjcuXMC1a9fw2GOP4dFHH5XXJ6ISUBTMzc31nDUBQLbVAZBFpj1FWI/zT9M0n4JYsKiW0xb58Tp/q2SkJMYj8ymZQkSIqAND/r4Qg6VGpTh0SxYQ8msq7v6LH9KvAd2dCGLcshA0URQh4NGbarWK+YWFsQWooihYWl7G6sYGjEE1CAQxASQOiE8MjDF4nodWq4Wg1ULkeWBZJkf/juKNcHBwgL/9u7/D6toazvKCrJL97IBjF9v7Sja13T8s/1kSDIOKHUX7IX88sfCJ3xslP92PZqOBhC8ywrEwiWNEcZzPuODnpXFRYPDq9igM8eqrr2J3bw/nz5/H1tbW4XUh9/f3PQ+GaaJSqcgogrykwrVGUYS25wHIxyVXKpWSbXPnczEKjDEZfi8KhphP05RzFMR58Oex2KEhagxESqffUC/xGMIdcpA7pVj0xdUVnwdd16EbBgxdh6brcly4eO+maYpWuw3f9xEGAUzbxsL8/FjpA0VVsbi8jJX19VLbKEHMEhIHxCcOkadut9toHxwg8n1kaQpT12HZdu4NP4BLly/j4sWLeOqppzC/sJCPKHbde9ZjL3P+KIgHdEcM0hnlv8WQJF3XoWpaSRCItjcRKRC0mk28/MorSOIYz3/xi1haWup63FarhSSO5eyETnFQas0EECcJ2ryTQVWU3Oyq87Uq+ClM6ytQjDQUQ/hFQSGjIEIcoNAqCkh/h+I1DUNOhOQiQOciYFidS8w7PZI4hh8EMC0LtUplcOdBAVVVsUCigLhHkDggPtFEUYRWq4XG3h5i3wdLErnYGYbR82bOAHz/lVdwd2cHn3vuORiGAVVVUalWxy52nCWigh2A7MnvNXdh5MfLMkRxjGazCc/zYJomLJ5G0VU137WaZteidffuXfzgBz+AZVl44YUXukdrKwqQZdg/OAAYQ42nKzrFgbim4jOacWMmIXyqlQr0fq2LnV4GM0A8p6xQ/ClSC+g413tpyCQiK0L4iojCsLHmjDGomoal5WWsbGz0HdtMELOGxAFxX5AkCdrtNva3t/NIQpJAU1U5QbBz0Q/DEH/z3/87HMfBk08+mReuKQpcx/nY+uzBGNKOPzexg84KFrhFF0N07GxFDYGofFeQG+iEQQDLtlGv12GaZs8wdRzH+NGPfoTLly7h2PHj+MIXvtB3sUmSBK1ms9QF0Usc9L5MhhYfCQ0ABh8H3TfN0P0Avb/c47+FeZI4bq/Cw2K9x72GMZanEMIw7xSJYynW6vV6f4HCa0MWFhexsrFBkQLinkPigLivyLIM7XYbe9vbCFstZFkGTVFgWla+KBYWoJ3dXbz88sswDANPP/00TNNEmqaHzowfQxShmCcXCIHQWdVe/J0oihBFkfwZ8XO6rucCIYpgGAbqc3NdoXIAuHnzJi5euIA4SfDE44/j7NmzPRdrkSoIeJGhaZpw+e52VHEgringxkgCYa9cKlYs3n4K4oghj65k/GeGjViWsyh63M4yPsPiXhPHMTyeEkt4jYSu60izDLVara9vgappuShYX5eRIIK415A4IO5LxI5s9+5d+M1mXnjGGDReFS565f0gwMsvv4wwDPHcc8/Btm0Z8nZdt+d8hyM9b6DvLpYBMhwuBIEovhMLI2MMpkgX8EK3NMvQODiAwrsKZIW9oiDwPFx8803cvHkTa+vrePrpp1HhTopdi20hF99sNJCmaT5q2zTlbIfOQs3OuoNO0iSBxy2ZGV8cHdfNJyxO+Px1IsZF90sx9euyOCoYy0dgR7w1U0QuVC56qtVqd2SHt6XOLS5ilUQB8QmAxAFx3+P7Pg52dtBqNJDyCYii6E1FXoz25sWL2NvfxzPPPIPFxUXEfH6AwaMI96oWobjIilHL0iCJ9/snfKep4HABF/UD/Xr7D/b3kTGW70h1HVmW4f3338fb77wDQ9Pw9Llz+XyEIbtvRVGQZhmaBweyhVEOZJpwkWWMIYwiBL4vd/aW6CKZ4PGEIEl5geegW9i9TikIzwcxUVNVVaTcOltVVdnFId5vCnIPiYXlZSwuL5MoID4xkDggHhiyLEPo+2gdHKDdbCIKgsMRxFmGd3/0I3x05w4eOnsWGxsbec6e36grfId8FDCeMsiEUQ7vi8/4LIMevwAoSt4CN6LZT6vVyof2uC6CIMCFN97A7t4eTp8+jSeffHJgnUVnyF6kFEQLI4CpxIEgzTL4nifnG6iaJkdNj4uYUNmZoijOMLiXUYOMt+HGcSyHT6mqKo2dRPGhnGSpaajPz2NxaQn1+fl7co4EMQ4kDogHljgM0drfh9dqIeZGOz/96U9x5f33sb6+jocfekgOHFIUJbeudZxyLrhHm1uXGVKHv0FRCKRZ1tOZT6Q2hFOhqqrQNA2aqkpXwGIlf5amAwv6fN+H53m4du0aPvjgA9RqNTzzzDM9WxQ76UwNNBoNZGkKp1KR8y5GEgfCiXLI8Tp318OmcxbJhkQKyqdT8JYQdQsj/eboCDtqz/elN4LDU1dRFCHLspLXhlOpYGFpCQtLSxOJIoK4V5A4IB54sixD6Hm5sVIQ4MqVK7h48SKWlpbw+OOPI0mSfCIeL1wzDKOruFFSdCrs51rY4+tFASCsiTVVlTbDQxGFen2iDTdu3sSbFy4gjCI8/thjePiRR0ayFu6k0xVR1jpg+shBkYy39IVBIH0RbMfpO3xrHFEgSNO0p7tisRMEKNRO8AmKPV9jYcONQzEV8eFSaZYBWZYPlLJthEGAiEeGXNeFywXB4vIynBEnZxLExw2JA+JTRRLHCFotXL1yBa+8/DJsx8EXn38euq6j3W7nYWHkC4thGLBME5quHy5Mhba54uciKt/9CyEg5h8UYYxNXkHP7Xwzvmt96623cP3aNcwvLOAzjz6K9dXV0dsGO+iVUgBmLw4ECS9YTLn9sME9LHRdn0gQCI4qpSAHV/FplkAubIQ5l2jhzBjD6toaVtfXUZ+fn0ioEcTHCYkD4lPLjevX8d+++12ojOHcuXNYW1tDlmUIgiAfNQyU7JvVMSxuhyFb7yYphGQMOzs7uHLlCj788EPoui7dILM0Ra1a7W88NASRUnA7ajDGEQfSinrEYzLGEIZh3vbHGJBlUDUtn61hGBMJnVm3LzLGEEeRHHHNeGeMZdswDSOfXxEE0AwD9YUFnD59Gs4QgyOC+CRD4oD4VNNqtfBf//RP8dH161heXMRnHn4YJ0+eBIAjFwnjRg/iMMTV69fx/pUraDWbcCsVnD59GidPnoRlmmjx8cqObU9U9d4vpQBMFjkYxZtAkAHIkiRPNfCOExHGNy0LVg+nx76Pi9lFDQaJAkPXEYYhwiiC47qYX1xEfWEBi4uLMxs1TRAfFyQOCALAjRs38MpLL+HKpUuouy4efvhhPHT2LHRd7ykSTO4zMC1Zmg6OHjCG7Z0dvP/++7jx4YdgjGF9fR2nT5/GsWPHuroMfN+HYRioViqlVIaYyCjo5U9w584dXHn/fXz44Yd4/8oVXL5yBa1WCwDwpRdewG/8xm8MvZ7/5z/8B7z00kt49tln8W//zb8pXAaTx5Qf/QYhcVvoYuieZRk0XR8pmjCLqEHGBzyFYYgky6DwVkTLtqFpmhw3Xa3XMbe0hGq1ikqlAmeCkcsE8UmEymUJAsDGxga+/a//NXZ3d/Hqq6/i9TffxDvvvouzp07h4YcfRq1WQxAEiOMYcZIgiiKoqiqFwqTRBFXTeg5g6hUlePzxx7F18iTsPla6Ymed9ihaFIvpd77zHfzJn/wJ/u9//+9xbHm59DO/9Vu/BUVV4bVaiMZwQyzy7Pnz+Md//Ef881tvSavgYnth6Zz6LKIKt8W2+LCniO/OsyxD4Hnw0T+aIPwPJlmg5TTLKEIUx1CQF0JqigLLdaGpKvwgAGMMtbk5zC0toVKpoFqtkr0x8cBB4oAgCiwuLuLnf/7n8eKLL+K1117DP/3wh/jJ5cs4ubWFRx56CHNzc4jiGHEUyVx5EATQ+a7WNIze3gUDUBVF5tq3d3e7ogRPP/10V5SgF5qmQVEU2UbZGdoWQUJV07q6KaIogqKqcte+tLiItY0NvP3WW+KXDx9D1BOww7HGYvF/6qmnoPNw+ztvv41z58717+oYAeF26ThOKZoQRxGiIMijCaYJnXeDZAVr6VFJ0xRRGOath+I6efeBZZpQ+OusKArmFhcxt7gI13XliGyCeBAhcUAQPXBdF1/96lfxwgsv4M0338T3v/99XPngA6wfP465uTm4lQosy4LBFydFUeC12/AVBYZIO/TJkUdRhFaziVarhWaziWajgQP+3yzLRooS9EJRFOi6jpjvfh3Hkd8b1uMfBAF+5sUXsbW1hUceeQRzc3O4e/cu/u2/+3fiwUsdG51LvXhsy7Lw5BNP4OLFi3j9jTdw7tw52To4TQZzYDQhCADG8pHYyLtFhFhQVRWarkNVlFIqQqYuwhBJHCPjRlUApAV3mqaIPQ+mZWFpZQXVeh3VarW3/TFBPGCQOCCIARiGgeeeew7nz5/He++9h39+6y18uLOD5pUrSOM4X3y4m2HFceA4DmzHgW1ZqLguNF1HwA2KWs0mDlotJLx+AQAcx0GtWsXx48dx6tQpzM/PY2lpaeK8tWmaXeJAFAb2W5zF/Iaf/dmfRb1en7rt7pnz53Hx4kVceOMNsF/7NRllGHQO49AZTYiTJH8t2OFUxrhofiSOzyMjaZrmvhbscNiVbdtwq1XYtp17XHCvC50LvUqlkttsU0si8SmBxAFBjICqqnjsscfw2GOPAcgXnSAIsLe3h/39ffl5d3sbH965g3arBaQpNFWFaRioV6uo1mo4ffIk6nNzqPNdaNGNcZq+foHBi/WyLEMSx9B49GLQ44rJiZZlzWTxO3/+PH73d38X+/v7uHL5cj4BckbCoIiMJvBpm2KoVZpl+STEJMlrRHgaKBHFpJaFqutKQSDmUYhhXZqmlf5NUQLi0wiJA4KYAEVR4PBIwfr6etf30zTFzs4OoiiCpmmIwhBxECD0/TxywPPYma5DNww5hlnscqc5L0PXc6OeKII7pP0vjuN8xoSizKyobmF+HmfPnMHly5fxxhtv4OzZs/LchMXyNNfYSSZsm4WLJB9eFXNxtFCvw6lUYFgWNE2DYRhwuTgQiz91GBBEGRIHBHEEaJqG48ePA+C98nGc9/CHYd4GF4YIPQ9t3wdrt6FrmhyypPC5CnK54gvpqAuYZVly3PMwrwExGMg0zZmGzJ85fx6XL1/G62+8gW9/+9vy69KOeVyBUPxZIaKQOywmXOAkfKql5Tio1utwajXYti1rFSwuDgiCGA6JA4I4YhRFkS2PQB5VECIhDMO8Uj4IEPk+Qj6QSNU0qIoCTYS3uQWzKO5Tygc4/Ddj0HVddi3Ecdx32qQIuYuc+yx59tln8Z0//mN8eP067ty9i+PHjh2eLv+A6NIonHux5bEoHYRhVJqmSEXKIEnA+Nht23UxV6nA5hEBIQYMHpUhCGI8SBwQxD1G07R8IA8fwlOMKsRxnO+EoyiPLgQBvHYbiqLIyntN0/JIg6rmFfhi4mAhr28YBoIggOd50DUt/zm+GGd8WmTb88CAvF1P7Og7xyD3oTiASC7mPLTPAJw4cQLLx47h9u3beP311/GNb3yjp/FSrzSKcDgsffCogKKqeeFgpZK3jvICQtM0pSCgokGCmB4SBwTxMWMYBgzDQK1WkxX0YlcfRREOdnfR2t3NF3ZeXCcEgcqHOymKAk1RoIiIA/9amqZotFqoVatSAKiqCq/dhsGL8CzLkiONAcjFWvglAEBSdB0UuX1+/nJp72hxPH/+PP7qr/4Kb7z+Or7+9a+Xfi5jLHcy7PhI0zTvKOjRemnYNlY3NuByfwHxQWKAIGYPiQOC+AQhvApEqx4ALC8v5739IpoQx4jDEEmS5H7/WYY0jvPFlbf2ZWmKJE3hex4AIAwCmV5YWFiQ9QiWbct2vlK6ggsJKSiK0YTC+OKiqGB8WqT4+rmnn8b3vvc9fHD1Kra3t2FbljxfRVXziZWGAd0wYPCaC03XofAIiWkYMEwzn47JowKUIiCIewOJA4K4DxCCATwVAUDWFIgdd6+PIAiwv7ub1zmkKeI0RcIYMgCGZSHiIkEs7FlhlkGr1cL8/DzAIxT7+/v5gUUEgXdYiA/w1If499bp04iSBC3fx807d/DM+fPQeHeG6BIQo607P0TdBEEQHw8kDgjiPkXlff79ECmKpaUlNJvN3CaYMbx76RJWt7awsbkphUHnBwBkN27grffeg6qq2HroIaxubgKqmkcRCkKgJBAA+e9XXnkFP/3gA6iqii+/+CLm5uZKYoAgiE8uJA4I4gFFpCjm5+fzCADn6tWrWFhYwHLH4KUiwuTp1q1bAIAwjrF+4kSXABi0u//rv/5r7O3t4etf/zpWVlZmc1EEQdwTSBwQBNGF6I4QBYmMt0iOw1/8xV8AAH7xF39x5udHEMTRQmW+BEHMnAsXLuDatWsASBwQxP0IiQOCIGbOn//5nwMAnnnmGZw4ceJjPhuCIMaF0goEQQAAXnrpJVy6dEn+9/b2tvz3pUuX8Hu/93uln//VX/3Vvo/13e9+FwDwzW9+c6bnSBDEvYHEAUEQAIDf+Z3fwe///u/3/N7LL7+Ml19+ufS1fuLg+vXruHDhAgBKKRDE/QqJA4L4FPHiiy8CQKl7YdaIqMHm5iaeeeaZIzsOQRBHh8JmPWSdIIhPNT/3cz+Hv/mbv8Fv/uZv4rd/+7c/7tMhCGICqCCRIIiZ0Wg08Pd///cAKKVAEPczJA4IgpgZf/mXf4k4jlGv12UKgyCI+w+qOSAIYmb83d/9Hebm5vDNb35TDnoiCOL+g2oOCIIgCIIoQWkFgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKLE/w9l88NEAIl3NAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also show multiple Bloch spheres side by side - simply set the `nrows` and `ncols` in the call to `cudaq.show()` accordingly. Make sure to have more spaces than spheres in your list, else it will throw an error! Let us show two spheres in a row:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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rK3j7Jz9BNpPBY48/jiCKYDsOFFVFKp1GOpNBYWAAQ0NDSKfTn9pn86Cj7wzcZbiui3q9DqteR61aRa1UguU4oFEEg6fbPNcFIQSGYciavtbKDeA1dU1VZdRNOR8g3gYkCIU0iuCFIXzPY6/hzgPltX3bdeF6HgilKJbL2NrYQBAEOLh/Pw4cPAiDL3jDMBCGIRqWBVVVkeNTxkgsE6FyUiONOQcCIkJpB0opLpw/j/mFBTx+9iwOT0/Dtm3UGg1YlgXNMJDJZpHKZDAiyhR9p6CPhwBRFLHsYb0Ou9FAZWsLtWoVQRgyW6Hr8HwfURhCIQSpVAqmaUpugIi+BRcn3lUkNnVf2A5uPyiloPz/Pd+H53nSzgBsg/bDEBYnM7ueh7X1dUYMTCRw/PhxFPJ56JoGwzBg6Dpq9TrCMEQ6lWJaJTESdNx2tAY04jx3bEeUAoqCcrmMt954A0RV8eVXXoGiqqzryrLghyGSqRQrI+TzGB8fR4bzl/roHX1n4C7BdV1UKhUUi0UUNzZQr1ah8rYdwZ5VFAVBEMDQNORyOWRzuR2pMeE9B8JTjv2q1aOWHIAw3F7kUQQ/COB5HhzPY61Fvg/f92E7DjY2NrC6uQkQgpHBQYyNjSGXySCbTjOiIU8t1up1ADzd16EuR3gGIL7IVU4oAtD+7yjFBx9+iLm5ObzyyisYKBRAwUiQ9Xod1XodIAQDg4PI5HIYGxtDNpvtEw77eCARRREajQZKpRK2NjdR3tqC53kwdR2mYTSl1ikAQ9cxMjS0Yx6A0BChUcQ4SLGfAdt8HmB7XQqtkTBmO4Td8D0Pjusi8H24noeGZWHx1i04rotkIoHJ/fuRzWSQzWSQTCTYORICy7Lg+T5Mw0Cyg+6IKBEIcSItVuqkwA6CtIBlWXj1tdcwUCjISYmipbFaq8GybaQyGeQKBQwODmJ4eBjJZPJOP5qHDn1n4BPCdV0Ui0VsbW2hVCzCtSyYqgrTNKFpmmzzSyQScDlTV1EU5GLp99ZHX2oJtIACrF+Yk2xEfbC1jCCMgIgMXN+H73lYXlnB4uIi0skkaraNA5OTSPP6PY0imalIJ5NMGATMGVB5diKMImk8pJBRm8eHcmdGkCB1TWM1PeG8RBFee+01hGEovXyFOxagFJV6nbU0ZrPIFwrIZrMo8H/7mYI+HgREUYR6vS5tR6VcBjiLX+f1e2E3DE1DtV4HpZTxfmKlAQHRHRCGYVt9EdGOHOcVBGHYbHtajheGIRzPg21ZuHrtGlzHQRhFOLB/PxLJJAxNQ8RT+slEAulkEiAEjutC58Jl4r3l+wuyMbAjqwhsC6JpmgZd16GrKrNP/LWrq6t484038IXHHsPhw4cBQJIlvSBAqVSC6/soDA8jwzMF+Xy+7xT0gL4zcIcIwxCbm5tYXV1FtVpF5PvQACQMA/l8HulUSvbrAoBl27AtCxRALp+H1mVTE1mBpnQdj/bjwh0AZJ1NJQQKZxELYlDg+3C5nsCNmzdx+/ZtTB04gJHRUbzzzjt44uxZqKqKIIpYtoEQFlWEIdwggKnrGB0eRjab3bFwQ+6wCLIjjSJZnoh4aYK2ODSapkHVNBiaJr38/QcO4IkWqdJ433IYRcjk88jmcshy0lCfaNjH/QzHcXD79m0Ui0XYtg2FZwvTqRSy2SzSqZTMhNEoQrlaRRRFrH03nd6ZTeQQTrhAFEXwfR8BLx8Kol4chJOIxb8iKvd4AOG6Lj744AMAwJEjRzBz8SLOPf00NE2D7TiMo0AIojBEBEgbkEmnMTk+zsqJsQynCCoCLpseDzBEEBS22DjCdRJUTYOh65i5eBGzN27glVdeQT6f33EfbNtGqVZDkvMJ8vk8MpkMCoVCP8PYBX0K5h1ga2sLi4uLqFarQBDA1DSWnspmkctktltnhBhQGMKxbVBCmH5AF0dAEIji0T+wTdgTi1nXdRh8gcR7byml8H0fjm1LgZGrH3+MjfV1nP3CFzA0PIwK7yQQUUa8Bci1bZYmDAI0bBu3l5cZ4z+f3ybn8Dqgqiigmta2/idqkB43QhF3FoIggMtfd+LECVy+cgUjQ0M4cPDg9t+DlR4GBwZg2TZq5TJrqcrnJdM5n8/3GcR93FfwfR+Li4tYW1tD4HlQoggpXUd2cBD5XK5JzltsnnXbBo0i1tefSrV1BChvLxQpfbHOolgULta4qiiSIKhzHROxWUecmOd6HgiAeq2GmZkZpNJpPHn2rOwUyIpuoEQClHcnBL4Py3HgEYJ6o4FiqQTP85DPZpHLZpvIhIIfFWjajjIoKIXlOAi4DQr4zATP8wDPgw0262BtdRU/eecdvPzyyzB1vSkbkkwmoRsGavU6iuvrqJbLGBkbg+d5zNlKp/stiW3Qt6Z7QKPRwPXr11GpVECCABohyOdyGCwUkBTRKl90JLbBNnj93dB1mKa547gUzKMWm6doKRRQFGWbZChS7uJvY153GARoWJZMxYVhiJmLF1GtVPDcc89haGgIFicGEkJgmCZSqRQajYZc1KNjY6BRhM1iEdVqlUUbvo/V9XXomoZCPo9si8OD2LUCLNrQVBVKrM0xDAJ4sdJGFEUYGxvDxsYGzn/4IRLJJNKZDOuTVlWAECiA7HOu1GoobWygUixi34ED8DwPuVyuzx7u477A0tIS5ubmEPk+SBQhqWkoDA+jEHdqhe3gHQGu68LjQmK5lomAwHYGTTjefhDsiKpVvuEb3HYoLZGxOKLjurBsWzL5i8UiZj76CCPDw3j2uefQsCz5N4qiIJvNMsl0TjrMZDIYGh6GbVlY29iA47pQCUGtXkelWkUmnUaBOzzCsimc4Bw/FwrA4NlD4cQEQYCACxYFQQAC4JHTp/Heu+/igw8+wMlHHmEcCz5cDZRCU1XkcznUGg3YloWF2VnkBwYwvm8fbNvutyO2Qf9u9Ijl5WXMzs4i8jwolGKgUMDw4CCSyaRkzzdxAPj3juvK36dTKenFE7BygMdTcfHNX+U1dF3ToInNsaWlUJQHhO6A4zhyMYsa2nsXLsD3fbz80ksoFApyQYtFQABJbqzX6wijCLV6HVmeWhMM5ZCT+8IowkaxiK1SCflcDvlsFpqmgfJUID8xANhBVlI1DQlVBXj0EwYB/CDAo1/4An7wwx9icXER00eOwHFdOWfB4I6EaRgYLBRQrlQQBAEW5+aQzWQwum8fBoeGkM/n+55+H/ckPM/Dxx9/jNLWFojvI2EYGB4dxUA+zwhzwpmPtRYL2I4DAEhyETLw11FK4fPAwY/r/YPJiau6zurtPPIOW4jIcYScwCi6FnRVxdr6Oj766CMcPHAATz75pOxKioPwzKLFZYUt20ZEKVKpFAYKBQRhCJ2XA8Xv6/U6krxdOZ1KQQFraRRdSfHSqLge0Umh6zqSYNlRPwiQSCRw5PBhXL9xAwenphAYBizHgWEYMHUdmq5DAZBLp6EqCizLQq1SgVWtYnBkBL7nYXhkZGf740OMvjOwCzzPw+zsLNbX1qAEAdLJJA7s34+kacrWHCW+kFsWtOM4oAASiQQUQhBGERsgImp4EH9G5IOs8B7fphRaSypeRA/xxQyxcAwDP3r9dQRhiJdeeonNGQdkyUFVlG1NAjDnI5vLoVavIwoC1Op1eb4EwMDAAAq5nGT7B2GIUrWKcrWKHHccZC2OEFmbbO2GiBs74WiIRT138yaOHD0q645BEMCybXZPuEOUz2ZRqdWgKAoaloWlhQXUazVMTE5iaGioTy7s457C5uYmrl+/Dt+yoAIYGR7G2NjYNvk2ijo+syJDGFGKBFcP9XkGQAiJCQiyXcIwZMeRROsajH3vcL0TkQ1IpVLY2NjAhx98gKPHj+OxL3wBFIDveYyUzIXG5NH534j1aLsua0FWFKaoahjIZrNwHQeVWg22bcNzXaysrcHg3KpUMsl4Ctw2BJwA2eq8iP8nfEaLYRg4deoU5ubmsLK8jOkjRxBxWXbXdaFyEreh64xjFAtSSsUirEYDjm1jfN++PgeJo+8MdMHW1hbmbt6EVa1CAzA4NITJiQlZ22pl9LbC5Up+QqmvVq83efIAq/0L5a/4Zkm4GqHoCIiwc4HYPBsgkOYKg2+88QZs28ZLX/qS7LcNYkxeTZCThOgIP79MKsUyAFwwSbQxEgBUUZDN5ZDJ5dCo11Gt1eDzGQilSgXpdBoDhQIMXUfIz1tcT9QhMhG/P3H8OG7OzmJ9dRVHjx2Dy2ufrYvbMAyk02nUGw1onKtQ2tyEVa/D2r8f+w8c6BOE+vjcEQQBFhYWsLK8jMh1kdB1HOCteEIwSKytdqC8bk7BMndiDcQ3eYUQZjdE5pBDtBfSKAIUZTuijwv+RBHrz/d9ACwYyKTTKG5t4d133sHBqSk89uijTdcjyoji/OLdRCYPjCzbhtVoyDJpxN9PNwyMDA+zmSm1Guo8eNnY2ICiKMjncsjlctJOSP4Tv5Z25RGAlV2PHT2K6zdu4PSZM6y04nnw+Gh3y7JgE8KyJLqOJGWjlyOeWbm9sIB6vY6p6em2RMSHDX1noA08z8PS0hK2NjZgV6tI6jrGx8dZOhrb6at27X9x2LYNx3F21NS1mEhHp8hATB0LeTkg7pFHYcjU+/hi1jQNmXQaCiF49733sFUs4sUXXkCOP+Ci5QhgWYF2KT+AGYV0JoNarSZnC6R4S450fChFJpNBJpOBZduoVKtSMa1WrSKRSmEwn4fOST1yYQseRRskEwlMHzqE2dlZHD12TLZj+twpEIvbtm3WdqQo8HhGBoTA9zwszM7Cqtdx/JFH+g5BH58bSqUSlpaWUN3aAgkCDORymJyYgGEYiCiFSohUAmyFKP0FYQjHcWA7DlLJJPyYjRDlM13XtxVBW8S/FEIQxmxGvI3P55oBYjNPJZNIJBKoVqt48623MDw8LEsDABs1HMRsxw7w1yW4sqnrOHBsG7qub2/uvByiaRoGBwaQz+WYU1CvIwgCbJRK2CiVMJjLIc35SNJaxrlJbXD02DFcn53FzdlZPHLypAxGvCCAy0u0goslSrSKaF9UVZS3tmDVajhy4gRGHvLpqn1noAWVSgW3b9+GW6/DrdeRz+UwygVwENtEWx/N+KhPSimqtRqK5TKiKEI2k2n25LlAR7eeTpGW04CmckK7xWyaJgghmJmZwe1bt/DMM89geHi46XiiRKBpGvyWNGMcGo8SqrUaG0/MjZcUAokZnlQyiVQyCcu2USyVYPGMgmVZGCgUkMtmd3AJwDdwyssg4p4dO34cN+fmMD8/jyNHjgCAdJgESUp0WYjWS8/zoHPDGIQhNtbWYFsWHjl1Csm+AlkfnyGiKMLi4iIqpRLsWg0qgMGREYwMD0PXNERRBLGVtpb8mnhEQYDNzU1YjgOdBwtCd0DT9WZBnta1JcDbBCmw3dfPFQ49zwM4wTedTkNVVVi2jR+/8QYymQyee/bZbZGjWGszgLZOdjxmTyWTUqnQsiymQNjy2ogfZ6BQQCGfR7laRalUghcEWC8WkWg0dkgZx4MKSbLkv0uYJqanp3FzdhbHjx6FyjuskqqKhGmy7iXuDMRF2VRCEBAmB+95Hq5cvAhrehoHpqYe2nLjw3nVHVAsFrEwNwe3WkXouhgZHsbw4CAyXJI3Dpkm4/8v0vm2baNUqaDCe4OTySRyfEpfMpmUTHlgZ9pfHFcek3+J2n2j0UCdi48ItqyoJ964fh3Xr1/HFx57DJP79zcdT0QbADoyaOPmRNd1mWnwPI+NSI2dk9jQxfENw8Dw8DBGx8aQ4ITErXIZy6urLDMSew/BswBYtkCIjKTTaRzYvx/Xrl3b4UCofGHncznk+EAnMau9wZ2PhGEAhKBeq+Hihx9iY3W17XX20cfdRhRFuHnzJipbW/BqNSQNA6MjIxgeHGQZslb+j/yGfSfaAmu1GnOq+eTAHBfbymSzTGhoF5KsyAKQ2JfKOTyVahUen2SaSCSkqqfn+3jzxz+GQgief/55aC3KhvGZAbtl3ET7o8haNOr1ZiJxC0kyohSpdJplXXM56IoC3/extLqKrVKpacKiKE0IGyJkkymA40ePwvd9zM3PN99XQM5+EcJDwrlyfR+2bTNFWF0HKMXi/Dw+vngRbsxmPUzoOwMcW1tbuLWwgMjzoKsqRoaGkE4mkeNyvK2pdRp74CilsB0HpUoFDduWSlsJ02SRga63VeprBQWk1xtf9kKy1Pd9EEVBMraYAeD27dv46KOPcPz4cRw9erTpmHHJYqCzMyDaegRM00QimQQBYPMWotjFS/EQn9cTCViqcGxsDIVcDjrvPV5ZW8Pm1haCdiWVmEMVUYrDR4/Ctiysr69v3w/+OnFPDO4UpFIpGIYBVVXhuC6K5bIc+Ww7DpYWF3F7fn7HyNY++ribCIIAs7OzaJTLoL6PHNfkyGUyrJbe8vw1kfs4L6jGybheEMD3fVhhA7998Xfwy9/9Ffzu+f9Brq+OYkNAUwCB2Pd+EMjWZlVV2eyPZJIJBUUR3n7zTTiOgxdeeKFJ56A1K6B1cQTidoMQwjKhvMRZbzR26JBElMLn2iMErFVxoFDA2Pg4kqYJXVFQq9WwtLLS1NLYBJ5ZjSiFmUhg3759mBfOQOtLwUocKW7PTdOUWYNqvS5VYWkUoVQqYfbqVdRrtY7X+6Ci7wyAsX6XFhcBz0OabzaqqiLDH+omBS1AerhNToBlIYoiaKoKwzSRzWSQSqWY/jY6L2RxzLhHH0cYRajVavB9HwpfaCK9RwjB1uYm3nvvPUxNTeHRM2eajys27Zh33y0F1vreiUQChmGAEiYk4vk+Uw8LQ0SCGMkZxiJToCgK8vk8JsfHWZSgqmjYNpaXl+W8g07vLWaULy0tyeFLO1woXrJImKY0vGLmQ50TNMMwZJ/L1hYWbtzYodrYRx93A0EQYG52Fk6lAiWKMMDZ8bquI5lMbov9cIh0t8jU1Wo1GbGLFlozkcC/uvL/wcf1j1H1a/jm7Lfwv3v1N1Byym3PoTWLGIftOKjzMemGriOfy8HUddmJ9O6776JUKuGLzz/PVEbjEOcpOpC69eTT5umIqqoyhwOshbheq0m10pCLCCnifGP8CdMwMDY2hqHBQSYkRCnWNzexur4Or8MaFqXGyclJVCoV1hHFywFNtiOW3UilUijERNSEQ+BxLQPXcTB3/Tq2NjY6X/MDiIfeGdjY2MDKrVugnicHbxBA1qJp7GEVi1kwZ8stToDQwhaLo3WYSDvQLiWDIAhQrVbhhyEUVUW2RcnL9zy8+957GBwcxBNPPimPFW/9aVrQ3IHoeC7YdiCEN29wSeWIM4HDFj1zQRBqPa6qaRgZGcHw8DAbpUwINkslLK+uNmcZWo61b/9+LC8vb9/z2BCVpuwKd0QSySRG+Ht4nFBp2TZq9Tob+VqrYfbq1Yc29dfHp4MgCDB7/TrcahU0ijA2MiLr+SmuJ0L5WokHEEEYMiGeSgUuJ7WZPAARkfmWtYWQbK+zi1uX8LVv/1O8vfqOfP9uTgAANGybiYlxJ0MENsIZuXnzJpaXlnDu3DkMDQ01rf24zZNcoy6ZAeFciPMS52aaJiMVcoJxu/bodsFJOpPB+NgYctksdEWB57pYXllBqVLpmOkbHx+HqqpYWVravkftbAcP7DRVxWChgGwmA01V4fPupUajwYalAVhaWMDK4mLH637Q8FA7A+vr61i9fRsRl80cGRqSDP1UMimFfpoWs++jXKnA4nK/aswJSJgmIsoU+0QtvRNkNoC2b7vzfJ+R+MIQqqIgl8k0p/gpxfvnzyMMQ5w7d2570A+2Sxc0YqNKfd9negI8yxGJ64pt/OL/RYlDLKCIUsYD4MOMrDZpu26Zj1QqhYmxMRSyWVk6aFcTFJicmIDH7/GOe8ZrhULeWCzspGmyDod0WrKWfd/HVqkkZzrcvH69K3Gyjz56hXAE/HodFMDk+LjMjIk2Nrm5xvg1lm2jyrtvKBj5rcB181X+zAKAaRqgLTmxklvCf/P6/wm/e+H/BTfs/BxTAPV6HY5tA5SyAUIt8ruVSgUzMzM4cvQo9k1OAtiOsEmM2CxnjFAqbUcYsxtiPYo1KbJywm4CTGiNECLr860QKfxWR0HlnQdjY2NMdIkQVCqVHTwkAU3TMDY2htXVValx0nRfKJVD3gQUzrtKpVLQdF0ORKo3GqhUqwCAjfV1rMUcjAcZD60zsLa2hpWlJUSeh4FcDqMjI1IDQEj+CuUu0RrTsCyUecpLVRQmscmdACmaEdt423nTwkB020Ad10WtVkMElt7LZrM7ZERnZ2exvLyMJ598su1ELrGwCSDnmctUH93WF5BRAP9q0jqIdRCkuRyq5/twXHfH+8UZvq1QFAX5QgETY2NIplKyJri6vr7DQAzxKH9lZWX7nsV+L0lE/LpEOlAMdzF0HSlOMgx8XxrGwPOwePNmn0PQxyeCECHz+KjtyYkJJJNJuZEnTFP28os1EQYBSjyAoGARs3QCxLrm3TGUUuSSubbvTSnFn1z7E/yvv/e/xVxlfsfvI8q6mIQoUTqd3mEbwiDAOz/5CTLZLB6NaQnE0cozEsPPALZhNGUQYn/TTnhN13UYpslUTrl2yY73E0JFbWCaJsbGxjA4OMimJEYRVtbXUW1T09+3bx+2ikX4ntf2eBG3b2IgUhSG0FQVKdNk5ykEisDawmv1OqIwxNrKCoqbm23P70HCQ+kMFItFrC4tAZ6HwXxetuGJBa3zKFjUtHzPQ7lalfKgpmliYGAAZiIhiSzi8Repar1NVoACgKJ07LcHWPTQaDSY0TAMScaJQ3r2R45gYmKi7XHiCzWuMbAb2joDvHshydXCLMtq8rCB7QeJdHEKdF3H2MgIBgcHoWkaAt/H8uqqvK/i7/ft24eVlRVpYBT2CwBoa0zEBm/yex4FAVLJJFSu5Gg7Dur1Ouq1Gm51IBn10cduiKIICwsLcPigr/3j4ywbyDcWAFB5f70oGTRsGxVeXlMIQZYP7tG49n782IKMq6jtNrIIBDzFX7mJ/9V3/zf4j9e/KY8RRBEq1aq0Ya0lRYEPPvwQlmXJbGIr4t0+0hnoUbdDZibj3UZgWVZB0GuXWWzqyOpw7Ewmg4nxcdaRpSjYKpWwvrnZ5NyPj48DYGOOxfHiAkbtAoEwimTLpmh7FC2SYRiixrlSS4uLDzyp8KFzBhzHwdKtW0AQYCCfx9DQkPxdEASgADROmqOUNi1mlRDksllk+RhRBZCpavYtS8lHlLJ2lRjkA9/BEaAA6o2GbONLJZPItBnCIzz7bC7X0bOPv18Yq5ftWYwn1j4IMEKhpusgXH60o0vDHYJOHRSZTAZjo6PSWK2ur6MUKwtMTEygWq2i0Wg0HS9+Lu0g5iwAkGlYMd/A5zLLW+vrWL51q5er76OPJqyursKuVKBSisnxcdlHL8qCiqKwNmDKhuuUq1UplGUaBgqFAkxhF2iz2p4UENN1aGozWY8phCpNqUQv8vC7F/6f+Oc/+jo26puoVavMRnECr97ibACs62h+fh6PPf74TsJgDOKcpNhQr3Yjlp6Pa64QRemaWYwHD006Ci1QNQ1jIyOsW0lRYNt2EwcpkUhgcHCQcY5a0JrJiENRFMbX4A6AoihSFZZSCsuy0Gg0sHDjRlOb9YOGh8oZiKIItxYWQHwfScPA4MBA8+/5AlV5v2trNiDP5XYlh6CFOBfwWrZCSPNkQf5vt9ager3eNb0nIDz7p59+umNqLd79EMUWdK/DfJo8fDSn6dO8Q0KoIDb9Xfz4nBzUaQGahoHxsTHWcaAoKFerrGwQRRgdHYWiKFhbW2v6m6jF0Ow4b8LmO4DrIwCMKJTl3RdRFKFuWbi1sMC6R/roo0dUq1UU19aAKGKEVe4IyIhTUaBxwrHVmg3gxGQxVIwQAtKywQpnwNB1aAqzHYKz023dvrfyPv6r7/5zzGxchK5pyPFOqFZYjQbOnz+PAwcOYCo2LrwbemkrjENooojvgW3bsVtmsfU43ZAvFDAyOsrKBmGI5bU11Dnpb2JiAutrazs5A13mQAAsGyxmzEjeWDqNJCd1ep6HcqWC61euwO7U7nif46FyBlaXlxE1GgClGOMbTjx9JB5kQSBpzQaIxQzEBIFEax0gx4dqfFrYboxf8Nc0eCoKlCLL+2DbQXr2Z8929ezj6KYe1gnC+LRTTFMUBSlOCnIdR5ZF4n8XRzeHQFEUjAwPo1AoQCcErutiZWUFQRAgn8+jXC7L14r0pRRU6WAgTcNgBCZOfiLsxNjcBi4u4rouFubmsDg31/M96ePhRRAEWLl1CyQMkU2nm9ce3VYP9H1/mxsQzwYYhuS6SLsB7AgkBPlVV3RElE0j6RYpa9CQRx4Vr4L/xwf/Azajrfa18ijCT955B7qu4+zjjzfV9ONobYWM7sB2tAqqxXUWmjKLvBTa6Ri7OQTJRALjnFyoKgo2ikVsbm2hkM8jjCLpHACxCapA81C5prck8nMSthhg5d4MJ2CGYYhyqYSLH33Uttxxv+OhcQYqpRLqm5vwggAjQ0PMi+e/Ey03URiiWq9LoYumbEDLYhYQBEOgOa22u8QQQ6PRYBkBSpHNZHa0I4rFaTUaeP/995lnf+BA0++jKELARwKLqWbiy7JteL6PwPflYCObs5ldz4PjOHBcV75e9NsGnBkcCvnf2KLWdV0qH1qWtT2joVPE3sUhAIBcLodR7ulTSrG6sYFkMokKLx3EZyvIY2JbmbHp52JRg0dbMSJlMpGQGRc/CHBzdhaz1693PK8++gCA23NzgG0z55WXFePpcEIIHMeRAUS7bEC7DU4O8uJsfFFqUIkChSidlhMAQIWKPPKgEUWIEMWoiItbM21fe+XyZZTKZTxz7lyTwqDoBAi42JHveXIeiO04cLg98LlanyPshusyu+E4bIhSi+3wfR+h6Ezi1yWQEZnFKJKZxbaWoQeHQLQv5zIZ6IqCeqMBlxO4he3Y0ZKMzsGEKFtG/LxlKUdRkEmlpKiZVa/jg/feQ+MB4xA8FLMJPMfB5tIS/CBAJpVClmvmiweC8Ki0zjUDACCXzcq6f6fFLCDIJ2HLdK/dHuaGbcPl9bNMJtNEOhSbvGAY/+Tdd6HqOo4fP876YEVbT5zRC6b9jdjPGpYltQHEgBTR3+y4LgzOcBZ/H182Pp81oKgqcwYIi1RE9BEEARMhiiLkMhl5j0hLdBLvyOiEBPf0N0slOLYNRdNQqlR2Fwxqw08Qym9iQcuXKgo0TUM6nYbtOAiDgGUHoghHTpzo/j59PJTYWFqC32jAoxT7Rkflcyzb8ChFo9GQdXCTT9YUEX23EqHILIq0tMJZ9braPjMoEHcEIhKhiip0VcfjI48D4M5FELBR46USrl69imMnTkA3DFSr1e22Yr6m4zoq4rz8IIDjOFAIgc3toJic6HgedL6RC5sRtx2izh7wTVVolSjYtg1iEqNowRbti00e0C7cI3HPBgYGYJomisUigiiCbhhY39zEvn37OnYPCYcgLv+uiA4wLmIm34MQUFVFIpmE4rrMSXIcfHD+PL7wxBM9Z2nvdTzwzkAUBFhbWEDgulAJwcjICAA0OQK2baNhWexBoGx+eJMjgN03dtGyQqMIKi8TdIPNp3tFUYREMomILyDZ58/nekdRhLW1NWxubODs2bMIogiE/y4Okd1oTegRsLJFIpFgvAEABudBiDno8QVBgSbBETkxUfQVA1BEWYUPOXH4xqrpOmgYArHZAWrs+90GgAiCUKVcZqpllGJ2bg7TXYaHyPtMyLZxwzajOQhDSNPKWd5EUZBOJtnglijCwvw8IgDH+g5BHzHUi0VUNjbgBQEGBwbY/AtsZwNCriIY8TG7QuZWEUS6HqJbAkjNDE3XmeCZ0tksK1CQQw6EEoAANmw8VvgCvnbm11BAAcVSSfb5R2GICx98gHQmg3379sHjRMfW9xfl0ni9P+BCapquy4hZ8KV0TdvmTsVth3AqROkkZsPAbQeJbc6u58EvFpHi+iCgVNoJaT9UVa7ZbkjxmQibW1vIZjJY39hApVpFdreBZZzwKe28ICDzrImwOwQAOLFQURSWcXVdzLz/Ph49exbZB2AE8gPtDERBgNLyMhzbhk8p9o2PNzkBlFLWhx6Lzl3Pk6I2TaM0d4HoRFA1jU0lbAEFyxwEQYCGbaPOe1hFD64XY9jGU5BRFOHatWsYn5jAxMSEVO0S/8qHVXi5sYxHGIZyQ8/FvFch2pFMJJBq07EgkEgk5HvkslkWUfBshPgeALu/vs/GlrI3ZvKjQQC/JR0nz1tVofHF3lqTzBcKgKLg4qVLqHI9gtHh4Y5zFYDt7EME1t5pGIYsd8TviTgHEIJUKgWvVgPCELcXFkCjCMdOnuyZaNnHgwu/Xsfmygr8METSNFHI5ZpsR8AVOaMoAlFVFAoFOSFP44N6erYdnMEunm+txRlQoEDn/xVQgEZ1KCBIJ1P4W8d/EY+NfEF2zACQTu/yygpq1Sqee+45JGNrGYSNR2/SBkALN8iyoBDSXFrzPDmDpJvdEPfI930kk0k2vpkHShHYRqvrOsBF1UKusBpFERAEiAAE4rxi/xJA2gtN03aQokXr8mI2i8qtWyhyYbN8rr1uAwDJKRLOXcQ7MjTDgOd5TfMaCKWAokA3DCQB1PjeMXPhAk4//jizW/cxHlhnIAoC+NUqk/0MAgxxIo9YAFEUoVavyxRdOp2GaRgolssI+aJOxh6EbqBgG6/QviaKIhW5Av4lamee67J2I7DWN3FOmqpuR9L8gSeE4PyFC4iiCE+cPduxw0CixQCJWv6eWwrl4bZTnYQQqIRszxfnSJgmKqrK0oG6DiOTYTU3nt0QwkBRrHYYhCGo70O4PyI9p2kaFH7/0qkUUskkXMdBGIZY39zc1SFgJ8vaOzWeBaG8zauViyGch2wmg1q9DhqGuM07DI4/8sgd3a8+HgwEjQaqxaLsSBkdHW1yBDzPY88MLwlmMxn43DlwPU/21fcCESSAEOhctluHhgQS0gFQOLUrgwwUqgKgqKOOx4cew5MTT0ibocRsh+04uDE7i+npaezbt2/382jJGNwJ8TiO+CYtRYtajqVpGmtRjiJkeATfajPE9+DiYpI71MY5ECqs+yYmMD83hygMUeZlkYHdIndKWZkXrNSj8ImOidY9gPMIdF1HMpWCZVmwuUPw6Nmz97VD8EA6A1EUIWg0UKvVEPo+NN57C6ApvReEIUAIsum09F4TpolGEMB2HPZQ7LKoxRJyXRee67LolD/IojYnUmlhGML3fZimyXQE4gpkbVAqlzF74wZOnzmzuyMANNXHgRgbuEfD1ApZD+xSsyOEIJVMsgyL40iPXVVVoGUDFqnLkJOWAl7XpDwT4/CZ48KpyhcKsHn5JghDrG1sYGRoqKvMs+u6rI1IVaHrOnxOrOw0J0LhJYOGbSMKQyzduoVEIoGD09N3csv6uM8Rui4Cx0GtWkUQRXIQltjcHMdBnetf6LrOZpmI7hsurGPbthyC0w0ULEp2PQ+e57EghVKkwhTSSCPu2ieRBKEENiyUUIIHD/9p+Vv46qO/iH3pnZv9zMwMCCE40zK8rNN5tEJ2WN2p7RDOQBfbYRoGPNeFD3ZfU8kkFE3bsSlRbDsJItspsrdRFCHwfVjc1qqKItd6FIbQdV2WcoZaWsmb3oOyoXMErH3a4bMK4qWCOBRCoGsaUtwh8DwPlz78EE8++2zHbrB7HQ9kN0FoWaBBgFq1Ci+KkBcLGiydX6lWZWqukMvBNE258ZlcmpLynvROEGz1RqOBUrnM5hUI0gxPTWuahmQigUw2i3QyyaQvUynkslnk8/ldve4PLlxANpfbMZa42zk13Ye7sKB7SZkboq7IhUA6QegvmIaBdCqFbC6HQj6PVDotPXuAGcggCJBMJlGr15nKWxDAc12sbWx0HHQUhqEs+aQTCSQMQ37mHa8RLLoQcsZhFGH2xg1sPQTyo300IwoCBFxgxg8CEEDaDoC1HAtHwORTMxXu/IN3EABMTtzv8sxFUQSbS45vbm2hwYdqRdwxPjZ4DJESwSY2KrQCTdXwU4e/gv/quf8DKkoFDhxEiBBGIX7/0h/sOP7m5iYWFhbw6JkzXR1nidYggpOTgd7VB9sftnXCwk4kufyv53kd75lYo4auM3uaySCXyyGbzTLtAs4rEF1HKuc6WCKQ4APfNra2OhIKLc7fUhUFyWSSdQ60EAlboRACQ9OQSiZZMOM4mPnggx2dT/cLHrjMQGBZiHwftXodQRAwXfxcDgSA43mocZ0BjU8BFBuyeEQU3pNeqVbhex5c15Wenhj64/EWHGEEALbxqpqGdDLJ+ml5uhtgi7/CFfsMXe8papibn8dmsYgvvfhiz5t567b9SVN98phdvHuBZDLJCEFhyGr2PUxsFEQnVVGgxtJxwvvP8PRrBCCXz6NULqNh25i7dQtjIyPIpFKybCBmR4BSpjPOMz2EH6+Thw9AzpJIJZOy++LSRx/hiXPnZPqyjwcbIpuIiI8MD0MM5POyDa4WEwVLJZNSwz7ecqxrGkzTZJ1J9TpzJLh9CIIAXhCw9r3YBhNwbpLBo0xN0/Di4POoKVX8ePlNHM8cxSvjryCtpZDJZPBzR38Gf3ztG/Lvv7/4Gv7+iV/CkcJheT4fXLiAgYEBHJya6u3iYx0FwHZGUSgq3glkP/8utkNTVSRMExZve85lsz1xLSj/EnNkAF4S5MFYIpGAbdtI8M+pVq9jc2sLtm1jZHi4KesrbDrAsgIKL9uKrGInh0qUVnQ+D8WyLFTKZVy9cgWnTp++77hHD5QzEPo+IscBKEWtVoMXhhgaGICqKHAcBzUuRCHSezISjQnaANtqWbZlMZIIf1ACzi+IE/x0w5CtMTQMkeIRpgQnKQqJYkHka20BjMP1PFycmcGhgweb5JK7oV37zSdN9QlHp5fRPqqiIJVIoGFZsG2bRfptFoMQNpH3EDszGqLMkM9mQaIIhqZJqeGtYhGe72NlZYUxvLnjJcSPRNlCXLfQfAg6LOp4F4Wqqsik06g3GvBcFxc/+ghPnTu3O0+hj/seYaMBykfY+r4PAqBQKCCilAUGfAPPptNsJgnQtmMglUrJQKFUKsEwDARBINeiUBQUka6QvVVVtSm9/DOH/wZ+ev9X5MCyTCYD0zDwDx/5+/jzm38OK2AkYAqKf33x9/Hfv/B/AwDcuHEDlVoNL7/0Uk+bUdyZERDneqflRQDMdvS4GSYSCbieh4C3M3biasWnrAoiclz/hPDUvUjfh1GEdCrFeFmKggofOR+sriKXy21LlfMSjWmacq3rus7OqVuUH+u+MHQdSCZh2TZWl5aQTqdx6D4rNT4wZYIoihBaFkCIJAaqqop8Pg/XddGwLMaEjaX3gM49rCpPEVWrVWxubkoJSqFjnc/lMFAoML5BTHGwdQNs2DZLORLCoky+SBT+RdosmksXLwIAznSZPbDr/bgLqb4407Zbr6+AmUgwTQKuYSBAY+fTKkJCuhzfTCRAOWFLzHw/sH8/cuk0NF3HVqmEeqOBcq2GGheLapVOFWqQQZt2zFYIomQ6lYKqaahXKvj4ypWerr2P+xeh4yDiqnNVnhXIFwoghEhuEeEZxrgjQACgTdpZ1zT2XFYqKJZKMtWsc42LQj6PQi7HauQ8em512ENepqRgm6UYwlVIDOCXH/llgFBEYHykt1bexszmRdi2jctXruDY0aN7IrJ1yih+khKBOG4vK0chBMlkkmVvHWdbxAzbDkBIt8fJk9gm3MnhSCYS8FwXOi/Vjo2OYmJiAgnDgB8EKFcqcGwbpVKJDSPyPBY48LUeJyCHbdZ/u+vSDUMOOZqfnW1SUL0f8MA4AyFP8dEoQpWPGS7k8wiCQNb5Erze1K7NDGAOheU4KJXLqNfrUDkJTVVVRJQimUwin88jzdN5soWPlwtISz+s67pyrniGj9VtAv8bIeRDCEGxWMTcwgJOnznziYgodyPVF/d8e4HCo3JCCGs3jJF+aMtCbn4b0jZ7kUgkmIGIORa6pmFiYgKFXA7pVAqlahWO57ESDF/olWqVRfe8qwBgmYHW+yAIXK3XLEoGRFGwuryM2/3BRg8sQt9HaFkghLCsgOdBIQSFXI45Avy5ycfUQaUjQLblyT1emiyVSnB5S5pY24J7kM1mkTTNpohbbHBNzyalaNTriCI2US/dQh7+u8e/isHEEDRFBVEVgFD8jzP/X3z44YfQVBWPnDrVU2kP/Bp23JO7mBno1ZE2dB2aYTCRIz7qOIy1MXe6nk62zUwkpKCbQJZPPkwnkwCl2CyV2HpXVeiGAcuyUK3VYAv+gKpCAXtGWu/Tjuvin7Wh60iYJoIgwOWPPtpxDvcyHghnILBt5tkDsoNAaOiL0oDBNaYlYgIZruOgWq+jVC6zB5Fv7olEAmPj42yWNiekWJa140GgXFQj7ggEQQCL628nk8mObPY4CCG4fOUKCrkcpg8d2j7+HdyTT1wiaD3eLos6AmSrlcpTbSKb0ku6sl2GRNd1KIrCHKqW1xYKBcboJQSOZaGQz7OMj6oiwja50+IyqqKfmRACGmM6d5JCjY8ynb1+/b7z8vvYHWEYsiCCMMGqSq2GgFLk8nnYXDsDhCCXy0kZX1EaAO9Katg2SuUyK0vycqKqqshls5jct49xCwhBNTZeOA4qZG9j67Qpm5hO71gXSS2Jf/TIPwAAEBAoRMWV4hW8ufw2Hn30UUZ+6zUA6FZe/ISZgZ7eHtsKrwnTREiZQmq7e9UJ8ZkxAolEArbj7Pi5mUggl83KsnAQhhgeHGSBFyEIeVdTtVaD67qMT8DLRnE71o6IKIIdUW6wLOu+yize985A6PsIbVuSVaxGAyFlA3/qgiyo680ZAb4JOK6LUrnMUoNiyh1P5Q0UCmy4Df9/8bA4rsuMRqyWFKG5RECjCHV+HoZh9NYWCGBjYwMbGxs4ffr09jhUQpqGmsRTY3HFrx335S6QByViXQVi8YraXdxzF2eRME2IcaV7Yda2evmEECSSyR0jTz3Pg9VoIJPJIGGaSKdSqFSrktGdy2TYVDneqiXUHaV2PL+mTlwIcR26rsM0DAS+j6sXL+4ujdzHfYWIZ+0AZgvCIAAohanr2zLh3AYQsj1jIApDlgVoCR5M00Qul0M+n2cqpoaBXC7HHFRKUa3X5RAjgZA7Fyp/9nfNJnL8/JGfw1h6TP4/BcVP8A72H9gvS2+tX9uvFd+036Sk7bgLgUTTBkq3lUyl/eCzAwST3+QKjkIYrcc32XGuCdNExInIAkI6OqIUBT5zRiEEtUYDSe4kCM5XBIASAtt1UalUmGPBnTbZMt4GwiFI8szQ+soKFhcW9nLLPjfc/84AT/GBM0mF0A+lFOCpnmwmsz1FizJt7VK5jEa9LlNiggeQE62GsQ2XgBmFDD9OGIaoVquwucEQKXnh3dc5I10Q0nrFpUuXUCgUMD4x0fwL4QjwiER4qa365wJxcQ7CpUbFBt70Orqtg9D6Rdt8Lxbubi1DmqpKwt+eFjV2evmJRAIuP0YURWhYFjOoYKnF/fv3M8IRpdjkrUOqqjYtbp1Lp7qeJwdRhUGws0QQg3imEqYJVVVRr9cxe+PGfePl99EdoeeBxqJPIQ2ua5p0BIQQGQApSlPnToDQ6W8NHppGl1PWtZTP5WR3jc2zkCKYiGcVm7KJ/LntBF3R8bVTv7p9PTTCerSB127/sOPfCKdABBk0tlGLbIcQByPYlimmbTa/drZDBiex14DX3IWdpbEvcU5xJLgTLwav7QXxYwmxIGF/fN+XZR9CCPL5PCbGx6Hx8lClWgXhaf5MOo18NosU39AjsOejWqvB5VoEHTMv/OciswhCMD872zRF8V7Ffe0MBI4jCTyE15qEYpV4mLPZrNxgHNdFtVpl7H4ubpNOpTAwMMA8whbWeJzxDgCmriOfz0PTNFAAlmWhXK2yYUL8/WzbZgIigBx92QvW1tawWSzi1KlTO2aBd0SHmr4gvhBCoHMiTGuaK764aZuv1uPthR0MbNf7hSRwz2jx8hOGAdtxYPNuEMH0TiYSSKfTUFUVw0ND0Pls8/XNze2RpXxxZzOZbbJWFMH3fVRrNVi23TFzIZniMR7E6tISSqVS79fSxz2LUJSe+MYnpvKJZzyZTLKNCcyxrvO1LhwFQ9fbBg8CklcAyCmGad7mJrRO6lyXRKzFOo9aDcPoSf30p6d+CodyU6CgbFYBgN+/+G/gh72ttyYxsdjGTQhTDCSxwENkRlqDilabEXcQhN1oHdfc9lzEJnqn2QG0cQYIQcOyUOcBQMTLmJlMBoauI8U5YJqioFKtymm1ALPlyWQSWd7FIfYVy7ZR5m3nHQMDfh467xbxfR9zs7P3fGbxvnUGwjBEGH9Y+AffaDRYipjr6SuKAtd1US6X0Wg0WJqYE8QGCgUk2yzkOFp/oyoKcnxzkVmCWo0Jh7gubM4pSLVECbvh8pUrGBocxNjY2O4vjqGTwxDGNsRWiEW+FwgHY/cTYq9RFQWGaULlpZW9QnQY6KYJ27LkhDONL+Y4uVJVVQwNDkIhTA+91FLfFy2GCdNElqs+hmHIlMvqdVZaanUKxD3i/IFkIgHf93FrcXHPRqqPewuB624HEYoiR3g7rsskwrlCaBhFqNfrKPJMgFCdy3PBG71DCa7TOknwjhgxCte2bdRrNTiuK4eUKXvIJipEwd89+FWEUST99KXGMv7z3F/u+redsnu0C9fojrvm9xBEANsbechHK+/trdh7iaFP1UqFdYNwTkImk2kKNrLZLAsqeNuy0yJopvIsZzqVQjKRYCWNMITNlSjdNk5B/GpFZnFzbQ3r6+v3dGbxvnUGqOcxz5Z7slEYol6rwQsC5vFlMqCUolKpoFavM2NP2HCaQZ4JiLOBdxw/5tm3QtSECoWC3JR838dmsYg6l+Q1e1H+4lhZWUFxawunY1mBT4K4935XOAMcu00OYy+Keeci5bfH7IAY3Sz030XqP81LNe2uyTAMDA0OQgVY2o/PMwe2a59CeCgZcwoiMBJRvVaDw+uC7a7F4HoSxfV1bGxs3LcqYw87oihiXAFsG+1GowHbspgjkEgwiVnHQalUgu26EP3rgg8gOlTabqYt2cRWaJxcmM1koClsHLIsPXgeMqlUz9lEAEitp3BAn2z62R9e+Xdwgu4Oa6f3+KSqpR3ebE8vVzj/gtxBdiCKItiOA4u3mYdhCNMwkMtmZbayFYV8nm3ahGBra6t5fHGsBV2UEMRxojCEw4fOtXIT5KUDSPOptKvLy6hWq3u6ns8S96UzEM8KiId6q1hkk/M0DYV8Hq7nsXQO/5CSqRTLBPCUL9B5wXZzBOJQCEEmlUI2m2Xtc2HIJvZxToHruj21+Fy+fBnDw8NyvHLsRLr+XbtaHrDNYQB6Y/L3ir0eKZ7ys3tY1GEYNrX3gJdyACa6ou+SaUkmk8gXCtBUFZVaTab9SCzKD/nAEyFBnE2nmXEnhCnHNRpyYTfdWUqR5M7Nxtpak7PRx/2DqGVN0jBEqVSCH4bIZDJIJhKMD8TbkYVQWI5H9HEy3g6uzi6OQByGriOXz7N2WN6CSwjTSGnEnsFuWF9fx+bWFv7xI7/e9PMtZwvfvPmtrn/bybLc7S4k4M4yCqI1U8xz6QZKqQweapwMLs7f5ATurtlfQjA4MCBl6OOlRnGcMIqYWiQhMA0DWf6siKF3jUaDDaCLlRf5wVlmMZlEaWsLZZ5luhdxXzoDlNfkhYH3fR+1ahU+H1dpWRbbfLg3NzAwIOu+AoKM13ZB7zGVE4YhNE1DKp1mYiVg0WbDslCqVGDZdsfFvby8jFKlglOnTu3pPeV1tPmZVBD7pFmBlgXU06Ju+RvZstMh5SeyAHW+kD3PAygTYUny1FzP7w3WS9yU9nMcCB2DiNIdn4NwCtK87BNxFTq5sMWzQJiGRMo0USkW0eBCJX3cP4iiSDoDwnZUajX5XGa4DLkQqMqk08wJaJ14yccAx7EXRyD+N2EUIcm7EFKJxDbRtVaTUxA7pZYvXb6MoYEBfPnkl/H02JNNv/sPV/8INb/W+b07kGfvivpgG+x2X1qvUOFpfdKFhBzydH2tVmNlFj790eCbdStPqhtUVcXQ0BA0TUMYhnKOgXAGAj6rQl4PdwpyXH8ins30BYE9dm2mrkPXNGyur6NW6/y5fJ6473RWoyhiksMclNf1XM9DGARyfDBRlO2WwA4QEaPw5joxXLueD29bo7wVKJfNQijwua4rB1g4jgNFUVi6WddlqvHypUsYGx3F8PDwjmPv5pJEUQTXdeE6jpTOjMIQruvCdhw5GEho84uxwiJivnbtGpKpFDRFYcM++L+qokDl31uOw8Yrx4R49nJ/RHZA3AOxcALuHPi8nUtA13UYXKIVgBR82gsK+TwCbig2i0WMjowwDgInAbWDruvI8tGvYoqcz0tOcVVDVdeh+j421taQTCbbfm593JuIxERLbuA910W9VkPg+9D4hEsRQKTT6a599m25OHs8H9txEIQhVEWR0xGDIGCDjrg8bxAEsMBannVuOxRFwerqKra2tvDiCy+AAPinZ/4J3l17Xx67HtTxRx//L/inZ/7xjvellMIPAmY3eC+9sA913jYtuh/CKEIUBAj5NEYAuPbxxyzajo1cV4TN4APHojBEEIZM619RoPOhYR3vJ3baO5PLFIdhCM/zpLSz7/vbs2E4pG2NzRwQ3KBeoWsahgcHsc6HoZUqFRSy2aZM647zJgTpZBKGpqFh26BRBKvRgK5pSJimfIYo70yqFItojIywtugeSKKfJe47Z4AKz55H9jYnDTqOA4XX6k3TRIo/hL1AjB0G9r6gG7yDIa4tLjgFSdOEzxd3wB9esSkSQrC5uYlytYqXz55tvkawVpZKtcpYzo4jN3zbtlkvsuMwUR/uxMQ7EMSiUgmBqmlskYpZ54rCnAZKUSyVgFJJjgelXC1wx4YpyircaUokEuzLNJFIJmHy700um5rJZJrIk6ZpwrJtuI7TdnEqfOyoaZo7PjOFkK4jlNuBECIXtet52NjcRIYzuTs5A+I8UqkUNF1nmQGeJdD5tDSxASQSCZS2tjB0jy7qPnZCZAVEO65wiuuNBhzfxyDXIcmkUjB6+DwppVAIkT3ne7UbQRjCsSwgimAmk/K51zQNGU1DlEyysehcq0MMzRGOwczMDIaGhjDMS4snBo/j5f0v4Qex1sL/eP1P8Sg9g0RowOE2w3VdlqlsydLF7YYCbG/w3GYofMYHjSI5pVUw7MWo37bZT0Kks6/rOpNWNk0kk0mY3GYI0mYylWrK4CqEzX7x63WUKxWmHRBfv5zLoes6dF3f4aApigLsMcNhGAYGBwdR5NMkRbAoeFidAiFd15HjwYQIJIIw3J6AyDs0dF3H2toakqlU2y6UzxP3lTMQRRFCx5FENt/z0Gg0UKtUAO6hZbNZ5tXu4SYT8NRfl42iHTzfh+e6rNWNR83NBybyQQX3xj3Pg+f7oJTixvXrKBQK2NjawuLt23AsS7bChJx7QMFIR6Zpyg13cGAAJhc1ERuzYRhQNQ2aqjL1tCBAKpWS7VFxFItFfOe738Uz586h0EbDXJD2giBAuVyGHwTQdR2+68JyHDi8Dcu2bdQ3N2UWRPwdwCa7pdNppNJpqcCoqioCw5DCHuLedCtnCJ0E0jKUZDcQQjA0OIjV9XV4vo9qo4FUItHVGQDYs2DqOlTeheJ5HnzPQxRFrD2RO1a6rmN9dZXd474zcM+DxrkCnLRnNRpwbBuqqm4rWO5x81AIF6+Koj3ZHMuyEIFlmtpN+BStbUne1SAmpQZBgLXVVZQrFRw/fhyXL1+GY9uwbRvHa0fxOn6EiMtpedTDH83+Mf5G+q8jwTskBgcGYHCnXWzM8Wi63miAEIKBNnahXC7jr77zHZzrYjfEGGHbcWDxVkkFrK27YVkyG1Gv1bDJbUfANT8owCa/ptPIpFKsTMidA40PFRI2Q+fTCrt9Xoqw6TGHpBekkkkEuRxK5TIq1SqSySTLUPKgr9v7pbitE5LGVqPBAiZOKE8kEigXi2iMjiKTycjpl/cC7itngPJ6MggBDUOUSyWWuuLp8MGBgd5G57YeFzxNJRZzDw+OULOiAGuhU5Su0YEfBNja2kKxWESlUmGiR40GaL2OYrEITVWRzmSQTKUwODiIdCaDDN9IDcOQnrpkt/LF0+167nQmAeGkF4W3BxqmKcexCuUwcQ8oTy8GQQDHtlGLDQ1qNBrY2NjYrr8DIKqKXCaDfD6PQqGAwcFBptLWYVELgo5CCCLSu9Y5BZNTHSgUsLm5ycRCFAXpHjduVVGQTCSkrGjIswTCkUmYJsqlEjyerelVZbKPzweRsB2KApuTVH2eelY0re3m1hUxgR1CCKii9LzhODw1D0p70hOwGg1scvJZjQ9OA6X4+No1JoyVTCKdSuHI8BF8Rf8y3i2/h5D/dz26gf/y3G9gMjspo9BODrHQ8LjTaFWKGvFSbZhIwDBNOVshitksYTeiMITr+7AbDVRrNdQbDViNBrMdm5vynCLwIXN8yNPA4CCGhoa6l4F5t4bI4PQKSinS6TQs20bkuqjV68jncrs6AwK6pkFNp2HzYMJxHERRJLOLCdNEcXMTqZizcy/g/nIGfB9CGW6zWIRl21B4H6iK7Va2vR+4mQFKgV09/YZtI+SbVIILS8RR55v81uYmNre2UKvVGJGE9xoL4Zwnn3qqufedp5fYKUSybiggNmoxFKl10JESK3l8Um1xIRcaUQqfZzNoFMELAtAwRNjazaAoyGSzSPPx0BofRUz4fIFN7ghZloVSqYSF+XlQSqFqGoaGhuTXAGf2iuuNX3vUY0o24q2kCS5OFFQqqFWrSPToLIr31TUN2UwGdS5aUm802KAqVYWp66iUyzL9ea8s6j6aEfk+60xRFHiui62tLakQapomdNOUNmAviG8vUq54l00njCKZFUgmk1KwTP6edzdsbW3JL2Fbsrkckjy7deToURw6dIj1vgPSTgxaQ7j45iX4dNse/fHMN/C107/eLHEesxeE//xuEY/FpD+h5eGqalPQEHHbEYduGBgYGMDAwIDMcKpcSKxWq2FjYwM1Pi9gfmEBVz/+GADrMhoeHsYgdw6y2eyObjFhK3vJLEqHhXcYrK2vw+HKkJkeonjxHBBCpIKhyDCK7KJpGKhVKrJ7Kr0HldpPE/eNMxDx1j0KsIxAo8FmjufzKJVKiHyfiQ3tAVJ4o5U1Twioqm5LbbYg4OQbUIp0JoOGZaFSqWB1dRWVchlFzmIH2AIeHhrC8ePHMTg0hEw6jXq9jm9/+9t44sknMTY62va8Au4Y+L7PJv9xhirljPhO3j0Fc0TEAlA4t0J6/IRIIpBl21JNEaIuJr7nTkCDt1hpMQZ1XJubALKmKEaAaqq6wxExDAOpdBq1QkFOlIyiCOVyWRq9Gzdu4PLlyxByoUNDQ5IYKep1rTPM2927qMWw5/N5dh2eh0qthnwu1/Hv20FRFKRTKRYp8DYikbGpVSooDAzcU4u6j2ZEfOCQ57pSI8JMJKDrOiqVSk9DxFrRLkMlO5Qo7RhMWHyWgaZpSCQSqNfrKJfLuLWwwKL/SgWU/35wYABHjx7F4NAQhgYGoGkaLly4AMM08eijjzYz/rnty2Yy+NLhF/Bns38Blf/37vp7+OmDX8H+zP6dDnwMDi+JGYYhFVrFdREwewH+r6br2/LDcQEjbjc8z4PrujA4YVi8Lh6hK7yOLmyL4Cg0OdXc0U6nUnA8D4auI5lMwrZtaTeKxSIWFhYASqEbBgaHhjA8NCTLFfKzQRdSNt05qljlbeoN20aDc7V6GjjH3lCSBhWejQrAOGapRAKqosBqNGQ56K7qOtwh7htngPKadLVSkTrPA0NDrDeUt6Ld8ajeNpAfKNDcl8yjQz8IUKtWcXN2FkvLy3BdF6qqYnBwEFNTUxgaGsLg0BCMNr3xszdvwjAMHNi/v/17c2KMqKsLRJywEwQBwiCQm3J84IcYFwzwVFyb4/s889CT/jfPPGiati1Ryr9UHlXEF2839q6macyI+D5c10UikZDZAIF6vY7NzU0Ui0Wsra0x6WhK8b3vfQ/79+/HxMQEMtnsjuxAq6GJQ1EUDPAN2+W1y3SPXn78GOlUCrZtIwhDWLbNmN08o1Ov15Hao2BMH58+KKWgfJMrFosIggCGYWB4eBilYpFxcvbIMRKtiZ02Flk2AJqcApdzjBqWhUathtXVVWxsbiLiaenhoSEc5LYjx2XU43B9H/MLCzh+4kTH1j9VVfHLp34Jf77wn1ELtlvY/mjxG/hvz/0LhEHASMLC4eeRsPyeb+ztGPSCdCiClF3vUYw0JwITOdmU247tl9OOAQ7ASrFezG4kk0ns378f+7kNDcOQZWK5g3D16lUEQYC5mzfhOg727duHsbGxHaqw4to7fZapdJpNU4wilCqVnonpQkGVgGlKKOm07OKqNRpImiaqlQpS6TQajQay2eyux/y0cd84AxFP1dT5Dc3m88im06jxzUI3jD0v6F5qfDL9B8CxLCzcuoWlpSUUSyXQKEI+n8fBgweRz+cxOjq6aw3QDwIszM/j8OHDe07HCc6AqqqgHRQOXU5oVBQFmUwGAHZ48EIeOJFIIJVOb2s2YLvuh1hUAKDpYe228e4Gk7cHeb7flniXyWSQyWQwMTGBt956ixkUsAzLtevXcfnKFaSSSYyPjWFichKDg4O9vS/vMLEsC+VKBaZh9CQXHU//EsIULB3e2eG4LpKJBOq1GvKFAhp8kmIf9w5oECDgQ2oc14VpGKyfXFXheR7CKEJiD2qhEruQ0mQkyjNZm5ubmF9YwNraGousVRVjY2N45NQpDAwMYLxNhrAVC/PziChtGm/eDnkzj79z9G/jD67+W/mzn6z/BNcqH+PUYHs9E0opCB+znOJZL5kl5HZDOAAJrtIoM478fojso9AGcF1XSjsLdJv41w0aD44iykYctxKjVVXFyMgIRkZGcOvWLWxsbAAABgYG0LAsvPvee1AIwcDQEPaNj2NiYgKJZJJlGXfZN/L5PFxe969UKhgYGOjpnMWEQ1A20CqTycjMkO26UHig12g0WCvr55wduC+cgSgImIa8ZSHwPGS4IhgIExyKKEVyLwt6Dw9jrVbDyvIyVlZWsLG5iTAMkS8UcOrkSew/cACZTAa1ep2VDXpwRhYWFuD7PqYPH+79fFtPf5ffq4oCTXQxtIFgthq6DrNL2qvjhk9Yz3674Sy7QedMfRFdtDvHer2ON954A1EU4eDBg1hbX8e5c+cQRRE2NzawsrqK24uL+Pj6dei6jomJCUxMTLT1/ONIpdPwuEErlssY3YNGQLytKMHrtI7jwHZd2PU68oUCLMvqOwP3GHzbZiJktg2dDxozDENm0UDp3soEMeJgN0RhiLX1dayurGBlZQUNywJRFIyNjeHxxx9n5UFCUCqXe1o3EaWYvXkTB/bv76l75avHvopv3vwWKt62Uua/vvT7+O0XfqttBlWUAsTE0Xb3xInbjR7tbes7dcuo7AbTMBBwzYF2XVIAcPXqVVy5cgVTU1NYXFzEgQMHcPjIEdi2jZWVFSwvLeHChx/i/IULyOfz2LdvH/ZNTCCXz3fM6qmKglwuh7ploW7bTLK6B8KwCMpElkXl2cVqvY4gimDzbpZEMgnHcT73zoL7whmwuLSv53lIp9NIp1IyTeYFAWP072EokEQH7z4MQyzfvo2bc3PY2tqCoqoYHx/Ho2fOIJvLIcUliHeoju2yqCmAGzduYHL//p4eps4H6vw+YgO/GyWTTqpqolOh9Sx6eUdCmEJYyAWTWo3O5uYm3nrrLaRSKXzxi1/E9evXpeiPoigYHR3F8MgIzpw+ja1iESvc2C4sLkLTNEwdPIjpQ4eQb8MOVwhBLpuVvcC1Wq2n9BwBI2PGuQpiGlkYhihubWGID5jq5OD08dkjDALUSyUp/yrY2wBrC47AoreeP68eNu1arYb5uTksLC7Cc11kMhlM7t+PbC6HdEzRkICz+rkNimuFtMPq6irq9Tqefuqpnk41pSXwD0/8Mv7fM/9K/mxmcwbvrr2LZ8bPtb88/m9PM0g+B+iGAZULNbWuM0opzp8/j8XFRZw6dQpHjx7F/MKCDA6SiQQOTU9j6tAh+J6H5ZUVrCwvy2xjIZ/H9OHDOHDgwA7ZcwLOeYoieL6PcqXChON62XN4ECFsqeAIWPU6PEqxtbmJyQMHYNt23xnYDY5lwa5UEHieVOGKe4UBZ7nf0YJuWdy1ahXzc3OYX1yE73kYGR3FuWeewfj4OCiAarmMiNIdtWGZEtzFWIgF/cSTT3Z9XezA7S8BnQ3Hp6Et3u484g94/Lx6gcHHEguSpFhUi4uLOH/+PEZHR3Hu3Dlomgbf9xnJMZauBAAoCgaHhlAYGMAjp07BajSwsLCA+fl53Lx5E4MDA5iensb+/fuhapq8X7qmwcjlUK5UUKnVZJ/1blAIQdjiPKaSSSY6RSmWbt/G1KFDMgLt4/MFpRSVzU2EERtZneLS1mKtCrtxJ63IrYjCEMsrK5ibm8Pm+jo0w8ChqSlMTU0hm8ux6XaOAy0WUYtME+Elt26CNgBwY3YWg4ODPZfFKICfnf4Z/MmNP8WavcbOk1D8T5f+DZ4eewoKabYPERcdA+5OICHReqxO5ZUeS7aGaSJsIfMFQYC33noLxWIRTz/9NPbv3y9lzYXtiGLlAF3XJd+ARhHW1tcxPzeHDy5cwMzMDMsmTE/vaDfNZbNy3k25WsVwj59FaxeUxh0C23FQ3NxEjr+P6HD5vHBPOwNBEKBWLMoatWGacrwlwG5exFN93fpNJVofOEIQBQGWlpcxPzeHjY0NGKaJQ4cO4dChQ00p31qthggsIhSbl1TK4iS63R7nmzdvopDPY6jXh6jNAtlN7Uws6E/bu6e71Ey7QVEUmAZTRfP4Zi/Se4cOHcLjjz8u763neVA6SBKLeiUFKwE8cuoUTp48idWVFdycn8d758/jo5kZTB08iANTU/K12UyGCSd5niwX7Oo80eZ+ZcG2TiWTqDcaqJbLKJVK0Ph0uz4+X1TLZYSOgzAIYCYSUFQVpkivU8rKRVHUewdS6/PHO23m5uawMD8P13UxPDyMp55+GvsmJ7c17cMQHp+GGc8Gxom40rHusAlXqlWsr63h6XPtI/p2IAAM1cCvPfIr+K3zv8PPGbhZvYkf3H4dXz7wctPrxXNNYlomdwPtsoqfBPFAQsgUv/HGG3AcBy+88IIkI4suLFXTdnQXgZOfQ95uOj4+jvHxcTi2jfn5eczPz2Nubg6DhQKmDx+WDhhRFAwODGBjYwOO4/RERBY2R3RBiadI13UpJrV8+zYOHz0Kx3E+146ke9oZqJRKAK/xCmEfWatqiRT36lE5rotr167h5twcPNfF8MgInj53Dvv27duxGALfh889zXbiMpJ01/rQxQxIrV7HysoKnnzyyZ4Z5+222l1rlXczM9ChTAAwR+VOygQChmHIuQqXL13CrVu3cPr0aRw7dow5aZx40zXt3qZ/mCgKJiYnMTE5CavRwNz8PBbm53Fjdhb5QgEHDhxAjouWrK2tIQwCNCwL2V1q/cJgCx0Hca1CdazeaGBtZQWZTEbqqPfx+cD3fTi1GkgYyj56Kf3K16iwHT3ZDUE25g7/2vo6rl69ivW1Nei6joMHD2J6ehrZNk6gY9uIwOxXu7SyGKAFoKPtmJ2dhZlIYN++fb3fBH6tXz7wZfzR9W9gobYgf/VvLv8BXtz3PHQ1lma/Qzn2TuiWJW3LG+gxuFAUBaauw/E8bGxs4MKFC1BVFS+99BIymYzMsoghYt1S+a3nkUgmcfKRR3DyxAmsrq1h7uZNvH/+PAxdx9jYGI4ePYpCoYBMNotarcbUCROJngIJQghoTMsBgJRXtup1lEolVgLvOwM74bouAsdhYxVjxC25YOKs1F42V/5az/dx7fp13LhxAzSKMDU1henpaWS61I4t2wbl79/OeIiNojVqp7HzujE7C9M0ZSvMnaLdJhyH5Ax8BmWC1l5q2qOTA0DqG3zw0UeolEp46qmnMDk5idapkb7vd1X36/aOqXQap0+fxqlHHsHS0hKu37iBmZkZLC4s4NTp08jxckGtXpcDVTqBdvheXEvCNFG3LFSrVQwMDPSdgc8R1XIZKqVSK0ThzgAA+WyJlrqeW0EJwcbWFi5fuoTNzU3kcjk8+eSTmNy/v6NDEQQBPC6DnOpA+ouXGNvZDtfzMD8/j5MnT97RJEFVUfG1U7+O/8tP/q8AJQABVqwV/NXCt/Hzh39Ovu5uCZW1ou1ApzYlxr3AME0sLS9jZmYG2VwOX/ziF2WLOcBsghBq0/YQSEgoCsYnJjA+MQG70cD1GzewcOsWbi8t4fD0NI4dOwZN0+DxLpV8Pt/1fIUSohiWFr8jQrBua32dBSkDA59bqeCedQaq1SoUnsahUQSVT6WSiKVse6lx+UGAGzdu4Pr16wijCEePHMFR/qF2/TuuCQ6gY9ugTPW1/pz/G1KK27du4eDUVNMUq10NUZvfd1tCQiEQuLsEwo7OVsvPexkoJI5pNRp47513YDkOHj97FpOTk21fHwRB5wXNzyGuutj2JXxxZ3M5VMpl3Jybw5tvvIGBwUFM7tuHRCq1+6KOs8jbXKdhGFAcB5VSCaVcDrlcrq858DnAdV14jgM99hk1qUO2yAbv+hlRilKphEuXL2NtbQ35QgFffO45DPfQCmhbFgsiTBNqBzvTSQxHnNXy0hKiKMIh3k4oN7w9PFtfnHgWJwrHcaV4Vf7s31399/jpqZ9GQjXv+Lhd0c0WtMsC9OIc8EBobm4OH83MYGhwEGefeKIt70PY7FYyYPNpkF27G5LpNI4ePYoD+/djeWUFN2/exNzcHKYOHkRhcBA13hbYbR8RxxfSyHFHSJRMa7aNRqMBy7I+N82Be9IZsCwLvusiQbdVoZqyAkDT990e4DAMMXvzJj7++GMEnofpw4dx4sQJJEwTFEwetOu5OI7MCnTymncjEK6trsJ1XUwdPNjUzw9skwHbkQI7cgY6XK/cEPkG+Umx2/Js9fDbljXotkCLeG25VMJbb70FRVVx7qmnoJtmE5EwDr/Dz1vPY9eWJf7eAwMDeGn/fqytr+Py5cu4ePEiCgMDGJ+Y6Lqo49kf/oMmAybLBfU6G6nNxVH6+GxRrVah8jUi5tEbsdIiAFkmANB1ql21UsGlK1ewvLSEbCaDc+fOYXJyEgToquQHsM1IDCTrOrdiF9uxuLiIsfFx+SztaOfl19XNBhJC8E9OfQ3/xx//N/JnRbeIP73xTfzyiV8CsG0H7yT70A7yajqRoHsJhmIOuMgYfjQzg9nZWRw+fBgHp6Y6jhf2fZ8JSu1mO3qQKY4ohaJpOHrsGI4dO4brPLN86/ZtjI6NwTCM7m3K/NwjEdy2nLOY6lqr1VAsFvvOgAClFLVaDVoYgqgqaBA0ZwXidfkuNW1KKebm53H1yhXYto1Dhw7h5MmTe2rpcz0PAZ+H0E1MaDdn4NatW8jn822JZaTlXyDmGHCj1ZRK7LKAPi3vvtPR4s6ATO/HyhjCUMXvysrKCt59910UCgU8++yzTNqZT3Js6wx0+PmOc9llUUdRxIymqgKEYGxsDGOjo1heWcHFS5dw9coVrK2t4ezjjyPXYTG2Zl5aSY2maaJWr6NSqWBra6tjtqOPTwe2bcN3HJixZ64pKxAvaYmsYpvjWJaFS5cuYWFxEelUCk8++SQOHDiwp2ybkO5NJJNdU+9ybbd5dhuWhc3NTTzVpZ1Q2IkmxNakOP5jo4/jyZEn8f7G+/Jlf3z9f8HPTf8MskasTfpuOQNdsopEvI94DSeISwGgNqXQMAzx7rvvYnV1FY8//jgOHTqESqUCn49fb3VigiBgUum7pNybCJydL0ZG9Zqm4dSpUzhy5AiuXL6MxVu3sLa2hsOHD+PkiRNtgzBCCCMr8mO1BhKEEKTSaWxtbCCbzX5unKN7zhmwbRuh78PEdqTblBVoWdC05WcAE615//33sbm5iQP79+ORU6eQ2Ssxg1I2bQ/YlSSidFnQXhBgaWkJp061V/5qh7iDEN/Yd2yuMadIRELA3eMLiPeRwzdiP2NvyxZwvLOgmzLh7OwsPvroI0xOTuKpp56SEtJiPCttGfYjhq/0EmHvlh0Qz0mTo0QI9u3bh4HBQVy/dg23l5bw6quv4pGTJ3Hs+PEm40+x8/PdYUQoRTqVQnFzE4VCARMTE5+7qtjDhFqtBi2KoKgqfN+XKVgATVkBgVbOAAUwd/MmZmZmoGkazj72GKamp6Huse7t8SACuwQRAKCK57bNsW7dugVVUTAxMdH1GG1OrjkdzTfbr53+dbzz2ru8C4eg5tXxR9e+gX965msywr7rz2ssg9K0PoXtED8Xdq3NfXBdF2+99RZq1Sqee+45jHFND03XEXFyt9pyn23HYd0jPThwCiE75hLEITstYscyTROPnz2LkfFxzM/N4eq1a1hZXsZTTz21o9woZJ4F2mYHDAO1ahUVPqRqz5/5XcA95ww4jgOVL+igXVYAaEqtNUXUlOLG7CwuXbyIRCKBL734Ioa7pG+aPLXW8/A8hEHARCJ2WdDC84/4ootvIstLSwjDEPsPHNj12tui9fy48Wi+ELbwhaKaFASK/a24Vsp/J1i3bZcA/zuRFhfM/nZ1TYrtckbH5UQpPvzoI9y8eRPHjx/H6dOn5a/ETPKQb/zxzgExUKnX8cAKbxdqewqCLd3ms04mEpicnERhYAArq6u4dOkSlvjCFlmCtvrligLEFjUhBAnDQKVaRaPRuCdUxR4WBEGAwPNgAMzQUtq1tNgauVqWhffPn8f62hqmp6dx5syZrvXmjqBUEo57YZqLTFXrc0sBLC4sYGLfvt7EbbqdEtgaPTFwDK9MvoTXl38kf/ens3+Kv3Xkb0KN1M62I7ZR09hXU4a25Xsx7XRHMCOuW5yX+F2HzbhWq+HNN99EFIZ48Utfaur9N3RdlmNaAwbbtnvPAnfLDsSdmTaf5ejwMEAphkZGsDA3h1dfew0nT5zACZ4liJdH5dtxO9yaHUgmk6hx2/F54J4KW0RLiBLzGs3WrEA7L11V0ajX8aPXX8dHH36IQ4cO4Stf+UpXR2D7j9t4jpTCsW1W7+thNG18JGgrkW3x1i0Mj4z0NLd81/Pr4r1KAqM4F2xHy2T7RfL/SXzxtn7Ff47uZMROi10gDEO89fbbmJubw9mzZ5scAXFOuq5DURQpFSwgUq09b6g8ImoH4Th2+n0ul4OqqpiYmMBzzz2HwPfx6quv4uOPP5ZO3o63a+NIKpoGQ9Ng85nsfXw2cF0XJIqggDm6pF0Q0QaEENycm8P3vvc91Go1vPDCCzj7+ON35giAlRbDIJCjzXeDzAy02I1KuYxqrYYDdxpExBBf779+6lehYDt17kUe/uer/347c9bOdpDteSVNT3sHe7FbeRH8mLthc2MDP/jBD6BpGl5+5ZUdIkBxJcfWAWm2ZfUcRACdMyJxnlC7M9Z1HalkEulUCo9+4Qs4fuwYrly9itdeew3lcrkjsXmHYxFFMHSdyZvz6aifNe4pZ8D3fURBIFW5QOn2oow/bBwidbu6soLvfu97aFgWXnzxRTz22GNSwvZO4HoewjBkQiU9ipKIhyn+IVqOg/W1NRw8ePCOz6WVS9ANd71M0MOiblJibPmd4zh4/fXXsbm5iS9+8YuSEd0KsagDPmdCwOZDqfbiSHUkV3YoKcXPIZVMMiOoqnjly1/G4cOHMXPxIl79/vdRq1Z7fj/dMGBZVt8Z+AzheV5TGlrX9R0loThoFMF1HLz/3ns4f/489k9O4qe+8hWM9tAl0A22IBz3OJZWZBVbMwO3bt2CrusY5SnxT4K43TiQPYD/4tBfa/r9X85/GxsWG+xzV9UH0d12dLNni4uL+PEbb2BoaAgvvfRSR30XwzBYIMF1BQQalrXnrFy7tRzPIHWyLblcDgohCIIAU4cO4eWXXkIYhvje97+Pq1eutM1Wkjb8L03TQKII9XodDh+9/FninnIGXNeFwkkkBGxoRhP5puXmVatVvHf+PG7evImJ8XF85ad+CiMiG9DjQ93uA3ZcV7YE9UrGU1R1W2+c4/atW4CifDIimSiJYBdPG58CgbDlHNr+qs37Ayy998Mf/hCO4+BLX/pSVyMrJpKBkKbRqJZts/TfXpybDouW9pDlEIva56NSz5w+jRdefBGu7+P73/8+bs7N9XQKhq7Dtu3PzcN/2EApZSPEucNHge12sw5ZgStXrmBmZgalSgXPnDuHs088wUpU3YhkMbR7igLfRxgEIIT0PMinXRARUYrFW7ewf4+kxU7n15qm/pUT/wCGsn1+FBR/PvcXkkx3V7CLLYpnK1rv+JUrV/D+++9jamoKzz33XNcyiQgkvCBous69ZgaA9rZBlDq6WSBVVZFJp6EQgmq9jlw+j5dfeQXHjh3D5StX8IMf/EBmOZvfMHZUntHVNO1zCyTuKWdAlgj4l9EiFBJHo9HAhfPnWZ1LUbC8vIzvfPvbeOfdd7G4uAin3c3vAYHvsw4CoOesALD9IMUX9eKtW9g3Pn7HKcc4eurhF4Qc/pBJpj//XvAEBHM3zvoXX4j/rCUz0K4k0G6xb/D0nq7reOXll3cV5QC2I7m4M2DfgXff6ZykKFQXY9e6qP0gwNDgIL7y5S9jamoKH3zwAS5dvNhMBkJLJoYyPXQC5qy2Rix93H34vo+IywsDkKUn8X1rqe3G9esolUqsVu/7eO/dd/Hqq6/i0qVL2NzauuPx3CKIMHm02gsURZH8HPG+G5ubsGz7E2UU24I7SyOpEfzC4Z/fPgcoeG/9fdyqLzXbBGErxPecCNdqK9Du/7vwiKTtaPk3iiK8//77uHr1Kk6fPo2zZ8/u6pzomiY7iYTQkMtHU+/ZdrTjY/UYYGUyGSZxHIaoNxoghODUqVN4+eWX4ToOfvDaa6iUyzuP3ZK9MgwDNp/Q+1njniIQ+r4PlT9wRFGaxSRiN61cKuGNN9+Eqqo48+ij0A0DuUwGq6urWFtbwzvvvgsFwMDgIMbHxzE2NoaBgYH2kXULkeNOFjTAMwMxzkC1VkOpVMKJ48f3dhNaIBZVFHOSdtMZ2NHyx9G0+Dp45fH/j1p+3hphRLFzE+zlhYUFXLhwoWnYUC8w+KwCMZJaIQQNy0LyTpwBtDCXEbs3u3ym2VwODcuC57qSAKiqKh4/exapTAYXZ2Zg2TaefPLJ5ucj9hypqgpFUdDo6w18JgiCQPKMFEVhznd8Xcf4MTMffYTrN25g//79GBweRobPqVhbW8PNuTlcvXoVhmGw1lP+1bH2HzPmURRJtcFeuAIChPNcIkIQhSEUTcOtW7eQTqUwODBwx/ckvlYjSrf5LXwz/3vH/h7+Yv6vYAUNKFBAQfGnN7+JM5OndhD9Om2IXW1HLJBo6ygIJ43bNN/38bYYNvTUU70TrgmBoetsTgEnIMvy4h3YjlYSspAe381uqKqKbDaLIlc0FeTRQqGAV155BW+8+SZ++PrreObZZ9n4amzb4fhnpes66pxv1JMWw13EPeUMUD7FTtU0NmmuRS0MYJP/fvKTnyCfy+Gpp57CVrEIPwjkRK9TjzwC27axur6OtdVVXL9+HVeuXIFpmnJxj46NweSORvxhbVrQezTgrZmBxcVF6JqGsfHx3q5dnEuLtyh+1ksJIL4A7wpajWkLxLmIqVyXLl3Cx1ev4tD0NBs2hG1io3RQOkRdiqJAU1VEUQTf86QQR7tRxLuCNEuNxoVJdku7EkLYeOIggNUyVvTo0aNIJhJ47/334TgOnn32WRmBxq9PGHgvpl7Zx6cHsTZ8bjuasgIcYRjivffew9Lt23j88ceRLxRQrlSgEIIDBw7gwIEDiMIQpVIJK6urWF9bw/u3bwNgQlVjY2MYHx+XQQVpOb7DuQK6rndUG+wEhXelRJQiCEPcvn0bR48c6f362U1oOh/xM1lebHnu82YOf/foV/EHV/8QCk8Qf7j1IS5uXcSZoTN7Ov8d59PCkm9FnLxMwbK8P/rxj+G6Ll548UU2yK0NWbFTvkbnswoC3p4sujk6SUDvBUK2erfAUPJEKhWEYQjX8yTXyTRNfOnFF/GTn/wEb77xBp548klMdcj6CL2Ehz4zoFAKly9oo82Cnp+bw/kLF7BvYgJPPf00FEXBVrEIytmkqqrKD+XQ1BQOHTyIiFIUt7awuraG1bU1LC4uggAYHBpiC3xsjM0lIAQuX9Bar7OqYxA1b/HwLN66xcbntnmIRJTd1tNu+Vm7ntR2iJcn7jqBcJcNVKT35ufncebRR3H86NHtzEObDIUEae6x1nUdfhDA832Ypgm70diTSFTTodmbys9ElJM6XYv4TCilSKfTLDvA25biGarJ/fuRSCTw1ttv44c//CGef/55JDnxUKRPCVimyHUcmbrs49NFxNX+CGJ69Pzz9zwPb731FkqlEp597jnsm5hAuVyGEIMRryWEYHBoCINDQzh9+jQcEVSsreHGjRsyayCyjSMjI+y9KIXnuqCUbk9G3AMUVQXxfURhiPX1dfi+3zYybrfpS7TaDbL7FNW/ffQX8Z9ufguexxzWECH+p0v/Br/z4m9/sog0vta7HYcQFDc38eabb0LVNLz88svbk2Lbbf6xjIKYjULBOUeKgiCK4AcBbMtiLel7yNDEzykuKCaVGbsQ0im2p5gmUykW2VtWE/FZ1TQ899xzOH/+PN577z04to3jx4/LLg1hA8X7NOr1hzwzEEUIufHUWwg4H1+9ikuXLuHw4cP4wmOPyQhP1NxCrknQBE78GB4exvDwMM6cPg3LcbC2soK1tTVcu3YNly5fhsFZu9lsFrl8/o4mRyk85RVRinK5jEajgcnHH++4IHv+kHsoD7CXbW/cn4m+OIfveXjjzTextbmJc+fO7W0Qk1jwvCxkivGkPCqPKL3zPn2RHaCUqX8R0lkJTtxj/r+6riNhmrD5mFKjhfMwNDyMl770Jbzx5pv4wQ9+gBdfeEE6lILboRICx/Pguu6dnX8fe4LI/miGwZ5//jk4joMfvf46XM/DiyLqBDO6hNLmlrSWdZNIJnHo4MHtoKJYxOrqqgwqoijCwOAghoeHkclmkc1k2urk7wZhy0JKsby8jHwuh2wm037z38PaJuguApbUkvgHJ/4+/u3M/wyAEQkvFi/hnfV38cxY7+OSu55Dl/NdWV7G22+/jXw+j3PPPLM7RyueUWjJPohSge/7sER58U7tYOz5CcMQlNKOMs3CERBIp1JoNBps0B6XUpccCUXBk08+iVQ6jYuXLsGxbXzhscea9EpEICcCic9SifCecgbC2NhJNTba89biIi5euoRTjzyCkydPNj1gKo/2gjBE29vWQh5KJRKYnp7G9PQ0QkqxtbWF5aUlLC8v4+bNmyCKgpHhYTnjOpvN9ra5ijIBpVhdWYGiqnK29idCLBXfDXG+wN3Cbgz8Rr2ON998E47j4MUXX0ThTmucwjMmBJqqIghD1KpV7KrrvguEspjYKDpladoZzFQ6Dcdx4DgOomx2R7Ylm8vh5Zdfxo9efx1vvv02Xnn5ZaaIhmZSYaeWxD7uHgilCDwPmqYx4xlzzN9++234vo+X+YhbAVGGFMHHbm6vQgiGh4YwPDQkg4rVlRUsr6zg+o0b8HhaeGJiAuPj4xgdHe08erv12DyjGQQBVlZWcGh6eldt/55AdpHZBfAzh/4G/ur6d1B1qog4S+j3L/0Bnh59Cgq5swzjbi3JlFLc4BNExycm9jTWvdPxNM4TCYIAtXqdZRQ7ZVF2gSjzhWHISo08q9j0nmgu3wroug5D1+F6HhqWhXyrBD0heOSRR5BMJHDhgw+QSqdx7NgxaTcA5qh63Jl4aJ2BgDsDce+6XCrh/fPnMTU1tcMRAGJ9utyD28uHrxKC0eFhDA4N4eDUFCrVKurVKoqlEq7wITapZJKlBcfHWVowVj6Qnjv/Uni0sbq6irHR0bs2DrQXbrPUGLibzoD4ps0xS1tbePOtt6AbBr78yitIJJO7Dn3qBZqmQQ0ClMtlgBBkuQEXZ7AnnjcvQ4QRM3NNzgCliNChfAGmbaBqmuQOtJOzTiQSeO6LX8QPXnsN77z7Lp579tltkibPWH0etb+HDZ7jyOdf1zTp3F24cAHlUglfanEEAJ4Z4EFE1wxYLBqNQwQV+/btQ6lcRqVSQaPRwPraGhYWFqAQgqHhYclTik+xlHaKbmveA0CpWITreRi/C9oC8tx3cQYM1cAvTv8C/vDKv5XOwM3qTfxw6XW8sv/lO3rbbnMJaBTho48+wuzNmzhx/DhOnjoFGkWfuAVXU1XW608pqtUqkyzm9/lObYfIKLZmW1uzAa1Ip9PwPA+WbSObybQlHx6anka90cDFixeRy+UwOjbW5ES5rvuZtyXfM86A2MxpzAtzbBtvvfUWCvl8xzYTlRtdoQXeFS1Zgvh7B76PZCKBfePjUFQVYRBgc3NTpgVvzs2x6GB4GGO8ZphJp7ePSQhUVYVlWSgWi3ji7NlPfE+AZjZvL7ib2uKdPPyl27fx/nvvoTAwgGeffRYGnzp4N6DrOmzHQaVaZW1+fCZ9E8OZ7JRM7QSFt/sIZ02SRXu4p6lkEjXe5tOpdJTJZHDu6afx5ttv48rly3jk1CnIgUaUwv0cxEMeNkScpCmUN8Ejz/n5eTz91FNtWfnCMYx4GWnXddNhY3Vcl43IHhtDmjscjUaDdTatrzcFFaNjYyyoGB5uCipEnXhjcxO6rmOAlzI+KQQfZ7eo+5mxp/Hdhe+hbJXlz/7gyh/ixX0vQFP2vkXItHjL+wZBIIcNnT17FtPT06CUItiDfesIQqBrGnzfR73RwLFcrql7QrymXXdD+8MxZ4CimS8gurq6QUy4DcIQdhdJ8tOnT6NWqeCdd97Byy+/jHQ6LR0ASiksy9rhxH6auHecAc9jKWJKpbzkW2+/DUopnnnmmY41G1XTmEe4Vy8q5vG7joMIjPUponmVdwKMjY/jC5SiVq9jlXMNZi5exEcffoh0JiNZxiMjI1AVBeVSCVEUYbzHLoLdQIEdOtbtEH4KZQKJWERz4/p1zFy8iAP797P2On6/7tb7qqoKlbfldRrl2a5dstPdaSJWEtJ+xkAHpNNp1Op1uag7KSGOjo/jzJkzuDgzg2wuh8nJSZmN8Ps6A586FEB2roSUYn1tDTMzMzh+7FjHXn3x3AqektJLOrYlSxDvPooTB9PpNI4cOYIjR44gDAJsbG5iZWUFa6urmLt5E0RVMcKzBuPj48hkMlAUBRsbGxgdHb2rBODe1iXBzx/+OVy6eFn+ZLmxgr9a+DZ+bvpn7+h9gWab4Ng23nzrLdTrdXzxueeauqzugisAgGUVG40GCNBe22SvjkGM/xPnHu0GRVGQSqVQq9VgddFKIYTgqXPn8IPXXsNbb7+Nl770JWavOL/JqteBT6iIuRfcM86AqPlFUQTP93Hp0iVUymV8qYMUpYCIBu40MqWcCYwo2tFOKEllUYRMOo2jR4/i6NGjCIIAGxsbWF1dxcrKCm7OzjKOwPAwrEYDmUwGiU9Q646D8A1st4dQDuL5NDID/Bw+/PBD3Jybw4njx3Hq9Onmc7qLToiqaag3GjjYA+eiXRtTfIGLPmFKdo4c3vU8VBVJ3qpk23ZXWeSjR46gXC7j/PnzMBMJSWTriw59+ojCkAUFioJyuYy33nkHo2NjOH2mc4ucQogMMMIowp5of/w5cz2vbfeRbOvjxNjR0VGmwPnYY6jX61IP5eKlS5j56COkMxlkcznUazUcnp7e8/Xvdp7dIJzlLww9ihOFY7hSvip/9+8+/v/hpw7+FBLqHln5LWWCarmMN996CxTAS1/6UlO78F4zn92g67pU7ts1oo45Bp2UEKMwZN1h3HbsJeDJpNOoc+GybiOJdV2XpcZ3330Xjz72GMAJi5+1CuG94wy4LosKNQ1rq6u4OT+Pcx1SfHFomsZSTXfoDHi+D0qI1DYQM7WbUkEtD4GmaZiYmGBjJilFtVbD2toaVpaXUa7VEAYBvvvd7zIS4tgYhoaG7hp/ANgeiRnxWhulbNyy5/uIYkpcTQSX2MNfrdUAsFHPsiYmrjNWY7MsS7bKzczMYGNzE48/9ljbGQN3Mx8RhSHCMNxzJ0GT88J+gICXn+402spkMrA4kVCwg9uCEDxx9qwcn33u3DmoXDeh0WjcUYdKH7sjDENEvB0ZhODC+fNIGAaefvrpXfkzQkLcDwLcSUe653kAYZMq4/aiWxo5k8k0BxWbm1hbXcWtxUVQQnDx4kWsb2xgnJcU7uZzE1cRFP8GQSCng/7K4X+I3zr/fwcAEBBQJ8I3L30Lf2Pqr4Fi227UhN0AZGsc+yP2M9/34TgOdF1HtVzG++fPI51K4Zlnn0W6ZU3frawA+LnYto1kKrVrKr/pHFoCChEECjvara2wE1RVRSKRYB1Jtt2VCChKjW+88QYKAwMYGh6GpqqwHlZnIPR9EM4mv3HjBsZGR3uS4zQMgwm8+D6LoPdo9D3PAyiFYRhNA5J6jnQJQS6XQy6Xw9DQEFa//30cOXwYYRDg1q1buH79OjRNw+jIiOxP3k0ZS4wjDrkIk8flNSkfC9oODh+upGlaz45R0zjSNvCDAL7rYuajj+A4Dh577DEMDg5KoyBY8yQ2qlNTFChcge9OYVkWCNjo4ogPjOoZfCHLPmGuFX8nCxpgz5ehaXB9H7ZtdyxdAGxzeeLsWbz66qtYWV7GKE+F1qrVvjPwKSHgrZu6rmNrcxOlchnPP/98Ty1+pmHAchy4riuJqr0iDEP2bPH3jrfJAujJfmiahonxcUyMj8NqNEDKZUxyQuJHH32E6MMPkc1m9xRUCLshMqw+DxA6rfMwDOHzKYtHc0fx2MCjuFzazg58b+F7eGHii0jrseeXxlpx2xzT5+PI1zc2MDc7i4HBQZw+fRpBEKDCu2sUwlp/FV6bVxQFiqJIYuedol6vI5NOI/D9O2Li0yhCyK9JED3v1JalUynYjgPHttt2JMUxOj6O/VNTuDk3h8GhISiKIpUUPyvcM86AaP/a3NyE1Wjgscce6+nvdF1nD1AQwImpPvUCyhdMRCl0Xd9RY9ur17q2ugpN03BwagqZdBqGYaBSqTAS4uoqLnzwASilyOdyGOOti7lcbnvz5/XL+IYfj/7jEBG9wheUp+vQVBWpVIrpdcdaEuPfA5DtVOlUSvYzxzsjKL83pVIJV69cAQjBuXPnkE6nmxwSCs5ViCJ27rGebQJG0lJ4+nYvTkK1WpV1tyAIYPSykYvzFs4cR+D7TBnOMO641SiTycArlXZ1BgAwHsnEBG7Oz0tnoM8b+PQQcvKgrmm4OTeHgYGBnmV8dcOACtzRhDjP8xCBlQjk+gJYVnGPx4qiCOsbG5icnMT+Awdw5tFH4fs+1rmK6u2WoEJ0NhmGITOBEV+DcTshnIJWyI2Yf5mGAU3TkEom8dVTX8Xbb/xzMNUBinJYxnfWvotfe+QfyVKksBvAdosd+5/t7oj5+XncvnULBw4cwMmTJ+V1xuXVEYYIYtk7eX7CMeDOwV6chFqthonxcRYQ9breRQAhugewvWY1VW2rhNsLEokENFVl2gcdOpLiOHb0KJaWlrCyvIxD09OfuXrpPeEMiE6CiFLMzs5idGICiUSie1pWgFLopgnH9+G4bs/OAKUUju8z5UJFgW4YO5T+mshpPTxYK6urGBkdlQMrCCEoFAooFAo4efIkXMfBysqK7E64+vHH0DUNg4ODGBoawuDgoFTNUjiJROHCOTIK5w5AO6ZuRKl8ALtBbMgKL8u0w+rqKmZmZpBMJvHC88/vyGaIdqCIf++LKCQMEXCSXhBFQMtGKKJ0TdOgqSo715ZrqVarklTlB8HuSmKUts2YRGGIkBsoTVVZluEOuANCDMXnmZpuz6Trujg8PY3l5WUsLS1hamqqP7nwU4TPHdu1tTXU63U8+dRTMkXdFZTCNAwQRYG/x6wipRQOb/1qZ29I7HW9bEZbW1tsKNbwMCOeRhF0Xcfk5CQmJycRRRFKxSJWeIfC+QsXQClFNpPB0OAgBoeH2cRNsa65tDe44I3YXEmbKFe0sGmaBtM08cjISTy77xm8vvy6fM2f3vwmfuHoz0NVmF3pZjeiMMTly5exvLKCEydO4NSpU80BFu+0EcPSQkrheZ50aOJfrflNlU/1E/aj3bV4rotMNstKIDxT2haxzEa77ocgDFkQwUtJvWg2tIIQggTvtPI8D+jiDIRhCEVRsG/fPiwsLGBy//67MuBuL7gnnAHhAS3fvg3XdXHuxAlQQmDZNnK7RGIgBKauwyJMThi7TMiLk3s81wUI2TWd1PQwxJn9cbas46BULDICCCAj5SAI4Ps+Au6l5woF5AoFHDt+HLVqFcViEcViEVeuXAEAFAoFjPPUYY4LVojF0Q13cy7B/Pw8Lly4gKGhIZw6daptWYMIz53/v6qq2waYUin2I6IT6STwOqUoZRAwwqBwDDRNQ6VSQS6f3yaGthrVeHTRBQFP44qOkyjmCJA9LG6FMIUzlzucmQ6LNAgCBGEI0zQxdfAgW9T79vWdgU8RAc/sXbt2DZOTkyhks1LSulupgIKXGHnU53rervNIxPMinl8CVmoAJ9i2YkeWsUMJYW1tDQnTRD6fRxCGCMIQhNsO8UwRVcW+yUnsm5yE73koFYsolstYWV3FwuIiDF3HCC9Fjk9MwDRNWTLoiQEfe82vn/pV/Hj5DURgwZEbufj3V/8D/tGhf9j1GJ7r4u2330axVMKZ06dxYGpqx3sTQkBUVY7L1QA5k4TdIipthsiWCvsXRpEUpgO2nR6N872q1SooWCeBoihtHXeROdyNECjsk3A6RMAqgrNekUwmmSKh5yHq0sIqlEqP8uzAwsICTp482fVv7jbuGWcgCALMzc9jenoaQ4ODqFSrCHwfrud1ng3OPxQzkdiVuS02crFoKTh5UPAFsHtZQD488Y2E/7u2toaIUgwODaHRaEhpzFYIr1bXNAwUCpKM5zoOVtfWsL62hrmbN/Hxxx/DMAyMjIxgdHQUw8PDHeU6dxsM0isopbh86RI+vnYNhw4dwvT0dM8PYtO7EgKVs7WbIjTK2nOCIJCOUtTiHCCKUK3VMD4xIQlJQRhC4xoBe1mIwrsXmZL48KJ4SWjXYxKCRCIBz/fhum7bdJ+IFkEpTF3HqVOnsHT7NhYWF/HI6dM9n3Mfe0MYhli+fRuO4+D06dPQ+Tx4y7Kgx4R+OsEwDLj8c+3kDLTO1/B46UkTw9TQg+0Atlt0tw8MEILVlRWMjo1JGe527HNCWB+9pmnQslmMjo5Kh7ZUKmGNdyicv3ABuHABhXweI6OjGBkZQaFQ6LiOozYOyv7MJP6LqZ/Gf174K/mz/zz/l/jpsa90vD6hRuq5Ls49/TRS6XRvgUnLpkwIaRvNU0oRxpwjEVx5vNQLMBusEIJUKiWzDfG/F+Rw8T4dT0mUXNBMHpTkQl4K2c2hoNgezU4phef7bSdaCvsXUYpcJoPD09OYm5vDIZ5VfKicgSAIsLG2Bs/3cfLECRBFQTKRQN2yYNs2jFhdLg7Rf2/wNi6fk+hayWKSPBMz+sIRUHjqCdhbtBhvR/E8D7eXlpDJZBBxJ4DyKFjntXyNL+ROH6yZSGBqakqmlUvFIlbX1qSXSCllI5l5b3I+n5fncDeGFEVhiPfffx+3bt/Go2fO4ND0NBqNRm+Khvye7ZqC5yWC+OcjeBJikdfqdURhCMM04TgOW9RRhGwms2cSYBjz7sX5xQtBcZErUS/cecrsmTBNE6hWWa24zQIVJRJQCsM0QQjBvslJLC0tNU1N7OPuQUSQt5eXceDAAcl/cVyXTY7rYXy0aRhoWFbbrGKrEyDgeZ4MIu4E4ikLogjVSgVbpRImeMQvInmFb4rCAeiUlieEyImtj5w6BddxsLa+jtXlZdycncXly5eh67rUNBgdHW0KKmRLcsuz/w9P/gN879ar8CIWYIUI8cdz38BxHNtxDsWtLbzF1UhffuUVycS/m2qohBDWwhnLPgYie8LJnLV6Hel0Gr7rosE7oQQnQiiC9gKZUeTlFfH+rd1KhH91tHjcWTATCUS2zZ7HFmdABBEUgMll+KenpzE/P4/l5eXPNKt4TzgDvuuiWC5joFCQ/flGIgHVcRBGERzHaa81wD8cEYH6QQDHcSRzu9NiFu8JQI4yBtBRZawVEffyfD6iloJJiQ5xZbFUIgEQwkiEprnn1L2iKBjiMsknTpyA4zhYXlnB6soKrl27hsuXLyORSDCp0/FxOQPhThefSO+VSyU8c+4cJvfv3ya99XLMWMZkr0QbVVGgGoY0rOtra2wATKHA7i2lsHl3gaIo0HRdOljdIPgCwLZ33+5KxCIXxKEdZ8+PYRgGU6YMQ3gtKWXK08wUXLiKH3NoeBi3bt3CVrG4p3vSR2/wfR+ObcNqNHCGZ19EF0q9Xofjuowk2MVBNvjn5bZk8TrZDpHZIsAdDSUKeAlDdAitb2xA4dwiQ9dBFQWGprFe/F0iz3YwEwkcPHgQBw4cQBAEKG5tMduxtoZbt26BEIKBgQE5e0Vsrq22YyQ5gr95+OfxjRt/In/2xvqbGESz9kc7NdJavQ6gxywl50PtGdxZEo5+RCmzwSMjjGtFCHzfl0ODhGOl6/qu3JAgCGTmR0DMOWk9h6hLMCGeHdMwYNs23DaZ69YgAmCZzEKhgGKx+JkGEveEMyAi4ampKfkzuagbDTiOwz7MLovP4G1Cjusy1nsXR4AC8IKARe4x7363sZ8iTSwcAAE/CGA7DsbHx1HI59HQNBmdkNg5xDeensBfm0gkMHXwIA5NTSGMIhS3trC2tobV1VUsLCyAKApyuRxGRkYwdfBgkw76bmjU63jjzTfh86luA9yxaKci1vk074yvIO6FiNAJISiWSsgXCsjncpIvIDZaofjmuS4UQqAbxrbX3wKfG2ylhYXcLXshFnY8nRj3yxOmCUt4+NwZEBoPQtxIbBAhpcjn81BVFctLS3u8M330Akop1tfXAQAjMaU20zDgcDlYIQLWqUvI5FkcL5ZV7FaO8oXQkKY1tfl1yypGlMJ1Xbiu2zS/gwBo1GpIplIY4+dfKpdlG3HcXuxlPYp7I4IK0dpnOw7W1tawtrqK69ev4/LlyzAMA4NDQ5jYtw+T+/Y1OTi/dOzv4T/P/xWsoMHvG8V7yvv4u/g76KZG2okb0ek8d7t/bf4IlLAJoaL86zoOLMvCqZEROVyu0WjIz1qk4m2+lxi8g6Ld/ZT6Am34Bu04EO2Cifj1JBIJoFKBz0sXwskIwxCO44BSigR3SgFmcwYGBjB74wbjstwlAbvdcE84A1ulEjzPw8jISNPPddOEwb3oRqOBTDYrI8LWx8bgbUIuT7l0e7BE763SWtMGdkS3EScaOi0LWVVVGLoOwzCwuroKAjCVMfB56q7LhqCgeQG3PvQE2+WOHZ5l7Ofid6qiYGRkBCMjIzhz5gwsy8LtpSWsrK7i5uwsbly/Lo2LSAt2YtRWymVcvHgRhmHg5ZdfltrqwN6cgZ4gOBstn0vr+2xubkopZ03TWOSmKEim00DE5pX7nDQmDKzOP4f4dbaWCARIl+yF/GziCzv2WpOrEcbHEotRpTSKkEylto/PnYNsLoel5eU7uWN97AJKKTY3NpDNZpt4RRRs6mS9VoMfBGhYVjPPI/aZCoa6HwSwXXd72l0HuL7POpja1PRbn+0gCOC4LisriNcB8nnVdR21eh3Dw8MyHS24Ma0T61qHHMWDjPjv4/egFclEAoemppqCisXFRWxyueQPzp/H0NCQLCnkcjn83aNfxR9c/UN5jNvkNvzAx4cffNBRjbRT6aEVO+xg5xdKWyg3ZMq7APh7bG1tgQIyS2qYJgsIFAWpREKSSqMYMVMhBIZpsjK0mFURhnL+gNbi7O1GHmwXTADb5GrqeXBjSruWbTNeQus+FLHR2GEU4dbiIvKPPtr1Pt4t3BPOwMrSEgghO0b+EgCpVEr20ta5Xr3apu/TNE0QRYHLPfxuKXNBNGyX5hOLOuA1R+FciPMxTROmaTY9KKViEclkUrYZiQ0o4NmHdt5k63W2bpTxLEVrdBBHKpXC1MGDcjaCbdtySMr8/DwURWHDlWI66AIXLlzA4OAgnnvuuR3GLe7h7gq+MOPnK68nfv27eP42T/nGnwOND42KOEtf52pvge/D9X2E3DnwfR+KqsLkRjYIAtYHfoftORHlg41iaTqx4YgWwzCKJIcgyVs6RRuoIBcNDQ1hYWGhtzbZPvaEKIqwsbGBR44ebfq5yAil0mk06nV4ngdbUVipsc0zaBgGbMeBbdusxNfp/fhzR9FSXuSIEwwd7iQKaKrKbAfnNwGs5FAqlXBmcnL7dZoma+HtOAmtgYV4X7le6bZOSLe1K4IKwzBw+MgRqIqCzc1NrK2v4+rVq7h06RKSqRSODE9jQC+g5JcBABmkcXFmBuVyBU888URbNdJeM4WEEDm6t8l2oE0UvktwsrW1xWwwj6I13rEQcIdA2O0oDGWZJqJUjik3dB2GaTYFEa3v1bZU0HI90laHYZNDYBoGfJ5ZTiWTTN2VBwzJZLIpSKGUIp1KwTAMzM/P48xD5QysrGCcp1RbQXjtvVarIeTSrtlMZkcEr+s6q91wec1W2cs44l0ErfB9H5Zlyf5lgC2chGnK+mIrtjhfIP56RVFAOeu1137RHU4C7+MHIYxIwjdZALLWRsFS0oL5Ojo2JjMUDctijsHqKi5dvoyZmRmkUinpBY8MD+O5L35xh6qZZN6Keh4/fjunBZQP8OC/j3dZxBnUvUB494OxqW0q1yEIgwDgNTXCSwS6YbAavutKr9+2bTQaDQS+D61NvVh4+LsRcwghTalacS6ixbDOyZURZeRC4dmLqxV/Ozw0hNnZWdy+fbut4ezjzlEsFuE6DgbbzLAQEXgqlULDsmA7DhT++bUikUigVqsxxbcugkUiwtdUte2acVwXjuM0tfkavJTVrsRZLpcRhWHT8y6zinuQV2+yG8Iu8mBCkqfjv49BRMGpVArT09OYnp5GFEXMMVhbw+raGv66+9N4W3kHiqrgjHsGtUp1x7Ch2I1gjjDQVL7bEcjQbVJ3qyOw45p6wNbWVlMQoSgKiKoCvD1RBG+KqiLBHTPf8+Byu+H5vvwihCDdjhzaY5mXUoqIkCZnSLQYOq6LeqMhCeZpkU2MQWSgh4eHMTc3t6f78EnwuTsDlFKUy2Ucm5xs60lSyoZ9ZDIZNkGOZwjSmUyTKBClFMlkEl4QMC34Ds5AEIbSI4tHakEQwLKs7b5ewnrLTZ5G6oQwilAsFnGmZSiKSAUFQXDH4hHx5SNr3fEShnwhj8Djiw9sBO/hw4dx+PBhhGGI9fV1XLp4EUU+WXFtfR1vvPkmJnhvcjrWDhTxY3TslRZoXRh3QCIU2NraQjqVahJyUbnoR9iBSKOqKpKpFBK8xchzXXg8JRhQioTrbveT7wE7sjQ89WcmEnBcF7VaDblsFoamyYxB3LsXmYFCoQACYGNjo+8M3GVsbm6CKAryXI8jDuHEGaYpSchWowGSyTStxwh85CzPKnZrZRYbdHxjF07A/7+9Pw2S40zPQ9En98zae9837AABkNgGnCFBDqkZSbZ1JHkkb0eyrAifa8c9vnLElSzZYUcch30lXXl8fKWwdaTwOYq48pXtYzmORtKMLVkac4bkkABJrCQWYutuAA2g1+ru2rJyz/vjWzqrOqu6GgOSAJEPgwGgu6pyqfze712e93nr9ToX0xEFgUei7ciLxWIRoig2TNjjWUXa1/5IZbqYDKMQ8zt2/kDjxi2IIm9LPHToECrVKo7PHsetW7dgBw6cwMP5CxcwODSEoYEB9NFJi3ydoDHSj70OWvpkmbfvpxjpeR7W1tcxOjbW8HNJkuCLInzP20Q4FliJgIkCMbtBMwZcGC3yrHAHZyv7FuEzsNezFkPHtmHV61AUBelUitulhowi/YyuQgH3PsUS4xPhDADUkMakz9lDJEoS0qwO6LqoUYcAoL2yggAjlUKtWoVZr/NF2YwoU1SgEadpWQ2SsYZhkFJABxvIeqmEwPfR1RRRyLK8qdd1u2Dp9qj3DEScBPr7KOO0VRQQBgFmZ2bw3eqbuKBegu7p+FvDP4PQCvHR5cv48KOPkMlkeOuibhg8/dhMuGuI+JtqeG1rf1ugWCxuivIkKgfqUw++lTY7SwWqqgqPOmGyKMJyHDiOA03XeYtqRyIskexBGIYArQXKksRbgQRq9DdOIuIM0D5lURCAT6lP+FnFVo6eYRgIgwC246BWqSCVyTTMExBppOh5Hqqm2d4ZCEM+HM2xbZiWxTdASZKQYjXoDp6x1dVVdHV3N5w/yyoG28wqRsGddnYO0RR05HdRoaTYdUsdCbNWw+zsLDLZLJzVVRw+fBjVahWL8/OYnZmBREuRg4OD6Ovv37Bb2LBHbDSvED1WxK6E3wc3aY0GN81lZlmS4DRlFePANn3FceA6DtctqJomZDpwiGcW0EgqjkVkTwuDgHQX0V+xsmY+l2vUMGBvZc+SIDzWCbSd4IlwBtjCiXscomlaWZaJQ1CtwnFd+JUKUobBNwhd03idt1WpgDNFRZG3nTCoqoqUYTSI02yFVRqdFCJjOQHwRex34Aw0k+hY+iwIQwiRlOOmM2pacK2Wk1Wv4/Tp03hYm8c54SKCMEBZqOB71jv41Zd/heg8LC1hYXER9x88wO3paUiShK6uLgwNDWFkeLhBhXDTQm4qG8Rd31bGkXn3zcOpWFpfEATCBdmipZCl53WaYXBsG34Yom5ZcGybOwXbRkjEkVzH4cdQIvVfgBg8kb+cfHePjYCZYBP4c9fiHkefO4Nyj2zHQbVSgW4Y0CIZKCOVQr1eR71WA5rWMkAcYpadCqk+AEvnMpKaqml87XZy7sWVFYzGDGNTZJm3MG7lDLCIkt2DhqFEzC40BRPNdiP6s2bMzs7i0qVLGBwYwO49e/DGG2+gp6cHO3fuBA4fRqVa5R0KH12+zEsOPT09GB8bI+TIpo6eMPJnQ2ATd33YOmtQLBYhSxJXbGXYKqvYDM/zoKgq9FQKkkDaTT3fR7VWIxlAKm7X9vsNG9UNBZrRNU2zoczQkHEQRYA+SyzwEjvJQDxmfObOAEOUGdoOiqoik82iWq3Cs21UfB+ZdBoSVQPbqlTgui7qlkVqyvQLkRUFKcNo+II6nV+9uraGrq6uTdEJG6zBBDGibSpsAceRZppf0wnakf1KdJY4AIwcHAE+2vjdfZO0vMmyjKHhYQwNDwNhiHK5jDv37qG4soIrly/j8uXLyGWzfLhST3d3a681zpGK1DHZ+TafaXF1Faw3vxmyJMGj93KrbdyhLYUyTd+rikJaQi0LfhgShTfb5l0KcWius4YhIRqxXnTWzhj4PsC4AjQ1yMAzA0lW4BNDO2Z3NKsI+vd0JgOBCpmZprkxJlsQkE6lsL621rJUwCbxsaiRfigMXYfONokOzovBrNdRr9fRE+ELMMiyDKFdVjEk7PqGlHwr5xzxnQ7R18XamTDE1atXcfPmTUxNTeH555/nEwcjH4xsNotsNstHMs/Pz+Phw4dYWl7G3Nwc4TH19xMCc/PE1phyxiYEQUNmLS6wWFlZQXdPz6br6DSryD6XtYzrtDOJCZ+5ngfH8+BWq1Cp8FGrvUEQRQgR++f7PkzTJIGCQKTvN9nO6HcX7cRo4h180vjMnYFOFk7zAyBLEtKZDMxajShPVSpIpVJQNa1tqcChIzQ9z4OWzZK0XioVS+7pRIM6DEOsrKxgdHR00++Y98fkdiU6NIQRZ7a86qaHjXv6MQ9hqzbAxYUFfPDBB8hkMvjil76Ey+uXtzoqIAjI5fOYHB/H+NgYdF1HsVjE4sIC7t29y6enDfT3Y2BwEP0DAw2Gs9UiaU4DNjsEq8UiFEWJnQrISzodePgunSanR1QlNVWFQss2NlU0dE0TCh3Osok13FQiME0Tru8j9H3ouk6evXq90Vg3lQi2uh8JHh9aGuaYTdCgmb96rUayRjSY2KpUUK3VUK3VuK3QNY1/1qZjdnDOqzFkWYZoN1LzJr9VJL0dtAoimBrp/fv3cfDQIezetaujQE2WZQwODCBLW8CDICAE5sVFfHjpEi4BDUHFJpnkuKhbFNvez4BytnY2dZSw6+o0q8jKxyI2RMokUUQ6lYJH9QA83+fS1UYqFUt4jz5vjIzObF2+UICzvMyVM+MCBWY7Posg4olxBlou6KbfMZKOJIrIZrOo1WpwHIc4Br7fslRgWRZK5TJ8Oskqm8221PqnJ7TlgmPtSHELGiCLw7Zt2K67qXVvK8R59+3Sofw1FDy9NziIEydOxLe1tbk+tp2pkelpCAnZc4GmBS9cuIAwCJDP5zEwOIiBgYFN3Im21xj5+9LSErq7uxtY/Ay8o2ALUhUbbMLqulGIoghd16GqKpE5pnMvPM9DKkLkaf480zT5aFjmONqOA5FmKjYuJmx4H0CMCRMjSfD4ERV76fANCEMyrVAWRVRpMFGuVpFJpzdKBabJSwV+EBBHoFIBABiaRuq932ebaHFtDelMZpMNCsOQb1qB78OJIb9teZlN/26V2o4jDzq2jTPvvYf19XV8gaqRbgdB5DNz2Sxy+Tz27N0L13VJKZIGFTdv3tyUNVA0raMgKfqa9fV12I6Dvt7e2Ixjp1lFNkdGiYn6ZUlCJp3eyCqDOIfNraJRsqRlWWQ4EbVFhmGQ0dGCwInlTEY/+t0wmyKyaYmfIj5zZwAgF+5GavfNYB5+XESdTqchSxKRe6Tem6qqvFSg6zpqpsknB0qyjEIu194RABp6xVthtVhEGIbcGYhG/sDGLGyPtjJuJ0rcVOdDa/W8htppGOLK1au4dfMmduzYgcOHD/PFvikCaLH0GuqN0fcIAgpdXSh0dWHfvn1wbBsLCwtYWFjAzMwMbly/DllROJmov7+/oS7bCq7jYKVYxPOHD5PDiBsjmgM62lOiaXnmzMV+TmRBt/KsRVFEKpWC5LqoVqvE2Fer3FEIw5AoHToOUQcDeRbYSGWAOHkhGvkgQRhyvgBb0IxECmDL5y3B9qHS1lLbsmIzSsCGwxCGZJIme5olGhDUajV4NLuoqiqRJqYOPMKQaNzTCXq6rqNQKGzpCHTCOVpdWUFvVO2TkXAj/KiApq636wx0mjVoDiKq1SrOnD4Nh6qRtgpytvOZDIqi8KmLLKh4OD+PhYUFXLxwAQCQzWbRPzCAgYEBEhh0EB0vLi5CURQeSEQ7zFhQsFVWMVoiaBe0KYoCie41luMQwTHX5cEEm5TIM4lBAE1VG7KPsiwjpHtR85ECansAsnfYjrP1+PbHiM/cGWC98YsrKx1tmHGPuabrECUJJu0vD4IAnuui5Dg8qgQIMYf1/W4JYWvFqfVSCZquQ9e02L51WZYJgYWSj7YjOrOdJGC0t//s2bMt03vNMVSra+OGbAtGq6ppGBsbw+jYGMKQTE9bWFzEg/v3ce7cOQBo0EEvFAqx2Y2lpSWEYdjQtxx1cARRhCTLGzrerZwBuvFuOcseRDRGzGZJO6nvE4+fCgPZNH3c7NUzyLIMhCHcyHjYKF+Ajz+VJDyYn4ckyw1S2wkeD0ZHR1GzLKysrKC3Sb0UIE4cc6DjODhiU3bRsW14ARmXu7K8vCEDSzkFUpuBQdtBEIZYL5UwPDKyiezHoNJZK67rNrTadoJY0l1MiTHqsKwWizh95gw0TcOrX/5ygzjZto69RaaX/hKFri7k8nns27cPtuNgaXGRqKjSia2bhiu1uAcLCwtkgiNr0Wv6fVT6txU8GkQIgrCl48WCCUEUYdHZOSyYCIKAdBqFIVckbSYry9SOxZ0PCy6YY1EsFrF7//625/M48Zk7A6IoYnR0FB+dPYtypRLbMwyg5aJhUBQF2VwO9XodCEPY9TpqlkVGBXd1IZ1KoVSpIAiCzjdlUQRivjTmyZdLJWSz2baT+hRF4YONvl8Futh0H/236zh459IllEolnDx5knjgce+PvrVVZoC9fovzaVA+Ezamp+3ZvRs2yxosLuLW7dv4+OOPoWka+mlKsL+/n3u9CwsLyGYyhMwVc85hSGQ+IYot77VHN28AHbdjSbRd1bZtmPU6KtUqfM+DqmlEv8AwYjsP+PfIWhijMsi0pMA+f2VlBf10ymSCxwtd15HN57FcLGJfi9eEgsCZ2q2QTqehqCrqtJVsbW0NlVoNo8PDZOiZIMAMw+2Jh7Ug7YFxUDwP2Wy2pUOuKApAeSlhEHTcZtaSeBzHNaJ/Li4u4vLly+ju6opVI90Owk5r3pHr1lSVBxXeCy/woGJpcbEhqGDOQVdXFyCQeQTF1VUcO3o0/hjMUads/VZ1epYViFOVbAUmf87kyavF4gY/SVGgp9Oxx2rggzR9Jw0ZRddFqVwmXRufEj5zZwAgHv7pt9/G4uJivIAIOiMaiqKIdDrNDbQAoFKtcp1n0MxDp949a+fjXxlj8dK/lyuV2IgkCsZm91wX2MbAibirjf0ZNS4ffvghfM9rm97rtEzB21selfwmEHGeiclJTExOIvB9ssBpSWHu3j3uPAwMDGB+YQFjTYIhzRBFkciB+j7v34+yqHlWoMXwkfanS17v0TKE4LpIp9MtWxBFGkE4dFx13EwE1k2ysryMky+/vK3zSdAZJEnC0MgIHs7Pt94wOyHrgqxTMZ2GSSdkMjXBTCbD1Ui35cw3Oe7sb0EYolQqAUDL0gYAXhoLwxBuC2ni7SBORyAMQ9y9exfT09MYGxvDsaNHt2zd3QodBxItAhtBFNHd04Punh4coCOZGQnx9vQ0rl+/DlVVMTA4yL+PgYGBlsdhJEIIAlFKpXaEl3TpvJMQ2BiPvAWiWiWCICDwPN7SqanqhrxwDBqcAXoODF7EdiwtL0OUZezYsaOjc3oceCKcAcMwkMnlsLS8jD27N8/LDtC6PaYZtWqVt6gJoog6JRfWajUEYYjMNidAsXG0ITuPCGO8Uq1iaosvi6WsvTYM0ljEXGvc47VSLOL8+fNQFAWvvfZaw7Chze/vLDPAI6ktzrXVd9LMbRAlCT29vejp7cVzBw+ibppc6vT69evwPA/37t6F67okLdjXt2lhclIVkwjeOAkAG9PkOikR8MukPcQsZZeOCC1Z9TqXtI0DG27DMhIiNW5R775qmqjbNqampjo+pwSdQ5IkjIyM4N7t21hbW9skWBWisYTWDj7lGBmGge7ublRNEyb9v2aapKVsG6lz1l8uNK0FQRBQqVYhs1kJbaAqCjzqcHbqDLS6ymZnIAwCXLl8GXP372Pnzp2Er/MYOl9YuXRLhzzu+4h5TzSoCGnnAAsqSqUSwjDE+++/TzoUBgaQy+c3HVuSJAh0w2a2I6S22GVk3w5KBBunHhLSOu0UkGQZGVXlwkqmacJIpWKDKYWWGD060ZY5jUEkoyhLEhYXFtDb17dJv+aTxBPhDMiyjMHhYTyYn4/lDfDaVpsFHYYhJwMBQKFQIN5euQzbtiFKEmzbJj3oisK7DtoiDHm9EWjcjGtUX7qddw9s1KEC6oF2xFdAfN2vub3w/v37OHvuHAr5PA4dOtTWESDn32GZoMPMQEvnbItODCOVwuTUFCanpvDxtWu4ceMGxsbGsLS0hLt37kAQRfRGpqdlsllI9Fz4eNeIo+K6Ls/gxLX7NMP1PD5tkMlTM/VCgAxMcumMixDxA60YMYkvano/mGMhSxLmHz6EoqqJDPEnBFmW0dfXB8t1STdKszMQsSUB+UHs57C59wAx7INDQ1hcWEDNNOHQIVgO1R4w6ACZtqBrtMEZiaBaqSDTohwahaIoEGybRK4dEpDjiMcAiGNP7ajnuvjggw+wuLSE/fv3E2f1MbXAxnUoxL6uabIfsJEBbtfDz4KKAwcO4L9861vo6umBJIq4ceMGrl29Cl3XeesiCypEUSRZ3kjJV6AtizZrRe4giAgikueMUySJItK5HOku8jzSckxHZ6fS6U02lOnhMNKiJMsNnUkso7i6uoqjJ09ueU6PE0+EMyBJEsbHx/H+7Cwezs9jZHiY/45lBQDEC9qAPECsTUgQBNJhQNnClmURz5pOsgtBWgKtep2T/+LKBowE0mpSVYUKcGQ7iBbYsV3X7cgZaFX34+2FYYibN2/i6tWrGB0dxc6dOztKYW5yssL4WipvD3pMBqIdFhcXMTAwgMPPPw+ASJ8uUjLRxx9/jCtXriCVSmFgcBD5XA7pTIYMHmlqhxIAzgaPc0ZYutWho6WZoVaouAhz2ACSqQJ9bhgHpXkDYB0FzPlkXQi83U0UMXv3LsYnJ7eMABM8GpiWR09/P2bv3sWevXs3tN7RfmNhcFwXJnUEFFnmDrVuGCTrQ7uTWDdSpVIhfBKqOLiZhwPy7FGeSxyxuFwuI7dFEAFsEJAD2s3QyRpvmRmg64GpkdZME8eOHUMul3usLWwdEQjJC1r8uLOzWV1dheN52L9vH7q6uxHS4Uqs7ZkFFT09Pejr7UWG2o4ofM8jZT2a3m+V6WTKlWxuAcIQEEXoETJ6SMnGaToYiw3VSze1LbMSIxOxai4byJKElWIRVdPErhjthE8ST4wz0N/fj67eXly9cgXDQ0MbvZvRnvMwjI2Yq9UqqfUKAjKZDI8ODV2HIsvwPA91Wv9LGQap4dNZ47ZlQdE0MoKWpnBYOilaF2p+SCqVCskwdMD0lWUZEARCVOnEw9/i9xcvXsSdO3ewb98+7Nq1CzXT7IhgtDkzEI+OF/T3Cdu2UVxdxdEjR/jPUuk0pnbswBQdrrSysoJFmha8MzsLPwzR092NoaEhMkOBzioPQZwB7ghEnh/uzUekWlkmgPX9Rr9fQRCQMgzUQTaLumUBgtCQIZAkiZRDIsY+6t3PLyxgdXUVf+Nv/s1P9B4+65AkCUePHsWl06dx5+5d7KAlmRBoWL9xEbMbdQQUhZAFKbLZLCw6ATOfzxOSoaIQJUtaXhItC7quQ6eqcpsIzk2ZPPIjovDZtwXXiIERkJu5KS3RKisnCCitrXE10ldfeQWiJPERv48DUeXOR+UbxXEb4rC4uAhVUXganQ9X6u/HoUOHYJomtxs3b96E7/tQVBWDQ0MYHBhAb18fXMchQYSi8O+PkZXDIIAfhrCpJgkAngnQ6L7CprVGy0AS1SSo1moImEOQyTTcDzEyTZF9LtcXkGVcuXoVff392Lt37yPdw0fFE+MMAMDhF17AxTNncOfOHTJKE5v73EU0pt6YrKggCCSd3PRg57JZOLaNdapSaOg6DMOAR408mzHtWBYkRSEef1PKKO4BrVQqHWUFgA0PHyFRI9yyrt2KMe95eO+997C4uIijx45hYmKCE+ce57bdiQoWy5zEOS6dnsvS0hKA1gQgSZIwQPuODx0+jGKxiIcPH6K4uoorly/jo48+QjqVQldPD/r7+hq+j5COJbVpqylzKjVVbRhFzQx43B1nRCDbcWDV65BEcaMMIQibHFNGNBMlCVevXMHg0NCnSgB6FiFJEnp6ejAyOopr165hfGyMZG2ahKua17AfBEQvHnQmSVMni6HrJE1fr6NSq6E/lULKMGDQqZUWdQpqtRrqpglVVaHreoP9YeS16Gbh2DZcx9mko98KiqLAcRzSYthJhqmF7VhYXMR7Z85wNVI2tpndm8eBTluS235Gh69bmJ/HwMBAy+OkUqmNoMLzcPfePT5DYXZmBqIgoKurC109PRgdHQX79gUatDFBMmbnJFmGoWmbOkriiJCiKCKTTnOemlWvNzxfIkBsDuOfscmNgoClxUWsrKzgr/71v/6pZGajeKKcgXyhgLGxMVy7dg1jY2MQYlSYoouapW4A0h4UNy40lU5DWFuDAFILZpuXrCjIUoJO3TRh2zYCWh+URBGqqkKlMrZxkUW5UkG2wwUNEO/TBolGtnIG4haEaZo4feYMzFoNL730Evr6+xtf20k9seluBi3KBJ1494IgtJ401uFDvLiwgHwut9HP3QaCICCby2FckjA5OQlV07C8uIh79+9jeXkZc/fu4dKlS6Sm2NODQj5PPpel9Gib4CYeBjvfFkaU9Q+7noeaaRLxIUHYuDf0fT7rRBBFPHzwAJVqFV9+/fWO7kOCRwezHfsPHMD33nwTt2/fxt59+zYb0qbv2KzVEIYhJFmObWkFSCBRrlR4hwH5GDKPwNA0PrrYpzoVFm1lVunkQlarjjqN5UoFYRhuyTViUBUFNVpT7oSAHPcUMzXSvv7+RjXSx5wBZMfeyg1ot+G3ElaLwqrXsVYqYWcM2TwOkiyjr7cXhUIBqXQaVr2O+w8eYGlxEdO3b+PWzZtIpdPo6+1Fd3c3cvk815hRZBlqOh07wZZlFWOvg+oR1Go1zlHiwmNNEssOzRiLkoSr166hr78fuz/lrADwhDgD0VnY+/fvxxtvvIHbt29jT5sb4tNNHMBGij8GgkAEQ8qlEmr09VFIoohMJgPDMLiEZHRxi5JEekdVlUsjh2GISqVCBvt0CEVVYbsujx7boTmKKZVKOH36NARRxJe//OWG2lcnHRYMotD8QLcnED4uUlEcfDrUZDuRM+MD+FQrorunB6l0msi2ui4WFxexUizi+rVr8AFk0mn09/VhaHgYmXQ6Vqehky4V3TDgV6sNXj77LGYKmPohwhDXP/4YIyMjGN7G85Hg0cCcAc0wsHPHDty4eRMTk5ObFR/DDSXCaDYxbpgZQ4oFGEGAejS6o4GBpmnQNI1kjujwM4eu8RrAA4qobapUKkTEqMOsIiMgs3KXvpUiXeRZDsONYUM7du7EoUOHml76eNc57yTYhsbApl91cJwHDx5AFAQMtmkpbAZLzQe+j3Qmg5HhYQz09wOUrLe8vIyFxUXcuXsXoiiiu7sbg4ODGB4aIo5AC9vRDpIkQTcMolho25AkiTyvjGBKCerMdiwtLqJSqeD4sWNbf8+fAJ4IZ0AQBJIOC0NoioIdU1P4+Pr1+EVNb2K1WgVASD9bSd4ahgFJkuDStF46nd6UGmYCNKlUitSYaWrO9zyYvg+hXocoy1BlGT5t9+nUuwfAo1Lf9zcJ1bTDwuIiPnj/fWSzWXzpS1+CqqoNnvN26vudcgY+jWEZi4uLcBwHo1voC0QhUzXJgI4SLpfLcKhkJ1NDHJ+YgACiWb64tISHDx9iZmYGsixzHfSBwcFtqbqJgrDJy4/emzAMeV1x7t491G0bBw8e7EiKOcH3B0bs9DwPe/ftw507d3D944/x/AsvNL6QZvcs227IJrZ7xgVBgEGNebVWQw/tVmjQHgERzdGoNDIbhuV7Hp+AKIDYKUXTUCmXN9WQt7xGSkB2HGfLTYJlIXzfx7lz5/DgwQMcPnyYiNc0RbKdhxGdIezQGWjnfHfinM/NzaGvv39b4kgsS8MmT7KW4nQqhe6uLhQKBRzYvx+2bWOlWMTS4iKZ2PrRR8hksxiiHQpd3d1E/yFyve0QJY+b9TpRdoxkFZkjEIYhrl+/jpHhYXR1dX0mtuOJcAYA4mU7tE6zb/9+zN69iw/efx8vvfTSRgsg9c7qpsmJHqkI6aclBIGMzqzXUSqXkTKMtmxWtriZRr3junAdB57rwnMcrK6uIggCqKq6rY2dlQocx2n7HrYYWHpvaGgIx48f35TeazjvTk6g6UVxi65TElDbBY2tmdz35uaQz+c7dqhCqvbn2jZMmsFxHIeweCWJjxZlvcKZTIZPk1wvlQiZaHERFy9eBADk8nnOR8jl81sa5wYv33G4WlkYWdDlchk3btzAzl27kEmnk3kEnwLYHAovCCBJEvbu3YvLly+jv79/U+bODwJY9TqA9tnEKHQaSHi0TGQ0jSuOgqlWGobBnQGHZhqZY7C6tkbGrFMb0InDraoqTCqXvWWpIAxhuy7eO3MG6+vrOPnii7w7q3nFxrVMfz/opEzw/R7LrNVQXF3F8WPHOn4PC8Asy4JH+UOsO0OSJCiKwgcUpdNprqLqeR6WlpawsLiIubk53Lp1CxINKgYHBtDX39+R/oOh62SuCuWpsOfHD0Ouj3Lt449h2TaeO3gQkix/32q1j4InZtg6Y+U7tJXniydPYml5GR9+9FGDcAjzsgDi2XcSEYe+jxRd1PV6HTVqELYCm3SXy2ZRKBSQoS2LJu0/D0OiQri2vo5KtcpJRa3AHhxWI2p5vmGIK1eu4OLFi9ixYwdOnjzZ8HA0bFzbyAw0b3ixtLnoebX7zC2893Zn47kuFubn26oOhkHAW/sqlQpK5TIZOEV1AWyadstls8jlctB1vaVoSCGfx969e/HqK6/gL/3Ij+DEiRPI53K4c+cO3n77bfzJn/wJPjh7Fvfu3YPdZmCWSo0GwpBHlwDtOKjXcenDD9HV3Y2DBw4goNoFUYyOjkIQBLzeAZfgypUrXMvgX/2rf7Xl659VCILA77Pruti1ezeGR0bw/gcfYH19veG1dbru5Q6yiQBxHlhWSJJlrK6udlyWY1yEQqHAn08IRHBIVVVUazWUSiWUymUiT9zGJoiiSLhLotjw3DWDaa289eabqFSrOHXqVEObdqs1+bg4A52UCba6f1udy9z9+5BEsW2JlgVxpmmiXC6jWqmQTC/dO8IwJMONurqI0mhk8mAUsixjeHgYR48cwV/4C38Br73+Ovbu2QPbtnHh4kX86Z/+Kb7zne/g6tWrWFlZacl1EOgzxAYm+b4PIQy5VsGdO3cw/+ABTpw4ERtEfFp244nJDDAPPwDZLPr6+nDkyBFcvHAB2WyW91yyBa11IhpE4QcBRElCJpOBaZpYXV2FoevbSoOzeo+maRBFkdcLmXY4EydBvc7LHrIsQ6HeJ7tGgZ5Pq64C3/fxwQcf4P79+zh8+HBsr2m09v39lAniNvSoxkC7z+ST1lodS2g91/3hw4fwg4BH7uzzPM/jXrxHNSNYpiIEKRMoqsqZuLKqNrSDdQJVUTA6OorR0VEiDbu+jrkHD7C4sIBz588DALoKhcaZ65HrNHQdHh00Evg+AhDp2o8++giyJOHFF1/kUxe1JmLkyZMn8Y1vfIOMft4ic/LzP//z8H0fO3fuxM/93M9t6xqfNWiahjoVHFNVFcePHcPbb7+NM6dP48uvvcYjddd1AUHoLJsIwvIOQbJM5UoFruOgUq22nJ/SCizyZM9OOp2GJEmkz51uDhbVymBRoUL/ZM+Iqihc/KhVO/NKsYjTp09D0zSiRtqGDxFd+4+NQNiJ+mAbsm4nuH/vHgaHhhqCIzYS2KO6AUwLgAtNUXusqSpC20Y6lYL8CNF3IZ8ngQWb2Lq4iIcPH+LOnTu4cfMmVEVpmL0S/Z5EUYSmqrBoNwlA9rnV1VXMTE9j/4EDGB4eRhiGmyYVflp244lxBgBwMo7reZAkCVOTk6hUKvjoo4+QyWTQ09PDNwnDMDruSfVpNNlVKPCaXrlSQeFRhscIAhzbhpFKIZvJgKnOsYyFS50DliIUQDxlTh4BSCrPcTY5A7Zt47333kOxWMSLL77YEQFteySgpm6CmLvX8VyCTn7fYtHfm5tDd1cXRElC3TTJpDjfb+BxhCHRKWdGUaaRkWSaWGMiQzGzx7cD1l6Uy+dxYN8+WLZN2o8WFzFDddA1VeXlBLbAFUXhHQZBGGL69m1YloVXXnkFqqpyTflmo/3iiy/iG9/4BkqlEm7evNmyj/hb3/oWvv3tbwMAvv71r3/fuvSfd2iaBogiPJrVkWUZL37xi/jud7+LM2fO4NVXX+VBhKqqkNsMvIoioA6frCjI5/NYW1vD+vo6MjRTsF0EYYjA95HLZpHP5fgmxiLWIOIIWyCrVaR2g0nnMj5As9Lm/fv3cfbsWXR1deHkyZMtnxlmM5t1NR4HWAX9UcuL9AUtf1WmmZRdu3fziYGB522IwkXahKMOlSxJnNfj1+vwgwCp72dNhUSEbGxsDENDQwjDEGvr61ikMxTigoquQoGQyGlZwPU8mJaFGx9/jOHhYezZswdAY6aL4dOyG0+eM1CpkKE+9IYcPHgQ1WoV77//Pk6cOMHnzoui2NCr2Qo+22hAHpBcLoe11VWUSiVk0+ltL2oBJC2s0xvNVNBkWYau6yTC9X141DHwPA8hrRcJVMWsVqsBdEKeRDMHddPE2bNn4XseXnnlFTKZqwPEXn2LqF1o6iYIEeNlUs/zcZAHmaPk07Yo3/dhWRaWl5exa9cuIuYT5SjQzV+iCzgu88PGU0u0y+NxQBAEIkmq6xgfH8f4+DiCMMQa1UFfXFrCvbk5CAB6mKZBLgfP8zC/uIi1tTWc/MIXOP8hDEMo7BmN4MUXX+R/P3fuXOyidl0X/+Af/AMAwCuvvIKvfe1rj+UaP89gdd+QEvdkWUbKMPClL30Jb731Fs6dPYvdu3cDgsDT9S1leyPgQjCiSJTlajVYto31UomTCbcD27ZJFwJ1EsVICzOwUdt2PY+PYmc/E2gQ4tKhZ0YqRdaIKGJmZgbXr1/H6OgoXjhyJLbFmoFn27Z99hSR4ENo+HHIo1a2bjuVUI5D1Gawv09PT0OUJKQzGViWxc8nxEZGhW3+zfaP6QeAcow6kS2PRSTIYYGeCKC7qwvdXV3YT0mIi0tLWFxYwMz0ND7++GMYdGJrd08PmVtSrWL2zh1kMhkcO3aM21wB2FTC+rTsxhPnDASiCN91+ZAPURDwhRMn8N3vfhfnz5/HsaNHNwQ7tljUQbihFS/Q9pBsJoMqre+vra+jt7d32+fpWFZLVq8gCIQ5LMswAO4csJSg7PuwqH61ZdtQggBra2u4cvkyVE3DETo5rFqtkgeN/c8eFrZRR9pTYoexdJiKa3amopK6cdcGbKQDA+poBfRnQbghyevSlF3zWSzOzyPwffT39/MNn4n5dFL2cT0PItCYaXkMaGYxi4KAnp4e9PT04LnnnoNZr2OJZg1u3r6NgDpyAYA9u3djYHCQvI8a4jhRGUYC9TwPZ8+exU/91E9tes1v/uZv4ubNmxAEAb/+67/+2K7v8w5N02DW63AihN6uQgEnTpzAe++9B0EUsX//fv7MbMVa56qUYchbWguFAlaWl1Eql5HN5VpOtWwF27YhCEJL2xEtRQIb2hX8f02D57qwHAcyLRvcuHEDD+fnMTU5iakdO2DWag12gk3pE6liJr/isEnNtRN7EQ0yYuxu6PsNtqP5zxAg8xHCkARp1GZEpbxZp1ZDxxR938OFBfTReQMStRuiJMVu/s1g9hfAo2fa4toLseH0MOVaTdMwPjaG8bExBGGI9fV1LMzPcyKiQM8bgoATx483BLYCfc6i+LTsxhPlDEiSBEXTENCWPlYflmUZR44exZn33sO5c+fwpZde4p55q0XdsEkikgqLLOpypYJsNrtt1rdl28h3OE0q6hzw65RlIiEsCFhbXcVHH32EfD6Pg4cOEeay68JrJglG/02dJEEQUDdN+LSzgs80iHjtvPYvCAi9ADJkBPARQIQUEo1sll4DANuyiEynIMCyrI1Rn5FFy4YFRe86WxQAWbweXexswbINf2llBT19feh5BCeM1wQFYduGeOPWbX5e2slOM6RTKUxOTmJychK+7/NyjijLGJ2YIC+KLuiYMdKGYeDw4cO4cOECzp49u+n3xWIR//yf/3MAwM/8zM/gaKs57Qk2QdM0VEWRZBV1nW90g4OD2L17N27evIkgCHD8+HHiENA1Evd9s7Qzi6DZ82EYBjRdh1+vY3V1dVt97sCGM9CpvWl2eFkrpEOliT+8eBErxSIO7N+Pwf5++J4Hj9mKVmQ2UYQQhlwzQxBF1CnPidmYqA1hP2flCWAjg8FsA+jn2Y7DybXRtcSkfRmLP1raZNwiphgZBAE83yf2TRR5wFApl2HWajj6wgukPW+bsG0bIuUOPHLWcwuHSYrMN2EQsJE1OHDgAJaWl/H+++8jCEP09fVx7QqRtitKsoxsU/n607IbT5QzAFAP3zThUmeALWpZlvGF48dx9do1vP3WWzh+4gRhowtEorjZk2TjJKMpHQbDMIiQTK2GtbU1DNKorlPUqSb5I1+jqsKyLMzMzmJ2Zgbj4+M4dvQoBIFMvrIp74B5y0E06maeNcgCsimZTaJM1Xaw6w7yYQ5+SKN/iHzkMwMTT2GCGHELwKc/Z5kblr1g0YgoEHVClkZDECAUBNTrdawUizgWmUWwHViWBYE6Ao+afgzC+CFQDDyKicmYsFTjB++/j6XlZTI8qVCAwqLNiJGJcwYAkvK7cOECLl26tKn2+0//6T/F+vo60uk0fvVXf/WRru9ZRXTOhE+dUIA4kGNjY9B1HVevXsXbb7+NL37xi9B1PZZzFH3m40x/d1cXbMuCWa/DNM2W6oVxYM/vo9oO1vZctyy8f+YMLNvGyy+/jL6+PlITdxyuVBjQzTdqQ8LIbA6Ptj6KokjKdY0Hil33VSqva5om4cVEicwgyoAQhE3jxznCEH7Ebgh0wxcFASLbpAUiPsccNiZ5fmthAZqmdTzTIQrG5wrDkLeMfxLgI5KjGUY2swJEcfX9Dz6AommcnMzA3tEqyPw07MYT6QxURZEwLilJkHmTqqbhlVOncOHiRXzw/vuoVirYv3//ps+Iaua3ivS6CgVY9TpMy0K1VkOmQ4Yxi0512m+8HQVABkEUcf3GDdy/fx979uzBc889R84VZFCFugUBh0fongefjUbWNKLJHjaS8OhfENJ0ZwiyGRKetN/AD2B/l2SZEK3opssihOhrmefeCowsxbIagiDg/v37EARhW8qNDJ7rIqDqjc3CS9tB3J2N+yQhMiGTOQhmrYZ3z5xB3TTx3MGDRGNCVfnAEfY8pFKplgb/xRdfxG/91m/BNE1cvXoVhw8fBgBcu3YN//bf/lsAwC/90i8l6oXbBOvecBwHruNAotkB5iCPDA+jp6cHZ06fxpvf/S6+9NJLyOVyDYFEM7Eu8uH8r7KiIJPNolQuY3VtDfo2upKYJG2raYadwKzXcf7cOUAQ8MqrryKXzfJzlmW5bamNOQi+5/EggrHco9cejfj5z5uzk3QTZ1mEwPf5OF7Ws8/WDbMVUTvTzpnn2YNIKfTe3BxGR0bIKOZtwrYsUovXNHKuj2g74hDdA+L2A/bv6elpfPjhh+jt68PIyAgs24ZKicgKnYAKtA8iPmm78cQ5A2wTCinrX1NVTuSRJQmSLOP48ePIZjK4eu0aqrUajh49CokyhIO4WhY2M2bZoi6Xy1hbW0PKMDpa1Iy48ijOQAjAdRy89/77WFpawoEDBzA8MtK4QW3xeXxTphuzSutnhmG09sgpDKyhiFUE9D9gc22blQDSmUzL1ptODFnzUg+DADMzMxgbHSVRHCLGt4Mo37JtBCAL2nVdeI/Ruxdi7nkY8fLDMMTq6irOnDkDUZJw4gtfAECmZUqSRIxq5FnItyF/RslAZ8+e5Yv6F37hF+B5HkZHRzkRKMH2YBgG7HIZtm3zyJ+PhlUUdKfTeO3113GaOgQnX3yRDLuhNqP5uW7VtpvP51Gr1eBssyvJtizeNhZ1NjtBGIZYWFzEmTNnkEqncfDgwU0O51aWSBQEgJIOAcDVNEiiSATZOgDbRDOZzKb2SiYAxpRcW11DJ/ayobwL4OH8PGzLwuTUFBfGCdFZJ5lHJ9SGYciF7TrlU22cROuyS2zJke5DzOn76KOPMD09jakdOzA5MYG19XVCMJYkeL7fUEJuRRz/NOzGEyM6xCAIAtLZLEJR5AIwTM8/Sv7Zt38/Tp48ifv37+Ptt9+GRZm629mc8/k8J2aUyuWO3mPbNqJtY1ulq0OAOynVahVvvvUW1tbW8NJLL2FocJDU+bZI78ciSsqJ/LvtW+LOr9mT7aRX+BGwtLQEs1bjswjYUQVa5ml3NJeyxAHS58+udVvRVbvrifkdW9RhGGJubg5vv/020uk0Xn75ZdI1EgRQqTENw5BnBwCSdWqF3bt3o5t6/+fOnQMA/Mmf/An+23/7bwCAX/3VX91W6jnBBnRdh6CqCGn9OkogZo5tKpXCq6++iu6eHrz7zju4PT3N1+gmtLAloigin8tBkiSUSiX+bG4FizopQPvnnR+enpcfBLg9PY13330XAwMDePHFF6GpaluBrHaIru3HpjFA79X31VbY4pxmpqfR3d2NfD7f4AQIWxwPIPccIKVZ1jm2rcxAG0eg3XsEQYDjODh9+jSmp6fxwvPPY+euXfx3ClWfDCLcq3Q63VIM69OwG0+cMwCQBStIEgLf56x0gHj30QdqdHQUp06dQq1axZ/9+Z/jzuwsYat2CFEUkcvlIEsS1tbWOlpc7DWMBNRqMUXT+WEYYm1tDW+++SY82jrYT6UshchnAmg9CTAG213IcQsnmkeJbq6Pey7BzMwMcrkcf6Abz4FAoP83d0WwBa3TFCtLT3a8qB9lQQOo2zY++OADnD17FkPDw3jp5Zc3VNZohMVKKdE2tHyLVB/DyZMnARAP3/M8/MIv/AIAwhr+6Z/+6W2fZwICQRCQzecRCgJs2yaOAI3Qos+zoij44he/iB07d+LDS5fwzjvvkHbfVp8b87NsNguVOh7LxWJH51e3rIaZGHHrl5X5ONs+CHD58mVcunQJO3fswMkvfIGLCUUZ8ttCNEJ/TM4AFyt7HHYjck7VahVLy8vYMTUV+1JGmo47qkeJ6CwrwLpCHidnIO7uBUGAhw8f4tvf/jaKxSK+9KUvYWR0lHdXSVRMShRFImzVQUYR+OTtxhPpDAiCAJ32bNuWxaPVuFaynp4efOWrX8Xg4CDOnz+Pd959lw8xAlqn+hiy2SwfZLS8srLlg8JJbJH2lOgmG80EsAWysLCAt996C4Zh4MuvvspT8yxl6NAohp5w2+NHEd04O3MM4oxPZHAJ2+jafN6jLCSzXsfCwgKmOpxQyLIFAL03rCWIlkHYou7oXj2CIxCGIWZnZvDtP/9zLC8v4/jx4zhx/HiDNrxEe4JlSeLENQB8/Gk7sJTf5cuX8Ru/8Ru4fv06AODXf/3XH3tG5llDNJCwLQsQhFi7IYoinn/+eXzxi19EqVTCf//2t3Hrxo3G1H2750YgracyZeN3klm0LauhkyD6XTMngBGFgQ010lu3buHw4cM4/PzzvPauqiogCITB/wTgkxpuNjM7C1VVMRJRK40DI5qzLGMYBBsl3UgQQV68DXuwxWubgzfTNPHee+/hvfffR6GrC1/5ylfQ19fHz0WWJKI0SVs9fdbCCqBriyDik7YbTxxngCGTy8FaWyOKXIw9GYaxBBJVVXHs6FGMjY7iwsWL+Pa3v40D+/djz549HdWVuru6iDKh62J1bQ29bQRFXNflBBkGdowg3FwTm5mZwYcffojhoSEcP3GiwTAxhSyPTuF7lME229rmYjMDG2Bm8LF49xHMzs5CkKS2swhiz4sypENsLGhygu3Jod8PyuUyLly4gGKxiMmJCRw8dIgPpLIjfBHLshAIAiRK2mTYakEDG4vacRz843/8jwEAP/mTP4mXX375sV/PswZBEKCn07BKJdiOsyHA04KbMjAwgK9+9au4du0aLl+9irn793H0yBFO5GpnYlVVRb5QwPr6OpE4N4y2La9OjOqoEN0QIs8RUyMtra/HqpGy+rfjONtSY6UH7fSVneMxlhfZtfi+j7t372J8YqJjJ4PdA8/zuJw0D9xoRtF/jHaDnWsQBJidncWVK1cgShJOfuELGBoaAgTSoh2EISTaKgkQZUkhYs9EUdyyXf2TthtPrDMgSRLUVApOrQbXdclNpNFYM9hX29/fj6985Sv4+No1XL1yBXNzczhEjXm7h1SSZXR3dxPtgXIZhmG01PUOm6aGRbXzWWsJ+/nly5dx+/Zt7Nq1C4cOHozdZDVN4wJE23YGhNb6/3GIu3vRjSx8nN49q+uHZBDH+NjYtrXALdsmi0iSSK11u6nNbWQFfN/HjRs3cOPGDRipFF4+dYq3MYX0XEJQmVNFIcNGaAtU9B52d6CfcPLkSU5OdF0Xmqbh61//emfXlGBLpPJ5WOUyZ6XLkRblKELKrpdlGYcPH8bY2BjOnzuH77z5Jnbv3InRsbFY8nEUuVyOdyUtLy9jaHBwy7HI7NhAfKmrUqngzOnT8DwPp06dinUwFUWBJMvw6WhjjWqydIrtcI06AZcxb3Ht2yVaA8D9Bw/gOA52TE5u61zCMOTy04ZhQJRlIGKbHydCAKVSiQcQUzt24OBzz/HxyqHvw2FlTl0nBEZB2KQS2dXdvaXd/aTtxhNZJmAwKEvXc10i54sYwhtd0AyyJOHQoUN4/StfgShJePedd3Djxo22074A8tCkMxnIkoSVYrElKYgpI0b5AAAxNOwLZuk9Rhw5fPhwy2ibRRJM6/5RfdaOigQxiyGIlAmCLbz77Zwb+4T5hw9hWVbLml8rsEg8CAIYbOQ0VVTjvc1bpHE7Yi4DWFpcxBtvvIEbN25gz549+IHXX2/oZ47yVhh50Pd9PpCIXW9Xdzc51y2Qz+exb98+/u+///f/Pqa2eX8StIaiKFDo9+C6LieabbIdQMNm2NXVhdd/4Adw8OBBTM/O4vTp01haXt7yOerp6YFEJwquNU1KjMKngQQPIGi9O7raVlZW8NZbb0EQRXz5tdfaZpp4qeARiITR5/ZxgJcJ4mxHh10EDOwTZmZm0N/fj/Q2RYaYIyDK8oZkObW/HZ1Fh06D47q4evky3njjDbiuiy+/+iqOHDnCHQEAqNEJt7IsQ2ZBRBg2tIAKAAZHRrY83idtN57YzABAZolXdR2gE8MQdq513VUo4LXXXsON69dx7do1zM/PY8eOHdi1e3dLg93d1QXbtmHZNpaLxViFMVa/jhWvEQTYloUzZ86gXCrhxRdfJKmiNhDoBETbsmDbNlKp1CM5BJ3cl61ewRd0i17l7bZCAcD0zAx6uruR28ZQqBCEbMUEhqIZhRBorP+1eL8Q9/00vWZhYQE3rl/HysoKcoUCXv/yl5FtStUFvg/LNImmvKpCkiQ+gCoEcT4dKgQ1vI0ySH9/Pz7++GP09fXhn/yTf9Lx+xJ0hlShgNr6OpG2bfHcxj3Poihi7549GB4exrmzZ/HRhx/i3t272Ld/P+lzj3nuJFlGT3c3isUiSqUSDMNAKsbGMLJYXDuaAODevXu4cOECenp6cPLkydipplHoqgqrXieKgI9CJHxMiF5TbNCzXd6OIPDSS7SlrhO4NFMCNHYekY8lLdlticcdnKvtOLh9+zamp6fhOA720ZJ0c2TPRtpLosiJo57nAVSRlSGTyXQ8i+aTtBtPtDMAAKlcDuvLy3xRy4rSsBG38zhFAFM7dqDQ1YUHDx5g9s4dTE9PY2JiAnv37t08ypSSghYXFjgpKNpPy1rI4lKOAFCtVPDOO+/A832c2sawIU1ViQxwJAPSKbZTKxRiE0ExZYIW3v12IAgCypUKJ+BtB45tcwXJlmptjEQY96s2nx2EIR4+eIDr16+TgTPd3fjSl76Enr6+Ru12kO/brNd5zzA7F8dxyERFReGvz2SzHX/fZ8+exVtvvQUA+Gf/7J8h/yjTMxO0hW4YEDUNsG3Yto1cNtugI88j81bE4kwGR48dw/LSEubu38cH77+Pa9ks9u7di/Hx8U3vS6XTqFsWgmoVKysrGBkaahiCxtoc41LBYRjixvXruHrtGsbHx3H0yJGOeDsCJRLa9BpVTevYdkQzmt8vWIAkIL5M8CjBzczsLHRd35Y6bAiigsgI3rFD6Oh3HqsRs4UjULcs3Lp1CzMzMwgB7JicxI5du2LL0K7n8YyNrus8wLIdB0EQkJHUVAp+pMMg4pO2G0++M5DNQlAUBK6LWq2GQqHARUK2FP0RBMh0Et6OqSkcfO45TM/M4PatW5i9cwfj4+PYu3cvnzYHREhBa2sNpKCopGfcZrm8vIz33nsPuq7j1KlTHQt5AODTtjzPg7MN7sB2CXRCzNKPThhgxJrHxQieZUzgDlJg/HwoCzgMQxip1JaRxibxlhYL2g8CzN27h5s3b6JcqaCvrw+nTp1Cb28vbw1sfhfz7AVBQJqeSxiGfPqcGpE27STNx/BLv/RLAMhEzr/zd/5Ox+9LsD2kcjnUSiUyGIwOlopiq2yaKJA5JsPDw6jVarhx8ybOnT2Lj69dw569ezExMdFACO7q6oJj27AcByurqxjo7weX9Wb95E3H9IMAFy9exL27d/HcgQPYvWfPtmrbqqbBtm1OTuyU/MvP5THU0Xlb4WMaHOY6Dubm5rB79+5t3QuLlhVbST7zGS3kH7y1j6OFPa3Warh16xbuzM5CkiTs3rULO6kTwKSfo2DZRIDYCIW2oLrUERAEMunW9Txouo6+DmdcfNJ244l3BgAg29WF0tIS6vU6Uuk06e2O1t3aPDCsFScIQ8iKgr1792LXrl24MzuLmzdv4u7duxgdHcXePXtQoJFdLptF3TQRWBaWlpYwODDAjxFgcyrs3r17OH/+PHp7e3HyxRf5vPvtQNd1VKtV4uFvQXhkiD6CHZUJ4gL+aGaA9cpv0XXQCWzbxuzsLHbv3r0t54LV+2QqiRyH5rNrIGU1LUzP93H3zh3cvHULtVoNg4ODOHbsWEMtNu67cl13I91oGIT9S1/L1NZURSEqmZq2ZesTw+/8zu/gzTffBEAmjT3OyYsJGpHv7sba4iJ810W1WkUun+cOdCeOtChJCGlauau7Gy+++CLKdKb8xUuXcO3aNezdswdTU1OQqbZ+d3c3lpaWUDNNlCsVTkQOyEEb1pbjunj//fexsryMY8eOYWJiYsN56BDRjiTbtjvirNAbsImv8Khoyxegv9/OcaZv30YYBJjaBnHQ9304dLBaKpXqyMkJae0+8P3YIKJcqeDmjRu4e+8eVFXF/gMHMDU11VC+af6motlETnymsCl5UI3sD0Ojox3Z7k/Dbjw1zkCNthmapolsNrtBgNnKu492IND2IkmSsHPXLkzt2IF7NFr8zne+g+6eHkyMj2NkdBS9vb14+PAhbNvGysrKBrM80k0QhiGuX7+Ojz/+GOMTEzgSmSW+ZW2qCZwd7DhcTrVjdOg9x5UJOKs5ooQVF11s1xmYnp4GAOzcubPj97BZ7QGAzFZGLZoVon+PKnqtrq5i7t49zN2/D8dxMDIyghdffHFTao3JDkcNQeD7qFPPXtO0hsXPZqKLdAojAPQNDrbslDBNEw8fPkSlUsE3v/lN/Mqv/AoA4O/+3b+LV199teN7k2D7EEUR6Xwe5ZUV2I5D+vyZTHEnzgCt5UedxVw+j+MnTmD/gQO4eeMGLl+5go+vX8fo6CjGx8bQ29uLbC6H9bU1LC8vQxoYgK7rCHy/IY1eq9Vw+vRpWJbFhw0xbFfm3NB1VKpV4pjq+paqfI8b7QSHWpVUW8H1PNyansbk5GRLNb64Y9TpkCSFEvXavRZoLBdFZ0W4nocH9+/j3twclpeWoOs6Dh8+jMnJydgNuPl7imYTU4bRsE+xrgKVSuxLktQya/pZ2I2nwhnQNA1qOo16qQTP82DV6zAo0W7LzABjoTPBmMgXKooiJicnMTE+jofz87h79y4uXLqES5cuYXBoCH20jlwzTcilErryebJ5iGJDem///v3Yt29fw3k0bzBbQRDInHOHOgOapnWcIut0sbVSPAMaBYeaI/m4iLsdXNfF7elpTE1NdTw7PAxDPj0tWmOLRcx1iIKAaq2Ge/fu4d6dO6hUq9B1HRPj45icmoode9oQGUR+VqOEQTkyWx4g0R1rMdQUhaSe2yxoAPgP/+E/bErpfeELX3jkmeMJtodUPg+rUiF2w7YhqypkOklzKyeaTdQDe/4jr0+n0zhy9Cj27d+P2dlZ3Lt3D7Ozs0inUhgZGUE6k4EoilheWWkkIgsC1tbWcPrMGUiiiFcjImTk1zQDuY1WOKZzD9rGtp1A4nFodbD109wuB2yP0wQAd2Zn4bkudu/Z0/F7HNvm6n7tMiOMqNkMPwiwuLCAu3fv4uH8PHzfR19vL44dP47R0dHW7ZK0m4x9T3HZxOhrHTo5kWUUBwYHW85x+CzsxlPhDIiiCCWVQmBZpK5O62OyoiCMtJq1giSK8LcoJYyMjGBkZASObWNubg537tzB3NwcFEVBoasLlUoFsiTxdN/pd9/FSrGI48ePY3x8PP68t5kdUClbnV3jo4gQtUfr9H+7ut92F/TMzAx838fu3bs7fo9FlSZFSYK+xXVHr8J1HNy/fx93793DarEIkW7Oz7/wAuEDtClR8Dav6HnU68RpFASiZhdpwWJ64wI2vPu+/v62muAXLlwAQBzayclJ/NW/+lfxi7/4i52ncxN8X9A0DUoqBatcBsIQddMkjmEHDoHERuoCvNTYDMMwcODAARzYvx/F1VXcvXMHt6en4bouZ4l7nofBgQHSk76+jgvnzyOXy/FRys0QQHRWtlNqNHQdrutyvZJOZqYArTuHtoOtVF47RRAEuHXrFsbHxzteH4xjBEGAoesdcSZYeXl9bQ335uYwNzcH27aRz2RwYP9+jI6Odsb5EjamL7bLJooALBqMsnZkURQxMDTUMqP4WdiNp8IZAEi06Ok6fNq3adbryHYoYsOJI8yLCzdPNmRQVBVTU1OYmppCpVrF3bt3cffOHSwtLmJmZgaqLMO0LEiShJdfeqntfG0mq9upQyAIAjRdh1erkRGXHXIHgK0zJAAgtuEMMMMjxWUPtpHl8H0ft27fxsTEBAw6RnYruK67wbxlmgJt4HkelpaXsTg/j6WlJfhBgP7+fhw/fhxDw8NEoEpor0Eel4p1bBuO6wLAJvJiCOrdU0VERVXhOA56+/vbOm2//du/jd/+7d/e4g4k+KSgKAoEWYaq65y9bds26QTZwiHgXJSmEdUtXoyu7m50FQo4ePgwjzTn5uZw59495HM5hGGImzdvYmhoCCdOnNhahGsb606hnAXf97cXSDyGkgIb0hXnWGwnGLp75w4sy8KevXs7en0Ikk5nx1a3uOYwDGGaJuYfPsT8wgJqtRo0TcPY2Bgmxsc32p/b7A9hGHKtk2hHG9MTkJqyiew8nQhfwPd9dHV3x2YrGT4Lu/HUOAOpVArVchkiTc/KkoR6vd5RXYmlh/wggEJ+EL+oaccA+5KzmQwOPvcc9u/fj5npadx/+BCLS0sIfB+GruPOnTuomSb6+/tj+4qjx+40HaerKur1OgLPg+u6HafZ2cYVUnZr4Psb8sj04bYtB1lkESKEDx8hQtTNOhRfJu1JjgNBEOB6HkRQycxtljtmZ2fh2Db2dpjmC4IApmmSaFvXYw1k6PtYpTXY5cVFFFdX4fo+spkM9h84gPHx8c3ZhEhNcNO9avp5COKQ1CNa5uw8WE0R4cbAJEmS4LsuhoaHkc1mH7see4LHB0EQYKTTMH0fQb2OAKS+LDE52GiQEPNehCHvshHID2OfqZB2DACALIoYGR7GyPAwKtUqrt+4gQcPHxLRIUFAvV7H9evX0T8wgO7ubi5R23BsgGvXd0YOJgx60zS3HUjQC+DMeM6QZ7aD2g9G7rUsi2zC1LZZlgUBgO55vMzIU/Id2o4wCHD95k2MjIwgk8l0NIDJsiwu5dzK/tpUGXJpaQmLi4tcGnh4aAjPP/88+vr7N3EsWgU/0WASAH8WzFoNAR1jno5kE4GNLKZtWUAYQpFlhACGRkaeuOmkT40zIMsyUpkMzCCAQPvQHcuCrCh8bkErsgr3WCNfsMgix8gXFyskBJIu3LlrF9LZLHzfx3qphPGJCSwvL+Pu3buAICCbzaK/vx8D/f3o7etr0CjfDiGIcQfqNP0V5wywNkfXdWHRQT5CpE2mue2S17Q8Dxo0hAjg00kEnufAgY26ZcGni5nzCEAlKinpkv/PlACbEAQBbt68ibGxMaRSKSKE0ua6Gzx7Wd7Y0MMQlXIZi8vLWF5awvLyMjzPg6Io6Ovtxb59+5DJZlEoFLaOgGIWdvN34ToOPw9VURocTClyH5nRE0URKm0JiralJngykc1mUa/XIXoeAs+DQHlAmUyGb2hxYJtatDbMo8HoC5uCiIZj09RzT3c33j97FgODg1AUBTMzM7h+/TokSUJfXx/6+vvR39+PQj7fUJraTrlAU1WideD7cFx3Q30vgiAMuYRx3bLgeh5Js4cb7drkkhodpDAM+Sh5NkuFZd9cWiev0/XB7DBLiTNNfpFO+oxzUubu30fdNLH3xRc74kC5ntfQPcBsvOd5KK6sYGlpCUuLi2SAVBgim89jeHgYmXQamVwOXYVCS2cpzuljQUHzz+qmCY+SQ1u1QgdhyAdKhQBGx8eRyWQ6DvQ+LTw1zgCwsahD34fo+/CCALVqFblslpAEY4g+wIaD4AcBLNvGL/z8z6NUKqGntxf/8l/+S8hs2Ezki7ZdF//vX/1V3Lp9G4os4x/+w3+I3bt24V46jXK5jL6+Phw8eBCe63LPc35hAdO3b0MQBHT19GCALvJ8Pg9VVTnLdCtoqko8WLqoFVmG7/uEbU8HN4G2IDHCChvKAYDXpKIeuiAI0KDCRA0+AgQIIECArCjQVA0udQRURYEUYdcGAIQwJAaApllDEGeK6SPIdDb33bt3YVkW9naY5mPM2yAMIVAdgMXlZSxRD14QRfR2d2PPnj3EUNIFXKvVtpSXZt+7AKKfwO970/Phui5q1BFQFIWUKSLvj3IqzHodPgBVkjA2OQnDMLZUiUvw2UOSJKTTaVR9HyLVCnFtG7YsE8evRSTIyosA2eh+8R/8A5RKJfT39+PrX/862YDoOuQRYIzd2L9vH3p6eqBIEmRZxoEDB3Di+HGUymUsLS1heWkJV69cweUwhK5p3DHo7urinVMdBRMskKjXYVsW0UcBkXN3PY8P7wFI66/recTRl2WSSYvUs5laHyL2g7PhFYXIclO7oMgyYfJLEslkUBsR0s4en9oxdg0SvQ8y/RMArt+4gcHBQeQj7Z+twDZhCAIkQUCpXMYKDRyKxSKCIIBuGBgYGMDuPXvQ39cHTdcRBAEqlEy6lR1udgjiHAGzVuN2M5VOb2QTGVGdvp5P0RUE5AsFFOj3+qThqXIGJElCKpVCjW6GkiDAdRyUazXkqJcPbPZqZTo/2vd9pFMp/A8/+qP497/3eyiurOCdd97Bq1/+coNnH4Qhfut/+99w6/ZtiIKA//v//D9jP9WEzuVyuP/gAWzHwcrKCvr7+jj5ECAtQ0tLS1haXsbt27fx8fXrQBgilUohl88jn8uRP/N5ZNLpWE9SEEWoioKybcMqFvnCY2Cet0QFlSRRRIY+XFILzxsAHNGBiTp1Bchmr2oKDN2A7ThQZBmZTIY/1Cxd6Pk+At+Hz/6ni5w5COx+36ALul0tzHVdlEslrK6vo1gsolapoFqtcgcnXyhgbGwM/f39RPO9KYUa7RPvNA3asJCjjoDnbTgCskymv0WjIWw4kmatRhY5gJHRURiG8UQu6ATxyGQyME0TgapCpnX1Wq1GyMltIjRZkmDTgVTMbiwtLeF73/sesRsAN/rt7EY+lyM8hTDEysoK5IEBFPJ5FPJ57Nm9G34QYLVYxNLyMpaWl3H//HlOXMzncsjmcshTu8GCizjomoZqrcb5L5Io8ueYkwZppk8NQ6R0nTDaqRPQDmwMr6ZpnPho2zZ834dMbQe5HaS0wOyG5/sI6DyPEBtaHTa9b8ViEdVyGUdeeIG8v8Xxa9Uq1tfXsVIsolKpoFypEKcgJBoy/b29OHT4MPr6+pDJZDbZB6bhsJ3ySfNgKfb3er0Oh2ZKjIgjAICXFUGvtWqaCMMQumFglJIjn8Qg4qlyBgCSHTBNE4EsQwfgex5c20ZNEMhAC8bwjHx5kiRBoOmxMAzxlR/4Afzpn/wJisUivvXNb+Lll15qWAj//t//e5w7fx4A8FM/9VM4eeIE/52RSnG2uWmaWF1bQ3dEhjadTnMCYhiGqFSrKK2vo1Quo1QqYfbOHZ5Ok0QRuXwehUIB+XweuWwWqqbxiN+2LASgaWnaPSHLMhRZJi1xEfUrpWmUbjziuwmiWZHofWAaDaIoAlGhjZBIq3o02nA9D/MPH8Ks1/Hcc8+hXK2SOe+WhVq1ilKphPVSCaVSCWatxj4cmXQauVwOY2NjyBUKKLQxcvycIsTATtXW4tJ+nudx9q8iyyTFF1n40TqiAKBMzzudzWJweBipVGrbUxgTfHYQRRHpdBoVmvLXNA11y0K1VkNWFDe+y6Y1JMsyHNeF7/sNduOb3/wmXj51qmFFtbMbAMn4CYIAQRSxuLyMoYEB/rxLokjKBX19eA5EkKi0vo5yuYx1un7m7t7l/AXDMJCntiOXyyGdTkOi4kOe68JxXbiui1Q6zYWJWDQuiCLq9Tos2yZtiTQz8CiI0xhgGRVZEABZRnRFB8xu+D4814UXhpiZnkZXTw9kWUa1UkEIoFKroVouE7u5vo5SqcQDBlVVkc5kMDw0tGE7c7ktN3ke8G3DbnD7SMEcAZd2B6RSKZIZibwnciNI9pPuO5O01fpJDSKeOmsmiiIymQwqlQoc1yWZAtPkaeU4UoYoinzcJ/Ni//Jf/sv4nd/5HSwtLeGtt97Ca6+/jjAM8Sd/+qf4sz//cwDAX/qLfxE//EM/1PBZjAuQyWRQq1ZRLpcRhiF6YqaLCYKAXDaLXDYLpj4dgnjY5XIZ6/QhXy0WMXvnDklfeh5EykjVVJWnrrsKBaQMA7phwDCM+Pa7Lch+8cqCYQNZp2E8M+KJj77vo16vE4NiWahWq7h16xZ0VcWNGzdgWxYsx+F1RVVVkc/lMDIyglwux+vykiyT7EiHnjrjM3SiPBl37cyQMvEqxv7Vdb2lIwBaXw48D34QYPfevRAEoW32I8GTCZYd8IMAou9DoZKwlXIZuVxuQ644UkpiWUXP85AyDG43lpeX8eabb+K1114DwhD/dQu7AQCarhPtCtqJMr+42OAQRKEqCncOGIIwRJlukOX1dayXSpidnSXE15DMTZFlmbS2qSo0TUM2k0E2m0WK2Q3DIJt0FI+wnvg5tdAYiGtHDsMQjutu2I56HfPz86hUq8jncnj/gw9gWxZxvjyPBAyZDLryeQwODfGspaqqMFKpBl5WOzTMo0DnLZD8dRHeiEUdgZBme6MRfpQvwYKPaq2GMAyRy+fR3dv7RAcRT+ZZbYFMJkP0uGm6Sdd18oCZ5sZAmebsgCxDoJGsLMt45ZVX8F/+63/FgwcP8M1vfQuvvPoqzp47h//0f/6fAIAvffGL+Bt/429sOraqqhBAe0llGevr66hWqwjDEL09PVueuwCSylN7e1Ho6oJlWVzetlaroVqtwqVevVWvo1qroVQu497cHJcL5hwAXYeqaVCpARAliaQAZZmT/cQI6c/G5jr73NwcimGRiOkEAeQHD0gdPwh45M9SfGwRe3Qx8HsrSfA9D4VCAalUihsjCAKymQxZMIIAjWp5u56HMAg2MW+3QrOU7JbvjDDFmRFwXZc7AjJtR4qegyRsHlZUrVbh0fbFVCqFQqGQyAg/hRAEMmugWCzC9X2oqsrLXZVKBfl8fiPCpQ5Bc1aR2Y35hw/xzW9+E6+88grOnTuH//gf/gMgCC3tBkDmCHieh/7+fiwtLcGmDsFAf/+W2hoAcWhzuRwymQz6ens5m95xXVTKZcIDcF04to1avU4GhS0tEdXMCPmROQxMN19TFEAUIVFVzShRWKJZSFmSYNFugsWlJZTKZQRBgJppwnNdvh6i9sKnGQA34gDwzTLcaN9TFAXpdBq6rkOlAkqqpiFNy3aSokBRFE50lBWlY0eAfe+sM4LxiNqBkwXZfQfgg2iQsNJAqklUiB0j6gg4dICU5/vYtWcPZFl+YrMCwFPqDAiCgK6uLqysrJABM54HXdNg2TapA9JoNOoQSFRb3qNfpiiK+ImvfQ3/+t/8G6ysrOB3f/d38e677yIMAhw4cAB/9+/8ndiHhklduo6D7u5uiKKI1bU11KgH2NPTs6UcqO/7MOt12FTaNqSpL9amFu3NtW0bdVZz0jTYjsMXVr1eR6lU4iNMfduG43kIaV2f1et8urE7ggNf8hH4AUKQ43740YfIClnuOCiSBIEufjFiHBRFQSaTgUEjDEPXOdnujTfewO49e3Dw4MGG62SKXLZtww+Jsl/dNKGoKgr5fMctec0dDh1FMmxxRl7j+36DI5BKpXiPNMsINDgCINoDLOu0Y9cufg8SPJ3QNA25XA7lchmWacJQVYRBAD8IUK5UyHRD2nIIuilIsgyflgpkWcZP/MRP4F//63/dYDcEQcD+/ftb2g2ARPtlKl7W39+P5eVlWLaNxaUl9Pf18TG3rRCGRCPBpMJYwEb2sbe7G0Hji1GmGzaLRK16HXXLQr1eR6VSgUkHglmOQ8qtkY2c1/ppYMA28SAIcPnyZX4YZiNEyl8SqRMhRpyKdDqN3t5ebjt0XYdhGJiensbNW7fw+uuvN2R0A+rgsAFMvu+jWi4jABFX2uo+MUTtBrt/vFW41T0GKXk024F6kyMgyXLDdxBGHQHapVapVuF6HoZHR5HJZPh+8aTiqXQGALK5d3V1oVgswnMcUlen6TdWB2QsV4QhWRAC6aFnD/YXTp7E+B//Me7du8eHQIxPTOD/+fM/Hz/+EuDtOkycJpvNAoKA1dVVskhXVtDX2xvrEAQ0zWTZNu/jVVWVyO/Sh6S5jUijI0oZ+YaRjtjnlUolgP6cRcCxfdAAanYV/78/+z1AFBH4pJvgh37oh5ATslwCObrRdcJgPnf+PCRJiu8gEASSuVBVWLaNtVqN1OtC0rMvCEJHRJpNCxpUv6FVa5AgbCIKOY7DOQLMEeCvR1NKMHK8SrUK23WxY+dO5HK5J9qzT9AZMpkMPM9DlTrlacOAaZrkZ9UqUun0Rto7JANnolnFF0+exB+NjzfajfFx/PzP/zwJFlqsG0VRSJQO8gz29fVtOASLi231Slh6nWXtRFEkmUFF4c9uQLsDAACCACOVQq1ahes4yFHCMgPjDGiahpRhtLQbALhuydraGr773e/i5VOnyMYmCNz+5CMtkcxhbwezXset27exe9euzaVdmpFJpVLQNY0EPBFCXt2yiM3cIuhqvp52MxTY68UmRyAIApINpd9bStehUOEgcqobGQF+3ZSsXqlWISsKxicm0NXV9cSWBxieXDelA6iqikKhAFHXYVsWJ9fx1C71dAGy+Fh6in2RYRiSmh9FPp/HL/7iLzYMmGgGS3mzRQ2QXuKenh5IlDCyvLKySXnLdhyUSiVYdGNXFQX5XA7pVKq9tyjQgRegzN0teo7b9c5KUtzDGPLPjKa+O6ETrZdKuHfvHvbt2xe/qUcWsOM4SKXTyKbT3ODVTRO1Wi22j3qTwEfTZwLx1xotCbDPYSUkgOpV0PJEtFsAMY6U53lYL5WQSqUwMjrKWxsTPP3I5/MksyWKMOt1/kzwTZelsWlmIJpVBBBrN7gj3ZRhYlBVlffkA8QmDfT3I0WDgaWlJZg0Fc8Q0Loz67gJAaRTKUK2jTgC7LhRMMl2Rhhsh3ZPNWsjZmucdTBFI+PtrourV69CaRVERNagQzlUKcNALpvlExor5TLv3e/0WraSTRaavjff91Gr1TYcAcPgnSc8E0A3/+bve319HZ7rYnBoCAMDA5+AtPzjx1PtDACEVZvN5yGqKk9BM6GOSrVKald0YSus75Yu6oWFBXzjG9/gn2XbdqP3FvPQsJ56Jp/LkEmnuUNQr9extLzMFQBrtRpqtRpxQgQiUJROp+PrznHHZIuafjZ/acz9aBfNx74eiGXnd6Jtfvmjj5DJZDA1NdXigCRlZtIyh0TbpHK5HKmRUg+6Wqs1OFcsPRvGOAlBiwUd5Qbw1wYBTNpmBRACVzpCWGSkIPb+hs8LAiwtLSEMQwwMDbUdWJLg6QMrNSrpNI822WZu2zZRlaPdOswmeDSruKXdwOaNBSD2h/GDGETaRWAYBkTaZcDaXT3PQ7lc5qlyRVGQy2b5xtLJ88iuyaEaJZETBNDo9G93Q+eji5vPY4uswNr6Oubm5rD/wIG20bLrOGRmCUiXFsvMsY4ImyohNgdeXD2xGRHeQCwi1+/SsddM0C2TyXCeAp902iITa9s21tbXoafTmJyaajmM6EnD58K6ZbNZGIUCV7STI55rpVKBTwUvmJiG57pYL5XwL7/+dVQqFc4MtywL3/rWtxo/nH7p/J8A0oaBGhOSiCCdTqO3rw8yfVDnFxZQoh6sT4UwctlsIwNVaFRAE1osJL6oHadxUVOwd7VbzoIQN8I42JhL0NRJ0A6LCwtYXl7GwYMHY40Sa1es1evw6OJJMYUugUin5ujCRkgEPBhBSGjhCAB01DJN3fKfxaT3fM9DtVYj94qmTHU6vpbcC2qsY4x2GAQorq3BrNWQyWaxb9++Jz7Fl2D7kCQJPb29kDQNPlXiYxut4zioVauEtMsMfxhifX0dv/Zrv7a13cBmhyCdyXDHIwpRFNHX20tq0YKApZUVFNfWUK5UeNYul89vyiIKaOoQirEdkiQRUp4gNGQdhDbv6RRxMwlCtLcdIYDLly8jm81icnKy5es8z0ONnq9OOyMAcj2ZTAYpShL3fB9VGvSx62llA5k+SoO9itgBBsuySAt0GJKOp0yGlI2ZfgCTfo+5d45tY3FhAQKA0bGxthNNnzR8LpwBQRDQ3d2NdD4PhCFsy4JE+2pZewfz3kNKZPsXX/86lpaXoes6/tE/+kc4duwYAOC///f/jrW1tcYDRMlrAPKFAsrlcuy5pAwDvbQdqFQuY2FpCa7nIZvJkKlaLVLbzQzbZvBFTdOa7H2t7kfsz2N+5gU+T3U1LJI25Qi2oHv7+jA0NNTydaZpconjdDq9aZCJSBe2pqoQJAlOEzkqDqyVic9iADY5Aow3EjKvPp3m7VucCNXCCPq+j3KlgnKpBFGW8dyhQ+jt7W15PgmebiiKgr6REUiKQspZtg2ddpi4nodKtQoEARRZhmXb+I3f+I3O7QYoiY2uxyx1HioxtkMURfT29iKdSiFwXTycnydkQ8pAj5tfwI9BP7/VumdEX6YE2BJt7E8c4jIDzcFNM+bn57GysoJDBw+2PF+PZgsFAKosN6iCMqiahkw6zZ2haq3W9tpYBpQFDuxco0TkIAhQq9XIHAEAiqYRuxXhj7Dx9XH3ybIsrK6vw3Zd5Lu6cPjw4aeq6+hz4QwANO03MECEh0A8NAGkTheGZFqV67oQBAH/8T/+R9ybm0MQBPi5n/s5TExM4Cd+4id469kf/eEfNn84ac2hXmQ2m0WlUml5LqqqEjU72pJUM80tB28wMZJ2izG6qK2mMkVHiziO1OjTbotoVmCLdOHt27dRrlZx6NChlq+pWxbpbACpcbZcFDRqT+k6IIpwPY+3IMWBLUQ2Xrb5TC3L2lAli3r12OgYCMIwNvPgeh5q1SppFQVRGtyxY0fbe5Hg6YeqqugZHCTBAgi/R6OONyOCQRDwn37/97G0tIQgDPH/+Ht/b2u7AWw8o4LAhYHKLWyHKIow6FoRaFCzVa0fwJbtcqIoQqc8KLNeJ45wizLBdgoFzKY1rO02mQbP8/DRRx+hr68PA4ODsa8JgoA7AlIT0bcZsiyT1mWqBVFnmcAY8PkS2BjAFuUWNfMDdMNo4I6xLEBcCYLtL/VaDbVqFZph4PALLzw15QGGz1XuUxRFFAYHISwuEhat5/GI2nEcWLaNP/qjP8LtW7dgaBr+2t/6Wzh48CDCMMTY2BiOHz+Os2fP4s233sJf+pEfQX9//6ZjCIKAfD7P9QGaBUNY2kqWZfT395Po2PexuLiIru5uMkehBVjaz2+xoJixqJsm6vV6I2kvssDZw/ru/Gm8O38aju8gCAME4eYN8N9e/t+RQQae6MKTPRzqOYSf2PmX0cosVKtVXL12Dbt27kShUIh9jW3bvE6f0nXejtnyugWBZz1Yar9er8cyq32qLc+7PSJefZT1q1LJ1IbBK9jICDQPqXIdByYVFHFsG909PThy7FhSHnhGoGcy6OrrQ2l5GY7rcqa9R4XK/vN//s+4MzsLAPif/vbfxuHnn0cQBBjt0G4AZP3mstnYzABAGPaObSOXyyGVTsM0TZimCcd10d/b27bzhm3krbZiXdPgUAIym9oIYNPG1vEcBGBTeXGrLoJrH3+MumXhpZdeavl5NZqeF0UR6VQqNoCJQpQkpFMpVE0THs36ptPpTZkUPwjI50YcAQamPcIifyYMFDKb2tTNFEUYBDCp9kq1VoOqqti5ezfGxsbwtOFzkxlgkBQFmZ4eZDIZsrFS2UtVUfDd73wHFy9cQCqVwg9+9as4depUg2f44z/+4zyd9Af/1//V8hi5XA4QhE0efhAEvItBEkUUCgUMDg7CSKUgCgJW19awQgdptIIoCG1bZjQ6XlcQBNTp6N9mCIKA88sX8MtnfwXfvf9dvDv/Ls4snMH7i+9veu3t9WlcX7+OD1c/wrml8/j/fvy7+MbMH8UeOwRw4eJF6JqG5w4ciH0NY2OHoOJKW7BoufERyNCkNI0EPNpn3Hz8MKbux8g+HhVXMVIpzrFgvcXs/ZEDk58FAeqmCZOes1mrIZPLYd+BA8iz+eYJnglo2SyyXV18aqVN21+/+53v4Pz581BkGT/w+us4TmWG2bP7Yz/+4yQFvYXdAEjnQVyJ0bZtWLREZhgGent70dfXB1mS4LkuFhYXN3UaNGMr3Y1UOs27klpF0EBnWcYwkl3ja7HN8Yurq7h9+zaeO3AgVr2TRddMMjjdYgJg5A0bbcCCgEwqxTsn4kiFnu8jFIRGrhElN2/iB0jSRudBMzkxYrtd2orq0QAiFAQMjIxg//79TyXZ+Ok74w6gGgaMQgGZbJYTCc+89x7eeOMNhGGI5w4exMkvfpEragGkBj06Oorjx4/D932cPnMGc3NzsZ+fzWYhCsLGNCoK1gcs0sFBAn34+np7kS8UIAlk4t7i0hLcNmWDrQR1jFQKCMlIUcbCb/ZYr6/e2PI+AYAMsjh8bJzPdGk69rWzs7NYWVnB0aNHY3UYHMfhswcUOtmsJaKLOQJFVUlGQBB4GyYAztoNgwCCKPIUbq1W40OEBCpwwgbCxJKJwo1WSuZE2K4LUSBDrxRNw/jkJMbHx9vctQSfR4iiCKNQQDqTIWRXAO+//z6+893vAgAOPPccTpw4gWqE2CcAGB8dxRe+8AV4vo93T59uaTcAEkiwEiN79pkYVhCSYTbMGTEMA4ODg9BoqXN5aYn39cdiizS/LMtQNQ0ibTWMywBsVXJgYOuSRdr0guJfGwS4cP48ugoF7Nq1a9PvWceVR6eiGpGRxLFgpO7o8ZhDQDfhhvKKsCEnzLIYjuMQUSDansj4AZu6lGL+zgOIWo07HY7rorurC3v27Glb2niS8bl0BgCS9lMNA7lcDtMzM/jjP/5jCKKIXbt24atf/SqCIMDa2hppXaGbhiAI+ImvfQ0CdSD+0+//fuyCEUUR2UyGePj0wWRKeyEdXtH8UOXzefT19UESRbiui4WFBT4FrBmsv75VhkCSJOj0GHUmYNSEQz2t6/kNnxXjDLzQ+/ym15n1Oq5cuYLJyckGvXQG27ZJqk0gQkKtxFMYYjUEKFRNg8K8/HqdD2vi3j1t7eTZAPqebCYTzxRuPDBJ7dVq3ADLsgxZUeC5LoaGhzE5NZWUB55RiJIEvVCApmmYvXMH3/iDPwDCELt37cL/8CM/AkmS4DgO1lZXN3hAgoCvfe1rRMEyDPGffv/3CS8F2PSM53I5eJ5HRLcoO52RZiVJ2iRLLMsyBgcHeSfBWqmE5eXlltlFPnq4BQxdJ/YNZM3GrcFOWgybyYPRYWfNuH79OqrVKo4eO7bps1lpwGNE48jU1Fi0sRssKwiQbiKHzkbh2jL0fbVajfCKaGCRpuRudh2trj8MQx5AOK4LEST76dO5FaOTkxgYGGh97k84PtcWT8vlUF9bw/Hjx3HkyBH+0IVhCMuyeOuh4zi87390dBT//vd+r6H/PKAPDfeaBQG5QmGj9hcSYZvA96EZRsuHmXn6KysrcF2X8Ai6ukjZoQnsgWSEt2bomgaXKi9aloV8Exfh+d6DONh9EFdWr7S8PwL9DwB8KmbaZ/ThtZHXGl4XArh48SJkWd4kOQxspDgFkIyAYRhb1h23SkWmDIO0hQYBuU5N42OUPc8jWvEgMyd0OgAGQGOUErOoLctCjeoeAICu65BEESvFInr6+zE0PNySC5Hg2YCsKNByObzwwgv4rd/6LVj0+Q4BLtlrWRYqlQp0XYemaRgdHcXv/d7vQRDoVE1angpAIi72XLK1Xi6VoOs6mUPiOAgBElG26Dbq7e3lw81My4KzuLiJRxDlDDBnu3mdCbQW79g26q4Lo025oB04eTBaIohZ8+vr67hx4wb279+/yc6x2QasQ4g5Aq1KGJ3wGSRJgm4YsCwLlm1DprNk/CCA7Tjw6MRZxlNiXKWtjsGIjQ7NJEiyDEPXYVIxqNHJSYyNjT2V5QGGp/fMO4AkSTAKBQhUPzuXzSJFPex0Ok1qxJT8UalUeJYAoIScaOsJXdw+/ZNnBkDSzQEln2ylW68oSiOPYH0dy8vLDfKWzQ9jrKdPa4AAMVDNalyCIOCn9/10+/tDfUGSFSDH/Ku7/goUsdGZmZubw+LiIo4cObKJxMQYzwFIqi2q7rf5lDvnKguMAS2KsB0HAY2gTDoDAiAbeaaJLBRVFIwioO1KzBGQZBm5TAayJKFYLKLQ3Y3B4WGMjo52fI4JPr9QDQNqNku4L7qObC4HWZJg6DoUSkhmc0LKlQqJbFmLX5MWAAso2MhbWZI438imjoCu61u2oeVyOcIjEEV4rov5hYVNpcrocVutN1lRoGoaJNpd0GxvWpUKeKkuUmprl84PwhDnL15ENpvFnj17Gn9HMwIBjdizkYxAqw2/I2JjSGa4MCfFc13SJWSasB2HDHKj9j8qA9/u8xzHQZkGjQB9HtJpomxqWRgeG8PY2NhTP8n0c+0MAMSDS3V1EaEIgYrd5HIwDIPMLqCbjO04ME0TFbqwGZjSFP83yEOZy+dRM03UqlVS2w4CaBFRm3YQBKGBR2DW63g4P88XdtxnxDkELK0oUjIhUzwMwxBBGOJw76G25QK5wRkAevVefHXiKw3pdcu28eGlSxgdHcVgUzuQZVm81KHreuvSQCTLsiUidX5VUSCAMP3X1tZ4Z4aqqshElNgAtNRpCGg9tlKpwKPiUwZdzACwUiwiWyhgcHgYU0l5IEEEejoNNZPhIldZyvJXFQWyLMOyLELGc11UKhVeh48KWzUjDEPkCwUsLS3BdRw49JnkglhbOMwsu6hrGhCGWFldxeLiYqOCZwSt2gVZ25xPsxzAxmYfhCEPD4KII8PZ9YLApZFFNtQp5txv3ryJ0vo6jh8/3rDpsiibdfQ0t/82fM42AggAXBBIVVWEApmdsL6+Dp+SunVdJ1yyrfr/qRNQqVaJbQ0CiJKEHC0p1Go1VGs1DI2NYXRsrGUHydOEz70zAAAidQjYRiFJEnJUFpd5tkyhq25ZKJfLGwubLqbmhTrQ3w9JFPHg4UM4to2AyoU29+22Qz6fR39/PycIsYXdKk0W5xAw75ZpmPs0e8Ecg5/e2zo7wMiDHsjxfnLXT0IRlIZzv3DhAgRRxOHDhxveW6dpOBbVbJokxjb+JmeqHaISn4wg6bgu6lSMCACMdJpPd4w6DiE7FkXUCWAkIVEUiRSyriPwfawUi0hnsxgaGcHOnTtj58oneLahZzJQ02nuyGqahv6+PjKwjGYDHdqWapomypUK1zMB4iP0wcFBLC0toU5LlRJtYwuxmb0eB1mWMTAwgHw+T9ROHQcP5+djtU+Y7Wo+B0GSOFHRtCxYrgs/CLjtYJlQ8uKmNRzpJIgy7/lrQcoDH1+/jt27dzeU3Xzaeh1QZccM1V7YBPaZnQQQ0eujjosXBKiWy7BsG7bjEJufzzfOCIizS81OAOUp6aqKXDYLSZZRq9VQrlYxPDaGkdHRtsJrTxOeCWcA2MgQMO9XEMhsc5aeEgQyECigDPVypcIXNgDO1GUpcFmW0dffj4WFBQAbMwt4ZN7sTbeArusYHBxEPpdrWNitFA7FaG2OLlpVUchcc6rlHcXBnufwQu8LsZ/FMgMePHRr3fih8a82/P76jRtYXFjAiRMneM9+yDIpVDrY0PWWXQPtSIINr6PXwhi/bDEy8REGnY4/bZgw2PRZvufBrNUanABZUZDOZIjhoeTQYrEILZXCyNgYduzYkTgCCVpCz2Yh6zrn7siKgu7ubmQyGYRBAE3ToCoK6vU6qtUqSqUSqYVHtCxYMBGGIQYGBuB7HorFItj4cmAjKg8p56Dd2mH2i3UbCIKAtbU1zC8sbM4SRAMaciAIIenlZxF7rVrdJMTVympFSwRxAY/runj/gw9QyOdx4MABzrdic0iYCmAmZj4Ld+o7CSAi9yeko5dZMOfYNiRFgSiKkCUJmqqS2QKRAKLh/oYhHNtucAIEmknIZjI862uaJkrlMoaoE/A0yQ1vhWcqJyqrKlJdXaitrpK+UiqHG4I8+JqmQZJlxL3xGAAAKBdJREFUOLbNhXPq9TqZw63rfNGyCH1wYAAfffQRPN+H3lRLjy4Srn7VtHkx54It7HQ6jWKxCN/3sb62hppporenZ7PYSKRmx0h0Km21s+j0xmi6+3/c8zdwaeXSpvsR7ST4K7t/Eqq0sSEuLS3h42vXsH//fs6QZb3AbGBLyjA2TeOKqvzFgt0HVjqghCum4hZtJwTAJZyZcpraIo3Pen2jhlCm7Y2yLMP3PAQg38VKsQhF1zE8OorJycn2LZAJEgBIFQqora7CcxwIIGQ/23EgShJRwKO8JMdxeH26rqpIpVLQKElVAOETFPJ5GIaBpeVlpGIEcqJObkj77sOmdD+zL4qiYGBgAJVKBevr63BpMMEGgkXT8yFIxiyqoqdpGiRJIm26pknGHEfT/i3IdJIkNYx55u8JQ5w9dw6e6+LUqVNksw9DMmuA8nWYIxBXr+8kgGABFsuksKmoNs1UAiQLmDYM2I5DMjXABich+llBAJdqmkQHtmmqCk3XN7RNKOl8fX0dg7Rk+nnjFz0zmQEGWVWRLhR45K7TNjYW0eeyWeRyOWQyGTJlzLZRWl/HysoKiqur5IGjD+vQ4CACAKurqw1yvnGIm7LHzoGl9JlqYVdXF0RJ4gt7fX2dZxnY65uJcgplxgoC0TKIevkHe57D0b6jDceXIEGAgBAhsloOPzT2g/x39XodZz/4AP0DA9i3bx8AcFlWPnkxk4ndRBscnqZFzVKhQWQxs3JApVLhss0CSM0vl82S2fKSRGqXdJ48/7wggE1Z3bVqlTsCiqIgk8lwARHmOAU0GpNlGUOjo5iamnpqe4ITfPpId3dD1jSyFkHmbQggkbCu68gXCsjmcsjQskLNNLG6uoqVlRXeGQOQzWZwcBDF5WUiMtbGdnDOEl1LAavpR20HiPbJ0NAQ5xCVy2UsLC5yJdAgCEjKG43lRpYRBcAja4ZWHJ9NMsSRz2PZxOMnTvC1ZVkWqhH9kTzVf4keh3VC8GNGeEZMX4SXUhihm6opVioVXrKURBEpwyD2yTAQBAH8IOCjhxk8z0OdlnXqtLWTZQIYp4y1IQdhiDr9LvuHhjA4NITx8fGnunMgDs9UZoBB1nVkentRW11FEARIp1IoUe9Q0zSS9lNVGKkU0bqv1+E6DqrVKsxaDZqmIZPJkNRzKoVisYjxiQmeYWiHhk2cPmxAo7eayWahqirWVldhOw5JO9IJemn6kG58DPl7GBKxDp9u2FEvn3AHfgoXli/w90WzAj+58yegySTCD4IA7733HiRZxokTJwAasddMk/QwUyZuVII0DBsnDUZHAzc4BJHoIYh45FEBF1XTuC48uz4/2JhUKAgCJ1550XSoQPQNNFUlSmSMTCgIQEAmxa2triKVzaJ/aAhTU1NPPfs3wacPI58nHS7VKmHlqyrJApgm0pkM0nQ6pk7JtdH/S+Uy0qkUstksBgYGMDM7u0HeC9sLjUU5CIiseb5pgg476u9HrVrF2vo6PBpMpNJpUu+OpPVZSZOv6VRqY6CbJPHNM9quyMCyfpIkNfy+OZvYPBNGU1XSIRR5DxdeotfCAqSGDAGzL/Q1vu/DchzekgkQJ0DTdT5mmP3M8zwEngdFURDQQU2u4zToNIhUsp6VW9j9ZM7W+vo66vU6BkZG0D8wgImJic+dIwA8o84AQDIE2f5+mGtrCC0LqqrCosI52WyWlA1onSmTTpP+dLpY6paFumVBkWUUCgUsLC0RAQvEL544bHIKIpsm69fvHxhAtVrF6urqhkFhETPzXOnxWDoxZRik1cl1YVJ9/xDA3q69ONZ/DGcXz8ELfGjQECCALCv4i5M/zI99+cMPsV4q4dVXXoGiKEQv3bIAQeCDQ9hApSjBMprab0jDhZHxwnQh244Dj7ZjsutlDljUCWDGwHMceL4Pn853j94rSZahKgoUWh9kmQk2bCnwPJQqFVSrVfQODKC3vx8TExNJRiDBI0EURaTyeciqinqpBD2SivZcF4qikNa1VAopw4BL5blrtRpcz0O5UkGFTiMURRErKysYHBxs2ITaBRQNDkPUKaD/FgFkKON9dW0NlWoVFXrMDCXfKrJMeATU7gRhSLQVNI2IeZkmsrTsIYgimWDKovGQDF/j5T56KnXTxPvvvYe+/n7s2buXlwUYUTBlGFAoUZp9ligICNA4QTSMXiN1Alh5z3UcOJ7XQLBmHVW8lErvQUDPk2mTWLZNzjtyHxVVhaIokCVp0+TTEKQtcWVlBZKiYHxqCgODgxgeHv5cOgLAM+wMAGRhZ3p6YFWrCMOQ8AQoCY+lwAVBIOTDdBqpVIorUJmmCdfzoOk6HNfFvbk5DA8Nkfpg1GPegkAYt7ijYPK6pXIZVZoKL66uoqKqyOdyRPWMlRDo4kkZBh/FKUkSVNrl8FO7/0ecXTgHEQKkkHz1Lw99EYpAFunc3Bxuz8zgyAsvIJ/Po8JYvwA0XeejXTedO3UUor3WLB0ZgqqBuS4c1230yEEyAdwJiCzkkNYZHddFuVqF7TgwNI07Fwp11Fg3SJzIiksXcygIGJ2YQF9/P0ZHR5P2wQTfN1TDgKwoqK2twaCBQq1WQzaXgxSJuhWaPchmsySlXa0SO+O6yKTTePjwIcbGxkirs6Jw53o7AQUfwRvh40iyjO7ubui6jhIl1FUpKTeTTiOXy/HBXSwS1+lgJs/zyLVkMiRrJwgk0xb9n2YUQurgn3nvPciKguPHj5PBP1SEjGUSY+v19NyjWUUWAAiUd+TSDIBLeUoMsixDo5t5gwMBMofA8zysl0rkPASBOwKyLEOl72PEyk3nFYao1GpYX11FvrsbfQMDGBsb+9yLkSVWEaR9SFZVeEGAUqmEaq2GENhol2M1elGEqqro7u5GoVAgNXTPgyJJePDgAVRVhUQ3KoNqjCuyzBd3px6/EPGYfUrW6e7qQi6bRblcJkpYto2llRVIosgJg+w4iqpCp2RC0zQh0sW4O78LI+kRzFXnIEOGCAl/Ze9f40zkCxcuYJz2zUbblFLpdEdse1mW4bFZAkEAmzoAXiRrIABQZBmKqpJzFkWI9Lx9z4NNjRHz/m2aFRBAOi90OqiJRTZNN5Efo1avEw2BbBZ9g4MYGRlBb2/vlteQIEGnEGUZ6Z4eSKoKd34etm2jUqkQ4h57FiNp+VQqhXQ6DdtxUCmXkc/ncffePSwsLiJlGCTK1XUyn0BRNlrutggoGARBIH32vs83bcMwYBgGYcGXSiSYoT3yLLUeBgFANVfSqRTK1SpXB0wzafUm/oDEsnBhiEuXLqFUKuGVU6cQBAEf8CTLMhd5azhH8peN+ygI8KgjE2AjePAcB9H+BlGgqoEsCGCOUxCQjIHrkrIA5XTU6nX4QYA07UJSmoKHOE2DwPextrqKumVhcHQUvX19mJiYeCa6jRJngEJWVfSOjhKhitVVVKtViKJIhG8iaSsGURSRz+WQMgysFIuYmZ3F5OQkAso6tSyLOAZU7Yux2iW6aW8Frn4IkvIOgwAy9fZzuRzK1GlxXBeVahWmZZGyBn1odV3nHnK1VkOGtlD+m1d+A//rB/8frMwX8dcP/DXk1DTK5TLeffdd5PN57Nu/H9VajROb2DjPLc8X4BMi65YFp6m9SZZlqHQGAEvnB0EAh05QYzLRzfeApV7TlNTJfh7HOBZA2p7K5TIqlQr6BgfR29eH8fHxpCyQ4BMBKxsMyjLm792D6zhYX19HgXILGtY6tSOqqqK3txeSJGF+YQErS0uYmJwka7VaRa1W4+UvnXYxyYrCM22t7Ec06PBotM/IxqlUCqlUCmathlK5TDhQlkWyf0GA3u5uCJT5n06l+NwPs15HOpXatDaZTbh65Qru3r1L1ElVFY5t87Jfp106zL5a9TpMy2ooG4ggtpkJPbF7GHge3KbAIQ6apqGnpweqpm3wLSLZB0R+Zts2JxiP7diBwcFBDA4Ofm7LAs1InIEIRFHEwNgYIAgor66iUqkgS7sKWAtgMxRFweTEBO7euYNqpYLde/agXq/DojLHlm3Dsm3UqlVSn6IPtSzLpFZF61XND5xAiW8AeEsSgyTL6O7pQS6fx+rqKhzLgmvbWFhchK5pZOqaYSCTTpNFTaU/s5kMdFnH/23X/4S3Hr6NofQgzHod77zzDnRdx6FDh8jURYGME04ZRvuFEIbwfB+u68L3PK5Kxlj9Es2ksHqe3/za5s0f4EODZFnmUwklSUKKjqRuEEKJvM8PyPjocrUKURAwQoeGjI6OPjOLOcFnBy2dxtDUFB7OzPBSopFKcdIe4xMB4JF+Kp3GxMQEbt++jaPHj3N2vO04ZJIh5RtIskyyaTRbIEsSae2jn90QeaMxCxntHAjCkJQ702k+PdWiffm24yBtGEil04QgnU6jWqvBdRyYkY4DBkmScOPGDdy8dQv79+9HV1cX11VIpVLcZrZCEARwPQ8+i+bDEC5te4xmD3knURCQ9mLKAWiGKIqQFQUKta3lcpmUPlS10RFo/pM6AZVKBWa1ikJvL/oHBzE2NhY7M+bzjMQZiEH/yAhCUUR5ZQW1ahUZWgdkbT7NSoDpTAYjY2OYuXMH+/btQz6fRy6XIzMDaJbA9TzYtI9eEIg+thRxBhgBTpJlcqwmQksUzLgoioKenh4IoohqpcJnlduOgzVRhKHrZAYDNS6VWg25TAYB9aT9IMD3vvc9iKKIw4cOcc/boMNXmhEEAd/IPc+LXZSCIECkPcgC5RHYltXyWiRJgswMXZOISbVahet5UGiUxLsDNg5GZg5Uq6jUagh9n4wgnprCyMgIurq6Wn7HCRI8bmi6jqEdO/Dwzh3S0kej6gCARG1HNKhQZBmjo6OYmZ7GzPQ0Djz3HFKpFHzKerdoF5PnebyrKWojJFGEKEmQKWlRpCXDVi2BUbuVTqcx0N+PUqlEeuypIFClWoWqqkin09BUleutRNPqIci8kqtXr2LHzp0YHh4m10MDCKHJ+Wa8Ah4MxAUCgkD0FkQRoiwjoKS/IMbG8NezoEqWG8SLPM8jGYYggEFHyTe9ucEJsEwToSCgb2gIo+PjGB4efibKAs1InIEYCIKAgaEhCIKA8vIyqpVKgwSuRx0Ctinruo7J8XHM3b2LmTt3sHvXLgiCAFVRSDdCJsOnkznME/Z9+GEIl4pdMNESmS5wUNKLQBc5iwBEutmKggCBbrqKLKPQ1YW0YcCkzGXf82CaJkzThCzLCMIQhqaRGd60pnbt6lX4vo9jx4+TthxNQ4oqrXmeR1r6mLyn7xPBEtpuw9qA2LlJ9JyZsInv+xAipQIBpMbKnKBoZNN875n2gElV3Jq1xAWBaKPXmBNAnZtsoYCDzz+P3t7eJBuQ4DOBrusYnpzEw3v34JgmREGAYRh8zUSdAVEUkclkMDY+jpnZWezbtw+yJPHI39B17hgwIp0fhghoOc5mLbd0A5UkCZIgwIvoGbByH7ch1HYwpyKVTiOfz0MAUDVN7oCsuy7hH1F1VgC8DXJ1dRUz09MYHR3F1MQEIBBZYVmWecDANvxoCZDppLC/N9gOULsRBEBT2p85ChLtwGC2gyE6DwIgw+ds2ybt4ZGMhgAgAJl1UimXyRhjGrTs3LMHO3bseKbLiYkz0AKCIKCP9spWV1dRLpeRymSgyvIGs5a+ThAE5AsFDA0N4fbt29i5cyeJBLDBmGXjMtMgEbZHI2xWZ/fpZuuHIVzLghcEpI8+Ek2IlHkrRBwRQZJQr9UIo59mGAqFAmzL4ovCcRwEvo/VtTWoioJKtYoQgOU4OHjgACHuUUelQlv3ogIgTPQDIJG8JMukN5cSAKOLkTGTo6lMFsFsEl5CTP2THrNG+RCMhAQamTC5aJYJEOg5Te3cif2HDiVOQILPHLquY3B0FEsPH8KqVuEHAVLpNCHKRmyHKJDBaRPj47h39y6mp6exl073Y+si6hjwCJtutqxmzgSFmIPg0uFHzCawFLkYsR0s2HAdh8h1p9PIUI0EyzRh1utkNghrA/Z9OHRTn56eRl9fH4aHh2HZNkRZxuraGo/iG1oFI5LsMs0CsrbKZjCZ4miwINHMaRwaUv8ULEgIgoALx7GuLot2VDAnACBtmM8fO5aQi5E4A20hiiL6BgYgSBKqq6uo0vnlBhXOADaEP1RVxfj4OB7Mz2N6ehp7du0CsHmzY+IgmqpCjbBhWeQddRLqlsUVsNiMdJ9G56yVEGFIVMNo/Z6JAfHam6KgTqeruY6DtdVVrCwtQQDQ090N1/Mg07ad6OJibUEi9ZxFuphZVCHQNKUQ+TdvNWrSOGcSxRwtCIAMjuuiTBXbcrkccQCqVUJMdBwumymJIvKFAvYeOID+pomKCRJ8lkilUugbGkJxaQl2tYpKqdTQYidgw9nO5fMYHBrCzVu3MDk5SYYgRcB4AGwzV2SZl91YP70XdRJcl4xGborEHdclg3yoM+L7Pmk/lmU4VLKXnZPEUvV0VLNjWSiurQEg5ZB8oUBU/8IQIh1DTE52I4MpiCIk2scvRH7G7IoYcVBEUYQTaU1uvv6NfzTZjqasABs1LEsSUroO0zTJqGHbJhlE6gxpqoqhkRHsO3AAaiJHDiBxBraEJEno7++HrutYLxbhUIW/VCrVIKkpCgIGBgcxMjSEK5cvo7enh9esow9rw98jrFZZliHIMiG7UBawYdukv7ZJipg7CNQxYLr+jGQUrccxmWND01CqVlGZnyd66iEZcBSGIWyamdA1DbqmcYGQqHMQLU9EHYaG9pww3FjQkXPYtMDZdUd/RP8MggAry8uwLQt+GBKnwHV5BkIEIRmm02kMjYxgcseOZDEneCKRTqehjo6iuLICc30dlWqV8HGaRp1rmoZdO3dicWEB58+fx8mTJzfEt+hrBKBh42N/SoIAkYpusZ95tN6+SfI88veAZhZY6UKnHKEwCIhgVxgSfX7XheM4eFirwbEsqIqCQj4PEeDlTlXXYdDugZRhQJQkHlwIwEZZk/EmsJEV5OueEpfJxW0QhVtlDht+RM+3VquhtL6OumVBNwwsLi8T+8junUjkhguFAiZ37ED/52Ta4OOCELZimyTYBMuysFosol4qIXBdPtsc2Hi4y5UK3n77bYiyjFdOnSKtiUBDX3wnrYUA8fqbW/Q2IQy5jCjLWrBjeNTz96jy2eXLlyEKAvbs3UsEk1QVnudxT52pGYI6ECqdxsZSe0qL9F7D+dJJgZ0gpO/xXJdwKjwPK8UiqlS/IZ/Pc4eL1f/YPR8ZG0Nvkg1I8BQgoPol5bU1ONUqiVrT6YZWwRDA9Y8/xuWrV3Ho4EHs2LGjgazMyoKdwm3Bum8+rzUa7efz+Y2x4KJIiMh0aNj09DTu37+PwcFBdHV1EdlzyimSRJGk3CMlRY1KijN2vyRJm4etNcGhjkUn4O2TlE/hU7n05ZUVWLYNTVX5fAim3ZCiJZBsLoexHTuS4WQxSJyBbcL3faytraFaLsOlC5uJCzHcv38fp8+cwcTEBJ47eJAz4VkkzVJjQHvHIAR4yaAdbNtG3TSh0YfedV0Ui0XcuXMHDx48wMzMDO7evYtqtYpsJoMXXngBP/U3/yYs6iiEVNiIaSQ4joMwCPDn3/42rly9iqmpKfz4j/0YAOLls9of6wSQKR/Ao+ShuOtgTobreaRWSeudfkRZzKSlAM/3kc/lkM1moRsGF2QBgGw+j/HJySQbkOCpg0mH3diVCkLX5eqbzOH1PA9nzpzB4tISvvilL6G3pyeWYNtczouDT8l7bc27IKBUKiH0feTyeYiCANtxUCwWMTc3h7m5Ody4cQMPHjyA67pIGQYOHDyIn/3Zn0W1WiWfEYYQ6bRG27K4rfpvf/ZnuHL1Knbs2EFsRxNnIGpDwjCE7Xm8lZohjPzJpgt6rguHERTp630qOe65LkRJQk93N4xUCoZh8IyHJEkYHB1NyoltkJQJtglJkoiIhaqirKpwTBPVapV7oIqiYGR0FHv27MHNmzfR1d2NwYGBBnlj9nDzVjmaqhPZv7GRYmP1tOaJXQDwjT/4A/zhH/0Rvv4v/gUUVUWtVuMtf/+vX/5liIIAx3FQr9d5uSDa1iRnMjApUS8IQ+iahkKhQPqdLQsH9u/HtatXsfDwIVn0VDbUDUM+fCQqUerR7oM4AxQlC0bTg+xnjm3D830oioLBwUH09vbycw3DEKlMBoMjI8h/ziVBE3x+wfrv1zUN9WoVrmnConNRmCjZsePH8cYbb+DKlSs4cuQI72LiBF1WKghDBAC3GcxWADQ1Dzqoh6XJm8Bsxy//8i8jZRioVCqcCPxrv/ZrCKgNcNnI5nQaAHiHQTaTQa1WIwqBYYiuQgGSLJMx5PU6Dhw4gBvXr2N5cZFE6JSwyBQKGcIwhE95D+zf7BqiVqRZP4GVJ4MggE05ArqmYWR0lDsAoPekZ2AAA4ODUGPapRNsIHEGHgGCICCXyyGVShFme6UCxzRRM02IIDXAQ4cOoVgs4vrHHyNlGJxnEN3M2aIG/TOI/JtnEoKAD/RAGPKhIWzRGIZBFi2t1TO9As9xSJ9uECCfy2F8YgK3bt2ih9o4fjqdhhiZrOb5PlKGgVQqhePHj+N//z/+D6Lzvb6OQ4cO8VYh9r/LZg6EIQTaIsk3/RgyEBNQYRECBAG2ZaFKWyRT2SxvdYIgIJ1OY3BkBLnECUjwOYCiKOjt7YVFN1OT2o5SuQxVlqHpOr5w4gS+9847mKWqpulIOTK6onjBrqn7h/8Y4A482zyZTQEIk96s13m5gon8mKZJdBIEAd1dXRgZGcHc/fvkQ+l7ZVlGNpslAYjvo1KpEAl2TYOSy+ELJ07gd5jtWFvDocOHSXuk68KnnVKu68J2XYhhiLCZI9GcEaH2QaITFWVJIp0DtRoJFlIpHqSBXndPfz8GhoY2jS9OEI/EGfg+IMsy8vk8XxTVahV1Oh/bsiwcOnQI77/3Hi5cuICDhw7xjXar+hnQqCDmUyIQ24BZ77FPa2JsKAprw5ufn8fE1BS6u7rw6quvYv+BA1heWsI//V/+Fz7vgCEEcSgkUeSZhVKpBCOVgqaqOPjcc7h46RLOnT+P5194AQpV+mIZjoAyiRk7uVOEYUhYvrSNKQgCGHTUqgAi5DQwPIx8IhyU4HMGgWoPGIYBJ5cjA4RKJZJlpB1Lu3fvxq1bt+C6Lnbv2gVHVYnOf4yD3Q5hEJD2Zdpt4FAFUJ9mJgUAoButqqq4cuUKJicnMTU1hR/8oR9Cf38/Fufn8cu/8iu8e4lfBx1CxMYU1+t1OI4DwzCgqioOPPccLn34Ic5duIAXjhyBqGlkFgI9L57mb5FNbAWmQVKnQkyqqiKbyfCBZ4kT8GhInIHHAFEUkc1mkclkUM/nUS6XYVUqgG3j+IkTuHjpEi5dvIidO3eiUChAo0Q/SRRb1v5YC6HP+nsjU73YVD8BZMymQevqNdPEmTNnsLK8jB94/XU8f/gwH0TiBwEEyvINmhTAwjCEoijI5nKomyacIIBpmrBtGy8cOYJLH36IixcvbugJ0PRcdJ56dBrhVnAihqNWq5GMQCaDfD6PQk8Penp7kc3nH/n7SJDgaQEbfJbL5VCr1VBeX4djmhgbH4coirhx/TrqtRp27NyJSrmMDN30GDMfaOrKYVoEEbXQ6NjgkHYfCYIAx7bJuGPDwPzCAm7fvg1BFPEzP/MzGBsbA5NEd5lcsONw28NBs3c2LUcysrKmaTh69Cg+/PBDXKK2g2U0WaYijJxzJ2ABhG1ZqNs2XNeFruvIZDIodHWhi9qOxAl4NCTOwGOEIAh8IIjX20vq9bUa0vk8zr73Hm7euIHxyUl09/SgUqlAVZSNNqMteu8BcPEeiUXnIGpboiTh7r17mJmZgaZpeOnllzEwMLBxXgCvybUb6iFRRTTHdYn6n+9j9549ZJJZuYyZmRlM7diBwPcbxEtY37IgCAgZwSlsHCcMEOUvRlpkgiYpOk51fHIS3X19yXjhBM8kWJYxl8sRMp5tI9fTg1Q2i0vnz+Pj69exa9cuUg4EKUXKkU6lVraDZQ2ZeE+U2W/bNizLwvXr11EqlTA+Po5Dhw836BwElB8EILZTiDkibJxwvV6HQzsR9uzZA1VVsb62hpnbtzG5Ywd904Zomk+7FyInTP6gn82GtFlUKyUMAph0CFwmm8XQyAiGR0eRyWYf+d4nIEgs7ycEVhNPpVLo6evD2I4d+NP/+l9x++ZNjJgmBgcHAUWBaZp82iDrzwXAtcYlKtjB0nlReL4PURTx9ptvQpIkjE1MYM+ePchHBmwwkp9LnQC3TesfMxyqokDO5VCv15EDMD45ibl793D27FmMDA9DbipzNJOX6IH5313HQd2ySKRC1Q4VRcHg8DAGh4e59HOCBM86BEEgrXmahlwuh6HRUQxPTOCbf/iHuPbxxxgbGUFPTw8vF2pspDd7P7Ah20vFwJpV+kAzfwKA02fOoLenB0deeAHjExONst8gNgY0A+i2CSQAUtJMp1JQFQVmvY5MJoMdO3fi/twczp4/j7Gmz2flgqYbwI8dBgGsep1oJlCioO/7yBUKKHR3Y2JiAik6yTTB94/EGfiUoKoqfvTHfxwXLlzAO9/7Hu7ev48ROh0rnUohoO19zMNuXsBMcTD0fcwvLGB2dha3bt1CEAQYGBzE4UOHADQScLhqIX0fsDFRsBWiKTvdMCDLMvbv24cH9+/j+vXrqH3lKxCogqKiqhBEkfAGms438H04jkPkTGm60rYsGIaB0clJFGhqNJt49AkStMX4xAT+1t/+2/j2n/0ZLl27hnwmg/HRUYyOjhJnPwi43LkY2WwZGEeobpq4e+cOZu/cwd179xAKAg4eOICdu3bx+SPMsWDiQywb0M5uRCeJhmEIkYqCWbaNA/v348GDB7h+/TrK5TLhJmgaacWOCQDCiO6I7TikW8CyEIYhCt3d6BsYQDqXQ1dXV0fcqwSdI3EGPkUIgoBjx47hhRdewNWrV3H69GnMnT2LnkKBiHkwFa9Uivf91ut1MpWvWkVpfR2ra2uwbRtdXV0YHhrC9evX8dyBAygUCiiVy1xxLDoEyK7XyWhhurjimMdRRLXFZVnG8ePH8Y1vfAO2ZWG9VEKhq4tMUaOzx9kURuaA2NQJ4IpnQYB0NoupPXuQ7+pCKpVCOp1uiBISJEjQGtlsFl/7yZ/E+vo63nvvPXx48SJuz8xgZHCQsPip+l8mk0E6k4Fj26jRtudKuYxSqYTVtTWSQRwdxY6pKdy4fh3j4+PQVJXoe7guwJQIQRwCh8oaR+WNyQsay4DNUb4gkOmnx44dwzf/+I/x8OFDlNbWUOjpgWua3DawCYwA+HRG3/M4aVoSRXT39aFvcBDpTAaZTAYaVWlN8HiROAOfASRJwuHDh3Ho0CHMzs7i3AcfYH5lBZVyGaAEH9YnzBZKKpVCNpPBxPg4RsfHUcjnsba6yj9ToONLXSofynQNArqgAcB2nFhvvBl8XCnF6Ogoent7sby0hBs3buAHXn+db/h+pM3Qp4tbUVVoug6dzk7PZLOc6GMYRjJMKEGCR0ShUMAP//AP49VXX8W5c+dw88YNPFhZgVWv83KdJIpEphikXTBD1+H4xATGRkchKwpmZ2f5Z8qqClAuD9vsBdryG1JRIY/JjDfZBgbGXWj+3djYGApdXcR23LqFV0dGyJwU14Xr++TvtPwgqyqZpJhKIZ3NIpVOw6AcLEacTPDJIXEGPkMIgoAdO3YQ6VGQaL1arWJpaQnLy8ukf9YwoCsKfDrGlM0IsCxr09Q/VVVJeo1KEwOEJBRd0B2dFzm5hoV99OhR/Nmf/RkuffghXn31VR4lyIqCVC4HVVWhaBpESYJhGMhms0Rdrc244gQJEjwaDMPAqVOncOrUKQAkqi4Wi1haWkKpVIKmaUgbBoQwhOc4ZMKnIBDZ36aonk1G9OnmrOs6z/AB6HgT5nYDaCA0Hj16FN/+8z/HpQ8/xKlTp4hdEUWkNA2qrkPVdUiKApW2T6ZSKc65aje1MMHjReIMPEEQBAHZbBbZbBY7d+4kxD+6uTM2rWvbRFDINBEEAbK5HMqVChlwJIqwbJuPNlaogAgoKZCl8VgyjzH/G1p9IkqCfDZ5EGD//v04c+YM5u7dg+W6yHd1oYfqfTPCk6ZpSeo/QYLPALIsY2BggHcRsY2d/e/QqaWuZaEWsR3VWg2arhNOD43YBVqe9H2fOAqyzAf+MBvSUCIIN6YjsrIga230fR/79u3DmdOnMT09DR9Az8AA9FSqwW7wdskEnxkSZ+AJhiAInBiUzWZ5yt+lBJu67+P23bswcjno2SwC34cehqhVKlhfX0dIe4xVOsY4n8/z2Qhr6+sbkX+EACRJEkRZJrU8TYOWSgGShOfzecz+838Os17H8toaDr7wAhQ2LS1ZxAkSPFGQJIlH2QC4zWDiQMx2qKkUtHQaEhUkslwX9aUlvvmnUilUymUUqO2QZBmrdLhR1G4INGMp0cFmGnUiRFHECz09mP5n/wylSgWLxSKee/55njVM8OQgcQaeIoh0BCcrAfhhiNuzs+ju68PwyAgXKSqXy6hUKrz1KJ1OY35+Hldu3oQoipjYswd9o6NkWiH5YJ6KY+2Mzf+/9dZbmJ6dhaqqePXVV5GmWuUJEiR48sEcd4BMKPTDELN376K7vx9DIyPwfR+9g4NYXV2F4zg8SylJEjA/jyu3b0MURYzv3o3BsTFiMyiHgA1eY7ai2Ya89dZbmL17F6qq4pVXXuGTVRM8WUicgaccAW0rim7OhUJhk8Kg4zh4uLBAiEGOg5GRES4cFP2/Ff7Lf/kvAIDXXnstaQdMkOBzAM/zoGkaMpFe/b6+Pq43wgaQOY6Dhw8fEs6B62Jom7bjW9/6FoDEdjzpSJyBzymii5NNPmSzA5j88HbAFvSP/uiPPr6TTJAgwRMHLiDG0v+J7XgmkNA0E2yJixcv4t69ewCSBZ0gQYLOkdiOpweJM5BgS/zxH/8xAODIkSMYHR39jM8mQYIETwsS2/H0ICkTfE7xzjvv4Pbt2/zfKysr/O+3b9/G7/7u7za8/md/9mdbftY3v/lNAMCP/diPPdZzTJAgwZOHxHY8m0icgc8pfud3fgf/7t/9u9jfvfvuu3j33XcbftZqQc/NzeHixYsAkjRfggTPAhLb8WwicQaeYnz5y18GQLoHPikwz35sbAxHjhz5xI6TIEGCTw+J7UjQDCFsN7EmwTOPH/zBH8S3v/1t/L2/9/fwm7/5m5/16SRIkOApQWI7ni4kBMIELVEul/Hmm28CSNJ8CRIk6ByJ7Xj6kDgDCVriT//0T+G6LnK5HE8rJkiQIMFWSGzH04eEM5CgJb7zne8gn8/jx37sx5LxoQkSJOgYie14+pBwBhIkSJAgQYJnHEmZIEGCBAkSJHjGkTgDCRIkSJAgwTOOxBlIkCBBggQJnnEkzkCCBAkSJEjwjCNxBhIkSJAgQYJnHIkzkCBBggQJEjzjSJyBBAkSJEiQ4BlH4gwkSJAgQYIEzzgSZyBBggQJEiR4xpE4AwkSJEiQIMEzjsQZSJAgQYIECZ5xJM5AggQJEiRI8IwjcQYSJEiQIEGCZxyJM5AgQYIECRI840icgQQJEiRIkOAZR+IMJEiQIEGCBM84EmcgQYIECRIkeMaROAMJEiRIkCDBM47EGUiQIEGCBAmecSTOQIIECRIkSPCMI3EGEiRIkCBBgmcc/3/cnOCF980SvQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:2], nrows=1, ncols=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can show them in a column too, if we want! Simply set the `nrows = 2` and `ncols = 1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:2], nrows=2, ncols=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Can we show the entire list of 4 Bloch spheres we created? Absolutely!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:], nrows=2, ncols=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if we had to add multiple vectors to a single Bloch sphere? CUDA-Q uses the [QuTiP](https://www.qutip.org) toolbox to construct Bloch spheres. We can then add multiple states to the same Bloch sphere by passing the sphere object as an argument to `cudaq.add_to_bloch_sphere()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qutip\n",
-    "\n",
-    "rng = np.random.default_rng(seed=47)\n",
-    "blochSphere = qutip.Bloch()\n",
-    "for _ in range(10):\n",
-    "    angleList = rng.random(3) * 2 * np.pi\n",
-    "    sph = cudaq.add_to_bloch_sphere(cudaq.get_state(kernel, angleList), blochSphere)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This created a single Bloch sphere with 10 random vectors. Let us see how it looks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "blochSphere.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unfortunately, there is no such handy visualization for multi-qubit states. In particular, a multi-qubit state cannot be visualized as multiple Bloch spheres due to the nature of entanglement that makes quantum computing so powerful. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Kernel Visualization\n",
-    "\n",
-    "A CUDA-Q kernel can be visualized using the `cudaq.draw` API which returns a string representing the drawing of the execution path, in the specified format. ASCII (default) and LaTeX formats are supported."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel_to_draw():\n",
-    "    q = cudaq.qvector(4)\n",
-    "    h(q)\n",
-    "    x.ctrl(q[0], q[1])\n",
-    "    y.ctrl([q[0], q[1]], q[2])\n",
-    "    z(q[2])\n",
-    "    \n",
-    "    swap(q[0], q[1])\n",
-    "    swap(q[0], q[3])\n",
-    "    swap(q[1], q[2])\n",
-    "\n",
-    "    r1(3.14159, q[0])\n",
-    "    tdg(q[1])\n",
-    "    s(q[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮                  ╭───────────╮       \n",
-      "q0 : ┤ h ├──●────●────╳───╳─┤ r1(3.142) ├───────\n",
-      "     ├───┤╭─┴─╮  │    │   │ ╰───────────╯╭─────╮\n",
-      "q1 : ┤ h ├┤ x ├──●────╳───┼───────╳──────┤ tdg ├\n",
-      "     ├───┤╰───╯╭─┴─╮╭───╮ │       │      ╰┬───┬╯\n",
-      "q2 : ┤ h ├─────┤ y ├┤ z ├─┼───────╳───────┤ s ├─\n",
-      "     ├───┤     ╰───╯╰───╯ │               ╰───╯ \n",
-      "q3 : ┤ h ├────────────────╳─────────────────────\n",
-      "     ╰───╯                                      \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.draw(kernel_to_draw))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\documentclass{minimal}\n",
-      "\\usepackage{quantikz}\n",
-      "\\begin{document}\n",
-      "\\begin{quantikz}\n",
-      "  \\lstick{$q_0$} & \\gate{H} & \\ctrl{1} & \\ctrl{2} & \\swap{1} & \\swap{3} & \\gate{R_1(3.142)} & \\qw & \\qw \\\\\n",
-      "  \\lstick{$q_1$} & \\gate{H} & \\gate{X} & \\ctrl{1} & \\targX{} & \\qw & \\swap{1} & \\gate{T^\\dag} & \\qw \\\\\n",
-      "  \\lstick{$q_2$} & \\gate{H} & \\qw & \\gate{Y} & \\gate{Z} & \\qw & \\targX{} & \\gate{S} & \\qw \\\\\n",
-      "  \\lstick{$q_3$} & \\gate{H} & \\qw & \\qw & \\qw & \\targX{} & \\qw & \\qw & \\qw \\\\\n",
-      "\\end{quantikz}\n",
-      "\\end{document}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.draw('latex', kernel_to_draw))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copy this output string into any LaTeX editor and export it to PDF.\n",
-    "\n",
-    "<img src=\"../images/circuit_pdf.png\" alt=\"Circuit in PDF format\" width=\"500\" \n",
-    "height=\"250\">\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/genindex.html b/pr-2308/genindex.html
deleted file mode 100644
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--- a/pr-2308/genindex.html
+++ /dev/null
@@ -1,2160 +0,0 @@
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-<html class="writer-html5" lang="en" >
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Index &mdash; NVIDIA CUDA-Q  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
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-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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-            NVIDIA CUDA-Q
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-<li class="toctree-l1"><a class="reference internal" href="using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_target"><code class="docutils literal notranslate"><span class="pre">get_target()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.reset_target"><code class="docutils literal notranslate"><span class="pre">reset_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.set_noise"><code class="docutils literal notranslate"><span class="pre">set_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.unset_noise"><code class="docutils literal notranslate"><span class="pre">unset_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.initialize_cudaq"><code class="docutils literal notranslate"><span class="pre">initialize_cudaq()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.Schedule"><code class="docutils literal notranslate"><span class="pre">Schedule</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.integrator.BaseIntegrator"><code class="docutils literal notranslate"><span class="pre">BaseIntegrator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.AsyncEvolveResult"><code class="docutils literal notranslate"><span class="pre">AsyncEvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.expressions.OperatorSum"><code class="docutils literal notranslate"><span class="pre">OperatorSum</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.expressions.ElementaryOperator"><code class="docutils literal notranslate"><span class="pre">ElementaryOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.expressions.ProductOperator"><code class="docutils literal notranslate"><span class="pre">ProductOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.expressions.RydbergHamiltonian"><code class="docutils literal notranslate"><span class="pre">RydbergHamiltonian</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.expressions.ScalarOperator"><code class="docutils literal notranslate"><span class="pre">ScalarOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.definitions.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.spin.x"><code class="docutils literal notranslate"><span class="pre">spin.x()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.spin.y"><code class="docutils literal notranslate"><span class="pre">spin.y()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.spin.z"><code class="docutils literal notranslate"><span class="pre">spin.z()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.cudm_state.CuDensityMatState"><code class="docutils literal notranslate"><span class="pre">CuDensityMatState</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.operator.cudm_state.to_cupy_array"><code class="docutils literal notranslate"><span class="pre">to_cupy_array()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.SampleResult"><code class="docutils literal notranslate"><span class="pre">SampleResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.AsyncSampleResult"><code class="docutils literal notranslate"><span class="pre">AsyncSampleResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.ObserveResult"><code class="docutils literal notranslate"><span class="pre">ObserveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.AsyncObserveResult"><code class="docutils literal notranslate"><span class="pre">AsyncObserveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.AsyncStateResult"><code class="docutils literal notranslate"><span class="pre">AsyncStateResult</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="api/default_ops.html">Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="api/default_ops.html#user-defined-custom-operations">User-Defined Custom Operations</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="api/default_ops.html#id1"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
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-      <li><a href="api/languages/cpp_api.html#_CPPv4N5cudaq5state5stateEP15SimulationState">cudaq::state::state (C++ function)</a>, <a href="api/languages/cpp_api.html#_CPPv4N5cudaq5state5stateERK5state">[1]</a>
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-      <li><a href="api/languages/cpp_api.html#_CPPv4I0ENK5cudaq5state7to_hostEvPNSt7complexI10ScalarTypeEENSt6size_tE">cudaq::state::to_host (C++ function)</a>
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-</li>
-      <li><a href="api/languages/cpp_api.html#_CPPv4N5cudaq15TensorStateDataE">cudaq::TensorStateData (C++ type)</a>
-</li>
-      <li><a href="api/languages/cpp_api.html#_CPPv4N5cudaq5TraceE">cudaq::Trace (C++ class)</a>
-</li>
-      <li><a href="api/languages/cpp_api.html#_CPPv4N5cudaq11unset_noiseEv">cudaq::unset_noise (C++ function)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.operator.cudm_state.CuDensityMatState">CuDensityMatState (class in cudaq.operator.cudm_state)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.operator.expressions.ElementaryOperator.define">define() (cudaq.operator.expressions.ElementaryOperator class method)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.DepolarizationChannel">DepolarizationChannel (class in cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.Target.description">description (cudaq.Target property)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.deserialize">deserialize() (cudaq.SampleResult method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.draw">draw() (in module cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.ObserveResult.dump">dump() (cudaq.ObserveResult method)</a>
-
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-        <li><a href="api/languages/python_api.html#cudaq.SampleResult.dump">(cudaq.SampleResult method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.State.dump">(cudaq.State method)</a>
-</li>
-      </ul></li>
-  </ul></td>
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-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.operator.expressions.ElementaryOperator">ElementaryOperator (class in cudaq.operator.expressions)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.evolve">evolve() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.evolve_async">evolve_async() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.EvolveResult">EvolveResult (class in cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.ObserveResult.expectation">expectation() (cudaq.ObserveResult method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.SampleResult.expectation">(cudaq.SampleResult method)</a>
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-      </ul></li>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.EvolveResult.expectation_values">expectation_values() (cudaq.EvolveResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.ObserveResult.expectation_z">expectation_z() (cudaq.ObserveResult method)</a>
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-      <ul>
-        <li><a href="api/languages/python_api.html#cudaq.SampleResult.expectation_z">(cudaq.SampleResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.PyKernelDecorator.extract_c_function_pointer">extract_c_function_pointer() (cudaq.PyKernelDecorator method)</a>
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-  </ul></td>
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-<h2 id="F">F</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.EvolveResult.final_expectation_values">final_expectation_values() (cudaq.EvolveResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.EvolveResult.final_state">final_state() (cudaq.EvolveResult method)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.mpi.finalize">finalize() (in module cudaq.mpi)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.gradients.ForwardDifference">ForwardDifference (class in cudaq.gradients)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.State.from_data">from_data() (cudaq.State static method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.gradients.CentralDifference.from_json">from_json() (cudaq.gradients.CentralDifference static method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.gradients.ParameterShift.from_json">(cudaq.gradients.ParameterShift static method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.optimizers.COBYLA.from_json">(cudaq.optimizers.COBYLA static method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.GradientDescent.from_json">(cudaq.optimizers.GradientDescent static method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.LBFGS.from_json">(cudaq.optimizers.LBFGS static method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.optimizers.NelderMead.from_json">(cudaq.optimizers.NelderMead static method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.PyKernelDecorator.from_json">(cudaq.PyKernelDecorator static method)</a>
-</li>
-      </ul></li>
-  </ul></td>
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-<h2 id="G">G</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.AsyncEvolveResult.get">get() (cudaq.AsyncEvolveResult method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.AsyncObserveResult.get">(cudaq.AsyncObserveResult method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.AsyncSampleResult.get">(cudaq.AsyncSampleResult method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.AsyncStateResult.get">(cudaq.AsyncStateResult method)</a>
-</li>
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-      <li><a href="api/languages/python_api.html#cudaq.NoiseModel.get_channels">get_channels() (cudaq.NoiseModel method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.get_marginal_counts">get_marginal_counts() (cudaq.SampleResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.Target.get_precision">get_precision() (cudaq.Target method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.get_register_counts">get_register_counts() (cudaq.SampleResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.get_sequential_data">get_sequential_data() (cudaq.SampleResult method)</a>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.ObserveResult.get_spin">get_spin() (cudaq.ObserveResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.get_state">get_state() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.get_state_async">get_state_async() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.get_target">get_target() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.get_targets">get_targets() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.State.getTensor">getTensor() (cudaq.State method)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.State.getTensors">getTensors() (cudaq.State method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.gradients.gradient">gradient (class in cudaq.gradients)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.optimizers.GradientDescent">GradientDescent (class in cudaq.optimizers)</a>
-</li>
-  </ul></td>
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-<h2 id="H">H</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.has_target">has_target() (in module cudaq)</a>
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-  </ul></td>
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-<h2 id="I">I</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.optimizers.COBYLA.initial_parameters">initial_parameters (cudaq.optimizers.COBYLA property)</a>
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-      <ul>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.GradientDescent.initial_parameters">(cudaq.optimizers.GradientDescent property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.LBFGS.initial_parameters">(cudaq.optimizers.LBFGS property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.NelderMead.initial_parameters">(cudaq.optimizers.NelderMead property)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.mpi.initialize">initialize() (in module cudaq.mpi)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.initialize_cudaq">initialize_cudaq() (in module cudaq)</a>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.EvolveResult.intermediate_states">intermediate_states() (cudaq.EvolveResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.Target.is_emulated">is_emulated() (cudaq.Target method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.mpi.is_initialized">is_initialized() (in module cudaq.mpi)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.State.is_on_gpu">is_on_gpu() (cudaq.State method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.items">items() (cudaq.SampleResult method)</a>
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-<h2 id="K">K</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.Kernel">Kernel (in module cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.kernel">kernel() (in module cudaq)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.KrausChannel">KrausChannel (class in cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.KrausOperator">KrausOperator (class in cudaq)</a>
-</li>
-  </ul></td>
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-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.optimizers.LBFGS">LBFGS (class in cudaq.optimizers)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.optimizers.COBYLA.lower_bounds">lower_bounds (cudaq.optimizers.COBYLA property)</a>
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-      <ul>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.GradientDescent.lower_bounds">(cudaq.optimizers.GradientDescent property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.LBFGS.lower_bounds">(cudaq.optimizers.LBFGS property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.NelderMead.lower_bounds">(cudaq.optimizers.NelderMead property)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
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-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.make_kernel">make_kernel() (in module cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.optimizers.COBYLA.max_iterations">max_iterations (cudaq.optimizers.COBYLA property)</a>
-
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-        <li><a href="api/languages/python_api.html#cudaq.optimizers.GradientDescent.max_iterations">(cudaq.optimizers.GradientDescent property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.LBFGS.max_iterations">(cudaq.optimizers.LBFGS property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.NelderMead.max_iterations">(cudaq.optimizers.NelderMead property)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.PyKernelDecorator.merge_kernel">merge_kernel() (cudaq.PyKernelDecorator method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.ComplexMatrix.minimal_eigenvalue">minimal_eigenvalue() (cudaq.ComplexMatrix method)</a>
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-      <li>
-    module
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-        <li><a href="api/languages/python_api.html#module-cudaq">cudaq</a>
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-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.most_probable">most_probable() (cudaq.SampleResult method)</a>
-</li>
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-</tr></table>
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-<h2 id="N">N</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.PyKernel.name">name (cudaq.PyKernel attribute)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.SimulationPrecision.name">(cudaq.SimulationPrecision property)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.Target.name">(cudaq.Target property)</a>
-</li>
-      </ul></li>
-      <li><a href="api/languages/python_api.html#cudaq.optimizers.NelderMead">NelderMead (class in cudaq.optimizers)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.NoiseModel">NoiseModel (class in cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.num_available_gpus">num_available_gpus() (in module cudaq)</a>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.ComplexMatrix.num_columns">num_columns() (cudaq.ComplexMatrix method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.Target.num_qpus">num_qpus() (cudaq.Target method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.State.num_qubits">num_qubits() (cudaq.State method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.mpi.num_ranks">num_ranks() (in module cudaq.mpi)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.ComplexMatrix.num_rows">num_rows() (cudaq.ComplexMatrix method)</a>
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-      <li><a href="api/languages/cpp_api.html#_CPPv4N5nvqir18MPSSimulationStateE">nvqir::MPSSimulationState (C++ class)</a>
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-      <li><a href="api/languages/cpp_api.html#_CPPv4N5nvqir24TensorNetSimulationStateE">nvqir::TensorNetSimulationState (C++ class)</a>
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-<h2 id="O">O</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.observe">observe() (in module cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.observe_async">observe_async() (in module cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.ObserveResult">ObserveResult (class in cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.operator.expressions.OperatorSum">OperatorSum (class in cudaq.operator.expressions)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.OptimizationResult">OptimizationResult (class in cudaq)</a>
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-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.optimizers.COBYLA.optimize">optimize() (cudaq.optimizers.COBYLA method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.optimizers.GradientDescent.optimize">(cudaq.optimizers.GradientDescent method)</a>
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-        <li><a href="api/languages/python_api.html#cudaq.optimizers.LBFGS.optimize">(cudaq.optimizers.LBFGS method)</a>
-</li>
-        <li><a href="api/languages/python_api.html#cudaq.optimizers.NelderMead.optimize">(cudaq.optimizers.NelderMead method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.optimizers.optimizer">optimizer (class in cudaq.optimizers)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.State.overlap">overlap() (cudaq.State method)</a>
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-<h2 id="P">P</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.gradients.ParameterShift">ParameterShift (class in cudaq.gradients)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.PhaseFlipChannel">PhaseFlipChannel (class in cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.Target.platform">platform (cudaq.Target property)</a>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.SampleResult.probability">probability() (cudaq.SampleResult method)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.operator.expressions.ProductOperator">ProductOperator (class in cudaq.operator.expressions)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.PyKernel">PyKernel (class in cudaq)</a>
-</li>
-      <li><a href="api/languages/python_api.html#cudaq.PyKernelDecorator">PyKernelDecorator (class in cudaq)</a>
-</li>
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-<h2 id="Q">Q</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="api/languages/python_api.html#cudaq.qreg">qreg (in module cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.QuakeValue">QuakeValue (class in cudaq)</a>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/languages/python_api.html#cudaq.qubit">qubit (class in cudaq)</a>
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-      <li><a href="api/languages/python_api.html#cudaq.qvector">qvector (class in cudaq)</a>
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-</tr></table>
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-<h2 id="R">R</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-<li class="toctree-l1"><a class="reference internal" href="using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#utilities">Utilities</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#namespaces">Namespaces</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.translate"><code class="docutils literal notranslate"><span class="pre">translate()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.evolve_async"><code class="docutils literal notranslate"><span class="pre">evolve_async()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#backend-configuration">Backend Configuration</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.has_target"><code class="docutils literal notranslate"><span class="pre">has_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_target"><code class="docutils literal notranslate"><span class="pre">get_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_targets"><code class="docutils literal notranslate"><span class="pre">get_targets()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.set_target"><code class="docutils literal notranslate"><span class="pre">set_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.reset_target"><code class="docutils literal notranslate"><span class="pre">reset_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.set_noise"><code class="docutils literal notranslate"><span class="pre">set_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.unset_noise"><code class="docutils literal notranslate"><span class="pre">unset_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.initialize_cudaq"><code class="docutils literal notranslate"><span class="pre">initialize_cudaq()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.num_available_gpus"><code class="docutils literal notranslate"><span class="pre">num_available_gpus()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.set_random_seed"><code class="docutils literal notranslate"><span class="pre">set_random_seed()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#data-types">Data Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.SimulationPrecision"><code class="docutils literal notranslate"><span class="pre">SimulationPrecision</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.Target"><code class="docutils literal notranslate"><span class="pre">Target</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.State"><code class="docutils literal notranslate"><span class="pre">State</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.Tensor"><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="api/default_ops.html">Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/default_ops.html#unitary-operations-on-qubits">Unitary Operations on Qubits</a><ul>
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-  <section id="cuda-q">
-<h1>CUDA-Q<a class="headerlink" href="#cuda-q" title="Permalink to this heading">¶</a></h1>
-<p>Welcome to the CUDA-Q documentation page!</p>
-<p>CUDA-Q streamlines hybrid application development and promotes productivity and scalability
-in quantum computing. It offers a unified programming model designed for a hybrid
-setting—that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for
-programming in Python and in C++.</p>
-<p>You are browsing the documentation for amd64-pr-2308 version of CUDA-Q. You can find documentation for all released versions <a class="reference internal" href="versions.html"><span class="doc">here</span></a>.</p>
-<p><strong>CUDA-Q</strong> is a programming model and toolchain for using quantum acceleration in heterogeneous computing architectures available in C++ and Python.</p>
-<div class="toctree-wrapper compound">
-<p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/run_kernel.html">   Running your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/troubleshooting.html">   Troubleshooting</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/quantum_operations.html">   Quantum Operations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/measuring_kernels.html">   Measuring Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/visualization.html">   Visualizing Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/executing_kernels.html">   Executing Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/expectation_values.html">   Computing Expectation Values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/operators.html">   Constructing Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/performance_optimizations.html">   Performance Optimizations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_teleportation.html">Quantum Teleporation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/readout_error_mitigation.html">Readout Error Mitigation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe.html">Variational Quantum Eigensolver</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/simulators.html">   Simulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/hardware.html">   Quantum Hardware</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/nvqc.html">   NVIDIA Quantum Cloud</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/platform.html">   Multi-Processor Platforms</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/install/local_installation.html">Local Installation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/install/data_center_install.html">Data Center Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/libraries.html">Integrating with Third-Party Libraries</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="specification/cudaq.html">   Language Specification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="specification/quake-dialect.html">   Quake Specification</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api/languages/cpp_api.html">C++ API</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api/languages/python_api.html">Python API</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api/default_ops.html">Quantum Operations</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="versions.html">   Other Versions</a></li>
-</ul>
-</div>
-</section>
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-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="using/quick_start.html" class="btn btn-neutral float-right" title="Quick Start" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
-    </div>
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-    <p>&#169; Copyright 2024, NVIDIA Corporation &amp; Affiliates.</p>
-  </div>
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-    color: #8c0;
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diff --git a/pr-2308/notebook_validation.py b/pr-2308/notebook_validation.py
deleted file mode 100644
index e0d464af93..0000000000
--- a/pr-2308/notebook_validation.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-import os
-import re
-import sys
-from pathlib import Path
-import subprocess
-from shutil import which
-
-if which('jupyter') is None:
-    print("Please install jupyter, e.g. with `pip install notebook`.")
-    exit(1)
-
-
-def read_available_backends():
-    available_backends = sys.stdin.readlines()
-    available_backends = ' '.join(available_backends).split()
-    return [backend.strip() for backend in available_backends]
-
-
-def validate(notebook_filename, available_backends):
-    with open(notebook_filename) as f:
-        lines = f.readlines()
-    for notebook_content in lines:
-        match = re.search('set_target[\\\s\(]+"(.+)\\\\"[)]', notebook_content)
-        if match and (match.group(1) not in available_backends):
-            return False
-    for notebook_content in lines:
-        match = re.search('--target ([^ ]+)', notebook_content)
-        if match and (match.group(1) not in available_backends):
-            return False
-    return True
-
-
-def execute(notebook_filename):
-    notebook_filename_out = notebook_filename.replace('.ipynb',
-                                                      '.nbconvert.ipynb')
-    try:
-        subprocess.run([
-            "jupyter", "nbconvert", "--to", "notebook", "--execute",
-            notebook_filename
-        ],
-                       check=True)
-        os.remove(notebook_filename_out)
-        return True
-    except subprocess.CalledProcessError:
-        print('Error executing the notebook "%s".\n\n' % notebook_filename)
-        return False
-
-
-def print_results(success, failed, skipped=[]):
-    if success:
-        print("Success! The following notebook(s) executed successfully:\n" +
-              " ".join(success))
-
-    if failed:
-        print(
-            "::error::The following notebook(s) raised one or more errors:\n" +
-            " ".join(failed))
-
-    if skipped:
-        print("::warning::Skipped validation for the following notebook(s):\n" +
-              " ".join(skipped))
-
-    if not failed and not skipped:
-        print("Success! All the notebook(s) executed successfully.")
-    elif failed:
-        exit(1)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) > 1:
-        notebook_filenames = sys.argv[1:]
-        notebooks_success, notebooks_failed = ([] for i in range(2))
-        for notebook_filename in notebook_filenames:
-            if (execute(notebook_filename)):
-                notebooks_success.append(notebook_filename)
-            else:
-                notebooks_failed.append(notebook_filename)
-        print_results(notebooks_success, notebooks_failed)
-    else:
-        available_backends = read_available_backends()
-        notebook_filenames = [
-            str(fn.relative_to(Path(__file__).parent))
-            for fn in Path(__file__).parent.rglob('*.ipynb')
-            if not fn.name.endswith('.nbconvert.ipynb')
-        ]
-
-        if not notebook_filenames:
-            print('Failed! No notebook(s) found.')
-            exit(10)
-
-        notebooks_success, notebooks_skipped, notebooks_failed = (
-            [] for i in range(3))
-        for notebook_filename in notebook_filenames:
-            if (validate(notebook_filename, available_backends)):
-                if (execute(notebook_filename)):
-                    notebooks_success.append(notebook_filename)
-                else:
-                    notebooks_failed.append(notebook_filename)
-            else:
-                notebooks_skipped.append(notebook_filename)
-
-        print_results(notebooks_success, notebooks_failed, notebooks_skipped)
diff --git a/pr-2308/objects.inv b/pr-2308/objects.inv
deleted file mode 100644
index 27576a5e16..0000000000
Binary files a/pr-2308/objects.inv and /dev/null differ
diff --git a/pr-2308/openapi.html b/pr-2308/openapi.html
deleted file mode 100644
index 757ce743f1..0000000000
--- a/pr-2308/openapi.html
+++ /dev/null
@@ -1,313 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head />
-  <body>
-    <script src="https://cdn.redoc.ly/redoc/latest/bundles/redoc.standalone.js"> </script>
-    <div id="redoc-container"></div>
-	<script type="application/json" id="openapi-spec">
-    {
-  "swagger": "2.0",
-  "info": {
-    "title": "CUDA-Q Remote Simulation Runtime REST API",
-    "version": "1.0.0"
-  },
-  "paths": {
-    "/job": {
-      "post": {
-        "summary": "Create a quantum simulation job",
-        "description": "Requests execution (simulation) of quantun kernels on the server. The server employs long polling, hence it will keep the request open until the simulation completed.\n",
-        "parameters": [
-          {
-            "name": "job request",
-            "in": "body",
-            "schema": {
-              "$ref": "#/definitions/RestRequest"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Execution results.",
-            "schema": {
-              "type": "object",
-              "required": [
-                "executionContext"
-              ],
-              "properties": {
-                "executionContext": {
-                  "$ref": "#/definitions/ResultExecutionContext"
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  },
-  "definitions": {
-    "RestRequest": {
-      "type": "object",
-      "required": [
-        "version",
-        "entryPoint",
-        "code",
-        "format",
-        "executionContext"
-      ],
-      "properties": {
-        "version": {
-          "type": "integer",
-          "format": "int64",
-          "description": "Version number of the payload. Each REST server version only supports a specific payload version.",
-          "example": 1
-        },
-        "clientVersion": {
-          "type": "string",
-          "description": "Version description of the CUDA-Q client generating the request.",
-          "example": "CUDA-Q Version 0.7.0 (https://github.com/NVIDIA/cuda-quantum 8650fb9d79e7e780ea95d9506a513162daf84b80)"
-        },
-        "entryPoint": {
-          "type": "string",
-          "description": "Entry point kernel identifier as a fully-qualified name.",
-          "example": "ns::ghz"
-        },
-        "simulator": {
-          "type": "string",
-          "description": "Name of the backend simulator to be used.",
-          "example": "nvidia-mgpu"
-        },
-        "executionContext": {
-          "type": "object",
-          "description": "Context to indicate how the CUDA-Q kernel should be executed.",
-          "$ref": "#/definitions/RequestExecutionContext"
-        },
-        "code": {
-          "type": "string",
-          "format": "binary",
-          "description": "Base64 encoded CUDA-Q kernel IR (Intermediate Representation)."
-        },
-        "args": {
-          "type": "string",
-          "format": "binary",
-          "description": "Base64 encoded kernel arguments."
-        },
-        "format": {
-          "type": "string",
-          "enum": [
-            "MLIR",
-            "LLVM"
-          ],
-          "description": "Format of the code IR."
-        },
-        "seed": {
-          "type": "integer",
-          "format": "int64",
-          "example": 123,
-          "description": "Random seed value for the simulation."
-        },
-        "passes": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "example": [
-            "inline",
-            "canonicalize",
-            "quake-to-qir"
-          ],
-          "description": "List of IR passes to be applied on the server."
-        }
-      }
-    },
-    "RequestExecutionContext": {
-      "type": "object",
-      "required": [
-        "name"
-      ],
-      "properties": {
-        "name": {
-          "type": "string",
-          "enum": [
-            "sample",
-            "observe",
-            "extract-state"
-          ],
-          "description": "Name of the execution context."
-        },
-        "shots": {
-          "type": "integer",
-          "description": "Number of measurement shots.",
-          "example": 1000
-        },
-        "hasConditionalsOnMeasureResults": {
-          "type": "boolean",
-          "description": "Flag to indicate that the kernel being executed in this context has conditional statements on measure results.",
-          "example": "false"
-        },
-        "registerNames": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "description": "For mid-circuit measurements in library mode keep track of the register names."
-        },
-        "spin": {
-          "type": "object",
-          "description": "Optional spin operator. Required for \"observe\" execution context.",
-          "$ref": "#/definitions/spin_op"
-        }
-      }
-    },
-    "ResultExecutionContext": {
-      "type": "object",
-      "required": [
-        "name"
-      ],
-      "properties": {
-        "name": {
-          "type": "string",
-          "enum": [
-            "sample",
-            "observe",
-            "extract-state"
-          ],
-          "description": "Name of the execution context."
-        },
-        "shots": {
-          "type": "integer",
-          "description": "Number of measurement shots.",
-          "example": 1000
-        },
-        "hasConditionalsOnMeasureResults": {
-          "type": "boolean",
-          "description": "Flag to indicate that the kernel being executed in this context has conditional statements on measure results.",
-          "example": "false"
-        },
-        "registerNames": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "description": "For mid-circuit measurements in library mode keep track of the register names."
-        },
-        "spin": {
-          "type": "object",
-          "description": "Optional spin operator. Required for \"observe\" execution context.",
-          "$ref": "#/definitions/spin_op"
-        },
-        "result": {
-          "type": "array",
-          "items": {
-            "type": "object",
-            "$ref": "#/definitions/ExecutionResult"
-          },
-          "description": "Execution result being populated by the server after execution."
-        },
-        "expectationValue": {
-          "type": "number",
-          "format": "double",
-          "example": 0.1234,
-          "description": "Optional computed expectation value."
-        },
-        "simulationData": {
-          "type": "object",
-          "description": "Underlying simulation data when using \"extract-state\" execution context.",
-          "properties": {
-            "dim": {
-              "type": "array",
-              "items": {
-                "type": "integer",
-                "example": 8
-              },
-              "description": "Dimensions of the state representation (flattened multi-dimensional arrays)."
-            },
-            "data": {
-              "type": "array",
-              "items": {
-                "type": "array",
-                "example": [
-                  0.5,
-                  0.5
-                ],
-                "items": {
-                  "type": "number",
-                  "format": "double"
-                }
-              },
-              "description": "State data as a vector of complex numbers. Each complex number is represented as an array of size 2 for the real and imaginary parts."
-            }
-          }
-        }
-      }
-    },
-    "spin_op": {
-      "type": "object",
-      "required": [
-        "num_qubits",
-        "data"
-      ],
-      "properties": {
-        "num_qubits": {
-          "type": "integer",
-          "description": "Number of qubits (spins) represented.",
-          "example": 4
-        },
-        "data": {
-          "type": "array",
-          "items": {
-            "type": "number",
-            "format": "double"
-          },
-          "description": "Serialized representation of this spin_op (as an array of floating point numbers)"
-        }
-      }
-    },
-    "ExecutionResult": {
-      "type": "object",
-      "properties": {
-        "counts": {
-          "type": "object",
-          "additionalProperties": {
-            "type": "integer"
-          },
-          "description": "Measurements and times observed",
-          "example": {
-            "00": 500,
-            "11": 500
-          }
-        },
-        "registerName": {
-          "type": "string",
-          "description": "Register name for the classical bits.",
-          "example": "res0"
-        },
-        "sequentialData": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "description": "Sequential bit strings observed (not collated into a map)",
-          "example": [
-            "00",
-            "10"
-          ]
-        },
-        "expectationValue": {
-          "type": "number",
-          "format": "double",
-          "description": "Optional <Z...Z> expected value",
-          "example": 1.234
-        }
-      }
-    }
-  }
-}
-	</script>
-  <script>
-    var spec = JSON.parse(document.getElementById("openapi-spec").innerHTML);
-    Redoc.init(spec, {
-      "expandResponses": "200"
-    }, document.getElementById('redoc-container'))
-  </script>
-  </body>
-</html>
\ No newline at end of file
diff --git a/pr-2308/py-modindex.html b/pr-2308/py-modindex.html
deleted file mode 100644
index 0bd4103367..0000000000
--- a/pr-2308/py-modindex.html
+++ /dev/null
@@ -1,788 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Python Module Index &mdash; NVIDIA CUDA-Q  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="_static/tabs.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="_static/cudaq_override.css" type="text/css" />
-      <link rel="stylesheet" href="_static/sg_gallery.css" type="text/css" />
-      <link rel="stylesheet" href="_static/_static/cudaq_override.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/tabs.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
-    <script src="_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="genindex.html" />
-    <link rel="search" title="Search" href="search.html" />
- 
-
-    <script>
-      DOCUMENTATION_OPTIONS.COLLAPSE_INDEX = true;
-    </script>
-
-
-</head>
-
-<body class="wy-body-for-nav"> 
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-side-nav-search"  style="background: #76b900" > 
-
-          
-          
-          <a href="index.html" class="icon icon-home">
-            NVIDIA CUDA-Q
-          </a>
-              <div class="version">
-                amd64-pr-2308
-              </div>
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
-    <input type="hidden" name="check_keywords" value="yes" />
-    <input type="hidden" name="area" value="default" />
-  </form>
-</div>
-
-  <style>
-    /* Sidebar header (and topbar for mobile) */
-    .wy-side-nav-search, .wy-nav-top {
-      background: #76b900;
-    }
-
-    .wy-side-nav-search a:link, .wy-nav-top a:link {
-      color: #fff;
-    }
-    .wy-side-nav-search a:visited, .wy-nav-top a:visited {
-      color: #fff;
-    }
-    .wy-side-nav-search a:hover, .wy-nav-top a:hover {
-      color: #fff;
-    }
-
-    .wy-menu-vertical a:link, .wy-menu-vertical a:visited {
-      color: #d9d9d9
-    }
-
-    .wy-menu-vertical a:active {
-      background-color: #76b900
-    }
-
-    .wy-side-nav-search>div.version {
-      color: rgba(0, 0, 0, 0.3)
-    }
-
-    /* override table width restrictions */
-    .wy-table-responsive table td, .wy-table-responsive table th {
-        white-space: normal;
-    }
-
-    .wy-table-responsive {
-        margin-bottom: 24px;
-        max-width: 100%;
-        overflow: visible;
-    }
-  </style>
-  
-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#measurements">Measurements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id13">Setting Credentials</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#utilities">Utilities</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#namespaces">Namespaces</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/quantum_operations.html#measurements">Measurements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/simulators.html">   Simulation</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#operators">Operators</a></li>
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-  <section id="cuda-q-releases">
-<h1>CUDA-Q Releases<a class="headerlink" href="#cuda-q-releases" title="Permalink to this heading">¶</a></h1>
-<p><strong>latest</strong></p>
-<p>The latest version of CUDA-Q is on the main branch of our <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a>
-and is also available as a Docker image. More information about installing the nightly builds can be found
-<a class="reference internal" href="using/install/install.html"><span class="doc">here</span></a></p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum">Docker image (nightly builds)</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/main/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p><strong>0.9.0</strong></p>
-<p>We are very excited to share a new toolset added for modeling and manipulating the dynamics of physical systems.
-The new API allows to define and execute a time evolution under arbitrary operators. For more information, take
-a look at the <a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/dynamics.html">docs</a>.
-The 0.9.0 release furthermore includes a range of contribution to add new backends to CUDA-Q, including backends
-from <a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies</a>,
-<a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/simulators.html#fermioniq">Ferimioniq</a>, and
-<a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#quera-computing">QuEra Computing</a>,
-as well as updates to existing backends from <a class="reference external" href="https://nvidia.github.io/cuda-quantum/latest/using/backends/hardware.html#orca-computing">ORCA</a>
-and <a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#oqc">OQC</a>.
-We hope you enjoy the new features - also check out our new notebooks and examples to dive into CUDA-Q.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.9.0">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.9.0">C++ installer</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.9.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.9.0/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.9.0">here</a>.</p>
-<p><strong>0.8.0</strong></p>
-<p>The 0.8.0 release adds a range of changes to improve the ease of use and performance with CUDA-Q.
-The changes listed below highlight some of what we think will be the most useful features and changes
-to know about. While the listed changes do not capture all of the great contributions, we would like
-to extend many thanks for every contribution, in particular those from external contributors.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.8.0">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.8.0">C++ installer</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.8.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.8.0/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.8.0">here</a>.</p>
-<p><strong>0.7.1</strong></p>
-<p>The 0.7.1 release adds simulator optimizations with significant performance improvements and
-extends their functionalities. The <code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend now supports user customization of the
-gate fusion level as controlled by the <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code> environment variable documented
-<a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.7.1/using/backends/simulators.html">here</a>.
-It furthermore adds a range of bug fixes and changes the Python wheel installation instructions.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.7.1">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.7.1">C++ installer</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.7.1">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.7.1/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.7.1">here</a>.</p>
-<p><strong>0.7.0</strong></p>
-<p>The 0.7.0 release adds support for using <a class="reference internal" href="using/backends/nvqc.html"><span class="doc">NVIDIA Quantum Cloud</span></a>,
-giving you access to our most powerful GPU-accelerated simulators even if you don’t have an NVIDIA GPU.
-With 0.7.0, we have furthermore greatly increased expressiveness of the Python and C++ language frontends.
-Check out our <a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.7.0/using/quick_start.html">documentation</a>
-to get started with the new Python syntax support we have added, and <a class="reference external" href="https://developer.nvidia.com/cuda-q">follow our blog</a>
-to learn more about the new setup and its performance benefits.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.7.0">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.7.0">C++ installer</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.7.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.7.0/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.7.0">here</a>.</p>
-<p><strong>0.6.0</strong></p>
-<p>The 0.6.0 release contains improved support for various HPC scenarios. We have added a
-<a class="reference internal" href="using/install/local_installation.html#distributed-computing-with-mpi"><span class="std std-ref">plugin infrastructure</span></a> for connecting CUDA-Q
-with an existing MPI installation, and we’ve added a <a class="reference internal" href="using/backends/platform.html#id1"><span class="std std-ref">new platform target</span></a> that distributes workloads across multiple virtual QPUs,
-each simulated by one or more GPUs.</p>
-<p>Starting with 0.6.0, we are now also distributing
-<a class="reference internal" href="using/install/local_installation.html#install-prebuilt-binaries"><span class="std std-ref">pre-built binaries</span></a> for using CUDA-Q with C++.
-The binaries are built against the <a class="reference external" href="https://www.gnu.org/software/libc/">GNU C library</a>
-version 2.28.
-We’ve added a detailed <a class="reference internal" href="using/install/data_center_install.html"><span class="doc">Building from Source</span></a> guide to build these binaries for older <code class="code docutils literal notranslate"><span class="pre">glibc</span></code> versions.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.6.0">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.6.0">C++ installer</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.6.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.6.0/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.6.0">here</a>.</p>
-<p><strong>0.5.0</strong></p>
-<p>With 0.5.0 we have added support for quantum kernel execution on OQC and IQM backends. For more information, see <a class="reference internal" href="using/backends/hardware.html"><span class="doc">CUDA-Q Hardware Backends</span></a>.
-CUDA-Q now allows to executing adaptive quantum kernels on quantum hardware backends that support it.
-The 0.5.0 release furthermore improves the tensor network simulation tools and adds a matrix product state simulator, see <a class="reference internal" href="using/backends/simulators.html"><span class="doc">CUDA-Q Simulation Backends</span></a>.</p>
-<p>Additionally, we are now publishing images for experimental features, which currently includes improved Python language support.
-Please take a look at <a class="reference internal" href="using/install/install.html"><span class="doc">Installation Guide</span></a> for more information about how to obtain them.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.5.0">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.5.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.5.0/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.5.0">here</a>.</p>
-<p><strong>0.4.1</strong></p>
-<p>The 0.4.1 release adds support for ARM processors in the form of multi-platform Docker images and <code class="code docutils literal notranslate"><span class="pre">aarch64</span></code> Python wheels. Additionally, all GPU-based backends are now included in the Python wheels as well as in the Docker image.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.4.1">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.4.1">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.4.1/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/0.4.1">here</a>.</p>
-<p><strong>0.4.0</strong></p>
-<p>CUDA-Q is now available on PyPI!
-The 0.4.0 release adds support for quantum kernel execution on Quantinuum and IonQ backends. For more information, see <a class="reference internal" href="using/backends/hardware.html"><span class="doc">CUDA-Q Hardware Backends</span></a>.</p>
-<p>The 0.4.0 PyPI release does not yet include all of the GPU-based backends.
-The fully featured version is available as a Docker image for <code class="code docutils literal notranslate"><span class="pre">linux/amd64</span></code> platforms.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://pypi.org/project/cuda-quantum/0.4.0">Python wheel</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.4.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/0.4.0/docs/sphinx/examples">Examples</a></p></li>
-</ul>
-<p>The full change log can be found <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases/tag/0.4.0">here</a>.</p>
-<p><strong>0.3.0</strong></p>
-<p>The 0.3.0 release of CUDA-Q is available as a Docker image for <code class="code docutils literal notranslate"><span class="pre">linux/amd64</span></code> platforms.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">Docker image</a></p></li>
-<li><p><a class="reference external" href="https://nvidia.github.io/cuda-quantum/0.3.0">Documentation</a></p></li>
-<li><p><a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/0.3.0/docs/sphinx/examples">Examples</a></p></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#utilities">Utilities</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/cpp_api.html#namespaces">Namespaces</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.translate"><code class="docutils literal notranslate"><span class="pre">translate()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.evolve_async"><code class="docutils literal notranslate"><span class="pre">evolve_async()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/languages/python_api.html#backend-configuration">Backend Configuration</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.has_target"><code class="docutils literal notranslate"><span class="pre">has_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="api/languages/python_api.html#cudaq.get_target"><code class="docutils literal notranslate"><span class="pre">get_target()</span></code></a></li>
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"Bernstein-Vazirani Algorithm", "Cost Minimization", "Deutsch\u2019s Algorithm", "Molecular docking via DC-QAOA", "Divisive Clustering With Coresets Using CUDA-Q", "Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements", "Hybrid Quantum Neural Networks", "Multi-reference Quantum Krylov Algorithm - <span class=\"math notranslate nohighlight\">\\(H_2\\)</span> Molecule", "Anderson Impurity Model ground state solver on Infleqtion\u2019s Sqale", "Max-Cut with QAOA", "Quantum Fourier Transform", "Quantum Teleporation", "Quantum Volume", "Readout Error Mitigation", "Factoring Integers With Shor\u2019s Algorithm", "Computing Magnetization With The Suzuki-Trotter Approximation", "Compiling Unitaries Using Diffusion Models", "Variational Quantum Eigensolver", "VQE with gradients, active spaces, and gate fusion", "Executing Quantum Circuits", "Measurements", "Noisy Simulation", "Operators", "Optimizers and Gradients", "Optimizing Performance", "Visualization", "CUDA-Q", "CUDA-Q Releases", "Language Specification", "<span class=\"section-number\">12. </span>Quantum Algorithmic Primitives", "<span class=\"section-number\">8. </span>Control Flow", "<span class=\"section-number\">9. </span>Just-in-Time Kernel Creation", "<span class=\"section-number\">13. </span>Example Programs", "<span class=\"section-number\">6. </span>Quantum Kernels", "<span class=\"section-number\">1. </span>Machine Model", "<span class=\"section-number\">2. </span>Namespace and Standard", "<span class=\"section-number\">5. </span>Quantum Intrinsic Operations", "<span class=\"section-number\">4. </span>Quantum Operators", "<span class=\"section-number\">10. </span>Common Quantum Programming Patterns", "<span class=\"section-number\">11. </span>Quantum Platform", "<span class=\"section-number\">7. </span>Sub-circuit Synthesis", "<span class=\"section-number\">3. </span>Quantum Types", "Specifications", "Quake Dialect", "CUDA-Q Applications", "CUDA-Q Backends", "CUDA-Q Dynamics", "CUDA-Q Hardware Backends", "NVIDIA Quantum Cloud", "Multi-Processor Platforms", "CUDA-Q Simulation Backends", "CUDA-Q Basics", "Building your first CUDA-Q Program", "What is a CUDA-Q kernel?", "Running your first CUDA-Q Program", "Troubleshooting", "Building Kernels", "CUDA-Q by Example", "Computing Expectation Values", "Using Quantum Hardware Providers", "Introduction", "Multi-control Synthesis", "Multi-GPU Workflows", "Quantum Computing 101", "Working with the CUDA-Q IR", "Extending CUDA-Q", "Create your own CUDA-Q Compiler Pass", "Extending CUDA-Q with a new Simulator", "Installation from Source", "Installation Guide", "Local Installation", "CUDA-Q and CMake", "Using CUDA and CUDA-Q in a Project", "Integration with other Software Tools", "Integrating with Third-Party Libraries", "Quick Start", "CUDA-Q Versions"], "terms": {"c": [0, 1, 3, 4, 9, 12, 14, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 51, 53, 54, 55, 57, 59, 60, 61, 62, 63, 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23, 25, 26, 27, 28, 29, 30, 32, 34, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 52, 53, 54, 55, 61, 64, 67, 69, 73, 75, 79, 81], "i\u03c0": 1, "two": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 26, 28, 30, 39, 48, 51, 52, 54, 55, 59, 61, 63, 64, 66, 68, 75, 79], "qubit_1": [1, 7, 14, 61], "qubit_2": [1, 16], "univers": [1, 2, 9, 16, 21, 53, 68], "three": [1, 10, 18, 19, 21, 23, 39, 52, 53, 63], "paramet": [1, 2, 3, 6, 8, 9, 11, 12, 13, 14, 17, 19, 21, 22, 24, 34, 38, 41, 46, 51, 52, 53, 55, 57, 61, 63, 64, 65, 67, 72, 73], "euler": [1, 51], "angl": [1, 2, 3, 6, 10, 13, 15, 18, 19, 28, 30, 37, 38, 41, 54, 57, 63, 64], "theta": [1, 8, 9, 10, 11, 14, 15, 22, 23, 26, 28, 30, 34, 36, 37, 53, 54, 61, 63], "phi": [1, 3, 4, 10, 34, 36, 52, 64, 69], "\u03c6": 1, "lambda": [1, 2, 8, 9, 12, 13, 14, 18, 19, 20, 23, 28, 34, 37, 38, 43, 51, 64, 66, 67, 69], "i\u03c6": 1, "np": [1, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 30, 37, 38, 43, 45, 51, 52, 54, 55, 61, 64, 67, 73], "m_pi": [1, 37, 45, 64], "m_pi_2": [1, 37, 38, 53], "adj": [1, 41, 61], "method": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 27, 28, 34, 36, 41, 46, 51, 52, 54, 55, 59, 61, 64, 65], "ani": [1, 2, 3, 9, 12, 14, 19, 20, 21, 23, 24, 26, 28, 30, 34, 38, 40, 41, 43, 45, 46, 51, 52, 53, 55, 57, 59, 60, 61, 64, 65, 66, 72, 73, 75, 79, 80], "alloc": [1, 2, 3, 6, 14, 21, 34, 36, 37, 38, 39, 46, 54, 55, 57, 59, 65, 66, 72], "now": [1, 4, 5, 7, 9, 13, 14, 15, 16, 19, 21, 22, 23, 32, 48, 52, 59, 64, 65, 66, 67, 75, 80, 81], "again": [1, 5, 23, 24, 34, 48, 75, 77], "initi": [1, 2, 3, 4, 5, 6, 8, 9, 12, 13, 14, 15, 16, 19, 20, 22, 23, 28, 34, 51, 52, 54, 61, 64, 67, 68, 73, 75], "ctrl": [1, 2, 5, 7, 14, 17, 19, 24, 28, 30, 34, 36, 37, 41, 53, 54, 57, 61, 63, 64, 65, 66, 68, 69, 75, 80], "condit": [1, 2, 15, 16, 21, 26, 34, 35, 36, 38, 39, 55, 58, 68, 69], "more": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 18, 19, 21, 23, 27, 30, 32, 36, 39, 41, 42, 48, 52, 53, 54, 55, 58, 59, 61, 64, 67, 68, 73, 75, 77, 79, 80, 81], "wikipedia": [1, 51], "entri": [1, 3, 12, 34, 38, 54, 61, 64, 69, 75, 79], "ctrl_1": 1, "ctrl_2": 1, "00": [1, 4, 9, 13, 16, 24, 26, 28, 59, 67, 68, 79, 80], "11": [1, 3, 4, 9, 11, 13, 16, 17, 18, 20, 21, 23, 24, 26, 28, 30, 34, 59, 67, 68, 69, 73, 75, 79, 80], "onli": [1, 2, 3, 5, 9, 16, 18, 19, 21, 23, 24, 25, 30, 34, 38, 39, 43, 46, 48, 51, 52, 53, 54, 58, 64, 68, 69, 71, 73, 75, 79, 80], "both": [1, 3, 4, 5, 7, 11, 13, 19, 39, 48, 52, 54, 55, 68, 73, 75, 77], "000": [1, 15, 16, 18, 19, 53, 59], "111": [1, 9, 15, 16, 18, 19], "follow": [1, 2, 3, 4, 5, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 27, 28, 30, 32, 34, 36, 38, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 57, 59, 60, 61, 63, 64, 65, 69, 71, 72, 73, 75, 76, 77, 79, 80, 81], "common": [1, 3, 9, 10, 18, 19, 21, 22, 23, 34, 38, 41, 42, 69, 73], "convent": [1, 8, 11, 14, 24], "all": [1, 2, 3, 9, 13, 16, 17, 18, 19, 20, 21, 23, 24, 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 46, 48, 52, 53, 54, 55, 59, 61, 63, 64, 67, 68, 69, 73, 75, 76, 77, 79, 80, 81], "howev": [1, 4, 9, 13, 15, 16, 18, 21, 23, 24, 48, 51, 52, 54, 79], "behavior": [1, 2, 3, 9, 23, 55], "chang": [1, 2, 5, 9, 13, 15, 19, 29, 32, 34, 38, 51, 59, 75, 80, 81], "instead": [1, 2, 4, 9, 26, 40, 43, 52, 54, 55, 59, 73, 75, 79], "when": [1, 2, 3, 9, 12, 13, 14, 18, 19, 21, 34, 39, 46, 48, 53, 54, 55, 59, 63, 64, 65, 69, 72, 73, 75, 79, 80], "negat": [1, 2, 3, 41, 45, 46], "polar": [1, 41, 45, 55], "syntax": [1, 9, 32, 33, 38, 39, 41, 43, 52, 61, 64, 77, 81], "preced": [1, 41, 52], "01": [1, 4, 7, 13, 16, 21, 26, 28, 68], "10": [1, 4, 7, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 21, 23, 26, 28, 30, 34, 37, 38, 55, 59, 61, 64, 65, 67, 68, 69, 73, 75, 77], "notat": [1, 16, 68], "context": [1, 2, 11, 39, 54, 55, 72], "valid": [1, 2, 3, 31, 38, 52, 55, 61, 64, 73, 75, 79], "either": [1, 7, 11, 14, 19, 39, 52, 54, 55, 64, 68, 73, 75, 80], "similarli": [1, 7, 23, 54, 60, 68], "condition": 1, "respect": [1, 2, 3, 4, 8, 13, 14, 19, 23, 28, 34, 51, 52, 54, 59, 63, 67, 68, 73, 75, 80], "e": [1, 2, 3, 4, 7, 8, 9, 10, 12, 14, 15, 16, 18, 19, 20, 21, 22, 27, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 48, 51, 52, 53, 54, 55, 64, 69, 72, 73, 75, 79, 80], "project": [1, 55, 72, 73, 75, 76, 79], "onto": [1, 68], "eigenvector": [1, 2, 10, 12], "non": [1, 2, 3, 8, 10, 12, 18, 19, 34, 38, 39, 46, 54, 55, 59, 63, 65], "linear": [1, 5, 9, 11, 12, 15, 22, 26, 53, 59, 64, 68], "avail": [1, 2, 3, 8, 9, 10, 21, 23, 28, 31, 32, 33, 34, 38, 39, 41, 44, 45, 46, 47, 50, 52, 53, 54, 55, 58, 59, 61, 62, 63, 64, 69, 73, 75, 80, 81], "first": [1, 2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 28, 30, 31, 34, 46, 52, 53, 54, 55, 56, 61, 63, 64, 65, 66, 69, 75, 80], "api": [1, 13, 24, 27, 30, 31, 32, 34, 36, 40, 41, 44, 46, 51, 52, 53, 54, 55, 59, 61, 64, 65, 72, 73, 75, 77, 79, 81], "regist": [1, 2, 3, 5, 10, 13, 15, 19, 22, 25, 34, 37, 39, 46, 52, 54, 61, 64, 65, 66, 69, 72], "outsid": [1, 9, 75, 79], "Then": [1, 18, 21, 25, 52, 64, 71, 72], "within": [1, 2, 3, 13, 22, 34, 38, 40, 42, 46, 52, 54, 55, 58, 59, 61, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80], "like": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 19, 21, 22, 23, 24, 28, 29, 32, 34, 38, 46, 52, 54, 59, 61, 64, 65, 67, 68, 69, 73, 75, 76, 79, 80, 81], "built": [1, 2, 4, 6, 8, 9, 12, 15, 23, 24, 26, 32, 52, 55, 58, 59, 73, 79, 80, 81], "abov": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 23, 24, 34, 36, 41, 51, 52, 53, 54, 55, 59, 61, 63, 64, 66, 68, 69, 72, 73, 75, 77, 79, 80], "level": [1, 2, 3, 13, 23, 29, 32, 33, 34, 39, 41, 51, 52, 53, 54, 55, 69, 72, 76, 81], "register_oper": [1, 13, 61], "accept": [1, 2, 3, 23, 24, 57, 73, 75, 80], "identifi": [1, 2, 3, 4, 13, 14, 19, 21, 73, 75], "string": [1, 2, 3, 5, 19, 27, 30, 34, 36, 37, 44, 51, 55, 59, 65, 69, 75, 79], "numpi": [1, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 30, 37, 51, 61, 64, 67, 73], "arrai": [1, 2, 3, 4, 7, 10, 12, 13, 15, 16, 17, 18, 21, 24, 26, 28, 46, 48, 51, 52, 54, 55, 61, 64, 65, 67, 69], "complex": [1, 2, 3, 4, 8, 12, 16, 20, 26, 42, 48, 51, 54, 59, 61, 67, 68, 79], "A": [1, 2, 3, 4, 7, 8, 9, 10, 12, 14, 16, 17, 18, 19, 20, 22, 28, 29, 30, 34, 37, 38, 41, 48, 51, 52, 59, 61, 64, 66, 67, 68, 71, 72, 73, 75], "1d": [1, 2], "interpret": [1, 8, 58, 64, 73], "row": [1, 2, 3, 30, 61], "major": [1, 73], "import": [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 37, 42, 51, 52, 53, 54, 55, 57, 59, 61, 63, 64, 65, 67, 68, 69, 73, 79, 80], "custom_h": 1, "custom_x": [1, 61], "def": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 34, 37, 38, 43, 45, 51, 53, 54, 57, 59, 61, 63, 64, 65, 66, 67, 68, 75, 80], "bell": [1, 13, 16, 34, 79], "sampl": [1, 2, 3, 5, 7, 8, 9, 13, 14, 17, 18, 19, 21, 26, 36, 52, 53, 54, 55, 56, 58, 61, 64, 65, 66, 67, 68, 72, 75, 79, 80], "dump": [1, 2, 3, 26, 34, 37, 53, 54, 59, 64, 65, 67, 79, 80], "macro": [1, 72], "cudaq_register_oper": 1, "uniqu": [1, 2, 3, 9, 14, 18, 21, 34, 39, 41, 46, 54, 77], "name": [1, 2, 3, 9, 13, 14, 16, 19, 34, 36, 41, 44, 50, 51, 52, 53, 54, 55, 63, 67, 68, 69, 72, 73, 75, 76, 79, 80], "represent": [1, 2, 3, 15, 16, 21, 24, 30, 34, 38, 48, 51, 55, 69, 71, 72], "includ": [1, 2, 3, 4, 12, 13, 14, 17, 23, 32, 34, 37, 46, 51, 53, 57, 58, 59, 61, 63, 64, 65, 66, 69, 71, 72, 73, 75, 77, 79, 80, 81], "m_sqrt1_2": 1, "__qpu__": [1, 2, 34, 37, 38, 45, 53, 54, 57, 59, 63, 64, 65, 66, 69, 79, 80], "void": [1, 2, 3, 34, 36, 37, 38, 41, 42, 44, 45, 46, 57, 59, 64, 66, 69, 71, 72, 77, 79, 80], "bell_pair": [1, 2, 3], "r": [1, 4, 13, 18, 19, 41, 46, 52, 53, 54, 55, 63, 64, 69, 75], "int": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 23, 24, 26, 34, 37, 38, 45, 46, 51, 53, 54, 57, 59, 61, 63, 64, 65, 66, 67, 69, 72, 73, 77, 79, 80], "main": [1, 4, 5, 9, 19, 21, 32, 34, 37, 48, 53, 59, 63, 64, 65, 66, 69, 73, 75, 77, 79, 80, 81], "auto": [1, 2, 34, 36, 37, 38, 42, 45, 46, 53, 54, 55, 57, 59, 63, 64, 65, 66, 69, 71, 79, 80], "count": [1, 2, 3, 5, 8, 9, 10, 12, 13, 14, 18, 19, 24, 34, 36, 37, 46, 52, 53, 54, 55, 59, 64, 65, 66, 67, 69, 72], "bit": [1, 2, 3, 4, 5, 7, 13, 16, 17, 18, 19, 24, 26, 34, 37, 39, 46, 48, 54, 55, 64, 65, 66, 68, 72], "printf": [1, 34, 37, 46, 54, 63, 65, 66, 73], "n": [1, 2, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 34, 36, 37, 38, 41, 42, 45, 53, 54, 59, 61, 63, 64, 65, 66, 67, 68, 69, 73, 77, 80], "data": [1, 2, 8, 11, 12, 13, 14, 15, 22, 31, 34, 38, 40, 42, 48, 54, 55, 63, 65, 66, 69, 72, 74, 75, 77, 79], "multi": [1, 4, 15, 16, 24, 30, 31, 32, 33, 36, 39, 41, 45, 50, 51, 52, 53, 59, 61, 62, 63, 68, 72, 73, 79, 80, 81], "msb": 1, "order": [1, 2, 3, 4, 9, 12, 13, 14, 16, 30, 34, 42, 51, 52, 55, 59, 63], "big": [1, 8, 17, 24], "endian": [1, 17, 24], "show": [1, 8, 9, 11, 13, 14, 15, 16, 18, 21, 22, 23, 30, 51, 54, 61, 63, 64, 73, 75], "differ": [1, 2, 3, 7, 9, 10, 11, 12, 13, 15, 16, 18, 19, 23, 24, 28, 30, 48, 52, 53, 54, 58, 59, 64, 67, 73, 75, 77, 80], "test": [1, 9, 11, 12, 17, 18, 19, 23, 31, 36, 67, 73, 75], "cnot": [1, 5, 9, 41, 57, 61, 68, 79], "my_cnot": 1, "print": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 30, 34, 37, 46, 51, 53, 54, 59, 61, 63, 64, 65, 66, 67, 68, 69, 73, 79, 80], "500": [1, 23, 68, 80], "exact": [1, 9, 10, 13, 17, 21, 55], "random": [1, 2, 3, 4, 5, 8, 9, 13, 14, 16, 17, 18, 19, 21, 22, 23, 28, 30, 54, 55, 67], "construct": [1, 2, 8, 9, 10, 12, 17, 18, 20, 23, 28, 30, 31, 34, 35, 36, 38, 46, 48, 51, 54, 57, 58, 59, 61, 62, 65, 69], "second": [1, 2, 3, 4, 7, 8, 9, 12, 14, 18, 20, 46, 51, 53, 55, 59, 61, 64], "1j": [1, 12, 13], "xy": [1, 30], "kron": [1, 18], "my_xi": 1, "custom_xy_test": 1, "undo": 1, "prior": [1, 55, 64, 68, 73, 75, 80], "1000": [1, 3, 9, 11, 13, 17, 18, 21, 24, 26, 34, 52, 59, 65, 67, 68, 80], "mycnot": 1, "myxi": 1, "hardwar": [1, 9, 15, 19, 21, 24, 31, 32, 50, 55, 59, 62, 79, 81], "synthes": [1, 3, 21, 41, 45, 69], "current": [1, 2, 3, 9, 30, 32, 34, 44, 52, 54, 55, 72, 75, 79, 81], "orca": [1, 2, 32, 50, 81], "which": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 28, 30, 32, 34, 36, 39, 42, 46, 48, 51, 52, 54, 55, 61, 63, 64, 65, 67, 68, 69, 72, 73, 75, 76, 79, 81], "doe": [1, 2, 3, 7, 9, 15, 16, 21, 30, 32, 34, 38, 46, 51, 53, 54, 73, 75, 77, 79, 80, 81], "increment": [1, 2, 67], "qumod": 1, "up": [1, 2, 3, 7, 9, 14, 15, 17, 21, 30, 36, 42, 51, 52, 55, 58, 63, 64, 67, 69, 72, 75], "maximum": [1, 3, 8, 9, 21, 55], "valu": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 28, 30, 31, 34, 36, 38, 42, 46, 48, 51, 53, 54, 55, 59, 62, 64, 67, 68, 69, 73, 79, 80], "repres": [1, 2, 3, 4, 7, 8, 9, 14, 15, 16, 21, 26, 30, 34, 48, 51, 52, 55, 64, 68, 69], "If": [1, 2, 3, 5, 7, 9, 11, 15, 16, 17, 18, 19, 20, 24, 25, 30, 34, 48, 51, 52, 53, 55, 59, 64, 67, 68, 73, 75, 79, 80], "where": [1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 29, 30, 33, 39, 41, 42, 46, 48, 51, 52, 55, 59, 61, 64, 67, 68, 72, 73, 75, 77, 80], "alreadi": [1, 2, 3, 13, 19, 30, 73, 75, 80], "ha": [1, 2, 3, 4, 5, 7, 9, 13, 14, 16, 17, 18, 19, 21, 24, 26, 28, 34, 41, 48, 52, 53, 55, 58, 59, 64, 67, 68, 73, 75, 79], "effect": [1, 13, 21, 48, 55, 59, 67, 68, 80], "u": [1, 2, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 19, 21, 22, 24, 30, 37, 38, 42, 43, 51, 52, 57, 58, 59, 64, 66, 67, 68, 69], "rangl": [1, 4, 15, 18, 19, 21, 34, 80], "3": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 32, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 51, 52, 53, 54, 55, 59, 61, 64, 66, 67, 69, 72, 73, 75, 76, 80, 81], "cdot": [1, 5, 9, 12, 19], "d": [1, 2, 3, 9, 12, 16, 37, 38, 39, 46, 52, 64, 73, 75], "reduc": [1, 2, 10, 13, 18, 19, 55], "minimum": [1, 14, 22, 53], "vacuum": [1, 64], "phase": [1, 2, 3, 5, 12, 15, 21, 41, 52, 64], "shifter": [1, 64], "add": [1, 2, 3, 9, 12, 14, 22, 26, 30, 32, 43, 51, 55, 64, 65, 69, 71, 72, 73, 75, 81], "a_1": [1, 18, 19], "creation": [1, 2, 4, 9, 33, 47, 51, 72], "dagger": [1, 4, 10, 12, 21, 26, 43, 51, 68], "shift": [1, 11, 53, 75], "p": [1, 4, 8, 9, 10, 18, 26, 73, 75], "left": [1, 2, 4, 5, 7, 10, 12, 13, 14, 15, 16, 24, 64, 72, 75], "right": [1, 4, 10, 13, 14, 15], "17": [1, 9, 13, 16, 17, 18, 21, 30, 61, 69, 77], "beam": [1, 52, 64], "splitter": [1, 52, 64], "act": [1, 2, 3, 6, 7, 9, 14, 27, 48, 51, 68], "togeth": [1, 18, 31, 55, 69, 80], "parameter": [1, 2, 3, 8, 9, 10, 13, 14, 20, 22, 23, 34, 36, 38, 41, 51, 53, 58, 61, 63, 65, 67], "relat": [1, 2, 9, 14, 19, 21, 69], "reflect": [1, 37, 55], "a_2": [1, 19], "b": [1, 9, 23, 28, 34, 61, 64], "_": [1, 4, 7, 11, 13, 18, 19, 21, 30, 51], "rang": [1, 2, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 32, 37, 38, 45, 46, 51, 53, 54, 57, 61, 67, 75, 80, 81], "34": [1, 9, 18], "return": [1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 28, 30, 34, 36, 37, 38, 41, 46, 48, 51, 53, 54, 55, 59, 63, 64, 65, 67, 69, 71, 72, 73, 77, 79], "result": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 34, 35, 36, 37, 39, 48, 51, 52, 53, 54, 55, 59, 61, 63, 64, 65, 66, 67, 68, 69, 72, 75, 79, 80], "input": [1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 15, 16, 21, 23, 27, 28, 34, 36, 37, 38, 45, 46, 54, 59, 61, 63, 64, 67], "class": [2, 3, 4, 9, 11, 21, 34, 36, 38, 42, 44, 46, 54, 55, 71, 72], "spin_op": [2, 8, 27, 34, 37, 41, 53, 54, 59, 63], "gener": [2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 18, 19, 22, 24, 27, 30, 34, 36, 37, 38, 39, 41, 42, 45, 47, 52, 53, 55, 58, 63, 64, 65, 66, 68, 69, 71, 75, 79], "sum": [2, 3, 4, 11, 12, 13, 14, 17, 18, 21, 27, 42, 46, 64, 68], "tensor": [2, 3, 11, 18, 27, 32, 42, 53, 54, 72, 81], "product": [2, 3, 5, 18, 27, 31, 32, 42, 53, 80, 81], "expos": [2, 3, 9, 13, 34, 36, 42, 44, 48, 54, 72], "typic": [2, 21, 34, 46, 53, 57, 58, 63, 69, 76, 77], "algebra": [2, 42, 63, 68], "programm": [2, 3, 34, 35, 36, 38, 39, 41, 43, 44, 46, 52, 54, 65], "defin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 33, 34, 35, 36, 37, 38, 42, 43, 44, 45, 46, 51, 53, 54, 55, 57, 58, 59, 61, 63, 64, 65, 66, 68, 69, 72, 73, 75, 77, 79, 81], "primit": [2, 14, 33, 36, 39, 42, 46, 47, 54, 58, 79], "them": [2, 3, 8, 9, 10, 13, 16, 18, 21, 30, 32, 48, 51, 57, 68, 69, 73, 75, 77, 79, 80, 81], "compos": [2, 3, 11, 14, 21, 38, 39, 52, 64, 69], "larger": [2, 3, 9, 10, 14, 21, 53, 55], "thereof": [2, 39, 42], "public": [2, 9, 34, 36, 42, 44, 46, 54, 71, 72, 75], "type": [2, 4, 5, 6, 7, 9, 13, 14, 15, 18, 20, 27, 33, 34, 36, 38, 39, 41, 42, 47, 51, 52, 54, 55, 57, 61, 63, 65, 68, 69, 72, 75, 79], "spin_op_term": 2, "bool": [2, 3, 13, 34, 38, 41, 42, 44, 54, 72, 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25, 26, 27, 28, 30, 34, 37, 41, 42, 51, 53, 54, 59, 63, 65, 67, 73], "y": [2, 3, 5, 7, 8, 9, 11, 12, 13, 15, 18, 19, 20, 24, 25, 27, 28, 30, 34, 37, 41, 42, 51, 53, 54, 59, 63, 65, 73, 75, 77, 80], "site": [2, 3, 8, 13, 64, 80], "csr_spmatrix": 2, "tupl": [2, 3, 9, 13, 28, 34, 38, 64], "doubl": [2, 3, 22, 23, 34, 36, 37, 38, 41, 42, 45, 53, 54, 55, 59, 63, 64, 72, 73, 75], "size_t": [2, 34, 36, 38, 42, 44, 46, 54, 64, 65, 72, 77, 79], "typedef": 2, "zero": [2, 3, 5, 6, 10, 11, 12, 18, 19, 21, 39, 51, 54, 59, 68], "spars": 2, "function": [2, 3, 5, 6, 7, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 30, 32, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 51, 52, 53, 54, 55, 57, 58, 59, 63, 65, 67, 69, 72, 77, 79, 81], "pair": [2, 3, 8, 12, 14, 16, 17, 38, 44, 48, 75], "const": [2, 34, 36, 37, 38, 41, 42, 44, 46, 53, 64, 71, 72, 77, 79], "termdata": 2, "constructor": [2, 3], "take": [2, 3, 4, 5, 7, 9, 12, 13, 14, 15, 17, 18, 20, 23, 28, 29, 32, 34, 36, 37, 38, 41, 42, 43, 44, 45, 46, 48, 52, 55, 58, 59, 61, 63, 64, 65, 66, 67, 69, 73, 75, 80, 81], "coeffici": [2, 3, 8, 12, 20, 27, 51, 68], "constant": [2, 7, 20, 46, 48, 69], "id": [2, 3, 34, 44, 46, 52, 54, 55, 73, 75], "coeff": 2, "qubit": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 34, 36, 37, 38, 39, 42, 44, 45, 48, 51, 52, 53, 54, 55, 57, 58, 59, 61, 63, 64, 65, 66, 67, 68, 69, 72, 75, 80], "unordered_map": [2, 34], "_term": 2, "full": [2, 3, 4, 13, 19, 29, 32, 53, 54, 55, 67, 68, 69, 71, 73, 75, 76, 81], "composit": [2, 51], "spin": [2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 20, 23, 24, 26, 28, 34, 37, 42, 48, 51, 53, 54, 59, 63, 64, 67, 69], "op": [2, 3, 12, 13, 20, 48, 63, 69], "map": [2, 3, 5, 8, 9, 13, 18, 21, 23, 26, 34, 46, 51, 69, 75], "individu": [2, 3, 39, 46, 54, 61], "bsf": 2, "from": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 34, 35, 37, 38, 39, 42, 46, 48, 51, 53, 54, 55, 58, 59, 61, 63, 64, 67, 69, 72, 75, 76, 77, 80, 81], "creat": [2, 3, 5, 8, 9, 13, 14, 15, 16, 18, 19, 21, 22, 26, 30, 31, 34, 36, 42, 45, 51, 52, 53, 54, 58, 59, 64, 65, 67, 69, 70, 72, 73, 75, 76, 77, 79, 80], "ident": [2, 12, 13, 14, 18, 19, 21, 48, 51, 63], "numqubit": [2, 37], "given": [2, 3, 4, 7, 8, 9, 10, 14, 18, 19, 20, 21, 23, 24, 34, 46, 51, 52, 54, 55, 59, 63, 72], "o": [2, 9, 10, 13, 15, 23, 37, 52, 53, 54, 55, 63, 64, 65, 66, 69, 73, 75, 77, 79, 80], "copi": [2, 16, 21, 30, 46, 48, 73, 75], "data_rep": 2, "serial": [2, 3, 12], "encod": [2, 3, 5, 9, 14, 19, 21, 34, 42, 54, 65, 68, 72], "via": [2, 3, 4, 5, 7, 11, 13, 16, 21, 24, 26, 31, 33, 34, 36, 39, 41, 43, 45, 46, 48, 51, 52, 54, 55, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73], "real": [2, 8, 10, 12, 13, 15, 17, 20, 21, 30, 58], "imaginari": [2, 4, 12], "part": [2, 3, 4, 12, 17, 19, 23, 34, 46, 69, 71, 73, 75], "append": [2, 3, 6, 8, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 28, 30, 38, 52, 54, 55, 61, 64, 67], "larg": [2, 4, 5, 12, 18, 23, 29, 41, 54, 55, 58, 68], "end": [2, 3, 4, 7, 10, 13, 15, 16, 17, 18, 20, 21, 24, 26, 30, 34, 46, 52, 54, 55, 59, 61, 64, 68, 73, 75], "total": [2, 3, 4, 8, 9, 11, 12, 13, 15, 21, 23, 53, 54, 55, 59, 64, 67, 75], "destructor": 2, "iter": [2, 3, 4, 9, 12, 19, 21, 23, 24, 27, 28, 34, 46], "begin": [2, 3, 4, 7, 9, 10, 12, 13, 15, 16, 18, 19, 21, 22, 24, 26, 30, 34, 46, 57, 64, 65, 68], "start": [2, 3, 4, 6, 13, 15, 16, 19, 21, 24, 31, 32, 41, 46, 52, 54, 59, 61, 64, 69, 71, 77, 81], "equal": [2, 10, 21, 24, 30, 48, 54, 55, 59, 68], "v": [2, 3, 4, 8, 10, 12, 13, 14, 19, 34, 37, 38, 43, 48, 53, 67, 69, 73], "noexcept": [2, 41], "subtract": 2, "multipli": [2, 12, 13, 19], "true": [2, 3, 4, 5, 9, 11, 12, 13, 16, 17, 18, 19, 21, 23, 28, 34, 38, 51, 52, 67, 73, 75, 80], "here": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 18, 19, 21, 22, 23, 26, 27, 31, 32, 34, 36, 37, 38, 41, 43, 51, 52, 53, 54, 61, 63, 64, 65, 66, 69, 71, 73, 75, 79, 80, 81], "consid": [2, 4, 5, 7, 9, 13, 17, 18, 19, 20, 21, 37, 39, 48, 53, 54, 69, 75], "num_qubit": [2, 3, 13, 20, 42, 53], "num_term": [2, 42], "get_coeffici": [2, 8, 12, 20, 42], "get": [2, 3, 10, 12, 13, 15, 17, 18, 19, 21, 23, 26, 28, 30, 32, 34, 36, 37, 46, 52, 53, 54, 59, 63, 64, 67, 72, 73, 77, 80, 81], "throw": [2, 30], "except": [2, 3, 9, 12, 13, 21, 30, 79], "get_raw_data": 2, "is_ident": [2, 42], "standard": [2, 3, 10, 21, 23, 28, 33, 34, 35, 36, 38, 41, 47, 54, 57, 58, 69, 71, 73, 75, 77, 79], "out": [2, 3, 7, 8, 9, 11, 12, 14, 15, 16, 19, 21, 23, 26, 28, 32, 34, 39, 46, 48, 54, 55, 59, 60, 61, 63, 64, 72, 75, 76, 79, 81], "to_str": [2, 8, 12, 20, 59], "printcoeffici": 2, "getdatarepresent": 2, "getdatatupl": 2, "fulli": [2, 3, 9, 11, 13, 21, 32, 33, 52, 54, 65, 69, 73, 75, 79, 81], "distribute_term": 2, "numchunk": 2, "distribut": [2, 10, 17, 18, 21, 23, 24, 26, 32, 39, 53, 55, 59, 63, 65, 73, 79, 80, 81], "chunk": [2, 39], "for_each_term": [2, 8, 12, 20, 42], "give": [2, 14, 15, 18, 24, 32, 34, 49, 54, 55, 73, 75, 81], "functor": 2, "reduct": 2, "captur": [2, 14, 19, 23, 30, 32, 38, 51, 61, 81], "variabl": [2, 9, 12, 14, 16, 19, 23, 29, 32, 38, 39, 51, 52, 53, 54, 60, 64, 67, 73, 75, 80, 81], "for_each_pauli": [2, 42], "thrown": [2, 79], "than": [2, 3, 12, 13, 14, 15, 16, 17, 19, 20, 23, 24, 25, 30, 41, 48, 53, 55, 59, 61, 68, 73, 75, 79], "user": [2, 3, 4, 5, 8, 9, 13, 18, 21, 29, 32, 34, 36, 37, 39, 40, 43, 46, 51, 52, 53, 54, 55, 64, 67, 69, 72, 73, 75, 81], "should": [2, 3, 13, 15, 19, 20, 23, 33, 34, 39, 41, 42, 44, 46, 52, 54, 55, 59, 72, 73, 75, 79, 80], "pass": [2, 3, 8, 9, 11, 12, 13, 17, 20, 23, 30, 31, 34, 38, 39, 42, 46, 52, 54, 55, 61, 64, 66, 69, 70, 75, 79], "index": [2, 3, 5, 9, 11, 12, 17, 42, 44, 46, 48, 51, 54, 55, 72], "complex_matrix": 2, "to_matrix": [2, 10, 12, 51], "dens": 2, "to_sparse_matrix": 2, "col": 2, "static": [2, 3, 34, 41, 46, 51, 69, 73, 79], "nterm": 2, "unsign": 2, "seed": [2, 3, 4, 8, 9, 13, 14, 18, 21, 22, 23, 30, 55, 67], "random_devic": 2, "specifi": [2, 3, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 23, 24, 25, 27, 28, 30, 34, 37, 38, 39, 41, 42, 43, 44, 46, 51, 52, 53, 54, 55, 59, 61, 64, 65, 67, 69, 72, 73, 75], "overrid": [2, 34, 55, 71, 73], "repeat": [2, 17, 18, 19, 23, 34], "from_word": 2, "pauliword": 2, "word": [2, 3, 8, 12, 19, 20], "g": [2, 3, 9, 14, 19, 20, 21, 27, 33, 34, 35, 38, 39, 40, 41, 42, 48, 51, 52, 53, 54, 55, 64, 69, 72, 73, 75, 76, 79, 80], "xyx": 2, "3rd": 2, "typenam": [2, 34, 36, 37, 38, 41, 45, 46, 77, 79], "qualifiedspinop": 2, "struct": [2, 34, 36, 37, 38, 41, 45, 53, 54, 63, 64, 65, 66, 69, 71, 79], "constexpr": [2, 36, 46, 53], "dyn": [2, 46], "qudit": [2, 35, 39, 41], "system": [2, 3, 4, 9, 12, 13, 16, 20, 22, 23, 24, 29, 32, 34, 39, 44, 46, 48, 51, 52, 54, 55, 58, 59, 63, 64, 67, 68, 71, 73, 76, 77, 79, 80, 81], "inlin": [2, 34, 69], "new": [2, 3, 4, 7, 8, 9, 12, 15, 21, 31, 32, 34, 36, 48, 58, 69, 70, 73, 75, 79, 80, 81], "enable_if_t": 2, "qreg": [2, 3, 12, 14, 53], "contain": [2, 3, 4, 9, 10, 13, 14, 18, 21, 31, 32, 34, 39, 41, 44, 48, 53, 55, 58, 59, 63, 64, 69, 72, 73, 79, 80, 81], "dynam": [2, 13, 31, 32, 36, 38, 39, 46, 52, 57, 58, 69, 79, 81], "time": [2, 3, 4, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 23, 24, 25, 26, 30, 31, 32, 33, 34, 37, 39, 46, 47, 52, 53, 54, 55, 59, 64, 65, 67, 68, 69, 73, 75, 80, 81], "By": [2, 9, 10, 23, 29, 34, 41, 51, 52, 53, 54, 55, 59, 64, 77], "value_typ": 2, "indic": [2, 3, 4, 8, 21, 38, 41, 42, 46, 51, 72], "underli": [2, 3, 9, 23, 34, 44, 46, 52, 54, 72], "interfac": [2, 3, 46, 55, 72, 73, 75, 77], "idx": [2, 3, 11, 13, 19, 21, 46, 51, 54], "qspan": 2, "front": [2, 37, 45, 46, 66], "back": [2, 21, 37, 46, 48, 54, 64, 65, 75], "last": [2, 18, 19, 21, 37, 46, 54, 61, 63], "slice": [2, 3, 46], "clear": [2, 3, 16, 34, 46, 72], "destroi": [2, 46], "postcondit": [2, 46], "own": [2, 3, 9, 18, 26, 39, 44, 46, 55, 69, 72, 73, 75, 79], "semant": [2, 3, 33, 38, 39, 40, 43, 45, 46, 48, 69], "held": 2, "explicit": [2, 10, 34, 45, 51, 55, 64, 79], "determin": [2, 4, 5, 7, 8, 9, 17, 20, 23, 31, 55, 59], "check": [2, 9, 19, 21, 32, 52, 53, 64, 68, 73, 75, 80, 81], "norm": [2, 18], "pre": [2, 3, 13, 21, 32, 34, 52, 55, 64, 66, 73, 79, 81], "exist": [2, 3, 8, 16, 21, 30, 32, 33, 34, 39, 40, 59, 71, 73, 75, 79, 80, 81], "could": [2, 9, 10, 12, 19, 23, 30, 53, 57, 68, 73], "from_data": [2, 3, 51], "retriev": [2, 3, 13, 24, 34, 51, 54, 64], "get_stat": [2, 3, 10, 15, 16, 17, 20, 21, 24, 30, 54, 61], "delet": [2, 46, 52, 75, 79], "cannot": [2, 3, 15, 30, 37, 38, 46, 48, 68, 75], "move": [2, 11, 46, 71, 73, 75, 80], "assign": [2, 9, 14, 44, 54, 55, 73], "qview": [2, 5, 10, 12, 15, 19, 37, 38, 45], "observe_result": [2, 3, 12, 34, 63], "encapsul": [2, 11, 34, 46, 54, 79], "observ": [2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 18, 20, 22, 23, 26, 28, 37, 51, 52, 54, 55, 56, 58, 63, 65, 66, 67, 68, 72, 80], "call": [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 19, 21, 22, 23, 24, 26, 28, 30, 36, 37, 38, 41, 45, 51, 54, 55, 58, 59, 61, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77], "measur": [2, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16, 18, 23, 24, 26, 31, 34, 35, 37, 38, 39, 41, 48, 51, 52, 54, 55, 57, 58, 59, 62, 64, 65, 69, 72, 80], "execut": [2, 4, 9, 13, 15, 21, 30, 31, 32, 33, 34, 36, 38, 39, 44, 45, 51, 52, 54, 55, 58, 59, 60, 62, 63, 64, 65, 67, 69, 72, 75, 76, 77, 79, 80, 81], "ansatz": [2, 4, 6, 9, 13, 14, 22, 23, 34, 37, 53, 54, 63], "circuit": [2, 3, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 28, 33, 34, 38, 39, 42, 43, 47, 48, 52, 53, 54, 55, 58, 59, 61, 63, 64, 69, 71], "global": [2, 3, 9, 14, 21, 34, 38, 53, 59, 64, 73], "expect": [2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 18, 20, 21, 22, 23, 24, 26, 28, 31, 34, 51, 53, 54, 55, 59, 62, 67, 73, 75, 79, 80], "h": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 30, 34, 36, 37, 41, 42, 43, 45, 46, 48, 51, 52, 53, 54, 57, 59, 61, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 75, 77, 79, 80], "precomput": 2, "psi": [2, 6, 7, 10, 12, 16, 24, 30, 34, 37, 59, 68], "sample_result": [2, 3, 19, 24, 34, 64, 79], "wa": [2, 3, 4, 7, 9, 12, 21, 34, 54, 65, 69, 73, 76, 80], "shot": [2, 3, 8, 9, 10, 17, 18, 19, 24, 34, 37, 52, 55, 59, 64, 65, 68, 72], "base": [2, 3, 8, 9, 11, 13, 15, 18, 19, 21, 23, 27, 32, 33, 34, 36, 40, 41, 46, 51, 52, 53, 54, 55, 59, 67, 69, 72, 73, 75, 77, 81], "raw_data": [2, 9, 34], "raw": [2, 3, 9], "convers": [2, 34], "simpli": [2, 30, 68, 75, 80], "ignor": [2, 9, 19, 55], "fine": [2, 34, 65, 66, 68], "grain": [2, 34, 65, 66], "explicitli": [2, 9, 10, 48, 52, 55, 69, 77, 79], "request": [2, 3, 34, 52, 53, 54, 55, 64, 75], "oppos": [2, 79], "observe_data": 2, "spinoptyp": [2, 34], "sub": [2, 3, 33, 34, 37, 38, 46, 47, 51, 75], "id_coeffici": [2, 34], "observe_opt": [2, 34], "option": [2, 3, 4, 7, 9, 10, 12, 13, 17, 18, 22, 23, 24, 28, 29, 34, 36, 41, 44, 51, 52, 53, 54, 59, 63, 64, 66, 67, 72, 73, 75], "async_observ": 2, "param": [2, 4, 13, 14, 17, 37, 38, 41, 53, 67], "run": [2, 3, 5, 7, 8, 9, 11, 12, 17, 18, 23, 24, 31, 33, 34, 37, 51, 52, 53, 54, 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-<li class="toctree-l2"><a class="reference internal" href="../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#ionq">IonQ</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#oqc">OQC</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#utilities">Utilities</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Tensor"><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.integrator.BaseIntegrator"><code class="docutils literal notranslate"><span class="pre">BaseIntegrator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.OperatorSum"><code class="docutils literal notranslate"><span class="pre">OperatorSum</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ProductOperator"><code class="docutils literal notranslate"><span class="pre">ProductOperator</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ScalarOperator"><code class="docutils literal notranslate"><span class="pre">ScalarOperator</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
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-<h1>Language Specification<a class="headerlink" href="#language-specification" title="Permalink to this heading">¶</a></h1>
-<p>NVIDIA CUDA-Q is a programming model in C++ for heterogeneous
-quantum-classical computing. As such, the model provides primitive types, concepts, syntax, and
-semantics that enable the integration of quantum processing units (QPUs) with
-existing NVIDIA multi-node, multi-GPU parallel programming models and architectures.
-The language is designed for C++. Extensions to other languages is fully supported
-via appropriate bindings.</p>
-<p>CUDA-Q implementations are intended to be enabled via library-based language
-extensions that define quantum code as callables in the classical language being extended.
-These callables are meant to be compiled with standard compilers and runtimes
-and run via quantum computer simulators or available physical quantum architectures. The
-language callables intended for quantum coprocessing must be annotated for
-compilers and runtimes to process. These annotations should directly enable quantum-specific
-optimization and transformation for optimal execution on simulators and QPUs.</p>
-<p>The rest of this document will detail all aspects of the language specification - its machine model,
-primitive types and operations, core concepts and abstractions, and algorithmic primitives. All code
-specification and demonstrative examples are provided in C++ (the native CUDA-Q classical language).
-Where possible, bindings for higher-level languages (e.g. Python) will be displayed to aid in understanding.</p>
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-<li class="toctree-l1"><a class="reference internal" href="cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cudaq/types.html">3. Quantum Types</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cudaq/operators.html">4. Quantum Operators</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cudaq/operations.html">5. Quantum Operations</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
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-  <section id="quantum-algorithmic-primitives">
-<h1><span class="section-number">12. </span>Quantum Algorithmic Primitives<a class="headerlink" href="#quantum-algorithmic-primitives" title="Permalink to this heading">¶</a></h1>
-<p><strong>[1]</strong> The general philosophy of the CUDA-Q specification is that quantum
-device code should be encapsulated as stand-alone callable instances of generic
-signature, and that operations or primitive algorithms targeting a quantum
-coprocessor be implemented as adaptors on those callable instances. Adaptors, by
-definition, are generic functions that take any quantum kernel as input along with
-the runtime arguments for that kernel. Runtime arguments passed to adaptor functions
-flow through the adaptor to the provided kernel. This pattern allows general pre-
-and post-processing around concrete kernel execution.</p>
-<section id="cudaq-sample">
-<span id="cudaq-sample-spec"></span><h2><span class="section-number">12.1. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code><a class="headerlink" href="#cudaq-sample" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> A common task for near-term quantum execution is to sample the state
-of a given quantum circuit for a specified number of shots (circuit
-executions). The result of this task is typically a mapping of observed
-measurement bit strings to the number of times each was observed. This
-is typically termed the counts dictionary in the community.</p>
-<p><strong>[2]</strong> The CUDA-Q model enables this functionality via template functions within the
-<code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> namespace with the following structure:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">ReturnType</span><span class="o">&gt;</span>
-<span class="k">concept</span><span class="w"> </span><span class="nc">HasVoidReturnType</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">is_void_v</span><span class="o">&lt;</span><span class="n">ReturnType</span><span class="o">&gt;</span><span class="p">;</span>
-
-<span class="c1">// Kernel only</span>
-<span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">requires</span><span class="w"> </span><span class="n">HasVoidReturnType</span><span class="o">&lt;</span><span class="n">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;</span>
-<span class="n">sample_result</span><span class="w"> </span><span class="n">sample</span><span class="p">(</span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="c1">// Specify shots</span>
-<span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">requires</span><span class="w"> </span><span class="n">HasVoidReturnType</span><span class="o">&lt;</span><span class="n">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;</span>
-<span class="n">sample_result</span><span class="w"> </span><span class="n">sample</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">shots</span><span class="p">,</span><span class="w"> </span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="c1">// Specify sample options (including shots and noise model)</span>
-<span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">requires</span><span class="w"> </span><span class="n">HasVoidReturnType</span><span class="o">&lt;</span><span class="n">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;</span>
-<span class="n">sample_result</span><span class="w"> </span><span class="n">sample</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">sample_options</span><span class="w"> </span><span class="o">&amp;</span><span class="n">options</span><span class="p">,</span>
-<span class="w">                     </span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-</pre></div>
-</div>
-<p><strong>[3]</strong> This function takes as input a quantum kernel instance followed by the
-concrete arguments at which the kernel should be invoked. CUDA-Q kernels
-passed to this function must be entry-point kernels and return <code class="code docutils literal notranslate"><span class="pre">void</span></code>.</p>
-<p><strong>[4]</strong> Overloaded functions exist for specifying the number of shots to sample and the
-noise model to apply.</p>
-<p><strong>[5]</strong> The function returns an instance of the <code class="code docutils literal notranslate"><span class="pre">cudaq::sample_result</span></code> type which encapsulates
-the counts dictionary produced by the sampling task. Programmers can
-extract the result information in the following manner:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">bell</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">};</span>
-
-<span class="c1">// Sample the state generated by bel</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">bell</span><span class="p">)</span>
-
-<span class="c1">// Print to standard out</span>
-<span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="c1">// Fine-grained access to the bits and counts</span>
-<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="o">&amp;</span><span class="w"> </span><span class="p">[</span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">counts</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Observed: %s, %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">bell</span><span class="p">():</span>
-   <span class="o">...</span>
-
-<span class="c1"># Sample the state generated by bell</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">bell</span><span class="p">)</span>
-
-<span class="c1"># Print to standard out</span>
-<span class="n">counts</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-
-<span class="c1"># Fine-grained access to the bits and counts</span>
-<span class="k">for</span> <span class="n">bits</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">counts</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Observed: </span><span class="si">{}</span><span class="s1">, </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bits</span><span class="p">,</span> <span class="n">count</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[6]</strong> CUDA-Q specifies the following structure for <code class="code docutils literal notranslate"><span class="pre">cudaq::sample_result</span></code>:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">CountsDictionary</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">unordered_map</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="p">;</span>
-<span class="w">  </span><span class="kr">inline</span><span class="w"> </span><span class="k">static</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;__global__&quot;</span><span class="p">;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">sample_result</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="n">sample_result</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span>
-<span class="w">      </span><span class="n">sample_result</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">sample_result</span><span class="w"> </span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="o">~</span><span class="n">sample_result</span><span class="p">();</span>
-
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="w"> </span><span class="n">register_names</span><span class="p">();</span>
-
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">count</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">bitString</span><span class="p">,</span>
-<span class="w">                  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">sequential_data</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-
-<span class="w">      </span><span class="n">CountsDictionary</span>
-<span class="w">      </span><span class="nf">to_map</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-
-<span class="w">      </span><span class="n">sample_result</span>
-<span class="w">      </span><span class="nf">get_marginal</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">marginalIndices</span><span class="p">,</span>
-<span class="w">             </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-
-<span class="w">      </span><span class="kt">double</span><span class="w"> </span><span class="nf">expectation</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-<span class="w">      </span><span class="kt">double</span><span class="w"> </span><span class="nf">probability</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">bitString</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">size</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="n">registerName</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">GlobalRegisterName</span><span class="p">);</span>
-
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">dump</span><span class="p">();</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">clear</span><span class="p">();</span>
-
-<span class="w">      </span><span class="n">CountsDictionary</span><span class="o">::</span><span class="n">iterator</span><span class="w"> </span><span class="nf">begin</span><span class="p">();</span>
-<span class="w">      </span><span class="n">CountsDictionary</span><span class="o">::</span><span class="n">iterator</span><span class="w"> </span><span class="nf">end</span><span class="p">();</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p><strong>[7]</strong> The <code class="code docutils literal notranslate"><span class="pre">sample_result</span></code> type enables one to encode measurement results from a
-quantum circuit sampling task. It keeps track of a list of sample results, each
-one corresponding to a measurement action during the sampling process and represented
-by a unique register name. It also tracks a unique global register, the implicit sampling
-of the state at the end of circuit execution. The API gives fine-grain access
-to the measurement results for each register. To illustrate this, observe</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">reg1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="n">reset</span><span class="w"> </span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="p">};</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">).</span><span class="n">dump</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-   <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-   <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-   <span class="n">reg1</span> <span class="o">=</span> <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-   <span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-   <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>should produce</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">{</span>
-<span class="w">  </span>__global__<span class="w"> </span>:<span class="w"> </span><span class="o">{</span><span class="w"> </span><span class="m">1</span>:1000<span class="w"> </span><span class="o">}</span>
-<span class="w">  </span>reg1<span class="w"> </span>:<span class="w"> </span><span class="o">{</span><span class="w"> </span><span class="m">0</span>:501<span class="w"> </span><span class="m">1</span>:499<span class="w"> </span><span class="o">}</span>
-<span class="o">}</span>
-</pre></div>
-</div>
-<p>Here we see that we have measured a qubit in a uniform superposition to a
-register named <code class="code docutils literal notranslate"><span class="pre">reg1</span></code>, and followed it with a reset and the application
-of an NOT operation. The <code class="code docutils literal notranslate"><span class="pre">sample_result</span></code> returned for this sampling
-tasks contains the default <code class="code docutils literal notranslate"><span class="pre">__global__</span></code> register as well as the user
-specified <code class="code docutils literal notranslate"><span class="pre">reg1</span></code> register.</p>
-<p>The contents of the <code class="code docutils literal notranslate"><span class="pre">__global__</span></code> register will depend on how your kernel
-is written:</p>
-<ol class="arabic simple">
-<li><p>If no measurements appear in the kernel, then the <code class="code docutils literal notranslate"><span class="pre">__global__</span></code>
-register is formed with implicit measurements being added for <em>all</em> the
-qubits defined in the kernel, and the measurements all occur at the end of
-the kernel. The order of the bits in the bitstring corresponds to the qubit
-allocation order specified in the kernel.  That is - the <code class="code docutils literal notranslate"><span class="pre">[0]</span></code> element
-in the <code class="code docutils literal notranslate"><span class="pre">__global__</span></code> bitstring corresponds with the first declared qubit
-in the kernel. For example,</p></li>
-</ol>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="n">b</span><span class="p">;</span>
-<span class="w">  </span><span class="n">x</span><span class="p">(</span><span class="n">a</span><span class="p">);</span>
-<span class="p">};</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">).</span><span class="n">dump</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>should produce</p>
-<blockquote>
-<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">{</span>
-<span class="w">  </span>__global__<span class="w"> </span>:<span class="w"> </span><span class="o">{</span><span class="w"> </span><span class="m">10</span>:1000<span class="w"> </span><span class="o">}</span>
-<span class="o">}</span>
-</pre></div>
-</div>
-</div></blockquote>
-<ol class="arabic simple" start="2">
-<li><p>Conversely, if any measurements appear in the kernel, then only the measured
-qubits will appear in the <code class="code docutils literal notranslate"><span class="pre">__global__</span></code> register. Similar to #1, the
-bitstring corresponds to the qubit allocation order specified in the kernel.
-Also (again, similar to #1), the values of the sampled qubits always
-correspond to the values <em>at the end of the kernel execution</em>. That is - if a
-qubit is measured in the middle of a kernel and subsequent operations change
-the state of the qubit, the qubit will be implicitly re-measured at the end
-of the kernel, and that re-measured value is the value that will appear in
-the <code class="code docutils literal notranslate"><span class="pre">__global__</span></code> register. For example,</p></li>
-</ol>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="n">b</span><span class="p">;</span>
-<span class="w">  </span><span class="n">x</span><span class="p">(</span><span class="n">a</span><span class="p">);</span>
-<span class="w">  </span><span class="n">mz</span><span class="p">(</span><span class="n">b</span><span class="p">);</span>
-<span class="w">  </span><span class="n">mz</span><span class="p">(</span><span class="n">a</span><span class="p">);</span>
-<span class="p">};</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">).</span><span class="n">dump</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>should produce</p>
-<blockquote>
-<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">{</span>
-<span class="w">  </span>__global__<span class="w"> </span>:<span class="w"> </span><span class="o">{</span><span class="w"> </span><span class="m">10</span>:1000<span class="w"> </span><span class="o">}</span>
-<span class="o">}</span>
-</pre></div>
-</div>
-</div></blockquote>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you don’t specify any measurements in your kernel and allow the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code>
-compiler to perform passes that introduce ancilla qubits into your kernel, it
-may be difficult to discern which qubits are the ancilla qubits vs which ones
-are your qubits. In this case, it is recommended that you provide explicit
-measurements in your kernel in order to only receive measurements from your
-qubits and silently discard the measurements from the ancillary qubits.</p>
-</div>
-<p><strong>[8]</strong> The API exposed by the <code class="code docutils literal notranslate"><span class="pre">sample_result</span></code> data type allows one to extract
-the information contained at a variety of levels and for each available
-register name. One can get the number of times a bit string was observed via
-<code class="code docutils literal notranslate"><span class="pre">sample_result::count</span></code>, extract a <code class="code docutils literal notranslate"><span class="pre">std::unordered_map</span></code> representation via
-<code class="code docutils literal notranslate"><span class="pre">sample_result::to_map</span></code>, get a new <code class="code docutils literal notranslate"><span class="pre">sample_result</span></code> instance over a subset of
-measured qubits via <code class="code docutils literal notranslate"><span class="pre">sample_result::get_marginal</span></code>, and extract the
-measurement data as it was produced sequentially (a vector of bit string observations
-for each shot in the sampling process). One can also compute probabilities and expectation
-values.</p>
-<p><strong>[9]</strong> There are specific requirements on input quantum kernels for the use of the
-sample function which must be enforced by compiler implementations. The kernel
-must be an entry-point kernel that returns <code class="code docutils literal notranslate"><span class="pre">void</span></code>.</p>
-<p><strong>[10]</strong> CUDA-Q also provides an asynchronous version of this function
-(<code class="code docutils literal notranslate"><span class="pre">cudaq::sample_async</span></code>) which returns a <code class="code docutils literal notranslate"><span class="pre">sample_async_result</span></code>.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="n">async_sample_result</span><span class="w"> </span><span class="n">sample_async</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qpu_id</span><span class="p">,</span><span class="w"> </span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>Programmers can asynchronously launch sampling tasks on any <code class="code docutils literal notranslate"><span class="pre">qpu_id</span></code>.</p>
-<p><strong>[11]</strong> The <code class="code docutils literal notranslate"><span class="pre">async_sample_result</span></code> wraps a <code class="code docutils literal notranslate"><span class="pre">std::future&lt;sample_result&gt;</span></code> and exposes the same
-<code class="code docutils literal notranslate"><span class="pre">get()</span></code> functionality to extract the results after asynchronous execution.</p>
-<p><strong>[12]</strong> For remote QPU systems with long queue times, the <code class="code docutils literal notranslate"><span class="pre">async_sample_result</span></code> type encodes job ID
-information and can be persisted to file and loaded from file at a later time. After loading from file,
-and when remote queue jobs are completed, one can invoke <code class="code docutils literal notranslate"><span class="pre">get()</span></code> and the results will
-be retrieved and returned.</p>
-</section>
-<section id="cudaq-observe">
-<h2><span class="section-number">12.2. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code><a class="headerlink" href="#cudaq-observe" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> A common task in variational algorithms is the computation of the expected
-value of a given observable with respect to a parameterized quantum circuit
-(<span class="math notranslate nohighlight">\(\langle H \rangle(𝚹) = \langle \psi(𝚹)|H|\psi(𝚹) \rangle\)</span>).</p>
-<p><strong>[2]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> function is provided to enable one to quickly compute
-this expectation value via execution of the parameterized quantum circuit
-with repeated measurements in the bases of the provided <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> terms. The
-function has the following signature:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Kernel only</span>
-<span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="n">observe_result</span><span class="w"> </span><span class="n">observe</span><span class="p">(</span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="c1">// Specify shots</span>
-<span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="n">observe_result</span><span class="w"> </span><span class="n">observe</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">shots</span><span class="p">,</span><span class="w"> </span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="c1">// Specify sample options (including shots and noise model)</span>
-<span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="n">observe_result</span><span class="w"> </span><span class="n">observe</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe_options</span><span class="w"> </span><span class="o">&amp;</span><span class="n">options</span><span class="p">,</span>
-<span class="w">                      </span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-</pre></div>
-</div>
-<p><strong>[3]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> takes as input an instantiated quantum kernel, the
-<code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code> whose expectation is requested, and the concrete
-arguments used as input to the parameterized quantum kernel.</p>
-<p><strong>[4]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> returns an instance of the <code class="code docutils literal notranslate"><span class="pre">observe_result</span></code> type which can be implicitly
-converted to a <code class="code docutils literal notranslate"><span class="pre">double</span></code> expectation value, but also retains all data directly
-generated and used as part of that expectation value computation. The
-<code class="code docutils literal notranslate"><span class="pre">observe_result</span></code> takes on the following form:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">observe_result</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">public</span><span class="o">:</span>
-<span class="w">    </span><span class="n">observe_result</span><span class="p">(</span><span class="kt">double</span><span class="w"> </span><span class="o">&amp;</span><span class="n">e</span><span class="p">,</span><span class="w"> </span><span class="n">spin_op</span><span class="w"> </span><span class="o">&amp;</span><span class="n">H</span><span class="p">);</span>
-<span class="w">    </span><span class="n">observe_result</span><span class="p">(</span><span class="kt">double</span><span class="w"> </span><span class="o">&amp;</span><span class="n">e</span><span class="p">,</span><span class="w"> </span><span class="n">spin_op</span><span class="w"> </span><span class="o">&amp;</span><span class="n">H</span><span class="p">,</span><span class="w"> </span><span class="n">MeasureCounts</span><span class="w"> </span><span class="n">counts</span><span class="p">);</span>
-
-<span class="w">    </span><span class="n">sample_results</span><span class="w"> </span><span class="nf">raw_data</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">data</span><span class="p">;</span><span class="w"> </span><span class="p">};</span>
-<span class="w">    </span><span class="k">operator</span><span class="w"> </span><span class="kt">double</span><span class="p">();</span>
-<span class="w">    </span><span class="kt">double</span><span class="w"> </span><span class="nf">expectation</span><span class="p">();</span>
-
-<span class="w">    </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">SpinOpType</span><span class="o">&gt;</span>
-<span class="w">    </span><span class="kt">double</span><span class="w"> </span><span class="n">expectation</span><span class="p">(</span><span class="n">SpinOpType</span><span class="w"> </span><span class="n">term</span><span class="p">);</span>
-
-<span class="w">    </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">SpinOpType</span><span class="o">&gt;</span>
-<span class="w">    </span><span class="n">sample_result</span><span class="w"> </span><span class="n">counts</span><span class="p">(</span><span class="n">SpinOpType</span><span class="w"> </span><span class="n">term</span><span class="p">);</span>
-<span class="w">    </span><span class="kt">double</span><span class="w"> </span><span class="nf">id_coefficient</span><span class="p">()</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">dump</span><span class="p">();</span>
-<span class="p">};</span>
-</pre></div>
-</div>
-<p><strong>[5]</strong> The public API for <code class="code docutils literal notranslate"><span class="pre">observe_result</span></code> enables one to extract the
-<code class="code docutils literal notranslate"><span class="pre">sample_result</span></code> data for each term in the provided <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code>.
-This return type can be used in the following way.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// I only care about the expected value, discard</span>
-<span class="c1">// the fine-grain data produced</span>
-<span class="kt">double</span><span class="w"> </span><span class="n">expVal</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">spinOp</span><span class="p">,</span><span class="w"> </span><span class="n">args</span><span class="p">...);</span>
-
-<span class="c1">// I require the result with all generated data</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">spinOp</span><span class="p">,</span><span class="w"> </span><span class="n">args</span><span class="p">...);</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">expVal</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">result</span><span class="p">.</span><span class="n">expectation</span><span class="p">();</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">X0X1Exp</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">result</span><span class="p">.</span><span class="n">expectation</span><span class="p">(</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">));</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">X0X1Data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">result</span><span class="p">.</span><span class="n">counts</span><span class="p">(</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">));</span>
-<span class="n">result</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># I require the result with all generated data</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="p">::</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">spinOp</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
-<span class="n">expVal</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-<span class="n">X0X1Exp</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">(</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-<span class="n">X0X1Data</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">counts</span><span class="p">(</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-<span class="n">result</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Here is an example of the utility of the <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> function:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span><span class="w"> </span><span class="nc">ansatz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()(</span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span><span class="w"> </span><span class="c1">// make it easier to use pauli X,Y,Z below</span>
-
-<span class="w">  </span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">              </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">  </span><span class="kt">double</span><span class="w"> </span><span class="n">energy</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">ansatz</span><span class="p">{},</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="mf">.59</span><span class="p">);</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Energy is %lf</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">energy</span><span class="p">);</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--5-input--2" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">ansatz</span><span class="p">(</span><span class="n">theta</span> <span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-   <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-   <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-   <span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-   <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="n">h</span> <span class="o">=</span> <span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span>
-                <span class="mf">.21829</span> <span class="o">*</span> <span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">6.125</span> <span class="o">*</span> <span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">energy</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">ansatz</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="mf">.59</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Energy is </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">energy</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[5]</strong> There are specific requirements on input quantum kernels for the use of the
-observe function which must be enforced by compiler implementations. The
-kernel must be an entry-point kernel that does not contain any conditional
-or measurement statements.</p>
-<p><strong>[6]</strong> By default on simulation backends, <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> computes the true
-analytic expectation value (i.e. without stochastic noise due to shots-based sampling).
-If a specific shot count is provided then the returned expectation value will contain some
-level of statistical noise. Overloaded <code class="code docutils literal notranslate"><span class="pre">observe</span></code> functions are provided to
-specify the number of shots and/or specify the noise model to apply.</p>
-<p><strong>[7]</strong> CUDA-Q also provides an asynchronous version of this function
-(<code class="code docutils literal notranslate"><span class="pre">cudaq::observe_async</span></code>) which returns a <code class="code docutils literal notranslate"><span class="pre">async_observe_result</span></code>.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="n">async_observe_result</span><span class="w"> </span><span class="n">observe_async</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qpu_id</span><span class="p">,</span><span class="w"> </span><span class="n">QuantumKernel</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="o">&amp;&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>Programmers can asynchronously launch sampling tasks on any <code class="code docutils literal notranslate"><span class="pre">qpu_id</span></code>.</p>
-<p><strong>[8]</strong> For remote QPU systems with long queue times, the <code class="code docutils literal notranslate"><span class="pre">async_observe_result</span></code> type encodes job ID
-information for each execution and can be persisted to file and loaded from file at a later time. After loading from file,
-and when remote queue jobs are completed, one can invoke <code class="code docutils literal notranslate"><span class="pre">get()</span></code> and the results will
-be retrieved and returned.</p>
-</section>
-<section id="cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">
-<h2><span class="section-number">12.3. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)<a class="headerlink" href="#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries" title="Permalink to this heading">¶</a></h2>
-<p>The primary use case for <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> is to leverage it as
-the core of a broader objective function optimization workflow.
-<code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> produces the expected value of a specified
-<code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> with respect to a given parameterized ansatz at a concrete
-set of parameters, and often programmers will require an extremal value of that expected value
-at a specific set of concrete parameters. This will directly require
-abstractions for gradient-based and gradient-free optimization strategies.</p>
-<p>The CUDA-Q model provides a <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> data type that exposes
-an <code class="code docutils literal notranslate"><span class="pre">optimize()</span></code> method that takes as input an
-<code class="code docutils literal notranslate"><span class="pre">optimizable_function</span></code> to optimize and the number of independent
-function dimensions. Implementations are free to implement this abstraction
-in any way that is pertinent, but it is expected that most approaches will
-enable optimization strategy extensibility. For example, programmers should
-be able to instantiate a specific <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> sub-type, thereby
-dictating the underlying optimization algorithm in a type-safe manner.
-Moreover, the optimizer should expose a public API of pertinent optimizer-specific
-options that the programmer can customize.</p>
-<p>CUDA-Q models the <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> as follows:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Encode the optimal value and optimal parameters</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">optimization_result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">tuple</span><span class="o">&lt;</span><span class="kt">double</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&gt;</span><span class="p">;</span>
-<span class="w">  </span><span class="c1">// Initialized with user specified callable of a specific signature</span>
-<span class="w">  </span><span class="c1">// Clients can query if the function computes gradients or not</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">optimizable_function</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Callable</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">optimizable_function</span><span class="p">(</span><span class="n">Callable</span><span class="o">&amp;&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="kt">bool</span><span class="w"> </span><span class="nf">providesGradients</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">_providesGradients</span><span class="p">;</span><span class="w"> </span><span class="p">}</span>
-<span class="w">      </span><span class="kt">double</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">dx</span><span class="p">);</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">optimizer</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="k">virtual</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">requiresGradients</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">      </span><span class="k">virtual</span><span class="w"> </span><span class="n">optimization_result</span><span class="w"> </span><span class="nf">optimize</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">dimensions</span><span class="p">,</span>
-<span class="w">                                          </span><span class="n">optimizable_function</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">opt_function</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>Here, <code class="code docutils literal notranslate"><span class="pre">optimization_result</span></code> should encode the optimal value and optimal
-parameters achieved during the optimization workflow
-(i.e. a <code class="code docutils literal notranslate"><span class="pre">tuple&lt;double,</span> <span class="pre">std::vector&lt;double&gt;&gt;</span></code>). The optimize method takes
-as input the number of parameters (or dimensions of the objective function),
-and a function-like object (i.e. <code class="code docutils literal notranslate"><span class="pre">std::function</span></code> or a lambda, something
-<code class="code docutils literal notranslate"><span class="pre">optimizable_function</span></code> can be constructed from) that takes a
-<code class="code docutils literal notranslate"><span class="pre">const</span> <span class="pre">std::vector&lt;double&gt;&amp;</span></code> and <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;double&gt;&amp;</span></code> for the
-function input parameters and gradient vector, respectively. The objective
-function must return a double representing the scalar cost for the
-objective function (e.g. the expected value from <code class="code docutils literal notranslate"><span class="pre">cudaq::observe()</span></code>).</p>
-<p>Here is an example of how the <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> is intended to be used:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">ansatz</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">phi</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{...};</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">H</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">;</span>
-
-<span class="n">cudaq</span><span class="o">::</span><span class="n">optimizers</span><span class="o">::</span><span class="n">cobyla</span><span class="w"> </span><span class="n">optimizer</span><span class="p">;</span>
-<span class="n">optimizer</span><span class="p">.</span><span class="n">max_eval</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">200</span><span class="p">;</span>
-
-<span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">opt_energy</span><span class="p">,</span><span class="w"> </span><span class="n">opt_params</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">optimize</span><span class="p">(</span>
-<span class="w">      </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="p">[</span><span class="o">&amp;</span><span class="p">](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">grad_vec</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">H</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">      </span><span class="p">});</span>
-</pre></div>
-</div>
-</section>
-<section id="cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">
-<h2><span class="section-number">12.4. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)<a class="headerlink" href="#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries" title="Permalink to this heading">¶</a></h2>
-<p>Typical optimization use cases will require the computation of gradients for the specified
-objective function. The gradient is a vector over all ansatz circuit
-parameters <span class="math notranslate nohighlight">\(∂H(𝚹) / ∂𝚹_i\)</span>. There are a number of potential strategies for
-computing this gradient vector, but most require additional evaluations
-of the ansatz circuit on the quantum processor.</p>
-<p>To enable true extensibility in gradient strategies, CUDA-Q programmers can
-instantiate custom sub-types of the <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> type. The <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code>
-type defines a <code class="code docutils literal notranslate"><span class="pre">compute(...)</span></code> method that takes a mutable reference to the
-current gradient vector and is free to update that vector in a strategy-specific way.
-The method also takes the current evaluation parameter vector, the <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code> used
-in the current variational task, and the computed expected value at the given parameters.
-The gradient strategy type takes the following form:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">gradient</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="n">gradient</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">function</span><span class="o">&lt;</span><span class="kt">void</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="p">)</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">kernel</span><span class="p">);</span>
-
-<span class="w">      </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">ArgsMapper</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">gradient</span><span class="p">(</span><span class="n">QuantumKernel</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">ArgsMapper</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">argsMapper</span><span class="p">);</span>
-
-<span class="w">      </span><span class="k">virtual</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="nf">compute</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">dx</span><span class="p">,</span>
-<span class="w">                        </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">exp_h</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-
-<span class="w">      </span><span class="k">virtual</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">compute</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">x</span><span class="p">,</span>
-<span class="w">              </span><span class="n">std</span><span class="o">::</span><span class="n">function</span><span class="o">&lt;</span><span class="kt">double</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="p">)</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">func</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="c1">// gradient is intended for subclassing</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">central_difference</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">gradient</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="n">compute</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">dx</span><span class="p">,</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="n">h</span><span class="p">,</span>
-<span class="w">              </span><span class="kt">double</span><span class="w"> </span><span class="n">exp_h</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>The <code class="code docutils literal notranslate"><span class="pre">compute</span></code> function can make use of the quantum kernel parameterized ansatz, the
-<code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> for which the expected value is being computed, the
-pre-computed expected value at the current iteration’s parameter, and the
-concrete arguments for the given quantum kernel at this iteration.</p>
-<p>A non-trivial aspect of the computation of gradients (in an extensible manner)
-is that we model the gradient as a derivative over concrete parameters for the
-circuit ansatz represented as a <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;double&gt;</span></code> when the actual
-quantum kernel may be defined with general variadic <code class="code docutils literal notranslate"><span class="pre">Args...</span></code> types.
-To address this issue, programmers can provide a default translation
-mechanism for mapping common quantum kernel ansatz functional expressions to a <code class="code docutils literal notranslate"><span class="pre">vector&lt;double&gt;</span></code> representation - the
-<code class="code docutils literal notranslate"><span class="pre">ArgsMapper</span></code> callable template type. This type must implement the
-<code class="code docutils literal notranslate"><span class="pre">std::tuple&lt;Args...&gt;(std::vector&lt;double&gt;&amp;)</span></code> callable concept.</p>
-<p>The overall CUDA-Q workflow for leveraging the <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code>
-will work as follows (here we demonstrate with an ansatz without the
-default <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;double&gt;</span></code> signature):</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">deuteron_n3_ansatz</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">double</span><span class="w"> </span><span class="n">x0</span><span class="p">,</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">x1</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">3</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">  </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">ry</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">ry</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">x</span><span class="o">&lt;</span><span class="n">vctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">ry</span><span class="p">(</span><span class="o">-</span><span class="n">x0</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="p">};</span>
-
-<span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">          </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h3</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mf">9.625</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">9.625</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">3.913119</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="w"> </span><span class="o">-</span>
-<span class="w">            </span><span class="mf">3.913119</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-
-<span class="c1">// The above ansatz takes 2 doubles, not a single std::vector&lt;double&gt;, which</span>
-<span class="c1">// the gradient type is expecting. So we must provide an ArgsMapper callable type</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">argsMapper</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="k">return</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">make_tuple</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]);};</span>
-
-<span class="c1">// Create the gradient strategy</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">gradients</span><span class="o">::</span><span class="n">central_difference</span><span class="w"> </span><span class="nf">gradient</span><span class="p">(</span><span class="n">deuteron_n3_ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">argsMapper</span><span class="p">);</span>
-
-<span class="c1">// Create the L-BFGS optimizer, requires gradients</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">optimizers</span><span class="o">::</span><span class="n">lbfgs</span><span class="w"> </span><span class="n">optimizer</span><span class="p">;</span>
-
-<span class="c1">// Run the optimization routine.</span>
-<span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">min_val</span><span class="p">,</span><span class="w"> </span><span class="n">opt_params</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">optimize</span><span class="p">(</span>
-<span class="w">    </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="p">[</span><span class="o">&amp;</span><span class="p">](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">grad_vec</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="c1">// Compute the cost, here its an energy</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">cost</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">deuteron_n3_ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">h3</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Compute the gradient, results written to the grad_vec reference</span>
-<span class="w">      </span><span class="n">gradient</span><span class="p">.</span><span class="n">compute</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">grad_vec</span><span class="p">,</span><span class="w"> </span><span class="n">h3</span><span class="p">,</span><span class="w"> </span><span class="n">cost</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Return the cost to the optimizer</span>
-<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">cost</span><span class="p">;</span>
-<span class="w">    </span><span class="p">});</span>
-
-<span class="c1">// Print the results</span>
-<span class="n">printf</span><span class="p">(</span><span class="s">&quot;Optimizer found %lf at [%lf,%lf]</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">min_val</span><span class="p">,</span><span class="w"> </span><span class="n">opt_params</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">opt_params</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-</pre></div>
-</div>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
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-  <section id="just-in-time-kernel-creation">
-<h1><span class="section-number">9. </span>Just-in-Time Kernel Creation<a class="headerlink" href="#just-in-time-kernel-creation" title="Permalink to this heading">¶</a></h1>
-<p><strong>[1]</strong> CUDA-Q provides a set of programming abstractions for dynamically constructing
-quantum kernel code at runtime.</p>
-<p><strong>[2]</strong> The callable <code class="code docutils literal notranslate"><span class="pre">cudaq::kernel_builder</span></code> abstraction
-facilitates the dynamic definition of quantum kernels that can optionally
-be parameterized by user-defined input arguments.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">kernel_builder</span></code> takes the following structure</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Type wrapping a value in the kernel IR</span>
-<span class="w">  </span><span class="k">struct</span><span class="w"> </span><span class="nc">Value</span><span class="p">;</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">kernel_builder</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">private</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">Value</span><span class="o">&gt;</span><span class="w"> </span><span class="n">arguments</span><span class="p">;</span>
-
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">Value</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">getArguments</span><span class="p">();</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="nf">name</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">getNumParams</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-
-<span class="w">      </span><span class="n">Value</span><span class="w"> </span><span class="nf">qalloc</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">nQubits</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">);</span>
-<span class="w">      </span><span class="n">Value</span><span class="w"> </span><span class="nf">qalloc</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">size</span><span class="p">);</span>
-
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">h</span><span class="p">(</span><span class="n">Value</span><span class="o">&amp;</span><span class="w"> </span><span class="n">qubit</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">//... all other quantum operations ...</span>
-
-<span class="w">      </span><span class="c1">// if (mz(q)) thenFunctor()</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">c_if</span><span class="p">(</span><span class="n">Value</span><span class="o">&amp;</span><span class="w"> </span><span class="n">result</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">function</span><span class="o">&lt;</span><span class="kt">void</span><span class="p">()</span><span class="o">&gt;&amp;&amp;</span><span class="w"> </span><span class="n">thenFunctor</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Invoke a predefined kernel</span>
-<span class="w">      </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">OtherKernelBuilder</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Values</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="n">call</span><span class="p">(</span><span class="n">OtherKernelBuilder</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernelToCall</span><span class="p">,</span><span class="w"> </span><span class="n">Values</span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// General multi-control on a predefined kernel</span>
-<span class="w">      </span><span class="c1">// models cudaq::control(...)</span>
-<span class="w">      </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">OtherKernelBuilder</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Values</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="n">control</span><span class="p">(</span><span class="n">OtherKernelBuilder</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernelToControl</span><span class="p">,</span><span class="w"> </span><span class="n">Value</span><span class="o">&amp;</span><span class="w"> </span><span class="n">ctrl</span><span class="p">,</span><span class="w"> </span><span class="n">Values</span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">values</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// General adjoint on a predefined kernel</span>
-<span class="w">      </span><span class="c1">// models cudaq::adjoint(...)</span>
-<span class="w">      </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">OtherKernelBuilder</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Values</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="n">adjoint</span><span class="p">(</span><span class="n">OtherKernelBuilder</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">kernelToAdjoint</span><span class="p">,</span><span class="w"> </span><span class="n">Values</span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">values</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// The constructed kernel is callable</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">Args</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="kt">void</span><span class="o">**</span><span class="w"> </span><span class="n">argsArray</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Enable structured bindings</span>
-<span class="w">      </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="k">decltype</span><span class="p">(</span><span class="k">auto</span><span class="p">)</span><span class="w"> </span><span class="n">get</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="k">if</span><span class="w"> </span><span class="k">constexpr</span><span class="w"> </span><span class="p">(</span><span class="n">N</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span>
-<span class="w">          </span><span class="k">return</span><span class="w"> </span><span class="o">*</span><span class="k">this</span><span class="p">;</span>
-<span class="w">        </span><span class="k">else</span>
-<span class="w">          </span><span class="k">return</span><span class="w"> </span><span class="n">arguments</span><span class="p">[</span><span class="n">N</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">];</span>
-<span class="w">      </span><span class="p">}</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-
-<span class="c1">/// Enable structured bindings on the kernel_builder type.</span>
-<span class="c1">/// auto [kernel, theta, phi] = std::make_kernel&lt;double,double&gt;();</span>
-<span class="k">namespace</span><span class="w"> </span><span class="nn">std</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">struct</span><span class="w"> </span><span class="nc">tuple_size</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">kernel_builder</span><span class="o">&lt;</span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;&gt;</span>
-<span class="w">    </span><span class="o">:</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">integral_constant</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="p">,</span><span class="w"> </span><span class="k">sizeof</span><span class="p">...(</span><span class="n">Args</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{};</span>
-
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">struct</span><span class="w"> </span><span class="nc">tuple_element</span><span class="o">&lt;</span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">kernel_builder</span><span class="o">&lt;</span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">using</span><span class="w"> </span><span class="n">type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">conditional_t</span><span class="o">&lt;</span><span class="n">N</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">kernel_builder</span><span class="o">&lt;</span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;</span><span class="p">,</span>
-<span class="w">                              </span><span class="n">cudaq</span><span class="o">::</span><span class="n">QuakeValue</span><span class="o">&gt;</span><span class="p">;</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span><span class="w"> </span><span class="c1">// namespace std</span>
-
-<span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">kernel_builder</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="n">make_kernel</span><span class="p">();</span>
-<span class="w">  </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="n">kernel_builder</span><span class="o">&lt;</span><span class="n">Args</span><span class="p">...</span><span class="o">&gt;</span><span class="w"> </span><span class="n">make_kernel</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p><strong>[3]</strong> The structure above allows one to leverage the provided factory functions (<code class="code docutils literal notranslate"><span class="pre">make_kernel</span></code>)
-to construct an empty CUDA-Q kernel with defined argument signature.</p>
-<p><strong>[4]</strong> For each type in the signature, the programmer is returned a new <code class="code docutils literal notranslate"><span class="pre">Value</span></code> instance which
-can be leveraged in the construction of the kernel. The intended mechanism for
-kernel creation and argument value extraction is via standard C++
-<a class="reference external" href="https://en.cppreference.com/w/cpp/language/structured_binding">structured bindings</a>.</p>
-<p><strong>[5]</strong> Once the kernel is created, the programmer is free to build up the kernel expression
-using the exposed API. There are methods for qubit allocation, quantum operation
-invocation, control and adjoint synthesis, and conditional branching based on
-<code class="code docutils literal notranslate"><span class="pre">boolean</span></code> values.</p>
-<p>Here is a simple example how one might build a CUDA-Q kernel dynamically.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">make_kernel</span><span class="p">();</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">qubits</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">kernel</span><span class="p">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="n">kernel</span><span class="p">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="n">kernel</span><span class="p">.</span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="n">kernel</span><span class="p">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">);</span>
-
-<span class="c1">// See algorithmic primitives section for more on sample</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-<span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Here is an example demonstrating how one may build a dynamic set of
-CUDA-Q kernels for executing the standard Hadamard test.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">xPrep</span><span class="p">,</span><span class="w"> </span><span class="n">qubitIn</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">make_kernel</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="o">&gt;</span><span class="p">();</span>
-<span class="n">xPrep</span><span class="p">.</span><span class="n">x</span><span class="p">(</span><span class="n">qubitIn</span><span class="p">);</span>
-
-<span class="c1">// Compute &lt;1|X|1&gt; = 0</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">hadamardTest</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">make_kernel</span><span class="p">();</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">q</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">hadamardTest</span><span class="p">.</span><span class="n">qalloc</span><span class="p">();</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">ancilla</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">hadamardTest</span><span class="p">.</span><span class="n">qalloc</span><span class="p">();</span>
-<span class="n">hadamardTest</span><span class="p">.</span><span class="n">call</span><span class="p">(</span><span class="n">xPrep</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-<span class="n">hadamardTest</span><span class="p">.</span><span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">);</span>
-<span class="n">hadamardTest</span><span class="p">.</span><span class="n">control</span><span class="p">(</span><span class="n">xPrep</span><span class="p">,</span><span class="w"> </span><span class="n">ancilla</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-<span class="n">hadamardTest</span><span class="p">.</span><span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">);</span>
-<span class="n">hadamardTest</span><span class="p">.</span><span class="n">mz</span><span class="p">(</span><span class="n">ancilla</span><span class="p">);</span>
-
-<span class="c1">// See algorithmic primitives section for more on sample</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">hadamardTest</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">xPrep</span><span class="p">,</span> <span class="n">qubitIn</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">)</span>
-
-<span class="c1"># Compute &lt;1|X|1&gt; = 0</span>
-<span class="n">hadamardTest</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-<span class="n">q</span> <span class="o">=</span> <span class="n">hadamardTest</span><span class="o">.</span><span class="n">qalloc</span><span class="p">()</span>
-<span class="n">hadamardTest</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="n">xPrep</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-<span class="n">hadamardTest</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-<span class="n">hadamardTest</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">xPrep</span><span class="p">,</span> <span class="n">ancilla</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-<span class="n">hadamardTest</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-<span class="n">hadamardTest</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">ancilla</span><span class="p">)</span>
-
-<span class="c1"># See algorithmic primitives section for more on sample</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">hadamardTest</span><span class="p">)</span>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
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-      <li class="breadcrumb-item active"><span class="section-number">13. </span>Example Programs</li>
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-  <section id="example-programs">
-<h1><span class="section-number">13. </span>Example Programs<a class="headerlink" href="#example-programs" title="Permalink to this heading">¶</a></h1>
-<section id="hello-world-simple-bell-state">
-<h2><span class="section-number">13.1. </span>Hello World - Simple Bell State<a class="headerlink" href="#hello-world-simple-bell-state" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">bell</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="kt">int</span><span class="w"> </span><span class="n">num_iters</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">nCorrect</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">num_iters</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">results</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">      </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="w">        </span><span class="n">nCorrect</span><span class="o">++</span><span class="p">;</span>
-
-<span class="w">      </span><span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">      </span><span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">nCorrect</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;N Correct = %d</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bell</span><span class="p">{}(</span><span class="mi">100</span><span class="p">));</span><span class="w"> </span><span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">bell</span><span class="p">(</span><span class="n">num_iters</span> <span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">nCorrect</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_iters</span><span class="p">):</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">results</span> <span class="o">=</span> <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-           <span class="n">nCorrect</span> <span class="o">=</span> <span class="n">nCorrect</span> <span class="o">+</span> <span class="mi">1</span>
-
-        <span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">nCorrect</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">bell</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;N Correct = </span><span class="si">{</span><span class="n">counts</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">counts</span> <span class="o">==</span> <span class="mi">100</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="ghz-state-preparation-and-sampling">
-<h2><span class="section-number">13.2. </span>GHZ State Preparation and Sampling<a class="headerlink" href="#ghz-state-preparation-and-sampling" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="nf">ghz</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">n_qubits</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">);</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">n_qubits</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span>
-<span class="w">    </span><span class="c1">// note use of ctrl modifier</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">  </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Sample the state produced by the ghz kernel</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">);</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">counts</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Observed %s %lu times.</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bits</span><span class="p">.</span><span class="n">c_str</span><span class="p">(),</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">ghz</span><span class="p">(</span><span class="n">numQubits</span><span class="p">:</span><span class="nb">int</span><span class="p">):</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">numQubits</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="o">.</span><span class="n">front</span><span class="p">())</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">qubit</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">qubits</span><span class="o">.</span><span class="n">front</span><span class="p">(</span><span class="n">numQubits</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)):</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubit</span><span class="p">,</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="k">for</span> <span class="n">bits</span><span class="p">,</span> <span class="n">count</span> <span class="p">:</span> <span class="n">counts</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Observed </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1"> times.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bits</span><span class="p">,</span> <span class="n">count</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="quantum-phase-estimation">
-<h2><span class="section-number">13.3. </span>Quantum Phase Estimation<a class="headerlink" href="#quantum-phase-estimation" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ phase_estimation.cpp -o qpe.x &amp;&amp; ./qpe.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/algorithm.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;stdio.h&gt;</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cmath&gt;</span>
-
-<span class="c1">// A pure device quantum kernel defined as a free function</span>
-<span class="c1">// (cannot be called from host code).</span>
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">iqft</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qview</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">size</span><span class="p">();</span>
-<span class="w">  </span><span class="c1">// Swap qubits</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">swap</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">N</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]);</span>
-<span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">j</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">i</span><span class="p">;</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">&gt;=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="o">--</span><span class="n">y</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="kt">double</span><span class="w"> </span><span class="n">denom</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="mi">1UL</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="p">(</span><span class="n">j</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">y</span><span class="p">));</span>
-<span class="w">      </span><span class="k">const</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">-</span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">denom</span><span class="p">;</span>
-<span class="w">      </span><span class="n">r1</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">j</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">y</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">N</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="p">}</span>
-
-<span class="c1">// CUDA-Q kernel call operators can be templated on</span>
-<span class="c1">// input CUDA-Q kernel expressions. Here we define a general</span>
-<span class="c1">// Phase Estimation algorithm that is generic on the eigenstate</span>
-<span class="c1">// preparation and unitary evolution steps.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">qpe</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="c1">// Define the CUDA-Q call expression to take user-specified eigenstate</span>
-<span class="w">  </span><span class="c1">// and unitary evolution kernels, as well as the number of qubits in the</span>
-<span class="w">  </span><span class="c1">// counting register and in the eigenstate register.</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">StatePrep</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">Unitary</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="k">operator</span><span class="p">()(</span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nCountingQubits</span><span class="p">,</span><span class="w"> </span><span class="n">StatePrep</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">state_prep</span><span class="p">,</span>
-<span class="w">                  </span><span class="n">Unitary</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">oracle</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="c1">// Allocate a register of qubits</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="n">nCountingQubits</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">    </span><span class="c1">// Extract sub-registers, one for the counting qubits</span>
-<span class="w">    </span><span class="c1">// another for the eigenstate register</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">counting_qubits</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="n">nCountingQubits</span><span class="p">);</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">state_register</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">back</span><span class="p">();</span>
-
-<span class="w">    </span><span class="c1">// Prepare the eigenstate</span>
-<span class="w">    </span><span class="n">state_prep</span><span class="p">(</span><span class="n">state_register</span><span class="p">);</span>
-
-<span class="w">    </span><span class="c1">// Put the counting register into uniform superposition</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">counting_qubits</span><span class="p">);</span>
-
-<span class="w">    </span><span class="c1">// Perform `ctrl-U^j`</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">nCountingQubits</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">j</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">j</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="p">(</span><span class="mi">1</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">i</span><span class="p">);</span><span class="w"> </span><span class="o">++</span><span class="n">j</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="n">cudaq</span><span class="o">::</span><span class="n">control</span><span class="p">(</span><span class="n">oracle</span><span class="p">,</span><span class="w"> </span><span class="n">counting_qubits</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">state_register</span><span class="p">);</span>
-<span class="w">      </span><span class="p">}</span>
-<span class="w">    </span><span class="p">}</span>
-
-<span class="w">    </span><span class="c1">// Apply inverse quantum Fourier transform</span>
-<span class="w">    </span><span class="n">iqft</span><span class="p">(</span><span class="n">counting_qubits</span><span class="p">);</span>
-
-<span class="w">    </span><span class="c1">// Measure to gather sampling statistics</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">counting_qubits</span><span class="p">);</span>
-
-<span class="w">    </span><span class="k">return</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">r1PiGate</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="o">&amp;</span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">r1</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">nQubits</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="p">{</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="mi">6</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">})</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span>
-<span class="w">        </span><span class="n">qpe</span><span class="p">{},</span><span class="w"> </span><span class="n">nQubits</span><span class="p">,</span><span class="w"> </span><span class="p">[](</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="o">&amp;</span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="p">},</span><span class="w"> </span><span class="n">r1PiGate</span><span class="p">{});</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">mostProbable</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">counts</span><span class="p">.</span><span class="n">most_probable</span><span class="p">();</span>
-<span class="w">    </span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">to_integer</span><span class="p">(</span><span class="n">mostProbable</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="p">(</span><span class="kt">double</span><span class="p">)(</span><span class="mi">1UL</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">nQubits</span><span class="p">);</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">piEstimate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">1.</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="p">(</span><span class="mi">2</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">theta</span><span class="p">);</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Pi Estimate(nQubits == %d) = %lf </span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">nQubits</span><span class="p">,</span><span class="w"> </span><span class="n">piEstimate</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span><span class="o">,</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Compute phase for U |psi&gt; = exp(-2 pi phase) |psi&gt;</span>
-<span class="c1"># This example will consider U = T, and |psi&gt; = |1&gt;</span>
-<span class="c1"># Define a Inverse Quantum Fourier Transform kernel</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">iqft</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">N</span> <span class="o">=</span> <span class="n">qubits</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">//</span> <span class="mi">2</span><span class="p">):</span>
-        <span class="n">swap</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">N</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="n">j</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-            <span class="n">r1</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">j</span> <span class="o">-</span> <span class="n">y</span><span class="p">),</span> <span class="n">qubits</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">y</span><span class="p">])</span>
-
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span>
-
-
-<span class="c1"># Define the U kernel</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">tGate</span><span class="p">(</span><span class="n">qubit</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">t</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="c1"># Define the state preparation |psi&gt; kernel</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">xGate</span><span class="p">(</span><span class="n">qubit</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-<span class="c1"># General Phase Estimation kernel for single qubit</span>
-<span class="c1"># eigen states.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">qpe</span><span class="p">(</span><span class="n">nC</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">nQ</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">statePrep</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">],</span> <span class="kc">None</span><span class="p">],</span>
-        <span class="n">oracle</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">],</span> <span class="kc">None</span><span class="p">]):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">nC</span> <span class="o">+</span> <span class="n">nQ</span><span class="p">)</span>
-    <span class="n">countingQubits</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">front</span><span class="p">(</span><span class="n">nC</span><span class="p">)</span>
-    <span class="n">stateRegister</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">back</span><span class="p">()</span>
-    <span class="n">statePrep</span><span class="p">(</span><span class="n">stateRegister</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">countingQubits</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nC</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">i</span><span class="p">):</span>
-            <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">oracle</span><span class="p">,</span> <span class="p">[</span><span class="n">countingQubits</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">stateRegister</span><span class="p">)</span>
-    <span class="n">iqft</span><span class="p">(</span><span class="n">countingQubits</span><span class="p">)</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">countingQubits</span><span class="p">)</span>
-
-<span class="c1"># Sample the state to get the phase.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">qpe</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">xGate</span><span class="p">,</span> <span class="n">tGate</span><span class="p">)</span>
-<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
-<span class="k">assert</span> <span class="s1">&#39;100&#39;</span> <span class="ow">in</span> <span class="n">counts</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="deuteron-binding-energy-parameter-sweep">
-<h2><span class="section-number">13.4. </span>Deuteron Binding Energy Parameter Sweep<a class="headerlink" href="#deuteron-binding-energy-parameter-sweep" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">deuteron_n2_ansatz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">              </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Perform parameter sweep for deuteron N=2 Hamiltonian</span>
-<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">param_space</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI</span><span class="p">,</span><span class="w"> </span><span class="n">M_PI</span><span class="p">,</span><span class="w"> </span><span class="mi">25</span><span class="p">);</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="k">auto</span><span class="o">&amp;</span><span class="w"> </span><span class="n">param</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">param_space</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="c1">// KERNEL::observe(...) &lt;==&gt;</span>
-<span class="w">    </span><span class="c1">// E(params...) = &lt;psi(params...) | H | psi(params...)&gt;</span>
-<span class="w">    </span><span class="kt">double</span><span class="w"> </span><span class="n">energy_at_param</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">deuteron_n2_ansatz</span><span class="p">{},</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="n">param</span><span class="p">);</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;&lt;H&gt;(%lf) = %lf</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">param</span><span class="p">,</span><span class="w"> </span><span class="n">energy_at_param</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">ansatz</span><span class="p">(</span><span class="n">angle</span><span class="p">:</span><span class="nb">float</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span>
-    <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">.21829</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">6.125</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Perform parameter sweep for deuteron N=2 Hamiltonian</span>
-<span class="k">for</span> <span class="n">angle</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">25</span><span class="p">):</span>
-     <span class="c1"># KERNEL::observe(...) &lt;==&gt;</span>
-     <span class="c1"># E(params...) = &lt;psi(params...) | H | psi(params...)&gt;</span>
-     <span class="n">energyAtParam</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">ansatz</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="mf">.59</span><span class="p">)</span>
-     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&lt;H&gt;(</span><span class="si">{}</span><span class="s1">) = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="n">energyAtParam</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="grover-s-algorithm">
-<h2><span class="section-number">13.5. </span>Grover’s Algorithm<a class="headerlink" href="#grover-s-algorithm" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --library-mode grover.cpp -o grover.x &amp;&amp; ./grover.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cmath&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;numbers&gt;</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">reflect_about_uniform</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">qs</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">ctrlQubits</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">qs</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="n">qs</span><span class="p">.</span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">);</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">lastQubit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">qs</span><span class="p">.</span><span class="n">back</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// Compute (U) Action (V) produces</span>
-<span class="w">  </span><span class="c1">// U V U::Adjoint</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">compute_action</span><span class="p">(</span>
-<span class="w">      </span><span class="p">[</span><span class="o">&amp;</span><span class="p">]()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="n">h</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">        </span><span class="n">x</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">      </span><span class="p">},</span>
-<span class="w">      </span><span class="p">[</span><span class="o">&amp;</span><span class="p">]()</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">z</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ctrlQubits</span><span class="p">,</span><span class="w"> </span><span class="n">lastQubit</span><span class="p">);</span><span class="w"> </span><span class="p">});</span>
-<span class="p">}</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">run_grover</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">CallableKernel</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="n">__qpu__</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()(</span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">n_qubits</span><span class="p">,</span><span class="w"> </span><span class="n">CallableKernel</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">oracle</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">n_iterations</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">round</span><span class="p">(</span><span class="mf">0.25</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">numbers</span><span class="o">::</span><span class="n">pi</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="w"> </span><span class="o">^</span><span class="w"> </span><span class="n">n_qubits</span><span class="p">));</span>
-
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">qs</span><span class="p">(</span><span class="n">n_qubits</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">n_iterations</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">oracle</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">      </span><span class="n">reflect_about_uniform</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">oracle</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="kt">long</span><span class="w"> </span><span class="n">target_state</span><span class="p">;</span>
-
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">qs</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">compute_action</span><span class="p">(</span>
-<span class="w">        </span><span class="p">[</span><span class="o">&amp;</span><span class="p">]()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">          </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;=</span><span class="w"> </span><span class="n">qs</span><span class="p">.</span><span class="n">size</span><span class="p">();</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">target_bit_set</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="mi">1</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="p">(</span><span class="n">qs</span><span class="p">.</span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">i</span><span class="p">))</span><span class="w"> </span><span class="o">&amp;</span><span class="w"> </span><span class="n">target_state</span><span class="p">;</span>
-<span class="w">            </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">target_bit_set</span><span class="p">)</span>
-<span class="w">              </span><span class="n">x</span><span class="p">(</span><span class="n">qs</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">          </span><span class="p">}</span>
-<span class="w">        </span><span class="p">},</span>
-<span class="w">        </span><span class="p">[</span><span class="o">&amp;</span><span class="p">]()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">          </span><span class="k">auto</span><span class="w"> </span><span class="n">ctrlQubits</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">qs</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="n">qs</span><span class="p">.</span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">);</span>
-<span class="w">          </span><span class="n">z</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ctrlQubits</span><span class="p">,</span><span class="w"> </span><span class="n">qs</span><span class="p">.</span><span class="n">back</span><span class="p">());</span>
-<span class="w">        </span><span class="p">});</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="w"> </span><span class="o">*</span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">secret</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">argc</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">strtol</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="nb">NULL</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="mb">0b1011</span><span class="p">;</span>
-<span class="w">  </span><span class="n">oracle</span><span class="w"> </span><span class="n">compute_oracle</span><span class="p">{.</span><span class="n">target_state</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">secret</span><span class="p">};</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">run_grover</span><span class="p">{},</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="n">compute_oracle</span><span class="p">);</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Found string %s</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">counts</span><span class="p">.</span><span class="n">most_probable</span><span class="p">().</span><span class="n">c_str</span><span class="p">());</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">reflect</span><span class="p">(</span><span class="n">qubits</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">ctrls</span> <span class="o">=</span> <span class="n">qubits</span><span class="o">.</span><span class="n">front</span><span class="p">(</span><span class="n">qubits</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">last</span> <span class="o">=</span> <span class="n">qubits</span><span class="o">.</span><span class="n">back</span><span class="p">()</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">compute_action</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">(</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">),</span> <span class="n">x</span><span class="p">(</span><span class="n">qubits</span><span class="p">)),</span>
-                          <span class="k">lambda</span><span class="p">:</span> <span class="n">z</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">ctrls</span><span class="p">,</span> <span class="n">last</span><span class="p">))</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">oracle</span><span class="p">(</span><span class="n">q</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="n">z</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="n">z</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">grover</span><span class="p">(</span><span class="n">N</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">M</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">oracle</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">],</span> <span class="kc">None</span><span class="p">]):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="p">):</span>
-        <span class="n">oracle</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-        <span class="n">reflect</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">grover</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">oracle</span><span class="p">)</span>
-<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span>
-<span class="k">assert</span> <span class="s1">&#39;101&#39;</span> <span class="ow">in</span> <span class="n">counts</span>
-<span class="k">assert</span> <span class="s1">&#39;011&#39;</span> <span class="ow">in</span> <span class="n">counts</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="iterative-phase-estimation">
-<h2><span class="section-number">13.6. </span>Iterative Phase Estimation<a class="headerlink" href="#iterative-phase-estimation" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ iterative_qpe.cpp -o qpe.x &amp;&amp; ./qpe.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">iqpe</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">8</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span>
-<span class="w">      </span><span class="n">r1</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">-5</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">8.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">cr0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span>
-<span class="w">      </span><span class="n">r1</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">-5</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">8.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cr0</span><span class="p">)</span>
-<span class="w">      </span><span class="n">rz</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">2.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">cr1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span>
-<span class="w">      </span><span class="n">r1</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">-5</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">8.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cr0</span><span class="p">)</span>
-<span class="w">      </span><span class="n">rz</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">4.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cr1</span><span class="p">)</span>
-<span class="w">      </span><span class="n">rz</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">2.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">cr2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">reset</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">r1</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">-5</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">8.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cr0</span><span class="p">)</span>
-<span class="w">      </span><span class="n">rz</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">8.</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cr1</span><span class="p">)</span>
-<span class="w">      </span><span class="n">rz</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI_4</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cr2</span><span class="p">)</span>
-<span class="w">      </span><span class="n">rz</span><span class="p">(</span><span class="o">-</span><span class="n">M_PI_2</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="cm">/*shots*/</span><span class="w"> </span><span class="mi">10</span><span class="p">,</span><span class="w"> </span><span class="n">iqpe</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
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-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3 current"><a class="current reference internal" href="#">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="quantum-kernels">
-<h1><span class="section-number">6. </span>Quantum Kernels<a class="headerlink" href="#quantum-kernels" title="Permalink to this heading">¶</a></h1>
-<p><strong>[1]</strong> To differentiate between host and quantum device code, the CUDA-Q programming
-model defines the concept of a <strong>quantum kernel</strong>. A quantum kernel is any callable
-in C++ annotated to indicate compilation and execution on an available quantum coprocessor.</p>
-<p><strong>[2]</strong> All quantum kernels must be annotated to indicate they are to be compiled for, and executed
-on, a specified quantum coprocessor. CUDA-Q requires the <code class="code docutils literal notranslate"><span class="pre">__qpu__</span></code> function
-attribute for quantum kernel declarations. Other language bindings may opt to use other language
-features to enable function annotation or decoration (e.g. a <code class="code docutils literal notranslate"><span class="pre">&#64;cudaq.kernel()</span></code> function
-decorator in Python).</p>
-<p><strong>[3]</strong> CUDA-Q specifically differentiates between kernels invoked from host code and those invoked
-from within another quantum kernel. The former are denoted <strong>entry-point</strong>
-quantum kernels, the latter are <strong>pure device</strong> quantum kernels. Entry point kernels can
-only take classical types as input and return classical output. Pure-device kernels
-can take quantum types as input.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Entry point lambda kernel</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">my_first_entry_point_kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">double</span><span class="w"> </span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="n">quantum</span><span class="w"> </span><span class="n">code</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">};</span>
-
-<span class="c1">// Entry point typed callable kernel</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">my_second_entry_point_kernel</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="kt">double</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">params</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="p">...</span><span class="w"> </span><span class="n">quantum</span><span class="w"> </span><span class="n">code</span><span class="w"> </span><span class="p">...</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="c1">// Pure device free function kernel</span>
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">my_first_pure_device_kernel</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qview</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="n">qubits</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">   </span><span class="p">...</span><span class="w"> </span><span class="n">quantum</span><span class="w"> </span><span class="n">code</span><span class="w"> </span><span class="p">...</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">my_first_entry_point_kernel</span><span class="p">(</span><span class="n">x</span> <span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-   <span class="o">...</span> <span class="n">quantum</span> <span class="n">code</span> <span class="o">...</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">my_second_entry_point_kernel</span><span class="p">(</span><span class="n">x</span> <span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">params</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-   <span class="o">...</span> <span class="n">quantum</span> <span class="n">code</span> <span class="o">...</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">my_first_pure_device_kernel</span><span class="p">(</span><span class="n">qubits</span> <span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-   <span class="o">...</span> <span class="n">quantum</span> <span class="n">code</span> <span class="o">...</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[4]</strong> Quantum kernel function bodies are programmed in a subset of the parent classical language.
-Kernels can be composed of the following:</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>Quantum intrinsic operations and measurements</p></li>
-<li><p>In-scope kernel calls with or without any kernel modifiers (e.g. <code class="code docutils literal notranslate"><span class="pre">cudaq::control</span></code>)</p></li>
-<li><p>Classical control flow constructs from the classical language (<code class="code docutils literal notranslate"><span class="pre">if</span></code>, <code class="code docutils literal notranslate"><span class="pre">for</span></code>, <code class="code docutils literal notranslate"><span class="pre">while</span></code>, etc.)</p></li>
-<li><p>Stack variable declarations for supported types.</p></li>
-<li><p>Arithmetic operations on integer and floating point stack variables</p></li>
-<li><p>Coherent conditional execution - <code class="code docutils literal notranslate"><span class="pre">if</span> <span class="pre">(</span> <span class="pre">boolExprFromQubitMeasurement</span> <span class="pre">)</span> <span class="pre">{</span> <span class="pre">x</span> <span class="pre">(another_qubit);</span> <span class="pre">}</span></code></p></li>
-<li><p>Syntax for common quantum programming patterns (e.g. compute-action-uncompute).</p></li>
-</ul>
-</div></blockquote>
-<p><strong>[5]</strong> CUDA-Q defines a set of allowed types that can be leveraged in quantum kernel
-function signatures (input and return types), in-function variable declaration or construction,
-and in variable capture from parent scope.</p>
-<p>The allowed types are as follows:</p>
-<blockquote>
-<div><ul class="simple">
-<li><p><code class="code docutils literal notranslate"><span class="pre">T</span></code> such that <code class="code docutils literal notranslate"><span class="pre">std::is_arithmetic_v&lt;T&gt;</span> <span class="pre">==</span> <span class="pre">true</span></code></p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">std::vector&lt;T&gt;</span></code> such that <code class="code docutils literal notranslate"><span class="pre">std::is_arithmetic_v&lt;T&gt;</span> <span class="pre">==</span> <span class="pre">true</span></code></p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">std::span&lt;T&gt;</span></code> such that <code class="code docutils literal notranslate"><span class="pre">std::is_arithmetic_v&lt;T&gt;</span> <span class="pre">==</span> <span class="pre">true</span></code></p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">std::vector&lt;V&gt;</span></code> such that type <code class="code docutils literal notranslate"><span class="pre">V</span></code> is a valid <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;T&gt;</span></code> (possibly recursively)</p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">struct</span></code> types composed of any valid CUDA-Q type.</p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">std::tuple&lt;Ts...&gt;</span></code> such that T is any allowed type.</p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">std::pair&lt;T,U&gt;</span></code> such that T is any allowed type.</p></li>
-</ul>
-</div></blockquote>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span><span class="w"> </span><span class="nc">MyCustomSimpleStruct</span><span class="w"> </span><span class="p">{</span>
-<span class="w">   </span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">   </span><span class="kt">int</span><span class="w"> </span><span class="n">j</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">   </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">angles</span><span class="p">;</span>
-<span class="p">};</span>
-
-<span class="c1">// Valid CUDA-Q Types used in Kernels</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">int</span><span class="w"> </span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">flag</span><span class="p">,</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="n">angle</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">layers</span><span class="p">,</span>
-<span class="w">         </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">parameters</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">recursiveVec</span><span class="p">,</span>
-<span class="w">         </span><span class="n">MyCustomSimpleStruct</span><span class="w"> </span><span class="n">var</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">kernelThatReturns</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">   </span><span class="p">...</span>
-<span class="w">   </span><span class="k">return</span><span class="w"> </span><span class="n">M_PI_2</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@dataclass</span>
-<span class="k">class</span> <span class="nc">MySimpleStruct</span><span class="p">:</span>
-    <span class="n">i</span> <span class="p">:</span> <span class="nb">int</span>
-    <span class="n">j</span> <span class="p">:</span> <span class="nb">int</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">N</span> <span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">flag</span> <span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">angle</span> <span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">layers</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
-            <span class="n">parameters</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">recursiveList</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]],</span>
-            <span class="n">var</span> <span class="p">:</span> <span class="n">MySimpleStruct</span><span class="p">):</span> <span class="o">...</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernelThatReturns</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-    <span class="o">...</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">2.0</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[6]</strong> Any variable with an allowed CUDA-Q type can be allocated on the stack within
-CUDA-Q kernels. Variables of type <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;T&gt;</span></code> for any allowed type
-<code class="code docutils literal notranslate"><span class="pre">T</span></code> can only be constructed with known size. Vector-like variables cannot be
-default constructed and later filled with type <code class="code docutils literal notranslate"><span class="pre">T</span></code> data (i.e. no dynamic memory allocation).</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Valid CUDA-Q Types used in Kernels</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="c1">// Not Allowed.</span>
-<span class="w">  </span><span class="c1">// std::vector&lt;int&gt; i;</span>
-<span class="w">  </span><span class="c1">// i.push_back(1);</span>
-
-<span class="w">  </span><span class="c1">// Valid variable declarations</span>
-
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="w"> </span><span class="n">i</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="w">  </span><span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</span><span class="p">;</span>
-
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">f</span><span class="w"> </span><span class="p">{</span><span class="mf">1.0</span><span class="p">,</span><span class="w"> </span><span class="mf">2.0</span><span class="p">,</span><span class="w"> </span><span class="mf">3.0</span><span class="p">};</span>
-
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">k</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-
-<span class="w">  </span><span class="kt">double</span><span class="w"> </span><span class="n">pi</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">3.1415926</span>
-<span class="p">};</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-   <span class="c1"># Not Allowed</span>
-   <span class="c1"># i = []</span>
-   <span class="c1"># i.append(1)</span>
-
-   <span class="n">i</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)]</span>
-   <span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
-
-   <span class="n">f</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]</span>
-
-   <span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
-
-   <span class="n">pi</span> <span class="o">=</span> <span class="mf">3.1415926</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[7]</strong> All entry-point kernel arguments adhere to pass-by-value semantics.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">v</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">   </span><span class="c1">// i == 2, allocate 2 qubits</span>
-<span class="w">   </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="n">i</span><span class="p">);</span>
-<span class="w">   </span><span class="c1">// v[1] == 2.0, angle here is 2.0</span>
-<span class="w">   </span><span class="n">ry</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">   </span><span class="c1">// Change the variables, caller does not see this</span>
-<span class="w">   </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</span><span class="p">;</span>
-<span class="w">   </span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">3.0</span><span class="p">;</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="n">k</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
-<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">d</span><span class="w"> </span><span class="p">{</span><span class="mf">1.0</span><span class="p">,</span><span class="w"> </span><span class="mf">2.0</span><span class="p">};</span>
-
-<span class="n">kernel</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">d</span><span class="p">);</span>
-
-<span class="c1">// k is still 2, pass by value</span>
-<span class="c1">// d is still {1.0, 2.0}, pass by value</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">i</span> <span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">v</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-    <span class="c1"># i = 2, allocate 2 qubits</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-    <span class="c1"># v[1] == 2.0, angle here is 2.0</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># Change the variables, caller does not see this</span>
-    <span class="n">i</span> <span class="o">=</span> <span class="mi">5</span>
-    <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">3.0</span>
-
-<span class="n">k</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
-<span class="n">kernel</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
-
-<span class="c1"># k is still 2, pass by value</span>
-<span class="c1"># d is still {1.0, 2.0}, pass by value</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[8]</strong> CUDA-Q kernel lambdas in C++ can capture variables of allowed type
-by value. CUDA-Q kernels defined as custom callable types can define non-reference type
-class members of any allowed type. These member variables can be set with
-pass-by-value semantics at kernel construction.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span><span class="w"> </span><span class="nc">kernel</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="p">;</span>
-<span class="w">  </span><span class="kt">float</span><span class="w"> </span><span class="n">f</span><span class="p">;</span>
-
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">     </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="n">i</span><span class="p">);</span>
-<span class="w">     </span><span class="n">ry</span><span class="p">(</span><span class="n">f</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="n">kernel</span><span class="p">{</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mf">2.2</span><span class="p">}();</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
-<span class="kt">double</span><span class="w"> </span><span class="n">f</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">2.2</span><span class="p">;</span>
-
-<span class="k">auto</span><span class="w"> </span><span class="n">kernelLambda</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="o">=</span><span class="p">]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Use captured variables</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="n">i</span><span class="p">);</span>
-<span class="w">  </span><span class="n">ry</span><span class="p">(</span><span class="n">f</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</span><span class="p">;</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">i</span><span class="p">;</span>
-<span class="p">};</span>
-
-<span class="k">auto</span><span class="w"> </span><span class="n">k</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">kernelLambda</span><span class="p">();</span>
-
-<span class="c1">// Pass by value semantics</span>
-<span class="n">assert</span><span class="p">(</span><span class="n">k</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">i</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">f</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">2.</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-   <span class="c1"># Use captured variables</span>
-   <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-   <span class="n">ry</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-   <span class="n">i</span> <span class="o">=</span> <span class="mi">5</span>
-   <span class="k">return</span> <span class="n">i</span>
-
-<span class="n">k</span> <span class="o">=</span> <span class="n">kernel</span><span class="p">()</span>
-
-<span class="c1"># Pass by value semantics</span>
-<span class="k">assert</span> <span class="n">k</span> <span class="o">!=</span> <span class="n">i</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[9]</strong> All quantum kernel invocations are synchronous calls by default.</p>
-<p><strong>[10]</strong> CUDA-Q kernels can serve as input to other quantum kernels and invoked by kernel
-function body code. To support CUDA-Q kernel parameterization on callable quantum kernel
-code, programmers can leverage standard C++ template definitions or dynamic typing in language bindings such as Python:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">MyStatePrep</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qview</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="n">qubits</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="p">...</span><span class="w"> </span><span class="n">apply</span><span class="w"> </span><span class="n">state</span><span class="w"> </span><span class="n">prep</span><span class="w"> </span><span class="n">operations</span><span class="w"> </span><span class="n">on</span><span class="w"> </span><span class="n">qubits</span><span class="w"> </span><span class="p">...</span>
-<span class="p">}</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">MyGenericAlgorithm</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">StatePrep</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="k">operator</span><span class="p">()(</span><span class="k">const</span><span class="w"> </span><span class="n">StatePrep</span><span class="o">&amp;</span><span class="w"> </span><span class="n">statePrep</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">10</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">statePrep</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="p">...</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="c1">// -or- with placeholder type specifiers</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">MyGenericAlgorithm2</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="k">const</span><span class="w"> </span><span class="k">auto</span><span class="o">&amp;</span><span class="w"> </span><span class="n">statePrep</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">10</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">statePrep</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="p">...</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">void</span><span class="w"> </span><span class="nf">callKernels</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="n">MyGenericAlgorithm</span><span class="w"> </span><span class="n">algorithm</span><span class="p">;</span>
-<span class="w">  </span><span class="n">algorithm</span><span class="p">(</span><span class="n">MyStatePrep</span><span class="p">);</span>
-
-<span class="w">  </span><span class="n">MyGenericAlgorithm2</span><span class="w"> </span><span class="n">anotherVersion</span><span class="p">;</span>
-<span class="w">  </span><span class="n">anotherVersion</span><span class="p">(</span><span class="n">MyStatePrep</span><span class="p">);</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--5-input--2" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">MyStatePrep</span><span class="p">(</span><span class="n">qubits</span> <span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">):</span>
-    <span class="o">...</span> <span class="n">apply</span> <span class="n">state</span> <span class="n">prep</span> <span class="n">operations</span> <span class="n">on</span> <span class="n">qubits</span> <span class="o">...</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">MyGenericAlgorithm</span><span class="p">(</span><span class="n">statePrep</span> <span class="p">:</span> <span class="n">typing</span><span class="o">.</span><span class="n">Callable</span><span class="p">[[</span><span class="n">cudaq</span><span class="o">.</span><span class="n">qview</span><span class="p">],</span> <span class="kc">None</span><span class="p">]):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">statePrep</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-    <span class="o">...</span>
-
-<span class="n">MyGenericAlgorithm</span><span class="p">(</span><span class="n">MyStatePrep</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>CUDA-Q kernel inputs can also be <a class="reference external" href="https://en.cppreference.com/w/cpp/language/constraints">constrained</a>.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="c1">// Generic constraint on Kernel Function Signatures</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Kernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">Signature</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">concept</span><span class="w"> </span><span class="nc">signature</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">is_convertible_v</span><span class="o">&lt;</span><span class="n">Kernel</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">function</span><span class="o">&lt;</span><span class="n">Signature</span><span class="o">&gt;&gt;</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// Specialized for taking a single qubit</span>
-<span class="w">  </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Kernel</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">concept</span><span class="w"> </span><span class="nc">takes_qubit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">signature</span><span class="o">&lt;</span><span class="n">Kernel</span><span class="p">,</span><span class="w"> </span><span class="kt">void</span><span class="p">(</span><span class="n">qubit</span><span class="o">&amp;</span><span class="p">)</span><span class="o">&gt;</span><span class="p">;</span>
-<span class="p">}</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">MyGenericAlgorithmOnQarray</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">signature</span><span class="o">&lt;</span><span class="kt">void</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&amp;</span><span class="p">)</span><span class="o">&gt;</span><span class="w"> </span><span class="k">auto</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">statePrep</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">10</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">statePrep</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="p">...</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">MyGenericAlgorithmOnQubit</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">takes_qubit</span><span class="w"> </span><span class="k">auto</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">statePrep</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">10</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">statePrep</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">...</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-</pre></div>
-</div>
-<p>This approach enables the development of generic libraries of quantum
-algorithms that are parameterized on sub-units of the global circuit representation.</p>
-</section>
-
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-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
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-</li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3 current"><a class="current reference internal" href="#">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<p><strong>[1]</strong> CUDA-Q presumes the existence of one or more classical host processors,
-zero or more NVIDIA graphics processing units (GPUs), and zero or more quantum processing units (QPUs).</p>
-<p><strong>[2]</strong> Each QPU is composed of a classical quantum control system (distributed FPGAs, GPUs, etc.)
-and a register of quantum bits (qubits) whose state evolves via signals from the classical control system.</p>
-<p><strong>[3]</strong> The machine model allows for three modes of quantum process parallelism: concurrent execution
-of quantum circuits on independent QPUs, dependent quantum parallelism via
-quantum message passing and inter-QPU entanglement, and QPU thread-level
-parallelism or the ability for concurrent execution of independent quantum
-circuits on a single QPU qubit connectivity fabric.</p>
-<p><strong>[4]</strong> The model assumes a
-classical memory space for the host processor which inherits the native language memory
-model semantics (e.g. C++ or Python).</p>
-<p><strong>[5]</strong> The model assumes a classical memory space for each control
-system driving the evolution of the multi-qubit state. This control system
-memory space should enable primitive arithmetic variable declarations,
-stores, and loads, as well as qubit measurement persistence and loading
-for fast-feedback and conditional circuit execution.</p>
-<p><strong>[6]</strong> The quantum memory
-space on an individual QPU is modeled as an infinite register of
-qubits and physical connectivity constraints are hidden from the
-programmer. Note that compiler implementations of the CUDA-Q model
-are free to enable developer access to the details of the QPU
-qubit connectivity for the purpose of developing novel placement strategies.</p>
-<p><strong>[7]</strong> CUDA-Q considers general <span class="math notranslate nohighlight">\(D\)</span>-level quantum information systems, e.g. qudits. Qudits
-are non-copyable, and can be allocated in chunks, via instantiation of
-user-level quantum container types. Quantum containers come in two flavors -
-quantum memory owning and non-owning (views). Moreover, the size of a quantum container
-can be specified at either compile-time or dynamically at runtime.
-As all qudits are non-copyable, qudits and containers
-thereof can only be passed by reference.</p>
-<p><strong>[8]</strong> Each allocated qudit is unique and if
-deallocated, is available for subsequent allocation. Deallocation occurs implicitly
-when the qudit goes out of scope. Uncomputation of qudit state should occur
-automatically via compiler implementations of the CUDA-Q model.</p>
-<p><strong>[9]</strong> The CUDA-Q model considers both remotely hosted QPU execution models as well as
-tightly-coupled quantum-classical architectures. Remotely hosted models
-support batch circuit execution where each circuit may contain simple
-quantum-classical operation integration. Tightly-coupled execution models provide
-streaming instruction execution, measure-reset, and fast-feedback of
-qubit measurement results. This multi-modal execution model directly
-influences the syntax and semantics of quantum kernel expressions
-and their associated host-code context.</p>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-  <section id="quantum-intrinsic-operations">
-<h1><span class="section-number">5. </span>Quantum Intrinsic Operations<a class="headerlink" href="#quantum-intrinsic-operations" title="Permalink to this heading">¶</a></h1>
-<p><strong>[1]</strong> To support low-level quantum programming and build foundational
-quantum kernels for large-scale applications, CUDA-Q specifies the <em>quantum
-intrinsic operation</em> abstraction for device-specific single-qudit unitary operations.</p>
-<p><strong>[2]</strong> A quantum intrinsic operation is modeled via a standard free function in the native classical language. It
-has a unique instruction name and general specified function operands.</p>
-<p><strong>[3]</strong> These operands can model classical rotation parameters or units of quantum information (e.g. the <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>).
-The syntax for these operations is</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="kt">void</span><span class="w"> </span><span class="nf">INST_NAME</span><span class="p">(</span><span class="w"> </span><span class="n">PARAM</span><span class="p">...</span><span class="o">?</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">N</span><span class="o">&gt;&amp;</span><span class="p">...);</span>
-</pre></div>
-</div>
-<p>where <code class="code docutils literal notranslate"><span class="pre">INST_NAME</span></code> is the name of the instruction, <code class="code docutils literal notranslate"><span class="pre">qudit&lt;N&gt;&amp;...</span></code> indicates one many
-<code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances, and <code class="code docutils literal notranslate"><span class="pre">PARAM...?</span></code> indicates optional parameters of
-floating point type (e.g. <code class="code docutils literal notranslate"><span class="pre">double</span></code>, <code class="code docutils literal notranslate"><span class="pre">float</span></code>). Quantum intrinsic operations return <code class="code docutils literal notranslate"><span class="pre">void</span></code>.</p>
-<p><strong>[4]</strong> All intrinsic operations should start with a base declaration targeting a single <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>, and overloads
-should be provided that take more than one <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances to model the application
-of that instruction on all provided <code class="code docutils literal notranslate"><span class="pre">cudaq::qudits</span></code>, e.g. <code class="code docutils literal notranslate"><span class="pre">void</span> <span class="pre">x(cudaq::qubit&amp;)</span></code> and
-<code class="code docutils literal notranslate"><span class="pre">x(cudaq::qubit&amp;,</span> <span class="pre">cudaq::qubit&amp;,</span> <span class="pre">cudaq::qubit&amp;)</span></code>, modeling the NOT operation on a single
-<code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code> or on multiple <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code>.</p>
-<p><strong>[5]</strong> The specific set of quantum intrinsic operations available to the programmer can
-be platform specific and provided via platform specific header files.</p>
-<p><strong>[6]</strong> Implementations should provide overloads to support broadcasting of single-qubit
-intrinsic operations across containers of <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>. For example,
-<code class="code docutils literal notranslate"><span class="pre">x(cudaq::qvector&lt;&gt;&amp;)</span></code> should apply a NOT operation on all
-<code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code> in the provided <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code>.</p>
-<p><strong>[7]</strong> Programmers can further modify quantum intrinsic operations via
-an extra specified template parameter, and CUDA-Q leverages this syntax
-for synthesizing control and adjoint variants of the operation. Specifically CUDA-Q
-provides the <code class="code docutils literal notranslate"><span class="pre">cudaq::ctrl</span></code>, and <code class="code docutils literal notranslate"><span class="pre">cudaq::adj</span></code> type modifiers for synthesizing control and
-adjoint forms of the operation. For language bindings that do not support template
-parameterization, implementations may rely on static method calls (e.g. <code class="code docutils literal notranslate"><span class="pre">x.ctrl(q,</span> <span class="pre">r)</span></code>)</p>
-<p>Here is an example of how one might modify an intrinsic operation for multi-control and adjoint operations.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">,</span><span class="w"> </span><span class="n">s</span><span class="p">;</span>
-<span class="c1">// Apply T operation</span>
-<span class="n">t</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="c1">// Apply Tdg operation</span>
-<span class="n">t</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">adj</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="c1">// Apply control Hadamard operation</span>
-<span class="n">h</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">,</span><span class="n">r</span><span class="p">,</span><span class="n">s</span><span class="p">);</span>
-<span class="c1">// Error, ctrl requires &gt; 1 qubit operands</span>
-<span class="c1">// h&lt;cudaq::ctrl&gt;(r);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(),</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="c1"># Apply T operation</span>
-<span class="n">t</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-<span class="c1"># Apply Tdg operation</span>
-<span class="n">t</span><span class="o">.</span><span class="n">adj</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-<span class="c1"># Apply control Hadamard operation</span>
-<span class="n">h</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">q</span><span class="p">,</span><span class="n">r</span><span class="p">,</span><span class="n">s</span><span class="p">)</span>
-<span class="c1"># Error, ctrl requires &gt; 1 qubit operands</span>
-<span class="c1"># h.ctrl(r);</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="operations-on-cudaq-qubit">
-<h2><span class="section-number">5.1. </span>Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code><a class="headerlink" href="#operations-on-cudaq-qubit" title="Permalink to this heading">¶</a></h2>
-<p>The default set of quantum intrinsic operations for the cudaq::qubit type is as follows:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">struct</span><span class="w"> </span><span class="nc">base</span><span class="p">;</span>
-<span class="w">    </span><span class="k">struct</span><span class="w"> </span><span class="nc">ctrl</span><span class="p">;</span>
-<span class="w">    </span><span class="k">struct</span><span class="w"> </span><span class="nc">adj</span><span class="p">;</span>
-
-<span class="w">    </span><span class="c1">// Single qubit operations, ctrl / adj variants, and broadcasting</span>
-<span class="w">    </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">base</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">QubitArgs</span><span class="o">&gt;</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">NAME</span><span class="p">(</span><span class="n">QubitArgs</span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">base</span><span class="o">&gt;</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">NAME</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">qvector</span><span class="o">&amp;</span><span class="w"> </span><span class="n">qr</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="k">template</span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctrl</span><span class="o">&gt;</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">NAME</span><span class="p">(</span><span class="n">qvector</span><span class="o">&amp;</span><span class="w"> </span><span class="n">ctrls</span><span class="p">,</span><span class="w"> </span><span class="n">qubit</span><span class="o">&amp;</span><span class="w"> </span><span class="n">target</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="c1">// Single qubit rotation operations and ctrl / adj variants</span>
-<span class="w">    </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">base</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">ScalarAngle</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">QubitArgs</span><span class="o">&gt;</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">ROTATION_NAME</span><span class="p">(</span><span class="n">ScalarAngle</span><span class="w"> </span><span class="n">angle</span><span class="p">,</span><span class="w"> </span><span class="n">QubitArgs</span><span class="w"> </span><span class="o">&amp;</span><span class="p">...</span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="n">MEASURE_OP</span><span class="p">(</span><span class="n">qubit</span><span class="w"> </span><span class="o">&amp;</span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="p">;</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">bool</span><span class="o">&gt;</span><span class="w"> </span><span class="n">MEASURE_OP</span><span class="p">(</span><span class="n">qvector</span><span class="w"> </span><span class="o">&amp;</span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="p">;</span>
-<span class="w">    </span><span class="kt">double</span><span class="w"> </span><span class="nf">measure</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="o">&amp;</span><span class="w"> </span><span class="n">term</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p><strong>[1]</strong> For the default implementation of the <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code> intrinsic operations, we let
-NAME be any operation name in the set <code class="code docutils literal notranslate"><span class="pre">{h,</span> <span class="pre">x,</span> <span class="pre">y,</span> <span class="pre">z,</span> <span class="pre">t,</span> <span class="pre">s}</span></code> and <code class="code docutils literal notranslate"><span class="pre">ROTATION_NAME</span></code> be any
-operation in <code class="code docutils literal notranslate"><span class="pre">{rx,</span> <span class="pre">ry,</span> <span class="pre">rz,</span> <span class="pre">r1</span> <span class="pre">(phase)}</span></code>. Implementations may provide appropriate
-function implementations using the above foundational functions to enable other
-common operations (e.g. <code class="code docutils literal notranslate"><span class="pre">cnot</span> <span class="pre">-&gt;</span> <span class="pre">x&lt;cudaq::ctrl&gt;</span></code>).</p>
-<p><strong>[2]</strong> Control qubits can be specified with positive or negative polarity.
-By this we mean that a control qubit can specify that a target operation is
-applied if the control qubit state is a <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> (positive polarity) or <code class="code docutils literal notranslate"><span class="pre">|1&gt;</span></code> (negative polarity).
-By default all control qubits are assumed to convey positive polarity. The
-syntax for negating the polarity is the not operator preceding the control
-qubit</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="o">!</span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="o">~</span><span class="n">q</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The set of gates that the official CUDA-Q implementation supports can be found in the <a class="reference internal" href="../../api/api.html"><span class="doc">API documentation</span></a>.</p>
-</section>
-</section>
-
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
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-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
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-  <section id="quantum-operators">
-<h1><span class="section-number">4. </span>Quantum Operators<a class="headerlink" href="#quantum-operators" title="Permalink to this heading">¶</a></h1>
-<section id="cudaq-spin-op">
-<h2><span class="section-number">4.1. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code><a class="headerlink" href="#cudaq-spin-op" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> CUDA-Q provides a native <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> data type in the <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> namespace for the
-expression of quantum mechanical spin operators.</p>
-<p><strong>[2]</strong> The <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> provides an abstraction for a general tensor product of Pauli
-spin operators, and sums thereof:</p>
-<div class="math notranslate nohighlight">
-\[H = \sum_{i=0}^M P_i, P_i = \prod_{j=0}^N \sigma_j^a\]</div>
-<p>for <span class="math notranslate nohighlight">\(a = {x,y,z}\)</span>, <span class="math notranslate nohighlight">\(j\)</span> the qubit index, and <span class="math notranslate nohighlight">\(N\)</span> the number of qubits.</p>
-<p><strong>[3]</strong> The <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> exposes common C++ operator overloads for algebraic expressions.</p>
-<p><strong>[4]</strong> CUDA-Q defines convenience functions in <code class="code docutils literal notranslate"><span class="pre">cudaq::spin</span></code> namespace that produce
-the primitive X, Y, and Z Pauli operators on specified qubit indices
-which can subsequently be used in algebraic expressions to build up
-more complicated Pauli tensor products and their sums.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span>\
-<span class="w">         </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-<span class="n">h</span> <span class="o">=</span> <span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> \
-         <span class="mf">.21829</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">6.125</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[5]</strong> The <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> also provides a mechanism for the expression of circuit
-synthesis tasks within quantum kernel code. Specifically, operations
-that encode <span class="math notranslate nohighlight">\(N\)</span><sup>th</sup>order trotterization of exponentiated <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code>
-rotations, e.g. <span class="math notranslate nohighlight">\(U = \exp(-i H t)\)</span>, where <span class="math notranslate nohighlight">\(H\)</span> is the provided <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code>.</p>
-<p><strong>[6]</strong> The <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> can be created within classical host code and quantum kernel
-code, and can also be passed by value to quantum kernel code from host code.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> should take on the following structure:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">spin_op</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="p">();</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="kt">bool</span><span class="w"> </span><span class="nf">empty</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">num_qubits</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">num_terms</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">complex</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">get_coefficient</span><span class="p">();</span>
-<span class="w">      </span><span class="kt">bool</span><span class="w"> </span><span class="nf">is_identity</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">for_each_term</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">function</span><span class="o">&lt;</span><span class="kt">void</span><span class="p">(</span><span class="n">spin_op</span><span class="w"> </span><span class="o">&amp;</span><span class="p">)</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">for_each_pauli</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">function</span><span class="o">&lt;</span><span class="kt">void</span><span class="p">(</span><span class="n">pauli</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="p">)</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">+=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">-=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">*=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="p">);</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">*=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">double</span><span class="p">);</span>
-<span class="w">      </span><span class="n">spin_op</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">*=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">complex</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="p">)</span>
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="k">namespace</span><span class="w"> </span><span class="nn">spin</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">spin_op</span><span class="w"> </span><span class="nf">x</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="p">);</span>
-<span class="w">    </span><span class="n">spin_op</span><span class="w"> </span><span class="nf">y</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="p">);</span>
-<span class="w">    </span><span class="n">spin_op</span><span class="w"> </span><span class="nf">z</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</section>
-</section>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
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-  <section id="common-quantum-programming-patterns">
-<h1><span class="section-number">10. </span>Common Quantum Programming Patterns<a class="headerlink" href="#common-quantum-programming-patterns" title="Permalink to this heading">¶</a></h1>
-<section id="compute-action-uncompute">
-<h2><span class="section-number">10.1. </span>Compute-Action-Uncompute<a class="headerlink" href="#compute-action-uncompute" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> CUDA-Q specifies syntax and semantics to support the compute-action-uncompute
-pattern, <span class="math notranslate nohighlight">\(W = U V U \dagger\)</span> (here <span class="math notranslate nohighlight">\(U\)</span> is the compute block, and <span class="math notranslate nohighlight">\(V\)</span> is the action block).</p>
-<p><strong>[2]</strong> Via special syntax for this pattern CUDA-Q enables compiler implementations to
-key on circuit optimizations via programmer intent. Specifically, controlled versions of <span class="math notranslate nohighlight">\(W\)</span> do not produce controlled
-operations of all instructions in <span class="math notranslate nohighlight">\(U\)</span>, <span class="math notranslate nohighlight">\(V\)</span>, and <span class="math notranslate nohighlight">\(U \dagger\)</span>, but instead we are free to only
-control <span class="math notranslate nohighlight">\(V\)</span>.</p>
-<p><strong>[3]</strong> The CUDA-Q specification requires the following syntax for this pattern:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Will invoke U V U^dag</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">compute_action</span><span class="w"> </span><span class="p">(</span>
-<span class="w">    </span><span class="p">[</span><span class="o">&amp;</span><span class="p">](){</span>
-<span class="w">      </span><span class="cm">/*U_code*/</span>
-<span class="w">    </span><span class="p">},</span>
-<span class="w">    </span><span class="p">[</span><span class="o">&amp;</span><span class="p">]()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="cm">/*V_code*/</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">computeF</span><span class="p">():</span>
-   <span class="o">...</span>
-
-<span class="k">def</span> <span class="nf">actionF</span><span class="p">():</span>
-   <span class="o">...</span>
-
-<span class="c1"># Can take user-defined functions</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">compute_action</span><span class="p">(</span><span class="n">computeF</span><span class="p">,</span> <span class="n">actionF</span><span class="p">)</span>
-
-<span class="c1"># Can take Pythonic CUDA-Q lambda kernels</span>
-<span class="n">computeL</span> <span class="o">=</span> <span class="k">lambda</span> <span class="p">:</span> <span class="p">(</span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">),</span> <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">),</span> <span class="n">ry</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">compute_action</span><span class="p">(</span><span class="n">computeL</span><span class="p">,</span> <span class="n">actionF</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[4]</strong> Compiler implementations must add the uncompute segment
-and optimize on any controlled versions of this block of code.</p>
-</section>
-</section>
-
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-<li class="toctree-l1"><a class="reference internal" href="../../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id12">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id15">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../using/install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
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-<h1><span class="section-number">11. </span>Quantum Platform<a class="headerlink" href="#quantum-platform" title="Permalink to this heading">¶</a></h1>
-<p><strong>[1]</strong> CUDA-Q provides an abstraction describing the underlying quantum computing resource(s).
-The underlying quantum platform can contain one or many quantum processing units (QPUs) each with
-its own qubit connectivity and noise configuration.</p>
-<p><strong>[2]</strong> CUDA-Q defines a <code class="code docutils literal notranslate"><span class="pre">cudaq::quantum_platform</span></code> in an effort to expose relevant system
-information and enable asynchronous quantum kernel invocations.</p>
-<p><strong>[3]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::quantum_platform</span></code> provides an API for querying the number of available
-quantum processing units (QPUs), with each QPU assigned a logical integer index (<code class="code docutils literal notranslate"><span class="pre">{0,1,2,...}</span></code>).</p>
-<p><strong>[4]</strong> Programmers can specify the ID of the desired QPU and all subsequent CUDA-Q
-kernel executions will target that QPU.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::quantum_platform</span></code>  should take the following structure</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">quantum_platform</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">public</span><span class="o">:</span>
-<span class="w">    </span><span class="n">quantum_platform</span><span class="p">();</span>
-<span class="w">    </span><span class="o">~</span><span class="n">quantum_platform</span><span class="p">();</span>
-
-<span class="w">    </span><span class="k">using</span><span class="w"> </span><span class="n">QubitEdge</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">pair</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="p">;</span>
-<span class="w">    </span><span class="k">using</span><span class="w"> </span><span class="n">QubitConnectivity</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">QubitEdge</span><span class="o">&gt;</span><span class="p">;</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">optional</span><span class="o">&lt;</span><span class="n">QubitConnectivity</span><span class="o">&gt;</span><span class="w"> </span><span class="n">connectivity</span><span class="p">();</span>
-
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">num_qpus</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">get_num_qubits</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qpu_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-
-<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="nf">is_simulator</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qpu_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="nf">is_remote</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qpuId</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">);</span>
-<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="nf">is_emulated</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qpuId</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="nf">name</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">get_current_qpu</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">;</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="nf">set_current_qpu</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">device_id</span><span class="p">);</span>
-
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>CUDA-Q provides the following public functions to interact with the current
-<code class="code docutils literal notranslate"><span class="pre">cudaq::quantum_platform</span></code></p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">quantum_platform</span><span class="w"> </span><span class="o">&amp;</span><span class="nf">get_platform</span><span class="p">()</span><span class="w"> </span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../using/backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/backends/dynamics.html">   Dynamics</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="../cudaq.html">   Language Specification</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="sub-circuit-synthesis">
-<h1><span class="section-number">7. </span>Sub-circuit Synthesis<a class="headerlink" href="#sub-circuit-synthesis" title="Permalink to this heading">¶</a></h1>
-<p><strong>[1]</strong> Execution of pure device quantum kernels can be modified via explicit intrinsic
-library calls that provide multi-qubit controlled application and adjoint,
-or circuit reversal, semantics.</p>
-<p><strong>[2]</strong> CUDA-Q defines the following library functions
-for enabling multi-controlled and adjoint operations on a general pure
-device kernel:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="kt">void</span><span class="w"> </span><span class="n">control</span><span class="p">(</span><span class="n">QuantumKernel</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">kernel</span><span class="p">,</span>
-<span class="w">             </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="o">&amp;</span><span class="w"> </span><span class="n">ctrl_qubit</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="w"> </span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumRegister</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">requires</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ranges</span><span class="o">::</span><span class="n">range</span><span class="o">&lt;</span><span class="n">QuantumRegister</span><span class="o">&gt;</span><span class="p">)</span>
-<span class="kt">void</span><span class="w"> </span><span class="n">control</span><span class="p">(</span><span class="n">QuantumKernel</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">kernel</span><span class="p">,</span>
-<span class="w">             </span><span class="n">QuantumRegister</span><span class="o">&amp;</span><span class="w"> </span><span class="n">ctrl_qubits</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="w"> </span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-
-<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">QuantumKernel</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="p">...</span><span class="w"> </span><span class="n">Args</span><span class="o">&gt;</span>
-<span class="kt">void</span><span class="w"> </span><span class="n">adjoint</span><span class="p">(</span><span class="n">QuantumKernel</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">Args</span><span class="w"> </span><span class="o">&amp;</span><span class="p">...</span><span class="w"> </span><span class="n">args</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>These functions can be leveraged in quantum kernel code in the following way</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">x_gate</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="o">&amp;</span><span class="w"> </span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="p">}</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">kernel</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()</span><span class="w"> </span><span class="p">()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">3</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="p">...</span>
-<span class="w">    </span><span class="c1">// Create Toffoli gate</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">ctrl_bits</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">control</span><span class="p">(</span><span class="n">x_gate</span><span class="p">,</span><span class="w"> </span><span class="n">ctrl_bits</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">...</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">void</span><span class="w"> </span><span class="nf">rx_and_h_gate</span><span class="p">(</span><span class="kt">double</span><span class="w"> </span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="o">&amp;</span><span class="w"> </span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">rx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="p">}</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="nf">kernel</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">N</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="n">N</span><span class="p">);</span>
-<span class="w">  </span><span class="p">...</span>
-<span class="w">  </span><span class="c1">// apply h(q[2]); rx(-pi, q[2]);</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">adjoint</span><span class="p">(</span><span class="n">rx_and_h_gate</span><span class="p">{},</span><span class="w"> </span><span class="n">M_PI</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">...</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">x_gate</span><span class="p">(</span><span class="n">q</span> <span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">kernelTestControl</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-    <span class="o">...</span>
-    <span class="n">ctrl_bits</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">front</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">x_gate</span><span class="p">,</span> <span class="n">ctrl_bits</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="o">...</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">rx_and_h_gate</span><span class="p">(</span><span class="n">x</span> <span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">q</span> <span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">rx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">q</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span><span class="p">()</span>
-<span class="k">def</span> <span class="nf">kernelTestAdjoint</span><span class="p">(</span><span class="n">N</span> <span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-    <span class="o">...</span>
-    <span class="c1"># apply h(q[2]); rx(-pi, q[2])</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">rx_and_h_gate</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="o">...</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[3]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::control(...)</span></code> function takes as input an instantiated pure
-device quantum kernel, a std::range of control qubits (<code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code>
-or <code class="code docutils literal notranslate"><span class="pre">cudaq::qview</span></code>), and the remaining arguments for the kernel itself.</p>
-<p><strong>[4]</strong> Compiler implementations are free to synthesize multi-controlled operations
-using any pertinent synthesis strategy available. Qubits may be aggregated into
-a range of control qubits with or without the use of the <code class="code docutils literal notranslate"><span class="pre">operator!</span></code>
-<a class="reference internal" href="operations.html"><span class="doc">negated polarity operator</span></a>.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">::</span><span class="n">control</span><span class="p">(</span><span class="n">kernel</span><span class="p">{},</span><span class="w"> </span><span class="p">{</span><span class="n">qubit0</span><span class="p">,</span><span class="w"> </span><span class="o">!</span><span class="n">qubit1</span><span class="p">},</span><span class="w"> </span><span class="n">kernel_arg</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="p">[</span><span class="n">qubit0</span><span class="p">,</span> <span class="o">~</span><span class="n">qubit1</span><span class="p">],</span> <span class="n">kernel_arg</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>[5]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::adjoint(...)</span></code> function takes as input an
-pure device quantum kernel and the remaining arguments for the kernel.</p>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">   Specifications</a><ul class="current">
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-<li class="toctree-l3"><a class="reference internal" href="namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="synthesis.html">7. Sub-circuit Synthesis</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="quantum-types">
-<h1><span class="section-number">3. </span>Quantum Types<a class="headerlink" href="#quantum-types" title="Permalink to this heading">¶</a></h1>
-<p>The CUDA-Q language extension provides certain fundamental types that are pertinent
-to quantum-classical computing. These types are provided via defined classical library
-types in C++ in the <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> namespace.</p>
-<section id="cudaq-qudit-levels">
-<h2><span class="section-number">3.1. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code><a class="headerlink" href="#cudaq-qudit-levels" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> models a <span class="math notranslate nohighlight">\(D\)</span>-level unit of quantum information. The state of
-this system (for <span class="math notranslate nohighlight">\(N\)</span> qudits) can be described by a <span class="math notranslate nohighlight">\(D\)</span><sup>N</sup>-dimensional vector in
-Hilbert space with the absolute square of all elements summing to 1.</p>
-<p><strong>[2]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> encapsulates a unique <code class="code docutils literal notranslate"><span class="pre">std::size_t</span></code> modeling the index of the
-qudit in the underlying quantum memory space (assuming an infinite register
-of available qudits).</p>
-<p><strong>[4]</strong> To adhere to the no-cloning theorem of quantum mechanics,
-the <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> is non-copyable and non-movable. Therefore, all <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>
-instances must be passed by reference, and the no-cloning theorem is satisfied
-at compile-time.</p>
-<p><strong>[5]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances can only be allocated within CUDA-Q quantum
-kernel code and can never be allocated from classical host code.</p>
-<p><strong>[6]</strong> All instantiated <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances start in the <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> computational basis
-state and serve as the primary input argument for quantum intrinsic operations
-modeling typical logical quantum gate operations.</p>
-<p><strong>[7]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances can be negated when leveraged as controls in
-quantum operation invocations. The mechanism for negation should be via overloading of <code class="code docutils literal notranslate"><span class="pre">qudit&lt;N&gt;::operator!()</span></code>.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> takes on the following structure</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="k">class</span><span class="w"> </span><span class="nc">qudit</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">protected</span><span class="o">:</span>
-<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">idx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">  </span><span class="k">public</span><span class="o">:</span>
-<span class="w">    </span><span class="n">qudit</span><span class="p">();</span>
-<span class="w">    </span><span class="n">qudit</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">qudit</span><span class="o">&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-<span class="w">    </span><span class="n">qudit</span><span class="p">(</span><span class="n">qudit</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">id</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">    </span><span class="k">static</span><span class="w"> </span><span class="k">constexpr</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">n_levels</span><span class="p">();</span>
-<span class="w">    </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="k">operator</span><span class="o">!</span><span class="p">();</span>
-<span class="p">};</span>
-</pre></div>
-</div>
-</section>
-<section id="cudaq-qubit">
-<h2><span class="section-number">3.2. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code><a class="headerlink" href="#cudaq-qubit" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> The specification provides a primitive <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code> type which models a
-single quantum bit (<span class="math notranslate nohighlight">\(2\)</span>-level) in the discrete quantum memory space.
-<code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code> is an alias for <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;2&gt;</span></code></p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">qubit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">qudit</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Allocate a qubit in the |0&gt; state</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">  </span><span class="c1">// Put the qubit in a superposition of |0&gt; and |1&gt;</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="c1">// cudaq::h == hadamard, ADL leveraged</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;ID = %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">id</span><span class="p">());</span><span class="w"> </span><span class="c1">// prints 0</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">r</span><span class="p">;</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;ID = %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">.</span><span class="n">id</span><span class="p">());</span><span class="w"> </span><span class="c1">// prints 1</span>
-<span class="w">  </span><span class="c1">// qubit out of scope, implicit deallocation</span>
-<span class="p">}</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="n">printf</span><span class="p">(</span><span class="s">&quot;ID = %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">id</span><span class="p">());</span><span class="w"> </span><span class="c1">// prints 0 (previous deallocated)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Allocate a qubit in the |0&gt; state</span>
-<span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="c1"># Put the qubit in a superposition of |0&gt; and |1&gt;</span>
-<span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ID = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">id</span><span class="p">()))</span> <span class="c1"># prints 0</span>
-
-<span class="n">r</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ID = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">id</span><span class="p">())</span> <span class="c1"># prints 1</span>
-<span class="c1"># qubits go out of scope, implicit deallocation</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="quantum-containers">
-<h2><span class="section-number">3.3. </span>Quantum Containers<a class="headerlink" href="#quantum-containers" title="Permalink to this heading">¶</a></h2>
-<p><strong>[1]</strong> CUDA-Q specifies abstractions for dealing with groups of <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances in the
-form of familiar C++ containers.</p>
-<p><strong>[2]</strong> The underlying connectivity of the <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code> instances stored in these containers is opaque to
-the programmer and any logical-to-physical program connectivity mapping should be done by compiler implementations.</p>
-<section id="cudaq-qview-levels-2">
-<h3><span class="section-number">3.3.1. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qview&lt;Levels</span> <span class="pre">=</span> <span class="pre">2&gt;</span></code><a class="headerlink" href="#cudaq-qview-levels-2" title="Permalink to this heading">¶</a></h3>
-<p><strong>[1]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qview&lt;N&gt;</span></code> is a non-owning reference to a subset of the discrete quantum memory space,
-and as such, it is a <code class="code docutils literal notranslate"><span class="pre">std::span</span></code>-like C++ range of <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>.</p>
-<p><strong>[2]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qview</span></code> does not own its elements and can therefore be passed by value or reference.</p>
-<p><strong>[3]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qview</span></code> is templated on the dimensionality of the contained quantum information unit,
-and defaults to <span class="math notranslate nohighlight">\(2\)</span> for qubit systems.</p>
-<p><strong>[4]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qview</span></code> provides an API for individual qubit extraction and sub-register slicing.
-Programmers can extract the front <span class="math notranslate nohighlight">\(N\)</span> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>, the back <span class="math notranslate nohighlight">\(N\)</span> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>, and the
-inner slice starting at a given index and including user-specified <code class="code docutils literal notranslate"><span class="pre">count</span></code> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::qview</span></code> should take on the following structure:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">Levels</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">qview</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">private</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">span</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">qudits</span><span class="p">;</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="c1">// Construct a span that refers to the qudits in `other`.</span>
-<span class="w">      </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">R</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="k">requires</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ranges</span><span class="o">::</span><span class="n">range</span><span class="o">&lt;</span><span class="n">R</span><span class="o">&gt;</span><span class="p">)</span>
-<span class="w">      </span><span class="n">qview</span><span class="p">(</span><span class="n">R</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">other</span><span class="p">);</span>
-<span class="w">      </span><span class="n">qview</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">qview</span><span class="o">&amp;</span><span class="w"> </span><span class="n">other</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Iterator interface.</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">begin</span><span class="p">();</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">end</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the qudit at `idx`.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="k">operator</span><span class="p">[](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">idx</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[0, count)` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">front</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the first qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">front</span><span class="p">();</span>
-<span class="w">      </span><span class="c1">// Returns the `[count, size())` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">back</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the last qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">back</span><span class="p">();</span>
-
-
-<span class="w">      </span><span class="c1">// Returns the `[start, start+count)` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the number of contained qudits.</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</section>
-<section id="cudaq-qvector-levels-2">
-<h3><span class="section-number">3.3.2. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qvector&lt;Levels</span> <span class="pre">=</span> <span class="pre">2&gt;</span></code><a class="headerlink" href="#cudaq-qvector-levels-2" title="Permalink to this heading">¶</a></h3>
-<p><strong>[1]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector&lt;Levels&gt;</span></code> is a container of elements from the discrete quantum memory space - a C++ container of <code class="code docutils literal notranslate"><span class="pre">cuda::qudit</span></code>.</p>
-<p><strong>[2]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code> is a dynamically constructed owning container for <code class="code docutils literal notranslate"><span class="pre">cuda::qudit</span></code> (<code class="code docutils literal notranslate"><span class="pre">std::vector</span></code>-like),
-and since it owns the quantum memory, it cannot be copied or moved.</p>
-<p><strong>[3]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code> is templated on the dimensionality of the contained
-quantum information unit, and defaults to <span class="math notranslate nohighlight">\(2\)</span> for qubit systems.</p>
-<p><strong>[4]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code> can only be instantiated within CUDA-Q kernels</p>
-<p><strong>[5]</strong> All qudits in the <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code> start in the <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> computational basis state.</p>
-<p><strong>[6]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector</span></code> provides an API for individual qubit extraction and sub-register slicing.
-Programmers can extract the front <span class="math notranslate nohighlight">\(N\)</span> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>, the back <span class="math notranslate nohighlight">\(N\)</span> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>, and the
-inner slice starting at a given index and including user-specified <code class="code docutils literal notranslate"><span class="pre">count</span></code> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::qview</span></code> should take on the following structure:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">Levels</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">qvector</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">private</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">qudits</span><span class="p">;</span>
-
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="c1">// Construct a qreg with `size` qudits in the |0&gt; state.</span>
-<span class="w">      </span><span class="n">qvector</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">size</span><span class="p">);</span>
-<span class="w">      </span><span class="n">qvector</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">qvector</span><span class="o">&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-
-<span class="w">      </span><span class="c1">// Iterator interface.</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">begin</span><span class="p">();</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">end</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the qudit at `idx`.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="k">operator</span><span class="p">[](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">idx</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[0, count)` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">front</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the first qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">front</span><span class="p">();</span>
-<span class="w">      </span><span class="c1">// Returns the `[count, size())` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">back</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the last qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">back</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[start, start+count)` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `{start, start + stride, ...}` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">stride</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">end</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the number of contained qudits.</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-
-<span class="w">      </span><span class="c1">// Destroys all contained qudits. Postcondition: `size() == 0`.</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">clear</span><span class="p">();</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Allocate 20 qubits, std::vector-like semantics</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">20</span><span class="p">);</span>
-<span class="c1">// Get first qubit</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">first</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">front</span><span class="p">();</span>
-<span class="c1">// Get first 5 qubits</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">first_5</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="c1">// Get last qubit</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">last</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">back</span><span class="p">();</span>
-<span class="c1">// Can loop over qubits with size() method</span>
-<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">size</span><span class="p">();</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="p">...</span><span class="w"> </span><span class="k">do</span><span class="w"> </span><span class="n">something</span><span class="w"> </span><span class="n">with</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="w"> </span><span class="p">...</span>
-<span class="p">}</span>
-<span class="c1">// Range based for loop supported</span>
-<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="w"> </span><span class="n">qb</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="p">...</span><span class="w"> </span><span class="k">do</span><span class="w"> </span><span class="n">something</span><span class="w"> </span><span class="n">with</span><span class="w"> </span><span class="n">qb</span><span class="w"> </span><span class="p">...</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Allocate 20 qubits, vector-like semantics</span>
-<span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
-<span class="c1"># Get the first qubit</span>
-<span class="n">first</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">front</span><span class="p">()</span>
-<span class="c1"># Get the first 5 qubits</span>
-<span class="n">first_5</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">front</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-<span class="c1"># Get the last qubit</span>
-<span class="n">last</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">back</span><span class="p">()</span>
-<span class="c1"># Can loop over qubits with size or len function</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">q</span><span class="p">)):</span>
-  <span class="o">..</span> <span class="n">do</span> <span class="n">something</span> <span class="k">with</span> <span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">..</span>
-<span class="c1"># Range based for loop</span>
-<span class="k">for</span> <span class="n">qb</span> <span class="ow">in</span> <span class="n">q</span><span class="p">:</span>
-  <span class="o">..</span> <span class="n">do</span> <span class="n">something</span> <span class="k">with</span> <span class="n">qb</span> <span class="o">..</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="cudaq-qarray-n-levels-2">
-<h3><span class="section-number">3.3.3. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qarray&lt;N,</span> <span class="pre">Levels</span> <span class="pre">=</span> <span class="pre">2&gt;</span></code><a class="headerlink" href="#cudaq-qarray-n-levels-2" title="Permalink to this heading">¶</a></h3>
-<p><strong>[1]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qarray&lt;N,</span> <span class="pre">Levels&gt;</span></code> (where <code class="code docutils literal notranslate"><span class="pre">N</span></code> is an integral constant) is a statically
-allocated container (<code class="code docutils literal notranslate"><span class="pre">std::array</span></code>-like). The utility of this type is in the compile-time
-knowledge of allocated containers of qudits that may directly enable ahead-of-time quantum
-optimization and synthesis.</p>
-<p><strong>[2]</strong> The second template parameter defaults to <span class="math notranslate nohighlight">\(2\)</span>-level <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>.</p>
-<p><strong>[3]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qarray</span></code> owns the quantum memory it contains, and therefore cannot be copied or moved.</p>
-<p><strong>[4]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qarray</span></code> can only be instantiated within CUDA-Q kernels</p>
-<p><strong>[5]</strong> All qudits in the <code class="code docutils literal notranslate"><span class="pre">cudaq::qarray</span></code> start in the <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> computational basis state.</p>
-<p><strong>[6]</strong> The <code class="code docutils literal notranslate"><span class="pre">cudaq::qarray</span></code> provides an API for individual qubit extraction and sub-register slicing.
-Programmers can extract the front <span class="math notranslate nohighlight">\(N\)</span> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>, the back <span class="math notranslate nohighlight">\(N\)</span> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>, and the
-inner slice starting at a given index and including user-specified <code class="code docutils literal notranslate"><span class="pre">count</span></code> <code class="code docutils literal notranslate"><span class="pre">qudits</span></code>.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::qarray</span></code> should take on the following structure:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">Levels</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">qarray</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">private</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">array</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="p">,</span><span class="w"> </span><span class="n">N</span><span class="o">&gt;</span><span class="w"> </span><span class="n">qudits</span><span class="p">;</span>
-
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="c1">// Construct a qreg with `size` qudits in the |0&gt; state.</span>
-<span class="w">      </span><span class="n">qarray</span><span class="p">();</span>
-<span class="w">      </span><span class="n">qarray</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">qvector</span><span class="o">&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-<span class="w">      </span><span class="n">qarray</span><span class="p">(</span><span class="n">qarray</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-
-<span class="w">      </span><span class="n">qarray</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">=</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">qarray</span><span class="w"> </span><span class="o">&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-
-<span class="w">      </span><span class="c1">// Iterator interface.</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">begin</span><span class="p">();</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">end</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the qudit at `idx`.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="k">operator</span><span class="p">[](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">idx</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[0, count)` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">front</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the first qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">front</span><span class="p">();</span>
-<span class="w">      </span><span class="c1">// Returns the `[count, size())` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">back</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the last qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">back</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[start, start+count)` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `{start, start + stride, ...}` qudits.</span>
-<span class="w">      </span><span class="n">qview</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">stride</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">end</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the number of contained qudits.</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-
-<span class="w">      </span><span class="c1">// Destroys all contained qudits. Postcondition: `size() == 0`.</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">clear</span><span class="p">();</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</section>
-<section id="cudaq-qspan-n-levels-deprecated-use-cudaq-qview-levels-instead">
-<h3><span class="section-number">3.3.4. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qspan&lt;N,</span> <span class="pre">Levels&gt;</span></code> (Deprecated. Use <code class="code docutils literal notranslate"><span class="pre">cudaq::qview&lt;Levels&gt;</span></code> instead.)<a class="headerlink" href="#cudaq-qspan-n-levels-deprecated-use-cudaq-qview-levels-instead" title="Permalink to this heading">¶</a></h3>
-<p><strong>[1]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qspan</span></code> is a non-owning reference to a part of the discrete quantum
-memory space, a <code class="code docutils literal notranslate"><span class="pre">std::span</span></code>-like C++ range of <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>
-(see C++ <a class="reference external" href="https://en.cppreference.com/w/cpp/container/span">span</a>). It does not
-own its elements. It takes a single template parameter indicating the levels for
-the underlying qudits that it stores. This parameter defaults to 2 for qubits.
-It takes on the following structure:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">Levels</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">qspan</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">private</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">span</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">qubits</span><span class="p">;</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="c1">// Construct a span that refers to the qudits in `other`.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ranges</span><span class="o">::</span><span class="n">range</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="k">auto</span><span class="o">&amp;</span><span class="w"> </span><span class="n">other</span><span class="p">);</span>
-<span class="w">      </span><span class="n">qspan</span><span class="p">(</span><span class="n">qspan</span><span class="w"> </span><span class="k">const</span><span class="o">&amp;</span><span class="w"> </span><span class="n">other</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Iterator interface.</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">begin</span><span class="p">();</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">end</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the qudit at `idx`.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="k">operator</span><span class="p">[](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">idx</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[0, count)` qudits.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">front</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the first qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">front</span><span class="p">();</span>
-<span class="w">      </span><span class="c1">// Returns the `[count, size())` qudits.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">back</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the last qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">back</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[start, start+count)` qudits.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the number of contained qudits.</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</section>
-<section id="cudaq-qreg-n-levels-deprecated-use-cudaq-qvector-levels-instead">
-<h3><span class="section-number">3.3.5. </span><code class="code docutils literal notranslate"><span class="pre">cudaq::qreg&lt;N,</span> <span class="pre">Levels&gt;</span></code> (Deprecated. Use <code class="code docutils literal notranslate"><span class="pre">cudaq::qvector&lt;Levels&gt;</span></code> instead.)<a class="headerlink" href="#cudaq-qreg-n-levels-deprecated-use-cudaq-qvector-levels-instead" title="Permalink to this heading">¶</a></h3>
-<p><strong>[1]</strong> <code class="code docutils literal notranslate"><span class="pre">cudaq::qreg&lt;N,</span> <span class="pre">Levels&gt;</span></code> models a register of the discrete quantum memory space - a
-C++ container of <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit</span></code>.  As a container, it owns its elements and
-their storage. <code class="code docutils literal notranslate"><span class="pre">qreg&lt;dyn,</span> <span class="pre">Levels&gt;</span></code> is a dynamically allocated container
-(<code class="code docutils literal notranslate"><span class="pre">std::vector</span></code>-like, see C++ <a class="reference external" href="https://en.cppreference.com/w/cpp/container/vector">vector</a>).
-<code class="code docutils literal notranslate"><span class="pre">cudaq::qreg&lt;N,</span> <span class="pre">Levels&gt;</span></code> (where N is an integral
-constant) is a statically allocated container (<code class="code docutils literal notranslate"><span class="pre">std::array</span></code>-like,
-see <a class="reference external" href="https://en.cppreference.com/w/cpp/container/array">array</a>).
-Its template parameters default to dynamic allocation and <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;2&gt;</span></code>.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">dyn</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">Levels</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="o">&gt;</span>
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">qreg</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">private</span><span class="o">:</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">conditional_t</span><span class="o">&lt;</span>
-<span class="w">        </span><span class="n">N</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">dyn</span><span class="p">,</span>
-<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&gt;</span><span class="p">,</span>
-<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">array</span><span class="o">&lt;</span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;</span><span class="p">,</span><span class="w"> </span><span class="n">N</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="o">&gt;</span><span class="w"> </span><span class="n">qudits</span><span class="p">;</span>
-<span class="w">    </span><span class="k">public</span><span class="o">:</span>
-<span class="w">      </span><span class="c1">// Construct a qreg with `size` qudits in the |0&gt; state.</span>
-<span class="w">      </span><span class="n">qreg</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">size</span><span class="p">)</span><span class="w"> </span><span class="k">requires</span><span class="w"> </span><span class="p">(</span><span class="n">N</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">dyn</span><span class="p">);</span>
-<span class="w">      </span><span class="n">qreg</span><span class="p">(</span><span class="n">qreg</span><span class="w"> </span><span class="k">const</span><span class="o">&amp;</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
-
-<span class="w">      </span><span class="c1">// Iterator interface.</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">begin</span><span class="p">();</span>
-<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">end</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the qudit at `idx`.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="k">operator</span><span class="p">[](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">idx</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[0, count)` qudits.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="o">&lt;</span><span class="n">dyn</span><span class="p">,</span><span class="w"> </span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">front</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the first qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">front</span><span class="p">();</span>
-<span class="w">      </span><span class="c1">// Returns the `[count, size())` qudits.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="o">&lt;</span><span class="n">dyn</span><span class="p">,</span><span class="w"> </span><span class="n">Levels</span><span class="o">&gt;</span><span class="w"> </span><span class="n">back</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">      </span><span class="c1">// Returns the last qudit.</span>
-<span class="w">      </span><span class="n">qudit</span><span class="o">&lt;</span><span class="n">Levels</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">back</span><span class="p">();</span>
-
-<span class="w">      </span><span class="c1">// Returns the `[start, start+count)` qudits.</span>
-<span class="w">      </span><span class="n">qspan</span><span class="o">&lt;</span><span class="n">dyn</span><span class="p">,</span><span class="w"> </span><span class="n">Levels</span><span class="o">&gt;</span>
-<span class="w">      </span><span class="n">slice</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">start</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-
-<span class="w">      </span><span class="c1">// Returns the number of contained qudits.</span>
-<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="nf">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
-
-<span class="w">      </span><span class="c1">// Destroys all contained qudits. Postcondition: `size() == 0`.</span>
-<span class="w">      </span><span class="kt">void</span><span class="w"> </span><span class="nf">clear</span><span class="p">();</span>
-<span class="w">  </span><span class="p">};</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p><code class="code docutils literal notranslate"><span class="pre">qreg</span></code> instances can only be instantiated from within quantum kernels,
-they cannot be instantiated in host code. All qubits in the <code class="code docutils literal notranslate"><span class="pre">qreg</span></code>
-start in the <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code> computational basis state.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Allocate 20 qubits, std::vector-like semantics</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">qreg</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">20</span><span class="p">);</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">first</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">front</span><span class="p">();</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">first_5</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">last</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">back</span><span class="p">();</span>
-<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">q</span><span class="p">.</span><span class="n">size</span><span class="p">();</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="p">...</span><span class="w"> </span><span class="k">do</span><span class="w"> </span><span class="n">something</span><span class="w"> </span><span class="n">with</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="w"> </span><span class="p">...</span>
-<span class="p">}</span>
-<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="w"> </span><span class="n">qb</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="p">...</span><span class="w"> </span><span class="k">do</span><span class="w"> </span><span class="n">something</span><span class="w"> </span><span class="n">with</span><span class="w"> </span><span class="n">qb</span><span class="w"> </span><span class="p">...</span>
-<span class="p">}</span>
-
-<span class="c1">// std::array-like semantics</span>
-<span class="n">cudaq</span><span class="o">::</span><span class="n">qreg</span><span class="o">&lt;</span><span class="mi">5</span><span class="o">&gt;</span><span class="w"> </span><span class="n">fiveCompileTimeQubits</span><span class="p">;</span>
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-<li class="toctree-l1"><a class="reference internal" href="../using/quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
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-</ul>
-</li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#python-support">Python Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../using/install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l1 current"><a class="current reference internal" href="#">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#utilities">Utilities</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Tensor"><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.ComplexMatrix"><code class="docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.OperatorSum"><code class="docutils literal notranslate"><span class="pre">OperatorSum</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ProductOperator"><code class="docutils literal notranslate"><span class="pre">ProductOperator</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ScalarOperator"><code class="docutils literal notranslate"><span class="pre">ScalarOperator</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
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-<p>The following specifications for the CUDA-Q programming model and toolchain are available:</p>
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-<li class="toctree-l2"><a class="reference internal" href="cudaq/machine_model.html">1. Machine Model</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../using/backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../using/backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../using/backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/backends/nvqc.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../using/backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../using/install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../using/extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../using/extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../using/extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../using/extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="index.html">   Specifications</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="cudaq.html">   Language Specification</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Quake Specification</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="quake-dialect">
-<h1>Quake Dialect<a class="headerlink" href="#quake-dialect" title="Permalink to this heading">¶</a></h1>
-<section id="general-introduction">
-<h2>General Introduction<a class="headerlink" href="#general-introduction" title="Permalink to this heading">¶</a></h2>
-<p>The quantum circuit model is the most widely used model of quantum
-computation.  It provides a convenient tool for formulating quantum
-algorithms and an architecture for the physical construction of quantum
-computers.</p>
-<p>A <em>quantum circuit</em> represents a computation as a sequence of quantum
-operators applied to quantum data.  In our case, the quantum data is a set
-of quantum bits, or qubits for short.  Physically, a qubit is an object with
-only two distinguishable states, i.e., it is a two-state quantum mechanical
-system such as a spin-1/2 particle.</p>
-<p>Conceptually, a <em>quantum operator</em> is an effect that might modify the state
-of a subset of qubits. Most often, this effect is unitary evolution—in
-this case, we say that the operator is a <em>unitary</em>.  The number of target
-qubits an operator acts upon is an intrinsic property.</p>
-<p>A <em>quantum instruction</em> is the embodiment of a quantum operator when applied
-to a specific subset of qubits.  The number of qubits must be equal to (or
-greater than) the number of target qubits intrinsic to the operator.  If
-greater, the extra qubits are considered controls.</p>
-</section>
-<section id="motivation">
-<h2>Motivation<a class="headerlink" href="#motivation" title="Permalink to this heading">¶</a></h2>
-<p>The main motivation behind Quake’s value model is to directly expose
-quantum and classical data dependencies for optimization purposes,
-i.e., to represent the dataflow in quantum computations.  In contrast
-to Quake’s memory model, which uses memory semantics (quantum
-operators act as side-effects on qubit references), the value model
-uses value semantics, that is quantum operators consume and produce
-values. These values are not truly SSA values, however, as operations
-still have side-effects on the value itself and the value cannot be
-copied.</p>
-<p>Let’s see an example to clarify the distinction between the models.  Take the
-following Quake implementation of some toy quantum computation:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">func</span><span class="p">.</span><span class="n">func</span><span class="w"> </span><span class="n">foo</span><span class="p">(</span><span class="o">%</span><span class="n">veq</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">veq</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="c1">// Boilerplate to extract each qubit from the vector</span>
-<span class="w">    </span><span class="o">%</span><span class="n">c0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">arith</span><span class="p">.</span><span class="n">constant</span><span class="w"> </span><span class="mi">0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">index</span>
-<span class="w">    </span><span class="o">%</span><span class="n">c1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">arith</span><span class="p">.</span><span class="n">constant</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">index</span>
-<span class="w">    </span><span class="o">%</span><span class="n">q0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">quake</span><span class="p">.</span><span class="n">extract_ref</span><span class="w"> </span><span class="o">%</span><span class="n">veq</span><span class="p">[</span><span class="o">%</span><span class="n">c0</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">veq</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="p">,</span><span class="w"> </span><span class="n">index</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">ref</span>
-<span class="w">    </span><span class="o">%</span><span class="n">q1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">quake</span><span class="p">.</span><span class="n">extract_ref</span><span class="w"> </span><span class="o">%</span><span class="n">veq</span><span class="p">[</span><span class="o">%</span><span class="n">c1</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">veq</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="p">,</span><span class="w"> </span><span class="n">index</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">ref</span>
-
-<span class="w">    </span><span class="c1">// We apply some operators to those extracted qubits</span>
-<span class="w">    </span><span class="c1">// ... bunch of operators using %q0 and %q1 ...</span>
-<span class="w">    </span><span class="n">quake</span><span class="p">.</span><span class="n">h</span><span class="w"> </span><span class="o">%</span><span class="n">q0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">ref</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="p">()</span>
-
-<span class="w">    </span><span class="c1">// We decide to measure the vector</span>
-<span class="w">    </span><span class="o">%</span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">quake</span><span class="p">.</span><span class="n">mz</span><span class="w"> </span><span class="o">%</span><span class="n">veq</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">veq</span><span class="o">&lt;</span><span class="mi">2</span><span class="o">&gt;</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="n">cc</span><span class="p">.</span><span class="n">stdvec</span><span class="o">&lt;</span><span class="n">i1</span><span class="o">&gt;</span>
-
-<span class="w">    </span><span class="c1">// And then apply another Hadamard to %q0</span>
-<span class="w">    </span><span class="n">quake</span><span class="p">.</span><span class="n">h</span><span class="w"> </span><span class="o">%</span><span class="n">q0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">quake</span><span class="p">.</span><span class="n">ref</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="p">()</span>
-<span class="w">    </span><span class="c1">// ...</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>Now imagine we want to optimize this code by removing pair of adjacent
-adjoint operators, e.g., if we have a pair Hadamard operations next to each
-other on the same qubit—visually:</p>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>    ┌───┐ ┌───┐         ┌───┐
-   ─┤ H ├─┤ H ├─  =  ───┤ I ├───  =  ─────────────
-    └───┘ └───┘         └───┘
-</pre></div>
-</div>
-<p>Where <code class="docutils literal notranslate"><span class="pre">I</span></code> is the identity operator. Now note that a naive implementation of
-this optimization for Quake would optimize away both <code class="docutils literal notranslate"><span class="pre">quake.h</span></code> operators
-being applied to <code class="docutils literal notranslate"><span class="pre">%q0</span></code>.  Such an implementation would have missed the fact
-that a measurement is being applied to the vector, <code class="docutils literal notranslate"><span class="pre">%veq</span></code>, which contains
-<code class="docutils literal notranslate"><span class="pre">%q0</span></code>.</p>
-<p>Of course it is possible to correctly implement this optimization for Quake.
-However such an implementation would be quite error-prone and require
-complex analyses.  For this reason, Quake has overloaded gates.</p>
-<p>In the value model operators consume values and return new values:</p>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>  %q0_1 = quake.op %q0_0 : (!quake.wire) -&gt; !quake.wire
-</pre></div>
-</div>
-<p>We can visualize the difference between memory and value representation as:</p>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>            Memory                                   Value
-
-        ┌──┐ ┌──┐     ┌──┐                  ┌──┐ %q0_1 ┌──┐     ┌──┐
-   %q0 ─┤  ├─┤  ├─···─┤  ├─ %q0  vs  %q0_0 ─┤  ├───────┤  ├─···─┤  ├─ %q0_Z
-        └──┘ └──┘     └──┘                  └──┘       └──┘     └──┘
-</pre></div>
-</div>
-<p>If we look at the implementation again, we notice that the problem
-with the naive optimization happens because the Hadamard operators are
-implicitly connected by the same value <code class="docutils literal notranslate"><span class="pre">%q0</span></code>. In value form, all the gates
-are explicitly connected by distinct values, which eliminates the need
-to do further analysis via implicit side-effects.
-The following is the implementation in value form.</p>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>func.func @foo(%array : !quake.qvec&lt;2&gt;) {
-    // Boilerplate to extract each qubit
-    %c0 = arith.constant 0 : index
-    %c1 = arith.constant 1 : index
-    %r0 = quake.extract_ref %array[%c0] : (!quake.qvec&lt;2&gt;, index) -&gt; !quake.qref
-    %r1 = quake.extract_ref %array[%c1] : (!quake.qvec&lt;2&gt;, index) -&gt; !quake.qref
-
-    // Unwrap the quantum references to expose the wires.
-    %q0 = quake.unwrap %r0 : (!quake.qref) -&gt; !quake.wire
-    %q1 = quake.unwrap %r1 : (!quake.qref) -&gt; !quake.wire
-
-    // Misc. operators applied
-    %q0_M = quake.h %q0_L : (!quake.wire) -&gt; !quake.wire
-
-    // Re-wrap the wire to its original source
-    quake.wrap %q0_M to %r0 : !quake.wire, !quake.qref
-    quake.wrap %q1_X to %r1 : !quake.wire, !quake.qref
-
-    // Measure the entire vector of quantum references
-    %result = quake.mz %array : (!quake.qvec&lt;2&gt;) -&gt; !cc.stdvec&lt;i1&gt;
-
-    // Unwrap the wire for qubit 0 again
-    %q0_P = quake.unwrap %r0 : (!quake.qref) -&gt; !quake.wire
-    ...
-    %q0_Z = quake.h %q0_Y : (!quake.wire) -&gt; !quake.wire
-    // Re-wrap the wire back to the original reference
-    quake.wrap %q0_Z to %r0 : !quake.wire, !quake.qref
-    return
-}
-</pre></div>
-</div>
-<p>In this code we can more straightforwardly see that the Hadamard
-operators cannot cancel each other.  One way of reasoning about this
-is as follows: In value form we need to follow a chain of values to
-know the qubit operators are being applied to, in this example:</p>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>Mmeory                          Value
-    %q0         [%q0_0, %q0_1 ... %q0_L, %q0_M; %q0_P ... %q0_Y, %q0_Z]
-</pre></div>
-</div>
-<p>We know that one Hadamard is applied to <code class="docutils literal notranslate"><span class="pre">%q0_L</span></code> and generates <code class="docutils literal notranslate"><span class="pre">%q0_M</span></code>, and
-the other is applied <code class="docutils literal notranslate"><span class="pre">%q0_Y</span></code> and generates <code class="docutils literal notranslate"><span class="pre">%q0_Z</span></code>.  Hence, there is no
-connection between them—which means they cannot cancel each other out.</p>
-</section>
-</section>
-
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ARWgywvO9HweiOzWDkHoNSwZLneBdeATLifl45UnmsifI8CMXqWPn8W0h+JSC/BI5PAmPnVWavaozV1Cv1VvJdKMCoPIXXI6GOo87vrluFmqvMKkJkFgMSP9b0CoyZEY4MmDVuwditHhXUzoQ7jjq/+7ldwTLnHYHJfqd+9iaRWqhGIRgX6qUPUbvejb5yFflSOD4Jily7j0tl/hi7WIVXeTV1BoOEIaMg5MDVge1o8DGqa3L3o+ZAZRchMkvBEf0eH+dJ4CAMs7+QR6CsoVeT7FV9Jt6a43SAxOtHr35y83zv5PEYmRmEW9wq92VQdppAko+nercDvmAV+TI4ngRjVSuPufKTWIXHzL4LBRcHoyPgm46b7K+8un5Vq3nheXQRkNlH3IORM8ZCfo1bQPLKqQPkAh7vd6Naxdt/+7no9sdzMBjxWvFe+L5wFYnzgXPgMsOP55qVcz8RPqsQPsekVO9Gr15FvgSOAzCq7dyrPiKV2WNwnz/gVXjMq6n5w1C1ypV/JLrjqHNyT92Li4W8mlv3LDA7KTBmVnBc4xaMC3gXC/k17ldu7FW8DcZ0Ua1Y1WqSr1+tIhGMGTlvFSAjbufZRcAxnfA5LdRLH6GP0x37ZavIp8PxCWBUnnvqI+N+FaupVebIvgKIAkwFoynUjoQ7VnUt7tpdLMK7uVXPBjP71ASMDEf+CNeoIckwWUNDaBUe463YdjQ2qtVqFq8brx893h+/6whMBcqdPMYWn8K5V0IAuj4+s6B+CrdLvQKQT4PjAIoR9xPlavXhYK78JFbhMbN3wRCpwDMF16OhjqWuQeXq3tz88HxyHbQtILNnOTCi5w93iRqSHGqV5UDJPoOB9ki4Y7mf2ZkZjm4VyfOwg+8AmdeGc87KYzo5MGIv4v44vP1OzwbkU+A4AKPa7j4Q9wEthVd1vkBVb4XMXtUMjDfhVXbQmmw7GnwsdQ3u2ifB81bNdfXcUuhZ+KJ3YMQPzUESr1VBgwGTfg0NpdXUZ+OtOVb3Mzsz3wfCkoPnhqEYMZt7jFQeL/fDZ83PJsexz7Ep3H6nZwLyYTi+GIyY2ataxdp4fPm4di+w8wpAClgTqJ0JPoa7Fr52Vbs5qea0ihCZPQtfcveBco1gZEjm9W6iVqutNW5hkzXm1dQq3v4bV0V3jA28AmNeP95HtYrEuVioxuPs5HnOWTnvlRiQ3Et19Y2eBciH4HgCjK5WH8vSeNVTH6r6kFfhVZ0vtKsrADlgnY3JcdR5VVb3VQXPnZvXyXPCnOI6ov5gHBhxnIJk1njdCg4MDgSlyhkb1RhvYvyZcFBOAHKPQYmwdLEL7wCZ98zPgeHEz6965vzsI273V/WNngHI03B8ARirjKF6GKupMbPvwkGlgtIRqE2jOpa6BpXdfVXzwvOqetVziiI75Xb1oVRwdJBEWC7C5/0oiHSgXKinogLn0eBjvQuP1561uicGZR4/a/aTZ7DFrXKuKzEYFRT5OOVxHwXkKTg+CEblVWaPkf1VbFuFx8zexVvjHZCOAO1oqGO5a3DXOwk1V25uq+eDdfYwcx+FH0TWC2X2DpIIRvzYEY4rbctAWC7C574KlhivhCOflz1fr4M/3hvPAXsFypzflXoMqJz73JefP/YqWKr6Ro8A8hQcG/GNYq08Zvaq5lhNrfJq6gwGCINFZQetybajwcdS16Cyuz83Hwt5nmf0HGHqlOop5Xb+KDhPAYlgRFguwuc9IjCmq8hVeFWfjbfmWG4VyZlBifeowgEyM0KQYYnKuVZiUKrnHXF/jOqYpwF5GI7NqpG3Ya38Qh57GNxfi3qFzH4Sb8I7EClgTaB2JvgY6hrc9U9CzVk319VzYp815omWeA4gcwyCET94hONK2zJ4deVAuYDHOd6oVvH2335VdMfoAInz4ODPoOQ5cYCMuAXjGrfPQEEKoaieL+cgr+obnQHkITj+IzC6WIVXeRIOKA4+DlhnY3Kc7nrU9avazU01j27e+Rk6j5n9WS2hP5T0HSAZlg6UeN8OJOzXmK0mOd4GY6bhALmQV/fA23AMBu4XcQtHBGNuP6p/CsgxHF8MRvZVrMJjZl+FgkoFn6NAOxLV8dR1qMy+CjVfKrv5D7Et4n5byvmjWsJ/EMqfASSDYBU9hkb6NfzPWfYcW7HtTPB5F+PxHt6FryLBh95Bcoceip9pPhf0TwHkEY3hWGiJW2Gt/NJ4FaupMbN3wfCogKOgxfWzwh23uqbunjB4bnje1LxOIkyNufLP0hK3H476YDAC/BL3YMw+e5wrt8rKeg2/auPnk8dT/UeCz1WtKF8JSYTiCj1+VqmXAPLI6nEEx8G/nU7hOOUX4VWdsTZ+Fb6KCiwOQBOAPSP42OpaunvoQs1ZNc9VROMxs1e1E34kTkvcfzDYVx/DCtsYkDt4PDbOEW5HMLqfq7mfgmXGK+CI58R6KfwrIIkrSF45Ov1TQLZwPPhzWvUXyOxVnQ9K1SpPo4OMg5TrPTP4+Op6MLNXtZqfai7d3HNE41VmX/VQCowTWEZ8jOEPp4slPj5chiWCcIHtLiaryLXxzw4FYwVGdd8IRgfIFB4jtUM+CsmXAHKiFo6FcEJUjb1FeFXng3J+Lfwa94DgnoKNA5TrvSJW4bnn7qUKNVcqu/l3zyiEV7nyE/H4R8DI2x4BJMNxjVsgdqvINe7BtApfxdt/YzH+32C/CpIL1XnNDEYGZCr3QWFPQfLVgJSarB5LOD74c5pzelXjg2GPmb17aSqvgFT1Xh3qmlRmz7WaF543Na9HIoRXufKq7oQvPvYW4Sst0YMSVzYOkAjHFfoq1Coy/Rr9alLFWzHmKCSXor8JrwAZ4NX9pxCImHPeKzlAprju+q3OrhyXTyu9yt1ErsJjxuCeA4iDj6u/Mty1qKzuTcVSeDWvkwhTVzmFNW87ItyXQVmtKlDZR68+mhW2KUDucQsNBGXGGsdXkZjZc2zFtowJIPFc6h6wr3zmoH0r5ZzzyhG9Ez8/9inuqTHt6tHCsVg1Tvs8adhXD4I95klUgFEwqvr/Z8Y+K9x1uXtR96nmZhG+mmMXUdRRZPaq7vos9eJjH+vJMZfQH04XS3x8vAxLXjWuopdRrSIRlIvwGN3KMuMIINU5+PorQCIoA8axcO4ZkJPnF3EPRs5B/pTOrhxT6mZ4gtB3sYpcRQUUBZ+j8QpIqmtTWd0jB8+RmkP2qhdiezQ+pXqqdr1OuA9DDfuL8J2WOAbKAL+EBuQOHud6sopMv0a/ipwAcmm2q/eiCwfIiM95X8BjP+ITiFwzKFPumee2xWSW7FerRwnHB1aNnNNXwQ9o+lAruLCfhoPhMyDprg2vnX0Xi/A8p+gnEcJXufKqdsJx7p/4bkz2z0Iy4mMsnzd7Co4OkNOV5DNWkRUg8fxnxvD1YiAM02eN2Qn/IZD1Sjni/hlWz3wMxKkeWTm6CcAJWuJ+nJvw7mFhMFQUdKahwDftTYKvyV2/uj/1wqpaZfZVhKmrXHlVT7e57QpcvC17CpIVLHM7egRiaqVtDEg+F/uMNZ63isSVHL4XWKv3yO1zJBiMWE+Eq8i83xBZiZ8XeuyhZN+tHu/g+IRVI/usM7jGUA+JH6wCSwWjCmzcP1pXwdexkl8LX73U6iVfQ8+fe+mj6UWRK6/qrj9V7u9eety2CI9jqmvk1QnDUMUSHx83w5L9Eh/PhHsdKFfYxh5DgVK9Q4uoXa+K1BLHwZjCucC5ibg9h5N65ijXb3V25egumiesmlR+KPyAFBDeRJ5GB7r/G/iq565pFZ7vR9XdXKh5q+a8ew7Kp6qeq7v+WeHx+KVf4vZjyTHKd8JjYe0ihM/5dF5F9XN7DQ3JrKvAc3BPjakCtcRxMDpY4X0iJFNuv5xX9NhzYz+bYvV4Fo6phTJ7VDfh7gG6UHBSMYHe/zXbVd3FKjzm1dT8Qqua5+voi63qKrueqrv+M5XnYIhhD2vlJ1rCg3iPz9Vd1kv4laSCY8IAw60iczyvHhGULvA9wl63X5DP+qh2yunVXPD5qmfGYGS5fqkbOA5/Uk/HVBPsogJDBRMFpykIOzBOV47qOlaR3T2oWIRfGj+Z+xBe5cqr2vVerTznWUhyRmUf/Q69iI/53ymW0ICc/MzOUKDEvMZjq0d1TowQufKVcM4wrzSO5yLHoVdaYFt67B3WIyvHbtJQPNlddA+5A9MRGD4Cye46VsjssVb3vAi/CK96IbZH4zFXXtWuV+nI+OkLvsT92DyPWl2kn1wLHzvrKpboAbmDr6KC5BIfz3+6euzGxCBXcvOB2zlPrqV6Vur5orAvx/BP6wkc8WLchfEYd4NdKBhMQamgWQGwg+NRMPI1rCIrQCoYqjlYhJ9ECM897nPP1a7ndGQsKvebQNKNXeL248gxynfCY3EPw/3cVlnFGrdQXKjmvjvONKLInRB0b824jBVybptck3sPFtiWHnuH9D84HvjfUaNwH7e/u0kOBQAHjg6KGB0YXwHHKrtQ91vNTRVh6ipPPcr1WdNxnfI4k5ddjeUe1uynsMyx6poQjDjGgZIDYcGQZEBOVo0YIbzKTvsgTyLnaCHP1zZ5Lvhc1fM4pMnKUcldnHsIuc2NOQNG9lNATrwCYgfIZ4CR75vnRvVCbOd+FLnyqnY9pem4o8rjTj4ANZZ7WC9x+wFWH2X2ua4iwC9xC8g8RoYDRcYjgAzI7JXwPpXPe3qjPIkKkOgDMl4DKvfptslx+NP6LBxR3aS6B5u1AyP2pmCsIMkrwQkYO0Dy+RwQHRwX4d2cTCIan6p6rnY9pem4R5XncR8Daon7cdzLGo87vRd3LAz+mZ3j8YPFVaT6lxP8zBUg1Tg+Dqq6R54zvu6MN+EdIPFfKGWPoYixm5zC+VPPgPsjdXDEC6gmUE169UBVj0PBREGSIdRB0sHu7M9qBUgFxKz53hbyC3lVT+a2yq6natdTmo57tvK83QegxnEPa+UnWkJfC4MRIbOEXkWmT1XvAIJx+t8e8n3hdbPnYPi9CY89BUWs+d4wdpFT0+d/SP8XEUf/vHGhzNvwoeE47qmHmxOlJmcCSoZkBTEVR8Gojl1dVwXFCo5VhKlVrryqXY81GfMVyuvoPhI1jntYs+fcKcdy8EoyIC+hf2rzs8btWJ8RzgdfkwsGZL7TO2W+Vwalu0cMNe94zagcd1rdyvFRLSJPooKJ266AVW1/Zqwir6Lma+d74jlQcxNFzT5rzOxV7XpK03Ffqbym7uNYxBjuYZ3+yD2rc6QYFllH3J6TV48oft7Zm2gCQ65VqO+RoZg9t3qcBgIy4v5Zd89ePU+pR+FYPYRFZBzfTQIHwwW9ig5oCDMHOTVmAkYGpLtmvC/2VUTjMbueql1PaTruXyqv0X4AZgz3sGbPeaIcn7CIuIVH+oCM4nfhrPAcb/F5Tegd/LrYhMfcvePq/rLHgMxtQb3sq/GtHoVjxOwDwxpvWnkVDiSPxgRyDpB4jJV6Lhbh+b4mEcJXmb2qXY81GfPdtET/YeR9dZBkf3Q++FrWuAXlSj4zCvev7qvb9mZyApEhydfZxaOADKrV3HfP9bDy31hXcDzy0NVNZd95dfOTmICke2hVKNhNwNfFIry6H66DauzhNuxNfdVjTcZ8Z+X1dx/TIsZgD4/DnvNRrXG7knSArOC4Q2aPsYYGI77zWK+07Qgg+f2tIkRG8fNRz2uyrdV05cgXeeTB84epPlTVr6J7IEfGTuLocaprXkSuIoo6isxe1a7Hmoz5Kcp7qT4aNWYp6vRVTnXgXKMHJAInexGfMEs/gSN6fIcr+C2mV0UesxuXc4MZg+f8ZZrCMVU9VBaOVfvxJKRX9ZlwD6172IvoVcdx492+kxcpI4o6isx+UitNxvxULdF/XDwm50N9oOmrnOoAqZ6jgssK4xiMa2gQ7lH/fMdwMOu+hyPBwr6bo5xTV7veIeHkvlpqUjC7ievGqHAPadJn4KV3wDvzQlSB4j57zCh1nKpWmoz56ZrcYze/ylfPiJ+zev7V+5FRvXfd+9m977ht8s1MIoTHHm5T27k/9af1KjjyBBrv/dAAACAASURBVJyRm1Q3roujD/NVEca7sSmseZvrqb4bh5qM+S2a3Gs3Rj0bztxTz1htc+8K1z8hovAo11P9o2PGWg/+B+DP1EJZbVNeTThvV2Pd/m7c0fFqn1R17ErqOLyd+9X4S1pn5unoPupZZa3eKdwWlNW7w33lq1DjUpPxaixKjeNt1ZhHVB3Hbntk5fisC6+0UK7UTWp3DPfA3X5qu3ohVO2urzvXo5ocZzLmL6p7p6rtR5/r9L3hdxX7yrsxXC8m3HhVZ6/a91u/a4/A8SfpXz6Eo+c+Ov7S71T3HkzhVElBqtsHdWTsj9NvhSP/W6qH/q2V0fSY03EpNf7oMS79fHXPfPKOT4+B47p9UEfG/jg9AsevmBj18Jy6l6U7Bp5rj/rc7mXqztkdszvXo5ocazLmL6p7NtV2N6fTvjsOv6uq171ratxuAjWpu33d/X8Lreovs/4idQ/P+eqhca+qq2OdGe/OqbZzz4m3u5rPpfwlrzPzdHQf9ayyVu8UbgvK6t3pxvJ+k/Gp7hjdteE2rHlbNeYRVcex2x5ZOVbiST8j9yCrsVVsgzFfHTHYnsKat7me6rtxqMmY36LJvXZj1LPhzD31jNW2s+9OFepb+IrvIwqPcj3VPzpmrKP/C5mzqibKbZ+OceFeABcrjFlFj//N3U7b2Kv7UPeklH/QjdvzuOgxo7jX1UqTMT9dbv5Ragz2lK+eM74HWKt3pQoHs01s53fdHUdt685zJoIyb1PbuT/1p3UUjnvMP5gc6/bhSUqv6rMxfYAMScwJwwTlTv3lvzrIoxi86t5R2Ucgcq0AmTnIuzqox5qM+Yly887icVXNz1Tl3dTTcPBiIG603e2jxqntav+uNw0W9tV21XfHeUhTOO5xfOWhxJOCGW+mm1B+gNUD7cZu8Qk1rhGMWPP2gF56dc1udcrCeeF5ZmBiT8EzfYQ+3uRZTsb8FPHcKakx3ON3WPX2xqvYhOde9d5jrsZMYjq2up8zEZR5G25/mSo47jH/IPhC1cfIx3M338UWt6s4N+ZIIBzV3+TGf6tbiuGIfVbCEL0DZF5TN/8KlNh3zyHHpFSPNRnznTX9mNQ47CnPPe7ju8k1v7fsFaTOvuf5fru62vZIuPtVESKjuKfGTLa1mq4cK+3Rr0aw5klgr2KLe6CcieqvrnR/J3CQT1XwmvTVz/dH/gUZAhFzCB+izl4HP7Xfd9b0Azn6IboPefJuZ0+BYyO/ka9675Sx/059rNm7fc/Aci9yFyHq7GF+qp71V7Pu4T+S3IYZx3cTw7HFDJDd391bjWVABvkjUg+U4bdAVn/GqVTNeUqBEee9AiLuV2k67l9q8vGoMdzj58gec+W795s9A6Z659kz1BTkVO32rc7vgu+F73MSKJ5Tt437mMd6FI6d9tCA7GKLe6ghPDDjS6EAyNswoqhTk4+/e1B73MMOe3xu9WecfJ0T8dyH8CFq11OajvtKTT+E6mPimp+ny+xVje+5qjEzaPi9Zj+F45GYHI+vzYW6DzU3LgJy+qpm8Vir/4uI2Pd9P/D/zrPH7UfH21L44fFHWkVCwfUZkvngFvIcqh/ksz6iPT7+b+OxVg+LI8GIXl1X3rOSegaVcl6Vj/DPdHKO6bhXCue9khrHPX6Gqsd9fL5cu9iEr0DC4aCIXsX/a/I08DqmkKzmoIqgnF6p2jZSt3Lco/5wcFzQduzhDSnwqUgAcmzkE3oIvw6EeJ3OR9xOMD+MrBmM+Xdv4Bj3IPe4hyRf63QVWT2fiNvnkD7Hs88xKdVTmo57ttTcKrln4GrlVa6eN9YVGFSeRAdFrBl8U0A6YLpzOSC6/iRC5BSOeZo6OE60R/0x5EXnB7mT5xdlgYy9DAXBDpApvs7JdafnWq0Wq7/Njcfucftv3VeR8/oYiLiSzPlkmLl7QzDys0kfonY9pem4R4XzWsmN4/5OWfVU3k2tYhP+GWBEUDG0KtBVgMSx3YrSXY+79m4O1Lxyxu3YwxrzIZ2F4x76xVcXgR9bfoTsFQwRiugdBDkH5Mor8UNRDwDjTXgFSPVXXTpARnzOSYIywcjzgXLPxmkCSXUOHOM0HXdU+Dw6qbHc4+eretzn3jQYDAoOkzgDRgW5bvX4/4yvQoERa3fvKqLoO7nt2Jdj8P9r4n9wPPjnjqk96o8o+xG3H5668QqQuT8DMoSvstMucuVVVIBcjc9YwDMs+f7Ru0jlsSfC55Men232UdPju/2Pao+51FjuYa08P3fnVU998AzGzEfA+C78FIxn4chgdD+/q2uZgHECycxuzNM0WTnucfuRqBecx7DUh6dudIMxDMjMHAnG3O+IuklXvSrcKtD9pekYk+Ms5DHO/NRGCO5NL0Ttek77px2Nj7jdZyI1nntYK68ye1Vz8MfOQDgCRgfFI3Cs/ixxAscKjOoaqvtx8zAJJ9y2Uz6sCRyd9rj9kII8j424/eAwq5cqx3OuPqrug1PnzjwNfsDYZ/hhrlaOKo4A8lk/tStYpg9Ru16lPZ4rdzzu75RVT+Xd1FVswjMQVI3RQTGzA6SDmPvzxAqCVa+6Bndv6juqIpptuV2pHcP/9403cCx+Wu8xAyB/PCj8yHAM3uBG4zfwmNNPP8SI+wnGXhfVQ8xtb9RDGO7gsd+BchH+X//UTh+hj8VjXq09tLiP9U6Ze5XHyL56NzbhN+G5p2CiYIgAQt/FZPWIvtvuQl2/u2fuVeHUPd/DemTlGPFx8gVykOexqsYXaKU6xcc7+uHxxHYPAIMfmHuQ2H+DnvIMxlX0eD6UX8Crn9qPgFKBUT1jHINy/WdpDy3uY628yuxVzbE1voMCx7vwmNm73tGV45Ftk1D3w3Ph5nE69yn0KNcvdRaOe3gA8kejhB9eTsgKnsdib/qxVRM4CX541cPc4n7liD3lEWoOlnyuhTz/dFeAXONWuS+CcgfPQjDu1Esfok65/lntocV9rJWv8m7qKjbK6TfyqnYAeReZvapVKKh14JuAtbqO6p5U8Fzys3Cqni9L9tXfiHAHx+FPa9XH7TwWT4wfFm7LSWEobDFTN4ER9xOugh+U6rmHm4Eg5Jp/bjsw8s9tnhsFxswMPwYmahe9So9CUm3rhPt227BWvsrsp7FRTr+Rn8S78B18pnEEdkdXiBl83SpjqHk8Ein0KNdvdXblGPFx0g6WKQdKrPHj32KunTL2j4R7WNzvXm6MDpKZO1B2K8kKkAHeRSqPOdFZSOK2R6SOgb2dMvcqr3ruw92KzF4FA6MCCwPobDwThu9xez18T6qn5kbNbRcBGT32ULLv/h4tCccnrB5xnLvQ1BafP/12qDe7x6fUxKR/A3801ENU/WkoSGKf4YherSJxnrJ2gEQ/idRONaoCI/qAGntnhcdyfeVVZq9qNeeqVpnfH6wZFA4mlX80HoHhe9xfi7sPdb88J2oO3fNA8XNluf5Ij6wcIz5Ozi889tg7bfEJRM6sapLSv5HnCcaXu4ojYyfxRn6n/pFV5GQludDxGIT5DyVU7qeeIesIJAN63K/k3pvuHejybuoqNuExs69CwUNlB6NnRAdId051jXxPqlZzpOaSnw9KPTuVWbJf/e2rFo4PrB65zpN3kMz+Fh6Qe9x+0DyBPGlvlDE201fjnhnVKhK3H1lF5vEQmAzGzAjGDWqMVB6n01FIBvSPivfDeh9k9tPYhN+EV/UWNTgm0PlXwefna1T3wvfu5obndRqp3WT2p3R25biHXlmkVznFdWqLT/ClxxxRf6g8eekdIHnlVT3QVwSvIjPv4I+sIvl+cp4dINFXkcLn7DSBJI6divd1dZXZu94mxmxFZl+FgggDyPmvCj6nuh6V1X2qeVHz6OZdPasovOupMeWqMaKB44H/vfUePSBzW4T+WCI+JkgBMvdXq49qAjMUIBEmW9zDU73czw4FSPTVz+1qFYlzj/c6BWQ+gz1u+yl8pkoVJF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deleted file mode 100644
index 4ab0ac179a..0000000000
--- a/pr-2308/sphinx/_static/cudaq_override.css
+++ /dev/null
@@ -1,27 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- *                                                                             *
- * File: cudaq_override.css                                                    *
- * Created Date: 16 Feb 2023                                                   *
- * Last Modified: 16 Feb 2023                                                  *
- ******************************************************************************/
-
-.wy-nav-content {
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deleted file mode 100644
index b45a3fd5b5..0000000000
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-{{ fullname | escape | underline}}
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-
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diff --git a/pr-2308/sphinx/_templates/autosummary/dataclass.rst b/pr-2308/sphinx/_templates/autosummary/dataclass.rst
deleted file mode 100644
index b8c35324ed..0000000000
--- a/pr-2308/sphinx/_templates/autosummary/dataclass.rst
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-{{ fullname | escape | underline}}
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deleted file mode 100644
index c0f02f29ab..0000000000
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diff --git a/pr-2308/sphinx/_templates/openapi.html b/pr-2308/sphinx/_templates/openapi.html
deleted file mode 100644
index d50627fd0a..0000000000
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+++ /dev/null
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-<!DOCTYPE html>
-<html>
-  <head />
-  <body>
-    <script src="https://cdn.redoc.ly/redoc/latest/bundles/redoc.standalone.js"> </script>
-    <div id="redoc-container"></div>
-	<script type="application/json" id="openapi-spec">
-    ##SPEC_JSON##
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diff --git a/pr-2308/sphinx/api/api.rst b/pr-2308/sphinx/api/api.rst
deleted file mode 100644
index 9a4cc41308..0000000000
--- a/pr-2308/sphinx/api/api.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-Code documentation
-******************
-
-.. toctree::
-  :caption: CUDA-Q API
-  :maxdepth: 1
-
-  C++ API <languages/cpp_api.rst>
-  Python API <languages/python_api.rst>
-  Quantum Operations <default_ops.rst>
\ No newline at end of file
diff --git a/pr-2308/sphinx/api/default_ops.rst b/pr-2308/sphinx/api/default_ops.rst
deleted file mode 100644
index 214c8f6575..0000000000
--- a/pr-2308/sphinx/api/default_ops.rst
+++ /dev/null
@@ -1,761 +0,0 @@
-Quantum Operations
-******************************
-
-CUDA-Q provides a default set of quantum operations on qubits. 
-These operations can be used to define custom kernels and libraries.
-Since the set of quantum intrinsic operations natively supported on a specific target 
-depends on the backends architecture, the :code:`nvq++` compiler automatically
-decomposes the default operations into the appropriate set of intrinsic operations 
-for that target.
-
-The sections `Unitary Operations on Qubits`_ and `Measurements on Qubits`_ list the default set of quantum operations on qubits.
-
-Operations that implement unitary transformations of the quantum state are templated.
-The template argument allows to invoke the adjoint and controlled version of the quantum transformation, see the section on `Adjoint and Controlled Operations`_.
-
-CUDA-Q additionally provides overloads to support broadcasting of
-single-qubit operations across a vector of qubits.  For example,
-:code:`x(cudaq::qvector<>&)` flips the state of each qubit in the provided
-:code:`cudaq::qvector`. 
-
-
-Unitary Operations on Qubits
-=============================
-
-:code:`x`
----------------------
-
-This operation implements the transformation defined by the Pauli-X matrix. It is also known as the quantum version of a `NOT`-gate.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # X = | 0  1 |
-        #     | 1  0 |
-        x(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // X = | 0  1 |
-        //     | 1  0 |
-        x(qubit);
-
-:code:`y`
----------------------
-
-This operation implements the transformation defined by the Pauli-Y matrix.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Y = | 0  -i |
-        #     | i   0 |
-        y(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Y = | 0  -i |
-        //     | i   0 |
-        y(qubit);
-
-:code:`z`
----------------------
-
-This operation implements the transformation defined by the Pauli-Z matrix.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Z = | 1   0 |
-        #     | 0  -1 |
-        z(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Z = | 1   0 |
-        //     | 0  -1 |
-        z(qubit);
-
-:code:`h`
----------------------
-
-This operation is a rotation by π about the X+Z axis, and 
-enables one to create a superposition of computational basis states.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # H = (1 / sqrt(2)) * | 1   1 |
-        #                     | 1  -1 |
-        h(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // H = (1 / sqrt(2)) * | 1   1 |
-        //                     | 1  -1 |
-        h(qubit);
-
-:code:`r1`
----------------------
-
-This operation is an arbitrary rotation about the :code:`|1>` state.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # R1(λ) = | 1     0    |
-        #         | 0  exp(iλ) |
-        r1(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // R1(λ) = | 1     0    |
-        //         | 0  exp(iλ) |
-        r1(std::numbers::pi, qubit);
-
-:code:`rx`
----------------------
-
-This operation is an arbitrary rotation about the X axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Rx(θ) = |  cos(θ/2)  -isin(θ/2) |
-        #         | -isin(θ/2)  cos(θ/2)  |
-        rx(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Rx(θ) = |  cos(θ/2)  -isin(θ/2) |
-        //         | -isin(θ/2)  cos(θ/2)  |
-        rx(std::numbers::pi, qubit);
-
-:code:`ry`
----------------------
-
-This operation is an arbitrary rotation about the Y axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Ry(θ) = | cos(θ/2)  -sin(θ/2) |
-        #         | sin(θ/2)   cos(θ/2) |
-        ry(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Ry(θ) = | cos(θ/2)  -sin(θ/2) |
-        //         | sin(θ/2)   cos(θ/2) |
-        ry(std::numbers::pi, qubit);
-
-:code:`rz`
----------------------
-
-This operation is an arbitrary rotation about the Z axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Rz(λ) = | exp(-iλ/2)      0     |
-        #         |     0       exp(iλ/2) |
-        rz(math.pi, qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // Rz(λ) = | exp(-iλ/2)      0     |
-        //         |     0       exp(iλ/2) |
-        rz(std::numbers::pi, qubit);
-
-:code:`s`
----------------------
-
-This operation applies to its target a rotation by π/2 about the Z axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # S = | 1   0 |
-        #     | 0   i |
-        s(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // S = | 1   0 |
-        //     | 0   i |
-        s(qubit);
-
-:code:`t`
----------------------
-
-This operation applies to its target a π/4 rotation about the Z axis.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # T = | 1      0     |
-        #     | 0  exp(iπ/4) |
-        t(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // T = | 1      0     |
-        //     | 0  exp(iπ/4) |
-        t(qubit);
-
-:code:`swap`
----------------------
-
-This operation swaps the states of two qubits.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit_1, qubit_2 = cudaq.qubit(), cudaq.qubit()
-
-        # Apply the unitary transformation
-        # Swap = | 1 0 0 0 |
-        #        | 0 0 1 0 |
-        #        | 0 1 0 0 |
-        #        | 0 0 0 1 |
-        swap(qubit_1, qubit_2)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit_1, qubit_2;
-
-        // Apply the unitary transformation
-        // Swap = | 1 0 0 0 |
-        //        | 0 0 1 0 |
-        //        | 0 1 0 0 |
-        //        | 0 0 0 1 |
-        swap(qubit_1, qubit_2);
-
-:code:`u3`
----------------------
-
-This operation applies the universal three-parameters operator to target qubit. The three parameters are Euler angles - theta (θ), phi (φ), and lambda (λ).
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation
-        # U3(θ,φ,λ) = | cos(θ/2)            -exp(iλ) * sin(θ/2)       |
-        #             | exp(iφ) * sin(θ/2)   exp(i(λ + φ)) * cos(θ/2) |
-        u3(np.pi, np.pi, np.pi / 2, q)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation
-        // U3(θ,φ,λ) = | cos(θ/2)            -exp(iλ) * sin(θ/2)       |
-        //             | exp(iφ) * sin(θ/2)   exp(i(λ + φ)) * cos(θ/2) |
-        u3(M_PI, M_PI, M_PI_2, q);
-
-
-Adjoint and Controlled Operations
-==================================
-
-.. tab:: Python
-
-    The :code:`adj` method of any gate can be used to invoke the 
-    `adjoint <https://en.wikipedia.org/wiki/Conjugate_transpose>`__ transformation:
-
-    .. code-block:: python
-
-        # Create a kernel and allocate a qubit in a |0> state.
-        qubit = cudaq.qubit()
-
-        # Apply the unitary transformation defined by the matrix
-        # T = | 1      0     |
-        #     | 0  exp(iπ/4) |
-        # to the state of the qubit `q`:
-        t(qubit)
-
-        # Apply its adjoint transformation defined by the matrix
-        # T† = | 1      0     |
-        #      | 0  exp(-iπ/4) |
-        t.adj(qubit)
-        # `qubit` is now again in the initial state |0>.
-
-    The :code:`ctrl` method of any gate can be used to apply the transformation
-    conditional on the state of one or more control qubits, see also this 
-    `Wikipedia entry <https://en.wikipedia.org/wiki/Quantum_logic_gate#Controlled_gates>`__.
-
-    .. code-block:: python
-
-        # Create a kernel and allocate qubits in a |0> state.
-        ctrl_1, ctrl_2, target = cudaq.qubit(), cudaq.qubit(), cudaq.qubit()
-        # Create a superposition.
-        h(ctrl_1)
-        # `ctrl_1` is now in a state (|0> + |1>) / √2.
-
-        # Apply the unitary transformation
-        # | 1  0  0  0 |
-        # | 0  1  0  0 |
-        # | 0  0  0  1 |
-        # | 0  0  1  0 |
-        x.ctrl(ctrl_1, ctrl_2)
-        # `ctrl_1` and `ctrl_2` are in a state (|00> + |11>) / √2.
-
-        # Set the state of `target` to |1>:
-        x(target)
-        # Apply the transformation T only if both 
-        # control qubits are in a |1> state:
-        t.ctrl([ctrl_1, ctrl_2], target)
-        # The qubits ctrl_1, ctrl_2, and target are now in a state
-        # (|000> + exp(iπ/4)|111>) / √2.
-
-.. tab:: C++
-
-    The template argument :code:`cudaq::adj` can be used to invoke the 
-    `adjoint <https://en.wikipedia.org/wiki/Conjugate_transpose>`__ transformation:
-
-    .. code-block:: cpp
-
-        // Allocate a qubit in a |0> state.
-        cudaq::qubit qubit;
-
-        // Apply the unitary transformation defined by the matrix
-        // T = | 1      0     |
-        //     | 0  exp(iπ/4) |
-        // to the state of the qubit `q`:
-        t(qubit);
-
-        // Apply its adjoint transformation defined by the matrix
-        // T† = | 1      0     |
-        //      | 0  exp(-iπ/4) |
-        t<cudaq::adj>(qubit);
-        // Qubit `q` is now again in the initial state |0>.
-
-    The template argument :code:`cudaq::ctrl` can be used to apply the transformation
-    conditional on the state of one or more control qubits, see also this 
-    `Wikipedia entry <https://en.wikipedia.org/wiki/Quantum_logic_gate#Controlled_gates>`__.
-
-    .. code-block:: cpp
-
-        // Allocate qubits in a |0> state.
-        cudaq::qubit ctrl_1, ctrl_2, target;
-        // Create a superposition.
-        h(ctrl_1);
-        // Qubit ctrl_1 is now in a state (|0> + |1>) / √2.
-
-        // Apply the unitary transformation
-        // | 1  0  0  0 |
-        // | 0  1  0  0 |
-        // | 0  0  0  1 |
-        // | 0  0  1  0 |
-        x<cudaq::ctrl>(ctrl_1, ctrl_2);
-        // The qubits ctrl_1 and ctrl_2 are in a state (|00> + |11>) / √2.
-
-        // Set the state of `target` to |1>:
-        x(target);
-        // Apply the transformation T only if both 
-        // control qubits are in a |1> state:
-        t<cudaq::ctrl>(ctrl_1, ctrl_2, target);
-        // The qubits ctrl_1, ctrl_2, and target are now in a state
-        // (|000> + exp(iπ/4)|111>) / √2.
-
-
-Following common convention, by default the transformation is applied to the target qubit(s)
-if all control qubits are in a :code:`|1>` state. 
-However, that behavior can be changed to instead apply the transformation when a control qubit is in 
-a :code:`|0>` state by negating the polarity of the control qubit.
-The syntax for negating the polarity is the not-operator preceding the
-control qubit: 
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit c, q;
-        h(c);
-        x<cudaq::ctrl>(!c, q);
-        // The qubits c and q are in a state (|01> + |10>) / √2.
-
-This notation is only supported in the context of applying a controlled operation and is only valid for control qubits. For example, negating either of the target qubits in the
-:code:`swap` operation is not allowed.
-Negating the polarity of control qubits is similarly supported when using :code:`cudaq::control` to conditionally apply a custom quantum kernel.
-
-
-Measurements on Qubits
-=============================
-
-:code:`mz`
----------------------
-
-This operation measures a qubit with respect to the computational basis, 
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-Z matrix.
-This is a non-linear transformation, and no template overloads are available.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-        mz(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-        mz(qubit);
-
-:code:`mx`
----------------------
-
-This operation measures a qubit with respect to the Pauli-X basis, 
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-X matrix.
-This is a non-linear transformation, and no template overloads are available.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-        mx(qubit)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-        mx(qubit);
-
-:code:`my`
----------------------
-
-This operation measures a qubit with respect to the Pauli-Y basis, 
-i.e., it projects the state of that qubit onto the eigenvectors of the Pauli-Y matrix.
-This is a non-linear transformation, and no template overloads are available.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qubit = cudaq.qubit()
-        kernel.my(qubit)
-        
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qubit qubit;
-        my(qubit);
-
-
-User-Defined Custom Operations
-==============================
-
-Users can define a custom quantum operation by its unitary matrix. First use 
-the API to register a custom operation, outside of a CUDA-Q kernel. Then the 
-operation can be used within a CUDA-Q kernel like any of the built-in operations
-defined above.
-Custom operations are supported on qubits only (`qudit` with `level = 2`).
-
-.. tab:: Python
-
-    The :code:`cudaq.register_operation` API accepts an identifier string for 
-    the custom operation and its unitary matrix. The matrix can be a `list` or
-    `numpy` array of complex numbers. A 1D matrix is interpreted as row-major.
-    
-
-    .. code-block:: python
-
-        import cudaq
-        import numpy as np
-
-        cudaq.register_operation("custom_h", 1. / np.sqrt(2.) * np.array([1, 1, 1, -1]))
-
-        cudaq.register_operation("custom_x", np.array([0, 1, 1, 0]))
-
-        @cudaq.kernel
-        def bell():
-            qubits = cudaq.qvector(2)
-            custom_h(qubits[0])
-            custom_x.ctrl(qubits[0], qubits[1])
-
-        cudaq.sample(bell).dump()
-
-        
-.. tab:: C++
-
-    The macro :code:`CUDAQ_REGISTER_OPERATION` accepts a unique name for the 
-    operation, the number of target qubits, the number of rotation parameters 
-    (can be 0), and the unitary matrix as a 1D row-major `std::vector<complex>` 
-    representation.
-    
-    .. code-block:: cpp
-
-        #include <cudaq.h>
-
-        CUDAQ_REGISTER_OPERATION(custom_h, 1, 0,
-                                {M_SQRT1_2, M_SQRT1_2, M_SQRT1_2, -M_SQRT1_2})
-
-        CUDAQ_REGISTER_OPERATION(custom_x, 1, 0, {0, 1, 1, 0})
-
-        __qpu__ void bell_pair() {
-            cudaq::qubit q, r;
-            custom_h(q);
-            custom_x<cudaq::ctrl>(q, r);
-        }
-
-        int main() {
-            auto counts = cudaq::sample(bell_pair);
-            for (auto &[bits, count] : counts) {
-                printf("%s\n", bits.data());
-            }
-        }
-
-
-For multi-qubit operations, the matrix is interpreted with MSB qubit ordering,
-i.e. big-endian convention. The following example shows two different custom
-operations, each operating on 2 qubits.
-
-
-.. tab:: Python
-
-    .. literalinclude:: ../snippets/python/using/examples/two_qubit_custom_op.py
-      :language: python
-      :start-after: [Begin Docs]
-      :end-before: [End Docs]
-
-
-.. tab:: C++
-
-    .. literalinclude:: ../snippets/cpp/using/two_qubit_custom_op.cpp
-      :language: cpp
-      :start-after: [Begin Docs]
-      :end-before: [End Docs]
-
-
-.. note:: 
-
-  When a custom operation is used on hardware backends, it is synthesized to a
-  set of native quantum operations. Currently, only 1-qubit and 2-qubit custom 
-  operations are supported on hardware backends.
-
-
-Photonic Operations on Qudits
-=============================
-
-These operations are valid only on the `orca-photonics` target which does not support
-the quantum operations above.
-
-:code:`create`
----------------------
-
-This operation increments the number of photons in a qumode up to a maximum value
-defined by the qudit level that represents the qumode. If it is applied to a qumode
-where the number of photons is already at the maximum value, the operation has no
-effect.
-
-:math:`U|0\rangle → |1\rangle, U|1\rangle → |2\rangle, U|2\rangle → |3\rangle, \cdots, U|d\rangle → |d\rangle`
-where :math:`d` is the qudit level.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = qudit(3)
-        create(q)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> q(1);
-        create(q[0]);
-
-:code:`annihilate`
----------------------
-
-This operation reduces the number of photons in a qumode up to a minimum value of
-0 representing the vacuum state. If it is applied to a qumode where the number of
-photons is already at the minimum value 0, the operation has no effect.
-
-:math:`U|0\rangle → |0\rangle, U|1\rangle → |0\rangle, U|2\rangle → |1\rangle, \cdots, U|d\rangle → |d-1\rangle`
-where :math:`d` is the qudit level.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = qudit(3)
-        annihilate(q)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> q(1);
-        annihilate(q[0]);
-
-:code:`phase_shift`
----------------------
-
-A phase shifter adds a phase :math:`\phi` on a qumode. For the annihilation (:math:`a_1`)
-and creation operators (:math:`a_1^\dagger`) of a qumode, the phase shift operator
-is defined  by
-
-.. math::
-    P(\phi) = \exp\left(i \phi a_1^\dagger a_1  \right)
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = qudit(4)
-        phase_shift(q, 0.17)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<4> q(1);
-        phase_shift(q[0], 0.17);
-
-:code:`beam_splitter`
----------------------
-
-Beam splitters act on two qumodes together and it is parameterized by a single angle 
-:math:`\theta`, relating to reflectivity.
-For the annihilation (:math:`a_1` and :math:`a_2`) and creation operators (:math:`a_1^\dagger`
-and :math:`a_2^\dagger`) of two qumodes, the beam splitter operator is defined by
-
-.. math::
-    B(\theta) = \exp\left[i \theta (a_1^\dagger a_2 + a_1 a_2^\dagger) \right]
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        q = [qudit(3) for _ in range(2)]
-        beam_splitter(q[0], q[1], 0.34)
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> q(2);
-        beam_splitter(q[0], q[1], 0.34);
-
-:code:`mz`
----------------------
-
-This operation returns the measurement results of the input qumode(s).
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        qumodes = [qudit(3) for _ in range(2)]
-        mz(qumodes)
-
-
-.. tab:: C++
-
-    .. code-block:: cpp
-
-        cudaq::qvector<3> qumodes(2);
-        mz(qumodes);
diff --git a/pr-2308/sphinx/api/languages/cpp_api.rst b/pr-2308/sphinx/api/languages/cpp_api.rst
deleted file mode 100644
index 34487dbefb..0000000000
--- a/pr-2308/sphinx/api/languages/cpp_api.rst
+++ /dev/null
@@ -1,266 +0,0 @@
-CUDA-Q C++ API
-******************************
-
-Operators 
-=============
-
-.. doxygenclass:: cudaq::spin_op
-    :members:
-
-Quantum
-=========
-
-.. doxygenvariable:: cudaq::dyn
-
-.. doxygenclass:: cudaq::qudit
-    :members:
-
-.. doxygenclass:: cudaq::qreg
-    :members:
-
-.. doxygenclass:: cudaq::qvector
-    :members:
-
-.. doxygenclass:: cudaq::qspan
-    :members:
-
-.. doxygenclass:: cudaq::qview
-    :members:
-
-.. doxygentypedef:: cudaq::qubit
-    
-Common
-=========
-
-.. doxygenclass:: cudaq::observe_result
-    :members:
-
-.. doxygenstruct:: cudaq::observe_options
-    :members:
-
-.. doxygenfunction:: cudaq::observe(const observe_options &options, QuantumKernel &&kernel, spin_op H, Args &&...args)
-.. doxygenfunction:: cudaq::observe(std::size_t shots, QuantumKernel &&kernel, spin_op H, Args &&...args)
-.. doxygenfunction:: cudaq::observe(QuantumKernel &&kernel, spin_op H, Args &&...args)
-.. doxygenfunction:: cudaq::observe(QuantumKernel &&kernel, const SpinOpContainer &termList, Args &&...args)
-
-.. doxygenclass:: cudaq::ExecutionContext
-    :members:
-
-.. doxygenclass:: cudaq::details::future
-    :members:
-
-.. doxygenclass:: cudaq::async_result
-    :members:
-
-.. doxygentypedef:: async_sample_result
-
-
-.. doxygenstruct:: cudaq::ExecutionResult
-    :members:
-
-.. doxygenclass:: cudaq::sample_result
-    :members:
-
-.. doxygenstruct:: cudaq::sample_options
-    :members:
-
-.. doxygenfunction:: cudaq::sample(const sample_options &options, QuantumKernel &&kernel, Args &&...args)
-.. doxygenfunction:: cudaq::sample(std::size_t shots, QuantumKernel &&kernel, Args &&...args)
-.. doxygenfunction:: cudaq::sample(QuantumKernel &&kernel, Args&&... args)
-
-.. doxygenclass:: cudaq::SimulationState
-
-.. doxygenstruct:: cudaq::SimulationState::Tensor
-    :members:
-
-.. doxygenenum:: cudaq::SimulationState::precision
-
-.. doxygenenum:: cudaq::simulation_precision
-
-.. doxygentypedef:: cudaq::tensor
-
-.. doxygentypedef:: cudaq::TensorStateData
-
-.. doxygentypedef:: cudaq::state_data
-
-.. doxygenclass:: cudaq::CusvState
-
-.. doxygenclass:: nvqir::MPSSimulationState
-
-.. doxygenclass:: nvqir::TensorNetSimulationState
-
-.. doxygenclass:: cudaq::RemoteSimulationState
-
-.. doxygenclass:: cudaq::registry::RegisteredType
-    :members:
-
-.. doxygenclass:: cudaq::complex_matrix
-    :members:
-
-.. doxygenclass:: cudaq::Trace
-
-.. doxygenfunction:: cudaq::range(ElementType total)
-.. doxygenfunction:: cudaq::range(ElementType begin, ElementType end, ElementType step)
-
-.. doxygenfunction:: cudaq::draw(QuantumKernel &&kernel, Args&&... args)
-
-.. doxygenfunction:: cudaq::get_state(QuantumKernel &&kernel, Args&&... args)
-
-.. doxygenclass:: cudaq::Resources
-
-.. doxygentypedef:: cudaq::complex_matrix::value_type
-
-Noise Modeling 
-================
-
-.. doxygenstruct:: cudaq::kraus_op
-    :members:
-
-.. doxygenclass:: cudaq::kraus_channel
-    :members:
-
-.. doxygenclass:: cudaq::amplitude_damping_channel
-    :members:
-
-.. doxygenclass:: cudaq::bit_flip_channel
-    :members:
-
-.. doxygenclass:: cudaq::phase_flip_channel
-    :members:
-
-.. doxygenclass:: cudaq::depolarization_channel
-    :members:
-
-.. doxygenclass:: cudaq::noise_model
-    :members:
-
-.. doxygenenum:: cudaq::noise_model_type
-
-Kernel Builder
-===============
-
-.. doxygenclass:: cudaq::kernel_builder
-    :members:
-
-.. doxygenclass:: cudaq::QuakeValue
-    :members:
-
-.. doxygenclass:: cudaq::details::kernel_builder_base
-    :members:
-
-.. doxygenclass:: cudaq::details::KernelBuilderType
-    :members:
-
-Algorithms
-===========
-
-.. doxygenclass:: cudaq::optimizer
-    :members:
-
-.. doxygenclass:: cudaq::optimizable_function
-    :members:
-
-.. doxygentypedef:: cudaq::optimization_result
-
-.. doxygenclass:: cudaq::state
-    :members:
-
-.. doxygenclass:: cudaq::gradient
-    :members:
-
-.. doxygenclass:: cudaq::gradients::central_difference
-    :members:
-
-.. doxygenclass:: cudaq::gradients::parameter_shift
-    :members:
-
-.. doxygenclass:: cudaq::gradients::forward_difference
-    :members:
-
-Platform
-=========
-
-.. doxygenclass:: cudaq::QPU
-    :members:
-
-.. doxygenclass:: cudaq::BaseRemoteRESTQPU
-
-.. doxygenclass:: cudaq::BaseRemoteSimulatorQPU
-
-.. doxygenclass:: cudaq::BaseNvcfSimulatorQPU
-
-.. doxygenclass:: cudaq::FermioniqBaseQPU
-
-.. doxygenclass:: cudaq::OrcaRemoteRESTQPU 
-
-.. doxygenclass:: cudaq::QuEraBaseQPU
-
-.. doxygenclass:: cudaq::quantum_platform
-    :members:
-
-.. doxygenstruct:: cudaq::RemoteCapabilities
-    :members:
-
-.. doxygenclass:: cudaq::SerializedCodeExecutionContext
-
-.. doxygentypedef:: cudaq::QuantumTask
-
-.. doxygentypedef:: cudaq::QubitConnectivity
-
-.. doxygentypedef:: cudaq::QubitEdge
-
-.. doxygentypedef:: cudaq::KernelExecutionTask
-
-.. doxygenstruct:: cudaq::KernelThunkResultType
-
-.. doxygentypedef:: cudaq::KernelThunkType
-
-Utilities
-=========
-
-.. doxygentypedef:: cudaq::complex
-
-.. doxygentypedef:: cudaq::real 
-
-.. doxygenfunction:: cudaq::range(std::size_t)
-    
-Namespaces 
-===========
-
-.. doxygennamespace:: cudaq
-    :desc-only:
-
-.. doxygenfunction:: cudaq::num_available_gpus
-.. doxygenfunction:: cudaq::set_random_seed
-.. doxygenfunction:: cudaq::set_noise
-.. doxygenfunction:: cudaq::unset_noise
-
-.. doxygennamespace:: cudaq::details
-    :desc-only:
-
-.. doxygennamespace:: cudaq::registry
-    :desc-only:
-
-.. doxygennamespace:: cudaq::mpi
-    :desc-only:
-
-.. doxygenfunction:: cudaq::mpi::initialize()
-.. doxygenfunction:: cudaq::mpi::initialize(int argc, char **argv)
-.. doxygenfunction:: cudaq::mpi::is_initialized
-.. doxygenfunction:: cudaq::mpi::finalize
-.. doxygenfunction:: cudaq::mpi::rank
-.. doxygenfunction:: cudaq::mpi::num_ranks
-.. doxygenfunction:: cudaq::mpi::all_gather(std::vector<double> &global, const std::vector<double> &local)
-.. doxygenfunction:: cudaq::mpi::all_gather(std::vector<int> &global, const std::vector<int> &local)
-.. doxygenfunction:: cudaq::mpi::all_reduce(const T&, const Func&)
-.. doxygenfunction:: cudaq::mpi::all_reduce(const T &localValue, const BinaryFunction &function)
-.. doxygenfunction:: cudaq::mpi::broadcast(std::vector<double> &data, int rootRank)
-.. doxygenfunction:: cudaq::mpi::broadcast(std::string &data, int rootRank)
-
-.. doxygennamespace:: cudaq::orca
-    :desc-only:
-
-.. doxygenfunction:: cudaq::orca::sample(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, int n_samples = 10000, std::size_t qpu_id = 0)
-.. doxygenfunction:: cudaq::orca::sample(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, std::vector<double> &ps_angles, int n_samples = 10000, std::size_t qpu_id = 0)
-.. doxygenfunction:: cudaq::orca::sample_async(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, int n_samples = 10000, std::size_t qpu_id = 0)
-.. doxygenfunction:: cudaq::orca::sample_async(std::vector<std::size_t> &input_state, std::vector<std::size_t> &loop_lengths, std::vector<double> &bs_angles, std::vector<double> &ps_angles, int n_samples = 10000, std::size_t qpu_id = 0)
diff --git a/pr-2308/sphinx/api/languages/python_api.rst b/pr-2308/sphinx/api/languages/python_api.rst
deleted file mode 100644
index d84ddc427c..0000000000
--- a/pr-2308/sphinx/api/languages/python_api.rst
+++ /dev/null
@@ -1,258 +0,0 @@
-CUDA-Q Python API
-******************************
-
-.. automodule:: cudaq
-
-Program Construction
-=============================
-
-.. autofunction:: cudaq::make_kernel
-.. [SKIP_TEST]: Reason - AttributeError: module 'cudaq' has no attribute 'from_state'
-.. .. autofunction:: cudaq::from_state
-
-.. autoclass:: cudaq::PyKernel
-.. autoclass:: cudaq::Kernel
-
-    .. automethod:: qalloc
-    .. automethod:: __str__
-    .. automethod:: __call__
-
-    .. automethod:: x
-    .. automethod:: cx
-    .. automethod:: y
-    .. automethod:: cy
-    .. automethod:: z
-    .. automethod:: cz
-    .. automethod:: h
-    .. automethod:: ch
-    .. automethod:: s
-    .. automethod:: sdg
-    .. automethod:: cs
-    .. automethod:: t
-    .. automethod:: tdg
-    .. automethod:: ct
-    .. automethod:: rx
-    .. automethod:: crx
-    .. automethod:: ry
-    .. automethod:: cry
-    .. automethod:: rz
-    .. automethod:: crz
-    .. automethod:: r1
-    .. automethod:: cr1
-    .. automethod:: swap
-    .. automethod:: cswap
-    .. automethod:: exp_pauli
-    .. automethod:: mx
-    .. automethod:: my
-    .. automethod:: mz
-    .. automethod:: c_if
-    .. automethod:: for_loop
-    .. automethod:: adjoint
-    .. automethod:: control
-    .. automethod:: apply_call
-    .. automethod:: u3
-
-.. autoclass:: cudaq::PyKernelDecorator
-    :members:
-    :special-members: __str__, __call__
-
-.. autofunction:: kernel
-    
-Kernel Execution
-=============================
-
-.. autofunction:: cudaq::sample
-.. autofunction:: cudaq::sample_async
-.. autofunction:: cudaq::observe
-.. autofunction:: cudaq::observe_async
-.. autofunction:: cudaq::get_state
-.. autofunction:: cudaq::get_state_async
-.. autofunction:: cudaq::vqe
-.. autofunction:: cudaq::draw
-.. autofunction:: cudaq::translate
-
-Dynamics
-=============================
-
-.. autofunction:: cudaq::evolve
-.. autofunction:: cudaq::evolve_async
-
-Backend Configuration
-=============================
-
-.. autofunction:: cudaq::has_target
-.. autofunction:: cudaq::get_target
-.. autofunction:: cudaq::get_targets
-.. autofunction:: cudaq::set_target
-.. autofunction:: cudaq::reset_target
-.. autofunction:: cudaq::set_noise
-.. autofunction:: cudaq::unset_noise
-.. automethod:: cudaq::initialize_cudaq
-.. automethod:: cudaq::num_available_gpus
-.. automethod:: cudaq::set_random_seed
-
-Data Types
-=============================
-
-.. autoclass:: cudaq::SimulationPrecision
-    :members:
-    
-.. autoclass:: cudaq::Target
-    :members:
-
-.. autoclass:: cudaq::State
-    :members:
-
-.. autoclass:: cudaq::Tensor
-
-.. autoclass:: cudaq::QuakeValue
-
-    .. automethod:: __add__
-    .. automethod:: __radd__
-    .. automethod:: __sub__
-    .. automethod:: __rsub__
-    .. automethod:: __neg__
-    .. automethod:: __mul__
-    .. automethod:: __rmul__
-    .. automethod:: __getitem__
-    .. automethod:: slice
-
-.. autoclass:: cudaq::qubit
-.. autoclass:: cudaq::qreg
-.. autoclass:: cudaq::qvector
-
-.. autoclass:: cudaq::ComplexMatrix
-    :members:
-    :special-members: __getitem__, __str__
-
-.. autoclass:: cudaq::Schedule
-.. autoclass:: cudaq.operator.integrator.BaseIntegrator
-.. autoclass:: cudaq::EvolveResult
-    :members:
-
-.. autoclass:: cudaq::AsyncEvolveResult
-    :members:
-
-.. autoclass:: cudaq::SpinOperator
-.. autoclass:: cudaq.operator.expressions.OperatorSum
-
-.. autoclass:: cudaq.operator.expressions.ElementaryOperator
-    
-    .. automethod:: define
-
-.. autoclass:: cudaq.operator.expressions.ProductOperator
-
-.. autoclass:: cudaq.operator.expressions.RydbergHamiltonian
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq.operator.expressions.ScalarOperator
-.. autoclass:: cudaq.operator.definitions.SpinOperator
-
-.. autofunction:: cudaq::spin.i
-.. autofunction:: cudaq::spin.x
-.. autofunction:: cudaq::spin.y
-.. autofunction:: cudaq::spin.z
-
-.. autoclass:: cudaq.operator.cudm_state.CuDensityMatState
-    :members:
-
-.. autofunction:: cudaq.operator.cudm_state.to_cupy_array
-
-.. autoclass:: cudaq::SampleResult
-    :members:
-    :special-members: __getitem__, __len__, __iter__
-
-.. autoclass:: cudaq::AsyncSampleResult
-    :members:
-
-.. autoclass:: cudaq::ObserveResult
-    :members:
-
-.. autoclass:: cudaq::AsyncObserveResult
-    :members:
-
-.. autoclass:: cudaq::AsyncStateResult
-    :members:
-
-.. autoclass:: cudaq::OptimizationResult
-    :members:
-
-
-Optimizers
------------------
-
-.. autoclass:: cudaq.optimizers::optimizer
-
-.. autoclass:: cudaq.optimizers::GradientDescent
-    :members:
-
-.. autoclass:: cudaq.optimizers::COBYLA
-    :members:
-
-.. autoclass:: cudaq.optimizers::NelderMead
-    :members:
-
-.. autoclass:: cudaq.optimizers::LBFGS
-    :members:
-
-Gradients
------------------
-
-.. autoclass:: cudaq.gradients::gradient
-    :members:
-
-.. autoclass:: cudaq.gradients::CentralDifference
-    :members:
-
-.. autoclass:: cudaq.gradients::ForwardDifference
-    :members:
-
-.. autoclass:: cudaq.gradients::ParameterShift
-    :members:
-
-Noisy Simulation
------------------
-
-.. autoclass:: cudaq::NoiseModel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::BitFlipChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::PhaseFlipChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::DepolarizationChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::AmplitudeDampingChannel
-    :members:
-    :special-members: __init__
-
-.. autoclass:: cudaq::KrausChannel
-    :members:
-    :special-members: __getitem__
-
-.. autoclass:: cudaq::KrausOperator
-    :members:
-
-MPI Submodule
-=============================
-
-.. automethod:: cudaq.mpi::initialize
-.. automethod:: cudaq.mpi::rank
-.. automethod:: cudaq.mpi::num_ranks
-.. automethod:: cudaq.mpi::all_gather
-.. automethod:: cudaq.mpi::broadcast
-.. automethod:: cudaq.mpi::is_initialized
-.. automethod:: cudaq.mpi::finalize
-
-ORCA Submodule
-=============================
-
-.. automethod:: cudaq.orca::sample
diff --git a/pr-2308/sphinx/applications/cpp/amplitude_estimation.cpp b/pr-2308/sphinx/applications/cpp/amplitude_estimation.cpp
deleted file mode 100644
index 8cb4a1de92..0000000000
--- a/pr-2308/sphinx/applications/cpp/amplitude_estimation.cpp
+++ /dev/null
@@ -1,168 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ amplitude_estimation.cpp -o mlae.x && ./mlae.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/optimizers.h>
-
-// This file implements Quantum Monte Carlo integration using Amplitude
-// Estimation. To compute a definite integral of the form sin^2(x) from
-// `0` to `bmax`, we use the maximum likelihood estimation method [1].
-// The kernels have been given similar names as their corresponding
-// unitary operators from equation (1) in [1].
-//
-// [1] Amplitude estimation without phase estimation by Suzuki et al.
-// (https://arxiv.org/pdf/1904.10246.pdf)
-
-namespace mlae {
-
-double loglikelihood(std::vector<int> &h, std::vector<int> &mk, int N_shots,
-                     double theta_val) {
-
-  double sum = 0;
-  for (int i = 0; i < h.size(); i++) {
-
-    double arg = (2 * mk[i] + 1) * theta_val;
-    sum += 2 * h[i] * log(fabs(sin(arg))) +
-           2 * (N_shots - h[i]) * log(fabs(cos(arg)));
-  }
-
-  return -sum;
-}
-
-double discretized_integral(const int n_qubits, double bmax) {
-
-  int N = pow(2, n_qubits) - 1;
-  double sum = 0.0;
-  double denom = pow(2, n_qubits);
-
-  for (int i = 0; i <= N; i++) {
-
-    sum += pow(sin((i + 0.5) * bmax / denom), 2);
-  }
-
-  return sum / denom;
-}
-
-int num_oracles(std::vector<int> &vec, const int N_shots) {
-
-  int total_oracles = 0;
-  for (int i = 0; i < vec.size(); i++) {
-    total_oracles += N_shots * (2 * vec[i] + 1);
-  }
-
-  return total_oracles;
-}
-} // namespace mlae
-
-struct statePrep_A {
-  void operator()(cudaq::qvector<> &q, const double bmax) __qpu__ {
-
-    int n = q.size();
-    // all qubits sans auxiliary
-    auto qubit_subset = q.front(n - 1);
-
-    h(qubit_subset);
-
-    ry(bmax / pow(2.0, n - 1), q[n - 1]);
-
-    for (int i = 1; i < n; i++) {
-      ry<cudaq::ctrl>(bmax / pow(2.0, n - i - 1), q[i - 1], q[n - 1]);
-    }
-  }
-};
-
-struct S_0 {
-  void operator()(cudaq::qvector<> &q) __qpu__ {
-
-    auto ctrl_qubits = q.front(q.size() - 1);
-    auto &last_qubit = q.back();
-
-    x(q);
-    h(last_qubit);
-    x<cudaq::ctrl>(ctrl_qubits, last_qubit);
-    h(last_qubit);
-    x(q);
-  }
-};
-
-struct run_circuit {
-
-  auto operator()(const int n_qubits, const int n_itrs,
-                  const double bmax) __qpu__ {
-
-    cudaq::qvector q(n_qubits + 1); // last is auxiliary
-    auto &last_qubit = q.back();
-
-    // State preparation
-    statePrep_A{}(q, bmax);
-
-    // Amplification Q^m_k as per evaluation schedule {m_0,m_1,..,m_k,..}
-    for (int i = 0; i < n_itrs; ++i) {
-
-      z(last_qubit);
-      cudaq::adjoint(statePrep_A{}, q, bmax);
-      S_0{}(q);
-      statePrep_A{}(q, bmax);
-    }
-    // Measure the last auxiliary qubit
-    mz(last_qubit);
-  }
-};
-
-int main() {
-
-  const int n = 10; // number of qubits (not including the auxiliary qubit)
-  const double bmax = M_PI * 0.25; // upper bound of the integral
-
-  // Specify your evaluation schedule
-  std::vector<int> schedule{0, 1, 2, 4};
-  std::vector<int> hits(schedule.size(),
-                        0); // #hits, input for post-processing
-
-  for (size_t i = 0; i < schedule.size(); i++) {
-    auto counts = cudaq::sample(run_circuit{}, n, schedule[i], bmax);
-    hits[i] = counts.count("1");
-  }
-
-  // Print the number of hits for the good state for each circuit
-  for (size_t i = 0; i < hits.size(); ++i)
-    printf("%d ", hits[i]);
-
-  printf("\n");
-
-  // Optimization (Replace with brute force)
-  cudaq::optimizers::cobyla optimizer;
-
-  // Specify initial value of optimization parameters
-  std::vector<double> theta{0.8}; // COBYLA is very sensitive to the start
-  optimizer.initial_parameters = theta;
-
-  // Specify bounds for theta
-  optimizer.lower_bounds = {0.0};
-  optimizer.upper_bounds = {M_PI / 2.0};
-
-  // Using 1000 shots in log-likelihood, which is the default #shots
-  auto [opt_val, opt_params] =
-      optimizer.optimize(1, [&](const std::vector<double> &theta) {
-        auto f = mlae::loglikelihood(hits, schedule, 1000, theta[0]);
-        printf("theta = %18.16e\n", theta[0]);
-        return f;
-      });
-
-  printf("opt val = %f, theta = %f\n", opt_val, opt_params[0]);
-
-  double a_estimated = sin(opt_params[0]) * sin(opt_params[0]);
-  printf("Estimated a is: %f\n", a_estimated);
-
-  // The integral is discretized using 2^n intervals
-  double a_discretized = mlae::discretized_integral(n, bmax);
-  printf("Discretized a is: %f\n", a_discretized);
-
-  printf("Relative error w.r.t. discretized version is: %3.2f %% \n",
-         100 * fabs((a_discretized - a_estimated) / a_estimated));
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/applications/cpp/bernstein_vazirani.cpp b/pr-2308/sphinx/applications/cpp/bernstein_vazirani.cpp
deleted file mode 100644
index 7ec7741eed..0000000000
--- a/pr-2308/sphinx/applications/cpp/bernstein_vazirani.cpp
+++ /dev/null
@@ -1,95 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ bernstein_vazirani.cpp -o bv.x --target nvidia --target-option mgpu
-// mpirun -np 4 ./bv.x
-// ```
-
-// This example is meant to demonstrate the cuQuantum GPU-accelerated multi-GPU
-// backend. A state vector simulation for up to 32 qubits can be run on a single
-// GPU (if the GPU memory is large enough). Larger state vectors can be
-// simulated using the multi-GPU backend. The amount of resources required for
-// the simulation doubles with with each additional qubit.
-
-#include <cudaq.h>
-#include <iostream>
-#include <random>
-#include <vector>
-
-#ifndef SIZE
-#define SIZE 5
-#endif
-
-template <int nrOfBits>
-std::vector<bool> random_bits(int seed) {
-
-  std::vector<bool> randomBits(nrOfBits);
-  std::default_random_engine generator(seed);
-  std::uniform_real_distribution<float> distribution(0.0, 1.0);
-
-  for (size_t i = 0; i < nrOfBits; i++) {
-    randomBits[i] = distribution(generator) >= 0.5;
-  }
-  return randomBits;
-}
-
-template <int nrOfBits>
-struct oracle {
-  auto operator()(std::vector<bool> &bitvector, cudaq::qview<> qs,
-                  cudaq::qubit &aux) __qpu__ {
-
-    for (size_t i = 0; i < nrOfBits; i++) {
-      if (bitvector[i] & 1) {
-        x<cudaq::ctrl>(qs[nrOfBits - i - 1], aux);
-      }
-    }
-  }
-};
-
-template <int nrOfBits>
-struct bernstein_vazirani {
-  auto operator()(std::vector<bool> bitvector) __qpu__ {
-
-    cudaq::qarray<nrOfBits> qs;
-    cudaq::qubit aux;
-    h(aux);
-    z(aux);
-    h(qs);
-
-    oracle<nrOfBits>{}(bitvector, qs, aux);
-    h(qs);
-    mz(qs);
-  }
-};
-
-// Construct the bit vector such that the last bit has highest significance.
-std::string asString(const std::vector<bool> &bitvector) {
-  char *buffer = static_cast<char *>(alloca(bitvector.size() + 1));
-  std::size_t N = bitvector.size();
-  buffer[N] = '\0';
-  for (std::size_t i = 0; i < N; ++i)
-    buffer[N - 1 - i] = '0' + bitvector[i];
-  return {buffer, N};
-}
-
-int main(int argc, char *argv[]) {
-  auto seed = 1 < argc ? atoi(argv[1]) : time(NULL);
-
-  // The number of qubits can be >32 when targeting the `nvidia-mgpu` backend.
-  const int nr_qubits = SIZE;
-  const int nr_shots = 100;
-  auto bitvector = random_bits<nr_qubits>(seed);
-  auto kernel = bernstein_vazirani<nr_qubits>{};
-  auto counts = cudaq::sample(nr_shots, kernel, bitvector);
-
-  if (!cudaq::mpi::is_initialized() || cudaq::mpi::rank() == 0) {
-    printf("Encoded bitstring:  %s\n", asString(bitvector).c_str());
-    printf("Measured bitstring: %s\n\n", counts.most_probable().c_str());
-
-    for (auto &[bits, count] : counts) {
-      printf("observed %s with %.0f%% probability\n", bits.data(),
-             100.0 * count / nr_shots);
-    }
-  }
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/applications/cpp/grover.cpp b/pr-2308/sphinx/applications/cpp/grover.cpp
deleted file mode 100644
index b4ac751ffd..0000000000
--- a/pr-2308/sphinx/applications/cpp/grover.cpp
+++ /dev/null
@@ -1,63 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --library-mode grover.cpp -o grover.x && ./grover.x
-// ```
-
-#include <cmath>
-#include <cudaq.h>
-#include <numbers>
-
-__qpu__ void reflect_about_uniform(cudaq::qvector<> &qs) {
-  auto ctrlQubits = qs.front(qs.size() - 1);
-  auto &lastQubit = qs.back();
-
-  // Compute (U) Action (V) produces
-  // U V U::Adjoint
-  cudaq::compute_action(
-      [&]() {
-        h(qs);
-        x(qs);
-      },
-      [&]() { z<cudaq::ctrl>(ctrlQubits, lastQubit); });
-}
-
-struct run_grover {
-  template <typename CallableKernel>
-  __qpu__ auto operator()(const int n_qubits, CallableKernel &&oracle) {
-    int n_iterations = round(0.25 * std::numbers::pi * sqrt(2 ^ n_qubits));
-
-    cudaq::qvector qs(n_qubits);
-    h(qs);
-    for (int i = 0; i < n_iterations; i++) {
-      oracle(qs);
-      reflect_about_uniform(qs);
-    }
-    mz(qs);
-  }
-};
-
-struct oracle {
-  const long target_state;
-
-  void operator()(cudaq::qvector<> &qs) __qpu__ {
-    cudaq::compute_action(
-        [&]() {
-          for (int i = 1; i <= qs.size(); ++i) {
-            auto target_bit_set = (1 << (qs.size() - i)) & target_state;
-            if (!target_bit_set)
-              x(qs[i - 1]);
-          }
-        },
-        [&]() {
-          auto ctrlQubits = qs.front(qs.size() - 1);
-          z<cudaq::ctrl>(ctrlQubits, qs.back());
-        });
-  }
-};
-
-int main(int argc, char *argv[]) {
-  auto secret = 1 < argc ? strtol(argv[1], NULL, 2) : 0b1011;
-  oracle compute_oracle{.target_state = secret};
-  auto counts = cudaq::sample(run_grover{}, 4, compute_oracle);
-  printf("Found string %s\n", counts.most_probable().c_str());
-}
diff --git a/pr-2308/sphinx/applications/cpp/iterative_qpe.cpp b/pr-2308/sphinx/applications/cpp/iterative_qpe.cpp
deleted file mode 100644
index 86653b3514..0000000000
--- a/pr-2308/sphinx/applications/cpp/iterative_qpe.cpp
+++ /dev/null
@@ -1,76 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// [Begin Documentation]
-// Compile and run with:
-// ```
-// nvq++ iterative_qpe.cpp -o qpe.x && ./qpe.x
-// ```
-
-#include <cudaq.h>
-
-struct iqpe {
-  void operator()() __qpu__ {
-    cudaq::qarray<2> q;
-    h(q[0]);
-    x(q[1]);
-    for (int i = 0; i < 8; i++)
-      r1<cudaq::ctrl>(-5 * M_PI / 8., q[0], q[1]);
-
-    h(q[0]);
-    auto cr0 = mz(q[0]);
-    reset(q[0]);
-
-    h(q[0]);
-    for (int i = 0; i < 4; i++)
-      r1<cudaq::ctrl>(-5 * M_PI / 8., q[0], q[1]);
-
-    if (cr0)
-      rz(-M_PI / 2., q[0]);
-
-    h(q[0]);
-    auto cr1 = mz(q[0]);
-    reset(q[0]);
-
-    h(q[0]);
-    for (int i = 0; i < 2; i++)
-      r1<cudaq::ctrl>(-5 * M_PI / 8., q[0], q[1]);
-
-    if (cr0)
-      rz(-M_PI / 4., q[0]);
-
-    if (cr1)
-      rz(-M_PI / 2., q[0]);
-
-    h(q[0]);
-    auto cr2 = mz(q[0]);
-    reset(q[0]);
-    h(q[0]);
-    r1<cudaq::ctrl>(-5 * M_PI / 8., q[0], q[1]);
-
-    if (cr0)
-      rz(-M_PI / 8., q[0]);
-
-    if (cr1)
-      rz(-M_PI_4, q[0]);
-
-    if (cr2)
-      rz(-M_PI_2, q[0]);
-
-    h(q[0]);
-    mz(q[0]);
-  }
-};
-
-int main() {
-  auto counts = cudaq::sample(/*shots*/ 10, iqpe{});
-  counts.dump();
-
-  return 0;
-}
-// [End Documentation]
diff --git a/pr-2308/sphinx/applications/cpp/phase_estimation.cpp b/pr-2308/sphinx/applications/cpp/phase_estimation.cpp
deleted file mode 100644
index 2ff5ea0588..0000000000
--- a/pr-2308/sphinx/applications/cpp/phase_estimation.cpp
+++ /dev/null
@@ -1,91 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ phase_estimation.cpp -o qpe.x && ./qpe.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <stdio.h>
-
-#include <cmath>
-
-// A pure device quantum kernel defined as a free function
-// (cannot be called from host code).
-__qpu__ void iqft(cudaq::qview<> q) {
-  int N = q.size();
-  // Swap qubits
-  for (int i = 0; i < N / 2; ++i) {
-    swap(q[i], q[N - i - 1]);
-  }
-
-  for (int i = 0; i < N - 1; ++i) {
-    h(q[i]);
-    int j = i + 1;
-    for (int y = i; y >= 0; --y) {
-      double denom = (1UL << (j - y));
-      const double theta = -M_PI / denom;
-      r1<cudaq::ctrl>(theta, q[j], q[y]);
-    }
-  }
-
-  h(q[N - 1]);
-}
-
-// CUDA-Q kernel call operators can be templated on
-// input CUDA-Q kernel expressions. Here we define a general
-// Phase Estimation algorithm that is generic on the eigenstate
-// preparation and unitary evolution steps.
-struct qpe {
-
-  // Define the CUDA-Q call expression to take user-specified eigenstate
-  // and unitary evolution kernels, as well as the number of qubits in the
-  // counting register and in the eigenstate register.
-  template <typename StatePrep, typename Unitary>
-  void operator()(const int nCountingQubits, StatePrep &&state_prep,
-                  Unitary &&oracle) __qpu__ {
-    // Allocate a register of qubits
-    cudaq::qvector q(nCountingQubits + 1);
-
-    // Extract sub-registers, one for the counting qubits
-    // another for the eigenstate register
-    auto counting_qubits = q.front(nCountingQubits);
-    auto &state_register = q.back();
-
-    // Prepare the eigenstate
-    state_prep(state_register);
-
-    // Put the counting register into uniform superposition
-    h(counting_qubits);
-
-    // Perform `ctrl-U^j`
-    for (int i = 0; i < nCountingQubits; ++i) {
-      for (int j = 0; j < (1 << i); ++j) {
-        cudaq::control(oracle, counting_qubits[i], state_register);
-      }
-    }
-
-    // Apply inverse quantum Fourier transform
-    iqft(counting_qubits);
-
-    // Measure to gather sampling statistics
-    mz(counting_qubits);
-
-    return;
-  }
-};
-
-struct r1PiGate {
-  void operator()(cudaq::qubit &q) __qpu__ { r1(1., q); }
-};
-
-int main() {
-
-  for (auto nQubits : std::vector<int>{2, 4, 6, 8}) {
-    auto counts = cudaq::sample(
-        qpe{}, nQubits, [](cudaq::qubit &q) __qpu__ { x(q); }, r1PiGate{});
-    auto mostProbable = counts.most_probable();
-    double theta = cudaq::to_integer(mostProbable) / (double)(1UL << nQubits);
-    auto piEstimate = 1. / (2 * theta);
-    printf("Pi Estimate(nQubits == %d) = %lf \n", nQubits, piEstimate);
-  }
-}
diff --git a/pr-2308/sphinx/applications/cpp/qaoa_maxcut.cpp b/pr-2308/sphinx/applications/cpp/qaoa_maxcut.cpp
deleted file mode 100644
index 0c14b578c6..0000000000
--- a/pr-2308/sphinx/applications/cpp/qaoa_maxcut.cpp
+++ /dev/null
@@ -1,91 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ qaoa_maxcut.cpp -o qaoa.x && ./qaoa.x
-// ```
-
-// [Begin Documentation]
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-#include <cudaq/spin_op.h>
-
-// Here we build up a CUDA-Q kernel for QAOA with p layers, with each
-// layer containing the alternating set of unitaries corresponding to the
-// problem and the mixer Hamiltonians. The algorithm leverages the CUDA-Q
-// VQE support to compute the Max-Cut of a rectangular graph illustrated below.
-//
-//        v0  0---------------------0 v1
-//            |                     |
-//            |                     |
-//            |                     |
-//            |                     |
-//        v3  0---------------------0 v2
-// The Max-Cut for this problem is 0101 or 1010.
-
-struct ansatz {
-  void operator()(std::vector<double> theta, const int n_qubits,
-                  const int n_layers) __qpu__ {
-    cudaq::qvector q(n_qubits);
-
-    // Prepare the initial state by superposition
-    h(q);
-
-    // Loop over all the layers
-    for (int i = 0; i < n_layers; ++i) {
-      // Problem Hamiltonian
-      for (int j = 0; j < n_qubits; ++j) {
-
-        x<cudaq::ctrl>(q[j], q[(j + 1) % n_qubits]);
-        rz(2.0 * theta[i], q[(j + 1) % n_qubits]);
-        x<cudaq::ctrl>(q[j], q[(j + 1) % n_qubits]);
-      }
-
-      for (int j = 0; j < n_qubits; ++j) {
-        // Mixer Hamiltonian
-        rx(2.0 * theta[i + n_layers], q[j]);
-      }
-    }
-  }
-};
-
-int main() {
-
-  using namespace cudaq::spin;
-
-  cudaq::set_random_seed(13); // set for repeatability
-
-  // Problem Hamiltonian
-  const cudaq::spin_op Hp = 0.5 * z(0) * z(1) + 0.5 * z(1) * z(2) +
-                            0.5 * z(0) * z(3) + 0.5 * z(2) * z(3);
-
-  // Problem parameters
-  const int n_qubits = 4;
-  const int n_layers = 2;
-  const int n_params = 2 * n_layers;
-
-  // Instantiate the optimizer
-  cudaq::optimizers::cobyla optimizer; // gradient-free COBYLA
-
-  // Set initial values for the optimization parameters
-  optimizer.initial_parameters = cudaq::random_vector(
-      -M_PI / 8.0, M_PI / 8.0, n_params, std::mt19937::default_seed);
-
-  // Call the optimizer
-  auto [opt_val, opt_params] =
-      cudaq::vqe(ansatz{}, Hp, optimizer, n_params, n_qubits, n_layers);
-
-  // Print the optimized value and the parameters
-  printf("Optimal value = %.16lf\n", opt_val);
-  printf("Optimal params = (%.16lf, %.16lf, %.16lf, %.16lf) \n", opt_params[0],
-         opt_params[1], opt_params[2], opt_params[3]);
-
-  // Sample the circuit using optimized parameters
-  auto counts = cudaq::sample(ansatz{}, opt_params, n_qubits, n_layers);
-
-  // Dump the states and the counts
-  counts.dump();
-
-  return 0;
-}
-// [End Documentation]
\ No newline at end of file
diff --git a/pr-2308/sphinx/applications/cpp/random_walk_qpe.cpp b/pr-2308/sphinx/applications/cpp/random_walk_qpe.cpp
deleted file mode 100644
index f3d1d26ebb..0000000000
--- a/pr-2308/sphinx/applications/cpp/random_walk_qpe.cpp
+++ /dev/null
@@ -1,61 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ random_walk_qpe.cpp -o qpe.x && ./qpe.x
-// ```
-
-#include <cudaq.h>
-
-// Here we demonstrate an algorithm expressed as a CUDA-Q kernel
-// that incorporates non-trivial control flow and conditional
-// quantum instruction invocation.
-
-struct rwpe {
-  double operator()(const int n_iter, double mu, double sigma) __qpu__ {
-    int iteration = 0;
-
-    // Allocate the qubits
-    cudaq::qvector q(2);
-
-    // Alias them
-    auto &aux = q.front();
-    auto &target = q.back();
-
-    x(q[1]);
-
-    while (iteration < n_iter) {
-      h(aux);
-      rz(1.0 - (mu / sigma), aux);
-      rz(.25 / sigma, target);
-      x<cudaq::ctrl>(aux, target);
-      rz(-.25 / sigma, target);
-      x<cudaq::ctrl>(aux, target);
-      h(aux);
-      if (mz(aux)) {
-        x(aux);
-        mu += sigma * .6065;
-      } else {
-        mu -= sigma * .6065;
-      }
-
-      sigma *= .7951;
-      iteration += 1;
-    }
-
-    return 2. * mu;
-  }
-};
-
-int main() {
-  int n_iterations = 24;
-  double mu = 0.7951, sigma = 0.6065;
-  auto phase = rwpe{}(n_iterations, mu, sigma);
-  printf("Phase = %lf\n", phase);
-}
diff --git a/pr-2308/sphinx/applications/cpp/trotter_kernel_mode.cpp b/pr-2308/sphinx/applications/cpp/trotter_kernel_mode.cpp
deleted file mode 100644
index 8edda798e1..0000000000
--- a/pr-2308/sphinx/applications/cpp/trotter_kernel_mode.cpp
+++ /dev/null
@@ -1,162 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ --enable-mlir -v trotter_kernel_mode.cpp -o trotter.x && ./trotter.x
-// ```
-
-#include <complex>
-#include <cudaq.h>
-#include <iostream>
-#include <string>
-
-// Compute magnetization using Suzuki-Trotter approximation.
-// This example demonstrates usage of quantum states in kernel mode.
-//
-// Details
-// https://pubs.aip.org/aip/jmp/article-abstract/32/2/400/229229/General-theory-of-fractal-path-integrals-with
-//
-// Hamiltonian used
-// https://en.m.wikipedia.org/wiki/Quantum_Heisenberg_model
-
-// If you have a NVIDIA GPU you can use this example to see
-// that the GPU-accelerated backends can easily handle a
-// larger number of qubits compared the CPU-only backend.
-//
-// Depending on the available memory on your GPU, you can
-// set the number of qubits to around 30 qubits, and run
-// the execution command with `-target nvidia` option.
-//
-// Note: Without setting the target to the `nvidia` backend,
-// there will be a noticeable decrease in simulation performance.
-// This is because the CPU-only backend has difficulty handling
-// 30+ qubit simulations.
-
-int SPINS = 11; // set to around 25 qubits for `nvidia` target
-int STEPS = 10; // set to around 100 for `nvidia` target
-
-// Compile and run with:
-// clang-format off
-// ```
-// nvq++ --enable-mlir -v trotter_kernel_mode.cpp -o trotter.x --target nvidia && ./trotter.x
-// ```
-// clang-format off
-
-// Alternating up/down spins
-struct initState {
-  void operator()(int num_spins) __qpu__ {
-    cudaq::qvector q(num_spins);
-    for (int qId = 0; qId < num_spins; qId += 2)
-      x(q[qId]);
-  }
-};
-
-std::vector<double> term_coefficients(cudaq::spin_op op) {
-  std::vector<double> result{};
-  op.for_each_term([&](cudaq::spin_op &term) {
-    const auto coeff = term.get_coefficient().real();
-    result.push_back(coeff);
-  });
-  return result;
-}
-
-std::vector<cudaq::pauli_word> term_words(cudaq::spin_op op) {
-  std::vector<cudaq::pauli_word> result{};
-  op.for_each_term(
-      [&](cudaq::spin_op &term) { result.push_back(term.to_string(false)); });
-  return result;
-}
-
-struct trotter {
-  // Note: This performs a single-step Trotter on top of an initial state, e.g.,
-  // result state of the previous Trotter step.
-  void operator()(cudaq::state *initial_state,
-                  std::vector<double> &coefficients,
-                  std::vector<cudaq::pauli_word> &words, double dt) __qpu__ {
-    cudaq::qvector q(initial_state);
-    for (std::size_t i = 0; i < coefficients.size(); ++i) {
-      cudaq::exp_pauli(coefficients[i] * dt, q, words[i]);
-    }
-  }
-};
-
-int run_steps(int steps, int spins) {
-  const double g = 1.0;
-  const double Jx = 1.0;
-  const double Jy = 1.0;
-  const double Jz = g;
-  const double dt = 0.05;
-  const int n_steps = steps;
-  const int n_spins = spins;
-  const double omega = 2 * M_PI;
-  const auto heisenbergModelHam = [&](double t) -> cudaq::spin_op {
-    cudaq::spin_op tdOp(n_spins);
-    for (int i = 0; i < n_spins - 1; ++i) {
-      tdOp += (Jx * cudaq::spin::x(i) * cudaq::spin::x(i + 1));
-      tdOp += (Jy * cudaq::spin::y(i) * cudaq::spin::y(i + 1));
-      tdOp += (Jz * cudaq::spin::z(i) * cudaq::spin::z(i + 1));
-    }
-    for (int i = 0; i < n_spins; ++i)
-      tdOp += (std::cos(omega * t) * cudaq::spin::x(i));
-    return tdOp;
-  };
-  // Observe the average magnetization of all spins (<Z>)
-  cudaq::spin_op average_magnetization(n_spins);
-  for (int i = 0; i < n_spins; ++i)
-    average_magnetization += ((1.0 / n_spins) * cudaq::spin::z(i));
-  average_magnetization -= 1.0;
-
-  // Run loop
-  auto state = cudaq::get_state(initState{}, n_spins);
-  std::vector<double> expResults;
-  std::vector<double> runtimeMs;
-  for (int i = 0; i < n_steps; ++i) {
-    const auto start = std::chrono::high_resolution_clock::now();
-    auto ham = heisenbergModelHam(i * dt);
-    auto coefficients = term_coefficients(ham);
-    auto words = term_words(ham);
-    auto magnetization_exp_val = cudaq::observe(
-        trotter{}, average_magnetization, &state, coefficients, words, dt);
-    auto result = magnetization_exp_val.expectation();
-    expResults.emplace_back(result);
-    state = cudaq::get_state(trotter{}, &state, coefficients, words, dt);
-    const auto stop = std::chrono::high_resolution_clock::now();
-    auto duration =
-        std::chrono::duration_cast<std::chrono::microseconds>(stop - start);
-    auto timeInSeconds = duration.count() / 1000.0 / 1000.0;
-    runtimeMs.emplace_back(timeInSeconds);
-    std::cout << "Step " << i << ": time [s]: " << timeInSeconds
-              << ", result: " << result << std::endl;
-  }
-  std::cout << std::endl;
-
-  // Print runtimes and results (useful for plotting).
-  std::cout << "Step times [s]: [";
-  for (const auto &x : runtimeMs)
-    std::cout << x << ", ";
-  std::cout << "]" << std::endl;
-
-  std::cout << "Results: [";
-  for (const auto &x : expResults)
-    std::cout << x << ", ";
-  std::cout << "]" << std::endl;
-
-  std::cout << std::endl;
-  return 0;
-}
-
-int main() {
-  const auto start = std::chrono::high_resolution_clock::now();
-  run_steps(STEPS, SPINS);
-  const auto stop = std::chrono::high_resolution_clock::now();
-  auto duration =
-      std::chrono::duration_cast<std::chrono::microseconds>(stop - start);
-  std::cout << "Total running time: " << duration.count() / 1000.0 / 1000.0
-            << "s" << std::endl;
-}
diff --git a/pr-2308/sphinx/applications/cpp/vqe_h2.cpp b/pr-2308/sphinx/applications/cpp/vqe_h2.cpp
deleted file mode 100644
index 50c65da2d6..0000000000
--- a/pr-2308/sphinx/applications/cpp/vqe_h2.cpp
+++ /dev/null
@@ -1,117 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ vqe_h2.cpp -o vqe.x && ./vqe.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/builder.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-
-// Here we build up a CUDA-Q kernel with N layers and each
-// layer containing an arrangement of random SO(4) rotations. The algorithm
-// leverages the CUDA-Q VQE support to compute the ground state of the
-// Hydrogen atom.
-
-// The SO4 random entangler written as a CUDA-Q kernel free function
-// since this is a pure-device quantum kernel
-__qpu__ void so4(cudaq::qubit &q, cudaq::qubit &r,
-                 const std::vector<double> &thetas) {
-  ry(thetas[0], q);
-  ry(thetas[1], r);
-
-  h(r);
-  x<cudaq::ctrl>(q, r);
-  h(r);
-
-  ry(thetas[2], q);
-  ry(thetas[3], r);
-
-  h(r);
-  x<cudaq::ctrl>(q, r);
-  h(r);
-
-  ry(thetas[4], q);
-  ry(thetas[5], r);
-
-  h(r);
-  x<cudaq::ctrl>(q, r);
-  h(r);
-}
-
-// The SO4 fabric CUDA-Q kernel. Keeps track of simple
-// arithmetic class members controlling the number of qubits and
-// entangling layers.
-struct so4_fabric {
-  void operator()(std::vector<double> params, int n_qubits,
-                  int n_layers) __qpu__ {
-    cudaq::qvector q(n_qubits);
-
-    x(q[0]);
-    x(q[2]);
-
-    const int block_size = 2;
-    int counter = 0;
-    for (int i = 0; i < n_layers; i++) {
-      // first layer of so4 blocks (even)
-      for (int k = 0; k < n_qubits; k += 2) {
-        auto subq = q.slice(k, block_size);
-        auto so4_params = cudaq::slice_vector(params, counter, 6);
-        so4(subq[0], subq[1], so4_params);
-        counter += 6;
-      }
-
-      // second layer of so4 blocks (odd)
-      for (int k = 1; k + block_size < n_qubits; k += 2) {
-        auto subq = q.slice(k, block_size);
-        auto so4_params = cudaq::slice_vector(params, counter, 6);
-        so4(subq[0], subq[1], so4_params);
-        counter += 6;
-      }
-    }
-  }
-};
-
-int main() {
-  // Read in the spin op from file
-  std::vector<double> h2_data{0, 0, 0, 0, -0.10647701149499994, 0.0,
-                              1, 1, 1, 1, 0.0454063328691,      0.0,
-                              1, 1, 3, 3, 0.0454063328691,      0.0,
-                              3, 3, 1, 1, 0.0454063328691,      0.0,
-                              3, 3, 3, 3, 0.0454063328691,      0.0,
-                              2, 0, 0, 0, 0.170280101353,       0.0,
-                              2, 2, 0, 0, 0.120200490713,       0.0,
-                              2, 0, 2, 0, 0.168335986252,       0.0,
-                              2, 0, 0, 2, 0.165606823582,       0.0,
-                              0, 2, 0, 0, -0.22004130022499996, 0.0,
-                              0, 2, 2, 0, 0.165606823582,       0.0,
-                              0, 2, 0, 2, 0.174072892497,       0.0,
-                              0, 0, 2, 0, 0.17028010135300004,  0.0,
-                              0, 0, 2, 2, 0.120200490713,       0.0,
-                              0, 0, 0, 2, -0.22004130022499999, 0.0,
-                              15};
-  cudaq::spin_op H(h2_data, /*nQubits*/ 4);
-  // For 8 qubits, 36 parameters per layer
-  int n_layers = 2, n_qubits = H.num_qubits(), block_size = 2, p_counter = 0;
-  int n_blocks_per_layer = 2 * (n_qubits / block_size) - 1;
-  int n_params = n_layers * 6 * n_blocks_per_layer;
-  printf("%d qubit Hamiltonian -> %d parameters\n", n_qubits, n_params);
-
-  // Define the initial parameters and ansatz.
-  auto init_params =
-      cudaq::random_vector(-1, 1, n_params, std::mt19937::default_seed);
-
-  so4_fabric ansatz;
-
-  // Run VQE.
-  cudaq::optimizers::lbfgs optimizer;
-  optimizer.initial_parameters = init_params;
-  optimizer.max_eval = 20;
-  optimizer.max_line_search_trials = 10;
-  cudaq::gradients::central_difference gradient;
-  auto [opt_val, opt_params] =
-      cudaq::vqe(ansatz, gradient, H, optimizer, n_params, n_qubits, n_layers);
-
-  printf("Optimal value = %.16lf\n", opt_val);
-}
diff --git a/pr-2308/sphinx/applications/python/afqmc.ipynb b/pr-2308/sphinx/applications/python/afqmc.ipynb
deleted file mode 100644
index 6f150dca1d..0000000000
--- a/pr-2308/sphinx/applications/python/afqmc.ipynb
+++ /dev/null
@@ -1,533 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quantum Enhanced Auxiliary Field Quantum Monte Carlo\n",
-    "\n",
-    "This work was done in collaboration with the Next Generation Computing team at [BASF](https://www.basf.com/global/en.html).\n",
-    "\n",
-    "In this tutorial we implement a quantum-classical hybrid workflow for computing the ground state energies of a strongly interacting molecular system. The algorithm consists of two parts:\n",
-    "\n",
-    "\n",
-    "1. A variational quantum eigensolver that uses the quantum-number-preserving ansatz proposed by [Anselmetti et al. (2021)](https://doi.org/10.1088/1367-2630/ac2cb3) to generate a quantum trial wave function $|\\Psi_T\\rangle$ using CUDA Quantum.\n",
-    "\n",
-    "2. An Auxiliary-Field Quantum Monte Carlo simulation that realizes a classical imaginary time evolution and collects the ground state energy estimates.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "# Package installs\n",
-    "!pip install pyscf==2.6.2 openfermion==1.6.1 ipie==0.7.1 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (>= 3.0.0) is not supported.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Relevant imports\n",
-    "\n",
-    "import cudaq\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from pyscf import gto, scf, ao2mo, mcscf\n",
-    "\n",
-    "from afqmc_src.vqe_cudaq_qnp import VQE, get_cudaq_hamiltonian\n",
-    "from afqmc_src.utils_ipie import get_coeff_wf, gen_ipie_input_from_pyscf_chk\n",
-    "\n",
-    "from ipie.hamiltonians.generic import Generic as HamGeneric\n",
-    "from ipie.qmc.afqmc import AFQMC\n",
-    "from ipie.systems.generic import Generic\n",
-    "from ipie.trial_wavefunction.particle_hole import ParticleHole\n",
-    "from ipie.analysis.extraction import extract_observable\n",
-    "\n",
-    "from ipie.config import config\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "\n",
-    "# Ipie has recently added GPU support however this remains a bit tricky to use as it requires manual installation of several packages.\n",
-    "# Once this is streamlined, we can set the GPU option to True in the tutorial.\n",
-    "config.update_option(\"use_gpu\", False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start by defining the structure of the molecule, the basis set, and its spin. We build the molecule object with PySCF and run a preliminary Hartree-Fock computation. Here we choose as an example a [chelating agent](https://doi.org/10.1021/acs.jctc.3c01375) representing a relevant class of substances industrially produced at large scales. Their use ranges, among the others, from water softeners in cleaning applications, modulators of redox behaviour in oxidative bleaching, scale suppressants, soil remediation and ligands for catalysts. In particular we focus here in a Fe(III)-NTA complex whose structure is given in the file imported below.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the molecular structure and the basis set for the Fenta molecule.\n",
-    "\n",
-    "atom = \"afqmc_src/geo_fenta.xyz\"\n",
-    "basis = \"cc-pVTZ\"\n",
-    "spin = 1\n",
-    "num_active_orbitals = 5\n",
-    "num_active_electrons = 5\n",
-    "\n",
-    "# You can swap to O3 which is a smaller system and takes less computational resources and time to run.\n",
-    "\n",
-    "atom = \"afqmc_src/geo_o3.xyz\"\n",
-    "basis = \"cc-pVTZ\"\n",
-    "spin = 0\n",
-    "num_active_orbitals = 9\n",
-    "num_active_electrons = 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-224.34048064812222"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# PYSCF helps to build the molecule and run Hartree-Fock.\n",
-    "\n",
-    "# Define the molecule.\n",
-    "molecule = gto.M(atom=atom, spin=spin, basis=basis, verbose=0)\n",
-    "\n",
-    "# Restriced open shell HF.\n",
-    "hartee_fock = scf.ROHF(molecule)\n",
-    "hartee_fock.chkfile = \"afqmc_src/output.chk\"\n",
-    "\n",
-    "# Run Hartree-Fock.\n",
-    "hartee_fock.kernel()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hamiltonian preparation for VQE\n",
-    "\n",
-    "Since this molecule contains of around 600 orbitals (which would correspond to 1200 qubits) and 143 total electrons, it is impossible to perform a full VQE with full statevector simulation. Therefore, we need to identify an active space with fewer orbitals and electrons that contribute to the strongly interacting part of the whole molecule. We then run a post Hartree-Fock computation with the PySCF's built-in CASCI method in order to obtain the one-body ($t_{pq}$) and two-body \n",
-    "($V_{prqs}$) integrals that define the molecular Hamiltonian in the active space:\n",
-    "\n",
-    "$$ H= \\sum_{pq}t_{pq}\\hat{a}_{p}^\\dagger \\hat {a}_{q}+\\sum_{pqrs}  V_{prqs}\\hat a_{p}^\\dagger \\hat a_{q}^\\dagger \\hat a_{s}\\hat a_{r} \\tag{1}$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from openfermion.transforms import jordan_wigner\n",
-    "from openfermion import generate_hamiltonian\n",
-    "\n",
-    "# Run a CASCI simulation for computing the Hamiltonian in the active space.\n",
-    "casci = mcscf.CASCI(hartee_fock, num_active_orbitals, num_active_electrons)\n",
-    "casci.fix_spin_(ss=(molecule.spin / 2 * (molecule.spin / 2 + 1)))\n",
-    "\n",
-    "# Executes the kernel to compute the hamiltonian in the active space.\n",
-    "casci.kernel()\n",
-    "\n",
-    "# Compute the one-body (h1) and two-body integrals (tbi) as shown in equation 1.\n",
-    "h1, energy_core = casci.get_h1eff()\n",
-    "\n",
-    "h2 = casci.get_h2eff()\n",
-    "h2_no_symmetry = ao2mo.restore('1', h2, num_active_orbitals)\n",
-    "\n",
-    "# V_pqrs terms in H.\n",
-    "tbi = np.asarray(h2_no_symmetry.transpose(0, 2, 3, 1), order='C')\n",
-    "\n",
-    "# Compute the hamiltonian and convert it to a CUDA-Q operator.\n",
-    "mol_ham = generate_hamiltonian(h1, tbi, energy_core.item())\n",
-    "jw_hamiltonian = jordan_wigner(mol_ham)\n",
-    "hamiltonian, constant_term = get_cudaq_hamiltonian(jw_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run VQE with CUDA-Q\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now execute the VQE algorithm using the quantum number preserving ansatz. At the end of the VQE, we store the final statevector that will be used in the classical AFQMC computation as an initial guess.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Using cudaq optimizer\n",
-      "# Num Params: 16\n",
-      "# Qubits: 18\n",
-      "# N_layers: 1\n",
-      "# Energy after the VQE: -224.3881035525103\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define some options for the VQE.\n",
-    "options = {\n",
-    "    'n_vqe_layers': 1,\n",
-    "    'maxiter': 100,\n",
-    "    'energy_core': constant_term,\n",
-    "    'return_final_state_vec': True\n",
-    "}\n",
-    "\n",
-    "n_qubits = 2 * num_active_orbitals\n",
-    "\n",
-    "vqe = VQE(n_qubits=n_qubits,\n",
-    "          num_active_electrons=num_active_electrons,\n",
-    "          spin=spin,\n",
-    "          options=options)\n",
-    "\n",
-    "results = vqe.execute(hamiltonian)\n",
-    "\n",
-    "# Best energy from VQE.\n",
-    "optimized_energy = results['energy_optimized']\n",
-    "\n",
-    "# Final state vector.\n",
-    "final_state_vector = results[\"state_vec\"]\n",
-    "\n",
-    "# Energies during the optimization loop.\n",
-    "vqe_energies = results[\"callback_energies\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Auxiliary Field Quantum Monte Carlo (AFQMC)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "AFQMC is a numerical method for computing relevant properties of strongly interacting molecules. AFQMC is a type of Quantum Monte Carlo method that combines the use of random walks with an auxiliary field to simulate the imaginary-time evolution of a quantum system and drive it to the lowest energy state. This method can provide accurate results for ground-state properties of a wide range of physical systems, including atoms, molecules, and solids. Here we summarize the main features of AFQMC while a detailed introduction to can be found [here](https://www.cond-mat.de/events/correl13/manuscripts/zhang.pdf).\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We consider the electronic Hamiltonian in the second quantization\n",
-    "\\begin{equation}\n",
-    "H = {H}_1 + {H}_2 \n",
-    "=\\sum_{pq} h_{pq} {a}_{p}^{\\dagger} {a}_{q} + \\frac{1}{2} \\sum_{pqrs} v_{pqrs}{a}_{p}^{\\dagger} {a}_r {a}^{\\dagger}_{q} {a}_s \\tag{2}\n",
-    "\\end{equation}\n",
-    "where ${a}_{p}^{\\dagger}$ and ${a}_{q}$ are fermionic creation and annihilation operators of orbitals $p$ and $q$, respectively. The terms $h_{pq} $ and \n",
-    "$v_{pqrs}$ are the matrix elements of the one-body, $H_1$, and two-body, $H_2$, interactions of $H$, respectively. Here, we omit the spin indices for simplicity.\n",
-    "\n",
-    "AFQMC realizes an imaginary time propagation of an initial state (chosen as a Slater determinant) $\\ket{\\Psi_{I}}$ towards the ground state $\\ket{\\Psi_0}$ of a given hamiltonian, $H$, with\n",
-    "\\begin{equation}\n",
-    "\\ket{\\Psi_0} \\sim\\lim_{n \\to \\infty} \\left[ e^{-\\Delta\\tau H  }  \\right]^{n} \\ket{\\Psi_{I}}\n",
-    "\\tag{3}\n",
-    "\\end{equation} \n",
-    "where $\\Delta\\tau$ is the imaginary time step.\n",
-    "\n",
-    "AFQMC relies on decomposing the two-body interactions $H_2$ in terms of sum of squares of one-body operators ${v}_\\gamma$ such that the Hamiltonian ${H}$ becomes\n",
-    "\\begin{equation}\n",
-    "H = v_0 - \\frac{1}{2}\\sum_{\\gamma=1}^{N_\\gamma} {v}_\\gamma^2\n",
-    "\\tag{4}\n",
-    "\\end{equation}\n",
-    "with ${v}_0 = {H}_1 $ and $\n",
-    "{v}_\\gamma = i \\sum_{pq} L^{\\gamma}_{pq} {a}_{p}^{\\dagger}{a}_{q}.\n",
-    "$\n",
-    "The $N_\\gamma$ matrices $L^{\\gamma}_{pq}$ are called Cholesky vectors as they are  obtained via a Cholesky decomposition of the two-body matrix elements \n",
-    "$v_{pqrs}$ via $v_{pqrs} = \\sum_{\\gamma=1}^{N_\\gamma} L^{\\gamma}_{pr} L^{\\gamma}_{qs}$.\n",
-    "\n",
-    "The imaginary time propagation evolves an ensemble of walkers $\\{\\phi^{(n)}\\}$ (that are Slater determinants) and allows one to access observables of the system. For example, the local energy\n",
-    "\\begin{equation}\n",
-    "\\mathcal{E}_{\\text{loc}}(\\phi^{(n)}) = \\frac{\\bra{\\Psi_\\mathrm{T}}H\\ket{\\phi^{(n)}}}{\\braket{\\Psi_\\mathrm{T}| \\phi^{(n)}}}\n",
-    "\\tag{5}\n",
-    "\\end{equation}\n",
-    "defined as the mixed expectation value of the Hamiltonian with the trial wave function $\\ket{\\Psi_\\mathrm{T}}$.\n",
-    "\n",
-    "\n",
-    "The trial wavefunction can be in general a single or a multi-Slater determinant coming from VQE for example. This might help in achieving more accurate ground state energy estimates.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The implementation of AFQMC we use here is from [ipie](https://github.com/JoonhoLee-Group/ipie) that supports both CPUs and GPUs and requires the following steps:\n",
-    "\n",
-    "\n",
-    "1. Preparation of the molecular Hamiltonian by performing the Cholesky decomposition\n",
-    "\n",
-    "2. Preparation of the trial state from the VQE wavefunction\n",
-    "\n",
-    "3. Executing AFQMC\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparation of the molecular Hamiltonian\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Number of electrons in simulation: (12, 12)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# AFQMC.\n",
-    "\n",
-    "# Generate the input Hamiltonian for ipie from the checkpoint file from pyscf.\n",
-    "ipie_hamiltonian = gen_ipie_input_from_pyscf_chk(hartee_fock.chkfile,\n",
-    "                                                 mcscf=True,\n",
-    "                                                 chol_cut=1e-5)\n",
-    "\n",
-    "h1e, cholesky_vectors, e0 = ipie_hamiltonian\n",
-    "\n",
-    "num_basis = cholesky_vectors.shape[1]\n",
-    "num_chol = cholesky_vectors.shape[0]\n",
-    "\n",
-    "system = Generic(nelec=molecule.nelec)\n",
-    "\n",
-    "afqmc_hamiltonian = HamGeneric(\n",
-    "    np.array([h1e, h1e]),\n",
-    "    cholesky_vectors.transpose((1, 2, 0)).reshape(\n",
-    "        (num_basis * num_basis, num_chol)), e0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparation of the trial wave function\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build the trial wavefunction from the state vector computed via VQE.\n",
-    "wavefunction = get_coeff_wf(final_state_vector,\n",
-    "                            n_active_elec=num_active_electrons,\n",
-    "                            spin=spin)\n",
-    "\n",
-    "trial = ParticleHole(wavefunction,\n",
-    "                     molecule.nelec,\n",
-    "                     num_basis,\n",
-    "                     num_dets_for_props=len(wavefunction[0]),\n",
-    "                     verbose=False)\n",
-    "\n",
-    "trial.compute_trial_energy = True\n",
-    "trial.build()\n",
-    "trial.half_rotate(afqmc_hamiltonian)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup of the AFQMC parameters\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we can choose the input options like the timestep $\\Delta\\tau$, the total number of walkers `num_walkers` and the total number of AFQMC iterations `num_blocks`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# random seed is 96264512\n",
-      "            Block                   Weight            WeightFactor            HybridEnergy                  ENumer                  EDenom                  ETotal                  E1Body                  E2Body\n",
-      "                0   1.0000000000000000e+02  1.0000000000000000e+02  0.0000000000000000e+00 -2.2437583763935545e+04  1.0000000000000000e+02 -2.2437583763935547e+02 -3.7639365190228011e+02  1.5201781426292453e+02\n",
-      "                1   4.2276634193515412e+02  1.4127560668989827e+03 -1.1711742028818304e+02 -2.2473358126540003e+04  9.9999999999999986e+01 -2.2473358126540006e+02 -3.7646854013277283e+02  1.5173495886737268e+02\n",
-      "                2   1.0031922288872407e+02  3.8320523739865604e+02 -1.1743088014788954e+02 -2.2489226882493567e+04  1.0000000000000001e+02 -2.2489226882493563e+02 -3.7650504938463922e+02  1.5161278055970348e+02\n",
-      "                3   9.9900990681040355e+01  1.0008400623205630e+02 -1.1736864885170948e+02 -2.2495677577437204e+04  9.9999999999999972e+01 -2.2495677577437212e+02 -3.7659644834889821e+02  1.5163967257452603e+02\n",
-      "                4   1.0009188692360159e+02  1.0005173726372723e+02 -1.1748969527283802e+02 -2.2495531836556856e+04  1.0000000000000001e+02 -2.2495531836556853e+02 -3.7675907314082951e+02  1.5180375477526098e+02\n",
-      "                5   9.9997269300807844e+01  1.0010618465796188e+02 -1.1752703012577417e+02 -2.2502732667629320e+04  1.0000000000000001e+02 -2.2502732667629317e+02 -3.7663343013337044e+02  1.5160610345707727e+02\n",
-      "                6   1.0012131352337956e+02  1.0019003056579172e+02 -1.1770170647504112e+02 -2.2513369839216481e+04  1.0000000000000000e+02 -2.2513369839216480e+02 -3.7660812717909516e+02  1.5147442878693036e+02\n",
-      "                7   9.9936984461419740e+01  9.9929966800671224e+01 -1.1765353928750643e+02 -2.2516138533920657e+04  1.0000000000000000e+02 -2.2516138533920659e+02 -3.7660292355465600e+02  1.5144153821544941e+02\n",
-      "                8   9.9902337463172714e+01  9.9910800755312891e+01 -1.1761532255317621e+02 -2.2518524275281430e+04  9.9999999999999986e+01 -2.2518524275281433e+02 -3.7674246483479845e+02  1.5155722208198404e+02\n",
-      "                9   1.0012943675389775e+02  1.0013880643723378e+02 -1.1780913595074867e+02 -2.2512465963277762e+04  1.0000000000000000e+02 -2.2512465963277762e+02 -3.7677999264623367e+02  1.5165533301345607e+02\n",
-      "               10   9.9628730363609819e+01  9.9223106824565718e+01 -1.1749814144939067e+02 -2.2517668156221851e+04  1.0000000000000000e+02 -2.2517668156221850e+02 -3.7688306341863290e+02  1.5170638185641434e+02\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Setup the AFQMC parameters.\n",
-    "afqmc_msd = AFQMC.build(molecule.nelec,\n",
-    "                        afqmc_hamiltonian,\n",
-    "                        trial,\n",
-    "                        num_walkers=100,\n",
-    "                        num_steps_per_block=25,\n",
-    "                        num_blocks=10,\n",
-    "                        timestep=0.005,\n",
-    "                        stabilize_freq=5,\n",
-    "                        seed=96264512,\n",
-    "                        pop_control_freq=5,\n",
-    "                        verbose=False)\n",
-    "\n",
-    "# Run the AFQMC.\n",
-    "afqmc_msd.run(estimator_filename='afqmc_src/estimates.0.h5')\n",
-    "afqmc_msd.finalise(verbose=False)\n",
-    "\n",
-    "# Extract the energies.\n",
-    "qmc_data = extract_observable(afqmc_msd.estimators.filename, \"energy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fea847f35e0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the energies.\n",
-    "\n",
-    "vqe_y = vqe_energies\n",
-    "vqe_x = list(range(len(vqe_y)))\n",
-    "plt.plot(vqe_x, vqe_y, label=\"VQE\")\n",
-    "\n",
-    "afqmc_y = list(qmc_data[\"ETotal\"])\n",
-    "afqmc_x = [i + vqe_x[-1] for i in list(range(len(afqmc_y)))]\n",
-    "plt.plot(afqmc_x, afqmc_y, label=\"AFQMC\")\n",
-    "\n",
-    "plt.xlabel(\"Optimization steps\")\n",
-    "plt.ylabel(\"Energy [Ha]\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/afqmc_src/geo_fenta.xyz b/pr-2308/sphinx/applications/python/afqmc_src/geo_fenta.xyz
deleted file mode 100644
index a8ef211dfc..0000000000
--- a/pr-2308/sphinx/applications/python/afqmc_src/geo_fenta.xyz
+++ /dev/null
@@ -1,28 +0,0 @@
-26
-Energy = -2155.935897674
-C    -1.0290441   -0.9471117   -1.9865887 
-C    -2.2505436   -0.7922495    0.1582250 
-C    -0.2593185   -2.2544373    0.0162178 
-O    -1.3454085    0.8783278   -3.5342318 
-O    -1.1347371    1.1483476    1.0151618 
-O     0.8453748   -2.7999130    2.0922098 
-O    -0.2780587    1.3040005   -1.6070564 
-O    -3.2498211    0.8000765    1.6534429 
-C    -0.9188318    0.5115628   -2.4417824 
-C    -2.2565356    0.4618854    1.0159078 
-C     0.5342286   -1.9274094    1.2848894 
-N    -0.9221422   -1.0235290   -0.5013883 
-Fe    0.2526900    0.4497192    0.0039380 
-O     1.8221685   -0.2815654   -1.0936915 
-H    -1.9555949   -1.4065777   -2.3473606 
-H    -0.1824638   -1.4867161   -2.4264081 
-H    -2.5130292   -1.6526686    0.7833244 
-H    -3.0320380   -0.6959329   -0.6040366 
-H     0.4617231   -2.6044401   -0.7316186 
-H    -0.9840969   -3.0551564    0.1969690 
-H     2.1227503    0.3853924   -1.7373941 
-H     2.5891208   -0.4447721   -0.5150575 
-O     0.9052095   -0.6681693    1.3941789 
-O     1.4573655    1.9796179    0.5273073 
-H     1.0868407    2.8424014    0.2685815 
-H     1.5840897    2.0352695    1.4913642 
\ No newline at end of file
diff --git a/pr-2308/sphinx/applications/python/afqmc_src/geo_o3.xyz b/pr-2308/sphinx/applications/python/afqmc_src/geo_o3.xyz
deleted file mode 100644
index a0c771460c..0000000000
--- a/pr-2308/sphinx/applications/python/afqmc_src/geo_o3.xyz
+++ /dev/null
@@ -1,5 +0,0 @@
-3
-Energy
-O     0.0000000    0.0000000    0.0000000
-O     0.0000000    0.0000000    1.2717000
-O     1.1383850    0.0000000    1.8385340
diff --git a/pr-2308/sphinx/applications/python/afqmc_src/utils_ipie.py b/pr-2308/sphinx/applications/python/afqmc_src/utils_ipie.py
deleted file mode 100644
index ef11a13371..0000000000
--- a/pr-2308/sphinx/applications/python/afqmc_src/utils_ipie.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import numpy as np
-
-from ipie.utils.from_pyscf import (load_from_pyscf_chkfile,
-                                   generate_hamiltonian, copy_LPX_to_LXmn,
-                                   generate_wavefunction_from_mo_coeff)
-
-
-def signature_permutation(orbital_list):
-    """
-    Returns the signature of the permutation in orbital_list
-    """
-    if len(orbital_list) == 1:
-        return 1
-
-    transposition_count = 0
-    for index, element in enumerate(orbital_list):
-        for next_element in orbital_list[index + 1:]:
-            if element > next_element:
-                transposition_count += 1
-
-    return (-1)**transposition_count
-
-
-def get_coeff_wf(final_state_vector, n_active_elec, spin=0, thres=1e-6):
-    """
-    :`param` `final_state_vector`: State vector from a VQE simulation
-    :`param` `n_active_elec`: Number of total electrons in active space
-    :`param` `spin`: spin
-    :`param` `thres`: Threshold for coefficients to keep from VQE wavefunction
-    :returns: Input for `ipie` trial: coefficients, list of occupied alpha, list of occupied bets
-    """
-    n_qubits = int(np.log2(final_state_vector.size))
-    n_elec = [(n_active_elec + spin) // 2, (n_active_elec - spin) // 2]
-
-    coeff = []
-    occas = []
-    occbs = []
-    for j, val in enumerate(final_state_vector):
-        if abs(val) > thres:
-            ket = np.binary_repr(j, width=n_qubits)
-            alpha_ket = ket[::2]
-            beta_ket = ket[1::2]
-            occ_alpha = np.where([int(_) for _ in alpha_ket])[0]
-            occ_beta = np.where([int(_) for _ in beta_ket])[0]
-            occ_orbitals = np.append(2 * occ_alpha, 2 * occ_beta + 1)
-
-            if (len(occ_alpha) == n_elec[0]) and (len(occ_beta) == n_elec[1]):
-                coeff.append(signature_permutation(occ_orbitals) * val)
-                occas.append(occ_alpha)
-                occbs.append(occ_beta)
-
-    coeff = np.array(coeff, dtype=complex)
-    ixs = np.argsort(np.abs(coeff))[::-1]
-    coeff = coeff[ixs]
-    occas = np.array(occas)[ixs]
-    occbs = np.array(occbs)[ixs]
-
-    return coeff, occas, occbs
-
-
-def gen_ipie_input_from_pyscf_chk(pyscf_chkfile: str,
-                                  verbose: bool = True,
-                                  chol_cut: float = 1e-5,
-                                  ortho_ao: bool = False,
-                                  mcscf: bool = False,
-                                  linear_dep_thresh: float = 1e-8,
-                                  num_frozen_core: int = 0):
-    """Generate AFQMC data from PYSCF (molecular) simulation.
-        Adapted from `ipie`.`utils`.from_`pyscf`: returns Hamiltonian and wavefunction instead of writing on files
-    """
-    if mcscf:
-        scf_data = load_from_pyscf_chkfile(pyscf_chkfile, base="mcscf")
-    else:
-        scf_data = load_from_pyscf_chkfile(pyscf_chkfile)
-    mol = scf_data["mol"]
-    hcore = scf_data["hcore"]
-    ortho_ao_mat = scf_data["X"]
-    mo_coeffs = scf_data["mo_coeff"]
-    mo_occ = scf_data["mo_occ"]
-    if ortho_ao:
-        basis_change_matrix = ortho_ao_mat
-    else:
-        basis_change_matrix = mo_coeffs
-
-        if isinstance(mo_coeffs, list) or len(mo_coeffs.shape) == 3:
-            if verbose:
-                print("# UHF mo coefficients found and ortho-ao == False. Using"
-                      " alpha mo coefficients for basis transformation.")
-            basis_change_matrix = mo_coeffs[0]
-    ham = generate_hamiltonian(
-        mol,
-        mo_coeffs,
-        hcore,
-        basis_change_matrix,
-        chol_cut=chol_cut,
-        num_frozen_core=num_frozen_core,
-        verbose=False,
-    )
-    # write_Hamiltonian(ham.H1[0], copy_`LPX`_to_`LXmn`(ham.`chol`), ham.`ecore`, filename=Hamiltonian_file)
-    ipie_ham = (ham.H1[0], copy_LPX_to_LXmn(ham.chol), ham.ecore)
-    nelec = (mol.nelec[0] - num_frozen_core, mol.nelec[1] - num_frozen_core)
-    if verbose:
-        print(f"# Number of electrons in simulation: {nelec}")
-    if mcscf:
-        # `ci`_`coeffs` = `scf`_data["`ci_coeffs`"]
-        # `occa` = `scf`_data["`occa`"]
-        # `occb` = `scf`_data["`occb`"]
-        # write_wavefunction((`ci_coeffs`, `occa`, `occb`), wavefunction_file)
-        return ipie_ham
-    else:
-        wavefunction = generate_wavefunction_from_mo_coeff(
-            mo_coeffs,
-            mo_occ,
-            basis_change_matrix,
-            nelec,
-            ortho_ao=ortho_ao,
-            num_frozen_core=num_frozen_core,
-        )
-        # write_wavefunction(wavefunction, wavefunction_file)
-        return ipie_ham, wavefunction
diff --git a/pr-2308/sphinx/applications/python/afqmc_src/vqe_cudaq_qnp.py b/pr-2308/sphinx/applications/python/afqmc_src/vqe_cudaq_qnp.py
deleted file mode 100644
index cec53d07df..0000000000
--- a/pr-2308/sphinx/applications/python/afqmc_src/vqe_cudaq_qnp.py
+++ /dev/null
@@ -1,293 +0,0 @@
-"""
-    Contains the class with the VQE using the quantum-number-preserving ansatz
-"""
-import numpy as np
-import cudaq
-from cudaq import spin as spin_op
-
-
-class VQE(object):
-    """
-        Implements the quantum-number-preserving ansatz from Anselmetti et al. NJP 23 (2021)
-    """
-
-    def __init__(self, n_qubits, num_active_electrons, spin, options):
-        self.n_qubits = n_qubits
-        self.n_layers = options.get('n_vqe_layers', 1)
-        self.number_of_Q_blocks = n_qubits // 2 - 1
-        self.num_params = 2 * self.number_of_Q_blocks * self.n_layers
-        self.options = options
-        num_active_orbitals = n_qubits // 2
-
-        # number of alpha and beta electrons in the active space
-        num_active_electrons_alpha = (num_active_electrons + spin) // 2
-        num_active_electrons_beta = (num_active_electrons - spin) // 2
-
-        # Define the initial state for the VQE as a list
-        # [n_1, n_2, ....]
-        # where n_j=(0,1,2) is the occupation of j-`th` the orbital
-
-        n_alpha_vec = [1] * num_active_electrons_alpha + [0] * (
-            num_active_orbitals - num_active_electrons_alpha)
-        n_beta_vec = [1] * num_active_electrons_beta + [0] * (
-            num_active_orbitals - num_active_electrons_beta)
-        init_mo_occ = [n_a + n_b for n_a, n_b in zip(n_alpha_vec, n_beta_vec)]
-
-        self.init_mo_occ = init_mo_occ
-        self.final_state_vector_best = None
-        self.best_vqe_params = None
-        self.best_vqe_energy = None
-        self.target = "nvidia"
-        self.initial_x_gates_pos = self.prepare_initial_circuit()
-
-    def prepare_initial_circuit(self):
-        """
-        Creates a list with the position of the X gates that should be applied to the initial |00...0>
-        state to set the number of electrons and the spin correctly
-        """
-        x_gates_pos_list = []
-        if self.init_mo_occ is not None:
-            for idx_occ, occ in enumerate(self.init_mo_occ):
-                if int(occ) == 2:
-                    x_gates_pos_list.extend([2 * idx_occ, 2 * idx_occ + 1])
-                elif int(occ) == 1:
-                    x_gates_pos_list.append(2 * idx_occ)
-
-        return x_gates_pos_list
-
-    def layers(self):
-        """
-            Generates the QNP ansatz circuit and returns the  kernel and the optimization parameters thetas
-
-            `params`: list/`np`.array
-            [theta_0, ..., theta_{M-1}, phi_0, ..., phi_{M-1}]
-            where M is the total number of blocks = layer * (n_qubits/2 - 1)
-
-            returns: kernel
-                     thetas
-        """
-        n_qubits = self.n_qubits
-        n_layers = self.n_layers
-        number_of_blocks = self.number_of_Q_blocks
-
-        # changed self.target to `nvidia`` to pass CI job, as it expects a string
-        # literal
-        # cudaq.set_target("`nvidia`")  # `nvidia` or `nvidia-mgpu`
-
-        kernel, thetas = cudaq.make_kernel(list)
-        # Allocate n qubits.
-        qubits = kernel.qalloc(n_qubits)
-
-        for init_gate_position in self.initial_x_gates_pos:
-            kernel.x(qubits[init_gate_position])
-
-        count_params = 0
-        for idx_layer in range(n_layers):
-            for starting_block_num in [0, 1]:
-                for idx_block in range(starting_block_num, number_of_blocks, 2):
-                    qubit_list = [qubits[2 * idx_block + j] for j in range(4)]
-
-                    # PX gates decomposed in terms of standard gates
-                    # and NO controlled Y rotations.
-                    # See Appendix E1 of Anselmetti et al New J. Phys. 23 (2021) 113010
-
-                    a, b, c, d = qubit_list
-                    kernel.cx(d, b)
-                    kernel.cx(d, a)
-                    kernel.rz(parameter=-np.pi / 2, target=a)
-                    kernel.s(b)
-                    kernel.h(d)
-                    kernel.cx(d, c)
-                    kernel.cx(b, a)
-                    kernel.ry(parameter=(1 / 8) * thetas[count_params],
-                              target=c)
-                    kernel.ry(parameter=(-1 / 8) * thetas[count_params],
-                              target=d)
-                    kernel.rz(parameter=+np.pi / 2, target=a)
-                    kernel.cz(a, d)
-                    kernel.cx(a, c)
-                    kernel.ry(parameter=(-1 / 8) * thetas[count_params],
-                              target=d)
-                    kernel.ry(parameter=(+1 / 8) * thetas[count_params],
-                              target=c)
-                    kernel.cx(b, c)
-                    kernel.cx(b, d)
-                    kernel.rz(parameter=+np.pi / 2, target=b)
-                    kernel.ry(parameter=(-1 / 8) * thetas[count_params],
-                              target=c)
-                    kernel.ry(parameter=(+1 / 8) * thetas[count_params],
-                              target=d)
-                    kernel.cx(a, c)
-                    kernel.cz(a, d)
-                    kernel.ry(parameter=(-1 / 8) * thetas[count_params],
-                              target=c)
-                    kernel.ry(parameter=(1 / 8) * thetas[count_params],
-                              target=d)
-                    kernel.cx(d, c)
-                    kernel.h(d)
-                    kernel.cx(d, b)
-                    kernel.s(d)
-                    kernel.rz(parameter=-np.pi / 2, target=b)
-                    kernel.cx(b, a)
-                    count_params += 1
-
-                    # Orbital rotation
-                    kernel.fermionic_swap(np.pi, b, c)
-                    kernel.givens_rotation((-1 / 2) * thetas[count_params], a,
-                                           b)
-                    kernel.givens_rotation((-1 / 2) * thetas[count_params], c,
-                                           d)
-                    kernel.fermionic_swap(np.pi, b, c)
-                    count_params += 1
-
-        return kernel, thetas
-
-    def get_state_vector(self, param_list):
-        """
-        Returns the state vector generated by the ansatz with parameters given by `param`_list
-        """
-        kernel, thetas = self.layers()
-        state = convert_state_big_endian(
-            np.array(cudaq.get_state(kernel, param_list), dtype=complex))
-        return state
-
-    def execute(self, hamiltonian):
-        """
-        Run VQE
-        """
-        options = self.options
-        mpi_support = options.get("mpi_support", False)
-        return_final_state_vec = options.get("return_final_state_vec", False)
-
-        if mpi_support:
-            cudaq.mpi.initialize()
-            print('# mpi is initialized? ', cudaq.mpi.is_initialized())
-            num_ranks = cudaq.mpi.num_ranks()
-            rank = cudaq.mpi.rank()
-            print('# rank', rank, 'num_ranks', num_ranks)
-
-        optimizer = cudaq.optimizers.COBYLA()
-        initial_parameters = options.get('initial_parameters')
-        if initial_parameters:
-            optimizer.initial_parameters = initial_parameters
-        else:
-            optimizer.initial_parameters = np.random.rand(self.num_params)
-
-        kernel, thetas = self.layers()
-        maxiter = options.get('maxiter', 100)
-        optimizer.max_iterations = options.get('maxiter', maxiter)
-        optimizer_type = "cudaq"
-        callback_energies = []
-
-        def eval(theta):
-            """
-            Compute the energy by cudaq.observe
-            """
-            exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()
-
-            callback_energies.append(exp_val)
-            return exp_val
-
-        if optimizer_type == "cudaq":
-            print("# Using cudaq optimizer")
-            energy_optimized, best_parameters = optimizer.optimize(
-                self.num_params, eval)
-
-            # We add here the energy core
-            energy_core = options.get('energy_core', 0.)
-            total_opt_energy = energy_optimized + energy_core
-            callback_energies = [en + energy_core for en in callback_energies]
-
-            print("# Num Params:", self.num_params)
-            print("# Qubits:", self.n_qubits)
-            print("# N_layers:", self.n_layers)
-            print("# Energy after the VQE:", total_opt_energy)
-
-            result = {
-                "energy_optimized": total_opt_energy,
-                "best_parameters": best_parameters,
-                "callback_energies": callback_energies
-            }
-
-            if return_final_state_vec:
-                result["state_vec"] = self.get_state_vector(best_parameters)
-            return result
-
-        else:
-            print(f"# Optimizer {optimizer_type} not implemented")
-            exit()
-
-
-def convert_state_big_endian(state_little_endian):
-
-    state_big_endian = 0. * state_little_endian
-
-    n_qubits = int(np.log2(state_big_endian.size))
-    for j, val in enumerate(state_little_endian):
-        little_endian_pos = np.binary_repr(j, n_qubits)
-        big_endian_pos = little_endian_pos[::-1]
-        int_big_endian_pos = int(big_endian_pos, 2)
-        state_big_endian[int_big_endian_pos] = state_little_endian[j]
-
-    return state_big_endian
-
-
-def from_string_to_cudaq_spin(pauli_string, qubit):
-    if pauli_string.lower() in ('id', 'i'):
-        return 1
-    elif pauli_string.lower() == 'x':
-        return spin_op.x(qubit)
-    elif pauli_string.lower() == 'y':
-        return spin_op.y(qubit)
-    elif pauli_string.lower() == 'z':
-        return spin_op.z(qubit)
-
-
-def get_cudaq_hamiltonian(jw_hamiltonian):
-    """ Converts an `openfermion` QubitOperator Hamiltonian into a `cudaq.SpinOperator` Hamiltonian
-
-    """
-
-    hamiltonian_cudaq = 0.0
-    energy_core = 0.0
-    for ham_term in jw_hamiltonian:
-        [(operators, ham_coeff)] = ham_term.terms.items()
-        if len(operators):
-            cuda_operator = 1.0
-            for qubit_index, pauli_op in operators:
-                cuda_operator *= from_string_to_cudaq_spin(
-                    pauli_op, qubit_index)
-        else:
-            cuda_operator = 0.0  #from_string_to_cudaq_spin('id', 0)
-            energy_core = ham_coeff
-        cuda_operator = ham_coeff * cuda_operator
-        hamiltonian_cudaq += cuda_operator
-
-    return hamiltonian_cudaq, energy_core
-
-
-def get_cudaq_operator(jw_hamiltonian):
-    """ Converts an `openfermion` QubitOperator Hamiltonian into a `cudaq.SpinOperator` Hamiltonian
-
-    """
-
-    hamiltonian_cudaq = 0.0
-    for ham_term in jw_hamiltonian:
-        [(operators, ham_coeff)] = ham_term.terms.items()
-        if len(operators):
-            cuda_operator = 1.0
-            for qubit_index, pauli_op in operators:
-                cuda_operator *= from_string_to_cudaq_spin(
-                    pauli_op, qubit_index)
-        else:
-            cuda_operator = from_string_to_cudaq_spin('id', 0)
-        if abs(ham_coeff.imag) < 1e-8:
-            cuda_operator = ham_coeff.real * cuda_operator
-        else:
-            print(
-                "In function get_cudaq_operator can convert only real operator to cuda_operator"
-            )
-            exit()
-        hamiltonian_cudaq += cuda_operator
-
-    return hamiltonian_cudaq
diff --git a/pr-2308/sphinx/applications/python/bernstein_vazirani.ipynb b/pr-2308/sphinx/applications/python/bernstein_vazirani.ipynb
deleted file mode 100644
index b0333b5cf8..0000000000
--- a/pr-2308/sphinx/applications/python/bernstein_vazirani.ipynb
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "29b3f948-cc6a-4776-ad75-1dc259e0c298",
-   "metadata": {},
-   "source": [
-    "# Bernstein-Vazirani Algorithm\n",
-    "\n",
-    "The Bernstein-Vazirani (BV) algorithm demonstrates an exponential speedup over classical methods for the particular task: \n",
-    "\n",
-    "Suppose we have an oracle that implements the function $f: \\{0,1\\}^n \\longrightarrow \\{0,1\\}$. \n",
-    "\n",
-    "$f(x)$ is promised to be the dot product between $x$ and a secret $n$-bit binary string $s$:\n",
-    "\n",
-    "$f(x) = x \\cdot s (mod \\space 2) =  (x_1s_1 + x_2s_2 + ... + x_ns_n) (mod \\space 2)$.\n",
-    "\n",
-    "Our goal is to find the secret string $s$.\n",
-    "\n",
-    "### Classical case\n",
-    "\n",
-    "For the case of $n=3$ consider $s=101$, which is a secret string and hence we dont have access to it. \n",
-    "\n",
-    "We have access to the function: $f(x) = x\\cdot s (mod \\space 2) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \\space 2)$\n",
-    "\n",
-    "Let us strategically query the function to determine $s$. \n",
-    "\n",
-    "$1^{st}$ query: $f(001) = 001 \\cdot s (mod \\space 2) = (0 \\cdot 1 + 0 \\cdot 0 + 1 \\cdot 1)(mod \\space 2) = 1 (mod \\space 2) = 1$\n",
-    "\n",
-    "$2^{nd}$ query: $f(010) = 010 \\cdot s (mod \\space 2) = (0 \\cdot 1 + 1 \\cdot 0 + 0 \\cdot 1)(mod \\space 2) = 0 (mod \\space 2) = 0$\n",
-    "\n",
-    "$3^{rd}$ query: $f(100) = 100 \\cdot s (mod \\space 2) = (1 \\cdot 1 + 0 \\cdot 0 + 0 \\cdot 1)(mod \\space 2) = 1 (mod \\space 2) = 1$\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Remember that from a user perspective, you only see $f(001) = 1$ since the inner mechanisms of the oracle and the value of $s$ is hidden from you. \n",
-    "\n",
-    "Why did we query $f(001)$ and not $f(011)$?\n",
-    "\n",
-    "For $f(001) = (x_1s_1 + x_2s_2 + x_3s_3) (mod \\space 2) = (0 + 0 + x_3s_3) (mod \\space 2) = (x_3s_3) (mod \\space 2) = 1 \\cdot s_3 (mod \\space 2) = s_3 (mod \\space 2) = 1$\n",
-    "\n",
-    "We have now isolated $s_3$ and know that $s_3 (mod \\space 2) = 1$. $s_3$ can only be 0 or 1 and in this case it has to be $1$ since $0 (mod \\space 2) = 0$. \n",
-    "\n",
-    "For $f(011) = (x_2s_2 + x_3s_3) (mod \\space 2)$, we will be left with $s_2$ and $s_3$ each of which will be more difficult to isolate via a combination of linear equations. Hence why we have to strategically query the function. \n",
-    "\n",
-    "In the classical case, we see that the secret string $s$ can be calculated in 3 queries. More generally speaking, it would take $n$ queries to generate enough information to determine $s$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebdfe818",
-   "metadata": {},
-   "source": [
-    "### Quantum case\n",
-    "\n",
-    "The BV quantum algorithm can take advantage of superposition and entanglement to encode the problem and produce an answer with a single oracle (black box function) query. The setup is a register of $n$ qubits in the $\\ket{0}$ state and a single auxiliary qubit in the $\\ket{1}$ state. The auxiliary qubit enables the phase kickback. Consider the register of $n$ qubits initialized here:\n",
-    "\n",
-    "$$ \\ket{0} $$\n",
-    "\n",
-    "Application of a Hadamard gate to each qubit results in:\n",
-    "$$ H \\ket{0}  = \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} \\ket{x}$$\n",
-    "\n",
-    "Next, the oracle, $U_f$, performs maps $\\ket{x}$ to $(-1)^{f(x)}\\ket{x}$ resulting in:\n",
-    "\n",
-    "$$ U_f \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} \\ket{x} = \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{f(x)}\\ket{x} =\\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{a*x}\\ket{x} $$\n",
-    "\n",
-    "Applying Hadamard gates again returns the following state:\n",
-    "$$ H \\frac{1}{\\sqrt{2^n}}\\sum_x^{n-1} (-1)^{a*x}\\ket{x}  =  \\frac{1}{\\sqrt{2^n}}\\sum_{x,y}^{n-1} (-1)^{a*x} (-1)^{y*x} \\ket{y}  = \\frac{1}{\\sqrt{2^n}}\\sum_{y}^{n-1} \\sum_{x}^{n-1} (-1)^{(a \\oplus y)*x}\\ket{y} $$\n",
-    "\n",
-    "In the case of $a \\neq y$, the entire term becomes zero. In the case of $a = y$, the state $\\ket{a}$ is returned with an amplitude of 1.  This means there is a 100% chance of the measurement result being the hidden bitstring $a$ with only one call to the oracle!\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fdbba1e-8eab-42a2-8964-6d7e86db0ad4",
-   "metadata": {},
-   "source": [
-    "### Implementing in CUDA-Q\n",
-    "\n",
-    "The cell below generates a random bitstring of length $n$. If you are running this on your CPU, keep the qubit count small and increase to around 30 if you are running on a GPU with the `nvidia` backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "883762a1-d271-4692-adad-d64daa20805d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "#cudaq.set_target('nvidia')  # GPU backend which enables scaling to large problem sizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d72225e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qubit_count = 5  # Set to around 30 qubits if you have GPU access\n",
-    "\n",
-    "secret_string = [1, 1, 0, 1,\n",
-    "                 0]  # Change the secret string to whatever you prefer\n",
-    "\n",
-    "assert qubit_count == len(secret_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3df178fe-ccf3-41aa-b5f7-037a1a1d4a69",
-   "metadata": {},
-   "source": [
-    "Next, the oracle kernel is defined. This will be used inside of the primary BV kernel, so it needs to take both the main register and auxiliary qubit as inputs as well as the secret bitstring. The oracle loops through the bits and applies a CNOT on the auxiliary qubit if the register qubit is a 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "91dbde75-486b-4015-9627-9ff404bdb475",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def oracle(register: cudaq.qview, auxiliary_qubit: cudaq.qubit,\n",
-    "           secret_string: List[int]):\n",
-    "\n",
-    "    for index, bit in enumerate(secret_string):\n",
-    "        if bit == 1:\n",
-    "            x.ctrl(register[index], auxiliary_qubit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "685bb02e-afdc-4b69-afb4-81af9c2fafbd",
-   "metadata": {},
-   "source": [
-    "The code below performs the steps described above for the BV algorithm, calling the oracle kernel as needed. You can see the circuit created below and verify that the procedure guessed the correct bitstring. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d93cb1db-b5c6-45ef-9efc-c426895aac18",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮          ╭───╮          \n",
-      "q0 : ┤ h ├───────●──┤ h ├──────────\n",
-      "     ├───┤       │  ╰───╯╭───╮     \n",
-      "q1 : ┤ h ├───────┼────●──┤ h ├─────\n",
-      "     ├───┤       │    │  ├───┤     \n",
-      "q2 : ┤ h ├───────┼────┼──┤ h ├─────\n",
-      "     ├───┤       │    │  ╰───╯╭───╮\n",
-      "q3 : ┤ h ├───────┼────┼────●──┤ h ├\n",
-      "     ├───┤       │    │    │  ├───┤\n",
-      "q4 : ┤ h ├───────┼────┼────┼──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╭─┴─╮╭─┴─╮╰───╯\n",
-      "q5 : ┤ x ├┤ h ├┤ x ├┤ x ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯╰───╯╰───╯     \n",
-      "\n",
-      "secret bitstring = [1, 1, 0, 1, 0]\n",
-      "measured state = 11010\n",
-      "Were we successful? True\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def bernstein_vazirani(secret_string: List[int]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(len(secret_string))  # register of size n\n",
-    "    auxiliary_qubit = cudaq.qubit()  # auxiliary qubit\n",
-    "\n",
-    "    # Prepare the auxillary qubit.\n",
-    "    x(auxiliary_qubit)\n",
-    "    h(auxiliary_qubit)\n",
-    "\n",
-    "    # Place the rest of the register in a superposition state.\n",
-    "    h(qubits)\n",
-    "\n",
-    "    # Query the oracle.\n",
-    "    oracle(qubits, auxiliary_qubit, secret_string)\n",
-    "\n",
-    "    # Apply another set of Hadamards to the register.\n",
-    "    h(qubits)\n",
-    "\n",
-    "    mz(qubits)  # measures only the main register\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(bernstein_vazirani, secret_string))\n",
-    "result = cudaq.sample(bernstein_vazirani, secret_string)\n",
-    "\n",
-    "print(f\"secret bitstring = {secret_string}\")\n",
-    "print(f\"measured state = {result.most_probable()}\")\n",
-    "print(\n",
-    "    f\"Were we successful? {''.join([str(i) for i in secret_string]) == result.most_probable()}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "388592de-3b47-45f4-b687-72abed6fdf8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum 176f1e7df8a58c2dc3d6b1b47bf7f63b4b8d3b63)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/cost_minimization.ipynb b/pr-2308/sphinx/applications/python/cost_minimization.ipynb
deleted file mode 100644
index 824ef41237..0000000000
--- a/pr-2308/sphinx/applications/python/cost_minimization.ipynb
+++ /dev/null
@@ -1,207 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cost Minimization\n",
-    "\n",
-    "Below we start with a basic example of a hybrid variational algorithm which involves flipping the Bloch vector of a qubit from the $\\ket{0}$ to the $\\ket{1}$ state. First we import the relevant packages and set our backend to simulate our workflow on NVIDIA GPUs. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───────╮╭───────╮\n",
-      "q0 : ┤ rx(0) ├┤ ry(0) ├\n",
-      "     ╰───────╯╰───────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Initialize a kernel/ ansatz and variational parameters.\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: List[float]):\n",
-    "    # Allocate a qubit that is initialized to the |0> state.\n",
-    "    qubit = cudaq.qubit()\n",
-    "    # Define gates and the qubits they act upon.\n",
-    "    rx(angles[0], qubit)\n",
-    "    ry(angles[1], qubit)\n",
-    "\n",
-    "\n",
-    "# Our Hamiltonian will be the Z expectation value of our qubit.\n",
-    "hamiltonian = cudaq.spin.z(0)\n",
-    "\n",
-    "# Initial gate parameters which intialize the qubit in the zero state\n",
-    "initial_parameters = [0, 0]\n",
-    "\n",
-    "print(cudaq.draw(kernel, initial_parameters))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We build our cost function such that its minimal value corresponds to the qubit being in the $\\ket{1}$ state. The observe call below allows us to simulate our statevector $\\ket{\\psi}$, and calculate $\\bra{\\psi}Z\\ket{\\psi}$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cost_values = []\n",
-    "\n",
-    "\n",
-    "def cost(parameters):\n",
-    "    \"\"\"Returns the expectation value as our cost.\"\"\"\n",
-    "    expectation_value = cudaq.observe(kernel, hamiltonian,\n",
-    "                                      parameters).expectation()\n",
-    "    cost_values.append(expectation_value)\n",
-    "    return expectation_value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# We see that the initial value of our cost function is one, demonstrating that our qubit is in the zero state\n",
-    "initial_cost_value = cost(initial_parameters)\n",
-    "print(initial_cost_value)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we use our built-in optimization suite to minimize the cost function. We will be using the gradient-free COBYLA alogrithm. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define a CUDA-Q optimizer.\n",
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "optimizer.initial_parameters = initial_parameters\n",
-    "\n",
-    "result = optimizer.optimize(dimensions=2, function=cost)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cost Value')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting how the value of the cost function decreases during the minimization procedure.\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x_values = list(range(len(cost_values)))\n",
-    "y_values = cost_values\n",
-    "\n",
-    "plt.plot(x_values, y_values)\n",
-    "\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Cost Value\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the final value or our cost function, $\\bra{\\psi}Z\\ket{\\psi} = -1$ demonstrating that the qubit is in the $\\ket{1}$ state."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/deutschs_algorithm.ipynb b/pr-2308/sphinx/applications/python/deutschs_algorithm.ipynb
deleted file mode 100644
index feaacacd9d..0000000000
--- a/pr-2308/sphinx/applications/python/deutschs_algorithm.ipynb
+++ /dev/null
@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deutsch's Algorithm "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a function which takes in a bit and outputs a bit. This can be represented as $f: \\{0,1\\} \\longrightarrow \\{0,1\\}$. \n",
-    "\n",
-    "The function $f$ has a property; either it is constant or balanced. \n",
-    "\n",
-    "If constant, the outputs are the same regardless of the inputs, i.e., $f(0) = f(1) = 0$ or $f(0) = f(1) = 1$.\n",
-    "\n",
-    "If balanced, the ouputs are balanced across their possibilities, i.e,  if $f(0) = 0$ then $f(1) = 1$ or if $f(0) = 1$ then $f(1) = 0$.\n",
-    "\n",
-    "The question we would like to answer is if the function is constant or balanced. \n",
-    " \n",
-    "Classically, if we are given a function $f$, we can solve to find its property via the code below: \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Treat the code snippet below like a black box, i.e., you don't have access to the value of the property argument and can just query f with different inputs.\n",
-    "\n",
-    "\n",
-    "def f(x, property='constant'):\n",
-    "\n",
-    "    if property == 'constant':\n",
-    "\n",
-    "        # The output is a 1 regardless of the input; we can also make the output to be 0.\n",
-    "\n",
-    "        if x == 0:\n",
-    "            return 1\n",
-    "        elif x == 1:\n",
-    "            return 1\n",
-    "\n",
-    "    if property == 'balanced':\n",
-    "\n",
-    "        # The output depends on the input.\n",
-    "\n",
-    "        if x == 0:\n",
-    "            return 1\n",
-    "        elif x == 1:\n",
-    "            return 0"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us now classically find out if the function we defined above is constant or balanced. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The function is constant\n"
-     ]
-    }
-   ],
-   "source": [
-    "if f(0) == 0:\n",
-    "    if f(1) == 0:\n",
-    "        print('The function is constant')\n",
-    "    elif f(1) == 1:\n",
-    "        print('The function is balanced')\n",
-    "\n",
-    "elif f(0) == 1:\n",
-    "    if f(1) == 1:\n",
-    "        print('The function is constant')\n",
-    "    elif f(1) == 0:\n",
-    "        print('The function is balanced')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you step through the `if` statements above, one can see that we require 2 calls to the function to determine its property. That is, we have to query $f$ twice.\n",
-    "\n",
-    "The claim is that Deutsch's algorithm can solve for this property with 1 function evalulation, demonstrating quantum advantage. \n",
-    "\n",
-    "Below we first go through the math and then the implementation in CUDA Quantum. \n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## XOR $\\oplus$\n",
-    "\n",
-    "You may have seen the $\\oplus$ symbol floating around. This is the exclusive OR or the XOR operation. It follows the rules of addition modulo 2. For example, \n",
-    "\n",
-    "\n",
-    "1. $3\\oplus5 = (3+5)_{\\%2} = 8_{\\%2} = 0$\n",
-    "\n",
-    "2. $5\\oplus4 = (5+4)_{\\%2} = 9_{\\%2} = 1$\n",
-    "\n",
-    "A quick way to perform the $\\oplus$ calculation is if the result of the addition is an even number, the result is 0 otherwise 1. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum oracles\n",
-    "\n",
-    "<img src=\"images/oracle.png\"  width=\"300\" height=\"150\">\n",
-    "\n",
-    "Suppose we have $f(x): \\{0,1\\} \\longrightarrow \\{0,1\\}$. We can compute this function on a quantum computer using oracles which we treat as black box functions that yield the output with an appropriate sequence of logic gates. \n",
-    "\n",
-    "Above you see an oracle represented as $U_f$ which allows us to transform the state $\\ket{x}\\ket{y}$ into: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{x}\\ket{y}  =  \\ket{x}\\ket{y \\oplus f(x)}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "If $y = 0$, then $U_f\\ket{x}\\ket{y} = U_f\\ket{x}\\ket{0}  =   \\ket{x}\\ket{0 \\oplus f(x)} =  \\ket{x}\\ket{f(x)}$ since $f(x)$ can either be $0/1$ and $0 \\oplus 0 = 0$ and $0 \\oplus 1 = 1$.\n",
-    "\n",
-    "This is remarkable because by setting $\\ket{y} = \\ket{0}$, we can extract the value of $f(x)$ by measuring the value of the second qubit. \n",
-    " \n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phase oracle\n",
-    "\n",
-    "\n",
-    "Since $f(x)$ can be either $0$ or $1$, we have that $0 \\oplus f(x) = 0 \\oplus 0 =  0 = f(x)$ or $0 \\oplus f(x) = 0 \\oplus 1 =  1 = f(x)$. \n",
-    "\n",
-    "Similarly, $1 \\oplus f(x) = 1 \\oplus 0 =  1 = \\overline{f(x)}$ or $1 \\oplus f(x) = 1 \\oplus 1 =  0 = \\overline{f(x)}$ where the bar on top of $f(x)$ denotes 'not $f(x)$'. \n",
-    "\n",
-    "\n",
-    "Let us now prove a result which we shall use later: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{x}\\ket{-}  &=   U_f \\ket{x} \\tfrac{1}{\\sqrt{2}}(\\ket{0} - \\ket{1}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (U_f\\ket{x}\\ket{0} - U_f\\ket{x}\\ket{1}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{0 \\oplus f(x)} - \\ket{x}\\ket{1 \\oplus f(x)}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{f(x)} - \\ket{x}\\ket{\\overline{f(x)}}) \\\\\n",
-    "&= \n",
-    "\\begin{cases}\n",
-    "      \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{0} - \\ket{x}\\ket{1}) = \\ket{x}\\ket{-} &   f(x) = 0 \\\\\n",
-    "      \\tfrac{1}{\\sqrt{2}}  (\\ket{x}\\ket{1} - \\ket{x}\\ket{0}) = -\\ket{x}\\ket{-} &   f(x) = 1 \\\\\n",
-    "\\end{cases} \\\\\n",
-    "&= (-1)^{f(x)}\\ket{x}\\ket{-}    \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "In summary, $U_f\\ket{x}\\ket{-} = (-1)^{f(x)}\\ket{x}\\ket{-}$ where the $\\ket{-}$ qubit was left unchanged and a phase was applied to $\\ket{x}$. \n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Quantum parallelism\n",
-    "\n",
-    "Consider: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "U_f\\ket{+}\\ket{0} &= U_f \\tfrac{1}{\\sqrt{2}}(\\ket{0}+\\ket{1}) \\ket{0} \\\\\n",
-    "&= U_f \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{0}+\\ket{1}\\ket{0}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}(U_f \\ket{0}\\ket{0} + U_f \\ket{1}\\ket{0})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(U_f \\ket{0}\\ket{0} + U_f \\ket{1}\\ket{0})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{0 \\oplus f(0)} + \\ket{1}\\ket{0 \\oplus f(1)})  \\\\   \n",
-    "&= \\tfrac{1}{\\sqrt{2}}(\\ket{0}\\ket{f(0)} + \\ket{1}\\ket{f(1)})  \\\\   \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We have calculated information about both $f(0)$ and $f(1)$ simultaneously. Quantum mechanics allows for parallelism by exploiting the ability of superposition states.  "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deutschs' Algorithm: \n",
-    "\n",
-    "Our aim is to find out if $f: \\{0,1\\} \\longrightarrow \\{0,1\\}$ is a constant or a balanced function? If constant, $f(0) = f(1)$, and if balanced, $f(0) \\neq f(1)$.\n",
-    "\n",
-    "We step through the circuit diagram below and follow the math after the application of each gate.\n",
-    "\n",
-    "<img src=\"images/deutsch.png\"  width=\"500\" height=\"210\">\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\ket{\\psi_0}  =  \\ket{01}\n",
-    "\\tag{1}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\\ket{\\psi_1}  =   H_1H_0\\ket{\\psi_0} = H_1H_0\\ket{01} = \\ket{+-} = \\frac{1}{\\sqrt{2}} (\\ket{0}+\\ket{1})\\ket{-}\n",
-    "\\tag{2}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\ket{\\psi_2}  =   U_f\\ket{\\psi_1} = \\frac{1}{\\sqrt{2}} (U_f\\ket{0}\\ket{-} + U_f\\ket{1}\\ket{-})\n",
-    "\\tag{3}\n",
-    "$$\n",
-    "\n",
-    "using $U_f\\ket{x}\\ket{-} = (-1)^{f(x)}\\ket{x}\\ket{-}$ which we proved in the phase oracle section above: \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_2}  =  \\frac{1}{\\sqrt{2}} ((-1)^{f(0)}\\ket{0}\\ket{-} + (-1)^{f(1)}\\ket{1}\\ket{-}) \\\\\n",
-    "= \\frac{1}{\\sqrt{2}} ((-1)^{f(0)}\\ket{0} + (-1)^{f(1)}\\ket{1}) \\ket{-} \\\\\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We now drop the second, qubit in the $\\ket{-}$ state from our derivations as the figure above has no further gate operations acting on it. Remember our aim is to find out if $f(x)$ is a constant or a balanced function. We have the following options: \n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\ket{\\psi_{2}}=\n",
-    "\\begin{cases}\n",
-    "      \\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1})  &   f(0) = 0, f(1) = 0, f(x)  \\text{ is constant} \\\\\n",
-    "      -\\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1})  &   f(0) = 1, f(1) = 1, f(x)  \\text{ is constant} \\\\\n",
-    "      \\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})  &   f(0) = 0, f(1) = 1, f(x)  \\text{ is balanced} \\\\\n",
-    "      -\\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})  &   f(0) = 1, f(1) = 0, f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} =\n",
-    "\\begin{cases}\n",
-    "      \\pm \\tfrac{1}{\\sqrt{2}} (\\ket{0} + \\ket{1}) = \\pm \\ket{+}  &    f(x)  \\text{ is constant} \\\\\n",
-    "      \\pm \\tfrac{1}{\\sqrt{2}} (\\ket{0} - \\ket{1})= \\pm \\ket{-}  &    f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{3}}= H\\ket{\\psi_{2}}\n",
-    "\\begin{cases}\n",
-    "      \\pm H\\ket{+} =  \\pm \\ket{0} &    f(x)  \\text{ is constant} \\\\\n",
-    "      \\pm H\\ket{-}  = \\pm \\ket{1}  &   f(x)  \\text{ is balanced} \\\\\n",
-    "\\end{cases} \n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "We can now measure the first qubit to yield either a $0$ or a $1$ to determine if $f(x)$ is constant or balanced. \n",
-    "\n",
-    "Deutsch's algorithm may not be practically useful but does demonstrate quantum advantage since it uses one function call to determine the property of $f$ in comparison to two for classical methods. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/qutip/__init__.py:66: UserWarning: The new version of Cython, (>= 3.0.0) is not supported.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import the CUDA-Q package and set the target to run on NVIDIA GPUs.\n",
-    "\n",
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "from typing import List\n",
-    "\n",
-    "cudaq.set_target(\"nvidia\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here we input the values of [f(0), f(1)] which allows us to represent constant or balanced functions.\n",
-    "\n",
-    "fx = [0, 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮          ╭───╮\n",
-      "q0 : ┤ h ├───────●──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╰───╯\n",
-      "q1 : ┤ x ├┤ h ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯     \n",
-      "\n",
-      "f(x) is a balanced function\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let us now code up the circuit shown above following the state Psi after each step.\n",
-    "\n",
-    "qubit_count = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(fx: List[int]):\n",
-    "    qubit_0 = cudaq.qubit()\n",
-    "    qubit_1 = cudaq.qubit()\n",
-    "\n",
-    "    # Psi 0\n",
-    "    x(qubit_1)\n",
-    "\n",
-    "    # Psi 1\n",
-    "    h(qubit_0)\n",
-    "    h(qubit_1)\n",
-    "\n",
-    "    # Psi 2 - oracle\n",
-    "    if fx[0] == 1:\n",
-    "        x.ctrl(qubit_0, qubit_1)\n",
-    "        x(qubit_1)\n",
-    "\n",
-    "    if fx[1] == 1:\n",
-    "        x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "    # Psi 3\n",
-    "    h(qubit_0)\n",
-    "\n",
-    "    # Measure the qubit to yield if the function is constant or balanced.\n",
-    "    mz(qubit_0)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, fx))\n",
-    "\n",
-    "result = cudaq.sample(kernel, fx, shots_count=1)\n",
-    "\n",
-    "if np.array(result)[0] == '0':\n",
-    "    print('f(x) is a constant function')\n",
-    "elif np.array(result)[0] == '1':\n",
-    "    print('f(x) is a balanced function')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/digitized_counterdiabatic_qaoa.ipynb b/pr-2308/sphinx/applications/python/digitized_counterdiabatic_qaoa.ipynb
deleted file mode 100644
index a3043b7a1b..0000000000
--- a/pr-2308/sphinx/applications/python/digitized_counterdiabatic_qaoa.ipynb
+++ /dev/null
@@ -1,427 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Molecular docking via DC-QAOA\n",
-    "\n",
-    "Drugs often work by binding to an active site of a protein, inhibiting or activating its function for some therapeutic purpose. Finding new candidate drugs is extremely difficult. The study of molecular docking helps guide this search and involves the prediction of how strongly a certain ligand (drug) will bind to its target (usually a protein).  \n",
-    "\n",
-    "One of the primary challenges to molecular docking arises from the many geometric degrees of freedom present in proteins and ligands, making it difficult to predict the optimal orientation and assess if the drug is a good candidate or not. One solution is to formulate the problem as a mathematical optimization problem where the optimal solution corresponds to the most likely ligand-protein configuration. This optimization problem can be solved on a quantum computer using methods like the Quantum Approximate Optimization Algorithm (QAOA). This tutorial demonstrates how this [paper](https://arxiv.org/pdf/2308.04098) used digitized-counteradiabatic (DC) QAOA to study molecular docking.  This tutorial assumes you have an understanding of QAOA, if not, please see the CUDA-Q MaxCut tutorial found [here](https://nvidia.github.io/cuda-quantum/latest/applications/python/qaoa.html).\n",
-    "\n",
-    "The next section provides more detail on the problem setup followed by CUDA-Q implementations below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setting up the Molecular Docking Problem\n",
-    "\n",
-    "The figure from the [paper](https://arxiv.org/pdf/2308.04098) provides a helpful diagram for understanding the workflow.\n",
-    "\n",
-    "![docking](./images/docking.png)\n",
-    "\n",
-    "\n",
-    "There are 6 key steps:\n",
-    "1.  The experimental protein and ligand structures are determined and used to select pharmacores, or an important chemical group that will govern the chemical interactions.\n",
-    "2. Two labeled distance graphs (LAGs) of size $N$ and $M$ represent the protein and the ligand, respectively. Each node corresponds to a pharmacore and each edge weight corresponds to the distance between pharmacores.\n",
-    "3.  A $M*N$ node binding interaction graph (BIG) is created from the LAGs. Each node in the BIG graph corresponds to a pair of pharmacores, one from the ligand and the other from the protein. The existence of edges between nodes in the BIG graph are determined from the LAGs and correspond to interactions that can feesibly coexist. Therefore, cliques in the graph correspond to mutually possible interactions. \n",
-    "4. The problem is mapped to a QAOA circuit and corresponding Hamiltonian. From there, the ground state solution is determined.\n",
-    "5.  The ground state will produce the maximum weighted clique which corresponds to the best (most strongly bound) orientation of the ligand and protein.\n",
-    "6.  The predicted docking structure is interpreted from the QAOA result and is used for further analysis.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### CUDA-Q Implementation\n",
-    "\n",
-    "First, the appropriate libraries are imported and the `nvidia` backend is selected to run on GPUs if available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "import numpy as np\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The block below defines two of the BIG data sets from the paper. The first is a smaller example, but it can be swapped with the commented out example below at your discretion. The weights are specified for each node based on the nature of the ligand and protein pharmacores represented by the node."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Edges:  [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4], [3, 4], [3, 5], [4, 5]]\n",
-      "Non-Edges:  [[0, 3], [1, 4], [2, 5]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The two graph inputs from the paper\n",
-    "\n",
-    "# BIG 1\n",
-    "\n",
-    "nodes = [0, 1, 2, 3, 4, 5]\n",
-    "qubit_num = len(nodes)\n",
-    "edges = [[0, 1], [0, 2], [0, 4], [0, 5], [1, 2], [1, 3], [1, 5], [2, 3], [2, 4],\n",
-    "         [3, 4], [3, 5], [4, 5]]\n",
-    "non_edges = [\n",
-    "    [u, v] for u in nodes for v in nodes if u < v and [u, v] not in edges\n",
-    "]\n",
-    "\n",
-    "print('Edges: ', edges)\n",
-    "print('Non-Edges: ', non_edges)\n",
-    "\n",
-    "weights = [0.6686, 0.6686, 0.6686, 0.1453, 0.1453, 0.1453]\n",
-    "penalty = 6.0\n",
-    "num_layers = 3\n",
-    "\n",
-    "# BIG 2 (More expensive simulation)\n",
-    "#nodes=[0,1,2,3,4,5,6,7]\n",
-    "#qubit_num=len(nodes)\n",
-    "#edges=[[0,1],[0,2],[0,5],[0,6],[0,7],[1,2],[1,4],[1,6],[1,7],[2,4],[2,5],[2,7],[3,4],[3,5],[3,6],\\\n",
-    "#    [4,5],[4,6],[5,6]]\n",
-    "#non_edges=[[u,v] for u in nodes for v in nodes if u<v and [u,v] not in edges]\n",
-    "#print('Edges: ', edges)\n",
-    "#print('Non-edges: ', non_edges)\n",
-    "#weights=[0.6686,0.6686,0.6886,0.1091,0.0770,0.0770,0.0770,0.0770]\n",
-    "#penalty=8.0\n",
-    "#num_layers=8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the Hamiltonian is constructed: \n",
-    "\n",
-    "$$H = \\frac{1}{2}\\sum_{i \\in V}w_i(\\sigma^z_i - 1) + \\frac{P}{4} \\sum_{(i,j) \\notin E, i \\neq j} (\\sigma^z_i -1)(\\sigma^z_j - 1) $$\n",
-    "\n",
-    "\n",
-    "The first term concerns the vertices and the weights of the given pharmacores.  The second term is a penalty term that penalizes edges of the graph with no interactions.  The penalty $P$ is set by the user and is defined as 6 in the cell above. The function below returns the Hamiltonian as a CUDA-Q `spin_op` object.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate the Hamiltonian\n",
-    "def ham_clique(penalty, nodes, weights, non_edges) -> cudaq.SpinOperator:\n",
-    "\n",
-    "    spin_ham = 0.0\n",
-    "    for wt, node in zip(weights, nodes):\n",
-    "        #print(wt,node)\n",
-    "        spin_ham += 0.5 * wt * spin.z(node)\n",
-    "        spin_ham -= 0.5 * wt * spin.i(node)\n",
-    "\n",
-    "    for non_edge in non_edges:\n",
-    "        u, v = (non_edge[0], non_edge[1])\n",
-    "        #print(u,v)\n",
-    "        spin_ham += penalty / 4.0 * (spin.z(u) * spin.z(v) - spin.z(u) -\n",
-    "                                     spin.z(v) + spin.i(u) * spin.i(v))\n",
-    "\n",
-    "    return spin_ham"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below strips the Hamiltonian into a list of coefficients and corresponding Pauli words which can be passed into a quantum kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.5+0j] IIZIIZ\n",
-      "[1.5+0j] ZIIZII\n",
-      "[-1.1657+0j] IZIIII\n",
-      "[1.5+0j] IZIIZI\n",
-      "[-1.42735+0j] IIIZII\n",
-      "[3.2791499999999996+0j] IIIIII\n",
-      "[-1.1657+0j] IIZIII\n",
-      "[-1.42735+0j] IIIIIZ\n",
-      "[-1.1657+0j] ZIIIII\n",
-      "[-1.42735+0j] IIIIZI\n",
-      "\n",
-      "[(1.5+0j), (1.5+0j), (-1.1657+0j), (1.5+0j), (-1.42735+0j), (3.2791499999999996+0j), (-1.1657+0j), (-1.42735+0j), (-1.1657+0j), (-1.42735+0j)]\n",
-      "['IIZIIZ', 'ZIIZII', 'IZIIII', 'IZIIZI', 'IIIZII', 'IIIIII', 'IIZIII', 'IIIIIZ', 'ZIIIII', 'IIIIZI']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Collect coefficients from a spin operator so we can pass them to a kernel\n",
-    "def term_coefficients(ham: cudaq.SpinOperator) -> list[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "    # Collect Pauli words from a spin operator so we can pass them to a kernel\n",
-    "\n",
-    "\n",
-    "def term_words(ham: cudaq.SpinOperator) -> list[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "ham = ham_clique(penalty, nodes, weights, non_edges)\n",
-    "print(ham)\n",
-    "\n",
-    "coef = term_coefficients(ham)\n",
-    "words = term_words(ham)\n",
-    "\n",
-    "print(term_coefficients(ham))\n",
-    "print(term_words(ham))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The kernel below defines a DC-QAOA circuit.  What makes the approach \"DC\" is the inclusion of additional counteradiabatic terms to better drive the optimization to the ground state. These terms are digitized and applied as additional operations following each QAOA layer.  The increase in parameters is hopefully offset by requiring fewer layers. In this example, the DC terms are the additional parameterized $Y$ operations applied to each qubit. These can be commented out to run conventional QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def dc_qaoa(qubit_num:int, num_layers:int, thetas:list[float],\\\n",
-    "    coef:list[complex], words:list[cudaq.pauli_word]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    h(qubits)\n",
-    "\n",
-    "    count = 0\n",
-    "    for p in range(num_layers):\n",
-    "\n",
-    "        for i in range(len(coef)):\n",
-    "            exp_pauli(thetas[count] * coef[i].real, qubits, words[i])\n",
-    "            count += 1\n",
-    "\n",
-    "        for j in range(qubit_num):\n",
-    "            rx(thetas[count], qubits[j])\n",
-    "            count += 1\n",
-    "\n",
-    "        #Comment out this for loop for conventional QAOA\n",
-    "        for k in range(qubit_num):\n",
-    "            ry(thetas[count], qubits[k])\n",
-    "            count += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The classical optimizer for the QAOA procedure can be specified as one of the built-in CUDA-Q optimizers, in this case Nelder Mead. The parameter count is defined for DC-QAOA, but can be swapped with the commented line below for conventional QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of parameters:  66\n",
-      "Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064, 0.37118004688049144, -0.03656087558321203, 0.08564174998504231, 0.21639801853794682, 0.11122286088634259, 0.1743727097033635, -0.36518146001762486, -0.15829741539542244, -0.3467434780387345, 0.28043500852894776, -0.09986021299050934, 0.14125225086023052, -0.19141728018199775, -0.11970943368650361, -0.3853063093646483, -0.1112643868789806, 0.3527177454825464, -0.22156160012057186, -0.1418496891385843, 0.32811766468303116, -0.367642000671186, -0.34158180583996006, 0.10196745745501312, 0.29359239180502594, -0.3858537615546677, 0.19366130907065582, 0.24570488114056754, -0.3332307385378807, 0.12287973244618389, 0.007274514934614895, -0.015799547372526146, 0.3578070967202224, -0.39268963055535144, -0.19872246354138554, 0.16668715544467982, -0.13777293592446055, -0.17514665212709513, 0.15350249947988204, 0.32872977428061945, -0.20068831419712105, -0.032919322131134854, -0.19399909325771983, -0.09477141125241506, 0.08210460401106645, 0.21392577760158515, -0.3393568044538389, 0.14615087942938465, 0.03790339186006314, -0.2843250892879255, -0.3151384847055956, -0.19983741137121905, -0.27348611567665115, 0.33457528180906904, 0.14145414847455462, -0.20604220093940323, 0.05410235084309195, 0.04447870918600966, -0.3355714098595045, 0.266806440171265, -0.07436189654442632, -0.2789176729721685, -0.2427508182662484]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Specify the optimizer and its initial parameters.\n",
-    "optimizer = cudaq.optimizers.NelderMead()\n",
-    "\n",
-    "#Specify random seeds\n",
-    "np.random.seed(13)\n",
-    "cudaq.set_random_seed(13)\n",
-    "\n",
-    "# if dc_qaoa used\n",
-    "parameter_count = (2 * qubit_num + len(coef)) * num_layers\n",
-    "\n",
-    "# if qaoa used\n",
-    "# parameter_count=(qubit_num+len(coef))*num_layers\n",
-    "\n",
-    "print('Total number of parameters: ', parameter_count)\n",
-    "optimizer.initial_parameters = np.random.uniform(-np.pi / 8, np.pi / 8,\n",
-    "                                                 parameter_count)\n",
-    "print(\"Initial parameters = \", optimizer.initial_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A cost function is specified which computes the expectation value of the DC-QAOA circuit and the Hamiltonian using the `observe` function. Running the optimization returns the minimized expectation value and the optimal parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "optimal_expectation = -2.0057493966746804\n",
-      "optimal_parameters = [2.0508763934174787, 0.013930789730781493, 0.5793211220774144, 0.878009560684498, 0.5277129177248182, 0.4404810513078178, 0.5755552245467919, 0.14125558672355468, 0.3724262117066903, 0.1318978057007808, -1.1228708513911436, 0.932342804955409, -0.8478237950658537, 0.46345886313018125, -0.5809397306340341, 0.2408342488137229, 0.11216088888484882, -0.009704173265255175, 0.4757346661223584, -0.7281211610985926, 0.06051951319169091, -0.7794512146826196, 0.09249435261907034, 0.09998378319110682, 1.255349350720572, 1.2607038244228248, 0.2060124032311757, 0.13991934581192997, 0.9874814082082164, -0.1591291464755939, 0.30815482837046393, -0.9701804681517978, -0.002609462845755913, 0.43533533568363353, 0.642630110681613, 0.6137063363954748, -0.7204687246344496, 0.08390768435524378, 0.5480630700433249, -0.38905723227347905, -0.6837811162838194, -0.17239016898719284, 0.1649341118754853, -0.46771209183422724, -0.008565327035838663, 1.982230359328883, -0.4232972687799105, 0.22765896988428905, 0.04207923928239914, -0.36758378917672285, -0.01825447063622079, -0.059755059728027485, -0.6849697218162497, 0.2711684382411018, -0.2904257415666667, -0.16359529445017368, -0.09168623367396612, 0.5786087806926155, -0.3476755367718726, 0.1209273564533628, 0.605136043801364, -0.19128215816141694, 0.16756583092588012, 1.0715488214105267, -0.5269641128095075, -0.3029128369198704]\n"
-     ]
-    }
-   ],
-   "source": [
-    "cost_values = []\n",
-    "\n",
-    "\n",
-    "def objective(parameters):\n",
-    "\n",
-    "    cost = cudaq.observe(dc_qaoa, ham, qubit_num, num_layers, parameters, coef,\n",
-    "                         words).expectation()\n",
-    "    cost_values.append(cost)\n",
-    "    return cost\n",
-    "\n",
-    "\n",
-    "# Optimize!\n",
-    "optimal_expectation, optimal_parameters = optimizer.optimize(\n",
-    "    dimensions=parameter_count, function=objective)\n",
-    "\n",
-    "print('optimal_expectation =', optimal_expectation)\n",
-    "print('optimal_parameters =', optimal_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sampling the circuit with the optimal parameters allows for the `most_probable` command to reveal the bitsting corresponding to the ideal graph partitioning solution. This indicates what sort of interactions are present in the ideal docking configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 110001:16 011100:4 111000:199979 011000:1 }\n",
-      "\n",
-      "The MVWCP is given by the partition:  111000\n"
-     ]
-    }
-   ],
-   "source": [
-    "shots = 200000\n",
-    "\n",
-    "counts = cudaq.sample(dc_qaoa,\n",
-    "                      qubit_num,\n",
-    "                      num_layers,\n",
-    "                      optimal_parameters,\n",
-    "                      coef,\n",
-    "                      words,\n",
-    "                      shots_count=shots)\n",
-    "print(counts)\n",
-    "\n",
-    "print('The MVWCP is given by the partition: ', counts.most_probable())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"./images/partition.png\" alt=\"dockin\" width=\"300\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The convergence of the optimization can be plotted below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x_values = list(range(len(cost_values)))\n",
-    "y_values = cost_values\n",
-    "\n",
-    "plt.plot(x_values, y_values)\n",
-    "\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Cost Value\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/sphinx/applications/python/divisive_clustering_coresets.ipynb b/pr-2308/sphinx/applications/python/divisive_clustering_coresets.ipynb
deleted file mode 100644
index 0cffbd7110..0000000000
--- a/pr-2308/sphinx/applications/python/divisive_clustering_coresets.ipynb
+++ /dev/null
@@ -1,1097 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Divisive Clustering With Coresets Using CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial will explore a CUDA-Q implementation of recent research (ArXiv Paper: https://arxiv.org/pdf/2402.01529.pdf) performed by a team from the University of Edinburgh. This tutorial was jointly developed by NVIDIA and the authors so users can better understand their method and explore how CUDA-Q removed barriers to scaling. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code for this tutorial is based off the MIT licensed code found here: https://github.com/Boniface316/bigdata_vqa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Clustering is a common unsupervised learning technique aimed at grouping data with similar characteristics. The unique properties of quantum computers could allow for enhanced pattern finding in clustering applications and enable more reliable data analysis. However, quantum computers today are severely limited by qubit count and noise.  Performing practical clustering applications would require far too many qubits.  The Edinburgh team developed a new method (extending the work of Harrow) to leverage coresets for clustering applications on quantum computers and use far fewer qubits.  This tutorial will walk through an example using this approach for divisive clustering and emphasize the utility of CUDA-Q for scaling quantum simulations.\n",
-    "\n",
-    "The goal of divisive clustering is to begin with all data points as one set, and iteratively bipartition the data until each point is its own cluster.  The branching behavior of this process can be used to understand similarities in the data points.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If you are running outside of a CUDA-Q container or CUDA-Q directory tree, you may need to uncomment these lines to fetch the files.\n",
-    "# If you are running inside a CUDA-Q tree, then this step can be skipped.\n",
-    "# !mkdir divisive_clustering_src\n",
-    "# !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py\n",
-    "# !wget -P divisive_clustering_src https://raw.githubusercontent.com/NVIDIA/cuda-quantum/main/docs/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "!pip install mpi4py==3.1.6 networkx==2.8.8 pandas==2.2.2 scikit-learn==1.4.2 tqdm==4.66.2 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "# Auxillary Imports\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import warnings\n",
-    "from typing import Tuple\n",
-    "from divisive_clustering_src.divisive_clustering import Coreset, DivisiveClustering, Dendrogram, Voironi_Tessalation\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The settings below are global parameters for the quantum simulation and can be toggled by the user.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "circuit_depth = 1\n",
-    "max_iterations = 75\n",
-    "max_shots = 1000\n",
-    "np.random.seed(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Given a data set $X = (x_1, x_2, \\cdots, x_N)$, a coreset is weighted data set of much smaller size ($X', w$) that represents $X$ enough such that analysis of ($X', w$) can allow us to draw reasonable approximate conclusions about $X$. There are various approaches to build coresets. They can be found in Practical Coreset Construction for Machine Learning (https://arxiv.org/pdf/1703.06476.pdf) and New Streaming Algorithms for Coresets in Machine Learning (https://arxiv.org/pdf/1703.06476.pdf).\n",
-    "\n",
-    "\n",
-    "Essentially, coreset construction boils down to finding the optimal coreset size and weights given some error tolerance.  Given the constraints of a quantum computer, in this work, a coreset size is selected $a$ $priori$, and the error is determined for each model.\n",
-    "\n",
-    "\n",
-    "The following is an example $M=10$ coreset constructed from a 1000-point data set and loaded into a pandas data frame. See the image below where the coreset is represented by the black stars, the size of which corresponds to the weights.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "          X         Y     weights Name\n",
-      "0  7.028364  1.669787  234.230716    A\n",
-      "1  7.167441  0.354792  101.319288    B\n",
-      "2  1.022889 -0.921443  125.158339    C\n",
-      "3  2.706134 -2.636852   13.650774    D\n",
-      "4  6.998497  0.455847  116.758239    E\n",
-      "5  7.507918  0.630311  120.727176    F\n",
-      "6 -2.102508  2.297727   53.294127    G\n",
-      "7  5.722463  1.400433   77.415840    H\n",
-      "8 -1.425868  2.341136   42.847985    I\n",
-      "9  7.985373 -0.063209  240.116237    J\n"
-     ]
-    }
-   ],
-   "source": [
-    "raw_data = Coreset.create_dataset(1000)\n",
-    "coreset = Coreset(\n",
-    "    raw_data=raw_data,\n",
-    "    number_of_sampling_for_centroids=10,\n",
-    "    coreset_size=10,\n",
-    "    number_of_coresets_to_evaluate=4,\n",
-    "    coreset_method=\"BFL2\",\n",
-    ")\n",
-    "\n",
-    "coreset_vectors, coreset_weights = coreset.get_best_coresets()\n",
-    "\n",
-    "coreset_df = pd.DataFrame({\n",
-    "    \"X\": coreset_vectors[:, 0],\n",
-    "    \"Y\": coreset_vectors[:, 1],\n",
-    "    \"weights\": coreset_weights\n",
-    "})\n",
-    "coreset_df[\"Name\"] = [chr(i + 65) for i in coreset_df.index]\n",
-    "print(coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(raw_data[:, 0], raw_data[:, 1], label=\"Raw Data\", c=\"#7eba00\")\n",
-    "plt.scatter(\n",
-    "    coreset_df[\"X\"],\n",
-    "    coreset_df[\"Y\"],\n",
-    "    s=coreset_df[\"weights\"],\n",
-    "    label=\"Coreset\",\n",
-    "    color=\"black\",\n",
-    "    marker=\"*\",\n",
-    ")\n",
-    "plt.xlabel(\"X\")\n",
-    "plt.ylabel(\"Y\")\n",
-    "plt.title(\"Raw data and its best 10 coreset using BFL2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to cluster data on a quantum computer, the task needs to be cast into the form of a binary optimization problem.  Each qubit represents a coreset point, and the quantum algorithm determines how to bipartition the coreset points at each iteration of the divisive clustering routine. \n",
-    "\n",
-    "The first step is to convert coreset points into a fully connected graph. The edge weight is calculated by:\n",
-    "\n",
-    "$e_{ij} = w_iw_jd_{ij}$ where $d_{ij}$ is the Euclidean distance between points $i$ and $j$. \n",
-    "\n",
-    "This process is handled by `Coreset.coreset_to_graph()`. The function returns a fully connected graph $G$ with edge weights."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The divisive clustering problem will be implemented on a quantum computer using a variational quantum algorithm (VQA) approach.  A VQA takes a Hamiltonian (encoded with the optimization problem) and a parameterized ansatz and evaluates expectation values (quantum computer) that inform updates to the ansatz parameters (classical computer).  The graph $G$ (Code in the \"src\" file) is used to construct the Hamiltonian, derived specifically for the divisive clustering problem, and motivated by a max-cut Hamiltonian.  The `spin.z(i)` method in CUDA-Q adds a Pauli Z operation that acts on qubit $i$ to the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_K2_Hamiltonian(G: nx.Graph) -> cudaq.SpinOperator:\n",
-    "    \"\"\"Returns the K2 Hamiltonian for the given graph G\n",
-    "\n",
-    "    Args:\n",
-    "        G (nx.Graph): Weighted graph\n",
-    "    \"\"\"\n",
-    "    H = 0\n",
-    "\n",
-    "    for i, j in G.edges():\n",
-    "        weight = G[i][j][\"weight\"]\n",
-    "        H += weight * (spin.z(i) * spin.z(j))\n",
-    "\n",
-    "    return H"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below constructs a quantum kernel, defining the circuit which will serve as an ansatz.  The structure of the circuit is a hardware efficient ansatz consisting of layers of parameterized $R_Z$ and $R_Y$ gate acting on each qubit, followed by a linear cascade of CNOT gates, and two more rotation gates.\n",
-    "\n",
-    "The `@cudaq.kernel` decorator allows us to define a quantum circuit in the new kernel mode syntax which provides performance benefits to JIT compilation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_VQE_circuit(number_of_qubits: int, circuit_depth: int) -> cudaq.Kernel:\n",
-    "    \"\"\"Returns the VQE circuit for the given number of qubits and circuit depth\n",
-    "\n",
-    "    Args:\n",
-    "        number_of_qubits (int): Number of qubits\n",
-    "        circuit_depth (int): Circuit depth\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq.Kernel: VQE Circuit\n",
-    "    \"\"\"\n",
-    "\n",
-    "    @cudaq.kernel\n",
-    "    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):\n",
-    "        \"\"\"VQE Circuit\n",
-    "\n",
-    "        Args:\n",
-    "            thetas (list[float]): List of parameters\n",
-    "            number_of_qubits (int): Number of qubits\n",
-    "            circuit_depth (int): Circuit depth\n",
-    "        \"\"\"\n",
-    "        qubits = cudaq.qvector(number_of_qubits)\n",
-    "\n",
-    "        theta_position = 0\n",
-    "\n",
-    "        for i in range(circuit_depth):\n",
-    "            for j in range(number_of_qubits):\n",
-    "                ry(thetas[theta_position], qubits[j])\n",
-    "                rz(thetas[theta_position + 1], qubits[j])\n",
-    "\n",
-    "                theta_position += 2\n",
-    "\n",
-    "            for j in range(number_of_qubits - 1):\n",
-    "                cx(qubits[j], qubits[j + 1])\n",
-    "\n",
-    "            for j in range(number_of_qubits):\n",
-    "                ry(thetas[theta_position], qubits[j])\n",
-    "                rz(thetas[theta_position + 1], qubits[j])\n",
-    "\n",
-    "                theta_position += 2\n",
-    "\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualize the circuit using the `cudaq.draw()` method. Below, we are drawing the circuit for 5 qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭────────────╮ ╭────────────╮      ╭────────────╮╭────────────╮»\n",
-      "q0 : ┤ ry(0.8904) ├─┤ rz(0.7335) ├───●──┤ ry(0.4343) ├┤ rz(0.2236) ├»\n",
-      "     ├────────────┤ ├────────────┤ ╭─┴─╮╰────────────╯├────────────┤»\n",
-      "q1 : ┤ ry(0.7937) ├─┤ rz(0.9981) ├─┤ x ├──────●───────┤ ry(0.3945) ├»\n",
-      "     ├───────────┬╯ ├────────────┤ ╰───╯    ╭─┴─╮     ╰────────────╯»\n",
-      "q2 : ┤ ry(0.696) ├──┤ rz(0.3352) ├──────────┤ x ├───────────●───────»\n",
-      "     ├───────────┴╮╭┴────────────┤          ╰───╯         ╭─┴─╮     »\n",
-      "q3 : ┤ ry(0.6658) ├┤ rz(0.05277) ├────────────────────────┤ x ├─────»\n",
-      "     ├───────────┬╯├─────────────┴╮                       ╰───╯     »\n",
-      "q4 : ┤ ry(0.791) ├─┤ rz(0.003569) ├─────────────────────────────────»\n",
-      "     ╰───────────╯ ╰──────────────╯                                 »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                                             \n",
-      "─────────────────────────────────────────────\n",
-      "╭────────────╮                               \n",
-      "┤ rz(0.4119) ├───────────────────────────────\n",
-      "├────────────┤╭────────────╮                 \n",
-      "┤ ry(0.3205) ├┤ rz(0.3504) ├─────────────────\n",
-      "╰────────────╯├────────────┤ ╭────────────╮  \n",
-      "──────●───────┤ ry(0.3913) ├─┤ rz(0.7392) ├──\n",
-      "    ╭─┴─╮     ├────────────┤╭┴────────────┴─╮\n",
-      "────┤ x ├─────┤ ry(0.3171) ├┤ rz(0.0008056) ├\n",
-      "    ╰───╯     ╰────────────╯╰───────────────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "parameter_count = 4 * circuit_depth * 5\n",
-    "parameters = np.random.rand(parameter_count)\n",
-    "\n",
-    "circuit = get_VQE_circuit(5, circuit_depth)\n",
-    "print(cudaq.draw(circuit, parameters, 5, circuit_depth))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next step is to select a classical optimizer. There are multiple [optimizers](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#optimizers) built-in to CUDA-Q that can be selected. The code below returns the optimizer with the proper number of initial parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,\n",
-    "                  **kwargs) -> Tuple[cudaq.optimizers.optimizer, int]:\n",
-    "    \"\"\"Returns the optimizer with the given parameters\n",
-    "\n",
-    "    Args:\n",
-    "        optimizer (cudaq.optimizers.optimizer): Optimizer\n",
-    "        max_iterations (int): Maximum number of iterations\n",
-    "        **kwargs: Additional arguments\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count\n",
-    "    \"\"\"\n",
-    "    parameter_count = 4 * kwargs[\"circuit_depth\"] * kwargs[\"qubits\"]\n",
-    "    initial_params = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,\n",
-    "                                       parameter_count)\n",
-    "    optimizer.initial_parameters = initial_params\n",
-    "\n",
-    "    optimizer.max_iterations = max_iterations\n",
-    "    return optimizer, parameter_count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Divisive Clustering Function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `DivisiveClusteringVQA` class enables the procedure to iteratively bipartition the coreset points until each is its own cluster. If you wish to develop on top of this or see how the underlying code works, please see the `divisive_clustering.py` file in the src directory. \n",
-    "\n",
-    "`run_divisive_clustering`, takes the current iteration's coreset points that will be bipartitioned as inputs, extracts the appropriate weights, and builds a graph $G$. The graph is then an input into the `get_counts_from_simulation` function. \n",
-    "\n",
-    "\n",
-    "`get_counts_from_simulation` handles preparation and execution of the quantum simulation. First, it takes $G$ and from it builds a spin Hamiltonian. Second, it defines a cost function, which in this case is a lambda function that returns the expectation value of our parameterized quantum circuit and the Hamiltonian. This value is obtained using the CUDA-Q `observe` command, accelerated by GPUs.  After the expectation value is minimized, the quantum circuit corresponding to the optimal parameters is sampled using the CUDA-Q `sample` function. The bitstrings and their associated counts are returned by `get_counts_from_simulation`.\n",
-    "\n",
-    "A subset of these counts is evaluated to compute their exact cost. The best bitstring is returned and later used to assign the coreset points to one of two clusters.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class DivisiveClusteringVQA(DivisiveClustering):\n",
-    "\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        **kwargs,\n",
-    "    ):\n",
-    "        super().__init__(**kwargs)\n",
-    "\n",
-    "    def run_divisive_clustering(\n",
-    "        self,\n",
-    "        coreset_vectors_df_for_iteration: pd.DataFrame,\n",
-    "    ):\n",
-    "        \"\"\"Runs the Divisive Clustering algorithm\n",
-    "\n",
-    "        Args:\n",
-    "            coreset_vectors_df_for_iteration (pd.DataFrame): Coreset vectors for the iteration\n",
-    "\n",
-    "        Returns:\n",
-    "            str: Best bitstring\n",
-    "\n",
-    "        \"\"\"\n",
-    "        coreset_vectors_for_iteration_np, coreset_weights_for_iteration_np = (\n",
-    "            self._get_iteration_coreset_vectors_and_weights(\n",
-    "                coreset_vectors_df_for_iteration))\n",
-    "\n",
-    "        G = Coreset.coreset_to_graph(\n",
-    "            coreset_vectors_for_iteration_np,\n",
-    "            coreset_weights_for_iteration_np,\n",
-    "            metric=self.coreset_to_graph_metric,\n",
-    "        )\n",
-    "\n",
-    "        counts = self.get_counts_from_simulation(\n",
-    "            G,\n",
-    "            self.circuit_depth,\n",
-    "            self.max_iterations,\n",
-    "            self.max_shots,\n",
-    "        )\n",
-    "\n",
-    "        return self._get_best_bitstring(counts, G)\n",
-    "\n",
-    "    def get_counts_from_simulation(self, G: nx.graph, circuit_depth: int,\n",
-    "                                   max_iterations: int,\n",
-    "                                   max_shots: int) -> cudaq.SampleResult:\n",
-    "        \"\"\"\n",
-    "        Runs the VQA simulation\n",
-    "\n",
-    "        Args:\n",
-    "            G (nx.graph): Graph\n",
-    "            circuit_depth (int): Circuit depth\n",
-    "            max_iterations (int): Maximum number of iterations\n",
-    "            max_shots (int): Maximum number of shots\n",
-    "\n",
-    "        Returns:\n",
-    "            cudaq.SampleResult: Measurement from the experiment\n",
-    "        \"\"\"\n",
-    "\n",
-    "        qubits = len(G.nodes)\n",
-    "        Hamiltonian = self.create_Hamiltonian(G)\n",
-    "        optimizer, parameter_count = self.optimizer_function(\n",
-    "            self.optimizer,\n",
-    "            max_iterations,\n",
-    "            qubits=qubits,\n",
-    "            circuit_depth=circuit_depth)\n",
-    "\n",
-    "        kernel = self.create_circuit(qubits, circuit_depth)\n",
-    "\n",
-    "        def objective_function(\n",
-    "            parameter_vector: list[float],\n",
-    "            hamiltonian: cudaq.SpinOperator = Hamiltonian,\n",
-    "            kernel: cudaq.Kernel = kernel,\n",
-    "        ) -> float:\n",
-    "            \"\"\"\n",
-    "\n",
-    "            Objective function that returns the cost of the simulation\n",
-    "\n",
-    "            Args:\n",
-    "                parameter_vector (List[float]):\n",
-    "                hamiltonian (cudaq.SpinOperator): Circuit parameter values as a vector\n",
-    "                kernel (cudaq.Kernel) : Circuit configuration\n",
-    "\n",
-    "            Returns:\n",
-    "                float: Expectation value of the circuit\n",
-    "\n",
-    "            \"\"\"\n",
-    "\n",
-    "            get_result = lambda parameter_vector: cudaq.observe(\n",
-    "                kernel, hamiltonian, parameter_vector, qubits, circuit_depth\n",
-    "            ).expectation()\n",
-    "\n",
-    "            cost = get_result(parameter_vector)\n",
-    "\n",
-    "            return cost\n",
-    "\n",
-    "        energy, optimal_parameters = optimizer.optimize(\n",
-    "            dimensions=parameter_count, function=objective_function)\n",
-    "\n",
-    "        counts = cudaq.sample(kernel,\n",
-    "                              optimal_parameters,\n",
-    "                              qubits,\n",
-    "                              circuit_depth,\n",
-    "                              shots_count=max_shots)\n",
-    "\n",
-    "        return counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An instance of the `DivisiveClusteringVQA` class is mostly constructed from variables previously discussed like the functions for building the Hamiltonians and quantum circuits. Parameters related to the quantum simulation can also be specified here such as `circuit_depth` and `max_shots`.  The ` threshold_for_max_cut` parameter specifies what percent of the sample results from the quantum computer are checked for the best bitstring value.\n",
-    "\n",
-    "The other options specify advanced features like if the data is normalized and how the graph weights are computed.\n",
-    "\n",
-    "\n",
-    "Finally, the `get_divisive_sequence` method performs the iterations and produces the clustering data which we will analyze below. Note that this postprocessing code is not exposed in this tutorial but can be found in the source code. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 129/129 [00:00<00:00, 12075.19it/s]\n",
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-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15827.56it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 13617.87it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "divisive_clustering = DivisiveClusteringVQA(\n",
-    "    circuit_depth=circuit_depth,\n",
-    "    max_iterations=max_iterations,\n",
-    "    max_shots=max_shots,\n",
-    "    threshold_for_max_cut=0.75,\n",
-    "    create_Hamiltonian=get_K2_Hamiltonian,\n",
-    "    optimizer=optimizer,\n",
-    "    optimizer_function=get_optimizer,\n",
-    "    create_circuit=get_VQE_circuit,\n",
-    "    normalize_vectors=True,\n",
-    "    sort_by_descending=True,\n",
-    "    coreset_to_graph_metric=\"dist\",\n",
-    ")\n",
-    "\n",
-    "hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(\n",
-    "    coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data can be nicely visualized with a Dendrogram which maps where the bipartitionings occurred. Early splits generally mark divisions between the least similar data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dendo = Dendrogram(coreset_df, hierarchial_clustering_sequence)\n",
-    "dendo.plot_dendrogram(plot_title=\"Dendrogram of Coreset using VQE\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each branch point in the dendrogram aboves corresponds to one of the plots below. Notice the first iterations are the most complicated, and the final iterations become trivial bipartitioning of two points. Occasionally, especially in the first iteration, the partitioning might be puzzling at first glance. The data might seem to naturally cluster into two groups. However, there are cases where a stray point seems to belong in the wrong cluster.  There are two explanations for this.  1) The quantum sampling is approximate and stochastic. It is possible that too few shots were taken to sample the ground state of the problem. 2) It is important to remember that we are clustering coresets and not data points. There can be cases where it is optimal to pay a penalty by excluding a point based on proximity if the weights are small enough that the penalty has less impact. Usually, if a point looks unusually clustered and you go look at the original coresets plotted above, that point will have a small weight."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x1200 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Dendrogram.plot_hierarchial_split(hierarchial_clustering_sequence, coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The hierarchical clustering can be converted to flat clustering by drawing a line perpendicular to the branches. Any data point that intersects the line is considered to be in the same cluster. The function below performs this task at threshold height of 1.5. If you want to use the number of clusters instead of height, you can use `dendo.get_clusters_using_k()` method. You pass the number of desired clusters as an argument. The figure below shows the clusters that are formed at threshold height of 1.5."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "threshold_height = 1\n",
-    "clusters = dendo.get_clusters_using_height(threshold_height)\n",
-    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\"]\n",
-    "dendo.plot_dendrogram(\n",
-    "    plot_title=\"Dendrogram of Coreset using VQE\",\n",
-    "    colors=colors,\n",
-    "    clusters=clusters,\n",
-    "    color_threshold=threshold_height,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can visualize the flat clusters using `dendo.plot_clusters()` method. The function takes the clusters and colors as arguments. The clusters are represented by different colors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gGRkZ9n4LFixg5syZ/O53v6NevXqcPn2aefPm4efnx0MPPQTkh4XGjRvz8ccf07BhQ2rUqEHTpk1p2rQpM2bMoEOHDjRr1oyBAwcSERHBsWPH2Lp1K4cPH+a///3vVd/n6dOnCQsLo3fv3vZHCX/++ed8/fXX1xwtnDBhAmvWrKFDhw688MILVKpUiTlz5pCbm8vf//73kn/Yl6lcuTIJCQl06dKFe++9l8cff5z27dtTuXJldu3axcKFCwkICGDixIlUrlyZSZMm8fTTT9OpUyeefPJJ+1JndevWZfjw4de8XqdOnfjjH/9IfHw8KSkpPPDAA1SuXJl9+/bxySefMG3aNHr37l2i76mk5wKIiopi1qxZTJgwgfr16xMUFMR9991XZI3+/v489thjTJ8+HcMwqFevHitWrCg0h/iHH37g/vvv5/HHH6dx48ZUqlSJZcuWcezYMX7/+99f1/dxOS8vL8aOHcuQIUO47777ePzxx0lPT2f+/PnUq1evVKPZffv25f3332fVqlX06dOnyIeNdOjQgX/+858MGzaM5s2bM2DAAEJCQtizZw/z5s3Dw8OD5cuXF7l82ZIlS4qcMtS1a9cyW1ZORMTOpWtNiJSxH374wRw4cKBZt25d08vLy/T19TXbt29vTp8+3WF5sSuXOjNN09yxY4fZpk0b08vLy6xdu7b5j3/8o9BSZ8nJyeaTTz5p1q5d2/5QhEceecT85ptvHM715ZdfmlFRUaaXl1ehZc/2799v9uvXz6xZs6ZZuXJlMzQ01HzkkUfMJUuW2PsUXPfKpbpyc3PNP/3pT2aLFi3sD6po0aKFOXPmzBJ9PsnJyWa3bt3MatWqmVWrVjWjo6PNL7/80qFPaZY6K3Dy5EnzlVdeMZs1a2ZWrVrVrFKlitm0aVNz9OjRZmZmpkPfjz/+2GzZsqVpsVjMGjVqXPUhF8WZO3euGRUVZXp7e5u+vr5ms2bNzJdfftk8evSo/X2W5HsqyblM0zSzsrLMhx9+2PT19S3RMmHHjx83Y2NjzapVq5oBAQHmH//4R/P77793WOrs559/NuPi4sxGjRqZPj4+pr+/v9mmTRtz8eLFDucqbqmzTz75xKFfUQ/RME3TfOutt8w6deqYFovFbN26tfnFF1+YUVFRZkxMzFXfw+UuXrxohoSEmID52WefXbXv5s2bzR49epi33367aRiGCZhBQUGF/h6Y5tWXOqOI5eJERMqCYZplfHeEiIjctPLy8ggMDKRXr17MmzfP6dcbP348r7zyCn/961+ZMGGC068nInItmvYgIlJB/frrr1gsFocpDu+//z4nTpygc+fO5VLDmDFjOHr0KBMnTqR27do899xz5XJdEZHiaORXRKSC2rBhA8OHD+exxx7jtttuIzk5mXfffZfIyEh27Njh8JAMERF3oZFfEZEKqm7dulitVt566y1OnDhBjRo16NevH2+88YaCr4i4LY38ioiIiIjb0Dq/IiIiIuI2FH5FRERExG1UuDm/eXl5HD16FF9f31It4i4iIiKuY5omp0+fplatWnh4aGxOnKfChd+jR49itVpdXYaIiIhch0OHDhEWFubqMqQCq3Dh19fXF8j/j8fPz8/F1YiIiEhJ5OTkYLVa7T/HRZylwoXfgqkOfn5+Cr8iIiK3GE1ZFGfTpBoRERERcRsKvyIiIiLiNhR+RURERMRtVLg5vyIiIiLXyzRNLl68iM1mc3UpUgqVK1fG09OzRH0VfkVEbjJbt26lQ4cOxMTEkJiY6OpyRNzG+fPnyczM5Ny5c64uRUrJMAzCwsKoVq3aNfsq/IqI3GTeffddhgwZwrvvvsvRo0epVauWq0sSqfDy8vI4cOAAnp6e1KpVCy8vL608cYswTZPjx49z+PBhGjRocM0RYIVfEZGbyJkzZ/j444/55ptvyMrKYv78+fzlL39xdVkiFd758+fJy8vDarVStWpVV5cjpRQYGEh6ejoXLly4ZvjVDW8iIjeRxYsX06hRI+644w769u3Lv/71L0zTdHVZIm5Dj1a+NZVmlF4jvyIirmSzwebNkJkJISG8+8479O3bF4CYmBiys7PZuHEjnTt3dm2dIiIVhP55IyLiKgkJULcuREfDH/7A3uhotm/dypOXnk5ZqVIlnnjiCd59913X1ikiUoEo/IqIuEJCAvTuDYcP25veBS4Ctf7v/6jk6UmlSpWYNWsWS5cuJTs722WlisitzzAMli9f7uoybgoKvyIi5c1mg6FD4bK5vBeB94EpQAqQEhhIyo4d/Pe//6VWrVosWrTINbWKyE0vKyuLIUOGEBERgcViwWq10r17d9auXeuU623YsAHDMDh16pRTzg9w4sQJ+vTpg5+fH9WrV+eZZ57hzJkzZXJuhV8RkfK2ebPDiC/ACuAk8AzQFGh67BhNT56kadOmxMbGauqDyK3CZoMNG2DRovw/nfywjPT0dKKioli3bh2TJ09m586dJCUlER0dTVxcnFOvfaMKHihSlD59+rBr1y7WrFnDihUr2LRpE88991yZXFfhV0SkvGVmFmp6F+gC+BfRLzY2lm+++YbvvvuuPKoTket1xTx+oqPzXyckOO2SL7zwAoZhsH37dmJjY2nYsCFNmjRhxIgRbNu2rchjihq5TUlJwTAM0tPTATh48CDdu3cnICAAHx8fmjRpwmeffUZ6ejrR0dEABAQEYBgGAwYMAPLXSo6Pjyc8PBxvb29atGjBkiVLCl135cqVREVFYbFY2LJlS6H6UlNTSUpK4p133qFNmzZ06NCB6dOn89FHH3H06NEb/sycGn5nzZpF8+bN8fPzw8/Pj7Zt27Jy5cqrHvPJJ5/QqFEjqlSpQrNmzfjss8+cWaKISPkLCSnU9B+g0LPcLvVr3bo1pmnSvHlzp5cmItepiHn8ABw5kt/uhAB84sQJkpKSiIuLw8fHp9D+6tWrX/e54+LiyM3NZdOmTezcuZNJkyZRrVo1rFYrS5cuBWDv3r1kZmYybdo0AOLj43n//feZPXs2u3btYvjw4fTt25eNGzc6nHvUqFG88cYbpKamFvn/a1u3bqV69eq0atXK3talSxc8PDz46quvrvs9FXDqUmdhYWG88cYbNGjQANM0WbBgAT169ODbb7+lSZMmhfp/+eWXPPnkk8THx/PII4+wcOFCevbsSXJyMk2bNnVmqSIi5adjRwgLy/+hWNQavoaRv79jx/KvTURKr4h5/Hammf/f9LBh0KMHXOMBDKWRlpaGaZo0atSozM5ZICMjg9jYWJo1awZARESEfV+NGjUACAoKsgfs3NxcXn/9dT7//HPatm1rP2bLli3MmTOHTp062Y9/7bXX6Nq1a7HXzsrKIigoyKGtUqVK1KhRg6ysrBt+b04d+e3evTsPPfQQDRo0oGHDhkycOJFq1aoVOww/bdo0YmJi+NOf/kRkZCTjx4/nrrvu4u2333ZmmSIi5cvTEy6NlHDlwuwFr6dOLdMfkiLiREXM43dgmnDoUH6/MuTMB+C8+OKLTJgwgfbt2/Pqq69ec9pVWloa586do2vXrlSrVs2+vf/+++zfv9+h7+Ujuq5QbnN+bTYbH330EWfPnrX/i+BKW7dupUuXLg5t3bp1Y+vWrcWeNzc3l5ycHIdNROSm16sXLFkCoaGO7WFh+e29ermmLhEpvSLm8d9QvxJq0KABhmGwZ8+eUh1X8BS7y8PzhQsXHPo8++yz/Pjjjzz11FPs3LmTVq1aMX369GLPWbASQ2JiIikpKfZt9+7dDvN+gSKnaFyuZs2a/PTTTw5tFy9e5MSJE9SsWfPab/AanB5+d+7cSbVq1bBYLAwaNIhly5bRuHHjIvtmZWURHBzs0BYcHHzVIe74+Hj8/f3tm9VqLdP6RUScplcvSE+H9eth4cL8Pw8cUPAVudUUMY//hvqVUI0aNejWrRszZszg7NmzhfYXtxRZYGAgAJmXhfGUlJRC/axWK4MGDSIhIYGRI0cyb948ALy8vID8gc0CjRs3xmKxkJGRQf369R220maztm3bcurUKXbs2GFvW7duHXl5ebRp06ZU5yqK08PvHXfcQUpKCl999RXPP/88/fv3Z/fu3WV2/tGjR5OdnW3fDh06VGbnFhFxOk9P6NwZnnwy/09NdRC59RTM479yGlMBwwCr1Snz+GfMmIHNZqN169YsXbqUffv2kZqayltvvVXsb9oLAunYsWPZt28fiYmJTJkyxaHPsGHDWLVqFQcOHCA5OZn169cTGRkJQJ06dTAMgxUrVnD8+HHOnDmDr68vL730EsOHD2fBggXs37+f5ORkpk+fzoIFC0r1niIjI4mJiWHgwIFs376dL774gsGDB/P73/+eWrVqXd8HdRmnh18vLy/q169PVFQU8fHxtGjRwn5X4JVq1qzJsWPHHNqOHTt21SFui8ViX02iYBMREREpNy6cxx8REUFycjLR0dGMHDmSpk2b0rVrV9auXcusWbOKPKZy5cosWrSIPXv20Lx5cyZNmsSECRMc+thsNuLi4uxBtGHDhsycOROA0NBQxo0bx6hRowgODmbw4MEAjB8/njFjxhAfH28/LjExkfDw8FK/rw8//JBGjRpx//3389BDD9GhQwfmzp1b6vMUxTCdOVu6CPfddx+1a9dm/vz5hfY98cQTnDt3jv/85z/2tnbt2tG8eXNmz55dovPn5OTg7+9Pdna2grCIiMgtwtU/v3/99VcOHDhAeHg4VapUub6TJCTkr/pw+c1vVmt+8NV0Jqcqzffn1KXORo8ezYMPPkjt2rU5ffo0CxcuZMOGDaxatQqAfv36ERoaSnx8PABDhw6lU6dOTJkyhYcffpiPPvqIb775psySvoiIiIjT9OqVv5zZ5s35N7eFhORPddB0ppuKU8PvTz/9RL9+/cjMzMTf35/mzZuzatUq+9puGRkZ9jsOIX+Ud+HChfztb3/jL3/5Cw0aNGD58uVa41dERERuDQXz+OWmVe7THpzN1b82ERERkdJz9c/vMpn2IC5Tmu+v3Nb5FRERERFxNYVfEREREXEbCr8iIiIi4jYUfkVERETEbSj8ioiIiIjbUPgVEREREbeh8CsiIiJSwRmGwfLly11dxk1B4VdERESkjNhssGEDLFqU/6fN5vxrZmVlMWTIECIiIrBYLFitVrp3787atWudcr0NGzZgGAanTp1yyvkBJk6cSLt27ahatSrVq1cv03M79QlvIiIiIu4iIQGGDoXDh39rCwuDadPyn3zsDOnp6bRv357q1aszefJkmjVrxoULF1i1ahVxcXHs2bPHORcuA6ZpYrPZqFSpcBw9f/48jz32GG3btuXdd98t0+tq5FdERETkBiUkQO/ejsEX4MiR/PaEBOdc94UXXsAwDLZv305sbCwNGzakSZMmjBgxgm3bthV5TFEjtykpKRiGQXp6OgAHDx6ke/fuBAQE4OPjQ5MmTfjss89IT08nOjoagICAAAzDYMCAAQDk5eURHx9PeHg43t7etGjRgiVLlhS67sqVK4mKisJisbBly5Yiaxw3bhzDhw+nWbNmN/4hXUEjvyIiIiI3wGbLH/E1zcL7TBMMA4YNgx49wNOz7K574sQJkpKSmDhxIj4+PoX238h0gbi4OM6fP8+mTZvw8fFh9+7dVKtWDavVytKlS4mNjWXv3r34+fnh7e0NQHx8PB988AGzZ8+mQYMGbNq0ib59+xIYGEinTp3s5x41ahRvvvkmERERBAQEXHeN10vhV0REROQGbN5ceMT3cqYJhw7l9+vcueyum5aWhmmaNGrUqOxOeklGRgaxsbH2kdeIiAj7vho1agAQFBRkD9i5ubm8/vrrfP7557Rt29Z+zJYtW5gzZ45D+H3ttdfo2rVrmddcUgq/IiIiIjcgM7Ns+5WUWdRQcxl58cUXef7551m9ejVdunQhNjaW5s2bF9s/LS2Nc+fOFQq158+fp2XLlg5trVq1ckrNJaXwKyIiInIDQkLKtl9JNWjQAMMwSn1Tm4dH/i1fl4fnCxcuOPR59tln6datG4mJiaxevZr4+HimTJnCkCFDijznmTNnAEhMTCQ0NNRhn8VicXhd1BSN8qQb3kRERERuQMeO+as6GEbR+w0DrNb8fmWpRo0adOvWjRkzZnD27NlC+4tbiiwwMBCAzMuGolNSUgr1s1qtDBo0iISEBEaOHMm8efMA8PLyAsB22TpujRs3xmKxkJGRQf369R02q9V6vW/RKRR+RURERG6Ap2f+cmZQOAAXvJ46tWxvdiswY8YMbDYbrVu3ZunSpezbt4/U1FTeeust+9zbKxUE0rFjx7Jv3z4SExOZMmWKQ59hw4axatUqDhw4QHJyMuvXrycyMhKAOnXqYBgGK1as4Pjx45w5cwZfX19eeuklhg8fzoIFC9i/fz/JyclMnz6dBQsWlPp9ZWRkkJKSQkZGBjabjZSUFFJSUuwjzDfErGCys7NNwMzOznZ1KSIiIlJCrv75/csvv5i7d+82f/nll+s+x9KlphkWZpr5t7jlb1ZrfrszHT161IyLizPr1Kljenl5maGhoeajjz5qrl+/3t4HMJctW2Z/vWXLFrNZs2ZmlSpVzI4dO5qffPKJCZgHDhwwTdM0Bw8ebNarV8+0WCxmYGCg+dRTT5k///yz/fjXXnvNrFmzpmkYhtm/f3/TNE0zLy/PnDp1qnnHHXeYlStXNgMDA81u3bqZGzduNE3TNNevX28C5smTJ6/5nvr3728ChbbL39PlSvP9GZc+kAojJycHf39/srOz8fPzc3U5IiIiUgKu/vn966+/cuDAAcLDw6lSpcp1n8dmy1/VITMzf45vx47OGfEVR6X5/nTDm4iIiEgZ8fQs2+XMpOxpzq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4VdERERE3IbCr4iIiEgFZxgGy5cvd3UZNwWFXxEREZEyYsuzsSF9A4t2LmJD+gZseTanXzMrK4shQ4YQERGBxWLBarXSvXt31q5d65TrbdiwAcMwOHXqlFPOn56ezjPPPEN4eDje3t7Uq1ePV199lfPnz5fJ+fV4YxEREZEykJCawNCkoRzOOWxvC/MLY1rMNHpF9nLKNdPT02nfvj3Vq1dn8uTJNGvWjAsXLrBq1Sri4uLYs2ePU65bFkzTxGazUamSYxzds2cPeXl5zJkzh/r16/P9998zcOBAzp49y5tvvnnD19XIr4iIiMgNSkhNoPfi3g7BF+BIzhF6L+5NQmqCU677wgsvYBgG27dvJzY2loYNG9KkSRNGjBjBtm3bijymqJHblJQUDMMgPT0dgIMHD9K9e3cCAgLw8fGhSZMmfPbZZ6SnpxMdHQ1AQEAAhmEwYMAAAPLy8oiPj7eP2LZo0YIlS5YUuu7KlSuJiorCYrGwZcuWQvXFxMTw3nvv8cADDxAREcGjjz7KSy+9REJC2XyGGvkVERERuQG2PBtDk4ZiYhbaZ2JiYDAsaRg97uiBp4dnmV33xIkTJCUlMXHiRHx8fArtr169+nWfOy4ujvPnz7Np0yZ8fHzYvXs31apVw2q1snTpUmJjY9m7dy9+fn54e3sDEB8fzwcffMDs2bNp0KABmzZtom/fvgQGBtKpUyf7uUeNGsWbb75JREQEAQEBJaonOzubGjVqXPf7uZzCr4iIiMgN2JyxudCI7+VMTA7lHGJzxmY61+1cZtdNS0vDNE0aNWpUZucskJGRQWxsLM2aNQMgIiLCvq8ghAYFBdkDdm5uLq+//jqff/45bdu2tR+zZcsW5syZ4xB+X3vtNbp27VriWtLS0pg+fXqZTHkAhV8RERGRG5J5OrNM+5WUaRYeaS4rL774Is8//zyrV6+mS5cuxMbG0rx582L7p6Wlce7cuUKh9vz587Rs2dKhrVWrViWu48iRI8TExPDYY48xcODA0r2JYjh1zm98fDx33303vr6+BAUF0bNnT/bu3XvVY+bPn49hGA5blSpVnFmmiIiIyHUL8Q0p034l1aBBAwzDKPVNbR4e+fHv8vB84cIFhz7PPvssP/74I0899RQ7d+6kVatWTJ8+vdhznjlzBoDExERSUlLs2+7dux3m/QJFTtEoytGjR4mOjqZdu3bMnTu3RMeUhFPD78aNG4mLi2Pbtm2sWbOGCxcu8MADD3D27NmrHufn50dmZqZ9O3jwoDPLFBEREbluHWt3JMwvDAOjyP0GBlY/Kx1rdyzT69aoUYNu3boxY8aMIrNVcUuRBQYGApCZ+dtIdEpKSqF+VquVQYMGkZCQwMiRI5k3bx4AXl5eANhsvy3j1rhxYywWCxkZGdSvX99hs1qtpX5vR44coXPnzkRFRfHee+/ZA3tZcOq0h6SkJIfX8+fPJygoiB07dnDvvfcWe5xhGNSsWdOZpYmIiIiUCU8PT6bFTKP34t4YGA43vhUE4qkxU8v0ZrcCM2bMoH379rRu3ZrXXnuN5s2bc/HiRdasWcOsWbNITU0tdExBIB07diwTJ07khx9+YMqUKQ59hg0bxoMPPkjDhg05efIk69evJzIyEoA6depgGAYrVqzgoYcewtvbG19fX1566SWGDx9OXl4eHTp0IDs7my+++AI/Pz/69+9f4vdUEHzr1KnDm2++yfHjx+37yiIflutSZ9nZ2QDXvFvvzJkz1KlTB6vVSo8ePdi1a1exfXNzc8nJyXHYRERERMpTr8heLHl8CaF+oQ7tYX5hLHl8idPW+Y2IiCA5OZno6GhGjhxJ06ZN6dq1K2vXrmXWrFlFHlO5cmUWLVrEnj17aN68OZMmTWLChAkOfWw2G3FxcURGRhITE0PDhg2ZOXMmAKGhoYwbN45Ro0YRHBzM4MGDARg/fjxjxowhPj7eflxiYiLh4eGlek9r1qwhLS2NtWvXEhYWRkhIiH0rC4bpzNnSl8nLy+PRRx/l1KlTRa7pVmDr1q3s27eP5s2bk52dzZtvvsmmTZvYtWsXYWFhhfqPHTuWcePGFWrPzs7Gz8+vTN+DiIiIOEdOTg7+/v4u+/n966+/cuDAAcLDw2/oXiNbno3NGZvJPJ1JiG8IHWt3dMqIrzgqzfdXbuH3+eefZ+XKlWzZsqXIEFucCxcuEBkZyZNPPsn48eML7c/NzSU3N9f+OicnB6vVqvArIiJyC6ko4VdcozTfX7ksdTZ48GBWrFjBpk2bShV8IX9ovmXLlqSlpRW532KxYLFYyqJMEREREangnDrn1zRNBg8ezLJly1i3bl2p53xA/pyTnTt3ltk8DxERERFxX04d+Y2Li2PhwoV8+umn+Pr6kpWVBYC/v7/9UXj9+vUjNDSU+Ph4IP+pH/fccw/169fn1KlTTJ48mYMHD/Lss886s1QRERERcQNODb8Fdxl27tzZof29995jwIABQP7j8y5fu+3kyZMMHDiQrKwsAgICiIqK4ssvv6Rx48bOLFVERERE3EC53fBWXlw9YV5ERERKz9U/v3XD262tNN9fua7zKyIiIiLiSgq/IiIiIuI2FH5FRERExG0o/IqIiIhUcIZhsHz5cleXcVNQ+BUREREpIzabjQ0bNrBo0SI2bNiAzWZz+jWzsrIYMmQIERERWCwWrFYr3bt3Z+3atU653oYNGzAMg1OnTjnl/ACPPvootWvXpkqVKoSEhPDUU09x9OjRMjm3wq+IiIhIGUhISKBu3bpER0fzhz/8gejoaOrWrUtCQoLTrpmenk5UVBTr1q1j8uTJ7Ny5k6SkJKKjo4mLi3PadcuCaZpcvHixyH3R0dEsXryYvXv3snTpUvbv30/v3r3L5LoKvyIiIiI3KCEhgd69e3P48GGH9iNHjtC7d2+nBeAXXngBwzDYvn07sbGxNGzYkCZNmjBixAi2bdtW5DFFjdympKRgGAbp6ekAHDx4kO7duxMQEICPjw9NmjThs88+Iz09nejoaAACAgIwDMP+7Ia8vDzi4+MJDw/H29ubFi1asGTJkkLXXblyJVFRUVgsFrZs2VJkjcOHD+eee+6hTp06tGvXjlGjRrFt2zYuXLhww5+ZUx9yISIiIlLR2Ww2hg4dSlGPTjBNE8MwGDZsGD169MDT07PMrnvixAmSkpKYOHEiPj4+hfZXr179us8dFxfH+fPn2bRpEz4+PuzevZtq1aphtVpZunQpsbGx7N27Fz8/P/tTe+Pj4/nggw+YPXs2DRo0YNOmTfTt25fAwEA6depkP/eoUaN48803iYiIICAgoETv88MPP6Rdu3ZUrlz5ut9TAYVfERERkRuwefPmQiO+lzNNk0OHDrF58+ZCT729EWlpaZimSaNGjcrsnAUyMjKIjY2lWbNmAERERNj31ahRA4CgoCB7wM7NzeX111/n888/p23btvZjtmzZwpw5cxzC72uvvUbXrl2vWcOf//xn3n77bc6dO8c999zDihUryuS9adqDiIiIyA3IzMws034l5cyH9L744otMmDCB9u3b8+qrr/Ldd99dtX9aWhrnzp2ja9euVKtWzb69//777N+/36Fvq1atSlTDn/70J7799ltWr16Np6cn/fr1K5P3rJFfERERkRsQEhJSpv1KqkGDBhiGwZ49e0p1nIdH/tjn5UHyyrm0zz77LN26dSMxMZHVq1cTHx/PlClTGDJkSJHnPHPmDACJiYmEhoY67LNYLA6vi5qiUZTbb7+d22+/nYYNGxIZGYnVamXbtm32keXrpZFfERERkRvQsWNHwsLCMAyjyP2GYWC1WunYsWOZXrdGjRp069aNGTNmcPbs2UL7i1uKLDAwEHAciU5JSSnUz2q1MmjQIBISEhg5ciTz5s0DwMvLC8BhGbfGjRtjsVjIyMigfv36DpvVar3et2iXl5cH5E+vuFEKvyIiIiI3wNPTk2nTpgEUCsAFr6dOnVqmN7sVmDFjBjabjdatW7N06VL27dtHamoqb731VrEjpAWBdOzYsezbt4/ExESmTJni0GfYsGGsWrWKAwcOkJyczPr164mMjASgTp06GIbBihUrOH78OGfOnMHX15eXXnqJ4cOHs2DBAvbv309ycjLTp09nwYIFpXpPX331FW+//TYpKSkcPHiQdevW8eSTT1KvXr0bHvUFhV8RERGRG9arVy+WLFlS6Ff+YWFhLFmyhF69ejnluhERESQnJxMdHc3IkSNp2rQpXbt2Ze3atcyaNavIYypXrsyiRYvYs2cPzZs3Z9KkSUyYMMGhj81mIy4ujsjISGJiYmjYsCEzZ84EIDQ0lHHjxjFq1CiCg4MZPHgwAOPHj2fMmDHEx8fbj0tMTCQ8PLxU76lq1aokJCRw//33c8cdd/DMM8/QvHlzNm7cWGgKxfUwTGfOlnaBnJwc/P39yc7Oxs/Pz9XliIiISAm4+uf3r7/+yoEDBwgPD6dKlSrXfR6bzcbmzZvJzMwkJCSEjh07OmXEVxyV5vvTDW8iIiIiZcTT07NMlzOTsqdpDyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIibGTBgAD179izUvmHDBgzDKPaxuCIVgcKviIiIiLgNhV8RERGRCs4wDJYvX+7qMm4KCr8iIiIiZSTPlkf6hnR2LtpJ+oZ08mx5Tr9mVlYWQ4YMISIiAovFgtVqpXv37qxdu9Yp1yvP6TG5ubnceeedGIZBSkpKmZxTjzcWERFxA7Y8G5szNpN5OpOsM1l8vuJzqlWr5tjHZnNRdRVDakIqSUOTyDmcY2/zC/MjZloMkb0inXLN9PR02rdvT/Xq1Zk8eTLNmjXjwoULrFq1iri4OPbs2eOU65YF0zSx2WxUqlR8HH355ZepVasW//3vf8vsuhr5FRERqeASUhOoO60u0Qui+UPCH1iVtopK9SoxackkUlJS7Ns777zj6lJvWakJqSzuvdgh+ALkHMlhce/FpCakOuW6L7zwAoZhsH37dmJjY2nYsCFNmjRhxIgRbNu2rchjihq5TUlJwTAM0tPTATh48CDdu3cnICAAHx8fmjRpwmeffUZ6ejrR0dEABAQEYBgGAwYMACAvL4/4+HjCw8Px9vamRYsWLFmypNB1V65cSVRUFBaLhS1bthT73lauXMnq1at58803b+xDuoJGfkVERCqwhNQEei/ujYnp0J5r5DLkqyEsqbOEXpG9ADh8+LArSrzl5dnySBqaxBUfcT4TMCBpWBJ39LgDD8+yG3c8ceIESUlJTJw4ER8fn0L7q1evft3njouL4/z582zatAkfHx92795NtWrVsFqtLF26lNjYWPbu3Yufnx/e3t4AxMfH88EHHzB79mwaNGjApk2b6Nu3L4GBgXTq1Ml+7lGjRvHmm28SERFBQEBAkdc/duwYAwcOZPny5VStWvW630dRFH5FREQqKFuejaFJQwsF38sNSxpGjzt64OnhWY6VVSwZmzMKjfg6MCHnUA4ZmzOo27lumV03LS0N0zRp1KhRmZ2zQEZGBrGxsTRr1gyAiIgI+74aNWoAEBQUZA/Yubm5vP7663z++ee0bdvWfsyWLVuYM2eOQ/h97bXX6Nq1a7HXNk2TAQMGMGjQIFq1amUfjS4rCr8iIiIV1OaMzRzOKX4018TkUM4hNmdspnPdzuVXWAVzOvN0mfYrKdMs/h81N+rFF1/k+eefZ/Xq1XTp0oXY2FiaN29ebP+0tDTOnTtXKNSeP3+eli1bOrS1atXqqteePn06p0+fZvTo0df/Bq5Cc35FREQqqMzTmWXaT4rmG+Jbpv1KqkGDBhiGUeqb2jw88uPf5eH5woULDn2effZZfvzxR5566il27txJq1atmD59erHnPHPmDACJiYkO88h3797tMO8XKHKKxuXWrVvH1q1bsVgsVKpUifr16wP5obl///4lf6PFUPgVERGpoEJ8Q4re8TvgycL9OnfujGmaNzRX1B3V7lgbvzA/MIrpYICf1Y/aHWuX6XVr1KhBt27dmDFjBmfPni20v7ilyAIDAwHIzPztHz1FLSNmtVoZNGgQCQkJjBw5knnz5gHg5eUFOK4O0rhxYywWCxkZGdSvX99hs1qtpXpfb731Fv/973/tAfqzzz4D4OOPP2bixImlOldRFH5FREQqqI61OxLmF4ZRTCozMLD6WelYu2M5V1axeHh6EDMtJv/FlR/1pdcxU2PK9Ga3AjNmzMBms9G6dWuWLl3Kvn37SE1N5a233rLPvb1SQSAdO3Ys+/btIzExkSlTpjj0GTZsGKtWreLAgQMkJyezfv16IiPzl2urU6cOhmGwYsUKjh8/zpkzZ/D19eWll15i+PDhLFiwgP3795OcnMz06dNZsGBBqd5T7dq1adq0qX1r2LAhAPXq1SMsLOw6PiVHCr8iIiIVlKeHJ9NipgEUCsAFr6fGTNXNbmUgslckjy95HL9QP4d2vzA/Hl/yuNPW+Y2IiCA5OZno6GhGjhxJ06ZN6dq1K2vXrmXWrFlFHlO5cmUWLVrEnj17aN68OZMmTWLChAkOfWw2G3FxcURGRhITE0PDhg2ZOXMmAKGhoYwbN45Ro0YRHBzM4MGDARg/fjxjxowhPj7eflxiYiLh4eFOee/XyzCdOFs6Pj6ehIQE9uzZg7e3N+3atWPSpEnccccdVz3uk08+YcyYMaSnp9OgQQMmTZrEQw89VKJr5uTk4O/vT3Z2Nn5+ftc+QEREpIJLSE1gaNJQh5vfrH5WpsZMtS9z5mqu/vn966+/cuDAAcLDw6lSpcp1nyfPlkfG5gxOZ57GN8SX2h1rO2XEVxyV5vtz6moPGzduJC4ujrvvvpuLFy/yl7/8hQceeIDdu3cXO9n5yy+/5MknnyQ+Pp5HHnmEhQsX0rNnT5KTk2natKkzyxUREamQekX2oscdPexPeAvxDaFj7Y4a8XUCD0+PMl3OTMqeU0d+r3T8+HGCgoLYuHEj9957b5F9nnjiCc6ePcuKFSvsbffccw933nkns2fPvuY1XP0vRxERESk9V//8LquRX3GN0nx/5ToOn52dDfy2OHJRtm7dSpcuXRzaunXrxtatW4vsn5ubS05OjsMmIiIiIlKUcgu/eXl5DBs2jPbt2191+kJWVhbBwcEObcHBwWRlZRXZPz4+Hn9/f/tW2uU0RERERMR9lFv4jYuL4/vvv+ejjz4q0/OOHj2a7Oxs+3bo0KEyPb+IiIiIVBzl8njjwYMHs2LFCjZt2nTN9dlq1qzJsWPHHNqOHTtGzZo1i+xvsViwWCxlVquIiIiIVFxOHfk1TZPBgwezbNky1q1bV6J13tq2bcvatWsd2tasWVPsQs0iIiIiIiXl1JHfuLg4Fi5cyKeffoqvr6993q6/vz/e3t4A9OvXj9DQUOLj4wEYOnQonTp1YsqUKTz88MN89NFHfPPNN8ydO9eZpYqIiIiIG3DqyO+sWbPIzs6mc+fOhISE2LePP/7Y3icjI8Ph2dLt2rVj4cKFzJ07lxYtWrBkyRKWL1+uNX5FRERE5IY5deS3JEsIb9iwoVDbY489xmOPPeaEikRERETcj2EYLFu2jJ49e7q6FJfT8/ZEREREykqeDY5tgPRF+X/m2Zx+yaysLIYMGUJERAQWiwWr1Ur37t0L3UNVVjZs2IBhGJw6dcop5weoW7cuhmE4bG+88UaZnLtcVnsQERERqfAOJcCOoXDu8G9tVcMgahpYeznlkunp6bRv357q1aszefJkmjVrxoULF1i1ahVxcXHs2bPHKdctC6ZpYrPZqFSp6Dj62muvMXDgQPtrX1/fMrmuRn5FREREbtShBNjc2zH4Apw7kt9+KMEpl33hhRcwDIPt27cTGxtLw4YNadKkCSNGjGDbtm1FHlPUyG1KSgqGYZCeng7AwYMH6d69OwEBAfj4+NCkSRM+++wz0tPTiY6OBiAgIADDMBgwYACQ/0Cz+Ph4wsPD8fb2tt+7deV1V65cSVRUFBaLhS1bthT73nx9falZs6Z98/HxubEP6xKFXxEREZEbkWfLH/GlqHudLrXtGFbmUyBOnDhBUlIScXFxRQbD6tWrX/e54+LiyM3NZdOmTezcuZNJkyZRrVo1rFYrS5cuBWDv3r1kZmYybdo0IP+pu++//z6zZ89m165dDB8+nL59+7Jx40aHc48aNYo33niD1NRUmjdvXmwNb7zxBrfddhstW7Zk8uTJXLx48brfz+U07UFERETkRhzfXHjE14EJ5w7l9wvuXGaXTUtLwzRNGjVqVGbnLJCRkUFsbCzNmjUDICIiwr6vRo0aAAQFBdkDdm5uLq+//jqff/65/dkMERERbNmyhTlz5tCpUyf78a+99hpdu3a96vVffPFF7rrrLmrUqMGXX37J6NGjyczM5B//+McNvzeFXxEREZEb8UvmtfuUpl8JlWRVrev14osv8vzzz7N69Wq6dOlCbGzsVUdp09LSOHfuXKFQe/78eVq2bOnQ1qpVq2tef8SIEfb/3bx5c7y8vPjjH/9IfHz8DT/ZV9MeRERERG6Ed0jZ9iuhBg0aYBhGqW9q8/DIj3+Xh+cLFy449Hn22Wf58ccfeeqpp9i5cyetWrVi+vTpxZ7zzJkzACQmJpKSkmLfdu/e7TDvF7iuubtt2rTh4sWL9jnJN0LhV0RERORGBHbMX9UBo5gOBlS15vcrQzVq1KBbt27MmDGDs2fPFtpf3FJkgYGBAA4PGUtJSSnUz2q1MmjQIBISEhg5ciTz5s0DwMvLCwCb7bc5zI0bN8ZisZCRkUH9+vUdNqvVer1v0aE+Dw8PgoKCbvhcCr8iIiIiN8LDM385M6BwAL70Ompqfr8yNmPGDGw2G61bt2bp0qXs27eP1NRU3nrrLfvc2ysVBNKxY8eyb98+EhMTmTJlikOfYcOGsWrVKg4cOEBycjLr168nMjISgDp16mAYBitWrOD48eOcOXMGX19fXnrpJYYPH86CBQvYv38/ycnJTJ8+nQULFpTqPW3dupWpU6fy3//+lx9//JEPP/zQfvNcQEDA9X1Ql1H4FREREblR1l7QcQlUDXVsrxqW3+6kdX4jIiJITk4mOjqakSNH0rRpU7p27cratWuZNWtWkcdUrlyZRYsWsWfPHpo3b86kSZOYMGGCQx+bzUZcXByRkZHExMTQsGFDZs6cCUBoaCjjxo1j1KhRBAcHM3jwYADGjx/PmDFjiI+Ptx+XmJhIeHh4qd6TxWLho48+olOnTjRp0oSJEycyfPhw5s6dex2fUGGG6czZ0i6Qk5ODv78/2dnZ+Pn5ubocERERKQFX//z+9ddfOXDgAOHh4VSpUuX6T5Rny1/V4ZfM/Dm+gR2dMuIrjkrz/Wm1BxEREZGy4uFZpsuZSdnTtAcRERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIhIBWcYBsuXL3d1GTcFhV8RERGRW1hWVhZDhgwhIiICi8WC1Wqle/furF271inX27BhA4ZhcOrUKaecv0BiYiJt2rTB29ubgIAAevbsWSbn1RPeRERERMqMDdgMZAIhQEfAeY83Tk9Pp3379lSvXp3JkyfTrFkzLly4wKpVq4iLi2PPnj1Ou/aNMk0Tm81GpUqF4+jSpUsZOHAgr7/+Ovfddx8XL17k+++/L5PrauRXREREpEwkAHWBaOAPl/6se6ndOV544QUMw2D79u3ExsbSsGFDmjRpwogRI9i2bVuRxxQ1cpuSkoJhGKSnpwNw8OBBunfvTkBAAD4+PjRp0oTPPvuM9PR0oqOjAQgICMAwDAYMGABAXl4e8fHxhIeH4+3tTYsWLViyZEmh665cuZKoqCgsFgtbtmwpVN/FixcZOnQokydPZtCgQTRs2JDGjRvz+OOPl8lnppFfERERkRuWAPQGzCvaj1xqXwL0KtMrnjhxgqSkJCZOnIiPj0+h/dWrV7/uc8fFxXH+/Hk2bdqEj48Pu3fvplq1alitVpYuXUpsbCx79+7Fz88Pb29vAOLj4/nggw+YPXs2DRo0YNOmTfTt25fAwEA6depkP/eoUaN48803iYiIICAgoNC1k5OTOXLkCB4eHrRs2ZKsrCzuvPNOJk+eTNOmTa/7PRVQ+BURERG5ITZgKIWDL5faDGAY0IOynAKRlpaGaZo0atSozM5ZICMjg9jYWJo1awZARESEfV+NGjUACAoKsgfs3NxcXn/9dT7//HPatm1rP2bLli3MmTPHIfy+9tprdO3atdhr//jjjwCMHTuWf/zjH9StW5cpU6bQuXNnfvjhB/v1r5emPYiIiIjckM3A4avsN4FDl/qVHdMsKmyXjRdffJEJEybQvn17Xn31Vb777rur9k9LS+PcuXN07dqVatWq2bf333+f/fv3O/Rt1arVVc+Vl5cHwF//+ldiY2OJiorivffewzAMPvnkkxt7Y2jkV0REROQGZZZxv5Jp0KABhmGU+qY2D4/8sc/Lw/OFCxcc+jz77LN069aNxMREVq9eTXx8PFOmTGHIkCFFnvPMmTNA/goNoaGhDvssFovD66KmaFwuJCQEgMaNGzucIyIigoyMjKseWxIa+RURERG5ISFl3K9katSoQbdu3ZgxYwZnz54ttL+4pcgCAwMByMz8LYynpKQU6me1Whk0aBAJCQmMHDmSefPmAeDl5QWAzWaz923cuDEWi4WMjAzq16/vsFmt1lK9r4Kb4fbu3Wtvu3DhAunp6dSpU6dU5yqKwq+IiIjIDekIhJE/t7coBmC91K9szZgxA5vNRuvWrVm6dCn79u0jNTWVt956yz739koFgXTs2LHs27ePxMREpkyZ4tBn2LBhrFq1igMHDpCcnMz69euJjIwEoE6dOhiGwYoVKzh+/DhnzpzB19eXl156ieHDh7NgwQL2799PcnIy06dPZ8GCBaV6T35+fgwaNIhXX32V1atXs3fvXp5//nkAHnvssev4lBwp/IqIiIjcEE9g2qX/fWUALng9FWes9xsREUFycjLR0dGMHDmSpk2b0rVrV9auXcusWbOKPKZy5cosWrSIPXv20Lx5cyZNmsSECRMc+thsNuLi4oiMjCQmJoaGDRsyc+ZMAEJDQxk3bhyjRo0iODiYwYMHAzB+/HjGjBlDfHy8/bjExETCw8NL/b4mT57M73//e5566inuvvtuDh48yLp164pcHaK0DNOZs6VdICcnB39/f7Kzs/Hz83N1OSIiIlICrv75/euvv3LgwAHCw8OpUqXKdZ4lgfxVHy6/+c1KfvAt22XOxFFpvj/d8CYiIiJSJnqRv5xZ+T3hTUpP4VdERESkzHgCnV1dhFyF5vyKiIiIiNtwavjdtGkT3bt3p1atWhiGwfLly6/av+CZz1duWVlZzixTRERERNyEU8Pv2bNnadGiBTNmzCjVcXv37iUzM9O+BQUFOalCEREREXEnTp3z++CDD/Lggw+W+rjLnxUtIiIiUl4KHq0rt5bSLF52U97wduedd5Kbm0vTpk0ZO3Ys7du3L7Zvbm4uubm59tc5OTnlUaKIiIhUIF5eXnh4eHD06FECAwPx8vLCMIp7aIXcTEzT5Pjx4xiGQeXKla/Z/6YKvyEhIcyePZtWrVqRm5vLO++8Q+fOnfnqq6+46667ijwmPj6ecePGlXOlIiIiUpF4eHgQHh5OZmYmR48edXU5UkqGYRAWFoan57WXlSu3h1wYhsGyZcvo2bNnqY7r1KkTtWvX5v/9v/9X5P6iRn6tVqseciEiInILcfVDLgqYpsnFixex2Wwuq0FKr3LlyiUKvnCTjfwWpXXr1mzZsqXY/RaLBYvFUo4ViYiISEVV8Kvzkvz6XG5NN/06vykpKYSEhLi6DBERERGpAJw68nvmzBnS0tLsrw8cOEBKSgo1atSgdu3ajB49miNHjvD+++8DMHXqVMLDw2nSpAm//vor77zzDuvWrWP16tXOLFNERERE3IRTw+8333xDdHS0/fWIESMA6N+/P/PnzyczM5OMjAz7/vPnzzNy5EiOHDlC1apVad68OZ9//rnDOURERERErle53fBWXm6WCfMiIiJScvr5LeXlpp/zKyIiIiJSVhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXymRAQMGYBhGoS0mJsbVpYmIiIiUmFOf8CYVS0xMDO+9955Dm8VicVE1IiIiIqWn8CslZrFYqFmzpqvLEBEREblumvYgIiI3jSunWN12223ExMTw3Xffubo0EakgFH7lKmzABmARkMWKFSuoVq2aw/b666+7tkQRqXBiYmLIzMwkMzOTtWvXUqlSJR555BFXlyUiFYSmPUgxEoChwGF7S3S0hVmzJgHd7G01atQo98pEpGK7fIpVzZo1GTVqFB07duT48eMEBga6uDoRudUp/EoREoDegOnQ6uOTS/36Q4AlQC8X1CUi7ubMmTN88MEH1K9fn9tuu83V5YhIBaDwK1ewkT/ia16lzzCgB+BZHgWJiJspmGIFcPbsWUJCQlixYgUeHpqpJyI3TuFXrrCZy6c6XC43F7KyTOAQ8CnQjkqVKnH77beXY30iUtHYbLB5M2RmQlYWdO4czezZswA4efIkM2fO5MEHH2T79u3UqVPHxdWW3oABAzh16hTLly93dSkigsKvFJJZ7J6kJAgJKXgVC8Add9zBnj17nF+WiFRICQkwdCgcvuzf3FWq+PDdd/XpdWl21TvvvIO/vz/z5s1jwoQJrilURCoM/Q5JrhBSZOv8+WCal2/rMU1TwVdErltCAvTu7Rh8AX79Nb89ISH/tWEYeHh48Msvv5R/kSJS4WjkV67QEQgDjlD0vF/j0v6O5VmUiFQwNlv+iK9Z5O0FuZhmFkOGQMOGJ5k1623OnDlD9+7dy7tMEamAFH7lCp7ANPJXezBwDMDGpT+nopvdRORGbN5ceMT3N0lACEePQps2vjRp0ohPPvmEzp07l1+BIlJhKfxKEXqRv5yZ4zq/+SO+U9EyZyJyozKLvb1g/qUt3zvvwJNPOr+esmSz2di8eTOZmZmEhIRgFj28LSIuovArxehF/nJmm8m/CS6E/KkOGvEVkRsXUvTtBdfd72aRkJDA0KFDOXzZsHbVqlVp0qSJC6sSkcvphje5Ck+gM/DkpT8VfEWkbHTsCGFhYBhF7zcMsFrz+90qEhIS6N27t0PwBTh37hxff/01CQV38ImISyn8iohIufP0hGnT8v/3lQG44PXUqfn9bgU2m42hQ4dedYrDsGHDsNls5ViViBRF4VdERFyiVy9YsgRCQx3bw8Ly23vdQrcXbN68udCI75UOHTrE5s2by6kiESmOwq+IiLhMr16Qng7r18PChfl/HjhwawVfgMzi7+C7rn63iqysLIYMGUJERAQWiwWr1Ur37t1Zu3atq0sTKZZueBMREZfy9IRbfRWzkBLemVfSfreC9PR02rdvT/Xq1Zk8eTLNmjXjwoULrFq1iri4OD0ESW5ahlnB1mDJycnB39+f7Oxs/Pz8XF2OiIi4AZvNRt26dTly5EiR834NwyAsLIwDBw7geatMZL6Ghx56iO+++469e/fi4+PjsO/UqVNUr169VOfTz28pL5r2ICIicoM8PT2ZdukOPuOKO/gKXk+dOrXCBN8TJ06QlJREXFxcoeALlDr4ipQnhV8REZEy0KtXL5YsWULoFXfwhYWFsWTJEnrdahOZi5BnyyN9QzpJM5IwTZOGDRu6uiSRUtOcXxERkTLSq1cvevTo4fCEt44dO1aIEd/UhFSShiaRcziHw5ee/rnijytobDQmsleki6sTKTmFXxERkTLk6elJ51v9Dr4rpCaksrj3Yrg0nbkGNQA49L9DLO69mMeXPK4ALLcMTXsQERGRYuXZ8kgammQPvgBVqUp96rOd7Zw3z5M0LIk8W559/6lTp8q/UJESUvgVERGRYmVsziDncE6h9od4CBOTecxj26FtbFy0kdTUVN566y3atm3rgkpFSkbTHkRERKRYpzNPF9legxr8kT+yiU2sYhXLnl5GUHAQUVFRzJo1q5yrFCk5p478btq0ie7du1OrVi0Mw2D58uXXPGbDhg3cddddWCwW6tevz/z5851ZooiIiFyFb4hv8fvw5WEeZjjD+WHNDxw+fJhPP/20ws15lorFqeH37NmztGjRghkzZpSo/4EDB3j44YeJjo4mJSWFYcOG8eyzz7Jq1SpnlikiIiLFqN2xNn5hfmAU08EAP6sftTvWLte6RK6XU6c9PPjggzz44IMl7j979mzCw8OZMmUKAJGRkWzZsoV//vOfdOvWrchjcnNzyc3Ntb/OySk8L0lERESuj4enBzHTYvJXezBwuPGtIBDHTI3Bw1O3Ecmt4ab6m7p161a6dOni0NatWze2bt1a7DHx8fH4+/vbN6vV6uwyRURE3Epkr0geX/I4fqGOjx32C/PTMmdyy7mpbnjLysoiODjYoS04OJicnBx++eUXvL29Cx0zevRoRowYYX+dk5OjACwiIlLGIntFckePO8jYnMHpzNP4hvhSu2NtjfjKLeemCr/Xw2KxYLFYXF2GiIhIhefh6UHdznVdXYbIDbmp/rlWs2ZNjh075tB27Ngx/Pz8ihz1FREREREpjZsq/LZt25a1a9c6tK1Zs0aLZYuIiIhImXBq+D1z5gwpKSmkpKQA+UuZpaSkkJGRAeTP1+3Xr5+9/6BBg/jxxx95+eWX2bNnDzNnzmTx4sUMHz7cmWWKiIiIiJtwavj95ptvaNmyJS1btgRgxIgRtGzZkldeeQWAzMxMexAGCA8PJzExkTVr1tCiRQumTJnCO++8U+wyZyIiIiIipWGYpmleu9utIycnB39/f7Kzs/Hz87v2ASIiIuJy+vkt5eWmmvMrIiIiIuJMCr8iIiIi4jYUfkVERETEbSj8ioiIiIjbUPgVEREREbeh8CsiIiIibkPhV0RERETchsKviIiIiLgNhV8RERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4VdERERE3IbCr4iIiIi4DYVfEREREXEbCr8iIiIi4jYUfkVERETEbSj8ioiIiIjbUPgVEREREbeh8CsiIiIibkPhV0RERETchsKviIiIiLgNhV8RERERcRsKvyIiIiLiNsol/M6YMYO6detSpUoV2rRpw/bt24vtO3/+fAzDcNiqVKlSHmWKiIiISAXn9PD78ccfM2LECF599VWSk5Np0aIF3bp146effir2GD8/PzIzM+3bwYMHnV2miIiIiLgBp4fff/zjHwwcOJCnn36axo0bM3v2bKpWrcq//vWvYo8xDIOaNWvat+DgYGeXKSIiIiJuwKnh9/z58+zYsYMuXbr8dkEPD7p06cLWrVuLPe7MmTPUqVMHq9VKjx492LVrV7F9c3NzycnJcdhERERERIri1PD7888/Y7PZCo3cBgcHk5WVVeQxd9xxB//617/49NNP+eCDD8jLy6Ndu3YcPny4yP7x8fH4+/vbN6vVWubvQ0REREQqhptutYe2bdvSr18/7rzzTjp16kRCQgKBgYHMmTOnyP6jR48mOzvbvh06dKicKxYRERGRW0UlZ5789ttvx9PTk2PHjjm0Hzt2jJo1a5boHJUrV6Zly5akpaUVud9isWCxWG64VhERERGp+Jw68uvl5UVUVBRr1661t+Xl5bF27Vratm1bonPYbDZ27txJSEiIs8oUERERETfh1JFfgBEjRtC/f39atWpF69atmTp1KmfPnuXpp58GoF+/foSGhhIfHw/Aa6+9xj333EP9+vU5deoUkydP5uDBgzz77LPOLlVEREREKjinh98nnniC48eP88orr5CVlcWdd95JUlKS/Sa4jIwMPDx+G4A+efIkAwcOJCsri4CAAKKiovjyyy9p3Lixs0sVERERkQrOME3TdHURZSknJwd/f3+ys7Px8/NzdTkiIiJSAvr5LeXlplvtQURERETEWRR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4fcGZGVlMXToUOrXr0+VKlUIDg6mffv2zJo1i3Pnzrm6PBERERG5QiVXF3Cr+vHHH2nfvj3Vq1fn9ddfp1mzZlgsFnbu3MncuXMJDQ3l0UcfdXWZIiIiInIZwzRN09VFlKWcnBz8/f3Jzs7Gz8/PadeJiYlh165d7NmzBx8fn0L7TdPEMAynXV9ERKQiKa+f3yKa9nAd/ve//7F69Wri4uKKDL6Agq+IiIjITUjh9zqkpaVhmiZ33HGHQ/vtt99OtWrVqFatGn/+859dVJ2IiIiIFEdzfksqzwbHN8MvmXDiZJFdtm/fTl5eHn369CE3N7ecCxQRERGRa1H4LYlDCbBjKJw7DED902AYsPerpfC739m7RUREAODt7e30kgYMGMCpU6dYvny5068lIiIiUlFo2sO1HEqAzb3twRfgNl/o2hTenvshZ/cudGFxIiIiIlIaCr9Xk2fLH/Gl8IIYM5+Gi3nQ6v7+fLxoEampqezdu5cPPviAPXv24OnpWf71ioiIiMhVadrD1Rzf7DDie7l6wfDtRHj93xcZPWokhzN/xmKx0LhxY1566SVeeOGFci5WRERERK5F4fdqfsm86u6QAJjeH6a3mwJ1n3R+PZffdPdLFpgW519TREREpAJR+L0a75Cy7Xcjrrjpjkwgt0p+u7WX868vIiIiUgFozu/VBHaEqmFAcQ+sMKCqNb+fMxVx0x0Atl/z2w8lOPf6IiIiIhWEwu/VeHhC1LRLL64MwJdeR03N7+csV7npzm7HsPx+IiIiInJVCr/XYu0FHZdA1VDH9qph+e3OnnJwlZvu8plw7lB+PxERERG5Ks35LQlrLwjt8dvNZt4h+VMdnDniW6CYm+7yTKjkce1+IiIiIvIbhd+S8vCE4M7lf91ibqb7KQfqB1+7n4iIiIj8RtMebnZX3HR38iysSIYNqdClKZTbTXciIiIiFYDC783uipvu/m8uDPoXjHwIekRdanb2TXciIiIiFYSmPdwKCm662zGUZcMvu/mtqjU/+GqdXxEREZESUfi9VbjypjsRERGRCqJcpj3MmDGDunXrUqVKFdq0acP27duv2v+TTz6hUaNGVKlShWbNmvHZZ5+VR5k3v4Kb7uo+mf+ngq+IiIhIqTg9/H788ceMGDGCV199leTkZFq0aEG3bt346aefiuz/5Zdf8uSTT/LMM8/w7bff0rNnT3r27Mn333/v7FJFREREpIIzTNO8yqPDblybNm24++67efvttwHIy8vDarUyZMgQRo0aVaj/E088wdmzZ1mxYoW97Z577uHOO+9k9uzZ17xeTk4O/v7+ZGdn4+fnV3ZvRERERJxGP7+lvDh15Pf8+fPs2LGDLl26/HZBDw+6dOnC1q1bizxm69atDv0BunXrVmz/3NxccnJyHDYRERERkaI4Nfz+/PPP2Gw2goODHdqDg4PJysoq8pisrKxS9Y+Pj8ff39++Wa3WsileRERERCqcW36d39GjR5OdnW3fDh065OqSREREROQm5dSlzm6//XY8PT05duyYQ/uxY8eoWbNmkcfUrFmzVP0tFgsWi6VsChYRERGRCs2pI79eXl5ERUWxdu1ae1teXh5r166lbdu2RR7Ttm1bh/4Aa9asKba/iIiIiEhJOf0hFyNGjKB///60atWK1q1bM3XqVM6ePcvTTz8NQL9+/QgNDSU+Ph6AoUOH0qlTJ6ZMmcLDDz/MRx99xDfffMPcuXOdXaqIiIiIVHBOD79PPPEEx48f55VXXiErK4s777yTpKQk+01tGRkZeHj8NgDdrl07Fi5cyN/+9jf+8pe/0KBBA5YvX07Tpk2dXaqIiIiIVHBOX+e3vGmdQBERkVuPfn5LebnlV3sQERERESkphV8RERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+HXTQwYMADDMAptaWlpri5NREREpNw4/QlvcvOIiYnhvffec2gLDAx0UTUiIiIi5U/h141YLBZq1qzp6jJEREREXEbTHkRERETEbSj8upEVK1ZQrVo1+/bYY4+5uiQRERGRcqVpDxVZng2Ob4ZfMuGXLKI7d2bW7Nn23T4+Pi4sTkRERKT8KfxWVIcSYMdQOHc4/3Um+ORWob7lO7D2cm1tIiIiIi6iaQ8V0aEE2Nz7t+BbwPZrfvuhBNfUJSIiIuJiCr8VTZ4tf8QXs/g+O4bl9xMRERFxMwq/Fc3xzYVHfB2YcO5Qfj8RERERN6M5vxXNL5lFNs8fVLJ+IiIiIhWZRn4rGu+Qsu0nIiIiUoEo/FY0gR2hahhgFNPBgKrW/H4iIiIibkbht6Lx8ISoaZdeXBmAL72OmprfT0RERMTNKPxWRNZe0HEJVA11bK8alt+udX5FRETETemGt4rK2gtCe/z2hDfvkPypDhrxFRERETem8FuReXhCcGdXVyEiIiJy09C0BxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4VdERERE3IbCr4iIiIi4DYVfEREREXEbCr8iIiIi4jYUfkVERETEbTgt/J44cYI+ffrg5+dH9erVeeaZZzhz5sxVj+ncuTOGYThsgwYNclaJIiIiIuJmKjnrxH369CEzM5M1a9Zw4cIFnn76aZ577jkWLlx41eMGDhzIa6+9Zn9dtWpVZ5UoIiIiIm7GKeE3NTWVpKQkvv76a1q1agXA9OnTeeihh3jzzTepVatWscdWrVqVmjVrOqMsEREREXFzTpn2sHXrVqpXr24PvgBdunTBw8ODr7766qrHfvjhh9x+++00bdqU0aNHc+7cuav2z83NJScnx2ETERERESmKU0Z+s7KyCAoKcrxQpUrUqFGDrKysYo/7wx/+QJ06dahVqxbfffcdf/7zn9m7dy8JCQnFHhMfH8+4cePKrHYRERERqbhKFX5HjRrFpEmTrtonNTX1uot57rnn7P+7WbNmhISEcP/997N//37q1atX5DGjR49mxIgR9tc5OTlYrdbrrkFEREREKq5Shd+RI0cyYMCAq/aJiIigZs2a/PTTTw7tFy9e5MSJE6Waz9umTRsA0tLSig2/FosFi8VS4nOKiIiIiPsqVfgNDAwkMDDwmv3atm3LqVOn2LFjB1FRUQCsW7eOvLw8e6AtiZSUFABCQkJKU6aIiIiISJGccsNbZGQkMTExDBw4kO3bt/PFF18wePBgfv/739tXejhy5AiNGjVi+/btAOzfv5/x48ezY8cO0tPT+fe//02/fv249957ad68uTPKFBERcUsDBgywr6dfuXJlgoOD6dq1K//617/Iy8tzdXkiTuW0h1x8+OGHNGrUiPvvv5+HHnqIDh06MHfuXPv+CxcusHfvXvtqDl5eXnz++ec88MADNGrUiJEjRxIbG8t//vMfZ5UoIiLitmJiYsjMzCQ9PZ2VK1cSHR3N0KFDeeSRR7h48aKryxNxGsM0TdPVRZSlnJwc/P39yc7Oxs/Pz9XliIiI3HQGDBjAqVOnWL58uUP7unXruP/++5k3bx7PPvtsudakn99SXpw28isiIiK3lvvuu48WLVpcdYlRkVud0x5vLCIiIjcTG7AZyASygKJXSmrUqBHfffddOdYlUr4UfkVERCq8BGAocPiytiqX2ns59DRNE8Mwyq80kXKmaQ8iIiIVWgLQG8fgC/DrpXbHKQ6pqamEh4eXT2kiLqDwKyIiUmHZyB/xvdq97cMu9cu/4W3nzp3ExsY6vzQRF9G0BxERkQprM4VHfPPl5kJWlonNdohjx94lKeln4uPjeeSRR+jXr1/5lilSjhR+RUREKqzMYvckJUFICFSqBAEBf6JFi9a89dZb9O/fHw8P/WJYKi6FXxERkQorpMjW+fPzt998CnR2ejUiNwP9005ERKTC6giEAcWt3mAA1kv9RNyDwq+IiEiF5QlMu/S/rwzABa+nXuon4h4UfkVERCq0XsASIPSK9rBL7b0KHSFSkWnOr4iISIXXC+jBb094CyF/qoNGfMX9KPyKiIi4BU90U5uIpj2IiIiIiBtR+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuA2FXxERERFxGwq/IiIiIuI2FH5FRERExG0o/IqIiIiI26hwT3gzTROAnJwcF1ciIiIiJVXwc7vg57iIs1S48Hv69GkArFariysRERGR0jp9+jT+/v6uLkMqMMOsYP/EysvL4+jRo/j6+mIYhqvLuWnk5ORgtVo5dOgQfn5+ri5HLqPv5uam7+fmpe/m5lba78c0TU6fPk2tWrXw8NCsTHGeCjfy6+HhQVhYmKvLuGn5+fnph8RNSt/NzU3fz81L383NrTTfj0Z8pTzon1YiIiIi4jYUfkVERETEbSj8ugmLxcKrr76KxWJxdSlyBX03Nzd9PzcvfTc3N30/crOqcDe8iYiIiIgURyO/IiIiIuI2FH5FRERExG0o/IqIiIiI21D4FRERERG3ofArIiIiIm5D4dcNzJgxg7p161KlShXatGnD9u3bXV2SAPHx8dx99934+voSFBREz5492bt3r6vLkiK88cYbGIbBsGHDXF2KXHLkyBH69u3Lbbfdhre3N82aNeObb75xdVluz2azMWbMGMLDw/H29qZevXqMHz8eLSwlNxOF3wru448/ZsSIEbz66qskJyfTokULunXrxk8//eTq0tzexo0biYuLY9u2baxZs4YLFy7wwAMPcPbsWVeXJpf5+uuvmTNnDs2bN3d1KXLJyZMnad++PZUrV2blypXs3r2bKVOmEBAQ4OrS3N6kSZOYNWsWb7/9NqmpqUyaNIm///3vTJ8+3dWlidhpnd8Krk2bNtx99928/fbbAOTl5WG1WhkyZAijRo1ycXVyuePHjxMUFMTGjRu59957XV2OAGfOnOGuu+5i5syZTJgwgTvvvJOpU6e6uiy3N2rUKL744gs2b97s6lLkCo888gjBwcG8++679rbY2Fi8vb354IMPXFiZyG808luBnT9/nh07dtClSxd7m4eHB126dGHr1q0urEyKkp2dDUCNGjVcXIkUiIuL4+GHH3b4b0hc79///jetWrXiscceIygoiJYtWzJv3jxXlyVAu3btWLt2LT/88AMA//3vf9myZQsPPvigiysT+U0lVxcgzvPzzz9js9kIDg52aA8ODmbPnj0uqkqKkpeXx7Bhw2jfvj1NmzZ1dTkCfPTRRyQnJ/P111+7uhS5wo8//sisWbMYMWIEf/nLX/j666958cUX8fLyon///q4uz62NGjWKnJwcGjVqhKenJzabjYkTJ9KnTx9XlyZip/ArchOIi4vj+++/Z8uWLa4uRYBDhw4xdOhQ1qxZQ5UqVVxdjlwhLy+PVq1a8frrrwPQsmVLvv/+e2bPnq3w62KLFy/mww8/ZOHChTRp0oSUlBSGDRtGrVq19N3ITUPhtwK7/fbb8fT05NixYw7tx44do2bNmi6qSq40ePBgVqxYwaZNmwgLC3N1OQLs2LGDn376ibvuusveZrPZ2LRpE2+//Ta5ubl4enq6sEL3FhISQuPGjR3aIiMjWbp0qYsqkgJ/+tOfGDVqFL///e8BaNasGQcPHiQ+Pl7hV24amvNbgXl5eREVFcXatWvtbXl5eaxdu5a2bdu6sDIBME2TwYMHs2zZMtatW0d4eLirS5JL7r//fnbu3ElKSop9a9WqFX369CElJUXB18Xat29faFnAH374gTp16rioIilw7tw5PDwco4Wnpyd5eXkuqkikMI38VnAjRoygf//+tGrVitatWzN16lTOnj3L008/7erS3F5cXBwLFy7k008/xdfXl6ysLAD8/f3x9vZ2cXXuzdfXt9Dcax8fH2677TbNyb4JDB8+nHbt2vH666/z+OOPs337dubOncvcuXNdXZrb6969OxMnTqR27do0adKEb7/9ln/84x/83//9n6tLE7HTUmdu4O2332by5MlkZWVx55138tZbb9GmTRtXl+X2DMMosv29995jwIAB5VuMXFPnzp211NlNZMWKFYwePZp9+/YRHh7OiBEjGDhwoKvLcnunT59mzJgxLFu2jJ9++olatWrx5JNP8sorr+Dl5eXq8kQAhV8RERERcSOa8ysiIiIibkPhV0RERETchsKviIiIiLgNhV8RERERcRsKvyIiIiLiNhR+RURERMRtKPyKiIiIiNtQ+BURERERt6HwKyIiIiJuQ+FXRERERNyGwq+IiIiIuI3/D41GRMsQk+8TAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dendo.plot_clusters(clusters,\n",
-    "                    colors,\n",
-    "                    plot_title=\"Clusters of Coreset using VQE\",\n",
-    "                    show_annotation=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function below uses the `dendo.get_voronoi_tessalation()` method to convert the clusters into regions. `coreset_df`, `clusters` and `colors` need to be passed as the arguments to create the regions. This function creates a region for each coreset point separately and then colors them according to the clusters with colors passed as arguments. Another option is to create regions using the centroids of the clusters. You need to pass `tesslation_by_cluster=True` to the function to perform this task.\n",
-    "\n",
-    "Once the region creation is complete, you can use `plot_voironi()` method to plot the regions. The function takes the clusters and colors as arguments. \n",
-    "\n",
-    "Remembering that these regions were based on coresets, they can overlay the original data set and be used to cluster the data based on the coreset analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "vt = Voironi_Tessalation(coreset_df,\n",
-    "                         clusters,\n",
-    "                         colors,\n",
-    "                         tesslation_by_cluster=False)\n",
-    "vt.plot_voironi(plot_title=\"Voironi Tessalation of Coreset using VQE\",\n",
-    "                show_annotation=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## QAOA Implementation\n",
-    "\n",
-    "CUDA-Q is designed to be a flexible tool for developers so they can test different implementations of the same code. For example, one can perform the same analysis, instead using a QAOA approach. Only the kernel needs to be changed as is done below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                             »\n",
-      "q0 : ──●──────────────────●──────────────────────────────────────────────────»\n",
-      "     ╭─┴─╮╭────────────╮╭─┴─╮                                                »\n",
-      "q1 : ┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──────────────────────────»\n",
-      "     ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮                        »\n",
-      "q2 : ────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──»\n",
-      "                             ╰───╯╰────────────╯╰───╯╭─┴─╮╭────────────╮╭─┴─╮»\n",
-      "q3 : ────────────────────────────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├»\n",
-      "                                                     ╰───╯╰────────────╯╰───╯»\n",
-      "q4 : ────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                             »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                        ╭───╮╭────────────╮╭───╮╭───────────╮\n",
-      "────────────────────────┤ x ├┤ rz(0.3527) ├┤ x ├┤ rx(1.111) ├\n",
-      "                        ╰─┬─╯╰────────────╯╰─┬─╯├───────────┤\n",
-      "──────────────────────────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "                          │                  │  ├───────────┤\n",
-      "──────────────────────────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "                          │                  │  ├───────────┤\n",
-      "──●──────────────────●────┼──────────────────┼──┤ rx(1.111) ├\n",
-      "╭─┴─╮╭────────────╮╭─┴─╮  │                  │  ├───────────┤\n",
-      "┤ x ├┤ rz(0.3527) ├┤ x ├──●──────────────────●──┤ rx(1.111) ├\n",
-      "╰───╯╰────────────╯╰───╯                        ╰───────────╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_QAOA_circuit(number_of_qubits, circuit_depth) -> cudaq.Kernel:\n",
-    "    \"\"\"Returns the QAOA circuit for the given number of qubits and circuit depth\n",
-    "\n",
-    "\n",
-    "    Args:\n",
-    "        number_of_qubits (int): Number of qubits\n",
-    "        circuit_depth (int): Circuit depth\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq.Kernel: QAOA Circuit\n",
-    "    \"\"\"\n",
-    "\n",
-    "    @cudaq.kernel\n",
-    "    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):\n",
-    "        qubits = cudaq.qvector(number_of_qubits)\n",
-    "\n",
-    "        layers = circuit_depth\n",
-    "\n",
-    "        for layer in range(layers):\n",
-    "            for qubit in range(number_of_qubits):\n",
-    "                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])\n",
-    "                rz(2.0 * thetas[layer], qubits[(qubit + 1) % number_of_qubits])\n",
-    "                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])\n",
-    "\n",
-    "            rx(2.0 * thetas[layer + layers], qubits)\n",
-    "\n",
-    "    return kernel\n",
-    "\n",
-    "\n",
-    "circuit = get_QAOA_circuit(5, circuit_depth)\n",
-    "\n",
-    "print(cudaq.draw(circuit, np.random.rand(2 * circuit_depth), 5, circuit_depth))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,\n",
-    "                  **kwargs) -> Tuple[cudaq.optimizers.optimizer, int]:\n",
-    "    \"\"\"\n",
-    "    Returns the optimizer with the given parameters\n",
-    "\n",
-    "    Args:\n",
-    "        optimizer (cudaq.optimizers.optimizer): Optimizer\n",
-    "        max_iterations (int): Maximum number of iterations\n",
-    "        **kwargs: Additional arguments\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple(cudaq.optimizers.optimizer, int): Optimizer and parameter count\n",
-    "    \"\"\"\n",
-    "\n",
-    "    parameter_count = 2 * kwargs[\"circuit_depth\"]\n",
-    "    optimizer.initial_parameters = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,\n",
-    "                                                     parameter_count)\n",
-    "    optimizer.max_iterations = max_iterations\n",
-    "    return optimizer, parameter_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 484/484 [00:00<00:00, 12163.89it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 52703.30it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 48/48 [00:00<00:00, 31987.07it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 36393.09it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 37957.50it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 42473.96it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "divisive_clustering = DivisiveClusteringVQA(\n",
-    "    circuit_depth=circuit_depth,\n",
-    "    max_iterations=max_iterations,\n",
-    "    max_shots=max_shots,\n",
-    "    threshold_for_max_cut=0.75,\n",
-    "    create_Hamiltonian=get_K2_Hamiltonian,\n",
-    "    optimizer=optimizer,\n",
-    "    optimizer_function=get_optimizer,\n",
-    "    create_circuit=get_QAOA_circuit,\n",
-    "    normalize_vectors=True,\n",
-    "    sort_by_descending=True,\n",
-    "    coreset_to_graph_metric=\"dist\",\n",
-    ")\n",
-    "\n",
-    "hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(\n",
-    "    coreset_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scaling simulations with CUDA-Q\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The University of Edinburgh team quickly encountered scaling challenges when they were developing this method. By developing with CUDA-Q they were able to port their code to an HPC environment once GPUs became available. GPUs massively accelerated their development and testing and allowed them to produce the 25 qubit experiments presented in their publication. If you have a GPU available, run the following code examples and see how the times compare on your device. Each call executes the divisive clustering procedure using the QAOA method, 100000 simulated shots for each VQE loop, and maximum 75 VQE iterations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, try a slightly larger N=18 problem using the CPU (`qpp-cpu`) backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Uncomment the following line if you want to explicitly execute this step on your system.\n",
-    "# !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now try the N=18 example on the GPU backend (`nvidia`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "100%|███████████████████████████████████████| 751/751 [00:00<00:00, 3460.26it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 42771.74it/s]\n",
-      "100%|█████████████████████████████████████| 4064/4064 [00:00<00:00, 6862.37it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 56871.92it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 44979.13it/s]\n",
-      "100%|██████████████████████████████████████| 128/128 [00:00<00:00, 19366.94it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 53773.13it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 54648.91it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 51941.85it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 56111.09it/s]\n",
-      "Total time for the execution: 461.866833317\n",
-      "Total time spent on CUDA-Q: 10.452308367999706\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Scaling up to N=25, the task becomes even more onerous on a CPU. Depending on your device, the simulation may not even run. Try it and feel free to interrupt after your patience has worn out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# target = 'qpp-cpu'\n",
-    "# Uncomment the following line if you want to explicitly execute this step on your system.\n",
-    "# !python3 divisive_clustering_src/main_divisive_clustering.py --target qpp-cpu --M 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "N=25 can still easily be completed by a single GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "100%|█████████████████████████████████████| 7352/7352 [00:03<00:00, 2063.82it/s]\n",
-      "100%|███████████████████████████████████| 16492/16492 [00:03<00:00, 4739.44it/s]\n",
-      "100%|██████████████████████████████████████| 256/256 [00:00<00:00, 15185.58it/s]\n",
-      "100%|████████████████████████████████████████| 64/64 [00:00<00:00, 23728.05it/s]\n",
-      "100%|██████████████████████████████████████| 256/256 [00:00<00:00, 15437.97it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 50840.05it/s]\n",
-      "100%|████████████████████████████████████████| 32/32 [00:00<00:00, 33562.82it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 54120.05it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 54560.05it/s]\n",
-      "100%|██████████████████████████████████████████| 8/8 [00:00<00:00, 55924.05it/s]\n",
-      "100%|████████████████████████████████████████| 16/16 [00:00<00:00, 42717.29it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 55007.27it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 53601.33it/s]\n",
-      "100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 47127.01it/s]\n",
-      "Total time for the execution: 67.61674502899999\n",
-      "Total time spent on CUDA-Q: 21.439895901\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia'\n",
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to push the simulation to an $N=34$ coreset, a single GPU (assuming A100) will run out of memory.  Run the code below to see for yourself. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "RuntimeError: NLOpt runtime error: nlopt failure\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia'\n",
-    "!python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia --M 34"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To compute a problem with 34 qubits, we need to pool the memory of multiple GPUs.  If you have multiple GPUs available, try the code below to run the same computation on 4 GPUs. You do not need to wait for the code to finish, just note how it does not fail immediately due to memory issues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "Using BFL2 method to generate coresets\n",
-      "^C\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target = 'nvidia-mgpu'\n",
-    "gpu_count = !nvidia-smi -L | wc -l\n",
-    "try:\n",
-    "    gpu_count = int(gpu_count[0])\n",
-    "except:\n",
-    "    gpu_count = 0\n",
-    "if gpu_count >= 4:\n",
-    "    !mpirun -np 4 python3 divisive_clustering_src/main_divisive_clustering.py --target nvidia-mgpu --M 34\n",
-    "else:\n",
-    "    print(\n",
-    "        f'Not enough GPUs found on this system ({gpu_count}) to run this step')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py b/pr-2308/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py
deleted file mode 100644
index ba588126ad..0000000000
--- a/pr-2308/sphinx/applications/python/divisive_clustering_src/divisive_clustering.py
+++ /dev/null
@@ -1,1273 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import cudaq
-import networkx as nx
-import numpy as np
-import pandas as pd
-from matplotlib import pyplot as plt
-from scipy.cluster.hierarchy import dendrogram, fcluster
-from scipy.spatial import Voronoi
-from scipy.stats import multivariate_normal
-from sklearn.cluster import KMeans
-from tqdm import tqdm
-
-np.random.seed(10)
-
-
-class Coreset:
-
-    def __init__(
-        self,
-        raw_data: np.ndarray,
-        number_of_sampling_for_centroids: int,
-        coreset_size: int,
-        number_of_coresets_to_evaluate: Optional[int] = 10,
-        coreset_method: Optional[str] = "BFL2",
-        k_value_for_BLK2: Optional[int] = 2,
-    ) -> None:
-        self._raw_data = raw_data
-        self._coreset_size = coreset_size
-        self._number_of_coresets_to_evaluate = number_of_coresets_to_evaluate
-        self._number_of_sampling_for_centroids = number_of_sampling_for_centroids
-        self._k_value_for_BLK2 = k_value_for_BLK2
-
-        if coreset_method not in ["BFL2", "BLK2"]:
-            raise ValueError("Coreset method must be either BFL2 or BLK2.")
-        else:
-            self._coreset_method = coreset_method
-
-    @property
-    def raw_data(self) -> np.ndarray:
-        return self._raw_data
-
-    @property
-    def coreset_size(self) -> int:
-        return self._coreset_size
-
-    @property
-    def number_of_coresets_to_evaluate(self) -> int:
-        return self._number_of_coresets_to_evaluate
-
-    @property
-    def number_of_sampling_for_centroids(self) -> int:
-        return self._number_of_sampling_for_centroids
-
-    @property
-    def coreset_method(self) -> str:
-        return self._coreset_method
-
-    @property
-    def k_value_for_BLK2(self) -> int:
-        return self._k_value_for_BLK2
-
-    @raw_data.setter
-    def raw_data(self, raw_data: np.ndarray) -> None:
-        self._raw_data = raw_data
-
-    @coreset_size.setter
-    def coreset_size(self, coreset_size: int) -> None:
-        self._coreset_size = coreset_size
-
-    @number_of_coresets_to_evaluate.setter
-    def number_of_coresets_to_evaluate(
-            self, number_of_coresets_to_evaluate: int) -> None:
-        self._number_of_coresets_to_evaluate = number_of_coresets_to_evaluate
-
-    @number_of_sampling_for_centroids.setter
-    def number_of_sampling_for_centroids(
-            self, number_of_sampling_for_centroids: int) -> None:
-        self._number_of_sampling_for_centroids = number_of_sampling_for_centroids
-
-    @coreset_method.setter
-    def coreset_method(self, coreset_method: str) -> None:
-        self._coreset_method = coreset_method
-
-    @k_value_for_BLK2.setter
-    def k_value_for_BLK2(self, k_value_for_BLK2: int) -> None:
-        self._k_value_for_BLK2 = k_value_for_BLK2
-
-    def get_best_coresets(self) -> Tuple[np.ndarray, np.ndarray]:
-        """
-        Get the best coreset vectors and weights for a given data.
-
-        Returns:
-            `Tuple[np.ndarray, np.ndarray]`: The coreset vectors and weights.
-        """
-
-        centroids = self.get_best_centroids()
-
-        if self._coreset_method == "BFL2":
-            print("Using BFL2 method to generate coresets")
-            coreset_vectors, coreset_weights = self.get_coresets_using_BFL2(
-                centroids)
-
-        elif self._coreset_method == "BLK2":
-            print("Using BLK2 method to generate coresets")
-            coreset_vectors, coreset_weights = self.get_coresets_using_BLK2(
-                centroids)
-        else:
-            raise ValueError("Coreset method must be either BFL2 or BLK2.")
-
-        coreset_vectors, coreset_weights = self.best_coreset_using_kmeans_cost(
-            coreset_vectors, coreset_weights)
-
-        self.coreset_vectors = coreset_vectors
-        self.coreset_weights = coreset_weights
-
-        return (np.array(coreset_vectors), np.array(coreset_weights))
-
-    def get_coresets_using_BFL2(
-        self, centroids: List[np.ndarray]
-    ) -> Tuple[List[np.ndarray], List[np.ndarray]]:
-        """
-        Generates coreset vectors and weights using the BFL2 algorithm.
-
-        Args:
-            `centroids (List[np.ndarray])`: The centroids to use for the coreset generation.
-
-        Returns:
-            `Tuple[List[np.ndarray], List[np.ndarray]]`: List of coreset vectors and weights.
-        """
-
-        coreset_vectors_list = []
-        coreset_weights_list = []
-        for i in range(self.number_of_coresets_to_evaluate):
-            coreset_vectors, coreset_weights = self.BFL2(centroids=centroids)
-            coreset_vectors_list.append(coreset_vectors)
-            coreset_weights_list.append(coreset_weights)
-
-        return (coreset_vectors_list, coreset_weights_list)
-
-    def get_best_centroids(self) -> List[np.ndarray]:
-        """
-        Get the best centroids using the D2 sampling algorithm.
-
-        Returns:
-            List[np.ndarray]: The best centroids.
-
-        """
-
-        best_centroid_coordinates, best_centroid_cost = None, np.inf
-
-        for _ in range(self.number_of_sampling_for_centroids):
-            centroids = self.D2_sampling()
-            cost = self.get_cost(centroids)
-            if cost < best_centroid_cost:
-                best_centroid_coordinates, best_centroid_cost = centroids, cost
-
-        return best_centroid_coordinates
-
-    def D2_sampling(self) -> List[np.ndarray]:
-        """
-        Selects the centroids from the data points using the D2 sampling algorithm.
-
-        Returns:
-            List[np.ndarray]: The selected centroids as a list.
-        """
-
-        centroids = []
-        data_vectors = self.raw_data
-
-        centroids.append(data_vectors[np.random.choice(len(data_vectors))])
-
-        for _ in range(self.coreset_size - 1):
-            p = np.zeros(len(data_vectors))
-            for i, x in enumerate(data_vectors):
-                p[i] = self.distance_to_centroids(x, centroids)[0]**2
-            p = p / sum(p)
-            centroids.append(data_vectors[np.random.choice(len(data_vectors),
-                                                           p=p)])
-
-        return centroids
-
-    def get_cost(self, centroids: Union[List[np.ndarray], np.ndarray]) -> float:
-        """
-        Computes the sum of between each data points and each centroids.
-
-        Args:
-            `centroids (Union[List[np.ndarray], np.ndarray])`: The centroids to evaluate.
-
-        Returns:
-            float: The cost of the centroids.
-
-        """
-
-        cost = 0.0
-        for x in self.raw_data:
-            cost += self.distance_to_centroids(x, centroids)[0]**2
-        return cost
-
-    def distance_to_centroids(
-            self, data_instance: np.ndarray,
-            centroids: Union[List[np.ndarray],
-                             np.ndarray]) -> Tuple[float, int]:
-        """
-        Compute the distance between a data instance and the centroids.
-
-        Args:
-            `data_instance (np.ndarray)`: The data instance.
-            `centroids (Union[List[np.ndarray], np.ndarray])`: The centroids as a list or `numpy` array.
-
-        Returns:
-            Tuple[float, int]: The minimum distance and the index of the closest centroid.
-        """
-
-        minimum_distance = np.inf
-        closest_index = -1
-        for i, centroid in enumerate(centroids):
-            distance_between_data_instance_and_centroid = np.linalg.norm(
-                data_instance - centroid)
-            if distance_between_data_instance_and_centroid < minimum_distance:
-                minimum_distance = distance_between_data_instance_and_centroid
-                closest_index = i
-
-        return (minimum_distance, closest_index)
-
-    def BFL2(
-        self, centroids: Union[List[np.ndarray], np.ndarray]
-    ) -> Tuple[List[np.ndarray], List[float]]:
-        """
-        Performs Algorithm 2 from https://arxiv.org/pdf/1612.00889.pdf BFL2. This will pick the coreset vectors and its corresponding weights.
-
-        Args:
-            centroids (List): The centroids to use for the coreset generation.
-
-        Returns:
-            Tuple[List, List]: The coreset vectors and coreset weights.
-        """
-
-        number_of_data_points_close_to_a_cluster = {
-            i: 0 for i in range(len(centroids))
-        }
-        sum_distance_to_closest_cluster = 0.0
-        for data_instance in self.raw_data:
-            min_dist, closest_index = self.distance_to_centroids(
-                data_instance, centroids)
-            number_of_data_points_close_to_a_cluster[closest_index] += 1
-            sum_distance_to_closest_cluster += min_dist**2
-
-        Prob = np.zeros(len(self._raw_data))
-        for i, p in enumerate(self._raw_data):
-            min_dist, closest_index = self.distance_to_centroids(p, centroids)
-            Prob[i] += min_dist**2 / (2 * sum_distance_to_closest_cluster)
-            Prob[i] += 1 / (
-                2 * len(centroids) *
-                number_of_data_points_close_to_a_cluster[closest_index])
-
-        if not (0.999 <= sum(Prob) <= 1.001):
-            raise ValueError(
-                "sum(Prob) = %s; the algorithm should automatically "
-                "normalize Prob by construction" % sum(Prob))
-        chosen_indices = np.random.choice(len(self._raw_data),
-                                          size=self._coreset_size,
-                                          p=Prob)
-        weights = [1 / (self._coreset_size * Prob[i]) for i in chosen_indices]
-
-        return ([self._raw_data[i] for i in chosen_indices], weights)
-
-    def kmeans_cost(self,
-                    coreset_vectors: np.ndarray,
-                    sample_weight: Optional[np.ndarray] = None) -> float:
-        """
-        Compute the cost of coreset vectors using k-means clustering.
-
-        Args:
-            `coreset_vectors (np.ndarray)`: The coreset vectors.
-            `sample_weight (np.ndarray)`: The sample weights.
-
-        Returns:
-            float: The cost of the k-means clustering.
-
-        """
-
-        kmeans = KMeans(n_clusters=2).fit(coreset_vectors,
-                                          sample_weight=sample_weight)
-        return self.get_cost(kmeans.cluster_centers_)
-
-    def best_coreset_using_kmeans_cost(
-            self, coreset_vectors: List[np.ndarray],
-            coreset_weights: List[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
-        """
-        Get the best coreset using k-means cost.
-
-        Args:
-            `coreset_vectors (List[np.ndarray])`: The coreset vectors.
-            `coreset_weights (List[np.ndarray])`: The coreset weights.
-
-        Returns:
-            Tuple: The best coreset vectors and coreset weights.
-        """
-
-        cost_coreset = [
-            self.kmeans_cost(
-                coreset_vectors=coreset_vectors[i],
-                sample_weight=coreset_weights[i],
-            ) for i in range(self._number_of_coresets_to_evaluate)
-        ]
-
-        best_index = cost_coreset.index(np.min(cost_coreset))
-        return (coreset_vectors[best_index], coreset_weights[best_index])
-
-    def get_coresets_using_BLK2(
-        self, centroids: Union[List[np.ndarray], np.ndarray]
-    ) -> Tuple[List[List[np.ndarray]], List[List[float]]]:
-        """
-        Generates coreset vectors and weights using Algorithm 2.
-
-        Args:
-            `centroids (List[np.ndarray])`: The centroids to use for the coreset generation.
-
-        Returns:
-            `Tuple[List[List[np.ndarray]], List[List[float]]]`: The coreset vectors and coreset weights.
-        """
-
-        coreset_vectors_list = []
-        coreset_weights_list = []
-        for i in range(self.number_of_coresets_to_evaluate):
-            coreset_vectors, coreset_weights = self.BLK2(centroids=centroids)
-            coreset_vectors_list.append(coreset_vectors)
-            coreset_weights_list.append(coreset_weights)
-
-        return (coreset_vectors_list, coreset_weights_list)
-
-    def BLK2(
-        self,
-        centroids: Union[List[np.ndarray], np.ndarray],
-    ) -> Tuple[List[np.ndarray], List[float]]:
-        """
-        Performs Algorithm 2 from  https://arxiv.org/pdf/1703.06476.pdf.
-
-        Args:
-            `centroids (List[np.ndarray])`: The centroids to use for the coreset generation.
-
-        Returns:
-            `Tuple[List, List]`: The coreset vectors and coreset weights.
-        """
-
-        alpha = 16 * (np.log2(self._k_value_for_BLK2) + 2)
-
-        B_i_totals = [0] * len(centroids)
-        B_i = [np.empty_like(self._raw_data) for _ in range(len(centroids))]
-        for data_instance in self._raw_data:
-            _, closest_index = self.distance_to_centroids(
-                data_instance, centroids)
-            B_i[closest_index][B_i_totals[closest_index]] = data_instance
-            B_i_totals[closest_index] += 1
-
-        c_phi = sum([
-            self.distance_to_centroids(data_instance, centroids)[0]**2
-            for data_instance in self._raw_data
-        ]) / len(self._raw_data)
-
-        p = np.zeros(len(self._raw_data))
-
-        sum_dist = {i: 0.0 for i in range(len(centroids))}
-        for i, data_instance in enumerate(self._raw_data):
-            dist, closest_index = self.distance_to_centroids(
-                data_instance, centroids)
-            sum_dist[closest_index] += dist**2
-
-        for i, data_instance in enumerate(self._raw_data):
-            p[i] = 2 * alpha * self.distance_to_centroids(
-                data_instance, centroids)[0]**2 / c_phi
-
-            closest_index = self.distance_to_centroids(data_instance,
-                                                       centroids)[1]
-            p[i] += 4 * alpha * sum_dist[closest_index] / (
-                B_i_totals[closest_index] * c_phi)
-
-            p[i] += 4 * len(self._raw_data) / B_i_totals[closest_index]
-        p = p / sum(p)
-
-        chosen_indices = np.random.choice(len(self._raw_data),
-                                          size=self._coreset_size,
-                                          p=p)
-        weights = [1 / (self._coreset_size * p[i]) for i in chosen_indices]
-
-        return [self._raw_data[i] for i in chosen_indices], weights
-
-    @staticmethod
-    def coreset_to_graph(
-        coreset_vectors: np.ndarray,
-        coreset_weights: np.ndarray,
-        metric: Optional[str] = "dot",
-        number_of_qubits_representing_data: Optional[int] = 1,
-    ) -> nx.Graph:
-        """
-        Convert coreset vectors to a graph.
-
-        Args:
-            `coreset_vectors (np.ndarray)`: The coreset vectors.
-            `coreset_weights (np.ndarray)`: The coreset weights.
-            `metric (str, optional)`: The metric to use. Defaults to "dot".
-            `number_of_qubits_representing_data (int, optional)`: The number of qubits representing the data. Defaults to 1.
-
-        Returns:
-            `nx.Graph`: The graph.
-        """
-
-        coreset = [(w, v) for w, v in zip(coreset_weights, coreset_vectors)]
-
-        vertices = len(coreset)
-        vertex_labels = [
-            number_of_qubits_representing_data * int(i) for i in range(vertices)
-        ]
-        G = nx.Graph()
-        G.add_nodes_from(vertex_labels)
-        edges = [(
-            number_of_qubits_representing_data * i,
-            number_of_qubits_representing_data * j,
-        ) for i in range(vertices) for j in range(i + 1, vertices)]
-
-        G.add_edges_from(edges)
-
-        for edge in G.edges():
-            v_i = edge[0] // number_of_qubits_representing_data
-            v_j = edge[1] // number_of_qubits_representing_data
-            w_i = coreset[v_i][0]
-            w_j = coreset[v_j][0]
-            if metric == "dot":
-                mval = np.dot(
-                    coreset[v_i][1],
-                    coreset[v_j][1],
-                )
-            elif metric == "dist":
-                mval = np.linalg.norm(coreset[v_i][1] - coreset[v_j][1])
-            else:
-                raise Exception("Unknown metric: {}".format(metric))
-
-            G[edge[0]][edge[1]]["weight"] = w_i * w_j * mval
-
-        return G
-
-    @staticmethod
-    def normalize_array(vectors: np.ndarray,
-                        centralize: bool = False) -> np.ndarray:
-        """
-        Normalize and centralize the array
-
-        Args:
-            `vectors (np.ndarray)`: The vectors to normalize
-            `centralize (bool, optional)`: Centralize the array. Defaults to False.
-
-        Returns:
-            `np.ndarray`: The normalized array
-        """
-
-        if centralize:
-            vectors = vectors - np.mean(vectors, axis=0)
-
-        max_abs = np.max(np.abs(vectors), axis=0)
-        vectors_norm = vectors / max_abs
-
-        return vectors_norm
-
-    @staticmethod
-    def create_dataset(
-        n_samples: float,
-        covariance_values: List[float] = [-0.8, -0.8],
-        n_features: Optional[int] = 2,
-        number_of_samples_from_distribution: Optional[int] = 500,
-        mean_array: Optional[np.ndarray] = np.array([[0, 0], [7, 1]]),
-        random_seed: Optional[int] = 10,
-    ) -> np.ndarray:
-        """
-        Create a data set with the given parameters.
-
-        Args:
-            `n_samples (float)`: The number of samples.
-            `covariance_values (List[float], optional)`: The covariance values. Defaults to [-0.8, -0.8].
-            `n_features (int, optional)`: The number of features. Defaults to 2.
-            `number_of_samples_from_distribution (int, optional)`: The number of samples from the distribution. Defaults to 500.
-            `mean_array (np.ndarray, optional)`: The mean array. Defaults to `np.array([[0, 0], [7, 1]])`.
-            `random_seed (int, optional)`: The random seed. Defaults to 10.
-
-        Returns:
-            `np.ndarray`: The data set created
-        """
-
-        random_seed = random_seed
-
-        X = np.zeros((n_samples, n_features))
-
-        for idx, val in enumerate(covariance_values):
-            covariance_matrix = np.array([[1, val], [val, 1]])
-
-            distr = multivariate_normal(cov=covariance_matrix,
-                                        mean=mean_array[idx],
-                                        seed=random_seed)
-
-            data = distr.rvs(size=number_of_samples_from_distribution)
-
-            X[number_of_samples_from_distribution *
-              idx:number_of_samples_from_distribution * (idx + 1)][:] = data
-
-        return X
-
-
-class DivisiveClustering(ABC):
-
-    def __init__(
-        self,
-        circuit_depth: int,
-        max_iterations: int,
-        max_shots: int,
-        threshold_for_max_cut: float,
-        create_Hamiltonian: Callable,
-        optimizer: cudaq.optimizers.optimizer,
-        optimizer_function: Callable,
-        create_circuit: Callable,
-        normalize_vectors: Optional[bool] = True,
-        sort_by_descending: Optional[bool] = True,
-        coreset_to_graph_metric: Optional[str] = "dot",
-    ) -> None:
-        self.circuit_depth = circuit_depth
-        self.max_iterations = max_iterations
-        self.max_shots = max_shots
-        self.threshold_for_maxcut = threshold_for_max_cut
-        self.normalize_vectors = normalize_vectors
-        self.sort_by_descending = sort_by_descending
-        self.coreset_to_graph_metric = coreset_to_graph_metric
-        self.create_Hamiltonian = create_Hamiltonian
-        self.create_circuit = create_circuit
-        self.optimizer = optimizer
-        self.optimizer_function = optimizer_function
-
-    @abstractmethod
-    def run_divisive_clustering(
-        self, coreset_vectors_df_for_iteration: pd.DataFrame
-    ) -> Union[List[str], List[int]]:
-        """
-        Run the divisive clustering algorithm.
-
-        Args:
-            `coreset_vectors_df_for_iteration (pd.DataFrame)`: The coreset vectors for the iteration.
-
-        Returns:
-            `Union[List[str], List[int]]`: The bitstring or the cluster. The return will depend on the name of the data point given.
-        """
-
-        pass
-
-    def get_hierarchical_clustering_sequence(
-        self,
-        coreset_vectors_df_for_iteration: np.ndarray,
-        hierarchial_sequence: List,
-    ) -> List:
-        """
-        Get the hierarchical clustering sequence.
-
-        Args:
-            `coreset_vectors_df_for_iteration (np.ndarray)`: The coreset vectors for the iteration.
-            `hierarchial_sequence (List)`: The hierarchical sequence.
-
-        """
-
-        bitstring = self.run_divisive_clustering(
-            coreset_vectors_df_for_iteration)
-        return self._add_children_to_hierarchial_clustering(
-            coreset_vectors_df_for_iteration, hierarchial_sequence, bitstring)
-
-    def _get_iteration_coreset_vectors_and_weights(
-        self, coreset_vectors_df_for_iteration: pd.DataFrame
-    ) -> Tuple[np.ndarray, np.ndarray]:
-        """
-        Gets the iteration coreset vectors and weights.
-
-        Args:
-            `coreset_vectors_df_for_iteration (pd.DataFrame)`: The coreset vectors for the iteration.
-
-        Returns:
-            `Tuple[np.ndarray, np.ndarray]`: The coreset vectors and weights.
-
-        """
-
-        coreset_vectors_for_iteration = coreset_vectors_df_for_iteration[[
-            "X", "Y"
-        ]].to_numpy()
-
-        coreset_weights_for_iteration = coreset_vectors_df_for_iteration[
-            "weights"].to_numpy()
-
-        if self.normalize_vectors:
-            coreset_vectors_for_iteration = Coreset.normalize_array(
-                coreset_vectors_for_iteration, True)
-            coreset_weights_for_iteration = Coreset.normalize_array(
-                coreset_weights_for_iteration)
-
-        return (coreset_vectors_for_iteration, coreset_weights_for_iteration)
-
-    def brute_force_cost_maxcut(self, bitstrings: list[Union[str, int]],
-                                G: nx.graph) -> Dict[str, float]:
-        """
-        Cost function for brute force method
-
-        Args:
-            bitstrings: list of bit strings
-            G: The graph of the problem
-
-        Returns:
-            Dict: Dictionary with bitstring and cost value
-        """
-
-        cost_value = {}
-        for bitstring in tqdm(bitstrings):
-            c = 0
-            for i, j in G.edges():
-                edge_weight = G[i][j]["weight"]
-                c += self._get_edge_cost(bitstring, i, j, edge_weight)
-
-            cost_value.update({bitstring: c})
-
-        return cost_value
-
-    def _get_edge_cost(self, bitstring: str, i: int, j: int,
-                       edge_weight: float) -> float:
-        """
-        Get the edge cost using MaxCut cost function.
-
-        Args:
-            bitstring: The bitstring
-            i: The first node
-            j: The second node
-            edge_weight: The edge weight
-
-        Returns:
-            float: The edge cost
-        """
-
-        ai = int(bitstring[i])
-        aj = int(bitstring[j])
-
-        return -1 * edge_weight * (1 - ((-1)**ai) * ((-1)**aj))
-
-    def _add_children_to_hierarchial_clustering(
-        self,
-        iteration_dataframe: pd.DataFrame,
-        hierarchial_sequence: list,
-        bitstring: str,
-    ) -> List[Union[str, int]]:
-        """
-        Add children to the hierarchical clustering sequence.
-
-        Args:
-            `iteration_dataframe (pd.DataFrame)`: The iteration data frame.
-            `hierarchial_sequence (list)`: The hierarchical sequence.
-            `bitstring (str)`: The bitstring.
-
-        Returns:
-            list: The hierarchical sequence.
-        """
-
-        iteration_dataframe["cluster"] = [int(bit) for bit in bitstring]
-
-        for j in range(2):
-            idx = list(
-                iteration_dataframe[iteration_dataframe["cluster"] == j].index)
-            if len(idx) > 0:
-                hierarchial_sequence.append(idx)
-
-        return hierarchial_sequence
-
-    @staticmethod
-    def get_divisive_cluster_cost(hierarchical_clustering_sequence: List[Union[
-        str, int]], coreset_data: pd.DataFrame) -> List[float]:
-        """
-        Get the cost of the divisive clustering at each iteration.
-
-        Args:
-            `hierarchical_clustering_sequence (List)`: The hierarchical clustering sequence.
-            `coreset_data (pd.DataFrame)`: The coreset data.
-
-        Returns:
-            List[float]: The cost of the divisive clustering sequence.
-        """
-
-        coreset_data = coreset_data.drop(["Name", "weights"], axis=1)
-        cost_at_each_iteration = []
-        for parent in hierarchical_clustering_sequence:
-            children_lst = Dendrogram.find_children(
-                parent, hierarchical_clustering_sequence)
-
-            if not children_lst:
-                continue
-            else:
-                children_1, children_2 = children_lst
-
-                parent_data_frame = coreset_data.iloc[parent]
-
-                parent_data_frame["cluster"] = 0
-
-                parent_data_frame.loc[children_2, "cluster"] = 1
-
-                cost = 0
-
-                centroid_coords = parent_data_frame.groupby("cluster").mean()[[
-                    "X", "Y"
-                ]]
-                centroid_coords = centroid_coords.to_numpy()
-
-                for idx, row in parent_data_frame.iterrows():
-                    if row.cluster == 0:
-                        cost += np.linalg.norm(row[["X", "Y"]] -
-                                               centroid_coords[0])**2
-                    else:
-                        cost += np.linalg.norm(row[["X", "Y"]] -
-                                               centroid_coords[1])**2
-
-                cost_at_each_iteration.append(cost)
-
-        return cost_at_each_iteration
-
-    def _get_best_bitstring(self, counts: cudaq.SampleResult,
-                            G: nx.Graph) -> str:
-        """
-        From the simulator output, extract the best bitstring.
-
-        Args:
-            `counts (cudaq.SampleResult)`: The counts.
-            `G (nx.Graph)`: The graph.
-
-        Returns:
-            `str`: The best bitstring.
-        """
-
-        counts_pd = pd.DataFrame(counts.items(),
-                                 columns=["bitstring", "counts"])
-        counts_pd[
-            "probability"] = counts_pd["counts"] / counts_pd["counts"].sum()
-        bitstring_probability_df = counts_pd.drop(columns=["counts"])
-        bitstring_probability_df = bitstring_probability_df.sort_values(
-            "probability", ascending=self.sort_by_descending)
-
-        unacceptable_bitstrings = [
-            "".join("1" for _ in range(10)),
-            "".join("0" for _ in range(10)),
-        ]
-
-        bitstring_probability_df = bitstring_probability_df[
-            ~bitstring_probability_df["bitstring"].isin(unacceptable_bitstrings
-                                                       )]
-
-        if len(bitstring_probability_df) > 10:
-            selected_rows = int(
-                len(bitstring_probability_df) * self.threshold_for_maxcut)
-        else:
-            selected_rows = int(len(bitstring_probability_df) / 2)
-
-        bitstring_probability_df = bitstring_probability_df.head(selected_rows)
-
-        bitstrings = bitstring_probability_df["bitstring"].tolist()
-
-        brute_force_cost_of_bitstrings = self.brute_force_cost_maxcut(
-            bitstrings, G)
-
-        return min(brute_force_cost_of_bitstrings,
-                   key=brute_force_cost_of_bitstrings.get)
-
-    def get_divisive_sequence(
-            self, full_coreset_df: pd.DataFrame) -> List[Union[str, int]]:
-        """
-        Perform divisive clustering on the coreset data.
-
-        Args:
-            `full_coreset_df (pd.DataFrame)`: The full coreset data.
-
-        Returns:
-            `List[Union[str, int]]`: The hierarchical clustering sequence.
-        """
-
-        index_iteration_counter = 0
-        single_clusters = 0
-
-        index_values = list(range(len(full_coreset_df)))
-        hierarchical_clustering_sequence = [index_values]
-
-        while single_clusters < len(index_values):
-            index_values_to_evaluate = hierarchical_clustering_sequence[
-                index_iteration_counter]
-            if len(index_values_to_evaluate) == 1:
-                single_clusters += 1
-
-            elif len(index_values_to_evaluate) == 2:
-                hierarchical_clustering_sequence.append(
-                    [index_values_to_evaluate[0]])
-                hierarchical_clustering_sequence.append(
-                    [index_values_to_evaluate[1]])
-
-            else:
-                coreset_vectors_df_for_iteration = full_coreset_df.iloc[
-                    index_values_to_evaluate]
-
-                hierarchical_clustering_sequence = self.get_hierarchical_clustering_sequence(
-                    coreset_vectors_df_for_iteration,
-                    hierarchical_clustering_sequence,
-                )
-
-            index_iteration_counter += 1
-
-        return hierarchical_clustering_sequence
-
-
-class Dendrogram:
-
-    def __init__(
-            self, coreset_data: pd.DataFrame,
-            hierarchical_clustering_sequence: List[Union[str, int]]) -> None:
-        self._coreset_data = self.__create_coreset_data(coreset_data)
-        self._hierarchial_clustering_sequence = self.__convert_numbers_to_name(
-            hierarchical_clustering_sequence, coreset_data)
-        self.linkage_matrix = []
-
-    @property
-    def coreset_data(self) -> pd.DataFrame:
-        return self._coreset_data
-
-    @coreset_data.setter
-    def coreset_data(self, coreset_data: pd.DataFrame) -> None:
-        self.linkage_matrix = []
-        self._coreset_data = coreset_data
-
-    @property
-    def hierarchical_clustering_sequence(self) -> List[Union[str, int]]:
-        return self._hierarchial_clustering_sequence
-
-    @hierarchical_clustering_sequence.setter
-    def hierarchical_clustering_sequence(
-            self, hierarchical_clustering_sequence: List[Union[str,
-                                                               int]]) -> None:
-        self.linkage_matrix = []
-        self._hierarchial_clustering_sequence = hierarchical_clustering_sequence
-
-    def __call__(self) -> List:
-        if not self.linkage_matrix:
-            self.get_linkage_matrix(self._hierarchial_clustering_sequence[0])
-
-        return self.linkage_matrix
-
-    def __create_coreset_data(self, coreset_data: pd.DataFrame) -> pd.DataFrame:
-        """
-        Creates coreset data that can be used for plotting.
-
-        Args:
-            `coreset_data (pd.DataFrame)`: The coreset data.
-
-        Returns:
-            `pd.DataFrame`: The coreset data.
-        """
-
-        _coreset_data = coreset_data.copy()
-        _coreset_data.index = _coreset_data.Name
-
-        return _coreset_data.drop(columns=["Name", "weights"])
-
-    def __convert_numbers_to_name(self,
-                                  hierarchical_clustering_sequence: List[int],
-                                  coreset_data: pd.DataFrame) -> List[str]:
-        """
-        Converts the int in the hierarchical sequence into the instance name. This would be used to plot the leaves of the dendrogram.
-
-        Args:
-            `hierarchical_clustering_sequence (List[int])`: The hierarchical clustering sequence.
-            `coreset_data (pd.DataFrame)`: The coreset data.
-
-        Returns:
-            List[str]: The converted hierarchical clustering sequence.
-        """
-
-        converted_hc = []
-        for hc in hierarchical_clustering_sequence:
-            converted_hc.append([coreset_data.Name[num] for num in hc])
-
-        return converted_hc
-
-    def plot_dendrogram(
-        self,
-        plot_title: Optional[str] = "DIANA",
-        orientation: Optional[str] = "top",
-        color_threshold: Optional[int] = None,
-        colors: Optional[List] = None,
-        clusters: Optional[np.ndarray] = None,
-        link_color_func: Optional[Callable] = None,
-    ):
-        """
-        Plots the dendrogram.
-
-        Args:
-            `plot_title (str, optional)`: The plot title. Defaults to "DIANA".
-            `orientation (str, optional)`: The orientation of the dendrogram. Defaults to "top".
-            `color_threshold (int, optional)`: The color threshold to convert hierarchical clustering into flat clustering. Defaults to None.
-            `colors (List, optional)`: The colors for the leaves. Defaults to None.
-            `clusters (np.ndarray, optional)`: Flat clustering results from applying threshold. Defaults to None.
-            `link_color_func (Callable, optional)`: Function to color the branches. Defaults to None.
-        """
-
-        if not self.linkage_matrix:
-            self.get_linkage_matrix(self._hierarchial_clustering_sequence[0])
-
-        if clusters is None:
-            clusters = np.array([0] * len(self._coreset_data))
-
-        fig = plt.figure(figsize=(10, 10), dpi=100)
-        plt.title(plot_title)
-        dn = dendrogram(
-            self.linkage_matrix,
-            labels=self._coreset_data.index,
-            orientation=orientation,
-            color_threshold=color_threshold * 100 if colors else None,
-        )
-
-        if color_threshold is not None:
-            plt.axhline(y=color_threshold, color="r", linestyle="--")
-
-        if colors is not None:
-            if len(colors) < len(set(clusters)):
-                raise ValueError(
-                    "Number of colors should be equal to number of clusters")
-            else:
-                colors_dict = {
-                    self._coreset_data.index[i]: colors[j]
-                    for i, j in enumerate(clusters)
-                }
-
-                ax = plt.gca()
-                xlbls = ax.get_xmajorticklabels()
-                for lbl in xlbls:
-                    lbl.set_color(colors_dict[lbl.get_text()])
-
-        plt.show()
-
-    def get_clusters_using_height(self, threshold: float) -> np.ndarray:
-        """
-        Get flat clusters from the hierarchical clustering using a threshold.
-
-        Args:
-            threshold (float): The height threshold to convert.
-
-        Returns:
-            `np.ndarray`: The flat cluster labels.
-        """
-
-        if not self.linkage_matrix:
-            self.get_linkage_matrix(self._hierarchial_clustering_sequence[0])
-
-        clusters = fcluster(self.linkage_matrix,
-                            threshold,
-                            criterion="distance")
-
-        return np.array(clusters) - 1
-
-    def get_clusters_using_k(self, k: int) -> np.ndarray:
-        """
-        Get flat clusters from the hierarchical cluster by defining the number of clusters.
-
-        Args:
-            k (int): The number of clusters.
-
-        Returns:
-            `np.ndarray`: The flat cluster labels.
-
-        """
-        if not self.linkage_matrix:
-            self.get_linkage_matrix(self._hierarchial_clustering_sequence[0])
-
-        clusters = fcluster(self.linkage_matrix, k, criterion="maxclust")
-
-        return np.array(clusters) - 1
-
-    def plot_clusters(
-        self,
-        clusters: np.ndarray,
-        colors: List[str],
-        plot_title: str,
-        show_annotation: Optional[bool] = False,
-    ):
-        """
-        Plot the flat clusters.
-
-        Args:
-            `clusters (np.ndarray)`: The flat clusters.
-            `colors (List[str])`: The colors for the clusters.
-            `plot_title (str)`: The plot title.
-            `show_annotation (bool, optional)`: Show annotation. Defaults to False.
-
-        """
-        if len(colors) < len(set(clusters)):
-            raise ValueError(
-                "Number of colors should be equal to number of clusters")
-        coreset_data = self._coreset_data.copy()
-        coreset_data["clusters"] = clusters
-        for i in range(coreset_data.clusters.nunique()):
-            data = coreset_data[coreset_data.clusters == i]
-            plt.scatter(data.X, data.Y, c=colors[i], label=f"Cluster {i}")
-        if show_annotation:
-            for _, row in coreset_data.iterrows():
-                plt.annotate(row.name, (row.X, row.Y))
-        plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
-        plt.title(plot_title)
-        plt.show()
-
-    def get_linkage_matrix(self, parent: List[str]) -> int:
-        """
-        Create the linkage matrix for the dendrogram and returns the index of the new branch.
-
-        Args:
-            parent (`List[str]`): The parent cluster.
-
-        Returns:
-            List: The linkage matrix.
-        """
-
-        if len(parent) < 2:
-            index_of_parent = np.argwhere(self._coreset_data.index == parent[0])
-            return index_of_parent[0][0]
-        children_1, children_2 = self.find_children(
-            parent, self._hierarchial_clustering_sequence)
-
-        index1 = self.get_linkage_matrix(children_1)
-        index2 = self.get_linkage_matrix(children_2)
-        self.linkage_matrix.append([
-            index1,
-            index2,
-            self.distance(index1) + self.distance(index2),
-            self.cluster_len(index1) + self.cluster_len(index2),
-        ])
-
-        return len(self.linkage_matrix) - 1 + len(self.coreset_data)
-
-    def distance(self, i: int) -> float:
-        """
-        Get the distance between two clusters.
-
-        Args:
-            i (int): The index of the cluster.
-
-        Returns:
-            float: The distance of the cluster.
-        """
-
-        if i >= len(self._coreset_data):
-            distance = self.linkage_matrix[i - len(self._coreset_data)][2]
-        else:
-            distance = sum(
-                self._coreset_data.iloc[i]) / (len(self.coreset_data) - 1)
-
-        return abs(distance)
-
-    def cluster_len(self, i: int):
-        """
-        Get the length of the cluster.
-
-        Args:
-            i (int): The index of the cluster.
-
-        Returns:
-            int: The length of the cluster.
-        """
-
-        if i >= len(self._coreset_data):
-            return self.linkage_matrix[i - len(self._coreset_data)][3]
-        else:
-            return 1
-
-    @staticmethod
-    def find_children(
-            parent: List[Union[str, int]],
-            hierarchical_clustering_sequence: List[Union[str, int]]) -> List:
-        """
-        Find the children of a given parent cluster.
-
-        Args:
-            parent (List): The parent cluster.
-            hierarchical_clustering_sequence (List): The hierarchical clustering sequence.
-
-        Returns:
-            List: The children of the parent cluster.
-        """
-
-        parent_position = hierarchical_clustering_sequence.index(parent)
-
-        found = 0
-        children = []
-        for i in range(parent_position + 1,
-                       len(hierarchical_clustering_sequence)):
-            if any(item in hierarchical_clustering_sequence[i]
-                   for item in parent):
-                children.append(hierarchical_clustering_sequence[i])
-                found += 1
-                if found == 2:
-                    break
-
-        return children
-
-    @staticmethod
-    def plot_hierarchial_split(
-            hierarchical_clustering_sequence: List[Union[str, int]],
-            full_coreset_df: pd.DataFrame):
-        """
-        Plots the flat clusters at each iteration of the hierarchical clustering.
-
-        Args:
-            hierarchical_clustering_sequence (List): The hierarchical clustering sequence.
-            `full_coreset_df` (`pd.DataFrame`): The full coreset data.
-        """
-        parent_clusters = [
-            parent_cluster
-            for parent_cluster in hierarchical_clustering_sequence
-            if len(parent_cluster) > 1
-        ]
-        x_grid = int(np.sqrt(len(parent_clusters)))
-        y_grid = int(np.ceil(len(parent_clusters) / x_grid))
-
-        fig, axs = plt.subplots(x_grid, y_grid, figsize=(12, 12))
-
-        for i, parent_cluster in enumerate(parent_clusters):
-            parent_position = hierarchical_clustering_sequence.index(
-                parent_cluster)
-            children = Dendrogram.find_children(
-                parent_cluster, hierarchical_clustering_sequence)
-            coreset_for_parent_cluster = full_coreset_df.loc[parent_cluster]
-            coreset_for_parent_cluster["cluster"] = 1
-            coreset_for_parent_cluster.loc[children[0], "cluster"] = 0
-
-            ax = axs[i // 3, i % 3]
-            ax.scatter(
-                coreset_for_parent_cluster["X"],
-                coreset_for_parent_cluster["Y"],
-                c=coreset_for_parent_cluster["cluster"],
-            )
-            for _, row in coreset_for_parent_cluster.iterrows():
-                ax.annotate(row["Name"], (row["X"], row["Y"]))
-
-            ax.set_xlabel("X")
-            ax.set_ylabel("Y")
-            ax.set_title(f"Clustering at iteration {parent_position}")
-
-        plt.tight_layout()
-        plt.show()
-
-
-class Voironi_Tessalation:
-
-    def __init__(
-        self,
-        coreset_df: pd.DataFrame,
-        clusters: np.ndarray,
-        colors: List[str],
-        tesslation_by_cluster: Optional[bool] = False,
-    ) -> None:
-        coreset_df["cluster"] = clusters
-
-        if tesslation_by_cluster:
-            cluster_means = coreset_df.groupby("cluster")[["X", "Y"]].mean()
-            coreset_df = cluster_means.reset_index()
-            coreset_df["cluster"] = [i for i in range(len(coreset_df))]
-
-        coreset_df["color"] = [colors[i] for i in coreset_df.cluster]
-
-        points = coreset_df[["X", "Y"]].to_numpy()
-
-        self.coreset_df = coreset_df
-
-        self.voronoi = Voronoi(points)
-
-    def voronoi_finite_polygons_2d(self,
-                                   radius: Optional[float] = None
-                                  ) -> Tuple[List, np.ndarray]:
-        """
-        Creates the Voronoi regions and vertices for 2D data.
-
-        Args:
-            radius (Optional[None]): The radius from the data points to create the Voronoi regions. Defaults to None.
-
-        Returns:
-            Tuple: The regions and vertices.
-        """
-
-        if self.voronoi.points.shape[1] != 2:
-            raise ValueError("Requires 2D input")
-
-        new_regions = []
-        new_vertices = self.voronoi.vertices.tolist()
-
-        center = self.voronoi.points.mean(axis=0)
-        if radius is None:
-            radius = self.voronoi.points.ptp().max()
-
-        all_ridges = {}
-        for (p1, p2), (v1, v2) in zip(self.voronoi.ridge_points,
-                                      self.voronoi.ridge_vertices):
-            all_ridges.setdefault(p1, []).append((p2, v1, v2))
-            all_ridges.setdefault(p2, []).append((p1, v1, v2))
-
-        for p1, region in enumerate(self.voronoi.point_region):
-            vertices = self.voronoi.regions[region]
-
-            if all(v >= 0 for v in vertices):
-                new_regions.append(vertices)
-                continue
-
-            ridges = all_ridges[p1]
-            new_region = [v for v in vertices if v >= 0]
-
-            for p2, v1, v2 in ridges:
-                if v2 < 0:
-                    v1, v2 = v2, v1
-                if v1 >= 0:
-                    continue
-
-                t = self.voronoi.points[p2] - self.voronoi.points[p1]
-                t /= np.linalg.norm(t)
-                n = np.array([-t[1], t[0]])
-
-                midpoint = self.voronoi.points[[p1, p2]].mean(axis=0)
-                direction = np.sign(np.dot(midpoint - center, n)) * n
-
-                far_point = self.voronoi.vertices[v2] + direction * radius
-
-                new_region.append(len(new_vertices))
-                new_vertices.append(far_point.tolist())
-
-            vs = np.asarray([new_vertices[v] for v in new_region])
-            c = vs.mean(axis=0)
-
-            angles = np.arctan2(vs[:, 1] - c[1], vs[:, 0] - c[0])
-            new_region = np.array(new_region)[np.argsort(angles)]
-
-            new_regions.append(new_region.tolist())
-
-        return new_regions, np.asarray(new_vertices)
-
-    def plot_voironi(
-        self,
-        plot_title: Optional[str] = "Voronoi Tessalation",
-        show_annotation: bool = False,
-        show_scatters: bool = False,
-    ):
-        regions, vertices = self.voronoi_finite_polygons_2d()
-        fig, ax = plt.subplots(figsize=(8, 8))
-        fig.tight_layout(pad=10)
-
-        for j, region in enumerate(regions):
-            polygon = vertices[region]
-            color = self.coreset_df.color[j]
-            # Uncomment this line for debugging
-            # `breakpoint()`
-            plt.fill(*zip(*polygon), alpha=0.4, color=color, linewidth=0)
-            if show_annotation:
-                plt.annotate(
-                    self.coreset_df.Name[j],
-                    (self.coreset_df.X[j] + 0.2, self.coreset_df.Y[j]),
-                    fontsize=10,
-                )
-
-        if show_scatters:
-            plt.plot(self.coreset_df.X, self.coreset_df.Y, "ko")
-
-        plt.xlim(min(self.coreset_df.X) - 1, max(self.coreset_df.X) + 1)
-        plt.ylim(min(self.coreset_df.Y) - 1, max(self.coreset_df.Y) + 1)
-        plt.xlabel("x")
-        plt.ylabel("y")
-        plt.title(plot_title)
-        plt.show()
diff --git a/pr-2308/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py b/pr-2308/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py
deleted file mode 100644
index 4065d93755..0000000000
--- a/pr-2308/sphinx/applications/python/divisive_clustering_src/main_divisive_clustering.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import argparse
-import os
-import sys
-import time
-import warnings
-from typing import Callable
-
-import cudaq
-import networkx as nx
-import numpy as np
-import pandas as pd
-from cudaq import spin
-from mpi4py import MPI
-
-np.random.seed(10)
-
-sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
-
-from divisive_clustering import Coreset, DivisiveClustering
-
-warnings.filterwarnings("ignore")
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument(
-    "-t",
-    "--target",
-    type=str,
-    choices=["qpp-cpu", "nvidia", "nvidia-mgpu"],
-    help=
-    "Quantum simulator backend. Default is qpp-cpu. See https://nvidia.github.io/cuda-quantum for more options.",
-)
-argparser.add_argument(
-    "-d",
-    "--depth",
-    type=int,
-    default=1,
-    help="Depth of the QAOA circuit. Default is 1.",
-)
-argparser.add_argument("-i",
-                       "--max_iterations",
-                       type=int,
-                       default=75,
-                       help="Max iterations for the optimizer.")
-argparser.add_argument("-s",
-                       "--max_shots",
-                       type=int,
-                       default=100000,
-                       help="Max shots for the simulation.")
-argparser.add_argument("-m",
-                       "--M",
-                       type=int,
-                       default=10,
-                       help="Size of the coreset.")
-
-args = argparser.parse_args()
-
-target = args.target
-coreset_size = args.M
-circuit_depth = args.depth
-max_iterations = args.max_iterations
-max_shots = args.max_shots
-
-
-class DivisiveClusteringVQA(DivisiveClustering):
-
-    def __init__(
-        self,
-        circuit_depth: int,
-        max_iterations: int,
-        max_shots: int,
-        threshold_for_max_cut: float,
-        create_Hamiltonian: Callable,
-        optimizer: cudaq.optimizers.optimizer,
-        optimizer_function: Callable,
-        create_circuit: Callable,
-        normalize_vectors: bool = True,
-        sort_by_descending: bool = True,
-        coreset_to_graph_metric: str = "dist",
-    ) -> None:
-        self.circuit_depth = circuit_depth
-        self.max_iterations = max_iterations
-        self.max_shots = max_shots
-        self.threshold_for_maxcut = threshold_for_max_cut
-        self.normalize_vectors = normalize_vectors
-        self.sort_by_descending = sort_by_descending
-        self.coreset_to_graph_metric = coreset_to_graph_metric
-        self.create_Hamiltonian = create_Hamiltonian
-        self.create_circuit = create_circuit
-        self.optimizer = optimizer
-        self.optimizer_function = optimizer_function
-        self.time_consumed = 0
-
-    def run_divisive_clustering(
-        self,
-        coreset_vectors_df_for_iteration: pd.DataFrame,
-    ):
-        coreset_vectors_for_iteration_np, coreset_weights_for_iteration_np = (
-            self._get_iteration_coreset_vectors_and_weights(
-                coreset_vectors_df_for_iteration))
-
-        G = Coreset.coreset_to_graph(
-            coreset_vectors_for_iteration_np,
-            coreset_weights_for_iteration_np,
-            metric=self.coreset_to_graph_metric,
-        )
-
-        counts = self.get_counts_from_simulation(
-            G,
-            self.circuit_depth,
-            self.max_iterations,
-            self.max_shots,
-        )
-
-        return self._get_best_bitstring(counts, G)
-
-    def get_counts_from_simulation(self, G, circuit_depth, max_iterations,
-                                   max_shots):
-        qubits = len(G.nodes)
-        Hamiltonian = self.create_Hamiltonian(G)
-        optimizer, parameter_count, initial_params = self.optimizer_function(
-            self.optimizer,
-            max_iterations,
-            qubits=qubits,
-            circuit_depth=circuit_depth)
-
-        kernel = self.create_circuit(qubits, circuit_depth)
-
-        def objective_function(parameter_vector: list[float],
-                               hamiltonian=Hamiltonian,
-                               kernel=kernel) -> tuple[float, list[float]]:
-            get_result = lambda parameter_vector: cudaq.observe(
-                kernel, hamiltonian, parameter_vector, qubits, circuit_depth
-            ).expectation()
-
-            cost = get_result(parameter_vector)
-
-            return cost
-
-        t0 = time.process_time()
-
-        energy, optimal_parameters = optimizer.optimize(
-            dimensions=parameter_count, function=objective_function)
-
-        counts = cudaq.sample(
-            kernel,
-            optimal_parameters,
-            qubits,
-            circuit_depth,
-            shots_count=max_shots,
-        )
-
-        tf = time.process_time()
-        self.time_consumed += tf - t0
-
-        return counts
-
-
-def get_K2_Hamiltonian(G: nx.Graph) -> cudaq.SpinOperator:
-    H = 0
-
-    for i, j in G.edges():
-        weight = G[i][j]["weight"]
-        H += weight * (spin.z(i) * spin.z(j))
-
-    return H
-
-
-def get_QAOA_circuit(number_of_qubits, circuit_depth):
-
-    @cudaq.kernel
-    def kernel(thetas: list[float], number_of_qubits: int, circuit_depth: int):
-        qubits = cudaq.qvector(number_of_qubits)
-
-        layers = circuit_depth
-
-        for layer in range(layers):
-            for qubit in range(number_of_qubits):
-                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])
-                rz(2.0 * thetas[layer], qubits[(qubit + 1) % number_of_qubits])
-                cx(qubits[qubit], qubits[(qubit + 1) % number_of_qubits])
-
-            rx(2.0 * thetas[layer + layers], qubits)
-
-    return kernel
-
-
-def get_optimizer(optimizer: cudaq.optimizers.optimizer, max_iterations,
-                  **kwargs):
-    parameter_count = 4 * kwargs["circuit_depth"] * kwargs["qubits"]
-    initial_params = np.random.uniform(-np.pi / 8.0, np.pi / 8.0,
-                                       parameter_count)
-    optimizer.initial_parameters = initial_params
-
-    optimizer.max_iterations = max_iterations
-    return optimizer, parameter_count, initial_params
-
-
-def create_coreset_df(
-    raw_data_size: int = 1000,
-    number_of_sampling_for_centroids: int = 10,
-    coreset_size: int = 10,
-    number_of_coresets_to_evaluate: int = 4,
-    coreset_method: str = "BFL2",
-):
-    raw_data = Coreset.create_dataset(raw_data_size)
-    coreset = Coreset(
-        raw_data=raw_data,
-        number_of_sampling_for_centroids=number_of_sampling_for_centroids,
-        coreset_size=coreset_size,
-        number_of_coresets_to_evaluate=number_of_coresets_to_evaluate,
-        coreset_method=coreset_method,
-    )
-
-    coreset_vectors, coreset_weights = coreset.get_best_coresets()
-
-    coreset_df = pd.DataFrame({
-        "X": coreset_vectors[:, 0],
-        "Y": coreset_vectors[:, 1],
-        "weights": coreset_weights,
-    })
-    coreset_df["Name"] = [chr(i + 65) for i in coreset_df.index]
-
-    return coreset_df
-
-
-if __name__ == "__main__":
-    cudaq.set_target(target)
-
-    coreset_df = create_coreset_df(
-        raw_data_size=1000,
-        number_of_sampling_for_centroids=10,
-        coreset_size=coreset_size,
-        number_of_coresets_to_evaluate=4,
-        coreset_method="BFL2",
-    )
-
-    optimizer = cudaq.optimizers.COBYLA()
-
-    divisive_clustering = DivisiveClusteringVQA(
-        circuit_depth=circuit_depth,
-        max_iterations=max_iterations,
-        max_shots=max_shots,
-        threshold_for_max_cut=0.5,
-        create_Hamiltonian=get_K2_Hamiltonian,
-        optimizer=optimizer,
-        optimizer_function=get_optimizer,
-        create_circuit=get_QAOA_circuit,
-        normalize_vectors=True,
-        sort_by_descending=True,
-        coreset_to_graph_metric="dist",
-    )
-
-    t0 = time.process_time()
-
-    hierarchial_clustering_sequence = divisive_clustering.get_divisive_sequence(
-        coreset_df)
-    tf = time.process_time()
-
-    print(f"Total time for the execution: {tf - t0}")
-
-    print(f"Total time spent on CUDA-Q: {divisive_clustering.time_consumed}")
diff --git a/pr-2308/sphinx/applications/python/hadamard_test.ipynb b/pr-2308/sphinx/applications/python/hadamard_test.ipynb
deleted file mode 100644
index 1d1f781d59..0000000000
--- a/pr-2308/sphinx/applications/python/hadamard_test.ipynb
+++ /dev/null
@@ -1,472 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements\n",
-    "\n",
-    "The Hadamard test is a quantum algorithm for estimating expectation values and is a useful subroutine for a number of quantum applications ranging from estimation of molecular ground state energies to quantum semidefinite programming. This tutorial will briefly introduce the Hadamard test, demonstrate how it can be implemented in CUDA-Q, and then parallelized for a Quantum Krylov Subspace Diagonalization application.\n",
-    "\n",
-    "The Hadamard test is performed using a register with an ancilla qubit in the $\\ket{0}$ state and a prepared quantum state $\\ket{\\psi}$, the following circuit can be used to extract the expectation value from measurement of the ancilla.\n",
-    "\n",
-    "\n",
-    "![Htest](./images/htest.png)\n",
-    "\n",
-    "The key insight is that $$P(0) = \\frac{1}{2} \\left[ I + Re \\bra{\\psi} O \\ket{\\phi} \\right]$$ and $$P(1) = \\frac{1}{2} \\left[ I - Re \\bra{\\psi} O \\ket{\\phi} \\right]$$ so their difference is equal to $$P(0)-P(1) = Re \\bra{\\psi} O \\ket{\\phi}.$$\n",
-    "\n",
-    "\n",
-    "More details and a short derivation can be found [here](https://en.wikipedia.org/wiki/Hadamard_test)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if you want to perform the Hadamard test to compute an expectation value like $\\bra{\\psi} O \\ket{\\phi}$, where $\\ket{\\psi}$ and $\\ket{\\phi}$ are different states and $O$ is a Pauli Operator? This is a common subroutine for the QKSD, where matrix elements are determined by computing expectation values between different states.\n",
-    "\n",
-    "Defining $O$ as \n",
-    "$$O = X_1X_2,$$\n",
-    "\n",
-    "and given the fact that\n",
-    "$$\\ket{\\psi} = U_{\\psi}\\ket{0} \\qquad \\ket{\\phi} = U_{\\phi}\\ket{0},$$\n",
-    "\n",
-    "we can combine the state preparation steps into the operator resulting in\n",
-    "$$\\bra{\\psi}O\\ket{\\phi} = \\bra{0}U_\\psi^\\dagger O U_\\phi\\ket{0},$$\n",
-    "which corresponds to the following circuit.\n",
-    "![Htest2](./images/htestfactored.png)\n",
-    "\n",
-    "By preparing this circuit, and repeatedly measuring the ancilla qubit, we estimate the expectation value as $$P(0)-P(1) = Re \\bra{\\psi} O \\ket{\\phi}.$$\n",
-    "\n",
-    "\n",
-    "The following sections demonstrate how this can be performed in CUDA-Q."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical result as a reference: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before performing the Hadamard test, let's determine the exact expectation value by performing the matrix multiplications explicitly. The code below builds two CUDA-Q kernels corresponding to $\\ket{\\psi} = \\frac{1}{\\sqrt{2}}\\begin{pmatrix}1 \\\\ 0 \\\\ 1 \\\\ 0\\end{pmatrix}$ and $\\ket{\\phi} = \\begin{pmatrix}0 \\\\ 1 \\\\ 0 \\\\ 0\\end{pmatrix}.$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "from functools import reduce\n",
-    "\n",
-    "cudaq.set_target('nvidia')\n",
-    "\n",
-    "qubit_num = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def psi(num: int):\n",
-    "    q = cudaq.qvector(num)\n",
-    "    h(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def phi(n: int):\n",
-    "    q = cudaq.qvector(n)\n",
-    "    x(q[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The state vectors can be accessed using the `get_state` command and printed as numpy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Psi state:  SV: [(0.707107,0), (0,0), (0.707107,0), (0,0)]\n",
-      "\n",
-      "Phi state:  SV: [(0,0), (1,0), (0,0), (0,0)]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "psi_state = cudaq.get_state(psi, qubit_num)\n",
-    "print('Psi state: ', psi_state)\n",
-    "\n",
-    "phi_state = cudaq.get_state(phi, qubit_num)\n",
-    "print('Phi state: ', phi_state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian operator ($O$ in the derivation above) is defined as a CUDA-Q spin operator and converted to a matrix with `to_matrix`. The following line of code performs the explicit matrix multiplications to produce the exact expectation value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hamiltonian:  [[0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n",
-      " [0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n",
-      " [0.+0.j 1.+0.j 0.+0.j 0.+0.j]\n",
-      " [1.+0.j 0.+0.j 0.+0.j 0.+0.j]] \n",
-      "\n",
-      "Numerical expectation value:  (0.7071067690849304+0j)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ham = cudaq.spin.x(0) * cudaq.spin.x(1)\n",
-    "ham_matrix = ham.to_matrix()\n",
-    "print('hamiltonian: ', np.array(ham_matrix), '\\n')\n",
-    "\n",
-    "exp_val = reduce(np.dot, (np.array(psi_state).conj().T, ham_matrix, phi_state))\n",
-    "\n",
-    "print('Numerical expectation value: ', exp_val)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `Sample` to perform the Hadamard test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Three CUDA-Q kernels are constructed below corresponding to $\\ket{\\psi}$, $\\ket{\\phi}$, and the Hamiltonian. A fourth kernel constructs the Hadamard test circuit and completes with a measurement of the ancilla qubit in the computational basis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "cudaq.set_target('nvidia')\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_psi(q: cudaq.qview):\n",
-    "    h(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_phi(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def ham_cir(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "    x(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(n: int):\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    q = cudaq.qvector(n)\n",
-    "    h(ancilla)\n",
-    "    cudaq.control(U_phi, ancilla, q)\n",
-    "    cudaq.control(ham_cir, ancilla, q)\n",
-    "    cudaq.control(U_psi, ancilla, q)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    mz(ancilla)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CUDA-Q `sample` method computes 100000 sample ancilla measurements, and from them we can estimate the expectation value. The standard error is provided as well. Try increasing the sample size and note the convergence of the expectation value and the standard error towards the numerical result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 0:85281 1:14719 }\n",
-      "\n",
-      "Observable QC:  0.70562 + - 0.0015844563982640861\n",
-      "Numerical result 0.7071067690849304\n"
-     ]
-    }
-   ],
-   "source": [
-    "shots = 100000\n",
-    "qubit_num = 2\n",
-    "count = cudaq.sample(kernel, qubit_num, shots_count=shots)\n",
-    "print(count)\n",
-    "\n",
-    "mean_val = (count['0'] - count['1']) / shots\n",
-    "error = np.sqrt(2 * count['0'] * count['1'] / shots) / shots\n",
-    "print('Observable QC: ', mean_val, '+ -', error)\n",
-    "print('Numerical result', np.real(exp_val))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test\n",
-    "\n",
-    "This example is small, but a more practical application of the Hadamard test such as QKSD will require much larger circuits. The QKSD method works by reducing the exponential $2^N$ Hilbert space into an exponentially smaller subspace using a set of non-orthogonal states which are easy to prepare on a quantum computer. The Hadamard test is used to compute the matrix elements of this smaller subspace which is then diagonalized using a classical method to produce the eigenvalues. [This paper](https://www.osti.gov/servlets/purl/1962060) described the method in more detail and is the source of the figure below.\n",
-    "\n",
-    "![Htest3](./images/QKSD.png)\n",
-    "\n",
-    "This method can be easily parallelized, and multiple QPUs, if available, could compute the matrix elements asynchronously.  The CUDA-Q `mqpu` backend allows you to simulate a computation across multiple simulated QPUs. The code below demonstrates how.\n",
-    "\n",
-    "First, the Hadamard test circuit is defined, but this time the $\\ket{\\psi}$ and $\\ket{\\phi}$ states contain parameterized rotations so that multiple states can be quickly generated, for the sake of example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_psi(q: cudaq.qview, theta: float):\n",
-    "    ry(theta, q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_phi(q: cudaq.qview, theta: float):\n",
-    "    rx(theta, q[0])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def ham_cir(q: cudaq.qview):\n",
-    "    x(q[0])\n",
-    "    x(q[1])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(n: int, angle: float, theta: float):\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    q = cudaq.qvector(n)\n",
-    "    h(ancilla)\n",
-    "    cudaq.control(U_phi, ancilla, q, theta)\n",
-    "    cudaq.control(ham_cir, ancilla, q)\n",
-    "    cudaq.control(U_psi, ancilla, q, angle)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    mz(ancilla)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the `nvidia-mqpu` backend is specified and the number of GPUs available is determined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of QPUs: 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "cudaq.set_target(\"nvidia\", option=\"mqpu\")\n",
-    "\n",
-    "target = cudaq.get_target()\n",
-    "qpu_count = target.num_qpus()\n",
-    "print(\"Number of QPUs:\", qpu_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `sample_async` command is then used to distribute the Hadamard test computations across multiple simulated QPUs. The results are saved in a list and accessed below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shots = 100000\n",
-    "angle = [0.0, 1.5, 3.14, 0.7]\n",
-    "theta = [0.6, 1.2, 2.2, 3.0]\n",
-    "qubit_num = 2\n",
-    "\n",
-    "result = []\n",
-    "for i in range(4):\n",
-    "    count = cudaq.sample_async(kernel,\n",
-    "                               qubit_num,\n",
-    "                               angle[i],\n",
-    "                               theta[i],\n",
-    "                               shots_count=shots,\n",
-    "                               qpu_id=i % qpu_count)\n",
-    "    result.append(count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The four matrix elements are shown below and can be classically processed to produce the eigenvalues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "{ 0:49865 1:50135 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.0027 + - 0.0022360598270171573\n",
-      "1\n",
-      "{ 0:49796 1:50204 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.00408 + - 0.002236049366181346\n",
-      "2\n",
-      "{ 0:49695 1:50305 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.0061 + - 0.002236026375068058\n",
-      "3\n",
-      "{ 0:49972 1:50028 }\n",
-      "\n",
-      "QKSD Matrix Element:  -0.00056 + - 0.002236067626884303\n"
-     ]
-    }
-   ],
-   "source": [
-    "mean_val = np.zeros(len(angle))\n",
-    "i = 0\n",
-    "for count in result:\n",
-    "    print(i)\n",
-    "    i_result = count.get()\n",
-    "    print(i_result)\n",
-    "    mean_val[i] = (i_result['0'] - i_result['1']) / shots\n",
-    "    error = np.sqrt(2 * i_result['0'] * i_result['1'] / shots) / shots\n",
-    "    print('QKSD Matrix Element: ', mean_val[i], '+ -', error)\n",
-    "    i += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classically Diagonalize the Subspace Matrix\n",
-    "\n",
-    "For a problem of this size, numpy can be used to diagonalize the subspace and produce the eigenvalues and eigenvectors in the basis of non-orthogonal states. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[-0.0027  -0.00408]\n",
-      " [-0.0061  -0.00056]]\n",
-      "Eigenvalues: \n",
-      "[-0.00782313  0.00456313]\n",
-      "Eigenvector: \n",
-      "[[-0.76575845  0.64312829]\n",
-      " [-0.64312829 -0.76575845]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "my_mat = np.zeros((2, 2), dtype=float)\n",
-    "m = 0\n",
-    "for k in range(2):\n",
-    "    for j in range(2):\n",
-    "        my_mat[k, j] = mean_val[m]\n",
-    "        m += 1\n",
-    "\n",
-    "print(my_mat)\n",
-    "\n",
-    "E, V = np.linalg.eigh(my_mat)\n",
-    "\n",
-    "print('Eigenvalues: ')\n",
-    "print(E)\n",
-    "\n",
-    "print('Eigenvector: ')\n",
-    "print(V)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/sphinx/applications/python/hybrid_qnns.ipynb b/pr-2308/sphinx/applications/python/hybrid_qnns.ipynb
deleted file mode 100644
index cc3f054e61..0000000000
--- a/pr-2308/sphinx/applications/python/hybrid_qnns.ipynb
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hybrid Quantum Neural Networks\n",
-    "\n",
-    "The example below highlights a hybrid quantum neural network workflow with CUDA-Q and PyTorch where both layers can GPU accelerated to maximise performance."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"images/hybrid.png\" alt=\"hybrid\" width=\"600\">\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We perform binary classification on the MNIST dataset where data flows through the neural network architecture to the quantum circuit whose output is used to classify hand written digits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "\n",
-    "!pip install matplotlib==3.8.4 torch==2.0.1+cu118 torchvision==0.15.2+cu118 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu118"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the relevant libraries\n",
-    "\n",
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch\n",
-    "from torch.autograd import Function\n",
-    "from torchvision import datasets, transforms\n",
-    "import torch.optim as optim\n",
-    "import torch.nn as nn\n",
-    "import torchvision\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "torch.manual_seed(22)\n",
-    "cudaq.set_random_seed(44)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set CUDAQ and PyTorch to run on either CPU or GPU.\n",
-    "\n",
-    "device = torch.device('cpu')\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "#cudaq.set_target(\"nvidia\")\n",
-    "#device = torch.device(\"cuda:0\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def prepare_data(target_digits, sample_count, test_size):\n",
-    "    \"\"\"Load and prepare the MNIST dataset to be used \n",
-    "    \n",
-    "    Args: \n",
-    "        target_digits (list): digits to perform classification of \n",
-    "        sample_count (int): total number of images to be used\n",
-    "        test_size (float): percentage of sample_count to be used as test set, the remainder is the training set\n",
-    "        \n",
-    "    Returns: \n",
-    "        dataset in train, test format with targets \n",
-    "    \n",
-    "    \"\"\"\n",
-    "\n",
-    "    transform = transforms.Compose(\n",
-    "        [transforms.ToTensor(),\n",
-    "         transforms.Normalize((0.1307), (0.3081))])\n",
-    "\n",
-    "    dataset = datasets.MNIST(\"./data\",\n",
-    "                             train=True,\n",
-    "                             download=True,\n",
-    "                             transform=transform)\n",
-    "\n",
-    "    # Filter out the required labels.\n",
-    "    idx = (dataset.targets == target_digits[0]) | (dataset.targets\n",
-    "                                                   == target_digits[1])\n",
-    "    dataset.data = dataset.data[idx]\n",
-    "    dataset.targets = dataset.targets[idx]\n",
-    "\n",
-    "    # Select a subset based on number of datapoints specified by sample_count.\n",
-    "    subset_indices = torch.randperm(dataset.data.size(0))[:sample_count]\n",
-    "\n",
-    "    x = dataset.data[subset_indices].float().unsqueeze(1).to(device)\n",
-    "\n",
-    "    y = dataset.targets[subset_indices].to(device).float().to(device)\n",
-    "\n",
-    "    # Relabel the targets as a 0 or a 1.\n",
-    "    y = torch.where(y == min(target_digits), 0.0, 1.0)\n",
-    "\n",
-    "    x_train, x_test, y_train, y_test = train_test_split(x,\n",
-    "                                                        y,\n",
-    "                                                        test_size=test_size /\n",
-    "                                                        100,\n",
-    "                                                        shuffle=True,\n",
-    "                                                        random_state=42)\n",
-    "\n",
-    "    return x_train, x_test, y_train, y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Classical parameters.\n",
-    "\n",
-    "sample_count = 1000  # Total number of images to use.\n",
-    "target_digits = [5, 6]  # Hand written digits to classify.\n",
-    "test_size = 30  # Percentage of dataset to be used for testing.\n",
-    "classification_threshold = 0.5  # Classification boundary used to measure accuracy.\n",
-    "epochs = 1000  # Number of epochs to train for.\n",
-    "\n",
-    "# Quantum parmeters.\n",
-    "\n",
-    "qubit_count = 1\n",
-    "hamiltonian = spin.z(0)  # Measurement operator.\n",
-    "shift = torch.tensor(torch.pi / 2)  # Magnitude of parameter shift."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train, x_test, y_train, y_test = prepare_data(target_digits, sample_count,\n",
-    "                                                test_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Xrl3bcqFChSx/9dVXlocNGxZ4DZ3h7vMmNwCA+EaZAAAAJIvOAAAAnovLMoFuHpGUlGS5WLFiYa/fvXu35blz51p+6aWXLJ86dSrN2gcAQGZBmQAAACSLzgAAAJ6LyzIBAACIDmUCAACQLDoDAAB4js4AAACeozMAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5+gMAADgOToDAAB4js4AAACeozMAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5+gMAADguWszugHwQ548eQKPFy1aZLlWrVqW77//fssbNmyIebsA+GHFihWW69SpY/nEiROB6+rWrWt506ZNsW9YJsHIAAAAnqMzAACA5ygTIF306NEj8LhmzZqWL126lN7NwRVUrlzZ8vLlyy0fO3YscF2jRo0s79q1K+bt8tlNN91kedWqVZZ37NhhuVWrVunapnigJUj9zLl8+bJl/R13zrlt27bFvF2ZESMDAAB4js4AAACe865McM01/9f/efLJJy0PGjQocF3hwoWTfa5Ro0ZZ7tatm+WJEydaPn36dOCe4cOHR/xeomnbtq3lgQMHRrxu6tSplrdu3RrTNiE8fX969uxp+YYbbgibnXNuzpw5lsuXLx+7xsFNmTLFcpkyZSyvXLkyI5oTNxo3bmw5a9aslg8dOmR58ODBgXvOnTsX+4ZlQowMAADgOToDAAB4zosyQalSpSy/9NJLlh9//HHLSUlJgXvGjh2b7PNqOaBIkSKWv/vuu4j3PP3005ZHjhyZ7GvEGy3DNG/e3HL27NkD1+nM9C5dusS8XfiJbv5UvXp1y/oe5M2b17LOug4VWjZA2unQoUPgsQ53q7lz56ZDa+LXXXfdFfbrBQsWtFyxYsXA97Zv3x7TNmVWjAwAAOA5OgMAAHjOizLB6NGjLRctWtRyr169LO/evTtwz8KFC1P0Gq1bt05d4xJMmzZtLDdr1izidR9++GE6tAahXnvtNctdu3ZN0b1nz54NPH7rrbfSpE34pdDPkyxZslieMGGC5XXr1qVbm+KRlgPUqVOnLB88eDC9mpOpMTIAAIDn6AwAAOC5hC0T3HrrrZbLli1r+bnnnrO8YMGC9GySF5o0aRLVdYsXL45xS/CzChUqWI72/QkndHOWESNGpPq58Ev9+vWzXL9+/cD39u3bZ3nmzJmWL168GPuGJaA9e/ZYDj2bwFeMDAAA4Dk6AwAAeC7uywQFChSwnDt3bst6VoCWDJ599lnLlAnSXtWqVcN+/V//+lfg8dKlS9OjOV7SI4idCw6D6oZCkejGUbqZ1v79+9OgdVAPPvig5eeff97y5s2bA9c1bNjQ8uHDh2PfsAQ3a9asjG5CpsPIAAAAnqMzAACA5+gMAADguUw7Z0DPni5RokTge4888ojlzp07W9Zap561/uc//9ny0aNH07SdCNaVr7/++rDXfPPNN4HHZ86ciWmbfKaHYTkXPFDoSgcP/axHjx6W2Z0t7ennlB6cpu9TgwYNAvccOXIk5u3yyY4dOzK6CZkOIwMAAHiOzgAAAJ7LVGUCLQ3cfffdlvVwFeecq1WrlmVdNnU1u6sh9e677z7Lv/rVr8JeE3rIDdKWLplt37594HvRlAbGjh1refz48Zbz589vOXRXvD/+8Y9hn6t3796WN23aZPncuXPJtsMH1apVs6xLCz/55BPLlAWQ3hgZAADAc3QGAADwXIaXCXRmre7ANXDgQMvffvtt4J5BgwZZDi0hIHOaNGlSRjch4egqm+7du0d1j5ZrRo0aZfmdd96xXLRoUcsTJkywrLvgXcnatWstDx06NGz2rWyUK1cuy/q3oD+H/v37p2ubAMXIAAAAnqMzAACA5zK8THDddddZfuWVV8Jeky9fvsDjd99913KfPn0sr1mzxvKuXbssMzM3tlq1ahWT59VDj0aPHm15586dlrt06RK458KFCzFpS2Y0bNgwy6VKlYrqHj0AR8ttv/3tby3Pnz/fcjQrEa6kX79+lnW1kG4E5gPdyKl06dKWBw8ebHnLli0R77///vstt2zZ0vLJkycta9nnxx9/TH1j49wdd9xh+ZZbbrGcJUuWjGhO3GBkAAAAz9EZAADAcxleJnjxxReTvUY3PnHOuVWrVlm+/fbbw96zbNkyyzNnzrTcoUOHqNo1efJkyxs2bLCcI0cOy0lJSVE9V6K76aab0uy59Oe7YMECywUKFLBcpUqVsNc751zr1q3TrC2ZkZZOdLhY6Qod55y7dOmS5ddff92y7os/YMCAsPfrvdGKdL9usOMbfa/Onz9v+dChQ5ZHjhxp+YEHHgjcrxsVKR36rlOnjmU9v+XEiROpaHH8uuuuuyxrmWDjxo2WP/roozR7vY4dO1rWz6x4OweHkQEAADxHZwAAAM9leJlgyJAhlnWzEl0lEGrJkiWW9+zZY7lcuXKWq1evblmHQ0OP0v2ZDkM751zPnj0t69GiWrL44osvLLdo0cIyqxdST8szoe9JOJGGTxNV+fLlLUea6R86tD9t2jTLWg645557wj6X3q9fP3z4cOB5P/3007Dt0tncev/VrkyIN7oCplKlSpZ1aF/PhNDVFnpOgXPOderUKexraOnl97//vWU9L2TFihUpaXbC0nNTtHyg/0OuRM/i6Nu3r+Wbb77Zctu2bS0/9thjgfu///776BubARgZAADAc3QGAADwHJ0BAAA8l+FzBrQ+qbWt1NS5dFmHKly4sOX9+/eHvUbrSc4Fz15v2rSp5bfeestyjRo1LJctW9ay7oTog9mzZ1uuVatW2Gt0uY9zzq1bty7sdSVLlgz7dd118t5777UcurRUl1dpTTtRtGvXLsX36Hty2223pejeTZs2Wdbd8pxzbuXKlZanTp1qWecM+KxevXqWQ5d7/kyXuOl8meXLlweu088jpUsTdc6AziVgzsBPihcvblnncITOGdD5L3qPzrcJXe7+M/1be++99wLfe+KJJyzr+5ZZMDIAAIDn6AwAAOC5DC8TpIdIpQF1pd2idLhHz3ofPny45blz51rWg0iOHz8edTvj1WeffWb5hx9+sJwnTx7LOnzvXPBMd/2ZatYS0gcffGBZSzJ60JVzzuXNmzclTY8LuutgNAcS6dJb5365m11yZs2aZblbt26WQ5cs9u/f33LoMqpwfPhbuPPOOy3r54B67rnnLE+YMMHy2bNnU/x6tWvXDvt1n5c36+eRHmpWpkwZy71797bcqFGjwP16aJfurqrlhM8//zzsaxcsWNBy6GeeLn3PjCVMRgYAAPAcnQEAADznRZkgLR08eDDs13W3PN1JzAe7du2yHKlMELpToK4u0J+pnsN+4MABy19++aXlYsWKXV2D40yRIkUs58uXL9nrCxUqFHicLVu2FL1e+/btLVeoUMFy6K6g0ZQGXn75Zcu6EidRNW/e3LKWsPSwoA8//NByakoDuXLlCvt6Z86csbx48eIUP2+i0JLvyZMnw16jpTfNzjl38eJFy/r7qwfeffXVV2GfV98PLR0759yTTz5pmTIBAADIdOgMAADgOcoESFPbt2+3rIeB6MZPzjn397//3bJuzvLQQw9Z1pKDbmSjKwZChwEjHUQVz/RgG82R6KZMzv1yFUByIh1aFK0tW7ZYHjNmjGUfVhPkzJkz7Nd1w6ZoD8aJRFcx6aZbgwYNsqxlNZ+lZnOfiRMnWtZD7qKhqwlC6QZGmREjAwAAeI7OAAAAnqNMkEI6uxq/1KxZM8urV6+2XKVKlcB1Wjb4z3/+Y/n999+3rEOgej670jMLnIu8GUg802F7zZGEDu1Hc0+k+6O9V3/udevWtexDaSAaV3teyVNPPWW5e/fulrUsNn78+Kt6jUTUpUsXy8uWLbMcelaK0s2BQsub4bzwwguW27ZtG/G60NUFmQ0jAwAAeI7OAAAAnqNMEIHO/OzVq5flrl27hr1+2rRplo8dOxarZmV6uomK7muvM6Cdc65hw4aW9XhXPeYzEj1romfPnqlpZlzRDUr03IGHH344Xduhqzucc65v376WdZMbSgO/pOdpbNy40bJuzKXHd+tnjnPOtW7d2rKuRtC/o8OHD6dNYxOIblym50Do55Fu4uRc8Gh6/Vnr51SkVTYXLlywrKtqnAse9Z4ZMTIAAIDn6AwAAOC5LJejnC4czWYn6aV+/fqW+/XrZ3ns2LGWFyxYYFlnuEfa175jx46Bx3pd7ty5k22T3j9jxoxkr/eNDoc651zTpk0tN2nSxHKrVq0snz592vKUKVMs6/HHusmRDypXrmxZf8f1PILQv9Vo/sS1tKUbOQ0ZMsTy119/Hbhn1apVyT6vb3TDJy2d6EZZ//jHPyzr+5k/f37L58+fDzzvvHnzLGt5Zu/evVfZYj/pzzq0JKOrA1SkMoGWHDZv3mx5/vz5V93OtBLNZwAjAwAAeI7OAAAAnqMzAACA5+JyzkD27Nktt2zZ0rKeS61nsufIkcNy1qxZr+q1dbe8Ro0aWT548KDl1BzuAqSUzsPQs9Jr1qwZuE7nZ0TSu3dvy3q4EFLv17/+tWWd25IvXz7LOg9jzpw5lseNGxd4rn379sWiifAEcwYAAECy6AwAAOC5uCwTRKJtvPba/9tcUXfvWr9+vWUdTi1atGjguSZPnmxZSwD649LdpgAAyIwoEwAAgGTRGQAAwHMJVSYAAABBlAkAAECy6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5+gMAADgOToDAAB4js4AAACeozMAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA566N9sLLly/Hsh0AACCDMDIAAIDn6AwAAOA5OgMAAHiOzgAAAJ6jMwAAgOfoDAAA4Dk6AwAAeI7OAAAAnqMzAACA5/4f5kKbg4Vh3lIAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot some images from the training set to visualise.\n",
-    "if device != 'cpu':\n",
-    "    sample_to_plot = x_train[:10].to(torch.device('cpu'))\n",
-    "else:\n",
-    "    sample_to_plot = x_train[:10]\n",
-    "\n",
-    "grid_img = torchvision.utils.make_grid(sample_to_plot,\n",
-    "                                       nrow=5,\n",
-    "                                       padding=3,\n",
-    "                                       normalize=True)\n",
-    "plt.imshow(grid_img.permute(1, 2, 0))\n",
-    "plt.axis('off')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class QuantumFunction(Function):\n",
-    "    \"\"\"Allows the quantum circuit to input data, output expectation values \n",
-    "    and calculate gradients of variational parameters via finite difference\"\"\"\n",
-    "\n",
-    "    def __init__(self, qubit_count: int, hamiltonian: cudaq.SpinOperator):\n",
-    "        \"\"\"Define the quantum circuit in CUDA Quantum\"\"\"\n",
-    "\n",
-    "        @cudaq.kernel\n",
-    "        def kernel(qubit_count: int, thetas: np.ndarray):\n",
-    "\n",
-    "            qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "            ry(thetas[0], qubits[0])\n",
-    "            rx(thetas[1], qubits[0])\n",
-    "\n",
-    "        self.kernel = kernel\n",
-    "        self.qubit_count = qubit_count\n",
-    "        self.hamiltonian = hamiltonian\n",
-    "\n",
-    "    def run(self, theta_vals: torch.tensor) -> torch.tensor:\n",
-    "        \"\"\"Excetute the quantum circuit to output an expectation value\"\"\"\n",
-    "\n",
-    "        #If running on GPU, thetas is a torch.tensor that will live on GPU memory. The observe function calls a .tolist() method on inputs which moves thetas from GPU to CPU.\n",
-    "\n",
-    "        qubit_count = [self.qubit_count for _ in range(theta_vals.shape[0])]\n",
-    "\n",
-    "        results = cudaq.observe(self.kernel, self.hamiltonian, qubit_count,\n",
-    "                                theta_vals)\n",
-    "\n",
-    "        exp_vals = [results[i].expectation() for i in range(len(results))]\n",
-    "        exp_vals = torch.Tensor(exp_vals).to(device)\n",
-    "\n",
-    "        return exp_vals\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def forward(ctx, thetas: torch.tensor, quantum_circuit,\n",
-    "                shift) -> torch.tensor:\n",
-    "\n",
-    "        # Save shift and quantum_circuit in context to use in backward.\n",
-    "        ctx.shift = shift\n",
-    "        ctx.quantum_circuit = quantum_circuit\n",
-    "\n",
-    "        # Calculate expectation value.\n",
-    "        exp_vals = ctx.quantum_circuit.run(thetas).reshape(-1, 1)\n",
-    "\n",
-    "        ctx.save_for_backward(thetas, exp_vals)\n",
-    "\n",
-    "        return exp_vals\n",
-    "\n",
-    "    @staticmethod\n",
-    "    def backward(ctx, grad_output):\n",
-    "        \"\"\"Backward pass computation via finite difference\"\"\"\n",
-    "\n",
-    "        thetas, _ = ctx.saved_tensors\n",
-    "\n",
-    "        gradients = torch.zeros(thetas.shape, device=device)\n",
-    "\n",
-    "        for i in range(thetas.shape[1]):\n",
-    "\n",
-    "            thetas_plus = thetas.clone()\n",
-    "            thetas_plus[:, i] += ctx.shift\n",
-    "            exp_vals_plus = ctx.quantum_circuit.run(thetas_plus)\n",
-    "\n",
-    "            thetas_minus = thetas.clone()\n",
-    "            thetas_minus[:, i] -= ctx.shift\n",
-    "            exp_vals_minus = ctx.quantum_circuit.run(thetas_minus)\n",
-    "\n",
-    "            gradients[:, i] = (exp_vals_plus - exp_vals_minus) / (2 * ctx.shift)\n",
-    "\n",
-    "        gradients = torch.mul(grad_output, gradients)\n",
-    "\n",
-    "        return gradients, None, None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class QuantumLayer(nn.Module):\n",
-    "    \"\"\"Encapsulates a quantum circuit into a quantum layer adhering PyTorch convention\"\"\"\n",
-    "\n",
-    "    def __init__(self, qubit_count: int, hamiltonian, shift: torch.tensor):\n",
-    "        super(QuantumLayer, self).__init__()\n",
-    "\n",
-    "        self.quantum_circuit = QuantumFunction(qubit_count, hamiltonian)\n",
-    "        self.shift = shift\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "\n",
-    "        result = QuantumFunction.apply(input, self.quantum_circuit, self.shift)\n",
-    "\n",
-    "        return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Hybrid_QNN(nn.Module):\n",
-    "    \"\"\"Structure of the hybrid neural network with classical fully connected layers and quantum layers\"\"\"\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        super(Hybrid_QNN, self).__init__()\n",
-    "\n",
-    "        self.fc1 = nn.Linear(28 * 28, 256)\n",
-    "        self.fc2 = nn.Linear(256, 128)\n",
-    "        self.dropout = nn.Dropout(0.25)\n",
-    "\n",
-    "        self.fc3 = nn.Linear(128, 64)\n",
-    "        self.fc4 = nn.Linear(64, 32)\n",
-    "        self.fc5 = nn.Linear(32, 2)\n",
-    "        self.dropout = nn.Dropout(0.25)\n",
-    "\n",
-    "        # The 2 outputs from PyTorch fc5 layer feed into the 2 variational gates in the quantum circuit.\n",
-    "        self.quantum = QuantumLayer(qubit_count, hamiltonian, shift)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "\n",
-    "        x = x.view(-1, 28 * 28)  # Turns images into vectors.\n",
-    "\n",
-    "        x = torch.relu(self.fc1(x))\n",
-    "        x = torch.relu(self.fc2(x))\n",
-    "        x = self.dropout(x)\n",
-    "\n",
-    "        x = torch.relu(self.fc3(x))\n",
-    "        x = torch.relu(self.fc4(x))\n",
-    "        x = torch.relu(self.fc5(x))\n",
-    "        x = self.dropout(x)\n",
-    "\n",
-    "        # Quantum circuit outputs an expectation value which is fed into the sigmoid activation function to perform classification.\n",
-    "        x = torch.sigmoid(self.quantum(x))\n",
-    "\n",
-    "        return x.view(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def accuracy_score(y, y_hat):\n",
-    "    return sum((y == (y_hat >= classification_threshold))) / len(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hybrid_model = Hybrid_QNN().to(device)\n",
-    "\n",
-    "optimizer = optim.Adadelta(hybrid_model.parameters(),\n",
-    "                           lr=0.001,\n",
-    "                           weight_decay=0.8)\n",
-    "\n",
-    "loss_function = nn.BCELoss().to(device)\n",
-    "\n",
-    "training_cost = []\n",
-    "testing_cost = []\n",
-    "training_accuracy = []\n",
-    "testing_accuracy = []\n",
-    "\n",
-    "hybrid_model.train()\n",
-    "for epoch in range(epochs):\n",
-    "\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "    y_hat_train = hybrid_model(x_train).to(device)\n",
-    "\n",
-    "    train_cost = loss_function(y_hat_train, y_train).to(device)\n",
-    "\n",
-    "    train_cost.backward()\n",
-    "\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    training_accuracy.append(accuracy_score(y_train, y_hat_train))\n",
-    "    training_cost.append(train_cost.item())\n",
-    "\n",
-    "    hybrid_model.eval()\n",
-    "    with torch.no_grad():\n",
-    "\n",
-    "        y_hat_test = hybrid_model(x_test).to(device)\n",
-    "\n",
-    "        test_cost = loss_function(y_hat_test, y_test).to(device)\n",
-    "\n",
-    "        testing_accuracy.append(accuracy_score(y_test, y_hat_test))\n",
-    "        testing_cost.append(test_cost.item())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(training_cost, label='Train')\n",
-    "plt.plot(testing_cost, label='Test')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Cost')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(training_accuracy, label='Train')\n",
-    "plt.plot(testing_accuracy, label='Test')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "16e3036d-b1be-43b8-8838-95dfc14cad60",
-   "metadata": {},
-   "source": [
-    "# Multi-reference Quantum Krylov Algorithm - $H_2$ Molecule\n",
-    "\n",
-    "The multireference selected quantum Krylov (MRSQK) algorithm is defined in [this paper](https://arxiv.org/pdf/1911.05163) and was developed as a low-cost alternative to quantum phase estimation. This tutorial will demonstrate how this algorithm can be implemented in CUDA-Q and accelerated using multiple GPUs. The [CUDA-Q Hadamard test tutorial](https://nvidia.github.io/cuda-quantum/latest/applications/python/hadamard_test.html) might provide helpful background information for understanding this tutorial.\n",
-    "\n",
-    "The algorithm works by preparing an initial state, and then defining this state in a smaller subspace constructed with a basis that corresponds to Trotter steps of the initial state. This subspace can be diagonalized to produce an approximate energy for the system without variational optimization of any parameters.\n",
-    "\n",
-    "In the example below, the initial guess is the ground state of the diagonalized Hamiltonian for demonstration purposes.  In practice one could use a number of heuristics to prepare the ground state such as Hartree Fock or CISD. A very promising ground state preparation method which can leverage quantum computers is the linear combination of unitaries (LCU). LCU would allow for the state preparation to occur completely on the quantum computer and avoid storing an inputting the exponentially large state vector.\n",
-    "\n",
-    "\n",
-    "Regardless of the method used for state preparation, the procedure begins by selecting a $d$-dimensional basis of reference states ${\\Phi_0 \\cdots \\Phi_d},$ where each is a linear combination of Slater determinants: \n",
-    "\n",
-    "$$ \\ket{\\Phi_I}  =  \\sum_{\\mu} d_{\\mu I}\\ket{\\phi_{\\mu}}. $$\n",
-    "\n",
-    "\n",
-    "From this, a non-orthogonal Krylov Space $\\mathcal{K} = \\{\\psi_{0} \\cdots \\psi_{N}\\}$ is constructed by applying a family of $s$ unitary operators on each of the $d$ reference states resulting in $d*s = N$ elements in the Krylov space where \n",
-    "$$ \\ket{\\psi_{\\alpha}} \\equiv \\ket{\\psi_I^{(n)}} = \\hat{U}_n\\ket{\\Phi_I}, $$\n",
-    "\n",
-    "Therefore, the general quantum state that we originally set out to describe is\n",
-    "\n",
-    "$$ \\ket{\\Psi} = \\sum_{\\alpha} c_{\\alpha}\\ket{\\psi_{\\alpha}} = \\sum_{I=0}^d \\sum_{n=0}^s c_I^{(n)}\\hat{U}_n\\ket{\\Phi_I}.  $$\n",
-    "\n",
-    "The energy of this state can be obtained by solving the generalized eigenvalue problem\n",
-    "$$ \\boldsymbol{Hc}=\\boldsymbol{Sc}E, $$\n",
-    "\n",
-    "where the elements of the overlap are\n",
-    "\n",
-    "$$S_{\\alpha \\beta} = \\braket{\\psi_{\\alpha}|\\psi_{\\beta}} =   \\braket{\\Phi_I|\\hat{U}_m^{\\dagger}\\hat{U}_n|\\Phi_J}$$ \n",
-    "\n",
-    "and Hamiltonian matrix is\n",
-    "\n",
-    "$$H_{\\alpha \\beta} = \\braket{\\psi_{\\alpha}|\\hat{H}|\\psi_{\\beta}} =   \\braket{\\Phi_I|\\hat{U}_m^{\\dagger}\\hat{H}\\hat{U}_n|\\Phi_J}.$$\n",
-    "\n",
-    "The matrix elements for $S$ are computed with the Hadamard test with a circuit shown below for the case of the overlap matrix elements. \n",
-    "\n",
-    "![Htest](./images/krylovcircuit.png)\n",
-    "\n",
-    "The $2\\sigma_+$ term refers to measurement of the expectation value of this circuit with the $X+iY$ operator.\n",
-    "\n",
-    "The Hamiltonian matrix elements are computed with a circuit that includes controlled application of the Hamiltonian. Once the $H$ and $S$ matrices are constructed, the diagonalization is performed classically to produce an estimate for the ground state in question.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b90de056-4dc5-473f-bf50-29c7d8b2ad05",
-   "metadata": {},
-   "source": [
-    "### Setup\n",
-    "\n",
-    "This cell installs the necessary packages. This application can be parallelized, so please uncomment the `mgpu` specification below if you would like to run on more than one GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba61665c-dc3b-4e43-b1cf-340855ea68fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "import scipy\n",
-    "\n",
-    "!pip install openfermion==1.6.1 pyscf==2.6.2 openfermionpyscf==0.5 -q\n",
-    "\n",
-    "# Single-node, single gpu\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "multi_gpu = False\n",
-    "\n",
-    "# Single-node, multi-GPU\n",
-    "#cudaq.set_target(\"nvidia\", option='mqpu,fp64')\n",
-    "#multi_gpu = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ba6e94e-a3e6-48aa-8392-e598429b0be7",
-   "metadata": {},
-   "source": [
-    "The molecule is defined below and its Hamiltonian is extracted as a matrix. The matrix is diagonalized to produce the ground state. The corresponding state vector will be used as the initial state to ensure good results for this demonstration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fb4192c-054a-4bff-b86f-a8d58cf8bac6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define H2 molecule\n",
-    "geometry = [('H', (0.0, 0.0, 0.0)), ('H', (0.0, 0.0, 0.7474))]\n",
-    "\n",
-    "hamiltonian, data = cudaq.chemistry.create_molecular_hamiltonian(\n",
-    "    geometry, 'sto-3g', 1, 0)\n",
-    "\n",
-    "electron_count = data.n_electrons\n",
-    "qubits_num = 2 * data.n_orbitals\n",
-    "\n",
-    "# Diagonalize Hamiltonian\n",
-    "spin_ham_matrix = hamiltonian.to_matrix()\n",
-    "e, c = np.linalg.eig(spin_ham_matrix)\n",
-    "\n",
-    "# Find the ground state energy and the corresponding eigenvector\n",
-    "print('Ground state energy (classical simulation)= ', np.min(e), ', index= ',\n",
-    "      np.argmin(e))\n",
-    "min_indices = np.argmin(e)\n",
-    "\n",
-    "# Eigenvector can be used to initialize the qubits\n",
-    "vec = c[:, min_indices]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "562f795e-cf4c-4e43-a0d9-3a3ae346277d",
-   "metadata": {},
-   "source": [
-    "The functions below take the original Hamiltonian defined above, and strip it into a list of Pauli words and a list of coefficients which will be uses later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "014e72eb-ee66-48f5-bb58-73210e621fad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Collect coefficients from a spin operator so they can pass to a kernel\n",
-    "def termCoefficients(ham: cudaq.SpinOperator) -> list[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Collect Pauli words from a spin operator so they can pass to a kernel\n",
-    "def termWords(ham: cudaq.SpinOperator) -> list[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Build the lists of coefficients and Pauli Words from the H2 Hamiltonian\n",
-    "coefficient = termCoefficients(hamiltonian)\n",
-    "pauli_string = termWords(hamiltonian)\n",
-    "\n",
-    "print(coefficient)\n",
-    "print(pauli_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74b4c079-8837-47ae-a484-52ec5f4a160e",
-   "metadata": {},
-   "source": [
-    "In this example, the unitary operators that build the Krylov subspace are first-order Trotter operations at different time steps. The performance here could potentially be improved by increasing the size of the time step, using a higher order Trotter approximation, or using other sorts of approximations. The CUDA-Q kernels below define the unitary operations that construct the $\\psi$ basis.  Each receives the target qubits, the time step, and components of the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c98db2b7-eaaf-4e3a-83bb-4f7ad0730471",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Applies Unitary operation corresponding to Um\n",
-    "@cudaq.kernel\n",
-    "def U_m(qubits: cudaq.qview, dt: float, coefficients: list[complex],\n",
-    "        words: list[cudaq.pauli_word]):\n",
-    "    # Compute U_m = exp(-i m dt H)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(dt * coefficients[i].real, qubits, words[i])\n",
-    "\n",
-    "\n",
-    "# Applies Unitary operation corresponding to Un\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def U_n(qubits: cudaq.qview, dt: float, coefficients: list[complex],\n",
-    "        words: list[cudaq.pauli_word]):\n",
-    "    # Compute U_n = exp(-i n dt H)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(dt * coefficients[i].real, qubits, words[i])\n",
-    "\n",
-    "\n",
-    "# Applies the gate operations for a Hamiltonian Pauli Word\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def apply_pauli(qubits: cudaq.qview, word: list[int]):\n",
-    "    # Add H (Hamiltonian operator)\n",
-    "    for i in range(len(word)):\n",
-    "        if word[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "        if word[i] == 2:\n",
-    "            y(qubits[i])\n",
-    "        if word[i] == 3:\n",
-    "            z(qubits[i])\n",
-    "\n",
-    "\n",
-    "# Performs Hadamard test circuit which determines matrix elements of S and H of subspace\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qfd_kernel(dt_alpha: float, dt_beta: float, coefficients: list[complex],\n",
-    "               words: list[cudaq.pauli_word], word_list: list[int],\n",
-    "               vec: list[complex]):\n",
-    "\n",
-    "    ancilla = cudaq.qubit()\n",
-    "    qreg = cudaq.qvector(vec)\n",
-    "\n",
-    "    h(ancilla)\n",
-    "\n",
-    "    x(ancilla)\n",
-    "    cudaq.control(U_m, ancilla, qreg, dt_alpha, coefficients, words)\n",
-    "    x(ancilla)\n",
-    "\n",
-    "    cudaq.control(apply_pauli, ancilla, qreg, word_list)\n",
-    "    cudaq.control(U_n, ancilla, qreg, dt_beta, coefficients, words)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c8f9fb4-4974-44bf-aa8c-c9e86ffb0dd4",
-   "metadata": {},
-   "source": [
-    "The auxillary function below takes a Pauli word, and converts it to a list of integers which informs applications of this word to a circuit with the `apply_pauli` kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5bed072a-9fb6-4394-9759-b45ba292d0a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pauli_str(pauli_word, qubits_num):\n",
-    "\n",
-    "    my_list = []\n",
-    "    for i in range(qubits_num):\n",
-    "        if str(pauli_word[i]) == 'I':\n",
-    "            my_list.append(0)\n",
-    "        if str(pauli_word[i]) == 'X':\n",
-    "            my_list.append(1)\n",
-    "        if str(pauli_word[i]) == 'Y':\n",
-    "            my_list.append(2)\n",
-    "        if str(pauli_word[i]) == 'Z':\n",
-    "            my_list.append(3)\n",
-    "    return my_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "999675e2-a5b0-43d1-ba80-827f1c81b3dc",
-   "metadata": {},
-   "source": [
-    "The spin operators necessary for the Hadamard test are defined below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "297dbc29-e48a-411b-bc42-4fabe180a641",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the spin-op x for real component and y for the imaginary component.\n",
-    "x_0 = cudaq.spin.x(0)\n",
-    "y_0 = cudaq.spin.y(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1880e50d-6d27-44e4-b27e-329d8c499bfb",
-   "metadata": {},
-   "source": [
-    "Finally, the time step for the unitary operations that build the Krylov space is defined as well as the dimension of the Krylov space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3ae6b210-13c4-40ba-be73-4fb906a1f7ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Define parameters for the quantum Krylov space\n",
-    "dt = 0.5\n",
-    "\n",
-    "# Dimension of the Krylov space\n",
-    "m_qfd = 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d59f13b-e9f8-44e6-b270-be4fc6a80c01",
-   "metadata": {},
-   "source": [
-    "### Computing the matrix elements \n",
-    "\n",
-    "The cell below computes the overlap matrix. This can be done in serial or in parallel, depending on the `multi_gpu` specification.  First, an operator is built to apply the identity to the overlap matrix circuit when `apply_pauli` is called. Next, the `wf_overlap` array is constructed which will hold the matrix elements. \n",
-    "\n",
-    "Next, a pair of nested loops iterate over the time steps defined by the dimension of the subspace.  Each m,n combination corresponds to computation of an off-diagonal matrix element of the overlap matrix $S$ using the Hadamard test.  This is accomplished by calling the CUDA-Q `observe` function with the X and Y operators, along with the time steps, the components of the Hamiltonian matrix, and the initial state vector `vec`.\n",
-    "\n",
-    "The observe function broadcasts over the two provided operators $X$ and $Y$ and returns a list of results. The `expectation` function returns the expectation values which are summed and stored in the matrix.\n",
-    "\n",
-    "The multi-gpu case completes the same steps, except the `observe_async` command is used. This allows for the $X$ and $Y$ observables to be evaluated at the same time on two different simulated QPUs. In this case, the results are stored in lists corresponding to the real and imaginary parts. These are then accessed later with the `get` command to build $S$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "875124e8-dba8-4ace-828b-d4b03352f9d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Single GPU:\n",
-    "if not multi_gpu:\n",
-    "\n",
-    "    # Add identity operator to compute overlap of basis\n",
-    "    observe_op = 1.0\n",
-    "    for m in range(qubits_num):\n",
-    "        observe_op *= cudaq.spin.i(m)\n",
-    "    identity_word = observe_op.to_string(False)\n",
-    "    pauli_list = pauli_str(identity_word, qubits_num)\n",
-    "\n",
-    "    # Empty overlap matrix S\n",
-    "    wf_overlap = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loop to solve for S matrix elements\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "            results = cudaq.observe(qfd_kernel, [x_0, y_0], dt_m, dt_n,\n",
-    "                                    coefficient, pauli_string, pauli_list, vec)\n",
-    "            temp = [result.expectation() for result in results]\n",
-    "            wf_overlap[m, n] = temp[0] + temp[1] * 1j\n",
-    "            if n != m:\n",
-    "                wf_overlap[n, m] = np.conj(wf_overlap[m, n])\n",
-    "\n",
-    "else:\n",
-    "\n",
-    "    ## Multi-GPU\n",
-    "\n",
-    "    # Add identity operator to compute overlap of basis\n",
-    "    observe_op = 1.0\n",
-    "\n",
-    "    for m in range(qubits_num):\n",
-    "        observe_op *= cudaq.spin.i(m)\n",
-    "\n",
-    "    identity_word = observe_op.to_string(False)\n",
-    "    pauli_list = pauli_str(identity_word, qubits_num)\n",
-    "\n",
-    "    # Empty overlap matrix S\n",
-    "    wf_overlap = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Empty lists to store results\n",
-    "    collect_overlap_real = []\n",
-    "    collect_overlap_img = []\n",
-    "\n",
-    "    # Loop to compute matrix elements of S\n",
-    "    count = 0\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            # The count variable determines which (simulated) QPU the computation is sent to\n",
-    "            # The results are stored in a list corresponding to the real and imaginary parts\n",
-    "            count_id = count % 2\n",
-    "            collect_overlap_real.append(\n",
-    "                cudaq.observe_async(qfd_kernel,\n",
-    "                                    x_0,\n",
-    "                                    dt_m,\n",
-    "                                    dt_n,\n",
-    "                                    coefficient,\n",
-    "                                    pauli_string,\n",
-    "                                    pauli_list,\n",
-    "                                    vec,\n",
-    "                                    qpu_id=count_id))\n",
-    "\n",
-    "            collect_overlap_img.append(\n",
-    "                cudaq.observe_async(qfd_kernel,\n",
-    "                                    y_0,\n",
-    "                                    dt_m,\n",
-    "                                    dt_n,\n",
-    "                                    coefficient,\n",
-    "                                    pauli_string,\n",
-    "                                    pauli_list,\n",
-    "                                    vec,\n",
-    "                                    qpu_id=count_id + 2))\n",
-    "            count += 1\n",
-    "\n",
-    "    tot_dim = 0\n",
-    "\n",
-    "    # A second loop takes the lists of results and, using `get`, populates the matrix elements\n",
-    "    for n in range(m_qfd):\n",
-    "        for m in range(n, m_qfd):\n",
-    "            observe_result = collect_overlap_real[tot_dim].get()\n",
-    "            real_val = observe_result.expectation()\n",
-    "\n",
-    "            observe_result = collect_overlap_img[tot_dim].get()\n",
-    "            img_val = observe_result.expectation()\n",
-    "\n",
-    "            wf_overlap[m, n] = real_val + img_val * 1j\n",
-    "            if n != m:\n",
-    "                wf_overlap[n, m] = np.conj(wf_overlap[m, n])\n",
-    "\n",
-    "            tot_dim += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fa0e0f1-0705-4dd6-a354-30db1526e266",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian matrix elements are computed in the same way, except this time the Hamiltonian is applied as part of the circuit. This is accomplished with the extra for loop, which iterates through the terms in the Hamiltonian, computing an expectation value for each one and then summing the results to produce one matrix element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "562a6063-b0fe-4d69-a4f4-c7d5309436da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Single GPU:\n",
-    "if not multi_gpu:\n",
-    "\n",
-    "    # Empty H\n",
-    "    ham_matrx = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loops over H matrix terms\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            # 2 entry array that stores real and imaginary part of matrix element\n",
-    "            tot_e = np.zeros(2)\n",
-    "\n",
-    "            # Loops over the terms in the Hamiltonian, computing expectation values\n",
-    "            for coef, word in zip(coefficient, pauli_string):\n",
-    "                #print(coef,word)\n",
-    "\n",
-    "                pauli_list = pauli_str(word, qubits_num)\n",
-    "                #print(pauli_list)\n",
-    "\n",
-    "                results = cudaq.observe(qfd_kernel, [x_0, y_0], dt_m, dt_n,\n",
-    "                                        coefficient, pauli_string, pauli_list,\n",
-    "                                        vec)\n",
-    "\n",
-    "                temp = [result.expectation() for result in results]\n",
-    "\n",
-    "                # Multiplies result by coefficient corresponding to Pauli Word\n",
-    "                temp[0] = coef.real * temp[0]\n",
-    "                temp[1] = coef.imag * temp[1]\n",
-    "\n",
-    "                # Accumulates results for each Pauli Word\n",
-    "                tot_e[0] += temp[0]\n",
-    "                tot_e[1] += temp[1]\n",
-    "\n",
-    "            # Sums real and imaginary totals to specify Hamiltonian entry\n",
-    "            ham_matrx[m, n] = tot_e[0] + tot_e[1] * 1j\n",
-    "            if n != m:\n",
-    "                ham_matrx[n, m] = np.conj(ham_matrx[m, n])\n",
-    "else:\n",
-    "\n",
-    "    ## Multi-GPU\n",
-    "\n",
-    "    # Empty H\n",
-    "    ham_matrx = np.zeros((m_qfd, m_qfd), dtype=complex)\n",
-    "\n",
-    "    # Loops over H matrix terms\n",
-    "    for m in range(m_qfd):\n",
-    "        dt_m = dt * m\n",
-    "        for n in range(m, m_qfd):\n",
-    "            dt_n = dt * n\n",
-    "\n",
-    "            #List storing results from real and imaginary circuit evaluations for each Pauli word\n",
-    "            ham_matrix_real = []\n",
-    "            ham_matrix_imag = []\n",
-    "\n",
-    "            # Loop to asynchronously compute real and imaginary parts for each Pauli word on two GPUs\n",
-    "            count = 0\n",
-    "            tot_e = np.zeros(2)\n",
-    "            for coef, word in zip(coefficient, pauli_string):\n",
-    "                count_id = count % 2\n",
-    "                #print(coef,word)\n",
-    "\n",
-    "                pauli_list = pauli_str(word, qubits_num)\n",
-    "                #print(pauli_list)\n",
-    "\n",
-    "                ham_matrix_real.append(\n",
-    "                    cudaq.observe_async(qfd_kernel,\n",
-    "                                        x_0,\n",
-    "                                        dt_m,\n",
-    "                                        dt_n,\n",
-    "                                        coefficient,\n",
-    "                                        pauli_string,\n",
-    "                                        pauli_list,\n",
-    "                                        vec,\n",
-    "                                        qpu_id=count_id))\n",
-    "                ham_matrix_imag.append(\n",
-    "                    cudaq.observe_async(qfd_kernel,\n",
-    "                                        y_0,\n",
-    "                                        dt_m,\n",
-    "                                        dt_n,\n",
-    "                                        coefficient,\n",
-    "                                        pauli_string,\n",
-    "                                        pauli_list,\n",
-    "                                        vec,\n",
-    "                                        qpu_id=count_id + 2))\n",
-    "\n",
-    "                count += 1\n",
-    "\n",
-    "            # Loops through coefficients to sum the real and imaginary parts of H matrix element\n",
-    "            i = 0\n",
-    "            for coef in coefficient:\n",
-    "\n",
-    "                observe_result = ham_matrix_real[i].get()\n",
-    "                real_val = observe_result.expectation()\n",
-    "\n",
-    "                observe_result = ham_matrix_imag[i].get()\n",
-    "                img_val = observe_result.expectation()\n",
-    "\n",
-    "                tot_e[0] += real_val * coef.real\n",
-    "                tot_e[1] += img_val * coef.imag\n",
-    "\n",
-    "                i += 1\n",
-    "\n",
-    "            # Enter final H matrix element\n",
-    "            ham_matrx[m, n] = tot_e[0] + tot_e[1] * 1j\n",
-    "            if n != m:\n",
-    "                ham_matrx[n, m] = np.conj(ham_matrx[m, n])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1917bd58-4ce3-4e3e-9ccf-096577c0da14",
-   "metadata": {},
-   "source": [
-    "### Determining the ground state energy of the subspace\n",
-    "\n",
-    "The final step is to solve the generalized eigenvaulue problem with the overlap and Hamiltonian matrices constructed using the quantum computer. The procedure begins by diagonalizing  $S$ with the transform $$S = U\\Sigma U^{\\dagger}$$\n",
-    "\n",
-    "The eigenvectors $v$ and eigenvalues $s$ are used to construct a new matrix $X':$\n",
-    "\n",
-    "$$ X' = S ^{\\frac{-1}{2}} = \\sum_k v_{ki} \\frac{1}{\\sqrt{s_k}} v_{kj}.$$\n",
-    "\n",
-    "The matrix $X'$ diagonalizes $H:$\n",
-    "\n",
-    "$$ X'^{\\dagger}HX' = ES^{\\frac{1}{2}}C.$$\n",
-    "\n",
-    "Using the eigenvectors of $H'$, ($^{\\frac{1}{2}}C$), the original eigenvectors to the problem can be found by left multiplying by $S^{\\frac{-1}{2}}C.$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8b281533-bf3d-4aed-823b-9d2f926dfe3e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def eig(H, s):\n",
-    "    #Solver for generalized eigenvalue problem\n",
-    "    # HC = SCE\n",
-    "\n",
-    "    THRESHOLD = 1e-20\n",
-    "    s_diag, u = scipy.linalg.eig(s)\n",
-    "    s_prime = []\n",
-    "    for sii in s_diag:\n",
-    "        if np.imag(sii) > 1e-7:\n",
-    "            raise ValueError(\n",
-    "                \"S may not be hermitian, large imag. eval component.\")\n",
-    "        if np.real(sii) > THRESHOLD:\n",
-    "            s_prime.append(np.real(sii))\n",
-    "\n",
-    "    X_prime = np.zeros((len(s_diag), len(s_prime)), dtype=complex)\n",
-    "\n",
-    "    for i in range(len(s_diag)):\n",
-    "        for j in range(len(s_prime)):\n",
-    "            X_prime[i][j] = u[i][j] / np.sqrt(s_prime[j])\n",
-    "\n",
-    "    H_prime = (((X_prime.conjugate()).transpose()).dot(H)).dot(X_prime)\n",
-    "    e_prime, C_prime = scipy.linalg.eig(H_prime)\n",
-    "    C = X_prime.dot(C_prime)\n",
-    "\n",
-    "    return e_prime, C"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74c1fa51-4fae-412b-b90d-262e0b3aaf53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eigen_value, eigen_vect = eig(ham_matrx[0:m_qfd, 0:m_qfd], wf_overlap[0:m_qfd,\n",
-    "                                                                      0:m_qfd])\n",
-    "print('Energy from QFD:')\n",
-    "print(np.min(eigen_value))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/logical_aim_sqale.ipynb b/pr-2308/sphinx/applications/python/logical_aim_sqale.ipynb
deleted file mode 100644
index f51bd10294..0000000000
--- a/pr-2308/sphinx/applications/python/logical_aim_sqale.ipynb
+++ /dev/null
@@ -1,1356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Anderson Impurity Model ground state solver on Infleqtion's Sqale"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ground state quantum chemistry—computing total energies of molecular configurations to within chemical accuracy—is perhaps the most highly-touted industrial application of fault-tolerant quantum computers. Strongly correlated materials, for example, are particularly interesting, and tools like dynamical mean-field theory (DMFT) allow one to account for the effect of their strong, localized electronic correlations. These DMFT models help predict material properties by approximating the system as a single site impurity inside a “bath” that encompasses the rest of the system. Simulating such dynamics can be a tough task using classical methods, but can be done efficiently on a quantum computer via quantum simulation.\n",
-    "\n",
-    "In this notebook, we showcase a workflow for preparing the ground state of the minimal single-impurity Anderson model (SIAM) using the Hamiltonian Variational Ansatz for a range of realistic parameters. As a first step towards running DMFT on a fault-tolerant quantum computer, we will use logical qubits encoded in the `[[4, 2, 2]]` code. Using this workflow, we will obtain the ground state energy estimates via noisy simulation, and then also execute the corresponding optimized circuits on Infleqtion's gate-based neutral-atom quantum computer, making the benefits of logical qubits apparent. More details can be found in our [paper](https://arxiv.org/abs/2412.07670)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This demo notebook uses CUDA-Q (`cudaq`) and a CUDA-QX library, `cudaq-solvers`; let us first begin by importing (and installing as needed) these packages:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    import cudaq_solvers as solvers\n",
-    "    import cudaq\n",
-    "    import matplotlib.pyplot as plt\n",
-    "except ImportError:\n",
-    "    print(\"Installing required packages...\")\n",
-    "    %pip install --quiet 'cudaq-solvers' 'matplotlib'\n",
-    "    print(\"Installed `cudaq`, `cudaq-solvers`, and `matplotlib` packages.\")\n",
-    "    print(\"You may need to restart the kernel to import newly installed packages.\")\n",
-    "    import cudaq_solvers as solvers\n",
-    "    import cudaq\n",
-    "    import matplotlib.pyplot as plt\n",
-    "\n",
-    "from collections.abc import Mapping, Sequence\n",
-    "import numpy as np\n",
-    "from scipy.optimize import minimize\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To prepare our ground state quantum Anderson impurity model circuits (referred to as AIM circuits in this notebook for short), we use VQE to train an ansatz to minimize a Hamiltonian and obtain optimal angles that can be used to set the AIM circuits. As described in our [paper](https://arxiv.org/abs/2412.07670), the associated restricted Hamiltonian for our SIAM can be reduced to,\n",
-    "$$  \n",
-    "\\begin{equation}\n",
-    "H_{(U, V)} = U (Z_0 Z_2 - 1) / 4 + V (X_0 + X_2),\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "where $U$ is the Coulomb interaction and $V$ the hybridization strength. In this notebook workflow, we will optimize over a 2-dimensional grid of Hamiltonian parameter values, namely $U\\in \\{1, 5, 9\\}$ and $V\\in \\{-9, -1, 7\\}$ (with all values assumed to be in units of eV), to ensure that the ansatz is generally trainable and expressive, and obtain 9 different circuit layers identified by the key $(U, V)$. We will simulate the VQE on GPU (or optionally on CPU if you do not have GPU access), enabled by CUDA-Q, in the absence of noise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if cudaq.num_available_gpus() == 0:\n",
-    "    cudaq.set_target(\"qpp-cpu\", option=\"fp64\")\n",
-    "else:\n",
-    "    cudaq.set_target(\"nvidia\", option=\"fp64\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This workflow can be easily defined in CUDA-Q as shown in the cell below, using the CUDA-QX Solvers library (which accelerates quantum algorithms like the VQE):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def ansatz(n_qubits: int) -> cudaq.Kernel:\n",
-    "    # Create a CUDA-Q parameterized kernel\n",
-    "    paramterized_ansatz, variational_angles = cudaq.make_kernel(list)\n",
-    "    qubits = paramterized_ansatz.qalloc(n_qubits)\n",
-    "\n",
-    "    # Using |+> as the initial state:\n",
-    "    paramterized_ansatz.h(qubits[0])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    paramterized_ansatz.rx(variational_angles[0], qubits[0])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "    paramterized_ansatz.rz(variational_angles[1], qubits[1])\n",
-    "    paramterized_ansatz.cx(qubits[0], qubits[1])\n",
-    "    return paramterized_ansatz\n",
-    "\n",
-    "\n",
-    "def run_logical_vqe(cudaq_hamiltonian: cudaq.SpinOperator) -> tuple[float, list[float]]:\n",
-    "    # Set seed for easier reproduction\n",
-    "    np.random.seed(42)\n",
-    "\n",
-    "    # Initial angles for the optimizer\n",
-    "    init_angles = np.random.random(2) * 1e-1\n",
-    "\n",
-    "    # Obtain CUDA-Q Ansatz\n",
-    "    num_qubits = cudaq_hamiltonian.get_qubit_count()\n",
-    "    variational_kernel = ansatz(num_qubits)\n",
-    "\n",
-    "    # Perform VQE optimization\n",
-    "    energy, params, _ = solvers.vqe(\n",
-    "        variational_kernel,\n",
-    "        cudaq_hamiltonian,\n",
-    "        init_angles,\n",
-    "        optimizer=minimize,\n",
-    "        method=\"SLSQP\",\n",
-    "        tol=1e-10,\n",
-    "    )\n",
-    "    return energy, params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing circuits in the `[[4,2,2]]` encoding"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `[[4,2,2]]` code is a quantum error detection code that uses four physical qubits to encode two logical qubits. In this notebook, we will construct two variants of quantum circuits: physical (bare, unencoded) and logical (encoded). These circuits will be informed by the Hamiltonian Variational Ansatz described earlier. To measure all the terms in our Hamiltonian, we will measure the data qubits in both the $Z$- and $X$-basis, as allowed by the `[[4,2,2]]` logical gateset. Full details on the circuit constructions are outlined in our [paper](https://arxiv.org/abs/2412.07670)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we create functions to build our CUDA-Q AIM circuits, both physical and logical versions. As we consider noisy simulation in this notebook, we will include some noisy gates. Here, for simplicity, we will just register a custom identity gate -- to be later used as a noisy operation to model readout error: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.register_operation(\"meas_id\", np.identity(2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def aim_physical_circuit(\n",
-    "    angles: list[float], basis: str, *, ignore_meas_id: bool = False\n",
-    ") -> cudaq.Kernel:\n",
-    "    kernel = cudaq.make_kernel()\n",
-    "    qubits = kernel.qalloc(2)\n",
-    "\n",
-    "    # Bell state prep\n",
-    "    kernel.h(qubits[0])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    # Rx Gate\n",
-    "    kernel.rx(angles[0], qubits[0])\n",
-    "\n",
-    "    # ZZ rotation\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.rz(angles[1], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    if basis == \"z_basis\":\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=2, function=lambda q_idx: getattr(kernel, \"meas_id\")(qubits[q_idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    elif basis == \"x_basis\":\n",
-    "        kernel.h(qubits)\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=2, function=lambda q_idx: getattr(kernel, \"meas_id\")(qubits[q_idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    else:\n",
-    "        raise ValueError(\"Unsupported basis provided:\", basis)\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def aim_logical_circuit(\n",
-    "    angles: list[float], basis: str, *, ignore_meas_id: bool = False\n",
-    ") -> cudaq.Kernel:\n",
-    "    kernel = cudaq.make_kernel()\n",
-    "    qubits = kernel.qalloc(6)\n",
-    "\n",
-    "    kernel.for_loop(start=0, stop=3, function=lambda idx: kernel.h(qubits[idx]))\n",
-    "    kernel.cx(qubits[1], qubits[4])\n",
-    "    kernel.cx(qubits[2], qubits[3])\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[3])\n",
-    "\n",
-    "    # Rx teleportation\n",
-    "    kernel.rx(angles[0], qubits[0])\n",
-    "\n",
-    "    kernel.cx(qubits[0], qubits[1])\n",
-    "    kernel.cx(qubits[0], qubits[3])\n",
-    "    kernel.h(qubits[0])\n",
-    "\n",
-    "    if basis == \"z_basis\":\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=5, function=lambda idx: getattr(kernel, \"meas_id\")(qubits[idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    elif basis == \"x_basis\":\n",
-    "        # ZZ rotation and teleportation\n",
-    "        kernel.cx(qubits[3], qubits[5])\n",
-    "        kernel.cx(qubits[2], qubits[5])\n",
-    "        kernel.rz(angles[1], qubits[5])\n",
-    "        kernel.cx(qubits[1], qubits[5])\n",
-    "        kernel.cx(qubits[4], qubits[5])\n",
-    "        kernel.for_loop(start=1, stop=5, function=lambda idx: kernel.h(qubits[idx]))\n",
-    "        if not ignore_meas_id:\n",
-    "            kernel.for_loop(\n",
-    "                start=0, stop=6, function=lambda idx: getattr(kernel, \"meas_id\")(qubits[idx])\n",
-    "            )\n",
-    "        kernel.mz(qubits)\n",
-    "    else:\n",
-    "        raise ValueError(\"Unsupported basis provided:\", basis)\n",
-    "    return kernel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the circuit definitions above, we can now define a function that automatically runs the VQE and constructs a dictionary containing all the AIM circuits we want to submit to hardware (or noisily simulate):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_circuit_set(ignore_meas_id: bool = False) -> object:\n",
-    "    u_vals = [1, 5, 9]\n",
-    "    v_vals = [-9, -1, 7]\n",
-    "    circuit_dict = {}\n",
-    "    for u in u_vals:\n",
-    "        for v in v_vals:\n",
-    "            qubit_hamiltonian = (\n",
-    "                0.25 * u * cudaq.spin.z(0) * cudaq.spin.z(1)\n",
-    "                - 0.25 * u\n",
-    "                + v * cudaq.spin.x(0)\n",
-    "                + v * cudaq.spin.x(1)\n",
-    "            )\n",
-    "            _, opt_params = run_logical_vqe(qubit_hamiltonian)\n",
-    "            angles = [float(angle) for angle in opt_params]\n",
-    "            print(f\"Computed optimal angles={angles} for U={u}, V={v}\")\n",
-    "\n",
-    "            tmp_physical_dict = {}\n",
-    "            tmp_logical_dict = {}\n",
-    "            for basis in (\"z_basis\", \"x_basis\"):\n",
-    "                tmp_physical_dict[basis] = aim_physical_circuit(\n",
-    "                    angles, basis, ignore_meas_id=ignore_meas_id\n",
-    "                )\n",
-    "                tmp_logical_dict[basis] = aim_logical_circuit(\n",
-    "                    angles, basis, ignore_meas_id=ignore_meas_id\n",
-    "                )\n",
-    "\n",
-    "            circuit_dict[f\"{u}:{v}\"] = {\n",
-    "                \"physical\": tmp_physical_dict,\n",
-    "                \"logical\": tmp_logical_dict,\n",
-    "            }\n",
-    "    print(\"\\nFinished building optimized circuits!\")\n",
-    "    return circuit_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9\n",
-      "Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1\n",
-      "Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7\n",
-      "Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9\n",
-      "Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1\n",
-      "Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7\n",
-      "Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9\n",
-      "Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1\n",
-      "Computed optimal angles=[-1.7301462729177735, 1.570796033796985] for U=9, V=7\n",
-      "\n",
-      "Finished building optimized circuits!\n"
-     ]
-    }
-   ],
-   "source": [
-    "sim_circuit_dict = generate_circuit_set()\n",
-    "circuit_layers = sim_circuit_dict.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up submission and decoding workflow "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we define various helper functions that will play a role in generating the associated energies of the AIM circuits based on the circuit samples (in the different bases), as well as decode the logical circuits with post-selection informed by the `[[4,2,2]]` code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _num_qubits(counts: Mapping[str, float]) -> int:\n",
-    "    for key in counts:\n",
-    "        if key.isdecimal():\n",
-    "            return len(key)\n",
-    "    return 0\n",
-    "\n",
-    "\n",
-    "def process_counts(\n",
-    "    counts: Mapping[str, float],\n",
-    "    data_qubits: Sequence[int],\n",
-    "    flag_qubits: Sequence[int] = (),\n",
-    ") -> dict[str, float]:\n",
-    "    new_data: dict[str, float] = {}\n",
-    "    for key, val in counts.items():\n",
-    "        if not all(key[i] == \"0\" for i in flag_qubits):\n",
-    "            continue\n",
-    "\n",
-    "        new_key = \"\".join(key[i] for i in data_qubits)\n",
-    "\n",
-    "        if not set(\"01\").issuperset(new_key):\n",
-    "            continue\n",
-    "\n",
-    "        new_data.setdefault(new_key, 0)\n",
-    "        new_data[new_key] += val\n",
-    "\n",
-    "    return new_data\n",
-    "\n",
-    "\n",
-    "def decode(counts: Mapping[str, float]) -> dict[str, float]:\n",
-    "    \"\"\"Decode physical counts into logical counts. Should be called after `process_counts`.\"\"\"\n",
-    "\n",
-    "    if not counts:\n",
-    "        return {}\n",
-    "\n",
-    "    num_qubits = _num_qubits(counts)\n",
-    "    assert num_qubits % 4 == 0\n",
-    "\n",
-    "    physical_to_logical = {\n",
-    "        \"0000\": \"00\",\n",
-    "        \"1111\": \"00\",\n",
-    "        \"0011\": \"01\",\n",
-    "        \"1100\": \"01\",\n",
-    "        \"0101\": \"10\",\n",
-    "        \"1010\": \"10\",\n",
-    "        \"0110\": \"11\",\n",
-    "        \"1001\": \"11\",\n",
-    "    }\n",
-    "\n",
-    "    new_data: dict[str, float] = {}\n",
-    "    for key, val in counts.items():\n",
-    "        physical_keys = [key[i : i + 4] for i in range(0, num_qubits, 4)]\n",
-    "        logical_keys = [physical_to_logical.get(physical_key) for physical_key in physical_keys]\n",
-    "        if None not in logical_keys:\n",
-    "            new_key = \"\".join(logical_keys)\n",
-    "            new_data.setdefault(new_key, 0)\n",
-    "            new_data[new_key] += val\n",
-    "\n",
-    "    return new_data\n",
-    "\n",
-    "\n",
-    "def ev_x(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * ((-1) ** int(k[0]) + (-1) ** int(k[1]))\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def ev_xx(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * (-1) ** k.count(\"1\")\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def ev_zz(counts: Mapping[str, float]) -> float:\n",
-    "    ev = 0.0\n",
-    "\n",
-    "    for k, val in counts.items():\n",
-    "        ev += val * (-1) ** k.count(\"1\")\n",
-    "\n",
-    "    total = sum(counts.values())\n",
-    "    ev /= total\n",
-    "    return ev\n",
-    "\n",
-    "\n",
-    "def aim_logical_energies(\n",
-    "    data_ordering: object, counts_list: Sequence[dict[str, float]]\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    counts_data = {\n",
-    "        data_ordering[i]: decode(\n",
-    "            process_counts(\n",
-    "                counts,\n",
-    "                data_qubits=[1, 2, 3, 4],\n",
-    "                flag_qubits=[0, 5],\n",
-    "            )\n",
-    "        )\n",
-    "        for i, counts in enumerate(counts_list)\n",
-    "    }\n",
-    "    return _aim_energies(counts_data)\n",
-    "\n",
-    "\n",
-    "def aim_physical_energies(\n",
-    "    data_ordering: object, counts_list: Sequence[dict[str, float]]\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    counts_data = {\n",
-    "        data_ordering[i]: process_counts(\n",
-    "            counts,\n",
-    "            data_qubits=[0, 1],\n",
-    "        )\n",
-    "        for i, counts in enumerate(counts_list)\n",
-    "    }\n",
-    "    return _aim_energies(counts_data)\n",
-    "\n",
-    "\n",
-    "def _aim_energies(\n",
-    "    counts_data: Mapping[tuple[int, int, str], dict[str, float]],\n",
-    ") -> tuple[dict[tuple[int, int], float], dict[tuple[int, int], float]]:\n",
-    "    evxs: dict[tuple[int, int], float] = {}\n",
-    "    evxxs: dict[tuple[int, int], float] = {}\n",
-    "    evzzs: dict[tuple[int, int], float] = {}\n",
-    "    totals: dict[tuple[int, int], float] = {}\n",
-    "\n",
-    "    for key, counts in counts_data.items():\n",
-    "        h_params, basis = key\n",
-    "        key_a, key_b = h_params.split(\":\")\n",
-    "        u, v = int(key_a), int(key_b)\n",
-    "        if basis.startswith(\"x\"):\n",
-    "            evxs[u, v] = ev_x(counts)\n",
-    "            evxxs[u, v] = ev_xx(counts)\n",
-    "        else:\n",
-    "            evzzs[u, v] = ev_zz(counts)\n",
-    "\n",
-    "        totals.setdefault((u, v), 0)\n",
-    "        totals[u, v] += sum(counts.values())\n",
-    "\n",
-    "    energies = {}\n",
-    "    uncertainties = {}\n",
-    "    for u, v in evxs.keys() & evzzs.keys():\n",
-    "        string_key = f\"{u}:{v}\"\n",
-    "        energies[string_key] = u * (evzzs[u, v] - 1) / 4 + v * evxs[u, v]\n",
-    "\n",
-    "        uncertainty_xx = 2 * v**2 * (1 + evxxs[u, v]) - u * v * evxs[u, v] / 2\n",
-    "        uncertainty_zz = u**2 * (1 - evzzs[u, v]) / 2\n",
-    "\n",
-    "        uncertainties[string_key] = np.sqrt(\n",
-    "            (uncertainty_zz + uncertainty_xx - energies[string_key] ** 2) / (totals[u, v] / 2)\n",
-    "        )\n",
-    "\n",
-    "    return energies, uncertainties\n",
-    "\n",
-    "\n",
-    "def _get_energy_diff(\n",
-    "    bf_energies: dict[str, float],\n",
-    "    physical_energies: dict[str, float],\n",
-    "    logical_energies: dict[str, float],\n",
-    ") -> tuple[list[float], list[float]]:\n",
-    "    physical_energy_diff = []\n",
-    "    logical_energy_diff = []\n",
-    "\n",
-    "    # Data ordering following `bf_energies` keys\n",
-    "    for layer in bf_energies.keys():\n",
-    "        physical_sim_energy = physical_energies[layer]\n",
-    "        logical_sim_energy = logical_energies[layer]\n",
-    "        true_energy = bf_energies[layer]\n",
-    "        u, v = layer.split(\":\")\n",
-    "        print(f\"Layer=({u}, {v}) has brute-force energy of: {true_energy}\")\n",
-    "        print(f\"Physical circuit of layer=({u}, {v}) got an energy of: {physical_sim_energy}\")\n",
-    "        print(f\"Logical circuit of layer=({u}, {v}) got an energy of: {logical_sim_energy}\")\n",
-    "        print(\"-\" * 72)\n",
-    "\n",
-    "        if logical_sim_energy < physical_sim_energy:\n",
-    "            print(\"Logical circuit achieved the lower energy!\")\n",
-    "        else:\n",
-    "            print(\"Physical circuit achieved the lower energy\")\n",
-    "        print(\"-\" * 72, \"\\n\")\n",
-    "\n",
-    "        physical_energy_diff.append(\n",
-    "            -1 * (true_energy - physical_sim_energy)\n",
-    "        )  # Multiply by -1 since negative energies\n",
-    "        logical_energy_diff.append(-1 * (true_energy - logical_sim_energy))\n",
-    "    return physical_energy_diff, logical_energy_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def submit_aim_circuits(\n",
-    "    circuit_dict: object,\n",
-    "    *,\n",
-    "    folder_path: str = \"future_aim_results\",\n",
-    "    shots_count: int = 1000,\n",
-    "    noise_model: cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel | None = None,\n",
-    "    run_async: bool = False,\n",
-    ") -> dict[str, list[dict[str, int]]] | None:\n",
-    "    if run_async:\n",
-    "        os.makedirs(folder_path, exist_ok=True)\n",
-    "    else:\n",
-    "        aim_results = {\"physical\": [], \"logical\": []}\n",
-    "\n",
-    "    for layer in circuit_dict.keys():\n",
-    "        if run_async:\n",
-    "            print(f\"Posting circuits associated with layer=('{layer}')\")\n",
-    "        else:\n",
-    "            print(f\"Running circuits associated with layer=('{layer}')\")\n",
-    "\n",
-    "        for basis in (\"z_basis\", \"x_basis\"):\n",
-    "            if run_async:\n",
-    "                u, v = layer.split(\":\")\n",
-    "\n",
-    "                tmp_physical_results = cudaq.sample_async(\n",
-    "                    circuit_dict[layer][\"physical\"][basis], shots_count=shots_count\n",
-    "                )\n",
-    "                file = open(f\"{folder_path}/physical_{basis}_job_u={u}_v={v}_result.txt\", \"w\")\n",
-    "                file.write(str(tmp_physical_results))\n",
-    "                file.close()\n",
-    "\n",
-    "                tmp_logical_results = cudaq.sample_async(\n",
-    "                    circuit_dict[layer][\"logical\"][basis], shots_count=shots_count\n",
-    "                )\n",
-    "                file = open(f\"{folder_path}/logical_{basis}_job_u={u}_v={v}_result.txt\", \"w\")\n",
-    "                file.write(str(tmp_logical_results))\n",
-    "                file.close()\n",
-    "            else:\n",
-    "                tmp_physical_results = cudaq.sample(\n",
-    "                    circuit_dict[layer][\"physical\"][basis],\n",
-    "                    shots_count=shots_count,\n",
-    "                    noise_model=noise_model,\n",
-    "                )\n",
-    "                tmp_logical_results = cudaq.sample(\n",
-    "                    circuit_dict[layer][\"logical\"][basis],\n",
-    "                    shots_count=shots_count,\n",
-    "                    noise_model=noise_model,\n",
-    "                )\n",
-    "                aim_results[\"physical\"].append({k: v for k, v in tmp_physical_results.items()})\n",
-    "                aim_results[\"logical\"].append({k: v for k, v in tmp_logical_results.items()})\n",
-    "    if not run_async:\n",
-    "        print(\"\\nCompleted all circuit sampling!\")\n",
-    "        return aim_results\n",
-    "    else:\n",
-    "        print(\"\\nAll circuits submitted for async sampling!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _get_async_results(\n",
-    "    layers: object, *, folder_path: str = \"future_aim_results\"\n",
-    ") -> dict[str, list[dict[str, int]]]:\n",
-    "    aim_results = {\"physical\": [], \"logical\": []}\n",
-    "    for layer in layers:\n",
-    "        print(f\"Retrieving all circuits counts associated with layer=('{layer}')\")\n",
-    "        u, v = layer.split(\":\")\n",
-    "        for basis in (\"z_basis\", \"x_basis\"):\n",
-    "            file = open(f\"{folder_path}/physical_{basis}_job_u={u}_v={v}_result.txt\", \"r\")\n",
-    "            tmp_physical_results = cudaq.AsyncSampleResult(str(file.read()))\n",
-    "            physical_counts = tmp_physical_results.get()\n",
-    "\n",
-    "            file = open(f\"{folder_path}/logical_{basis}_job_u={u}_v={v}_result.txt\", \"r\")\n",
-    "            tmp_logical_results = cudaq.AsyncSampleResult(str(file.read()))\n",
-    "            logical_counts = tmp_logical_results.get()\n",
-    "\n",
-    "            aim_results[\"physical\"].append({k: v for k, v in physical_counts.items()})\n",
-    "            aim_results[\"logical\"].append({k: v for k, v in logical_counts.items()})\n",
-    "\n",
-    "    print(\"\\nObtained all circuit samples!\")\n",
-    "    return aim_results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running a CUDA-Q noisy simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we will first explore the performance of the physical and logical circuits under the influence of a device noise model. This will help us predict experimental results, as well as understand the dominant error sources at play. Such a simulation can be achieved via CUDA-Q's density matrix simulator: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "cudaq.set_target(\"density-matrix-cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_device_noise(\n",
-    "    depolar_prob_1q: float,\n",
-    "    depolar_prob_2q: float,\n",
-    "    *,\n",
-    "    readout_error_prob: float | None = None,\n",
-    "    custom_gates: list[str] | None = None,\n",
-    ") -> cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.NoiseModel:\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "    depolar_noise = cudaq.DepolarizationChannel(depolar_prob_1q)\n",
-    "\n",
-    "    noisy_ops = [\"z\", \"s\", \"x\", \"h\", \"rx\", \"rz\"]\n",
-    "    for op in noisy_ops:\n",
-    "        noise.add_all_qubit_channel(op, depolar_noise)\n",
-    "\n",
-    "    if custom_gates:\n",
-    "        custom_depolar_channel = cudaq.DepolarizationChannel(depolar_prob_1q)\n",
-    "        for op in custom_gates:\n",
-    "            noise.add_all_qubit_channel(op, custom_depolar_channel)\n",
-    "\n",
-    "    # Two qubit depolarization error\n",
-    "    p_0 = 1 - depolar_prob_2q\n",
-    "    p_1 = np.sqrt((1 - p_0**2) / 3)\n",
-    "\n",
-    "    k0 = np.array(\n",
-    "        [[p_0, 0.0, 0.0, 0.0], [0.0, p_0, 0.0, 0.0], [0.0, 0.0, p_0, 0.0], [0.0, 0.0, 0.0, p_0]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k1 = np.array(\n",
-    "        [[0.0, 0.0, p_1, 0.0], [0.0, 0.0, 0.0, p_1], [p_1, 0.0, 0.0, 0.0], [0.0, p_1, 0.0, 0.0]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k2 = np.array(\n",
-    "        [\n",
-    "            [0.0, 0.0, -1j * p_1, 0.0],\n",
-    "            [0.0, 0.0, 0.0, -1j * p_1],\n",
-    "            [1j * p_1, 0.0, 0.0, 0.0],\n",
-    "            [0.0, 1j * p_1, 0.0, 0.0],\n",
-    "        ],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    k3 = np.array(\n",
-    "        [[p_1, 0.0, 0.0, 0.0], [0.0, p_1, 0.0, 0.0], [0.0, 0.0, -p_1, 0.0], [0.0, 0.0, 0.0, -p_1]],\n",
-    "        dtype=np.complex128,\n",
-    "    )\n",
-    "    kraus_channel = cudaq.KrausChannel([k0, k1, k2, k3])\n",
-    "\n",
-    "    noise.add_all_qubit_channel(\"cz\", kraus_channel)\n",
-    "    noise.add_all_qubit_channel(\"cx\", kraus_channel)\n",
-    "\n",
-    "    if readout_error_prob is not None:\n",
-    "        # Readout error modeled with a Bit flip channel on identity before measurement\n",
-    "        bit_flip = cudaq.BitFlipChannel(readout_error_prob)\n",
-    "        noise.add_all_qubit_channel(\"meas_id\", bit_flip)\n",
-    "    return noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, with our example noise model defined above, we can synchronously & noisily sample all of our AIM circuits by passing `noise_model=cudaq_noise_model` to the workflow containing function `submit_aim_circuits()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example parameters that can model execution on hardware at the high, simulation, level:\n",
-    "# Take single-qubit gate depolarization rate: ~0.2% or better (fidelity ≥99.8%)\n",
-    "# Take two-qubit gate depolarization rate: ~1–2% (fidelity ~98–99%)\n",
-    "cudaq_noise_model = get_device_noise(0.002, 0.02, readout_error_prob=0.02)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running circuits associated with layer=('1:-9')\n",
-      "Running circuits associated with layer=('1:-1')\n",
-      "Running circuits associated with layer=('1:7')\n",
-      "Running circuits associated with layer=('5:-9')\n",
-      "Running circuits associated with layer=('5:-1')\n",
-      "Running circuits associated with layer=('5:7')\n",
-      "Running circuits associated with layer=('9:-9')\n",
-      "Running circuits associated with layer=('9:-1')\n",
-      "Running circuits associated with layer=('9:7')\n",
-      "\n",
-      "Completed all circuit sampling!\n"
-     ]
-    }
-   ],
-   "source": [
-    "aim_sim_data = submit_aim_circuits(sim_circuit_dict, noise_model=cudaq_noise_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_ordering = []\n",
-    "for key in circuit_layers:\n",
-    "    for basis in (\"z_basis\", \"x_basis\"):\n",
-    "        data_ordering.append((key, basis))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_physical_energies, sim_physical_uncertainties = aim_physical_energies(\n",
-    "    data_ordering, aim_sim_data[\"physical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_logical_energies, sim_logical_uncertainties = aim_logical_energies(\n",
-    "    data_ordering, aim_sim_data[\"logical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To analyze our simulated energy results in the above cells, we will compare them to the brute-force computed exact ground state energies for the AIM Hamiltonian. For simplicity, these are already stored in the dictionary `bf_energies` below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bf_energies = {\n",
-    "    \"1:-9\": -18.251736027394713,\n",
-    "    \"1:-1\": -2.265564437074638,\n",
-    "    \"1:7\": -14.252231964940428,\n",
-    "    \"5:-9\": -19.293350575766127,\n",
-    "    \"5:-1\": -3.608495283014149,\n",
-    "    \"5:7\": -15.305692796870582,\n",
-    "    \"9:-9\": -20.39007993367173,\n",
-    "    \"9:-1\": -5.260398644698076,\n",
-    "    \"9:7\": -16.429650912487233,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the above metric, we can assess the performance of the logical circuits against the physical circuits by considering how far away the respective energies are from the brute-force expected energies. The cell below computes these energy deviations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer=(1, -9) has brute-force energy of: -18.251736027394713\n",
-      "Physical circuit of layer=(1, -9) got an energy of: -15.929\n",
-      "Logical circuit of layer=(1, -9) got an energy of: -17.46016175277361\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, -1) has brute-force energy of: -2.265564437074638\n",
-      "Physical circuit of layer=(1, -1) got an energy of: -1.97\n",
-      "Logical circuit of layer=(1, -1) got an energy of: -2.176531948420889\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, 7) has brute-force energy of: -14.252231964940428\n",
-      "Physical circuit of layer=(1, 7) got an energy of: -12.268\n",
-      "Logical circuit of layer=(1, 7) got an energy of: -13.26321740664324\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -9) has brute-force energy of: -19.293350575766127\n",
-      "Physical circuit of layer=(5, -9) got an energy of: -16.8495\n",
-      "Logical circuit of layer=(5, -9) got an energy of: -18.46681284816878\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -1) has brute-force energy of: -3.608495283014149\n",
-      "Physical circuit of layer=(5, -1) got an energy of: -3.1965000000000003\n",
-      "Logical circuit of layer=(5, -1) got an energy of: -3.4531715120183297\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, 7) has brute-force energy of: -15.305692796870582\n",
-      "Physical circuit of layer=(5, 7) got an energy of: -13.336\n",
-      "Logical circuit of layer=(5, 7) got an energy of: -14.341784541550897\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -9) has brute-force energy of: -20.39007993367173\n",
-      "Physical circuit of layer=(9, -9) got an energy of: -17.802\n",
-      "Logical circuit of layer=(9, -9) got an energy of: -19.339249509416753\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -1) has brute-force energy of: -5.260398644698076\n",
-      "Physical circuit of layer=(9, -1) got an energy of: -4.8580000000000005\n",
-      "Logical circuit of layer=(9, -1) got an energy of: -5.1227150992242025\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, 7) has brute-force energy of: -16.429650912487233\n",
-      "Physical circuit of layer=(9, 7) got an energy of: -14.3635\n",
-      "Logical circuit of layer=(9, 7) got an energy of: -15.448422736181264\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "sim_physical_energy_diff, sim_logical_energy_diff = _get_energy_diff(\n",
-    "    bf_energies, sim_physical_energies, sim_logical_energies\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Both physical and logical circuits were subject to the same noise model, but the `[[4,2,2]]` provides additional information that can help overcome some errors. Visualizing the computed energy differences from the above the cell, our noisy simulation provides a preview of the benefits logical qubits can offer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2200x1400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(11, 7), dpi=200)\n",
-    "\n",
-    "layer_labels = [(int(key.split(\":\")[0]), int(key.split(\":\")[1])) for key in bf_energies.keys()]\n",
-    "plot_labels = [str(item) for item in layer_labels]\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    sim_physical_energy_diff,\n",
-    "    yerr=sim_physical_uncertainties.values(),\n",
-    "    ecolor=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    color=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Physical\",\n",
-    ")\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    sim_logical_energy_diff,\n",
-    "    yerr=sim_logical_uncertainties.values(),\n",
-    "    color=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    ecolor=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Logical\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Hamiltonian Parameters (U, V)\", fontsize=18)\n",
-    "ax.set_ylabel(\"Energy above true ground state (in eV)\", fontsize=18)\n",
-    "ax.set_title(\"CUDA-Q AIM Circuits Simulation (lower is better)\", fontsize=20)\n",
-    "ax.legend(loc=\"upper right\", fontsize=18.5)\n",
-    "plt.xticks(fontsize=16)\n",
-    "plt.yticks(fontsize=16)\n",
-    "\n",
-    "ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=2)\n",
-    "plt.ylim(\n",
-    "    top=max(sim_physical_energy_diff) + max(sim_physical_uncertainties.values()) + 0.2, bottom=-0.2\n",
-    ")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running logical AIM on Infleqtion's hardware "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The entire workflow we've seen thus far can be seamlessly executed on real quantum hardware as well. CUDA-Q has integration with Infleqtion's gate-based neutral atom quantum computer, [Sqale](https://arxiv.org/html/2408.08288v2), allowing execution of CUDA-Q kernels on neutral-atom hardware via Infleqtion’s cross-platform Superstaq compiler API that performs low-level compilation and optimization under the hood. Indeed, the AIM research results seen in [our paper](https://arxiv.org/abs/2412.07670) were obtained via this complete end-to-end workflow.\n",
-    "\n",
-    "To do so, users can obtain a Superstaq API key from [superstaq.infleqtion.com](https://superstaq.infleqtion.com/) to gain access to Infleqtion's neutral-atom simulator, with [pre-registration](https://www.infleqtion.com/sqale-preregistration) open for access to Infleqtion’s neutral atom QPU."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a tutorial, let us reproduce the workflow we've run so far but on Infleqtion's QPU. We begin with the same GPU-enhanced VQE to generate the AIM circuits:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "\n",
-    "if cudaq.num_available_gpus() == 0:\n",
-    "    cudaq.set_target(\"qpp-cpu\", option=\"fp64\")\n",
-    "else:\n",
-    "    cudaq.set_target(\"nvidia\", option=\"fp64\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computed optimal angles=[1.5846845738799267, 1.5707961678256028] for U=1, V=-9\n",
-      "Computed optimal angles=[4.588033710930825, 4.712388365176642] for U=1, V=-1\n",
-      "Computed optimal angles=[-1.588651490745171, 1.5707962742876598] for U=1, V=7\n",
-      "Computed optimal angles=[1.64012940802256, 1.5707963354922125] for U=5, V=-9\n",
-      "Computed optimal angles=[2.1293956916868737, 1.5707963294715355] for U=5, V=-1\n",
-      "Computed optimal angles=[-1.6598458659836037, 1.570796331040382] for U=5, V=7\n",
-      "Computed optimal angles=[1.695151467539617, 1.5707960973500679] for U=9, V=-9\n",
-      "Computed optimal angles=[2.4149519241823376, 1.5707928509325972] for U=9, V=-1\n",
-      "Computed optimal angles=[-1.7301462945564499, 1.570796044872433] for U=9, V=7\n",
-      "\n",
-      "Finished building optimized circuits!\n"
-     ]
-    }
-   ],
-   "source": [
-    "device_circuit_dict = generate_circuit_set(\n",
-    "    ignore_meas_id=True\n",
-    ")  # Setting `ignore_meas_id=True` drops the noisy-identity gate from earlier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, we change backends! Before selecting an Infleqtion machine in CUDA-Q, we must first set our Superstaq API key, like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ['SUPERSTAQ_API_KEY'] = \"api_key\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we declare the type of execution we would like on Infleqtion's machine based on the keyword options specified:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cudaq.reset_target()\n",
-    "\n",
-    "# Set the following to run on Infleqtion's Sqale QPU:\n",
-    "cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\")\n",
-    "\n",
-    "# Set the following to run an ideal dry-run on Infleqtion's Sqale QPU:\n",
-    "# cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\", method=\"dry-run\")\n",
-    "\n",
-    "# Set the following to run a device-realistic noisy simulation of Infleqtion's Sqale QPU:\n",
-    "# cudaq.set_target(\"infleqtion\", machine=\"cq_sqale_qpu\", method=\"noise-sim\")\n",
-    "\n",
-    "# Set the following to run a local, ideal emulation:\n",
-    "# cudaq.set_target(\"infleqtion\", emulate=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With that, we're all set! That simple change instructs our AIM circuits to execute on Infleqtion's QPU (or simulator). Due to the general queue wait time of running on hardware, we optionally recommend enabling the `run_async=True` flag to asynchronously sample the circuits. This will allow the cell to be executed and not wait synchronously until all the jobs are complete, allowing other classical code to be run in the meantime. When using `run_async`, an optional directory to store the job information can be specified with `folder_path` (this will be important to later retrieve the job results from the same directory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Posting circuits associated with layer=('1:-9')\n",
-      "Posting circuits associated with layer=('1:-1')\n",
-      "Posting circuits associated with layer=('1:7')\n",
-      "Posting circuits associated with layer=('5:-9')\n",
-      "Posting circuits associated with layer=('5:-1')\n",
-      "Posting circuits associated with layer=('5:7')\n",
-      "Posting circuits associated with layer=('9:-9')\n",
-      "Posting circuits associated with layer=('9:-1')\n",
-      "Posting circuits associated with layer=('9:7')\n",
-      "\n",
-      "All circuits submitted for async sampling!\n"
-     ]
-    }
-   ],
-   "source": [
-    "submit_aim_circuits(\n",
-    "    device_circuit_dict, folder_path=\"hardware_aim_future_results\", shots_count=1000, run_async=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the above cell execution, all the circuits will post to execute on QPU. We can then return at a later time to retrieve the job results with the cell below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Retrieving all circuits counts associated with layer=('1:-9')\n",
-      "Retrieving all circuits counts associated with layer=('1:-1')\n",
-      "Retrieving all circuits counts associated with layer=('1:7')\n",
-      "Retrieving all circuits counts associated with layer=('5:-9')\n",
-      "Retrieving all circuits counts associated with layer=('5:-1')\n",
-      "Retrieving all circuits counts associated with layer=('5:7')\n",
-      "Retrieving all circuits counts associated with layer=('9:-9')\n",
-      "Retrieving all circuits counts associated with layer=('9:-1')\n",
-      "Retrieving all circuits counts associated with layer=('9:7')\n",
-      "\n",
-      "Obtained all circuit samples!\n"
-     ]
-    }
-   ],
-   "source": [
-    "aim_device_data = _get_async_results(circuit_layers, folder_path=\"hardware_aim_future_results\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "physical_energies, physical_uncertainties = aim_physical_energies(\n",
-    "    data_ordering, aim_device_data[\"physical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logical_energies, logical_uncertainties = aim_logical_energies(\n",
-    "    data_ordering, aim_device_data[\"logical\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Layer=(1, -9) has brute-force energy of: -18.251736027394713\n",
-      "Physical circuit of layer=(1, -9) got an energy of: -17.626499999999997\n",
-      "Logical circuit of layer=(1, -9) got an energy of: -17.69666562801761\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, -1) has brute-force energy of: -2.265564437074638\n",
-      "Physical circuit of layer=(1, -1) got an energy of: -2.1415\n",
-      "Logical circuit of layer=(1, -1) got an energy of: -2.2032104443266585\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(1, 7) has brute-force energy of: -14.252231964940428\n",
-      "Physical circuit of layer=(1, 7) got an energy of: -12.9955\n",
-      "Logical circuit of layer=(1, 7) got an energy of: -13.76919450035401\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -9) has brute-force energy of: -19.293350575766127\n",
-      "Physical circuit of layer=(5, -9) got an energy of: -18.331\n",
-      "Logical circuit of layer=(5, -9) got an energy of: -18.85730052910377\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, -1) has brute-force energy of: -3.608495283014149\n",
-      "Physical circuit of layer=(5, -1) got an energy of: -3.476\n",
-      "Logical circuit of layer=(5, -1) got an energy of: -3.5425689231532203\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(5, 7) has brute-force energy of: -15.305692796870582\n",
-      "Physical circuit of layer=(5, 7) got an energy of: -14.043500000000002\n",
-      "Logical circuit of layer=(5, 7) got an energy of: -14.795918428433312\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -9) has brute-force energy of: -20.39007993367173\n",
-      "Physical circuit of layer=(9, -9) got an energy of: -19.4715\n",
-      "Logical circuit of layer=(9, -9) got an energy of: -19.96524696701215\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, -1) has brute-force energy of: -5.260398644698076\n",
-      "Physical circuit of layer=(9, -1) got an energy of: -4.973\n",
-      "Logical circuit of layer=(9, -1) got an energy of: -5.207315773582224\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n",
-      "Layer=(9, 7) has brute-force energy of: -16.429650912487233\n",
-      "Physical circuit of layer=(9, 7) got an energy of: -15.182\n",
-      "Logical circuit of layer=(9, 7) got an energy of: -16.241375689575516\n",
-      "------------------------------------------------------------------------\n",
-      "Logical circuit achieved the lower energy!\n",
-      "------------------------------------------------------------------------ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "physical_energy_diff, logical_energy_diff = _get_energy_diff(\n",
-    "    bf_energies, physical_energies, logical_energies\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As before, we use the same metric of comparing against the true ground state energies; however, this time, both the physical and logical circuits are fully exposed to real hardware noise. Yet, we expect the use of logical qubits afforded to us by the `[[4,2,2]]` code to achieve energies closer to the true ground state than the bare physical circuits (up to a certain error threshold). And indeed they do! Visually, we can plot the energy deviations of both the physical and logical circuits from the cell above and observe that the logical circuits are able to outperform the physical circuits by obtaining much lower energies, demonstrating the power of error detection and the beginning possibilities of fault-tolerant quantum computation: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XY25uXpSMNXLkSMydO1fuO3Dr1i1cuXIFb7/9dqnGqq6xY8di1qxZRf9rGve///4rmmKm+Mgjb5L4+HhcuXIF4eHhSElJgY2NDSpXrozatWujVatWMDU1NXSIpUYQBISFheHu3buIjo5GWloaLCws4OLigipVqqBly5aoXLmyXmN48eIFLl68iIcPHyI1NRVWVlZwdnZGw4YN0bx5c5iZ8VAqLy8PoaGhuHfvHp4/f46MjAxYWFjA0dERtWrVQsuWLSVHc9K3O3fu4MaNG3j69CmysrLg5OQEZ2dneHt7w8PDo9TjKY+ysrLw4MED3Lt3D/Hx8UhJSUF+fj6cnJxQsWJFuLu7o2XLljA3NzdIbFevXsX9+/cRHx+PzMxM2NjYoEqVKhgxYoTK7URFReHKlSuIjIxEeno6bGxsULVqVTRq1AiNGzfmdGZKCIKAqKgohIWFISoqCikpKcjIyICdnV3R7/Htt9/W+3pckbCwMNy+fbto+2JqaoqKFSuie/fuaq8jIiIicPv2bTx58gSpqamQyWRF26omTZpwKrYyylg+14SEBFy/fh2RkZFISEhAVlYWrKysYG9vjxo1aqBBgwbw8PDg+qiUJSQk4OrVq4iIiEBSUhJycnKKthFeXl5o2rRpqe8nZmZm4sqVKwgLC8PLly9hYmICZ2dnVK9eHe3bt4etrW2pxlOS4jcEmJubo3fv3gaKRrnHjx/jxo0bePz4MVJTUyEIAipUqIAaNWrgrbfeQr169QwdolGLiYnBjRs3EBkZiZSUFBQUFMDFxQUuLi5o0KCB6EYTfSsoKMDNmzdx9+5dPHv2DOnp6TA3N0flypXRp08fg+2baCIqKgqhoaF49uxZ0b6WlZUVbGxsUKlSJXh6eqJWrVqS0y0bo2fPnuHq1at49OgRUlJSYGZmBhcXFzRu3BjNmzeHiYn+72s3lu1/WWIM526K0/Z4cODAgdiyZYtcWUBAADp27KivkIn0TyAiIoOIjo4WTE1NBQByD09PTyEnJ8egsfn7+4viAiA8evRIp/14eHiI+hg9erTK9aVinDt3rk5jFARBaNOmjWRf69at07jNgoICwdPTU9TmL7/8UrRMWlqaUKFCBdEyO3bs0MGr0q20tDTB3t5eFKuNjY3w9OlTnff37NkzwdbWVtSfs7OzwX8/hUaNGiWKr0+fPnLL+Pn5iZaZOHGiTvrX9vcxd+5cUf327dsLgiAIUVFRgomJidxzn376qdox9urVS64Ne3t7IT09XRAE6fWDPn7fJdH2fZSqX9z+/fuFTp06id7T1x+Ojo7C2LFjhcjISJX7Hj16tML21Hn4+/uL2n706JFoOQ8PD5VjU+Ts2bPCmDFjhIoVKyqNycTERGjTpo2wdOlSISMjQ+t+X7d//37Bz89PkMlkCvt3cnISPv30UyEiIkKurtR7LvX+CYL0e6jpQ9l7L/Vb1va3dPToUWHgwIGCjY2N0rhkMpng7e0trFixQkhLS9O4P6n9kuL7C0lJScLChQsl1x2vP7y8vITly5cL2dnZWr0H2lL0+Sv6vmjK19dX1Ievr6/a7eTk5AiHDh0Spk6dKrRo0ULp+qrwYW1tLXTq1EnYsGGDkJubq/PXcfLkSblljh07JvTu3VuwsrJSGFNJ8vPzhQ0bNggtWrRQ+tpcXV2F//3vf0JsbKzacb7u33//FS3fu3dvtd6biIgIyRjNzc3V/t1Jve7Q0FCV6t66dUtYvHix8N5770nuE0qtHxo2bCh8/fXXWu8rqrKOePz4sTB9+nShWrVqCmNS9fcXGhoqTJo0SWlbhY+mTZsKc+fOFRISErR6jbogtT1Q5XehK6mpqUL9+vVF/VerVk2Ii4vTuN2BAweK2jQzMxPOnj2rVjvG8rlGRUUJc+bMERo1alRiHMCr45+hQ4cK27ZtE7KyskpsX5Xfi7rU+V4p+h5q8lC2PyO1P6DN+YzU1FRh+fLlwttvv610HxGAYGtrK3zwwQfC8ePHNe5PEFTbply/fl0YNmyY0n0yCwsLoVu3bsK5c+e0ikdXzp8/L4rxnXfe0bg9fRyXPHnyRPjmm28kz9kUf1StWlWYPHmyEBYWpnY/06dPF7X3888/q9XGX3/9JRlXu3bt1GonMTFRdI6yUqVKQkFBgVrtCMKrfZOvv/5aqFWrVonvX7169YRp06YJMTExavdTSJXjnTt37giffPKJUKlSJYWxKNtn05a6+4iKXL16Vfjss8+EKlWqqLyudHNzEwYOHCisW7dOePHihe5fnBIlrYvz8/OFjRs3Ct7e3krXrc7OzsL06dNF+966oO/tv76O+0+ePKmzdjU5PiztczeldTwoCIKQmZkpasPe3l6l/SwiY8VkECIiA5k/f77kTskPP/xg6NCYDFLMkSNHJPvq0KGDxm0GBgaK2jMzMxMd2EhdWHz33Xe1fUk6p+g7M2bMGL31OXbsWMk+//77b731qaqUlBTJE3K7d++WW07qfXN0dBQyMzO1jkHb34eyZBBBEITu3bvLPefk5KTWgdGzZ88EMzMzuTbGjRtX9PybkAzy7Nkz0ftY0sPS0lJYtWqVSn2XpWSQW7duCZ06ddIoPjc3N2Hz5s0a913o+fPnwrvvvqtW39bW1sLy5cuL2ijvySC3b98WOnTooFGMzs7OQkBAgEb9lnTh6uDBg4Krq6ta8dSpU0e4e/euRvHoQllJBklOThY++ugjwcnJSevv6D///KPT11F48i8uLk7o2bOnSnEoc+/ePaFly5ZqvS4nJydh27ZtKsUpJSUlRbQtdHBwEPLy8lR+b9atW6cwvsOHD6vczsuXL0VJPs7OziVeBFqzZo3KF60VPSwtLYUpU6ZonKRV0jpi6dKlgrW1dYlxlPT7e/z4sdC/f3+NXmPFihWF5cuXa3RRTVcMnQwiCIJw8+ZNyc+ia9euQn5+vtrtrVy5UvI1/fTTTyq3YSyfa3x8vDBhwgTROkGdR6VKlYT//vtPaT9MBlHfX3/9pfQCsrJHx44dNd7fULZNycnJEaZOnapScubrjzFjxhj85okZM2aI4lq4cKHG7enyuCQjI0P46quvBHNzc7U/axMTE2HcuHFCYmKiyv0dPnxY1E7Pnj3Vinn48OGS8ZiZmQkpKSkqt7N3715RGwMHDlQrlvj4eGHcuHGSN76V9LCxsRHmzJmj0cVWZcc7ubm5wtdff63SutWYk0FevnwpjB07tsRktJIepqamQlRUlN5eZ3HK1sUPHz4UWrdurVb89vb2woYNG3QSW2lt/8tbMoihzt2UxvHg66Re47FjxzSKncgY6H9sJSIikrRnzx5Rmbm5OT766CMDREPKdOvWDZ6enqLy4OBgxMfHa9TmX3/9JSp79913RcM3jh49WrTcsWPHEBMTo1G/+rJ3717J8o8//lhvfSr6rezevVtvfapqx44dyMjIkCurWLGiaN7JgQMHiobsTUpKwj///KPvELVWfDqXxMREteLeuHEj8vLylLZZnt27dw+tWrXC0aNH1aqXnZ2NyZMn44cfftBTZKVv3bp1aNmyJU6ePKlR/ejoaIwYMQJfffWVaOotVT148ACtW7fGkSNH1KqXmZmJL774AlOmTNGo37Lk77//hre3N86ePatR/RcvXmD06NEYM2aM5BR5mlq1ahV69eqFZ8+eqVUvPDwcPj4+uHHjhs5iKY/i4uLw559/IjExUat2Hj9+jH79+uG7777TUWSvREZGwtvbGwcPHtSqnXPnzqFVq1YICQlRq15iYiKGDh2KZcuWadSvnZ0dvL295cqSk5Nx5coVldsIDAzU6LniTp48iYKCArmyTp06lTj9xMaNG3Hr1i2V+5GSnZ2NFStWwM/PD8+fP9eqrdcJgoAPP/wQ06dPR2ZmplZtHThwAE2aNJE8hlNFQkICvvjiCwwfPlyn68CyplGjRvjtt99E5cePH8fChQvVauvKlSuYPn26qLxnz5746quvVGrDWD7X06dPo3Hjxvjjjz9E+8fqePnyJR4/fqxxfZKXk5ODESNG4MMPP8TLly81auP06dN4++23FR4vayI9PR3du3fHzz//LFpvl2TDhg3o27evVt8zbR0+fFhU5ufnV/qBFBMdHY327dtjyZIlGv2eCwoKsG7dOrz99tu4e/euSnV8fHxEU+qdPn1arc+n+NTChfLy8nD69GmV25HaZ+jcubPK9YODg9GkSROsW7cO+fn5KtcrlJGRgfnz56NHjx5ISUlRu76U7OxsvP/++1i8eLFBv/PaevbsGXx8fODv76/x8W6h/Px8o3gvbt++jXbt2uHixYtq1UtJScGYMWPwzTffaNW/sWz/yxpjOHdTnK6OB4vr1KmTqOzQoUM67YOoNHGiayIiA3j27BmuX78uKvfz8yszczm+SWQyGfr27Yvly5fLlefn5+P06dPo37+/Wu0lJydLHnBIJX74+fnBw8ND7qReQUEBAgICMHPmTLX61Zfc3FzJExCenp5o37693vpt3749PD09ERkZKVd+/PhxFBQUlMp8oopIJfsMHToUFhYWcmUVKlRA//79sWnTJlH9IUOG6DVGbfXt2xdOTk5yFwj9/f0xePBgler7+/vL/V+/fn20bdtWpzEaq6dPn6Jr166IioqSK7e3t4enpycqVaqEtLQ0REREKDzxPHv2bPj4+MDHx6c0QtabBQsW4Ntvv1X4vIWFBWrXro1KlSpBJpMhNjYW4eHhkie+ly5disTERKxfv16tGGJjY9G1a1eFF09MTExQs2ZNVKtWDdnZ2YiKihIlHqxcuRI1a9ZUq9+yZPv27Rg+fLjCCw6Wlpbw9PRE1apVkZKSgidPnij87gYEBCAlJQW7du3Sej29adMmfP7556ITSTVq1ICrqytsbW0RHx+PsLAwyZN08fHxGDZsGK5evSpaP1PJbGxsUKNGDTg4OMDOzg6ZmZlISEhAeHi45ElmQRAwb948ODg44IsvvtC6/9TUVHTv3l302zU3N0fNmjXh4uKC/Px8REdH4+nTpwovTNy6dQvvvfeewgsPhe25uroiNTUVT548ESUDf/XVV5KJw6ro0qULgoOD5coCAwPRunVrleorughU2I6qpJbt0qWLyvWLk8lkcHd3h5OTExwcHCCTyZCcnIxHjx4hKSlJsk5wcDB69+6Nc+fOiS6OaWLWrFmi/Q0AqFatGlxdXWFnZ4fnz58jKioK6enpCtsJCAjAhx9+qHAdaGpqilq1aqFy5cqwtLQs2lZJrXe2bduGhIQEHDx4EKamppq/uDJs7NixOHXqFAICAuTKv/vuO/j4+Kh0UTgpKQmDBg1CTk6OXHmNGjWwcePGEpOYAOP5XP/55x8MGTIE2dnZCpdxcHBA9erV4ezsjPz8fCQmJiIyMlLp95a0k5+fj4EDB+LAgQMKl6lUqRLc3d3h4OCAZ8+eITIyUvJzTE9PxwcffIDt27dj4MCBWsfVv39/0YW4wn1mZ2dn5OXlITo6Gk+ePJFs49ChQ/jpp58wa9YsrWLRRGxsLEJDQ+XKLC0tRYmRpe358+fw8/PDw4cPFS5To0YNVKtWDebm5oiJiUFkZKTkxcyHDx/Cz88Pp0+fhpeXl9J+bW1t0bp1a7lk69TUVFy6dAnt2rUrMe47d+4oTYgODAxEz549S2yncNniVN0POH78OPr06aMw8VImk6FmzZpwdnaGjY0NXrx4gYcPH0ouf/LkSfj5+eHUqVOws7NTqX9Fxo4dK3nzhYeHB6pUqQJra2vExMQgOjoaWVlZWvWlLwUFBejTpw/u3LmjcJmqVavC3d296Eaj5ORkvHz5Ek+ePNHZBXddSkhIQP/+/REbGytXbm1tDU9PT1SpUgVJSUmIjIxUuM+4ePFi2NnZYfbs2Wr3byzb/7LGGM7dFKer40Epvr6+orIjR47gl19+0SpmIoMx3KAkRERvLqnhFwEIs2bNMnRogiBwmhgp+/fvl+xv5syZarf1xx9/iNpRNsXG7NmzRcvXrVtX25ekM1evXpV8b4YMGaL3vgcPHizZtybz9epKWFiYZEwXL16UXP7EiROiZU1MTIQnT55oFYe2v4+SpokRBEGYNGmSKG5VhhyVmie6+HDe5XmamI4dOxb9LZPJhMGDBwtnz54VTQuQn58vnDlzRvDx8ZFsx8vLS+mwpOHh4UJwcLDco2rVqqJ29uzZI1ru9UdcXJyobV0Mx7xz507J1yWTyYT3339fOHLkiJCeni6q9+LFC+H333+XfC0A1B42tlevXpLtODg4CEuWLBGePXsmqnP9+nVh4sSJcsP0WlpaCm3atBG1o2jagaysLLn3+aOPPhLV/eijj5R+NoWPq1evKnx92k4Tc+/ePYXz0NetW1fYuHGjaAjqgoIC4cyZM8IHH3wgWQ+A8OOPP6ocg9R+ibe3t9xUAxUrVhR+/PFHyX2V5ORkYfXq1YKzs7NkLNoMTa6psjJNzIMHD+Te4+HDhwtbt24V7t27p3BKh8zMTCEwMFAYMWKE5PD1FhYWwpUrV7R+HW+99Zbc/02aNBF27NghJCcni+o/e/ZM8juXk5MjNG7cWPKzqF69urBu3TrJ4d7Pnz8vDB06VG75SpUqCQ0bNhS1U9IQ4FJDPHfu3Fml9+XmzZsKf2OF69P4+HiV2vLy8hLVDw8PL7Fe+/btBeDVsOMdO3YUli5dKly4cEFIS0tTWOf+/fvC/PnzFa7Hp02bplLMhaTWEQ0aNJD7/tnZ2QnfffedcP/+fVH9nJwcYffu3UJwcLDouXPnzikcWt7Hx0fYvXu3kJSUJKqXnJwsbN68WahTp45k3Xnz5qn1GnXBGKaJKZSeni76DQMQqlatKjx//rzE+v369RPVNTMzE86fP69S/8byuZ49e1awtLSUbMvW1lb45ptvhIsXL0qub/Pz84Vbt24Jq1atEjp06FC0T1LSdsTQ08RERUVpvW9a+FB23KHtNDHff/+9wnXr4MGDhTNnzoj2w1NSUoSNGzcKtWvXlqxXoUIFldarhaS2fa8fRwAQWrVqJezdu1dITU0V1b9//77w4YcfSk4rYWFhIURERKgci678888/oliaN2+uVZvaHpcUFBQIXbt2lfzMLC0thRkzZkhuO6KiooRFixYJdnZ2knWbNm2q0vRnUuvm+fPnqxT7r7/+qnQ/oEmTJiq18/TpU1FdNzc3leqGh4cL9vb2Ct+DjRs3Sh5PZmRkCP/884/QokULybrqTDks9R4W38a4uLgIy5cvl1xvpKenCwEBAXo9j6TpNDFr1qyRfH8aNmwo/PXXX6Kppl+XlpYmXLhwQViwYIHQpk2bonWBrs/tKiO1Li5+fqN+/frCtm3bRMf++fn5wokTJ4QePXpIvgcmJibCuXPn1IrHENt/fR33Jycnyz333nvvidqdPXu2Su3evn1b6ftmDOdu9HU8qEhSUpLk601ISFC5DSJjwmQQIiIDmDNnjuROkDZzqesSk0HEHj9+LNlfnz591G6rVatWonYmTJigcPn79+9L9n369GktXpHu/PXXX5LxLV26VO99L1myRLLvrVu36r1vRb7++mtRPPXr11e4fH5+vlCjRg1RHVVPACmi7e9DlWSQK1euiJZR5aLqxx9/LFfH1NRUdMG9PCeDFD6cnJxUOgGUn58vjBo1SrKNo0ePqvV6dDV/urYnXR8+fCh50rBKlSpCYGCgSm2kpKQI3bp1E7VhZ2cnREZGqtTG9u3bJd/XFi1aqJTYdOrUKcHBwUHp56zqxX1tkzb00W5ubq7g7e0t+bo+/PBDISMjo8Q2du7cKXmhy9zcXGkSy+sU7ZcUPjp37qzSSZmIiAjB3d1dVN/NzU1hYoO+lKVkEG9vb2Hbtm1Cbm6u2v1funRJ8j3v0aOHWu1IvY7i32lNPsN58+ZJttezZ0/JJJDi9uzZo/BCbuGjpPV8VlaWXGITAMHKykrIzMwssf/ly5eL1n+mpqZyZX///XeJ7URHR4vidnd3L7GeIAhCnz59hNmzZ0smzpUkKSlJGDJkiKhvMzMzITo6WuV2SlpHeHt7axRfQkKC5Pe3QoUKwrZt21RqIzs7Wxg9erTka1SUKKwvxpQMIgiCcOfOHcHW1lYUT5cuXZT+nn/55RfJ16HqsYexfK6JiYlC9erVJV9Lr1691P7OFl7437Jli9LlDJ0MUpyu9k112e7ly5clLxZaW1urdM4mPT1dGDFihOT70qZNG5W3p8q2fSYmJsIvv/yiUjvr1q2TbGPGjBkq1delWbNmieIYNWqUVm1qe1yiaJ1Su3btEi+QCoIgREZGCs2bN5dsQ5XkxlOnTonqqbqv1rdvX7l6jo6Ocv/LZDLJRIziNm/erNHnkp2dLbRs2VJU19zcXOXvZ0FBgTBz5kzJ92/37t0qtaFo+1b46Nmzp+TF4dKkaTJI69atRfX69++v8GYyZcLCwoQJEyYIMTExGrwCzUiti19/fPTRRyrt865evVoysa1BgwYqJV0JgvFs//V13C8Vly6OLY3l3I2+jgeVkfq+HDt2TKd9EJUWJoMQERmAohMDjx8/NnRogiAwGUSR4ifqgVcXDNVx+/ZtybhLuoutXbt2ojpjx47V5uXojNQJHQDCf//9p/e+//vvP8m+tU2k0FReXp7g6uoqiueHH35QWk/q5EetWrWUjvpQEm1/H6okgwiCIDRp0kRumZJGrUlPTxfdPfX++++LlivvySA2NjZCaGioyu1kZWUJdevWFbUzdOhQtV6PsSSDvPPOO6L6Li4uat+hmJOTI7pDEoAwadKkEuvm5uZKXoSpU6eOSidNC50+fVrpxeCynAyiKNlv6NChaq2f9uzZIzlCRKdOnVSqr+xCb6dOnYScnByVYwkMDJRsR93EKm0pSgYpjYc6ySDabIcKRURECE5OTnIxyGQy4d69eyq3oezkn6YjLMTGxkr+dn18fFQ6KV1oz549kienCx+qnOiXWieeOHGixHrFRzbq06ePKOlYWcJxoYCAAFH/qu5navsdyc/PlxzlQZ3R95StI5o0aSIavUhVxZNXgVfb70uXLqndVvGRZIBXF6dKU0kXy3TxUOX7/rqNGzdKtqNoO3XhwgXB3NxctHyvXr1U7tNYPtfPPvtM8rWPHTtWNFqcLjEZpGRS+5ampqbCv//+q3L/+fn5wsCBAyXfm4CAAJXaULbtW79+vcqxCIIgjBw5UtRGtWrV1GpDF6Tu8F+8eLFWbWpzXJKcnCw5soerq6taxyVxcXFCvXr1RO2YmJiU+L3Lzs4WjcJnYWEheZf96/Lz80XJH999950okWn79u0lxj927FiNvqcLFiyQfM379u0rsW5xM2bMELXVqFEjleoq275169ZNrWMFfdEkGSQxMVG0j1mhQgWDJ7aoQ1kyyKBBg9S6eK9oJJyVK1eqVN9Ytv9lLRnEGM7dCIJ+jgdL0rNnT1FfJZ3fJTJW2k3STEREGomOjpYsr1y5cilHQuqoVq2aqOzp06dqtfHXX3+JyurVq4e2bdsqrTd69GhR2d9//20U80THxMRIllepUkXvfbu4uEiWK/qN6dvhw4dFc/aamJhg5MiRSutJfb4RERE4ffq0TuPTh7Fjx8r9/+DBA7k5j4v7+++/kZqaqrSNN8HSpUvRpEkTlZe3tLTE119/LSo/d+6cLsMqFVevXsWJEydE5bt27ULNmjXVasvc3Bxbt24tmh+5UEBAAJKTk5XWPXDggOT6a/369XB2dlY5Bh8fH3z11VcqL1+W/Prrr6Ky6tWrY+3atZDJZCq3069fP3zyySei8pMnT+LWrVsax+fo6IgtW7bA3Nxc5TqdO3dG+/btReVl8bdUGtT5nBWpWbMmvvvuO7kyQRCwdetWrdv29vbWaK5wAPD390d2drZcmZWVFQICAmBlZaVyO/369cOYMWM0iqFQ586dRWWBgYFK6+Tn5+PUqVNyZV26dEGXLl3UakfRMlIxSdH2O2JiYoLff/8dNjY2cuVbtmzRql0AMDMzg7+/P+zs7NSu++zZM2zcuFFUvmbNGnh7e6vd3po1a+Dm5iZXdujQITx48EDttsqTkSNH4qOPPhKVf//996LvZWJiIgYPHozc3Fy5cg8PDwQEBKjUn7F8rjExMfjjjz9E5d7e3li7di1MTU3VjoV048aNG5LHYJMnT0bPnj1VbsfExAR//vknqlatKnpOav9KHYMGDZL83SgzZ84cUdnTp0/x6NEjrWJRl9Rvo0aNGqUaw+s2bNggOjYFgN9//12t4xJnZ2cEBASItokFBQX47bfflNa1sLBAhw4d5MpycnKUHlMDQEhICJKSkuTK+vXrJ1qXqbIf8N9//4nKStoPyMrKwsqVK0Xl3333HXr37l1in8XNnz8fLVu2lCu7deuWSvErYm9vjz///FOtYwVjEh0dDUEQ5Mo6duwIe3t7A0WkOy4uLli7di1MTFS/PDl58mR07dpVVC61PS3OWLb/ZY2xnLtRRpvjwZJIbZ/Cw8P10heRvjEZhIjIABITE0Vl5ubmopOgZFwcHR1FZeokY+Tl5WHz5s2i8pISBYBXJ3yKX5hIS0vDzp07Ve5fXxISEiTLHRwc9N631GcCAPHx8XrvW4pUsk+nTp1EB4nF1atXD23atFGpPWMzfPhw0ckVf39/hcsXf65y5cro1auXXmIzVjVq1MC4cePUrjdw4EDRyZInT54Y7PuuqSVLlojKBg8eDB8fH43aq169Oj788EO5MlXWj2vXrhWV9e3bF76+vmrHMGPGDIXJaWVVcHAwrl27JipftGgRKlSooHZ7CxculNwulHSCXJkJEybA1dVV7XqDBg0SlYWEhGgcB5Vs+PDhMDMzkysLDg7Wut05c+ZodNFUEASsW7dOVP7ZZ5+pfWITAH788Ue1EkiKK57AAZR88ebKlStISUkRtVO8rQcPHiAqKkppW5pcBNKlqlWronv37nJljx8/FiXYqqtPnz5o0aKFRnVXrlyJnJwcubLWrVtjxIgRGrVnZ2eHL7/8Uq5MEARs2LBBo/bKk19//RWNGzeWKysoKMDw4cPx/PnzorIxY8bg8ePHcsuZm5tjx44dcHJyUqkvY/lc169fL0pqMTExQUBAgGhdSaVLar/EyclJlNSoCnt7eyxcuFBUfuXKFVy8eFGj+GQymUax1KlTB82bNxeVl+b+jyAIePLkiai8evXqpRZDcVKfd+fOndG3b1+122rTpo3kuuTPP/9EVlaW0rqa7AcU33a7uLigUaNGaieFPnz4ULRurVevXonnMAICAhAXFydX5uHhoXGSvJmZGWbOnCkq1+acyLhx40p8Hcas+H4egHKRCAIAc+fO1eic4c8//ywqu3PnDs6cOaO0nrFs/8saYzl3o4ymx4OqkFp/REZG6qUvIn1jMggRkQFkZmaKykrjwjlpx9raWlQm9VkqcvDgQcTGxsqVyWQylZJBHB0d0adPH1G5sovupUXRe6AoUUOXFP1u1PlcdOXFixf4999/ReVSo35IkVpu165dkncqGRNnZ2e8//77cmU7d+6UTJR6+PCh6E67ESNGlNk7dTQ1atQojU70Ozo6olatWqLysnT3SXZ2Nvbu3Ssq//zzz7VqV2o9quyEUFZWluSJUXXvsixkY2ODYcOGaVTXWB09elRU5ujoKJlIoQonJyfJulL9qErTz6v4XYdA2fodlUUVK1YUrb8uXbokuttRHa6urujRo4dGdR88eICHDx+KyjX9Trm4uGiV2NiyZUvRPk1ISIjSu+SKr8NcXV3RsGFDtG/fHpaWlkqXfd39+/dFI6o1aNBAckQ8fZK6K/PChQtatanp5wkA27dvF5Vpu62SuuBQ0sWLN4G1tTX+/vtvUaJhbGwshg4divz8fCxbtgz79+8X1f3pp5/QunVrlfsyls/177//FpX16NEDDRo00CoW0p7UfsngwYM1Pl8zdOhQyYu3mu7/tGvXDvXr19eorqH3f54/fy66GAtIj8BaGiIjI3H//n1R+fjx4zVuc8KECaKyxMREXLp0SWk9TZJBij/fuXNnyGQyUTJnRESE0ouXUv1IxVOc1Pp0/Pjxon0QdfTp00c0mpc220lt9gOMgdQ5tStXrmi1/2wMrKysMHz4cI3qNmrUSHK7f+TIEaX1jGX7X5YYy7kbZbQ5HlSF1PZJKqmRqCxgMggRkQHk5eWJyrQ5YKLSUVBQICpTZ2hsqTsafH194eHhoVL9UaNGicrOnDlj8CHq8vPzJctL4zutqA+pk0z6tnnzZtEdfhUqVED//v1Vqj948GDR68nIyMCOHTt0FqO+FJ/mJS0tDbt27RIt5+/vLzpx8SZOEaPpXRTAq7v6itNmSM3SdunSJdG0DK6uriVOlVWS5s2bi4YbPX/+vMLlr127Jvq92tvbi+5MV8fgwYM1rmuMpEZt6Nu3r1brdqmTfo8ePRIlSqrC1dVV8vegCmP+Hc2ePRvBwcE6e0jdBWwoxUfPSUpKwsuXLzVur2PHjhrfBSZ1R3bjxo3h5eWlcTyaJkoBgKmpqWhUovz8fAQFBSmsI3URCHh1gr1du3ZKly3pudIcFaSQ1OhKUgk7qjIxMUHHjh01qhsdHS26cGZhYaH1SGYuLi6i79iVK1dE26PSpst1jqYjsXh5eUmO2BUUFIRhw4ZhxowZouf69u0ruitXGWP5XJ89e4bbt2+Lysv6Rcvy4NmzZ6IREgBgyJAhGrdpbW0teTyo6ehYZfk4oviUJoU0GXFOF6Q+A2tra42mOCnUrl07eHp6qtTX65o3by4a4ejatWuSowoDry7SFp9GpjCBo127dqIbmXS9H5CTkyO5LzVgwACl9Upiamoq2oeJiorSaBrgKlWqlPkEu1q1aolGngsPD8f8+fMNFJFudO3aVasbIqX2uZWNtmQs2/+yxljO3SijzfGgKqS2T4q2ZUTGjmMPEhEZgNQw0sZyEYIUK74TDEiPFiIlNjYWhw4dEpWrOmoEAHTv3h1Vq1aVGy4ZeHWBXWr42dKiaFj0lJQUVKxYUa99Sw2bCUB0YFEapEZpGTBggMqxODk5oVevXqIkCn9/f3z88cc6iVFfevToIfpu+vv7y32/CwoKRHO0tmzZEk2aNCm1OI1Fw4YNNa4rdWdhWdp+SM3B3rJlS7US66SYmZnBzc0N9+7dKyp7+PAhMjIyJKdgk7o7r1mzZlqNUtO8eXOYmZlJJnyWNYIgSJ5QU+fuayne3t4wMTERJVcGBwerPRy3Nid3jfl3VLt2bclpwzSlj6GkExIScPDgQYSGhuLGjRuIjIxEamoqUlJSkJGRoVZbSUlJqFy5skZxSN3hrCqpdYAm84Xrsn6XLl1EIx8EBgZKjgyXlZUlOmn6+l28Xbp0wcmTJ4v+l5oG5vU+pGLRVGESy4ULF3Djxg3cvXsXSUlJSElJQWpqqmRytSLanGytX7++xvuDp06dEpXVq1dPdLeyJjw9PeW2VZmZmXjw4IFW+wba0uU6RxtDhw7FqVOnsGbNGrlyqaHDPT091R4h0Vg+V0XTcmhzkZ90Q2o0IhMTE63X723atBFNHaDpyEdl+ThC0T6CqudUdE3qM2jSpInW8bRp00Z04bmkZBATExP4+fnJ3YVfUFCAkydPKkwmKj4aauG229LSEu3bt8eJEyeKngsMDJRMOBMEQW5/AXh1w1OnTp2Uxnv58mVR/3Z2dlol1RaSSqa5ceOG2tO9aLOfaCysrKzQpUsXHDx4UK583rx5OHfuHL7++mt07txZNJWssdN2nSr12RaOmCJ1XsFYtv9ljbGcu1FG379zqXjUmS6eyJgwGYSIyACkTkympaUhPz9frxmtpB2pu1dVvYtl06ZNoguEtra2GDhwoMr9m5qaYvjw4Vi2bJlc+caNG/H9998rPQBMSUnBnTt3VO7rdbVr14azs7PC5xXtrCclJek9GUTRRYLSnkf1ypUruHnzpqhcnWSfwuWLJ4OcP38e9+/fR7169bSKUZ/MzMwwcuRIuflET58+jYiIiKJpAY4fP46oqCi5em/iqCAAtPpdSJ2cLGkOamNy69YtUZmtra3WUwEAkEzkSEhIkFxHSd1t3rRpU636t7S0RP369SVfY1mTnJwseXFA21EmbGxs4OXlhbt378qVazLU6pv8OzKUs2fPYvHixThy5IjO7njT5mK/NnPASw2Xru06wMPDA46Ojhq/JnWGiD9//rzoO1s8GWT27NlF/z99+hR3794VJVEJgiAafaTwgpS6Xr58iR9++AFbtmzRaLQfKYb6fkitxytWrKiTbZVUMkxCQoLW7ZYXy5cvx8WLF3H9+nWFy1hYWGDnzp1qT0lpLJ+r1LQctWrV0jgxjnRHalSQevXqqX1xqjip/afCJDl1j1vL8v6PomQQRTeX6JvU562LEdWaN28umpJClX3dLl26iKZkCAwMlEwGKZ7kWbNmTdSsWVOurdeTQRQlhd68eRMvXryQK2vWrBkqVaqkNFap9WnVqlV1sj6VmvJXk+2kNvsBxmTmzJk4dOiQaITV48eP4/jx43BxccF7770HPz8/dOjQAbVr1zZQpKrTdp9bqn5ycjKSkpJEI+wAxrP9L2uM5dyNMvr+nSuaLl5R4hGRMWMyCBGRAVStWlWyPDk5We8Xz0lzxUfkAFSf31bqzrV+/fqpPSTq6NGjRckg0dHROHbsGN59912F9a5evVri3R2K+Pv7Y8yYMQqfV/R9Lo2h8xTdyVTaySBSn6+7u7vaF1PeffdduLi4IC4uTq78r7/+wo8//qhNiHo3duxYuWQQQRCwYcOGoiFMi0+TZGlpiWHDhpVqjMZC1yPXlKU5g6WS6nbs2KG36ZASEhIkTxBIrZ8UrcvUUbVq1XKRDKJoSGpXV1et265WrZooGURRf8oYYgSoN1VaWhomTZqETZs26Xx9o82dVdoMLy31nZOapkRdzs7OGu//vPXWW6hSpYpcIsWdO3fw7Nkz0W+veJJInTp14O7uXvS/t7c37O3t5UZQCwwMFCWDXL9+XbRelhqqviQBAQGYOnWqzk9+G+r7IbWtOn36tNbDYitSXi4a6IKVlRX+/vtvtGzZUuEIgEuWLNHormJj+VxjYmJEZeXlomVZJ7Vt0NW+j6L+1D1uLcvHEYpG4MvLy4OFhUWpxVGoND9vVfZ1paZmUZQUWry8eEJp8f9jY2Nx69YtNGrUqMT2VZkqTmp9+uDBA6PaTmqzH2BM2rVrh3nz5mHu3LmSz8fFxWHDhg1Fow85OzvDx8cHnTp1Qrdu3YzypiJt97kdHR1hYWEhmh46MTFRch/WWLb/ZY2xnLtRRt+/c6kbIMzMzJgIQmVS2RpDioionKhRo4Zk+aNHj0o5EvXo+kRBWbqAGRkZKXnXjKLP8nUXL16UHJVD3VEjgFfz2UvdraLuMMm6pOg9kLrrTdfu378vWf76XTH6lpWVhW3btonKR44cqfYBgpmZGYYPHy4q37RpE/Lz8zWOsTQ0aNBANIVEQEAACgoKkJCQgH379sk917dvX7UvNlHZV9onRhQljEldrNVFEllpJ6Lpi6IT1rp4fVInbDRJBqHSkZKSgu7du2Pjxo162W/Tpk1t7tTW1zpA2xOSql4IKukikKmpKXx9feXKXr9DWFnb6k4Rs3TpUowZM0Yv63dDfT+MZVv1pqpTp47CKVNatGiBzz//XKN2jeVzTUtLE5WpO8oJ6YfU/og+tw1v2v6PovWy1EgQpaE0P29VPusGDRqIklHu3buH6OhoubK0tDTRdHfFt90tWrQQrVd0uR9gLOtTZbQd0ceYzJkzB6tWrVJpFJ0XL15gz549+Oyzz+Dl5YWWLVvi119/NdjvTIq+jrsV/c7KwvfVGJWF903fv3OpEa14UwqVVRwZhIjIAN566y3J8kuXLhnFvJaWlpaS5erOA18Sqbv9DDVEaEkUDVVc/M4KKcVHRABeHbjY2NhoNLxemzZtcO3aNbmyffv2KcyC1zdF3+fLly9j8ODBeu378uXLkuWKYtKHvXv3Sh50enl5afT5Ss0t+vTpUxw9ehTvvfeeRjGWlrFjx+LixYtF/z958gT//fcfwsLCkJ2dLVqW3jylMWLQ6xQlUUmdDNPFfOWGmvNc1xSdLNTXe8R5d43X2LFjcf78ecnn7Ozs0KZNG7Ro0QI1atRAtWrVYGNjAysrK8l9yU8//VS0/2IoxbdJAHRyV7KifWhVde7cWZRgGhgYiBEjRhT9n5KSgitXrsgtI3XhpkuXLjhw4EDR/0FBQaIpKaWGjVfljuBC+/fvx1dffSX5nImJCRo3boy2bduiZs2aqFGjBhwcHGBlZQUrKyvR9IYHDx7EggULVO5bn4xlW/WmWr9+PQ4ePCj53LVr13Do0CGN9omN5XOVKi8v+w9lXWnvH75p+z+KRkU11EXq0vy8Vf2sO3fujC1btsiVBQYGyt1IdPr0abkpiGUymWjbbWpqCj8/P/zzzz9y7XzxxRdF/+fl5eH06dNy9czNzRUm473OWNanb5JJkyahT58+WLhwIbZs2YLU1FSV6l29ehVXr17FDz/8gKVLl0refFTa9LXPLbV/D/D7qim+b9LbCSaDUFnFZBAiIgNQlPBx+fJlTJw4sZSjEVN0Z5LUXUzakDp4MdaRAhQNz1lS8k5mZqbkEHopKSlo3769TmIDXh30bNmyBZMnT9ZZm6pq0aKFZHnxCxX6oKiP0kwGUTQqy6hRo3Taz19//WX0ySBDhgzBl19+KXfA5O/vL5oSws3NDV27di3t8MgImJkZx+GHnZ2dqEwX2zhVT8oZO0V3a6Wmpmp9sVvqPSovwziXN8eOHcOePXtE5R4eHliwYAEGDRqk1slcY7pDU+o7p4vfr6JpLVQlldRRfB/01KlTcidLZTKZ5FSAxdtKTk5GSEgIWrVqBeDVsMfFLwJZWFiodBEIeDUy2usXlQqZmppi6tSpmDJlCqpXr65SW4D0vOSGYizbqjfRzZs3lY78IQgCRo0ahevXr6s9lLixfK5Sx9rarjtIN6T2f3SxbVDUxpu2/6NoCpYXL14YZKqk0vy8Vf2su3TpUmIySPH9gkaNGklOu9GlSxe5ZJBTp04hLy+vaF14+fJlUaytWrVSaSpjY1mfvmnc3NywevVqLFu2DAcPHsTx48cRFBSk0qi8z58/x4gRI3Dp0iWsWLGiFKJVTF/73IrOZfP7qhm+b6+2T8WpOl08kbHhNDFERAbQokULyYPBU6dOGcXUKYoSMnQ51F12drZofkdlfRtSQUGBaIoL4NWOcceOHZXW3b17d6kNEahsqhg/Pz8IgqDRY8yYMUr7dXV1hZeXl6j84sWLes0kT0pKEg2PCry6SOXu7q63fl/35MkThYlCunbgwAHJOTuNiYODA/r37y9XtnPnTtGd4KNHjxbdEUxvBqmTrl9//bXG66eSHn5+fpJx6OtCTHm5mKNoW6yL1ye1TaxYsaLW7ZLu/frrr6Kyt956CyEhIRgxYoTad/UZ03D4UusAXeyvadtGzZo1RVPdRUVFyZ3kL77f0bRpU1SuXFnUVqNGjVClShW5stfrXrx4UXSncps2bVRO2jl48KBoiksTExPs378fixcvVisRBDCu74fUtmrQoEF621aVtK/9pkhLS8MHH3xQ4igBL1++xJAhQ+TujFeFsXyuUtu80r77Vpdyc3MNHYLOSO3/6GvfB3jz9n+sra0lkxZiYmIMEE3pft6qftaqJIWWNFWcovLU1FS5kVW1mSpOan3aqlUrva1P582bp1JcbwobGxt88MEHWLt2Le7fv4/nz59j165d+Pzzz9GkSROldVeuXGnwZBBt95cLCgrUmnLNWLb/ZY2xnLsxJKntk4eHhwEiIdIez8ITERmAubk5unfvLiqPiIgotQvLyig6UL13757O+ggLC1Orb0M6cuQIoqKiROUdOnQoMXlFWYKGrl29ehU3btwotf5e17NnT1FZVlaW6K4WXdqyZQuysrJE5X369NFbn8UFBASgoKCgVPrKycnB5s2bS6UvbRSf/kXqRD2niHlz1ahRQ1RmiCQnqW1N8YuamtBFG8ZA0bYtIiJC67YfPnyocn9kOBkZGZL7pOvXr0elSpU0ajM+Pl7bsHRGKnlClbsqlcnMzNTJBS2paVpe/yxUvQgEQDRiiLJ2FPWtyP79+0Vl48aN03gUM2P6fhjLtupNM2HCBNHxpqmpKb788kvRsufOncOsWbPUat9YPlep9U94eHip9S91p6+6iTWvK0+/Dan9EX3t+yjqr7zz9PQUlUVHR5d+ICjdz1vVz9rd3R21a9eWK3v69GnR+bP4+HjROR9F+wENGjQQjcaiq/0AY1mf0itVqlTBgAEDsGLFCoSGhuLx48f46aefFF60njdvnkGTcLXd537w4AEEQf5GSplMpvB8Mr+vmuH7Jp0MIrUdIyoLmAxCRGQgI0eOlCz/448/SjkSsVq1akneFajL4ZsVtdW4cWOd9aELyu5CKCnbOzIyEidPntR9UEr89ddfpdpfodfnsn/d+vXr9dbnn3/+KVleWskggiBgw4YNpdJXodJMLtJU586dlWbKd+zYUXSCi94cDRs2FJU9fvy41ONo1KiRqOz69etatZmUlGSQ16IPtra2kiMsFR/lR13x8fGSJ/ylvhdkWHfu3BHdnV+nTh20adNGo/ZiYmIQFxeni9B0omnTpqIybdcBN27c0Mlc18ruCo6Li8Pt27flnnvnnXdUbuvcuXNFibT//fefSn0rEhISIipTdHyjiqtXr2pcV9eMZVv1Jlm3bp1kEvn8+fPx888/Y9y4caLnlixZgkOHDqnch7F8rs2aNROVvXjxQmHCgK7peqo8Q43qoA9S35GYmBjJYeLVIbX/5OnpCWtra63aLYukzvdoe2FYU1Kft7b7uoraUGdfV9l+wMmTJ+UugpuZmcHX11dhW8WTOwrbyczMRHBwsNxzNjY2aNu2rUoxSr2ep0+flquRgsoyd3d3fP311wgLC8P48eNFzyclJeHAgQMGiOwVbfe5pep7eXnByspKcnlj2f6XNXzfgPv374vKShp9h8hYMRmEiMhAevToIXmhZd++fTo5ANWGmZkZWrduLSo/c+aMzvo4d+6cqMzKygotWrTQWR+68Ntvv8kNpVmoSpUqGDJkiNK6GzZsEGWrOzo6IisrSydD6C1dulTU55YtWwxyAN68eXN4e3uLyq9fv44dO3bovD+pqUeAV3e/FL8LVl9OnToleefQhQsXdPL5XrlyRdR2aGiowdcPJZHJZEoTpTgqyJtN6gTj+fPnJacN06dWrVqJym7duqXVHVLabiNlMplW9XVN6rM6deqUVm1KJUiamZlJbj/IsGJjY0VlDRo00Lg9Xe5D6oLUfu7riRKa0NXoflJ35RZe/Pnvv//k9i3Nzc3h4+OjsK3iF5SysrJw/vx5ZGRk4MKFC3LP2draSr4viujyO5Kbm4uLFy9qVFcfpNZ/4eHhBrt7vby7ceMGpkyZIirv3r07ZsyYAeDVsPbFT74LgoBRo0ap/LkYy+faokULyYSM48ePl0r/UtPVPn/+XOP2pI7r1WFM+z/e3t4wNTUVletj/0fVi+7ljdQ+382bNw0QifRnEBYWJrl9U1VOTg7Onz+vUl+KKEsGKb6v0apVK8n1iaK2goODkZmZiXPnziE7O1vuuQ4dOqg8BaDUbyUzM9OotuX06hzr6tWrJZOgz549a4CIXtF2n1kqoVnq+L6QsWz/C+lru6frdo3l3I2h5Ofni5LwAentGFFZwGQQIiIDMTU1xbfffisqz8vLw6hRo0QHZrqkyrBu7du3F5XdvXtXJ9OQ5OXl4e+//xaVe3t7w9zcXOv2dSUoKAjTp0+XfG7+/PmwtLRUWFfRqBH9+/dXWk8dgwcPhomJ/KY8Pj5ectju0iD1fQaAKVOm6HQIyqSkJMkTxsCr+StL64Si1CgstWvXVutCijItW7ZEvXr1VOrX2IwePVryc6hQoQI++OADA0RExqJTp06ikacyMjJw5MiRUo2jfv36ooshubm52LVrl8ZtajstltS2wZB317Vr105UdvToUa3ujt24caOorEmTJpKjkZFhSc3lXaFCBY3bUzSal6FI7XMmJyfj33//1bjNTZs2aRsWgFcJx2+99ZZc2cuXL3H9+nXRyfM2bdrA1tZWYVs1a9ZEzZo15coCAwNx5swZ0YlcHx8ftfbDdfkd2blzJ1JTUzWqqw8NGzYUvW8AsGfPHgNEU76lpaXhgw8+EI1EVL16dWzatKlof9LKygp///236Dv28uVLDBkyRKVpTozlczU1NUWHDh1E5atXry6V/qtVqyYqu3XrlsZTX2p77GlM+z8VKlSQvONXm6k6Y2NjcezYMVE5k0H+j6Gmum3Tpo3omLWgoADbtm3TuM0DBw5InvtQ5/Pu1KmTKK6goCAUFBSoNVWc1PPZ2dk4e/as1lPF2dvbo2PHjqJybieNj4mJieQ5GG2SALV17949yZufVJGdnS15Plnq2LWQsWz/C+lru6frdo3l3I2hPHjwQLR/amdnBy8vLwNFRKQdJoMQERnQ2LFjJU823Lp1C1999ZVe+vz3339LnN4EgMILtqtWrdI6hu3bt0vOCz5o0CCt29aV/fv34/3335dMymnbti0+/vhjpfUDAwMlh88bNmyYzmJ0c3OTvBvUUFOJ9OrVS3KI0tjYWEyYMEE0SoqmPv30U8kD1zp16mD48OE66aMkKSkp2L17t6h86NChOu1Hqr2tW7fqNVlMF2rWrImjR49i7969co8jR44ovWhF5Z+1tTUGDhwoKp8/f36pxmFiYiIZx2+//abRxZAnT55g3759WsUkdVdfenq6Vm1qo2/fvqI7/vLy8jTeD7h7967kiSMmiBknqTvHnz59qlFb165dw4kTJ7QNSafs7Owkp5VbvHixRvsr+/btQ1hYmC5CA6D4rmB1LwJJLSPVjqptvU5X35H8/Hz8/PPPatfTN6kpbxYvXqzV6DEk9sknn4iG4DY1NcW2bdvg7OwsV16vXj2sWbNG1Ma5c+cwe/Zslfozls9V6nj8xo0bWu9LqKJu3bqifY709HTJ0QxKEhoaqvUd3sa2/yO1f3jo0CHJoeJVsXLlSlGykpmZGfr166dRe2Vd06ZN4eTkJFcWGxuLJ0+elHosjo6OklOt/f777xrd+S4IAn755RdReYMGDSSnqFTE2dlZNJ1OYmIi/vnnH4SHh8uVl7Ttdnd3R506deTKdLUfILU+XbNmjVYjq5B+VK5cWVSmi6kNtfHTTz9pVG/dunWihCsrK6sSjymNZfsP6G+7p+t2jeXcjaFIjRLesWNH0U2ZRGUFv7lERAZkamqKjRs3St6F9+uvv+J///ufzi6g5+Tk4Ouvv0bv3r1VuvOuSZMmkgfG69ev1+hEUaGkpCTJ0TacnJyMYvqIuLg4fPLJJ+jbt6/kTnPlypWxY8eOEnf+pBIyXF1ddT6FiVRyyZEjR/Ds2TOd9qOq9evXS859vHPnTnzxxRdaf5+nTZsmeaeOiYkJ/P39S21kmR07diAjI0NUrstkH0XtJSQklMqJYm117doVffv2lXtIjThEb54vv/xSdLdbSEgIfvjhh1KNQ2r+5NDQUKxbt07ttqZNm6b1iaTiJ8YBIDIyUqs2teHu7q7wYvmjR4/Ubm/SpEmiiyFWVlYYN26cxjGS/kjdOX7hwgUkJSWp1U52djZGjx6to6h0S2odcPnyZbXXAenp6fjyyy91FRYA6btz/f39Rb89TZJBrly5Inknvzp3BAPS35HDhw+r1Qbw6mLA1atX1a6nbxMnThTdDRkTE6NwdDpS39q1a7F161ZR+fz58xVOfzRs2DDJ7cbixYtV+v4Zy+c6YMAA1K5dW1Q+fvx4rUbgUoVMJpOcmnX9+vVqtZOTk4OPPvpI63iMbf9n3Lhxorurc3Nz8fnnn6vd1v379yWndu3Xrx/c3Nw0jrEsMzU1Rbdu3UTlQUFBpR8MgM8++0xU9uDBA42SFDdu3Cg5bdLkyZPVHr1UaptcfCRWGxsblUYcKb4f8M8//4i2u46OjmpP2Tx06FDRvkBGRgbGjh1r8EQDkvfw4UNRWfXq1Q0Qyf/ZtWuX5KhJysTGxmLOnDmi8oEDB6JixYpK6xrL9h/Q33ZPH+0ay7kbQ5Ca4u29994zQCREusFkECIiA2vatCl+++03yed++ukn9OrVCzExMVr1cejQITRu3BhLlixR62K81OgkgiCgX79+Gp20ffnyJbp37y55p8DEiRMNNmJAfn4+zp8/j4kTJ6JmzZpYu3at5PtUsWJFHDt2DDVq1FDaXnJyMvbu3Ssql5rWRVsDBw4UJUDk5+dLDsVfGurUqYO1a9dKPrdy5UoMHDhQ7QtJwKuROIYOHarwpMzUqVMlh1vWF6mpWpo1a4YGDRrotJ969eqhZcuWonJDjf5CpAvNmjWTvCN29uzZ+P3337VuPyMjA6tXr5acwuB1rVq1Qps2bUTlU6dOxYULF1Tub9myZVpNL1OoYcOGorKLFy/qLClUE1988YWoLCsrC3369FFr+q9p06ZJnkwZNWoUKlWqpE2IpCdNmjQRjfyQnZ2N7777TuU2cnJyMHz4cNy8eVPX4enEO++8g+bNm4vKP//8c5XvdM/JyUHfvn01SpBSxs/PTzQyz507d+T+t7W1VWlqus6dO8udxM3Pz8e9e/fklqlYsSKaNWumVoxSF+t/+uknpKSkqNzGjh07MHfuXLX6LS1Vq1bFN998Iypfu3YtZsyYofGUGoVyc3OxZcsWREREaNVOWRUaGip5AaZ79+6YMWOG0rorV65E06ZN5coEQcCoUaMQHR2ttK6xfK6mpqaYOXOmqDwuLg5du3bVOLE/Ly9PpWQSqTt9N27ciNOnT6vUT25uLsaOHYuQkBC1YyxOav8nODhY63Y15ezsjBEjRojKjx49ilmzZqnczosXL9C7d2/JESak9q/eJFIX0wyVDNKzZ0/UrVtXVD5nzhwcOnRI5XaCg4MxadIkUXnFihUxatQoteOSSvYsvh/QoUMHWFhYqN3WvXv3RMkafn5+ap+rsrKywqJFi0Tlhw8fxocffqh1onxBQQEOHDig8XQi5cHu3bvx+++/S96IpKqEhATJqQzffvttbULTiSFDhqh8nJCcnIwePXqIjkFNTU0xderUEusby/Yf0N9xvz62p8Zy7sYQpM5f9OjRwwCREOmIQERERuHbb78VAEg+KlSoIEyfPl14+PChyu0lJycL/v7+QosWLUTt+fr6qtzOuHHjFMb0/fffC6mpqSW2kZ+fL2zevFmoWbOmZFtNmzYVMjMzVY6pkFRbH330kRAcHCz5OHfunBAYGCjs3btXWL16tfD1118LPXr0EBwdHRW+94WPevXqCbdu3VIprtWrV0u2cenSJbVfoyp69eol6qt+/fp66UtVc+fOVfheVq1aVVi1apWQlpZWYjsZGRnC2rVrherVqytsr3///kJeXl4pvKpX7t69KxnH4sWL9dLfsmXLRH2ZmJgI0dHRSutJxTh37lyV+5X6DNu3b6/lq1Gdh4eHVvHrirbvo1R9bYwePVrUnr+/v8r1pd7XR48eqR3Ho0ePRO14eHioXD8pKUkyFgDCgAEDhAcPHqgVT0FBgXDx4kVh+vTpgpOTkwBAePbsWYn1rl+/LpiZmYlicHJyEnbv3q20bk5OjjBnzhxBJpPJ/TY1/XxycnIES0tLUf3Vq1erVF8Rqd+yOt9hRfsBTZs2FW7cuKG0bkpKisL61atXFxISElSKwd/fX1R/9OjRKr8GKbr+bapL6jek7u9ZFb6+vqI+VN0PHD58uGSMixYtEgoKCpTWffDggdCpUye5eqampqK2Tp48qfHrULWuMiEhIZJxWVtbC8uWLRPy8/MV1r1z547QunVruXo2NjY6i7NVq1aS73/ho0ePHiq31bhxY6VtDRgwQO34jh8/LtmWj4+PEBsbq7RuZmam8N1338mtM6U+B1V/5/pYRwjCq/Wyt7e35Ov09fUVrl69qnabN2/eFObNmydUq1ZNACAEBwdrHaeqFO2fKzp20vQRGRmpNI6UlBShXr16ojiqVasmxMXFqfRa7t27J9jZ2Yna6NChg5Cbm6u0rjF9rv369ZOMw93dvcT9kNclJiYKq1atEjw8PFTajiQkJAhWVlaifh0cHIQjR44orXvt2jWhQ4cOStd76mxTN27cKKpbuXJlISoqSuU2pGizzxsfHy+4urpKvq7JkyeXeBx79epVoWHDhpL1J06cqPJr0PW2T1/rSnUlJCQIFhYWcnFUr169xH0LRbQ9Ljlz5ozkPry1tbWwZs2aEuPavn27wnNKO3bs0Og1paSkSB6jvP746aefVGrrxYsXcscrUo9ff/1VozgFQRD69u0r2WaTJk2EoKAgtdt7+PChsHTpUqFOnToCAGHbtm0l1tH2eKc0aPJ7/uWXXwQAQqVKlYSvv/5aCAkJUavPx48fS+5PWlhYCC9fvtTi1ahOal38+nbDyclJ2Lhxo9I2Ll68qHCdOm3aNJVjMZbtv76O+x8/fiz52g4fPqxVu8Zy7kZfx4NS7t27J+qrefPmeumLqLSYgYiIjML8+fNha2uLGTNmiLKB09LSsHTpUixduhTNmjVDhw4d0LBhQ7i7u8POzg6mpqZIT0/H06dPce/ePQQHB+PChQvIzs7WOq6VK1fiypUruHbtmiimb7/9FkuWLIGvry/8/PxQo0YNVKxYEebm5khISEBsbCzOnz+PwMBAhaOb2NvbY9euXbCystI6VgD4888/8eeff+qkLeBVlvknn3yCH3/8UXL+RSlSo0bUrVsX3t7eOovrdcOGDcOBAwfkysLCwnD+/Hm0a9dOL32WZN68eZDJZJg3b57ouefPn2Py5MmYMWMGunbtinbt2qFu3bpwdHSETCZDcnIywsPDERwcjKNHjyrNEO/evTu2bdsmuntWn6Q+X5lMhqFDh+qlvyFDhuCrr76Su1OhoKAAAQEBkncUEpUFDg4O+Pfff+Hj4yMaLWj37t3Ys2cP3n33XXTr1g1t27ZFjRo14OTkBDMzMyQnJyM5ORmRkZG4efMmQkNDcezYMTx9+lTtOJo2bYqvvvpKdFdbYmIiBgwYgK5du2LUqFFo27YtqlWrhuzsbERHR+Pw4cP466+/EBYWVlTHzc0N3t7ekiNDqcLc3Bx9+vTBzp075conTpyIQ4cOoUePHqhVqxbs7OxEd+5ZWlpKjnCgC7/88guCgoLw4MEDufLQ0FC0bNkSQ4YMweDBg9G4cWNUqVIFqampiIyMxP79++Hv7y95l7ZMJkNAQIDkULZkPGbNmoVt27aJ7pSbMWMG9uzZgwkTJqBjx46oXr06ZDIZnj9/jtDQUOzduxfbtm2Tuxva19cXBQUFOHPmTGm/DKVatGiBb775RjTUcWZmJqZNm4aVK1di8ODBaN68OVxdXZGWlobHjx/jwIEDOHHihNzUR3379kViYiJOnTqlk9g6d+6MS5cuKXxelSliXm9L2Z2X6k4RA7waWaVt27aiOw7PnDmDhg0bYsKECXj//fdRv3592NraIiEhAZGRkTh06BA2btwoN2y1lZUVPvvsMyxZskTtOPTJ3Nwce/fuRbt27fDkyRO5506dOoUWLVrAx8cHPXv2RLt27VCzZk04OTnB0tISKSkpSE5ORnR0NG7cuIEbN24gMDBQcqh2Q1NlmgF1TJkyBcuXL1f4/Pjx43H//n25MlNTU2zbtg3Ozs4q9VGvXj2sWbNGNKXi2bNnMXv2bPz4448K6xrT5+rv74/Q0FDRncRPnjzBgAED0Lx5c/Tv3x/vvPMOatSogcqVKyM/Px+JiYl4+PAhrly5gpMnT+LYsWOSI1Ao4uTkhK+//hrz58+XK09OTsa7776Lzp07o0+fPqhbty4qVKiA+Ph4PHjwAEeOHEFQUJDc+YpVq1bhww8/1Oj1A8D7778PKysruVEE4uPj0bRpU4wcORKtW7dG1apVJacidXNz08t0K5UqVYK/vz969OghOjezatUq7N+/H2PGjEHv3r3h4eEBOzs7xMbG4saNG9i+fTt27NghmhoPALy8vCSnjXnTODk54f3338eePXuKymJiYnDhwgWdr49U0aFDB3zzzTei44HMzEx88sknWL9+PUaPHo0uXbqgWrVqMDU1xdOnT3H27Fls3LhR4agmI0eOxKBBgzSKyc7ODm+//bbS0QpV3Q+oXLkymjRpgtDQUIXLaLIfUGjjxo3w9fUVnTO8ceMG/Pz80KxZM/Tr1w/t27dH3bp14eTkBBsbG6SmpiI5ORnPnj3DzZs3cePGDQQFBeHWrVsax1JevXz5EosXL8bixYtRs2ZNvPPOO2jZsiWaNWsGV1fXovc0IyMDT58+xc2bN/Hvv/9i+/btkueFZ82aVeK0Kvq0cOHCoikWExMTMWrUKCxatAiDBg3CW2+9hSpVqiApKQkRERHYu3cvzpw5IzlqRt26dUXbMWWMZfuvr+N+d3d3tGrVSnT80KtXL3zwwQfo0qULPDw8YGtrK5r6xd7eXnJkEcB4zt2Upt27d4vKjHXqUyKVGTYXhYiIijt06JDg7OysNGtfm4e9vb3w+++/qxVTXFyc6O5OXTw8PDzUzmx/nb7eIwCCmZmZMGzYsBLvei7u1q1bku3NmTNH49dZkvT0dKFChQqiPj/++GO99amq7du3S8ami8f06dNLvPNP13Jzc4WqVauKYunYsaNe+5X6/dWtW1dpHan3jCODqE/b91GqvjbKy8ggha5evSr5m9LFQ5W7SwTh1e+6e/fuWvVlYWEhnDp1SuvP5+zZsyXeuSf1UPbe6+JOubCwMJ1+TqtWrVKrf44MojltRgYRBEGYPXu21p+3h4eHEBMTo9XdXPq8Eyw/P1/o37+/Vq/Ry8tLSEhI0GmcikbeKHxcu3ZN5bb279+vtK2wsDCNYrx+/bpga2ur1Xsnk8mETZs2afU71/fd7o8ePRK8vLx0tg58/WEMI4Po+jFlyhSFMSgaQXHBggUavabx48dLfqcOHTpUYl1j+VwfPnwoeHp66qxvVbcj2dnZQtOmTbXq65tvvhEEQfttqqJRxEp6KNuf0cU+7/Lly3X2uVSrVk3tu6fL68gggiAI//zzjygWde7wf50ujkvy8vKEgQMH6uzz9vX1FdLT0zV6PYVmzZqlsH0nJyelo5cVN3XqVIVtubq6ahWnILwaTadt27Y6e/9ef3BkEN2+n126dCnV82iK1sXTp0/X6nVUqVJFuH//vkYxGcP2Xx/H/YIgCFu2bNF4nVUSQ5+7Kc2RQVq2bCnXj5mZmcqj1xEZK/UmgyMiIr3r0aMHwsLC8Omnn8Lc3Fxn7VpaWmLKlCl4+PAhJk6cqFZdZ2dnHDt2DNOmTYOZmW4GlXr33Xdx5coVtGjRQift6YKlpSV8fX2xYsUKPHnyBFu2bEHjxo3VakNq1AgAorvWdMnGxgZ9+vQRle/cuVOruUV1YfDgwbhz5w769u2rszZr1qyJ/fv3Y8mSJTr7Pqrq8OHDeP78uahcn5+vovYfPHiAs2fP6rVfIn1r3rw5QkJC0K1bN522K5PJVB4xyMzMDHv37kWvXr006svKygrbt29Hx44dNar/uvbt20vOZWxoXl5eOH/+PJo2bapVOzY2Nti2bZvknOpknObPn6/RXPeF6tWrhxMnTqBatWo6jEq3TExMsGPHDowbN06j+k2aNEFgYKDOR7pp3749LC0tJZ+rXLmyWr9HX19fhevE6tWrw8vLS6MYmzZtiu3bt0vesa8Kc3NzrF+/HiNGjNCofmnx9PTExYsXMXz4cJ23Xdr7soZ0/fr1ojuBX9etWzeNR7tbsWKF6LcgCAJGjRolOTLV64zlc61VqxYuXbqE7t276zwOZSwsLHD8+HGNRhaTyWT4/vvvlY7Aoo5ly5ahXr16OmlLl6ZMmYJNmzZpvI4r1Lx5c5w/fx516tTRUWRlX8+ePUWjuuzcuVM0GllpKRyd6IsvvtC6reHDh+Pw4cOwsbHRqh1lI3906tRJNGKApm1pMypIoUqVKuHkyZP48ssv1YpLFbo8L/qmGzJkCA4dOmQU+x6LFy/G1KlTNapbv359nD17FnXr1tWovjFs//V13D9s2DAMGTJE5+0CxnHupjQ8ePAAISEhcmV9+/ZVefQ6ImPFZBAiIiNUsWJF/Pbbb4iIiMD//vc/uLu7a9xWq1atsGrVKsTExGD58uWoXLmyRu2YmZlh6dKlCA8Px6RJkzQ6ISKTydCrVy+cP38ehw8f1jgWTchkMlhaWsLe3h5ubm5o3rw53n33XUyaNAm//vorTp8+jeTkZAQFBeHzzz+Hq6ur2n3k5uZi8+bNovIWLVpofJJdVVLJAikpKdi1a5de+1VFjRo1sHfvXoSEhGD48OEanxSpUKECli9fjrCwMI0v2mpLKtnH3NwcH3zwgV77HThwICwsLFSKh6isqVatGo4ePYo9e/ZonSBYv359LFiwAJGRkWodrFtbW2Pfvn1YtmyZylOCAa8uAp89exb9+vXTJFxJixYtwpo1aww6dK+UmjVr4tKlS/j+++/h6OioVl2ZTIZ+/frh1q1bejs5RfpROKXPL7/8AltbW5XrmZiYYPz48bh06VKZuPhlZmaGtWvXYvfu3ahVq5ZKdaytrfH111/jwoULqF69us5jsra2VjhcfqdOnUTDOytjb2+vcLpCbS8Cvf/++wgODlY4tLQizZo1w9mzZ7WaXqI0OTg4YPPmzQgKCoKvr69Wbbm7u+N///sf7t69i7fffltHERq31NRUDBo0SG4qEODVPsDmzZvV+j6/zsrKCjt37hRtu+Pj4zF06FDJqTpeZyyfq7OzM44cOYKtW7dqfNxoY2ODsWPHws/PT61+g4KCMHXqVMljDSlNmjRBUFAQZs+erVGcUuzs7HDhwgUMHTpU5xeStTVixAjcvHkTffr0Uft76uTkhB9++AEXL16Eh4eHniIsm8zMzPDZZ5/JlUVFReHw4cMGiuhVTL/88gsCAwPRrFkztevXrVsXu3fvxubNm7VOIAKAdu3aKZxOWZ2p4gCgY8eOCpMqdJEMAry6uernn3/G1atX0bt3b61+yy4uLvjss89w+fJlDBgwQCfxlUWF5yw9PT21aqd+/fo4cOAAtm3bpvK6Xt9kMhmWLVuGf/75R+V9b0tLS0yfPh1XrlzR+vjCGLb/+jru37x5M77//nu1jt1UZQznbvRt7dq1ojJNE5eIjIlMECQm3CIiIqNz9epVnD17FpcvX8bDhw/x5MkTJCcnIzMzExYWFnBycoKTkxOqVq2KFi1awNvbG61bt9YqkUSZjIwMBAcH48yZMwgODsbz58+RkJCAhIQE5OXlwcnJCRUrVkTlypXRvHlzdOzYET4+PnBxcdFLPMYgOjoa69evF5W3b98eXbt21WvfeXl5+OGHH0R30jRu3NjoDp7T09MRGBiIs2fPIjQ0FI8ePUJcXBzS09OVnrA1MTHBtm3bNJ53V1uCIGDRokWi+birV6+u8d3E6vjzzz8RFRUlV2ZnZ4dp06bpvW8qP0aPHo3Y2Fi5soCAAFSpUsVAEYlduXIFe/bswenTp3H9+nWkp6dLLlehQgV4eXnhrbfegq+vb9EcuNqKj4/HunXrsGvXLly/fl20XnVyckKnTp0wYsQI9OnTR+5E55gxYxAQECC3vL+/P8aMGaN2HNnZ2di/fz+CgoIQGhqKyMhIpKamIj09Hfn5+XLLenh4IDIyUu0+NJWamoodO3Zg3759OH/+PBISEkTLmJubo1mzZujevTtGjBih96RI0r+XL1/i999/x7///ouQkBDR99Dc3BxNmjTBu+++i48++gg1a9aUe37v3r2IiYmRK+vXr59eEim0kZ+fj3///Rf79u3D5cuX8fjxY6Snp8PGxgZVqlRBo0aN0LVrVwwePFjvic3Hjh3D+fPnReXvvPMOOnTooFZbhw4dEs0hDry60NCmTRuNYyxUUFCAvXv3YtOmTTh9+jQSExNFy9SoUQOdOnXCsGHD0K1bN7kLq2FhYThx4oTc8g0aNFD7gldpuXv3Lnbv3o3//vsP165dE82hXsja2hp169ZFw4YN0aFDB3Tp0gX169cv3WBJZcbwuQqCgMDAQOzevRunT59GWFiY5GgJVlZWqFu3Llq3bo3u3bujW7dusLe317jfqKgo7N27F0eOHMH9+/fx4sULZGRkwM7ODrVq1ULr1q3Rv39/vf8mo6OjsWPHDly5cgU3b97EixcvkJaWJjni5dy5czFv3jy9xvO6sLAwbNq0CcePH8f169eRm5srWqZSpUpo164d+vTpg0GDBqmVZPymSUpKQo0aNZCWllZU1qtXL+zfv9+AUf2foKAgbN++HSdPnsSDBw8gdQnF09MTHTt2xKBBg9CjRw+dJzOtXbsWT58+FZV//PHHopFVSvL7778jLi5OVD5hwgRUrVpV4xgVefz4MXbt2oUTJ04gJCQEL168kFzOwsICtWvXRoMGDdCuXTt06dIFTZs21ThJsLwKCwvDuXPnEBwcjGvXruHhw4dITk6WXNbGxgaNGzdG69atMXjwYLRr166Uo1VPfn4+Dh06hAMHDiAkJASPHj1CamoqTE1Ni/a9u3XrhmHDhuktacCQ2399HfenpaVh165dOHfuHEJDQxEdHY20tDSkp6eL9it8fX0RFBSkduyGPnejazk5OXBzc5NbX7Vp0wbBwcEGjIpIN5gMQkREREYnMDAQ7733nijxwsLCAgcPHsQ777xjoMiIqLTFxcXhxYsXyMzMhJmZGezs7ODg4FAqo0tlZWUhMjISaWlpsLS0hLOzs9KTpbpMBilL4uPj8fz5c2RmZsLc3BxOTk5wc3MzquFeSbcyMzMRGxuLhIQEmJiYwMHBATVq1DCKYafJ8AoKChAbG4uXL18iOzsbFSpUQLVq1cr1RdGEhISi9aCJiQns7Oxgb2+PypUrG91IB6Q6Y/hcc3NzER0djeTkZOTl5cHW1hb29vZwdXXld8uA8vPzER0djcTEROTm5sLa2hpVq1Yt1dFPy4OvvvoKS5cuLfrf1NQU4eHhWo+EoGtZWVmIiopCSkoKAMDW1hY1atTQy5335VVKSgqePn2KjIwMCIJQtD51dnbmMYOGEhMT8fLlS6SlpSE/P7/oONnZ2ZnbBy0Zw/a/LDLkuRtd2LJli2j6yn379qF3794GiohId5gMQkREREZp69atGDFihOgOnAoVKuC///5TONw5EZGhvKnJIERERERE6oqPj0etWrWQmppaVDZp0iSsWrXKgFEREdGbRhAENG3aFDdv3iwqa926NS5cuGDAqIh0h2lsREREZJSGDRuGJUuWiMrT0tLw3nvv4d69ewaIioiIiIiIiIi0VblyZXz55ZdyZX/99ZfCKUWIiIj04eDBg3KJIACwcOFCA0VDpHtMBiEiIiKjNW3aNNHJIeDVHUTdunVDdHS0AaIiIiIiIiIiIm1NmzYNVapUKfo/MzMTy5YtM2BERET0pime+NGtWzd06dLFQNEQ6R6TQYiIiMioLVu2DEOGDBGVP3nyBN27d0dCQoIBoiIiIiIiIiIibdjb2+Onn36SK1u5ciViYmIMFBEREb1J9uzZIzcdjLm5OVauXGnAiIh0j8kgREREZNRkMhkCAgLQuXNn0XN37txBz549kZGRYYDIiIiIiIiIiEgbo0aNQvv27Yv+z8zMxNy5cw0YERERvQny8vIwY8YMubKpU6fCy8vLQBER6YeZoQMgIiIiKomFhQX27t2L8ePHIykpSfT8unXrMGXKlNIPjIiIiIiIiIg0JpPJsG7dOuzYsaOozMzMDLm5uTA3NzdgZEREVJ49efIEQ4cOLfpfJpNh+vTpBoyISD9kgiAIhg6CiIiIiIiorBszZgwCAgLkyvz9/TFmzBjDBERERERERERERERvLE4TQ0RERERERERERERERERERFSOMBmEiIiIiIiIiIiIiIiIiIiIqBxhMggRERERERERERERERERERFROSITBEEwdBBEREREREREREREREREREREpBscGYSIiIiIiIiIiIiIiIiIiIioHGEyCBEREREREREREREREREREVE5YmboAIiMSVZWFm7evAkAcHZ2hpkZfyJERERERERERERERERERKQ/eXl5ePHiBQCgcePGsLKy0rpNXukmes3NmzfRqlUrQ4dBRERERERERERERERERERvoEuXLsHb21vrdjhNDBEREREREREREREREREREVE5wpFBiF7j7Oxc9PelS5fg6upqwGiIiIiIiIiIiIiIiIiIiKi8e/bsWdEMFq9fs9YGk0GIXmNm9n8/CVdXV7i5uRkwGiIiIiIiIiIiIiIiIiIiepO8fs1aG5wmhoiIiIiIiIiIiIiIiIiIiKgcYTIIERERERERERERERERERERUTnCZBAiIiIiIiIiIiIiIiIiIiKicoTJIERERERERERERERERERERETlCJNBiIiIiIiIiIiIiIiIiIiIiMoRJoMQERERERERERERERERERERlSNMBiEiIiIiIiIiIiIiIiIiIiIqR5gMQkRERERERERERERERERERFSOMBmEiIiIiIiIiIiIiIiIiIiIqBxhMggRERERERERERERERERERFROcJkECIiIiIiIiIiIiIiIiIiIqJyhMkgREREREREREREREREREREROUIk0GIiIiIiIiIiIiIiIiIiIiIyhEmgxARERERERERERERERERERGVI0wGISIiIiIiIiIiIiIiIiIiIipHmAxCREREREREREREREREREREVI4wGYSIiIiIiIiIiIiIiIiIiIioHGEyCBEREREREREREREREREREVE5wmQQIiIiIiIiIiIiIiIiIiIionKEySBERERERERERERERERERERE5QiTQYiIiIiIiIiIiIiIiIiIiIjKETNDB0BE8goKCpCWlobU1FTk5uaioKAA+fn5hg6LiIh0yNTUFKamprC0tISDgwOsrKwgk8kMHRYREREREREREREREZUTTAYhMgKCICA1NRWpqalIS0tDQUGBoUMiIiI9ysvLAwBkZGQgMTERFhYWcHBwgKOjI8zMuHtGRERERERERERERETa4dUGIgMTBAHPnj1DcnKy5PMymQympqalHBUREelTfn4+BEEo+j8nJwcvXrxAUlISPDw8YG5ubsDoiIiIiIiIiIiIiIiorGMyCJEBSSWCmJqaws7ODnZ2drCxsYGJiYkBIyQiIn3Jz89HamoqkpOTkZGRAQDIzc1FVFQUPDw8mAhIREREREREREREREQaYzIIkYFIJYK4urrCwcEBMpnMgJEREVFpMDU1haOjIxwdHZGTk4MnT54gNzcX2dnZiIqKgru7OxMCiYiIiIiIiIiIiIhII7zCQGQghXeDF6pevTocHR2ZCEJE9AaysLCAu7t70WggmZmZSExMNHBURERERERERERERERUVjEZhMhAUlNTi/52dXWFvb29AaMhIiJDs7CwgJubW9H/aWlpBoyGiIiIiIiIiIiIiIjKMiaDEBlAQUFB0UU+U1NTODg4GDgiIiIyBjY2NrCwsAAAZGRkID8/38ARERERERERERERERFRWcRkECIDSEtLQ0FBAQDAzs6OU8MQEVEROzu7or85OggREREREREREREREWmCySBEBvD6FDGvX/QjIiKqUKFC0d9MBiEiIiIiIiIiIiIiIk0wGYTIAHJzc4v+trGxMWAkRERkbKytrYv+fn17QUREREREREREREREpComgxAZQOEUMTKZDCYm/BkSEdH/kclkRdOHFW4viIiIiIiIiIiIiIiI1MGr0EQGkJ+fDwAwNTU1cCRERGSMCrcPhdsLIiIiIiIiIiIiIiIidTAZhIiIiIiIiIiIiIiIiIiIiKgcYTIIERERERERERERERERERERUTnCZBAiIiIiIiIiIiIiIiIiIiKicsTM0AEQ0Ztj9ZojWL32iM7bnTj+XUz85F2dt0tEREREREREREREREREVBYxGYSISk1qWiaePU/US7tERERERERERERERERERPQKk0GIqNTYVbCGa1Unhc8XFAiIjUuSK6vi4ggTE1mJ7RIRERERERERERERERER0StMBiGiUjPxE+XTucS/TEGDJp/JlQWd+B6VK9nrOzQiIiIiIiIiIiIiIiIionLDxNABEBEREREREREREREREREREZHuMBmEiIiIiIiIiIiIiIiIiIiIqBxhMggRERmdDRs2QCaTyT2CgoIMHVa5M2/ePNH7HBkZaeiwjA6/j0REREREREREREREVNaYGToAIiIyXjKZTOVlTUxMYG9vD0dHR7i4uKBly5Zo1aoVevTogSpVqugxSiIiIiIiIiIiIiIiIiJ6HUcGISIinSgoKEBSUhIiIyNx6dIlrF69GmPHjoW7uzuGDx+O27dvGzpEIiIiIiIiIiIiIiIiojcCk0GIiEivcnJysHXrVrRs2RLLly83dDhERERERERERERERERE5R6niSEiIrXY2dnBxEScS1hQUIDU1FSF9bKzs/Hll18iPj4eCxYs0GeIRERERERERERERERERG80jgxCRERquXHjBpKSkkSPlJQU5ObmIiwsDKtWrULdunUl6y9cuBBbt24t5aiJiIiIiIiIiIiIiIiI3hxMBiEiIp0xMzODl5cXJk2ahLt372Lq1KmSy33zzTfIzs4u5eiouHnz5kEQBLmHp6enocMiIiIiIiIiIiIiIiIiLTEZhIiI9MLU1BTLli3D+PHjRc9FR0djw4YNpR8UERERERERERERERER0RuAySBERKRXy5YtQ8WKFUXl//77rwGiISIiIiIiIiIiIiIiIir/mAxCRAaXk5OHfQcuYdbcLaLn+n3wI6ZM+xP7DlxCTk6eAaIjbVWoUAEjR44UlZ86dQr5+fkGiIiIiIiIiIiIiIiIiIiofDMzdABE9ObKzc3DH+uOYvXao3jxIllymbB7MQi7F4Ot20/DxcUBE8Z1x4Rx3WFuztVXWeLn54cVK1bIlaWmpiIuLg6urq5atR0WFoarV6/i6dOnyMnJQeXKleHq6ooOHTrAyclJq7aNRUpKCm7evIkHDx4gOTkZqampMDc3h42NDSpVqgRPT0/UqVMHLi4uhg61yP3793Hv3j3Ex8cjPj4eeXl5sLe3h6urKxo2bIi6devC1NRUqz6ysrKK+nnx4gVSUlIgk8lQsWJFVKpUCY0bN0bdunV19IqIiIiIiIiIiIiIiIjKDl5NJSKDCLsXjUlT1uLGzccq14mLS8b8hTvxz/6L+G3FeNT3ctNjhKRL7u7ukuXx8fEaJYPk5ORgzZo1WLlyJcLDwyWXMTU1RYcOHTB//nx07NhRpXbff/99HDx4UK4sNDQUTZo0UTvG1w0dOhTbt2+XKztz5gw6dOigsE52djY2bNiATZs24fz58xAEocR+PDw80K5dO/Tt2xc9e/aEra2t0uXnzZuH7777Tq7s0aNH8PT0LLEvKZcuXcLq1atx4sQJREdHK13W3t4enTt3Rt++ffHBBx/AxsamxPYFQcDZs2dx8OBBBAUFISQkBHl5ykcMqlKlCt577z1Mnz4dDRs2VOv1EBERERERERERERERlVWcJoaISt2lyw/wXp8FaiWCvO7Gzcd4r88CXLr8QMeRkb7Y29tLlqempqrd1t27d9GiRQt8/vnnChNBACA/Px+nTp2Cr68vJk+ejIKCghLbnjhxoqhs7dq1asf4uvj4eOzdu1eurGHDhkoTQc6cOYNGjRphwoQJOHfunEqJIADw+PFjbNu2DYMHD8bkyZO1ilsdt27dwnvvvYfWrVtjw4YNJSaCAK9GO/nnn38wZswYVKtWDc+fP1e6/NatW+Hu7o6OHTvip59+wsWLF0tMBAGA2NhY+Pv7o1GjRhgzZgwyMjJUfl1ERERERERERERERERlFZNBiKhUhd2LxpCRy5CamqlVO6mpmRgychnu3Y/RUWSkT8nJ0tMAOTg4qNXOlStX0LZtW9y+fVuter/99hvGjRtX4nI9evSAh4eHXNmWLVuQman59zUgIADZ2dlyZePHj1e4/OHDh9GtWzeliS6qUDWBRFu7d+9GmzZtcPjwYY3bSE5ORlZWltJlLl26pFKSiSKCICAgIADt27dHbGysxu0QERERERERERERERGVBZwmhohKTW5uHiZNWat1Ikih1NRMfPr5Ghw5MAfm5lydGbNHjx5JlleuXFnlNqKiojB16lS5xBJPT0907twZbm5usLW1RVxcHM6ePYtLly6JkiH++usv9O7dG3369FHYh4mJCT755BPMnDmzqCwpKQk7duzAmDFjVI71devWrZP738rKCqNGjZJc9sWLFxgxYoRkYkT16tXRvn171K5dG/b29jA1NUVKSgpevnyJO3fu4MaNG0hMTNQoRk39/vvvmDx5smTiiampKby9vdGyZUs4OzvD2toaSUlJiImJwZUrVxAWFqbSaC2KmJiYoFatWmjUqBFq1aoFe3t72NraIj09HXFxcQgNDZUcPeT69esYPnw4jh07BhMT5sQSEREREREREREREVH5xKunRFRq/lh3VOOpYRS5cfMx/lh3FJ992lOn7ZJunTx5UlTm6OgIZ2dnldv48ssv8fLlSwBA48aNsXTpUnTr1k1y2eDgYIwYMQIRERFy5dOmTUPv3r0hk8kU9vPRRx9h7ty5yM3NLSpbu3atRskgp06dwr179+TKBg4cCCcnJ8nlly1bhoSEBLmyOnXq4LffflP4WgsJgoDLly/jwIED+Ouvv9SOVV2nT5/GlClTRIkgNjY2+OKLLzB16lRUqlRJYf24uDjs2bMH/v7+uHTpkkp9mpqaolevXujfvz/ee+89pe0X9vHbb79h8eLFcgk2gYGBWLlyJb744guV+iUiIiIiIiIiIiIiIipreEssEZWKnJw8/LHuqF7a/mPdUeTm5pW8IBlESkoKNm/eLCr38/NTa2SGwkSQ9957D8HBwUqTI9q2bYtTp06hYsWKcuUPHz5EUFCQ0n5cXFzQv39/ubLg4GDcunVL5VgLrV27VlT2ySefKFx+165dcv87Ozvj7NmzJSaCAIBMJkOrVq3w/fff4/Hjx/jmm2/UjldVSUlJGDRokGjUDXd3d1y5cgULFy4sMVHDxcUFEyZMwMWLFxEYGAhHR0ely/fr1w8RERHYu3cvRo4cWWL7hX189913OHv2rOi78PPPP4viJyIiIiIiIiIiIiIiKi+YDEJEpeLw0auIi0sueUENxMUl49CRq3ppm7Q3efJkpKSkiMp79eqldlteXl7YuXMnbG1tS1zWzc0N8+fPF5Xv3bu3xLoTJ04UlUkldiiTkJCA3bt3y5U1bNgQHTp0kFw+JycHDx8+lCsbO3YsqlSpola/AGBmZoYGDRqoXU9Vq1atQmxsrFxZxYoVcfbsWY367dy5c4nJIL6+vnB3d1e7bQBo2bKlaLqeqKgoHDhwQKP2iIiIiIiIiIiIiIiIjB2TQYioVPwXdFOv7Z88pd/2SX3Z2dn45JNPsGnTJtFznp6eGDlypNptrly5UqVEkEIjRoyAlZWVXFlISEiJ9Xx9fdGwYUO5ss2bN8tNNVKSgIAAZGdny5WNGzdO4fLx8fGistq1a6vcX2nJyMjAihUrROVr1qxBjRo1DBCRavr37486derIlUlNX0RERERERERERERERFQeMBmEiEpF6I3IMt0+laygoAAJCQm4fPkyFi1ahDp16kiOpiGTybBs2TKYm5ur1X79+vVVmi7ldQ4ODmjWrJlc2Y0bN1SqO2HCBLn/ExMTsXPnTpX7Lj4ShZWVFUaNGqVw+QoVKojKHj16pHJ/peXo0aOixJUmTZpg4MCBBopIdb6+vnL/X7hwwUCREBERERERERERERER6ReTQYioVDyMeKbf9h8+12v79H9q1qwJmUwmepiamqJSpUpo1aoVZs6ciejoaMn6CxcuRP/+/dXut2vXrhrF+9Zbb8n9n5aWhpycnBLrjRo1CjY2NnJlxRM8FDl79izu3r0rVzZw4EBUrFhRYR17e3u4urrKla1Zswbh4eEq9VlagoKCRGXFE2eMVdWqVeX+v3PnjoEiISIiIiIiIiIiIiIi0i8mgxBRqcjOztNr+1nZuXptn7Rna2uLtWvXYsaMGRrVb9GihUb1nJycRGXJyckl1nNwcMDQoUPlyqSSPKRIjYgyfvz4Euv17t1b7v/ExES8/fbbWLhwIZ49029ClapOnz4tKuvUqVOpx5GYmIiAgABMmTIFnTt3Rq1ateDs7AwrKyvJZCWZTIaFCxfKtZGeno7cXK47iIiIiIiIiIiIiIio/GEyCBGVCktLM722b2Wp3pQjVHpsbW0xbtw4hIaGYty4cRq3U7lyZY37Ly4jI0OluhMnThSVSSV6vC4xMRG7du2SK2vQoAF8fHxK7O+bb74RxZucnIzZs2ejevXqaNOmDWbOnInDhw8jKSmp5BegBxEREXL/Ozg4wMvLq9T6v3PnDvr374+qVatizJgxWLlyJU6ePIlHjx4hPj4e2dnZarVnqPeRiIiIiIiIiIiIiIhIn/R7dZaI6P+rXcsVt+880V/7tauWvBDphJ2dHUxMxLmEJiYmsLOzg6OjI1xcXNCiRQu0atUK77zzDhwcHLTut0KFClq3UUgQBJWWa9myJby9vXH58uWiso0bN+LHH3+EpaWlZJ1NmzYhMzNTrkyVUUGAV1PwbN26FYMGDRIlNQiCgIsXL+LixYtYtGgRZDIZGjduDD8/P3Tt2hXdunWDhYWFSv1oKjc3FykpKXJlVatWhUwm02u/hebPn4/vv/8eeXm6G2lI1cQgIiIiIiIiIiIiIiKisoTJIERUKpo28dRrMkjTJp56a5vk3bhxA56enoYOo9RMnDhRLhkkISEBu3btwvDhwyWXX7dundz/VlZWGDVqlMr99e7dG+fPn8enn36KixcvKlxOEATcuHEDN27cwMqVK+Hk5ISRI0fim2++QbVq1VTuTx0JCQmiMkdHR730Vdy0adPw888/q7SsmZkZrKysYGpqKleelZUlmWRDRERERERERERERERU3nCaGCIqFZ39Guu1/U6++m2f3lxDhgwRJTwomirm/PnzuHXrllzZwIEDUbFiRbX6bNGiBS5cuIDjx49jxIgRKiVcJCYmYuXKlahduzZWrVqlVn/G7siRI5KJINbW1hg+fDjWrFmDixcvIjo6Gnl5ecjNzUVqaiqSkpLkHv/73/8MED0REREREREREREREVHp48ggRFQqenRvARcXB8TFJeu8bRcXB7z3bgudt0sEvEo4GD16NFasWFFUdvr0ady7dw9eXl5yy0oliag6RYyUd955B++88w4KCgpw/fp1nD59GmfPnsXZs2cRGxsrWScrKwufffYZYmNj8f3332vctxSppJakpCSd9iFl2rRporL+/ftj3bp1aiXapKen6zIsIiIiIiIiIiIiIiIio8WRQYioVFhYmGHCuO56aXvCuO4wN2duG+nPhAkTRGXFEz+Sk5Oxc+dOubIGDRrAx8dH6/5NTEzQokULfPHFF9i1axeeP3+OsLAw/Pzzz2jTpo1knQULFiA4OFjrvl9nbm4Oe3t7ubLY2Fi9TrVy+/Zt3LlzR66sbdu22Llzp9ojrkhNc0NERERERERERERERFQeMRmEiErNhHHd0aSxh07bbNrEExPHv6vTNomKq1+/Pjp16iRXtnHjRmRnZxf9v2nTJmRmZsoto82oICXx8vLCl19+ieDgYJw7dw61a9cWLbN06VKd91u8n6SkJNy/f1/n/RQKDAwUlX3zzTcwNTVVuy19xklERERERERERERERGRMmAxCRKXG3NwMv60YDzs7a520Z29vg99WjIeZmfoXhYnUNXHiRLn/4+PjsXfv3qL/161bJ/e8lZUVRo0aVSqxtWvXDkeOHIGFhYVc+YkTJ3TeV8eOHUVlJ0+e1Hk/hZ4+fSoqa9eundrtZGdn4/Lly7oIiYiIiIiIiIiIiIiIyOgxGYSISlV9Lzds3zRN64QQe3sbbNs4FV71qusoMiL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",
-      "text/plain": [
-       "<Figure size 2200x1400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(11, 7), dpi=200)\n",
-    "\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    physical_energy_diff,\n",
-    "    yerr=physical_uncertainties.values(),\n",
-    "    ecolor=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    color=(20 / 255.0, 26 / 255.0, 94 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Physical\",\n",
-    ")\n",
-    "plt.errorbar(\n",
-    "    plot_labels,\n",
-    "    logical_energy_diff,\n",
-    "    yerr=logical_uncertainties.values(),\n",
-    "    color=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    ecolor=(0, 177 / 255.0, 152 / 255.0),\n",
-    "    capsize=4,\n",
-    "    elinewidth=1.5,\n",
-    "    fmt=\"o\",\n",
-    "    markersize=8,\n",
-    "    markeredgewidth=1,\n",
-    "    label=\"Logical\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Hamiltonian Parameters (U, V)\", fontsize=18)\n",
-    "ax.set_ylabel(\"Energy above true ground state (in eV)\", fontsize=18)\n",
-    "ax.set_title(\"CUDA-Q AIM Infleqtion Hardware Execution (lower is better)\", fontsize=20)\n",
-    "ax.legend(loc=\"upper left\", fontsize=18.5)\n",
-    "plt.xticks(fontsize=16)\n",
-    "plt.yticks(fontsize=16)\n",
-    "\n",
-    "ax.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=2)\n",
-    "plt.ylim(top=max(physical_energy_diff) + max(physical_uncertainties.values()) + 0.2, bottom=-0.2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/qaoa.ipynb b/pr-2308/sphinx/applications/python/qaoa.ipynb
deleted file mode 100644
index b064182dd6..0000000000
--- a/pr-2308/sphinx/applications/python/qaoa.ipynb
+++ /dev/null
@@ -1,376 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Max-Cut with QAOA\n",
-    "\n",
-    "[Farhi et al.](https://arxiv.org/abs/1411.4028) introduced the quantum approximation optimization algorithm (QAOA) to solve optimization problems like the max cut problem. Before diving into the details of QAOA, we'll first define the max cut problem. \n",
-    "\n",
-    "[Max Cut](https://en.wikipedia.org/wiki/Maximum_cut) is the problem of finding a partition of a graph's nodes into two sets which maximizes the edges between the two sets. Although this problem is relatively easy to solve for graphs with few vertices, this problem is [NP-hard](https://en.wikipedia.org/wiki/NP-hardness#:~:text=7%20References-,Definition,complete%20problem%20G%20to%20H.). The max cut problem has a wide range of applications including [machine learning](https://jmlr.org/papers/volume14/wang13a/wang13a.pdf), [circuit design](https://math.mit.edu/research/highschool/primes/materials/2020/Hong-Lee-Wei.pdf) and [statistical physics](https://www.researchgate.net/publication/262162554_An_Application_of_Combinatorial_Optimization_to_Statistical_Physics_and_Circuit_Layout_Design), among others. Furthermore, the QAOA algorithm presented in this tutorial can be adapted to other related optimization problems with an even wider application field including [portfolio optimization](https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.043204) and [job shop scheduling](https://www.sciencedirect.com/science/article/pii/S0377221723002072), just to name a few.\n",
-    "\n",
-    "We take the convention that $G=(V,E)$ represents a graph with vertex set $V\\subseteq \\mathbb{N}$ and edge set $E\\subseteq V\\times V$. We use the terms vertex and node interchangeably.  For this tutorial we assume that the graphs are undirected (that is, $(u,v)$ and $(v,u)$ represent the same edge). Our graphs contain no self loops (i.e., for every vertex $v$, there is no edge $(v,v)$). A *cut* of the graph $G$ is a partition, $(V_0,V_1)$, of the vertex set such that every vertex of $V$ is a member of exactly one of $V_0$ or $V_1$ (i.e., $V_0\\bigcup V_1 = V$ and $V_0\\bigcap V_0=\\emptyset$). The *cut value* for a partition is the sum of the edges with one node in $V_0$ and one node in $V_1$.\n",
-    "\n",
-    "In the images below, we illustrate two cuts of a graph with the dotted lines. Each of these cuts partitions the graph into two disjoint sets. The cut on the left is not optimal, and the cut on the right is the max cut. The cut on the left divides the graph into disjoint sets $\\{1,2\\}$ and $\\{0,3,4\\}$, and that cut contains 3 edges. To more easily visualize the cut, we have colored the vertices in one set of the partition green and the vertices in the other set of the partition gray.\n",
-    "The cut depicted in the diagram on the right divides the graph vertices into two disjoint sets $V_0=\\{0,2\\}$, colored gray, and $V_1=\\{1,3,4\\}$, colored green. The number of edges connecting vertices in the distinct sets is computed by $$\\sum_{\\substack{u \\in V_0; v\\in V_1\\\\ (u,v) \\in E}}1.$$ For the graph on the right, the number of edges in the cut (in this case there are $5$ edges) is maximal, and this value is referred to as the *max cut value*. The partitioning $(V_0,V_1)$  &mdash; and sometimes the set of edges connecting vertices in $V_0$ and $V_1$  &mdash; is referred to as a *max cut of a graph*. Note that the max cut of a graph need not be unique; that is, two distinct partitions may produce the same cut value.\n",
-    " \n",
-    "![](images/max-cut-illustration.png)\n",
-    " \n",
-    "We will use bitstrings to identify vertices in each of the two partitions. For example using the ordering of the vertices, the bitstring `01100` captures the partition in the image above on the left with vertices $1$ and $2$ in $V_1$, and the bitstring `01011` codes the partition in the image on the right with vertices $1$, $3$, and $4$ in $V_1$.\n",
-    "\n",
-    "Let's code our graph data as lists of integers so that we can call these variables when we create the `cudaq.kernel` for QAOA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "from typing import List\n",
-    "\n",
-    "# We'll use the graph below to illustrate how QAOA can be used to\n",
-    "# solve a max cut problem\n",
-    "\n",
-    "#       v1  0--------------0 v2\n",
-    "#           |              | \\\n",
-    "#           |              |  \\\n",
-    "#           |              |   \\\n",
-    "#           |              |    \\\n",
-    "#       v0  0--------------0 v3-- 0 v4\n",
-    "# The max cut solutions are 01011, 10100, 01010, 10101 .\n",
-    "\n",
-    "# First we define the graph nodes (i.e., vertices) and edges as lists of integers so that they can be broadcast into\n",
-    "# a cudaq.kernel.\n",
-    "nodes: List[int] = [0, 1, 2, 3, 4]\n",
-    "edges = [[0, 1], [1, 2], [2, 3], [3, 0], [2, 4], [3, 4]]\n",
-    "edges_src: List[int] = [edges[i][0] for i in range(len(edges))]\n",
-    "edges_tgt: List[int] = [edges[i][1] for i in range(len(edges))]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "QAOA is a variational algortihm with a particular ansatz. QAOA is made up of a variational quantum circuit (i.e., a kernel that depends on a set of parameter values) and a classical optimizer. The aim of QAOA is to use the classical optimizer to identify parameter values that generate a quantum circuit whose expectation value for a given cost Hamilitonian is minimized. \n",
-    "\n",
-    "What distinguishes QAOA from other variational algorithms is the structure of the quantum circuit. For each vertex in the graph, there is an associated qubit in the circuit. The circuit is initialized in a superposition state. The remainder of the QAOA circuit is made up of blocks (referred to as layers). The more layers there are, the better the approximation the algorithm achieves.\n",
-    "\n",
-    "![diagram of QAOA circuit layers](images/qaoa-circuit-layers.png)\n",
-    "\n",
-    "\n",
-    "\n",
-    "Each layer contains a problem kernel and a mixer kernel. The mixer kernel is composed of parameterized rotation gates applied to each qubit, depicted in green in the image above. The problem kernel encodes the graph edges. The image below shows an example of an graph edge encoded with controlled-X gates and a parameterized rotation gate.\n",
-    "\n",
-    "![diagram of a QAOA problem kernel for a max cut problem](images/qaoa-problem-kernel.png)\n",
-    "\n",
-    "Let's implement the QAOA circuit for our max cut problem in CUDA-Q."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Problem parameters\n",
-    "# The number of qubits we'll need is the same as the number of vertices in our graph\n",
-    "qubit_count: int = len(nodes)\n",
-    "\n",
-    "# We can set the layer count to be any positive integer.  Larger values will create deeper circuits\n",
-    "layer_count: int = 2\n",
-    "\n",
-    "# Each layer of the QAOA kernel contains 2 parameters\n",
-    "parameter_count: int = 2 * layer_count\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qaoaProblem(qubit_0: cudaq.qubit, qubit_1: cudaq.qubit, alpha: float):\n",
-    "    \"\"\"Build the QAOA gate sequence between two qubits that represent an edge of the graph\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    qubit_0: cudaq.qubit \n",
-    "        Qubit representing the first vertex of an edge\n",
-    "    qubit_1: cudaq.qubit \n",
-    "        Qubit representing the second vertex of an edge\n",
-    "    thetas: List[float]\n",
-    "        Free variable   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.Kernel\n",
-    "        Subcircuit of the problem kernel for Max-Cut of the graph with a given edge\n",
-    "    \"\"\"\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "    rz(2.0 * alpha, qubit_1)\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "\n",
-    "# We now define the kernel_qaoa function which will be the QAOA circuit for our graph\n",
-    "# Since the QAOA circuit for max cut depends on the structure of the graph,\n",
-    "# we'll feed in global concrete variable values into the kernel_qaoa function for the qubit_count, layer_count, edges_src, edges_tgt.\n",
-    "# The types for these variables are restricted to Quake Values (e.g. qubit, int, List[int], ...)\n",
-    "# The thetas plaeholder will be our free parameters\n",
-    "@cudaq.kernel\n",
-    "def kernel_qaoa(qubit_count: int, layer_count: int, edges_src: List[int],\n",
-    "                edges_tgt: List[int], thetas: List[float]):\n",
-    "    \"\"\"Build the QAOA circuit for max cut of the graph with given edges and nodes\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    qubit_count: int \n",
-    "        Number of qubits in the circuit, which is the same as the number of nodes in our graph\n",
-    "    layer_count : int \n",
-    "        Number of layers in the QAOA kernel\n",
-    "    edges_src: List[int]\n",
-    "        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    edges_tgt: List[int]\n",
-    "        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    thetas: List[float]\n",
-    "        Free variables to be optimized   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.Kernel\n",
-    "        QAOA circuit for Max-Cut for max cut of the graph with given edges and nodes\n",
-    "    \"\"\"\n",
-    "    # Let's allocate the qubits\n",
-    "    qreg = cudaq.qvector(qubit_count)\n",
-    "    # And then place the qubits in superposition\n",
-    "    h(qreg)\n",
-    "\n",
-    "    # Each layer has two components: the problem kernel and the mixer\n",
-    "    for i in range(layer_count):\n",
-    "        # Add the problem kernel to each layer\n",
-    "        for edge in range(len(edges_src)):\n",
-    "            qubitu = edges_src[edge]\n",
-    "            qubitv = edges_tgt[edge]\n",
-    "            qaoaProblem(qreg[qubitu], qreg[qubitv], thetas[i])\n",
-    "        # Add the mixer kernel to each layer\n",
-    "        for j in range(qubit_count):\n",
-    "            rx(2.0 * thetas[i + layer_count], qreg[j])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As mentioned earlier, QAOA is a variational algorithm that aims to minimize a cost function. To apply QAOA to the max cut problem, we assign a binary variable $z_v$ to each vertex $v$. These variables will take on the vales $-1$ or $1$. Notice that an assignment of variable values corresponds to a partition of the graph.  For example, $\\bar z = [-1, 1, -1, 1, 1]$ would partition the graph into two sets: $\\{0,2\\}$ and $\\{1,3,4\\}.$ We can compute the cut value of the graph for an arbirtrary assignment of variable values by the formula: $$C(\\bar{z})= \\frac{1}{2} \\sum_{(u,v)\\in E} (1-z_uz_v),$$ where $E$ is the set of edges of our graph.  Our goal is to find a variable assignment that maximizes $C(\\bar z)$.  Equivalently, we want to find a variable assignment that minimizes $-C(\\bar z)$. Since QAOA identifies minimum values, we have reframed the max cut optimization problem from one of maximization to minimization. Furthermore, we can promote this equation to a matrix equation by replacing $z_uz_v-1$ with Pauli-Z operators acting on qubits associated with nodes $u$ and $v$, respectively, and replacing 1 with the identity matrix. This leads to the reformulation of the problem from one of maximizing $C(\\bar{z})$ to one of minimizing the eigenvalues of $$H= \\frac{1}{2}\\sum_{(u,v)\\in E} (Z_uZ_v-II).$$  \n",
-    "\n",
-    "We can code this Hamiltonian using the `cudaq.spin` operators."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The problem Hamiltonian\n",
-    "# Define a function to generate the Hamiltonian for a max cut problem using the graph\n",
-    "# with the given edges\n",
-    "\n",
-    "\n",
-    "def hamiltonian_max_cut(edges_src, edges_tgt):\n",
-    "    \"\"\"Hamiltonian for finding the max cut for the graph with given edges and nodes\n",
-    "        \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    edges_src: List[int]\n",
-    "        List of the first (source) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "    edges_tgt: List[int]\n",
-    "        List of the second (target) node listed in each edge of the graph, when the edges of the graph are listed as pairs of nodes\n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    cudaq.SpinOperator\n",
-    "        Hamiltonian for finding the max cut of the graph with given edges \n",
-    "    \"\"\"\n",
-    "\n",
-    "    hamiltonian = 0\n",
-    "\n",
-    "    for edge in range(len(edges_src)):\n",
-    "\n",
-    "        qubitu = edges_src[edge]\n",
-    "        qubitv = edges_tgt[edge]\n",
-    "        # Add a term to the Hamiltonian for the edge (u,v)\n",
-    "        hamiltonian += 0.5 * (spin.z(qubitu) * spin.z(qubitv) -\n",
-    "                              spin.i(qubitu) * spin.i(qubitv))\n",
-    "\n",
-    "    return hamiltonian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To inititiate the QAOA algorithm, we need to identify initial parameters and specify the classical optimization routine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters =  [0.21810696323572243, -0.20613464375211488, 0.2546877639814583, 0.3657985647468064]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Specify the optimizer and its initial parameters.\n",
-    "cudaq.set_random_seed(13)\n",
-    "optimizer = cudaq.optimizers.NelderMead()\n",
-    "np.random.seed(13)\n",
-    "optimizer.initial_parameters = np.random.uniform(-np.pi / 8, np.pi / 8,\n",
-    "                                                 parameter_count)\n",
-    "print(\"Initial parameters = \", optimizer.initial_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can either use the `observe` primitive or the `vqe` primitive to code up the optimization loop of QAOA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.5+0j] IIZIZ\n",
-      "[0.5+0j] IZZII\n",
-      "[-3+0j] IIIII\n",
-      "[0.5+0j] ZZIII\n",
-      "[0.5+0j] IIIZZ\n",
-      "[0.5+0j] IIZZI\n",
-      "[0.5+0j] ZIIZI\n",
-      "\n",
-      "optimal_expectation = -4.495973826282007\n",
-      "Therefore, the max cut value is at least  4.495973826282007\n",
-      "optimal_parameters = [0.51349181993727, -0.21299416361632417, 0.3250526425808945, 0.886630847343767]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#cudaq.set_target('nvidia')\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "\n",
-    "# Generate the Hamiltonian for our graph\n",
-    "hamiltonian = hamiltonian_max_cut(edges_src, edges_tgt)\n",
-    "print(hamiltonian)\n",
-    "\n",
-    "# Define the objective, return `<state(params) | H | state(params)>`\n",
-    "# Note that in the `observe` call we list the kernel, the hamiltonian, and then the concrete global variable values of our kernel\n",
-    "# followed by the parameters to be optimized.\n",
-    "\n",
-    "\n",
-    "def objective(parameters):\n",
-    "    return cudaq.observe(kernel_qaoa, hamiltonian, qubit_count, layer_count,\n",
-    "                         edges_src, edges_tgt, parameters).expectation()\n",
-    "\n",
-    "\n",
-    "# Optimize!\n",
-    "optimal_expectation, optimal_parameters = optimizer.optimize(\n",
-    "    dimensions=parameter_count, function=objective)\n",
-    "\n",
-    "# Alternatively we can use the vqe call (just comment out the above code and uncomment the code below)\n",
-    "# optimal_expectation, optimal_parameters = cudaq.vqe(\n",
-    "#    kernel=kernel_qaoa,\n",
-    "#    spin_operator=hamiltonian,\n",
-    "#    argument_mapper=lambda parameter_vector: (qubit_count, layer_count, edges_src, edges_tgt, parameter_vector),\n",
-    "#    optimizer=optimizer,\n",
-    "#    parameter_count=parameter_count)\n",
-    "\n",
-    "print('optimal_expectation =', optimal_expectation)\n",
-    "print('Therefore, the max cut value is at least ', -1 * optimal_expectation)\n",
-    "print('optimal_parameters =', optimal_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have identified the optimal parameters, we can read out the partitioning(s) that gives us a max cut of the graph using the `sample` primitive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11111:4 11110:1 01101:2 01010:148 01110:47 01000:3 00110:54 10101:165 00101:2 01011:154 00100:3 01001:41 00000:1 00011:3 01100:8 10011:7 00010:2 01111:2 11011:3 00111:10 11100:7 10001:65 10010:7 10100:144 10110:40 10000:5 10111:3 11000:7 11101:1 11001:59 11010:2 }\n",
-      "\n",
-      "The max cut is given by the partition:  10101\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sample the circuit using the optimized parameters\n",
-    "# Since our kernel has more than one argument, we need to list the values for each of these variables in order in the `sample` call.\n",
-    "counts = cudaq.sample(kernel_qaoa, qubit_count, layer_count, edges_src,\n",
-    "                      edges_tgt, optimal_parameters)\n",
-    "print(counts)\n",
-    "\n",
-    "# Identify the most likely outcome from the sample\n",
-    "max_cut = max(counts, key=lambda x: counts[x])\n",
-    "print('The max cut is given by the partition: ',\n",
-    "      max(counts, key=lambda x: counts[x]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/quantum_fourier_transform.ipynb b/pr-2308/sphinx/applications/python/quantum_fourier_transform.ipynb
deleted file mode 100644
index 52cd4e8e40..0000000000
--- a/pr-2308/sphinx/applications/python/quantum_fourier_transform.ipynb
+++ /dev/null
@@ -1,413 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quantum Fourier Transform "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classical Fourier transforms aid many areas such as signal processing and data compression. Below, we quantize it to enable Quantum Fourier Transforms (QFT) in CUDA-Q.\n",
-    "\n",
-    "QFT is a key ingredient in Shor's algorithm which is exponentially faster at factoring prime numbers and also plays a vital role in many other interesting quantum algorithms. \n",
-    "\n",
-    "The idea with classical Fourier transforms is to take the problem from the frequency domain, to the time domain where it is easier to solve. This is the same analogy we draw with QFT, where we take the basis state from the computational basis to the Fourier basis. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Quantum Fourier Transform is defined to be the linear operation which takes a basis state, $\\ket{x}$, \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\text{QFT}\\ket{x}  =   \\frac{1}{\\sqrt{N}}\\sum_{y=0}^{N-1} e^{\\frac{2\\pi ixy}{N}} \\ket{y}\n",
-    "\\tag{1}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "where $n$ is the number of qubits and $N= 2^n$. \n",
-    "\n",
-    "It is convenient to represent states like $\\ket{x}  = \\ket{101}$ in their binary representation which in this case would be $2^2 \\times 1 + 2^1 \\times 0 + 2^0 \\times 1 = \\ket{5}$. \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the $n=1$ qubit case, we have the following basis states: $\\ket{0}, \\ket{1}$. The QFT on these is as follows: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|0\\rangle &= \\frac{1}{\\sqrt{2}}\\sum_{y=0}^{2-1} e^{\\frac{2\\pi i(0)y}{2}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}\\left(e^0|0\\rangle + e^0|1\\rangle\\right) \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}(|0\\rangle + |1\\rangle) \\\\\n",
-    "&= \\ket{+} \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{2}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|1\\rangle &= \\frac{1}{\\sqrt{2}}\\sum_{y=0}^{2-1} e^{\\frac{2\\pi i(1)y}{2}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}\\left(e^0|0\\rangle + e^{\\pi i}|1\\rangle\\right) \\\\\n",
-    "&= \\frac{1}{\\sqrt{2}}(|0\\rangle - |1\\rangle) \\\\\n",
-    "&= \\ket{-} \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{3}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "             \n",
-    "\n",
-    "To summarize, $\\text{QFT}|0\\rangle = \\ket{+}$ and $\\text{QFT}|1\\rangle = \\ket{-}$ which can be achieved by applying Hadamard gates. \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Let us now extend this and see how we can apply this to $n=3$ qubits. \n",
-    "\n",
-    "For $n=3, N = 2^3 = 8$ and we would like to perform $\\text{QFT}|101\\rangle$ which in binary is $2^2 \\times 1 + 2^1 \\times 0 + 2^0 \\times 1 = 5$: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|101\\rangle = \\text{QFT}|5\\rangle &= \\frac{1}{\\sqrt{8}}\\sum_{y=0}^{8-1} e^{\\frac{2\\pi i(5)y}{8}}|y\\rangle \\\\\n",
-    "&= \\frac{1}{\\sqrt{8}} [ e^{0}\\ket{0} + e^{\\frac{5i\\pi (1)}{4}}\\ket{1}  + e^{\\frac{5i\\pi (2)}{4}}\\ket{2} +...++ e^{\\frac{5i\\pi (7)}{4}}\\ket{7} ]   \\\\\n",
-    "&= \\frac{1}{\\sqrt{8}} [ \\ket{000} + e^{\\frac{5i\\pi (1)}{4}}\\ket{001}  + e^{\\frac{5i\\pi (2)}{4}}\\ket{010} +...++ e^{\\frac{5i\\pi (7)}{4}}\\ket{111} ]   \\\\\n",
-    "&=  (0.35+0i) \\ket{000} + (-0.25-0.25i)\\ket{001}  + (0+0.35i)\\ket{010} +...+ (-0.25+0.25i)\\ket{111}    \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{4}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "where we have used the fact that $e^{i\\theta} = \\cos(\\theta) + i\\sin(\\theta).$\n",
-    "\n",
-    "Here, we see how in addition to the Hadamard gates, we need some phase gates to apply the appropriate phases to the basis states. It turns out that the gate we need is the controlled $R_k$ rotation where the $R_k$ gate is denoted by:\n",
-    "\n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "R_k = \\begin{bmatrix}\n",
-    "1 & 0 \\\\\n",
-    "0 & e^{2\\pi i/ 2^{k}}\n",
-    "\\end{bmatrix}\n",
-    "\\tag{5}\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "Here is a generalized ciruit for a $n$-qubit quantum Fourier transform:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "<img src=\"images/qft.png\" alt=\"qft\" width=\"600\">\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we show the code for a QFT implementation in CUDA-Q based on the circuit diagram above: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "from typing import List\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform(input_state: List[int]):\n",
-    "    '''Args: \n",
-    "    input_state (list[int]): specifies the input state to be Fourier transformed.  '''\n",
-    "\n",
-    "    qubit_count = len(input_state)\n",
-    "\n",
-    "    # Initialize qubits.\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # Initialize the quantum circuit to the initial state.\n",
-    "    for i in range(qubit_count):\n",
-    "        if input_state[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮╭───────────╮╭────────────╮                       \n",
-      "q0 : ┤ x ├┤ h ├┤ r1(1.571) ├┤ r1(0.7854) ├───────────────────────\n",
-      "     ╰───╯╰───╯╰─────┬─────╯╰─────┬──────╯╭───╮╭───────────╮     \n",
-      "q1 : ────────────────●────────────┼───────┤ h ├┤ r1(1.571) ├─────\n",
-      "     ╭───╮                        │       ╰───╯╰─────┬─────╯╭───╮\n",
-      "q2 : ┤ x ├────────────────────────●──────────────────●──────┤ h ├\n",
-      "     ╰───╯                                                  ╰───╯\n",
-      "\n",
-      "[ 0.35+0.j   -0.25-0.25j  0.  +0.35j  0.25-0.25j -0.35+0.j    0.25+0.25j\n",
-      " -0.  -0.35j -0.25+0.25j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Can be changed to 'nvidia' for single gpu, 'nvidia-mgpu' for multi-GPU or quantum hardware.\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "# The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.\n",
-    "input_state = [1, 0, 1]\n",
-    "\n",
-    "# Number of decimal points to round up the statevector to.\n",
-    "precision = 2\n",
-    "\n",
-    "# Draw the quantum circuit.\n",
-    "print(cudaq.draw(quantum_fourier_transform, input_state))\n",
-    "\n",
-    "# Print the statevector to the specified precision\n",
-    "statevector = np.array(cudaq.get_state(quantum_fourier_transform, input_state))\n",
-    "print(np.round(statevector, precision))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can verify that this is the correct answer as above we worked out that: \n",
-    "\n",
-    "$$\n",
-    "\\begin{equation} \n",
-    "\\begin{aligned}\n",
-    "\\text{QFT}|101\\rangle = \\text{QFT}|5\\rangle &= \\frac{1}{\\sqrt{8}}\\sum_{y=0}^{8-1} e^{\\frac{2\\pi i(5)y}{8}}|y\\rangle \\\\\n",
-    "&=  (0.35+0i) \\ket{000} + (-0.25-0.25i)\\ket{001}  + (0+0.35i)\\ket{010} +...+ (-0.25+0.25i)\\ket{111}.   \\\\\n",
-    "\\end{aligned}\n",
-    "\\tag{6}\n",
-    "\\end{equation}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Barring the gates used in the state preparation step, the circuit starts off by implementing a Hadamard gate and then $n-1$ rotations on the zeroth qubit - a total of $n$ gates. This is followed by a Hadamard and $n-2$ conditional rotations on the first qubit which totals to $n -1$ gates. If we continue the series, we get $n + (n-1) + (n-2)... = n(n+1)/2$ gates. Hence, this circuit provides an algorithm with $O(n^2)$ gates for computing the QFT. \n",
-    "\n",
-    "The best classical algorithms for computing fast Fourier transforms on $2^{n}$ elements require $O(n2^{n})$ gates. This means that it requires exponentially more operations to compute the fast Fourier transform on a classical computer than it does to implement the QFT on a quantum computer.\n",
-    "\n",
-    "Why are we not using QFT for real life applications then? The problem lies in quantum measurement. The quantum-Fourier-transformed amplitudes cannot be accessed by quantum measurement. However there are other quantum algorithms that do make use of QFT such as Shor's algorithm. \n",
-    "\n",
-    "One can also invert the QFT where the circuit is executed in reverse with the inverse of each gate. The figure below shows the general $n$-qubit inverse QFT. Why don't you try implementing this in CUDA-Q?\n",
-    "\n",
-    "<img src=\"images/inverse_qft.png\" alt=\"qft\" width=\"600\">\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Fourier Transform revisited\n",
-    "We provided one implementation of the Quantum Fourier Transform above.  To demonstrate some of the other features of CUDA-Q, let's define a new kernel for the Quantum Fourier Transform, which we'll call `quantum_fourier_transform2`.  Pay attention to how the kernel definition differs from the example above.\n",
-    "\n",
-    " Since the Quantum Fourier Transform is a unitary operator, its inverse is its adjoint.  Therefore, once we have implemented the Quantum Fourier Transform as a kernel in CUDA-Q, we can use the built-in `adjoint` operation to create the Inverse Quantum Fourier Transform."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define kernels for the Quantum Fourier Transform and the Inverse Quantum Fourier Transform\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform2(qubits: cudaq.qview):\n",
-    "    '''Args: \n",
-    "    qubits (cudaq.qview): specifies the quantum register to which apply the QFT.'''\n",
-    "    qubit_count = len(qubits)\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def inverse_qft(qubits: cudaq.qview):\n",
-    "    '''Args: \n",
-    "    qubits (cudaq.qview): specifies the quantum register to which apply the inverse QFT.'''\n",
-    "    cudaq.adjoint(quantum_fourier_transform2, qubits)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see that this approach gives us the same results as the section above. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮╭─────────╮╭──────────╮                                      »\n",
-      "q0 : ┤ x ├┤ h ├┤ r1(1.5) ├┤ r1(0.75) ├──────────────────────────────────────»\n",
-      "     ╰───╯╰───╯╰────┬────╯╰────┬─────╯╭───╮╭─────────╮          ╭──────────╮»\n",
-      "q1 : ───────────────●──────────┼──────┤ h ├┤ r1(1.5) ├──────────┤ r1(-1.5) ├»\n",
-      "     ╭───╮                     │      ╰───╯╰────┬────╯╭───╮╭───╮╰────┬─────╯»\n",
-      "q2 : ┤ x ├─────────────────────●────────────────●─────┤ h ├┤ h ├─────●──────»\n",
-      "     ╰───╯                                            ╰───╯╰───╯            »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "     ╭───────────╮╭──────────╮╭───╮\n",
-      "─────┤ r1(-0.75) ├┤ r1(-1.5) ├┤ h ├\n",
-      "╭───╮╰─────┬─────╯╰────┬─────╯╰───╯\n",
-      "┤ h ├──────┼───────────●───────────\n",
-      "╰───╯      │                       \n",
-      "───────────●───────────────────────\n",
-      "                                   \n",
-      "\n",
-      "[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.-0.j 0.-0.j 0.+0.j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def verification_example(input_state: List[int]):\n",
-    "    '''Args: \n",
-    "    input_state (list[int]): specifies the input state to be transformed with QFT and the inverse QFT.  '''\n",
-    "    qubit_count = len(input_state)\n",
-    "    # Initialize qubits.\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # Initialize the quantum circuit to the initial state.\n",
-    "    for i in range(qubit_count):\n",
-    "        if input_state[i] == 1:\n",
-    "            x(qubits[i])\n",
-    "\n",
-    "    # Apply the quantum Fourier Transform\n",
-    "    quantum_fourier_transform2(qubits)\n",
-    "\n",
-    "    # Apply the inverse quantum Fourier Transform\n",
-    "    inverse_qft(qubits)\n",
-    "\n",
-    "\n",
-    "# The state to which the QFT operation is applied to. The zeroth element in the list is the zeroth qubit.\n",
-    "input_state = [1, 0, 1]\n",
-    "\n",
-    "# Number of decimal points to round up the statevector to.\n",
-    "precision = 2\n",
-    "\n",
-    "print(cudaq.draw(verification_example, input_state))\n",
-    "\n",
-    "# Print the statevector to the specified precision\n",
-    "statevector = np.array(cudaq.get_state(verification_example, input_state))\n",
-    "print(np.round(statevector, precision))  # The result should be the input state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version latest (https://github.com/NVIDIA/cuda-quantum a726804916fd397408cbf595ce6fe5f33dcd8b4c)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/quantum_teleportation.ipynb b/pr-2308/sphinx/applications/python/quantum_teleportation.ipynb
deleted file mode 100644
index ddf7b98ac5..0000000000
--- a/pr-2308/sphinx/applications/python/quantum_teleportation.ipynb
+++ /dev/null
@@ -1,396 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quantum Teleporation\n",
-    "\n",
-    "\n",
-    "Below we shall study an interesting quantum phenomena - teleportation.\n",
-    "\n",
-    "Let us start by implementing the circuit shown below and discuss its implications later. We normally assume that the input qubits are initialised to the $\\ket{0}$ state unless specified otherwise. In this case however, the circuit diagram depicts the 0<sup>th</sup> qubit to be initialised in an arbitary quantum state, $\\ket{\\psi} = \\alpha\\ket{0} + \\beta\\ket{1}$, and the 1<sup>st</sup> and 2<sup>nd</sup> qubit to be in the state $\\ket{\\beta_{00}} = \\tfrac{1}{\\sqrt{2}} [\\ket{00} + \\ket{11}]$.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![tene.png](./images/teleportation.png)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')\n",
-    "cudaq.set_random_seed(23)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We now replicate the circuit shown in the diagram above and step through each intermediate stage from psi0 to psi4.\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def teleportation():\n",
-    "\n",
-    "    # Initialize a 3 qubit quantum circuit\n",
-    "    qubits = cudaq.qvector(3)\n",
-    "\n",
-    "    # Psi0 - random quantum state, psi, on qubit 0.\n",
-    "    rx(3.14, qubits[0])\n",
-    "    ry(2.71, qubits[0])\n",
-    "    rz(6.62, qubits[0])\n",
-    "\n",
-    "    # Psi0 - create a maximally entangled state on qubits 1 and 2.\n",
-    "    h(qubits[1])\n",
-    "    cx(qubits[1], qubits[2])\n",
-    "\n",
-    "\n",
-    "# Let us save the statevector of the circuit so far for later use.\n",
-    "psi_0 = cudaq.get_state(teleportation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now we continue with the rest of the circuit\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def teleportation():\n",
-    "\n",
-    "    # Initialize a 3 qubit quantum circuit\n",
-    "    qubits = cudaq.qvector(3)\n",
-    "\n",
-    "    # Psi0 - random quantum state, psi, on qubit 0.\n",
-    "    rx(3.14, qubits[0])\n",
-    "    ry(2.71, qubits[0])\n",
-    "    rz(6.62, qubits[0])\n",
-    "\n",
-    "    # Psi0 - create a maximally entangled state on qubits 1 and 2.\n",
-    "    h(qubits[1])\n",
-    "    cx(qubits[1], qubits[2])\n",
-    "\n",
-    "    # Psi1\n",
-    "    cx(qubits[0], qubits[1])\n",
-    "\n",
-    "    # Psi2\n",
-    "    h(qubits[0])\n",
-    "\n",
-    "    # Psi3 - measure qubits 0 and 1 and store it in variables m1 and m2\n",
-    "    m1 = mz(qubits[0])\n",
-    "    m2 = mz(qubits[1])\n",
-    "\n",
-    "    # Psi4 - apply conditioned pauli operators dependent on the measurement result of qubits 0 and 1.\n",
-    "    if m1 == 1:\n",
-    "        z(qubits[2])\n",
-    "\n",
-    "    if m2 == 1:\n",
-    "        x(qubits[2])\n",
-    "\n",
-    "\n",
-    "psi_4 = cudaq.get_state(teleportation)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The state of this 3 qubit system is described by a $2^3 = 8$ dimensional vector. Sometimes we would like to extract the state of a single qubit from a multipartite state. We can use the partial trace operation defined in the function below which allows us to translate our statevectors to the density matrix representation to trace out subsystems. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to be used below to calculate the partial trace yielding a density matrix.\n",
-    "\n",
-    "\n",
-    "def partial_trace(state_vector, trace_systems):\n",
-    "    \"\"\"\n",
-    "    Partial trace of multi-particle quantum state. \n",
-    "\n",
-    "    Arguments:\n",
-    "        state_vector: complex vector  of size 2**n\n",
-    "        trace_systems (list(int)): a list of subsystems (starting from 0) to trace over. \n",
-    "        dimensions (list(int)): a list of the dimensions of the subsystems.\n",
-    "  \n",
-    "    Returns:\n",
-    "        ndarray: A density matrix with the appropriate subsystems traced over.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    n_qubits = int(np.log2(state_vector.shape[0]))\n",
-    "\n",
-    "    dimensions = [2 for i in range(n_qubits)]\n",
-    "\n",
-    "    trace_systems = len(dimensions) - 1 - np.array(trace_systems)\n",
-    "\n",
-    "    rho = state_vector.reshape(dimensions)\n",
-    "    rho = np.tensordot(rho, rho.conj(), axes=(trace_systems, trace_systems))\n",
-    "    d = int(np.sqrt(np.prod(rho.shape)))\n",
-    "\n",
-    "    return rho.reshape(d, d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|qubit_0> == |qubit_2>?  True\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Trace out qubits 1 and 2 leaving us with qubit 0.\n",
-    "state_of_q0 = partial_trace(state_vector=np.array(psi_0), trace_systems=[1, 2])\n",
-    "\n",
-    "# Trace out qubits 0 and 1 leaving us with qubit 2.\n",
-    "state_of_q2 = partial_trace(state_vector=np.array(psi_4), trace_systems=[0, 1])\n",
-    "\n",
-    "print(f\"|qubit_0> == |qubit_2>?  {np.allclose(state_of_q0, state_of_q2)}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us discuss what has happened. \n",
-    "\n",
-    "We started with a 3 qubit quantum circuit with the 0<sup>th</sup> qubit in $\\ket{\\psi}$ and the 2<sup>nd</sup> qubit being a portion of the maximally entangled bell state $\\ket{\\beta_{00}}$.\n",
-    "\n",
-    "If we look closely at the code cell above, the output state on the 2<sup>nd</sup> qubit, `state_of_q2`, is the same as the random input state, $\\ket{\\psi}$, on the 0<sup>th</sup> qubit, `state_of_q0`. \n",
-    "\n",
-    "We have **teleported** a quantum state from one qubit to another. There is nothing to restrict how close qubits 0 and 2 have to be since they have no entangling operations between them. They can be placed in different labs or infinitely far apart. \n",
-    "\n",
-    "Moreover, we still obey the no-cloning theorem since the initial state $\\ket{\\psi}$ no longer exists on the 0<sup>th</sup> qubit and hence we only have 1 copy of it as shown below \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Using the final statevector, psi4, we show that psi has been teleported and only 1 copy of psi is preserved.\n",
-    "\n",
-    "# Trace out qubits 1 and 2 leaving us with qubit 0\n",
-    "state_of_q0 = partial_trace(state_vector=np.array(psi_4), trace_systems=[1, 2])\n",
-    "\n",
-    "# Trace out qubits 0 and 1 leaving us with qubit 2\n",
-    "state_of_q2 = partial_trace(state_vector=np.array(psi_4), trace_systems=[0, 1])\n",
-    "\n",
-    "np.allclose(state_of_q0, state_of_q2)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "None of this violates the universal limit of information travelling faster than the speed of light. Notice how we have conditioned operations meaning we have to communicate classical information via classical channels which adhere to this limit."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Teleportation explained\n",
-    "\n",
-    "We have stepped through the circuit above using code. We will now do the same using mathematics to enhance our understanding. \n",
-    "\n",
-    "\n",
-    "We start by gathering experimentalists in a lab and performing the following\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "CX_{01}H_{0}\\ket{00} &= CX_{01}\\tfrac{1}{\\sqrt{2}}(\\ket{0}+\\ket{1})\\ket{0} \\\\\n",
-    "                        &= CX_{01}\\tfrac{1}{\\sqrt{2}}(\\ket{00}+\\ket{10}) \\\\\n",
-    "                        &= \\tfrac{1}{\\sqrt{2}}(\\ket{00}+\\ket{11}) \\\\\n",
-    "                        &= \\ket{\\beta_{00}}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "to create an entangled bell pair. We then hand each qubit in $\\ket{\\beta_{00}}$ to two parties, namely Alice and Bob for safekeeping. Many moons later, Alice comes in posession of an arbitary quantum state $\\ket{\\psi} = \\alpha\\ket{0} + \\beta\\ket{1}$ which she would like to send to Bob who is many miles away. This entails communicating values of $\\alpha$ and $\\beta$. However, measurement would lead to state collapse and only yield one bit of information, 0 or a 1.\n",
-    "\n",
-    "Referring to circuit in the figure above, the 0<sup>th</sup> and the 1<sup>st</sup> qubits are in posession of Alice and the 2<sup>nd</sup> qubit is with Bob. The 1<sup>st</sup> and 2<sup>nd</sup> qubits have been entangled but are now seperated in distance. Let us now step through the circuit to describe its evolution. Our input state is \n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{0}} &=  \\ket{\\psi} \\ket{\\beta_{00}}  = (\\alpha\\ket{0} + \\beta\\ket{1})(\\tfrac{1}{\\sqrt{2}} (\\ket{00} + \\ket{11}) \\\\\n",
-    "&= \\tfrac{1}{\\sqrt{2}}[\\alpha\\ket{0}(\\ket{00} + \\ket{11}) +\\beta\\ket{1}(\\ket{00} + \\ket{11})]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "Alice then performs a CX gate between the qubits she posses i.e. qubit 0 and 1 \n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{1}} &= CX_{01}\\ket{\\psi_{0}} \\\\\n",
-    "                &= \\tfrac{1}{\\sqrt{2}}[\\alpha\\ket{0}(\\ket{00} + \\ket{11}) +\\beta\\ket{1}(\\ket{10} + \\ket{01})]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "which is then followed by Alice performing a Hadamard on the 0<sup>th</sup> qubit\n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= H_{0}\\ket{\\psi_{1}} \\\\\n",
-    "                &= \\tfrac{1}{{2}}[\\alpha(\\ket{0}+\\ket{1})(\\ket{00} + \\ket{11}) +\\beta(\\ket{0}-\\ket{1})(\\ket{10} + \\ket{01})].\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "If we expand all the inner brackets\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= \\tfrac{1}{{2}}[(\\alpha\\ket{0} + \\alpha\\ket{1})(\\ket{00} + \\ket{11}) + (\\beta\\ket{0} - \\beta\\ket{1})(\\ket{10} + \\ket{01})] \\\\\n",
-    "                &= \\tfrac{1}{{2}}[\\alpha\\ket{000}+\\alpha\\ket{011}  +\\alpha\\ket{100}  +\\alpha\\ket{111} +\\beta\\ket{010}+\\beta\\ket{001}-\\beta\\ket{110}-\\beta\\ket{101}]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "and then collect like terms whilst maintaining qubit ordering, we yield \n",
-    "\n",
-    "\n",
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "\\ket{\\psi_{2}} &= \\tfrac{1}{{2}}[  \\ket{00}(\\alpha\\ket{0}+\\beta\\ket{1}) + \\ket{01}(\\alpha\\ket{1}+\\beta\\ket{0}) +\n",
-    " \\ket{10}(\\alpha\\ket{0}-\\beta\\ket{1}) + \\ket{11}(\\alpha\\ket{1}+\\beta\\ket{0})                              ]\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "where the qubit ordering notation states that the left most qubit is the 0<sup>th</sup> qubit. This expression naturally breaks down into 4 terms where each term has all 3 qubits and the 4 terms represent all the possibilities they can be in after they have been evolved.  \n",
-    "\n",
-    "We now ask our friend Alice to measure her qubits. Quantum mechanically we know that upon measurement the possibilities of the 3 qubit system will collapse regardless of the distance between them and the unmeasured outcomes will therefore be deterministic. If her measurement result yields a 00, the first term in $\\ket{\\psi_{2}}$ tells us that Bob's qubit will be in the state $\\alpha\\ket{0}+\\beta\\ket{1}$ which is the the original state, $\\ket{\\psi}$, we wanted to teleport. Alice has other potential measurement outcomes all of which are summarised in the figure below \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "| Alices' measurement        | Bobs' state           | \n",
-    "| ---------------------------|:---------------------:| \n",
-    "|  $$00$$ | $$  \\ket{\\psi_3(00)} = \\alpha\\ket{0} + \\beta\\ket{1} $$ | \n",
-    "|  $$01$$ | $$ \\ket{\\psi_3(01)} =  \\alpha\\ket{1} + \\beta\\ket{0} $$ | \n",
-    "|  $$10$$ | $$ \\ket{\\psi_3(10)} = \\alpha\\ket{0} - \\beta\\ket{1} $$ | \n",
-    "|  $$11$$ | $$ \\ket{\\psi_3(11)} = \\alpha\\ket{1} - \\beta\\ket{0} $$ | \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A quick glance at the figure above shows us that Bob's qubit is nearly in the state $\\ket{\\psi}$ which is what we are after pending some minor corrections. The circuit diagram depicts conditioned gates that are applied depending on Alice's measurement result fulfilling the minor corrections required to complete the teleportation protocol. The final gate operations are summarised below "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$ \n",
-    "\\begin{aligned} \n",
-    "M1M2 = 00, \\ket{\\psi_4} = Z^{0}X^{0}\\ket{\\psi_3(00)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 01, \\ket{\\psi_4} = Z^{0}X^{1}\\ket{\\psi_3(01)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 10, \\ket{\\psi_4} = Z^{1}X^{0}\\ket{\\psi_3(10)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \\\\\n",
-    "M1M2 = 11, \\ket{\\psi_4} = Z^{1}X^{1}\\ket{\\psi_3(11)} = \\alpha\\ket{0} + \\beta\\ket{1} = \\ket{\\psi} \n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is clear to see that in all instances, we recover $\\ket{\\psi}$ on Bob's qubit thus fulfilling our ambition of teleporting an unknown quantum state between 2 parties. \n",
-    "\n",
-    "It is important to note that quantum teleportation does not allow communication faster than the speed of light. The state $\\ket{\\psi}$ does not instantly appear with Bob. Alice has to use a classical communication channel which is bound by the speed of classical physics to communicate her measurement results to Bob so that he can make the minor corrections required. \n",
-    "\n",
-    "Moreover teleportation does not violate the no-cloning theorem. The protocol does not allow us to create a copy of $\\ket{\\psi}$ leaving us with $\\ket{\\psi\\psi}$ but rather transmits $\\ket{\\psi}$ from Alice to Bob. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The images used in this notebook are courtesey of the Quantum Computation and Quantum Information textbook by Nielsen & Chuang.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/quantum_volume.ipynb b/pr-2308/sphinx/applications/python/quantum_volume.ipynb
deleted file mode 100644
index a567ba857f..0000000000
--- a/pr-2308/sphinx/applications/python/quantum_volume.ipynb
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0b2a1f13-07b1-4b72-9c81-4da1ba62aec6",
-   "metadata": {},
-   "source": [
-    "# Quantum Volume\n",
-    "\n",
-    "[Quantum volume](https://en.wikipedia.org/wiki/Quantum_volume) (QV) is a metric for determining the power of a noisy quantum device.  The QV test is performed for a specified qubit number $N$. If the test is passed, then the device can claim a quantum volume of $2^N$.  In practice, the procedure repeats, until the device reaches a qubit number for which the test fails and its greatest passing score is the device's quantum volume. Though imperfect, the test is a reasonable  approximation of the devices usable processing power. This tutorial will demonstrate how CUDA-Q can be used to perform the quantum volume test.\n",
-    "\n",
-    "\n",
-    "The test consists of the following steps (see figure below):\n",
-    "1. A special random circuit is constructed (details below)\n",
-    "2. A simulation determines the exact probability distribution of every bitstring and the median probability is determined.\n",
-    "3. Every bitstring which has an associated probability greater than the median, is considered a heavy bitstring for that particular circuit.\n",
-    "4. The circuit is sampled on the noisy device, and the percent of shots resulting in heavy bitstring are\n",
-    "5. The process is repeated many times and the resulted averaged. The test is passed if the average is greater than 2/3.\n",
-    "\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/QVprocedure.png\" alt=\"Drawing\" style=\"width: 700px;\"/>\n",
-    "\n",
-    "the circuits used are square, meaning they have the same number of layers as qubits. Each layer consists of a random permutation of qubits, followed by random SU4 operations applied to n/2 pairs of qubits.  (See the first step in the figure above).  For CUDA-Q implementation, the SU4 gates are decomposed using the KAK decomposition (figure from this [paper](https://arxiv.org/pdf/1811.12926)).\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/kakdecomp.png\" alt=\"Drawing\" style=\"width: 500px;\"/>\n",
-    "\n",
-    "The cell below specifies a circuit size `n` and two CUDA-Q kernels, one performing an SU4 operation and another building the entire QV circuit. This example is constructed for an even number of qubits for simplicity.\n",
-    "\n",
-    "The QV kernel concludes with application of a bit flip operation on each qubit. This is not part of the QV circuit, but will be used later to introduce noise to the circuit. Otherwise the test would pass every time!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b748ca48-8c65-4158-8c8a-126bbbb39afe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "# Select an even number\n",
-    "n = 4\n",
-    "su4_per_circuit = int(n / 2 * n)\n",
-    "n_params_in_su4 = 21\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def su4_gate(q0: cudaq.qubit, q1: cudaq.qubit, params: list[float]):\n",
-    "    u3(params[0], params[1], params[2], q0)\n",
-    "    u3(params[3], params[4], params[5], q1)\n",
-    "    x.ctrl(q0, q1)\n",
-    "    u3(params[6], params[7], params[8], q0)\n",
-    "    u3(params[9], params[10], params[11], q1)\n",
-    "    x.ctrl(q1, q0)\n",
-    "    u3(params[12], params[13], params[14], q0)\n",
-    "    x.ctrl(q0, q1)\n",
-    "    u3(params[15], params[16], params[17], q0)\n",
-    "    u3(params[18], params[19], params[20], q0)\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def qv(n: int, params: list[float], permutations: list[int]):\n",
-    "\n",
-    "    reg = cudaq.qvector(n)\n",
-    "    param_index = 0\n",
-    "\n",
-    "    for layer in range(n):\n",
-    "        for gate in range(n / 2):\n",
-    "            su4_gate(reg[permutations[layer * n + gate * 2]],\n",
-    "                     reg[permutations[layer * n + gate * 2 + 1]],\n",
-    "                     params[param_index:param_index + 21])\n",
-    "            param_index += 21\n",
-    "\n",
-    "    x(reg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3d3fb4f-ca18-4eea-9b65-609fbc31317c",
-   "metadata": {},
-   "source": [
-    "Each circuit must be random. These function randomly choose parameters and permutations for each circuit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b139613f-f6ca-48de-a4c2-59aa7d705764",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_random_params() -> list[float]:\n",
-    "\n",
-    "    params = np.random.uniform(0, 2 * np.pi, n_params_in_su4 * su4_per_circuit)\n",
-    "\n",
-    "    params_list = params.tolist()\n",
-    "\n",
-    "    return params_list\n",
-    "\n",
-    "\n",
-    "\n",
-    "def generate_random_permutations() -> list[int]:\n",
-    "\n",
-    "    circuit_permutations = []\n",
-    "\n",
-    "    for i in range(n):\n",
-    "        circuit_permutations.extend(\n",
-    "            np.random.permutation(n).astype(np.int64).tolist())\n",
-    "\n",
-    "    return circuit_permutations\n",
-    "\n",
-    "\n",
-    "parameters = generate_random_params()\n",
-    "permutations = generate_random_permutations()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d11fd5a-da31-4645-976d-7d944ba4e18e",
-   "metadata": {},
-   "source": [
-    "This function is an auxillary function used later to convert an integer into a \"big endian\" bitstring. This is used to help determine the heavy bitstrings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ded42cc6-3bf7-4b56-90ca-0e69cbe85dad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_bitstring(integer) -> str:\n",
-    "\n",
-    "    return bin(integer)[2:].zfill(n)[::-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d631da6-97e3-473c-b59f-beb2302fa75e",
-   "metadata": {},
-   "source": [
-    "The `percent_heavy_sampled` function takes the random circuit parameters and permutations and the error rate and returns the percent of heavy bitstrings produced by a noisy circuit sample.  \n",
-    "\n",
-    "The function first sets up the noise model.  It assumes that each $X$ gate applied at the end of the circuit will fail with some probability denoted by the `error_rate`.\n",
-    "\n",
-    "Next, the noiseless simulation is performed on a GPU simulated with the `nvidia` backend to obtain the state vector.  The `density-matrix-cpu` backend is used to sample the noisy circuit. \n",
-    "\n",
-    "The rest of function processes these results to determine the heavy bitstring sample probabilities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e852e3c5-29a0-4588-ac56-a102463aa100",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def percent_heavy_sampled(circuit_params,\n",
-    "                          layer_permutations,\n",
-    "                          error_rate,\n",
-    "                          print_output=False) -> float:\n",
-    "\n",
-    "    # Includes option to print results for a single circuit\n",
-    "\n",
-    "    # Define a bit flip error applied to all qubits\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "    bf = cudaq.BitFlipChannel(error_rate)\n",
-    "    for i in range(n):\n",
-    "        noise.add_channel('x', [i], bf)\n",
-    "\n",
-    "    # Gets noiseless probability distribution\n",
-    "    cudaq.set_target(\"nvidia\")\n",
-    "    clean_result = np.array(\n",
-    "        cudaq.get_state(qv, n, circuit_params, layer_permutations))\n",
-    "\n",
-    "    # Performs noisy sampling\n",
-    "    cudaq.set_target(\"density-matrix-cpu\")\n",
-    "    noisy_result = cudaq.sample(qv,\n",
-    "                                n,\n",
-    "                                circuit_params,\n",
-    "                                layer_permutations,\n",
-    "                                noise_model=noise,\n",
-    "                                shots_count=1000)\n",
-    "\n",
-    "    # Converts SV amplitudes to probabilities\n",
-    "    probs = clean_result * np.conjugate(clean_result)\n",
-    "\n",
-    "    # Determines the median value\n",
-    "    cutoff = np.median(probs).real\n",
-    "\n",
-    "    if print_output:\n",
-    "        print('The Median for this circuit is:')\n",
-    "        print(np.median(probs).real)\n",
-    "\n",
-    "    # Determines if a bitstring is heavy and saves the bitstring in a list if so.\n",
-    "    heavy = []\n",
-    "    index = 0\n",
-    "    circuit_prob = 0\n",
-    "\n",
-    "    for outcome_prob in probs:\n",
-    "        if outcome_prob.real > cutoff:\n",
-    "            heavy.append(make_bitstring(index))\n",
-    "            circuit_prob += outcome_prob.real\n",
-    "        index += 1\n",
-    "\n",
-    "    if print_output:\n",
-    "\n",
-    "        print('The heavy bitstrings for this circuit are')\n",
-    "        print(heavy)\n",
-    "\n",
-    "        print('This circuit has an ideal havy sampling probability of:')\n",
-    "        print(circuit_prob)\n",
-    "\n",
-    "    # Determines percent of noisy sample results that are heavy\n",
-    "    prob_heavy_in_noisy = 0\n",
-    "    for heavy_bitstring in heavy:\n",
-    "        prob_heavy_in_noisy += noisy_result.probability(heavy_bitstring)\n",
-    "\n",
-    "    if print_output:\n",
-    "        print('Percent of time noisy sample returned heavy bitstring')\n",
-    "        print(prob_heavy_in_noisy)\n",
-    "\n",
-    "    # Returns this probability\n",
-    "    return prob_heavy_in_noisy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8eb4fff-c031-40d1-b521-7aea8bc1014f",
-   "metadata": {},
-   "source": [
-    "You can test a single circuit below to see if it passes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9cd7d79d-f489-426e-97a7-8664e62799a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The Median for this circuit is:\n",
-      "0.04363711\n",
-      "The heavy bitstrings for this circuit are\n",
-      "['0000', '0100', '0010', '1010', '0101', '1101', '0011', '0111']\n",
-      "This circuit has an ideal havy sampling probability of:\n",
-      "0.8153219893574715\n",
-      "Percent of time noisy sample returned heavy bitstring\n",
-      "0.488\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.488"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "percent_heavy_sampled(parameters, permutations, 1, True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecd2b646-df3e-4b4c-b9be-851f6bdb6116",
-   "metadata": {},
-   "source": [
-    "The true quantum volume is detemined by repeating the process many times and averaging the results. This function repeatedly applies the `percent _heavy_sampled` function for `n_circuit`number of times and prints if the test is passed and returns the average."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f94562c0-7ceb-4609-9e55-ae8791cd8dc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_qv(n_circuits, circuit_size, prob_of_error) -> float:\n",
-    "\n",
-    "    n = circuit_size\n",
-    "    su4_per_circuit = int(n / 2 * n)\n",
-    "    number_of_circuits = n_circuits\n",
-    "\n",
-    "    counter = 0\n",
-    "    circuit_results = []\n",
-    "\n",
-    "    # Loop over n_circuits\n",
-    "    while counter < number_of_circuits:\n",
-    "        parameters = generate_random_params()\n",
-    "        permutations = generate_random_permutations()\n",
-    "        circuit_results.append(\n",
-    "            percent_heavy_sampled(parameters,\n",
-    "                                  permutations,\n",
-    "                                  prob_of_error,\n",
-    "                                  print_output=False))\n",
-    "\n",
-    "        counter += 1\n",
-    "\n",
-    "    # Average the results\n",
-    "    score = sum(circuit_results) / len(circuit_results)\n",
-    "\n",
-    "    print('The score is:')\n",
-    "    print(score)\n",
-    "\n",
-    "    # Determined if QV test is passed\n",
-    "    if score > 2 / 3:\n",
-    "        print('passed!')\n",
-    "        print('Quantum Volume')\n",
-    "        print(2**n)\n",
-    "\n",
-    "    else:\n",
-    "        print('failed QV Test')\n",
-    "\n",
-    "    return score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2279de84-bb73-48a9-a33b-3aa2efa66573",
-   "metadata": {},
-   "source": [
-    "Try running the QV procedure for 100 four qubit circuits with a 10% chance of error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3cab12da-7cbc-4ff3-80d8-2a81007942ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The score is:\n",
-      "0.7280300000000003\n",
-      "passed!\n",
-      "Quantum Volume\n",
-      "16\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.7280300000000003"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n = 4\n",
-    "calc_qv(100, n, .1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afbc7d0a-2507-42e4-9946-3aa93384c645",
-   "metadata": {},
-   "source": [
-    "an interesting benefit of simulation is the ability to explore how noise might affect the QV results.  In this case, the noise model is trivial, but it is still possible to see a relationship between the probability of error in the $X$ gates and the QV outcome. \n",
-    "\n",
-    "\n",
-    "<img src=\"./images/qvplot.png\" alt=\"Drawing\" style=\"width: 900px;\"/>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/readout_error_mitigation.ipynb b/pr-2308/sphinx/applications/python/readout_error_mitigation.ipynb
deleted file mode 100644
index ec366366bd..0000000000
--- a/pr-2308/sphinx/applications/python/readout_error_mitigation.ipynb
+++ /dev/null
@@ -1,922 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5b2ac00e-c7fc-45cc-888e-8c26dabf80d2",
-   "metadata": {},
-   "source": [
-    "# Readout Error Mitigation\n",
-    "\n",
-    "Readout errors are caused by imperfect qubit measurement and are a common source of error in quantum computing. Properly modelling these errors in simulation can give the user tools to understand better what these errors are and how to mitigate them when running on actual quantum devices.\n",
-    "\n",
-    "Readout errors can be mitigated with a confusion matrix $A$. It is a square matrix of size $2^n \\times 2^n$ and tells about the probability of observing the state $|y\\rangle$ given the true state $|x\\rangle$. The confusion matrix characterizes the readout error of the device and is circuit-independent. Once $A$ is estimated, we can compute its pseudoinverse $A^+$ which can be applied to the noisy probability distribution $p_{\\text{noisy}}$ to obtain an adjusted quasi-probability distribution \n",
-    "\n",
-    "$$\n",
-    "p_{\\text{mitigated}} = A^+ p_{\\text{noisy}}\n",
-    "$$\n",
-    "\n",
-    "In this tutorial, we show how to build a confusion matrix with the following approaches.\n",
-    "\n",
-    "- Using a  single qubit model\n",
-    "- Using $k$ local confusion matrices\n",
-    "- A full confusion matrix for each $2^n$ combination\n",
-    "\n",
-    "The last method works well for correcting correlated errors (which affect multiple qubits together). However, it becomes impractical for large numbers of qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "04b58854-7dab-4f1b-8c59-d8b9506bce44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "\n",
-    "!pip install matplotlib==3.8.4 pandas==2.2.2 scipy==1.13.1 seaborn==0.13.2 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "710a2a46-4e55-4508-a0b7-21969aac6a87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import itertools\n",
-    "from functools import reduce\n",
-    "from scipy.optimize import minimize\n",
-    "from typing import Union\n",
-    "\n",
-    "sns.set_style(\"dark\")\n",
-    "\n",
-    "cudaq.set_target(\"density-matrix-cpu\")\n",
-    "seed = 42\n",
-    "\n",
-    "cudaq.set_random_seed(seed)\n",
-    "np.random.seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8eab761a-0a82-4e20-8cf8-54602cd05d6a",
-   "metadata": {},
-   "source": [
-    "To model the readout error, we apply a bitflip channel on every qubit at the end of the circuit using an Identity gate. The probability of bitflip `probs` can be the same or different for all the qubits.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-success\"><b>Note: </b>\n",
-    "In principle, readout error is applied to the measurement gate but we use the Identity gate because adding a quantum error on the measurement gate is not yet supported. Also, the Identity gate is not present in the <kbd>add_channel</kbd> method, so we model the Identity gate using a rotation gate with an angle of 0.0.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d84bb420-4fab-4c26-919a-7efa8d3a219b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_noise(n_qubits: int, probs: Union[float, list[float]]):\n",
-    "    \"\"\"\n",
-    "    Creates a noise model with specified probabilities of bit flip errors on each qubit.\n",
-    "\n",
-    "    Args:\n",
-    "        n_qubits (int): The number of qubits in the quantum circuit.\n",
-    "        probs (Union[float, list[float]]): Probabilities of a bit flip error occurring on each qubit.\n",
-    "        If a single probability value is provided, it will be applied to all qubits. If a list of\n",
-    "        probabilities is provided, each qubit will have its own probability.\n",
-    "\n",
-    "    Returns:\n",
-    "        cudaq noise model\n",
-    "    \"\"\"\n",
-    "    noise = cudaq.NoiseModel()\n",
-    "\n",
-    "    if not isinstance(probs, list):\n",
-    "        probs = [probs] * n_qubits\n",
-    "\n",
-    "    for i, p in enumerate(probs):\n",
-    "        bit_flip = cudaq.BitFlipChannel(p)\n",
-    "        noise.add_channel(\"rx\", [i], bit_flip)\n",
-    "\n",
-    "    return noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb4c7a14-1c0c-4fab-a6aa-7174c23baa7f",
-   "metadata": {},
-   "source": [
-    "We define a cudaq kernel which will help create a set of quantum circuits to take measurements of the basis states for $n$ qubits. The kernel takes the number of qubits and the basis state as arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "53e3f450-000f-49b8-ad5c-57aa74461694",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(n_qubits: int, state_label: list[int]):\n",
-    "    qvector = cudaq.qvector(n_qubits)\n",
-    "    for i, val in enumerate(state_label):\n",
-    "        if val == 1:\n",
-    "            x(qvector[i])\n",
-    "        rx(0.0, qvector[i]\n",
-    "          )  # Identity gate at the end on each qubit to model readout error\n",
-    "    mz(qvector)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95411463-cd1e-499f-baef-d624acc0122c",
-   "metadata": {},
-   "source": [
-    "For the tutorial, we will use the GHZ state on three qubits to test readout error mitigation. The GHZ state on 3 qubits is described as:\n",
-    "\n",
-    "$$\n",
-    "\\frac{|000\\rangle + |111\\rangle}{\\sqrt{2}}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c1476028-923d-4f2b-b5fc-39ebcc8cbab9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def ghz_kernel(n_qubits: int):\n",
-    "    qvector = cudaq.qvector(n_qubits)\n",
-    "\n",
-    "    h(qvector[0])\n",
-    "    for i in range(n_qubits - 1):\n",
-    "        cx(qvector[i], qvector[i + 1])\n",
-    "\n",
-    "    # Apply identity gates for readout error mitigation\n",
-    "    for i in range(n_qubits):\n",
-    "        rx(0.0, qvector[i])\n",
-    "\n",
-    "    mz(qvector)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "900d2110-d613-426a-8aa9-6c12b1238536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to draw the confusion matrix\n",
-    "def plot_cmat(mat):\n",
-    "    fig, ax = plt.subplots()\n",
-    "    n = len(mat)\n",
-    "    im2 = ax.matshow(mat, cmap=plt.cm.Reds, vmin=0, vmax=1.0)\n",
-    "    ax.set_yticks(np.arange(n))\n",
-    "    ax.set_xticks(np.arange(n))\n",
-    "    ax.set_yticklabels(n * [\"\"])\n",
-    "    ax.set_xticklabels(n * [\"\"])\n",
-    "    ax.set_title(r\"Confusion Matrix\", fontsize=16)\n",
-    "    ax.set_xlabel(\"Prepared State\")\n",
-    "    ax.xaxis.set_label_position(\"top\")\n",
-    "    ax.set_ylabel(\"Measured State\")\n",
-    "    fig.colorbar(im2, ax=ax)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "032f6898-c953-4d30-a2bf-e8b46ee77c62",
-   "metadata": {},
-   "source": [
-    "It is possible that the adjusted quasi-probability distribution $p_{\\text{mitigated}}$ obtained by application of $A^+$ to $p_{\\text{noisy}}$ may be non-positive. We need to find the closest positive probability distribution in such a case.\n",
-    "\n",
-    " $$ p'' = \\min_{p_{\\rm positive}} \\|p_{\\rm noisy} - p_{\\rm positive}\\|_1$$\n",
-    "\n",
-    "Then, we can draw samples from $p''$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9b355dcb-bf80-42a1-ae11-dcc9f11ba1e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_closest_distribution(empirical_dist):\n",
-    "    \"\"\"\n",
-    "    Find the closest distribution to an empirical distribution by minimizing the L1 norm.\n",
-    "\n",
-    "    Args:\n",
-    "        empirical_dist: Empirical distribution that you want to find the closest distribution to.\n",
-    "\n",
-    "    Returns:\n",
-    "        Closest distribution to `empirical_dist`\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def objective(x):\n",
-    "        return np.linalg.norm(empirical_dist - x, ord=1)\n",
-    "\n",
-    "    # Constraint: all elements of p must be positive, and the distribution must sum to 1\n",
-    "    cons = (\n",
-    "        {\n",
-    "            \"type\": \"ineq\",\n",
-    "            \"fun\": lambda p: p\n",
-    "        },\n",
-    "        {\n",
-    "            \"type\": \"eq\",\n",
-    "            \"fun\": lambda p: np.sum(p) - 1\n",
-    "        },\n",
-    "    )\n",
-    "    bnds = [(0, 1) for _ in range(len(empirical_dist))]\n",
-    "    initial_value = np.random.uniform(size=len(empirical_dist))\n",
-    "\n",
-    "    res = minimize(\n",
-    "        objective,\n",
-    "        initial_value,\n",
-    "        method=\"SLSQP\",\n",
-    "        options={\"maxiter\": 1000},\n",
-    "        bounds=bnds,\n",
-    "        constraints=cons,\n",
-    "    )\n",
-    "\n",
-    "    return res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0ceb5a15-cecf-47ed-b1cc-cea79fa936f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_counts_from_distribution(n_qubits, size, dist):\n",
-    "    \"\"\"\n",
-    "    Generates samples based on a given distribution and returns the counts of each sample value.\n",
-    "\n",
-    "    Args:\n",
-    "        n_qubits: The number of qubits in the quantum circuit.\n",
-    "        dist: The probability distribution from which samples are drawn.\n",
-    "\n",
-    "    Returns:\n",
-    "        An array of counts for each possible value in the distribution. The array has a length of 2^n_qubits.\n",
-    "    \"\"\"\n",
-    "    samples = np.random.choice(np.arange(2**n_qubits), size=size, p=dist)\n",
-    "    values, counts = np.unique(samples, return_counts=True)\n",
-    "    res = np.zeros(2**n_qubits, dtype=int)\n",
-    "    res[values] = counts\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1225d4de-fcc8-4a56-846b-44d6a52a272f",
-   "metadata": {},
-   "source": [
-    "Let's run the `ghz_kernel` with the noise model. We will perform two experiments. First, we keep the bitflip probability the same for all the qubits. In the second experiment, we use different bitflip probabilities for qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "53453484-a8d5-416c-8aad-7c982cad7191",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_qubits = 3\n",
-    "shots = 1024\n",
-    "\n",
-    "labels = list(map(list, itertools.product([0, 1], repeat=n_qubits)))\n",
-    "states = list(map(lambda label: \"\".join(map(str, label)), labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2d147fe7-ce5e-475c-9014-59d4ff63a644",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bitflip probability: 0.1\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'000': 352,\n",
-       " '010': 43,\n",
-       " '111': 367,\n",
-       " '001': 47,\n",
-       " '011': 47,\n",
-       " '100': 54,\n",
-       " '101': 61,\n",
-       " '110': 53}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = 0.1\n",
-    "print(f\"bitflip probability: {p}\\n\")\n",
-    "\n",
-    "noise_1 = get_noise(n_qubits, p)\n",
-    "\n",
-    "ghz_result = cudaq.sample(ghz_kernel,\n",
-    "                          n_qubits,\n",
-    "                          shots_count=shots,\n",
-    "                          noise_model=noise_1)\n",
-    "noisy_dict_1 = dict(list(ghz_result.items()))\n",
-    "\n",
-    "noisy_res_1 = np.array(\n",
-    "    [noisy_dict_1.get(state, 0) for i, state in enumerate(states)])\n",
-    "\n",
-    "noisy_dict_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "976f13da-277b-4754-9a58-277a5988f739",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bitflip probability: [0.149816047538945, 0.3802857225639665, 0.292797576724562]\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'000': 212,\n",
-       " '010': 122,\n",
-       " '111': 189,\n",
-       " '001': 111,\n",
-       " '011': 87,\n",
-       " '100': 78,\n",
-       " '101': 131,\n",
-       " '110': 94}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = list(np.random.uniform(low=0.0, high=0.4, size=n_qubits))\n",
-    "print(f\"bitflip probability: {p}\\n\")\n",
-    "\n",
-    "noise_2 = get_noise(n_qubits, p)\n",
-    "\n",
-    "ghz_result = cudaq.sample(ghz_kernel,\n",
-    "                          n_qubits,\n",
-    "                          shots_count=shots,\n",
-    "                          noise_model=noise_2)\n",
-    "noisy_dict_2 = dict(list(ghz_result.items()))\n",
-    "\n",
-    "noisy_res_2 = np.array(\n",
-    "    [noisy_dict_2.get(state, 0) for i, state in enumerate(states)])\n",
-    "\n",
-    "noisy_dict_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1889f50a-f051-4f0e-bace-18477122eafe",
-   "metadata": {},
-   "source": [
-    "## Inverse confusion matrix from single-qubit noise model\n",
-    "\n",
-    "Here we assume that the readout error of each qubit is independent with the same confusion probabilities. Then, the confusion matrix $A_1$ for a single qubit can be defined as\n",
-    "\n",
-    "$$\n",
-    "A_1 = \\begin{bmatrix}\n",
-    "P(0|0) & P(0|1)\\\\\n",
-    "P(1|0) & P(1|1)\n",
-    "\\end{bmatrix}\n",
-    "$$\n",
-    "\n",
-    "where $P(y|x)$ is the probability of observing state $|y\\rangle$ when measuring the true state $|x\\rangle$. Using $A_1$, the full $2^n \\times 2^n$ confusion matrix $A$ can be written as \n",
-    "\n",
-    "$$\n",
-    "A = A_1 \\otimes \\dots \\otimes A_1\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e6ceb0a9-c2ec-4fcd-bb23-457890a8b57e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 becomes {'0': 924, '1': 100}\n",
-      "1 becomes {'0': 104, '1': 920}\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_qubit_labels = [[0], [1]]\n",
-    "single_qubit_states = [\"0\", \"1\"]  # Used for dictionary keys\n",
-    "\n",
-    "# we use noise_1 which has same bitflip probability for all qubits\n",
-    "results = [\n",
-    "    cudaq.sample(kernel, 1, label, shots_count=shots, noise_model=noise_1)\n",
-    "    for label in single_qubit_labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(single_qubit_states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9f030f99-0ad9-4f89-8d99-a570b3c5e745",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.90234375 0.1015625 ]\n",
-      " [0.09765625 0.8984375 ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Construct confusion matrix for single qubit\n",
-    "\n",
-    "A_1 = np.zeros((2, 2))\n",
-    "for i, state in enumerate(single_qubit_states):\n",
-    "    true_state = int(state, 2)\n",
-    "    for key, val in results[i].items():\n",
-    "        observed_state = int(key, 2)\n",
-    "        A_1[observed_state][true_state] = val / shots\n",
-    "\n",
-    "print(A_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cbc4a228-935f-413c-9ad3-b6e26f22e802",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[476   3   0   1  12  19   9 501]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = reduce(np.kron, [A_1] * n_qubits)  # joint confusion matrix\n",
-    "A_pinv = np.linalg.pinv(A)  # Generate pseudoinverse confusion matrix.\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_1),\n",
-    "                     dtype=int)  # Obtain mitigated counts\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "b65c4657-87f7-44df-b205-7fc6195b95a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    \"states\": states,\n",
-    "    \"noisy\": np.around(noisy_res_1 / sum(noisy_res_1), 3),\n",
-    "    \"mitigated_sg\": np.around(mitigated / sum(mitigated), 3),\n",
-    "})\n",
-    "\n",
-    "ax = df.plot(x=\"states\",\n",
-    "             y=[\"noisy\", \"mitigated_sg\"],\n",
-    "             kind=\"bar\",\n",
-    "             figsize=(8, 5))\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_sg\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2927e72-98bb-41cf-8e77-35ea9572474a",
-   "metadata": {},
-   "source": [
-    "## Inverse confusion matrix from k local confusion matrices\n",
-    "\n",
-    "This method works under the assumption that the readout errors for different qubits are independent of each other and the confusion matrix $A$ can be factorized into the product of $k$ small local confusion matrices. We consider the special case when $k = n$ for $n$ qubits and different $2 \\times 2$ confusion matrices for each qubit\n",
-    "\n",
-    "$$\n",
-    "A = A_1 \\otimes \\dots \\otimes A_n\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "1e8fd13a-e0cf-4139-84ca-d81f7a71baa6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "000 becomes {'000': 388, '010': 235, '111': 18, '001': 174, '011': 79, '100': 58, '101': 28, '110': 44}\n",
-      "111 becomes {'000': 23, '010': 38, '111': 370, '001': 40, '011': 60, '100': 107, '101': 224, '110': 162}\n"
-     ]
-    }
-   ],
-   "source": [
-    "local_labels = [[0] * n_qubits, [1] * n_qubits]\n",
-    "local_states = [\"0\" * n_qubits, \"1\" * n_qubits]\n",
-    "\n",
-    "results = [\n",
-    "    cudaq.sample(kernel,\n",
-    "                 n_qubits,\n",
-    "                 label,\n",
-    "                 shots_count=shots,\n",
-    "                 noise_model=noise_2) for label in local_labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(local_states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f592fb8-2ded-498e-b4fe-eff6eac74750",
-   "metadata": {},
-   "source": [
-    "The local confusion matrices are generated using the marginal probability distribution for each qubit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "7fabcc1f-8d9c-42c2-b02a-3ce42c1e34c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counts = [\n",
-    "    dict(list(results[i].items())) for i, state in enumerate(local_states)\n",
-    "]\n",
-    "matrices = []\n",
-    "\n",
-    "for k in range(n_qubits):\n",
-    "    matrix = np.zeros([2, 2], dtype=float)\n",
-    "    marginalized_counts = []\n",
-    "    total_shots = []\n",
-    "    for i in range(2):\n",
-    "        marginal_cts = dict(results[i].get_marginal_counts([k]).items())\n",
-    "        marginalized_counts.append(marginal_cts)\n",
-    "        total_shots.append(sum(marginal_cts.values()))\n",
-    "\n",
-    "    # matrix[i][j] is the probability of counting i for expected j\n",
-    "    for i in range(2):\n",
-    "        for j in range(2):\n",
-    "            matrix[i][j] = marginalized_counts[j].get(str(i),\n",
-    "                                                      0) / total_shots[j]\n",
-    "    matrices.append(matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7f016f9b-3243-42b3-ab59-4d03f7765b82",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.85546875 0.15722656]\n",
-      " [0.14453125 0.84277344]]\n",
-      "\n",
-      "[[0.6328125  0.38476562]\n",
-      " [0.3671875  0.61523438]]\n",
-      "\n",
-      "[[0.70800781 0.32226562]\n",
-      " [0.29199219 0.67773438]]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for m in matrices:\n",
-    "    print(m)\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "51734299-307f-4081-b8cc-1968ebfc7d52",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[559  -1 -58  32 -28  26 -16 510]\n",
-      "\n",
-      "Corrected for negative counts:\n",
-      "[521   0   0  35   0  22   0 446]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# To find the joint pseudoinverse confusion matrix, we can first perform pseudoinverse of the\n",
-    "# local confusion matrices and then do the tensor product\n",
-    "\n",
-    "pinv_confusion_matrices = [np.linalg.pinv(m) for m in matrices]\n",
-    "A_pinv = reduce(np.kron, pinv_confusion_matrices)\n",
-    "\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_2), dtype=int)\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")\n",
-    "\n",
-    "A_joint = reduce(np.kron, matrices)\n",
-    "plot_cmat(A_joint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "4261d8a3-b701-4939-8219-c0d823d42747",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    \"states\": states,\n",
-    "    \"noisy\": np.around(noisy_res_2 / sum(noisy_res_2), 3),\n",
-    "    \"mitigated_k_local\": np.around(mitigated / sum(mitigated), 3),\n",
-    "})\n",
-    "\n",
-    "ax = df.plot(x=\"states\",\n",
-    "             y=[\"noisy\", \"mitigated_k_local\"],\n",
-    "             kind=\"bar\",\n",
-    "             figsize=(8, 5))\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_k_local\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9329a30d-9224-42b1-9a66-1e87ba7c2674",
-   "metadata": {},
-   "source": [
-    "## Inverse of full confusion matrix\n",
-    "\n",
-    "Here we generate a quantum circuit for each of the basis states of $n$ qubits (i.e., $2^n$ combinations). This gives more accurate readout error mitigation. Since it scales exponentially with the number of qubits, it is only practical for small devices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "cf6025e8-1755-4c8f-96fa-1b67830d448d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "000 becomes {'000': 367, '010': 248, '111': 17, '001': 159, '011': 83, '100': 68, '101': 35, '110': 47}\n",
-      "001 becomes {'000': 161, '010': 93, '111': 46, '001': 363, '011': 253, '100': 17, '101': 76, '110': 15}\n",
-      "010 becomes {'000': 234, '010': 378, '111': 27, '001': 100, '011': 150, '100': 36, '101': 25, '110': 74}\n",
-      "011 becomes {'000': 106, '010': 145, '111': 65, '001': 251, '011': 386, '100': 11, '101': 30, '110': 30}\n",
-      "100 becomes {'000': 83, '010': 34, '111': 99, '001': 29, '011': 18, '100': 388, '101': 144, '110': 229}\n",
-      "101 becomes {'000': 22, '010': 13, '111': 247, '001': 70, '011': 43, '100': 163, '101': 371, '110': 95}\n",
-      "110 becomes {'000': 32, '010': 77, '111': 157, '001': 14, '011': 30, '100': 233, '101': 85, '110': 396}\n",
-      "111 becomes {'000': 14, '010': 31, '111': 393, '001': 38, '011': 60, '100': 95, '101': 233, '110': 160}\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = [\n",
-    "    cudaq.sample(kernel,\n",
-    "                 n_qubits,\n",
-    "                 label,\n",
-    "                 shots_count=shots,\n",
-    "                 noise_model=noise_2) for label in labels\n",
-    "]\n",
-    "\n",
-    "for i, state in enumerate(states):\n",
-    "    res = dict(list(results[i].items()))\n",
-    "    print(f\"{state} becomes {res}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "28f2b2cb-13d3-4806-852d-06ea91e58c1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# generate full confusion matrix\n",
-    "A_full = np.zeros((2**n_qubits, 2**n_qubits))\n",
-    "for i, state in enumerate(states):\n",
-    "    true_state = int(state, 2)\n",
-    "    for key, val in results[i].items():\n",
-    "        observed_state = int(key, 2)\n",
-    "        A_full[observed_state][true_state] = val / shots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "c3b0b80b-63ac-438a-9f3d-a6a3687da167",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_cmat(A_full)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "507852a8-6715-4802-b6a0-10967d09ed94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.09734490298929"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Distance between full confusion matrix and tensored confusion matrix\n",
-    "np.linalg.norm(A_joint - A_full)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "9e6c3874-2905-4c25-9bf0-22ab27756fed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mitigated counts:\n",
-      "[ 714 -100 -211  152  -49   63   32  422]\n",
-      "\n",
-      "Corrected for negative counts:\n",
-      "[683   0   0  39   0   0   0 302]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A_pinv = np.linalg.pinv(A_full)\n",
-    "mitigated = np.array(np.dot(A_pinv, noisy_res_2), dtype=int)\n",
-    "print(f\"Mitigated counts:\\n{mitigated}\")\n",
-    "\n",
-    "if not np.all(mitigated >= 0):\n",
-    "    positive_dist = find_closest_distribution(mitigated / shots)\n",
-    "    mitigated = get_counts_from_distribution(n_qubits, shots, positive_dist)\n",
-    "    print(f\"\\nCorrected for negative counts:\\n{mitigated}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "cb109820-5478-4179-84b8-66e6af1008fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[\"mitigated_full\"] = np.around(mitigated / sum(mitigated), 3)\n",
-    "\n",
-    "ax = df.plot(\n",
-    "    x=\"states\",\n",
-    "    y=[\"noisy\", \"mitigated_k_local\", \"mitigated_full\"],\n",
-    "    kind=\"bar\",\n",
-    "    figsize=(12, 5),\n",
-    ")\n",
-    "ax.bar_label(ax.containers[0], labels=df[\"noisy\"])\n",
-    "ax.bar_label(ax.containers[1], labels=df[\"mitigated_k_local\"])\n",
-    "ax.bar_label(ax.containers[2], labels=df[\"mitigated_full\"])\n",
-    "ax.set_ylabel(\"probabilities\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/shors.ipynb b/pr-2308/sphinx/applications/python/shors.ipynb
deleted file mode 100644
index 8063162ee4..0000000000
--- a/pr-2308/sphinx/applications/python/shors.ipynb
+++ /dev/null
@@ -1,986 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "628c747b-084c-4fb6-843e-7c7f4cbf07a1",
-   "metadata": {},
-   "source": [
-    "# Factoring Integers With Shor's Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d0627c4-95bd-47b9-9ab5-e8374d25508e",
-   "metadata": {},
-   "source": [
-    "The most famous application of quantum computers is factoring integers using Shor's algorithm. This algorithm is particularly significant because an efficient factorization algorithm could potentially break modern asymmetric encryption schemes, such as RSA.\n",
-    "\n",
-    "For small integers, this quantum algorithm can be simulated on classical computers. The main challenge in classical implementation lies in the order-finding algorithm. We will first introduce the classical implementation of this algorithm as a preliminary step, and then proceed to explain the quantum order-finding algorithm.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "148568d8",
-   "metadata": {},
-   "source": [
-    "First let's install some libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "486fb3d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install contfrac==1.0.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "88619b35-624e-4853-8d2e-6d9952390e5e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from math import gcd, log2, ceil\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import cudaq\n",
-    "from cudaq import *\n",
-    "import fractions\n",
-    "import matplotlib.pyplot as plt\n",
-    "import contfrac"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8523c6f-d729-4d5e-8df9-1780c343a58e",
-   "metadata": {},
-   "source": [
-    "## Shor's algorithm\n",
-    "The factoring problem for an integer $N$ aims to find integers $a_1$, $a_2$ which are factors of $N$.  This problem is interesting when $N$ is not prime and the solutions $a_1$ and $a_2$ are non-trivial.  In other words, we will attempt to find integers $a_1$ and $a_2$ satisfying $N=a_1\\cdot a_2$ with $a_1\\neq 1$ and $a_2\\neq 1$.\n",
-    "\n",
-    "The algorithm consists of two ideas:\n",
-    "\n",
-    "* Reduce the problem of factoring the integer $N$ to the order-finding problem.\n",
-    "* Solve the order-finding problem: \n",
-    "Given integers $a$ and $N$ so that $a$ and $N$ share no common factors (i.e., the greatest common divisor of $a$ and $N$ is 1), find the smallest positive integer which satisfies $a^r \\equiv 1 \\mod N$.  This value $r$ is refered to as the *order of* $a \\mod N$.\n",
-    "\n",
-    "These two ideas are combined in the following steps in Shor's algorithm:\n",
-    "\n",
-    "0. Rule out the easy case that $N$ is even\n",
-    "1. Select a random integer $a$ between $2$ and $N-1$\n",
-    "2. Check to see if $a$ is a factor of $N$ (if so we're done!)\n",
-    "3. Find the order of $a \\mod N$, (i.e., find $r$ so that $a^r\\equiv 1 \\mod N$)\n",
-    "4. Check to see if $a^{\\frac{r}{2}}-1$ or $a^{\\frac{r}{2}}+1$ are integers and share common, non-trivial, divisors with $N$ (if so we're done!)\n",
-    "5. If no factor is found, repeat steps 1 through 4 above.\n",
-    "\n",
-    "The function `shors_algorithm` below outlines these steps for both the classical and the quantum implementation of Shor's algorithm. For the purposes of demonstration, we will also control the initial random integer selected in step 1 so that we can investigate cases in which the selected integer produces common divisors of $N$ in step 4 and others in which step 4 produces no common factors.\n",
-    "\n",
-    "\n",
-    "**Note about terminology:** Some literature refers to the \"period-finding problem\" in Shor's algorithm.  The order-finding problem, as we have described above, can be recast as finding the period of the function $f(x) = a^x\\mod N$, by noticing that the period of $f(x)$ is one more than the order of $a\\mod N$. The period finding problem is more general than the order-finding problem since it aims to find the period of any periodic function, not just modular exponentiation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7dd05729",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shors_algorithm(N, initial, quantum):\n",
-    "    \"\"\" Factor N using Shor's algorithm with initial starting value and choice of \n",
-    "    using either classical or quantum approach for the period finding step\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    N: int \n",
-    "        Integer to be factored (assumed to be non-prime and >1)\n",
-    "    initial: int \n",
-    "        Initial choice of the random integer between 2 and N-1\n",
-    "    quantum: boolean\n",
-    "        True if we will call the quantum order-finding algorithm, and false if we call the classical one for step 3.   \n",
-    "        \n",
-    "    Returns\n",
-    "    -------\n",
-    "    a1, a2: int\n",
-    "        Non-trivial factors of N\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # 0. Check to see if N is even.\n",
-    "    if N % 2 == 0:\n",
-    "        divisor1 = 2\n",
-    "        divisor2 = N // 2\n",
-    "        print(\"Found factors:\", divisor1, divisor2)\n",
-    "        return (divisor1, divisor2)\n",
-    "\n",
-    "    attempts = [initial]\n",
-    "    while (True):\n",
-    "        # 1. Select a random integer between 2 and N-1\n",
-    "        if len(attempts) == 1:\n",
-    "            a = initial\n",
-    "        else:\n",
-    "            a = random.choice(\n",
-    "                [n for n in range(N - 1) if n not in attempts and n != 1])\n",
-    "\n",
-    "        # 2. See if the integer selected in step 1 happens to factor N\n",
-    "        print(\"Trying a =\", a)\n",
-    "        divisor1 = gcd(a, N)\n",
-    "        if divisor1 != 1:\n",
-    "            divisor2 = N // divisor1\n",
-    "            print(\"Found factors of N={} by chance: {} and {}\".format(\n",
-    "                N, divisor1, divisor2))\n",
-    "            return (divisor1, divisor2)\n",
-    "\n",
-    "        # 3. Find the order of a mod N (i.e., r, where a^r = 1 (mod N))\n",
-    "        if quantum == True:\n",
-    "            r = find_order_quantum(a, N)\n",
-    "        else:\n",
-    "            r = find_order_classical(a, N)\n",
-    "        print(\"The order of a = {} is {}\".format(a, r))\n",
-    "\n",
-    "        # 4. If the order of a is found and it is\n",
-    "        # * even and\n",
-    "        # * not a^(r/2) = -1 (mod N),\n",
-    "        # then test a^(r/2)-1 and a^(r/2)+1 to see if they share factors with N.\n",
-    "        # We also want to rule out the case of finding the trivial factors: 1 and N.\n",
-    "        divisor1, divisor2 = test_order(a, r, N)\n",
-    "        if (divisor1 != 0):  # test_order will return a 0 if no factor is found\n",
-    "            print(\"Found factors of N = {}: {} and {}\".format(\n",
-    "                N, divisor1, divisor2))\n",
-    "            return divisor1, divisor2\n",
-    "\n",
-    "        # 5. Repeat\n",
-    "        print(\"retrying...\")\n",
-    "        attempts.append(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed682e81",
-   "metadata": {},
-   "source": [
-    "Let's first explore the idea of reducing the factoring problem into the order-finding problem. This gets captured in step 4 described above.  In this step, we have already established that $a$ and $N$ share no factors other than $1$ (i.e., $\\gcd(a,N)=1$) and we have found $r$, the order of $a\\mod N.$  With this information we know that $a^r \\equiv 1 \\mod N.$  Rewritten in another way:\n",
-    "$$\n",
-    "a^r -1 \\equiv 0\\mod N\n",
-    "\\tag{1}.\n",
-    "$$ \n",
-    " \n",
-    " If $r$ is even, we can rewrite $a^r$ as $x^2$ where $x=a^\\frac{r}{2}$. Next, we can factor equation $(1)$ using the identity $x^2-1 = (x-1)(x+1)$:\n",
-    " $$ \n",
-    " (a^{\\frac{r}{2}} - 1)(a^{\\frac{r}{2}} + 1) \\equiv 0\\mod N\n",
-    "\\tag{2}.\n",
-    "$$  \n",
-    "\n",
-    "If, in addition, the equation\n",
-    "$$\n",
-    "a^{\\frac{r}{2}}  \\not\\equiv -1\\mod N\n",
-    "\\tag{3}\n",
-    "$$\n",
-    "is satisfied, then  at least one of the terms $a^{\\frac{r}{2}} - 1$ or $a^{\\frac{r}{2}} + 1$ must share a common factor with $N$. [Peter Shor](https://arxiv.org/abs/quant-ph/9508027) demonstrated that there is greater than a $50\\%$ chance of randomly selecting a value for $a$ satisfying these properties.\n",
-    "\n",
-    "The code block below defines a function that tests whether $r$ is even and whether equation $(3)$ is satisfied and searches for a non-trivial factor of $N$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "6c8eed12",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_order(a, r, N):\n",
-    "    \"\"\"Checks whether or not a^(r/2)+1 or a^(r/2)-1 share a non-trivial common factor with N\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "    r: int\n",
-    "    N: int\n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    int, int factors of N, if found; 0,0 otherwise  \n",
-    "    \"\"\"\n",
-    "\n",
-    "    if r != None and (r % 2 == 0) and a**r % N == 1:\n",
-    "        if (a**(int(r / 2))) % N != -1:\n",
-    "            possible_factors = [gcd(r - 1, N), gcd(r + 1, N)]\n",
-    "            for test_factor in possible_factors:\n",
-    "                if test_factor != 1 and test_factor != N:\n",
-    "                    return test_factor, N // test_factor\n",
-    "    # period did not produce a factor\n",
-    "    else:\n",
-    "        print('No non-trivial factor found')\n",
-    "        return 0, 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a43af433-905c-4070-94b3-0f6be6f67f6b",
-   "metadata": {},
-   "source": [
-    "### Solving the order-finding problem classically\n",
-    "\n",
-    "The key component of Shor's algorithm is an efficient quantum algorithm to find the order $r$ of $a \\mod N$. While a straightforward classical algorithm can solve this problem, it is notably inefficient:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "14f4b0d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_order_classical(a, N):\n",
-    "    \"\"\"A naive classical method to find the order r of a (mod N).\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "        an integer in the interval [2,N-1]\n",
-    "    N: int\n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    r: int \n",
-    "        Period of a^x (mod N)\n",
-    "    \"\"\"\n",
-    "    assert 1 < a and a < N\n",
-    "    r = 1\n",
-    "    y = a\n",
-    "    while y != 1:\n",
-    "        y = y * a % N\n",
-    "        r += 1\n",
-    "    return r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aae6b36d",
-   "metadata": {},
-   "source": [
-    "Let's see how Shor's algorithm works on a few examples using the classical order-finding problem.  Notice how often our choice of value for the initial guess for $a$ produces factors of $N$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "3f75b314-9f0c-40c5-a7bc-58ae235fc183",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trying a = 42\n",
-      "Found factors of N=123 by chance: 3 and 41\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(3, 41)"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_integer = 123  #edit this value to try out a few examples\n",
-    "# Edit the value in the line below to try out different initial guesses for a.\n",
-    "# What happens when you choose a = 42 for the integer 123?\n",
-    "# What happens when you choose a = 100 for the integer 123?\n",
-    "initial_value_to_start = 42  # edit this value; it should be less than my_integer\n",
-    "\n",
-    "shors_algorithm(my_integer, initial_value_to_start, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "275efced-b10e-4438-9b5e-f3534079304b",
-   "metadata": {},
-   "source": [
-    "### Solving the order-finding problem with a quantum algorithm\n",
-    "\n",
-    "\n",
-    "The Fourier transform is a classical computation that provides a more efficient algorithm than the one encoded in `find_order_classical` for identifying the period of $f(x) = a^x\\mod N$. The quantum version of the Fourier transform is the central idea of Shor's Algorithm. This efficient quantum solution derives the period from a measurement of $n = \\lceil log2(N) \\rceil$ qubits. \n",
-    "\n",
-    "The image below outlines the quantum kernel used to find the order of $a$. Notice the last step involves applying the Inverse Quantum Fourier Transform."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aba9fbd7",
-   "metadata": {},
-   "source": [
-    "![](images/shor_circuit.png)\n",
-    "\n",
-    "Figure. Circuit diagram for finding the phase of the modular multiplication gate $U|x\\rangle = |a^x\\mod N\\rangle $, which will be used to compute the order of $a$ modulo $N$.  The number of qubits in the control register determines the accuracy of estimating the phase of $U$. The size of the work register depends on $N$. The goal of this section is to code the  diagram as a kernel named `phase_kernel`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7eff3b6",
-   "metadata": {},
-   "source": [
-    "We will need to create a quantum kernel for the Inverse Quantum Fourier Transform.  Additionally we'll need to create a kernel for modular multiplication: $g(y) = ay \\mod N$, which can be repeatedly applied $x$-times to $y=1$ to carry our modular exponentation $f(x)=a^x\\mod N$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "567e104e",
-   "metadata": {},
-   "source": [
-    "#### Inverse quantum Fourier transform\n",
-    "In the code block below we define a kernel for the quantum Fourier transform and then use `cudaq.adjoint` to create a kernel for the inverse quantum Fourier transform used in the quantum order-finding algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "0e04a0a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define kernels for the quantum Fourier transform and the inverse quantum Fourier transform\n",
-    "@cudaq.kernel\n",
-    "def quantum_fourier_transform(qubits: cudaq.qview):\n",
-    "    qubit_count = len(qubits)\n",
-    "    # Apply Hadamard gates and controlled rotation gates.\n",
-    "    for i in range(qubit_count):\n",
-    "        h(qubits[i])\n",
-    "        for j in range(i + 1, qubit_count):\n",
-    "            angle = (2 * np.pi) / (2**(j - i + 1))\n",
-    "            cr1(angle, [qubits[j]], qubits[i])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def inverse_qft(qubits: cudaq.qview):\n",
-    "    cudaq.adjoint(quantum_fourier_transform, qubits)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39db9e6a",
-   "metadata": {},
-   "source": [
-    "#### Quantum kernels for modular exponentiation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b8b8884",
-   "metadata": {},
-   "source": [
-    "\n",
-    "While Shor's algorithm provably can factor numbers faster than known classical techniques, the resources required for implementing Shor's algorithm are hefty. A full-scale implementation to factor an $L$-bit number could require a quantum kernel with $5L+1$ qubits to achieve accuracy for the continued fractions part of the algoirthm and as many as $72L^3$ quantum gates for the modular exponentiaion [(Beckman, Chari, Devabhaktuni, & Preskill, 1996)](https://arxiv.org/pdf/quant-ph/9602016).  Both of these are well beyond the capabilities of modern quantum hardware for numbers of interest. Advancements to reduce the number of gates and qubits required focus on optimizing the kernel for $f(x) = a\\cdot x \\mod N$ for properties of the number to be factored. The difficulty is to create efficient quantum kernels (in terms of qubit and gate count) that compute $a\\cdot x \\mod{N}$.  \n",
-    "\n",
-    "For the purposes of this demonstration, we will consider only the order-finding problem for the values of $a$ to be either $4$ or $5$ with $N=21$. We'll be using the quantum circuits depicted in [this paper](https://arxiv.org/abs/2103.13855) and [this report](https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf), respectively. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a48d6fa1",
-   "metadata": {},
-   "source": [
-    "##### The case N = 21 and a = 5:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "c4b8e2e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def modular_mult_5_21(work: cudaq.qview):\n",
-    "    \"\"\"\"Kernel for multiplying by 5 mod 21\n",
-    "    based off of the circuit diagram in \n",
-    "    https://physlab.org/wp-content/uploads/2023/05/Shor_s_Algorithm_23100113_Fin.pdf\n",
-    "    Modifications were made to change the ordering of the qubits\"\"\"\n",
-    "    x(work[0])\n",
-    "    x(work[2])\n",
-    "    x(work[4])\n",
-    "\n",
-    "    swap(work[0], work[4])\n",
-    "    swap(work[0], work[2])\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def modular_exp_5_21(exponent: cudaq.qview, work: cudaq.qview,\n",
-    "                     control_size: int):\n",
-    "    \"\"\" Controlled modular exponentiation kernel used in Shor's algorithm\n",
-    "    |x> U^x |y> = |x> |(5^x)y mod 21>\n",
-    "    \"\"\"\n",
-    "    x(work[0])\n",
-    "    for exp in range(control_size):\n",
-    "        ctrl_qubit = exponent[exp]\n",
-    "        for _ in range(2**(exp)):\n",
-    "            cudaq.control(modular_mult_5_21, ctrl_qubit, work)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c644786",
-   "metadata": {},
-   "source": [
-    "Verify in the code block below that the kernels defined above do in fact carry out multiplication and exponentiation with $a=5$ and $N=21$ by changing the `iterations` variable below. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "e93fcf39",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮      \n",
-      "q0 : ┤ x ├┤ x ├─╳──╳─\n",
-      "     ╰───╯╰───╯ │  │ \n",
-      "q1 : ───────────┼──┼─\n",
-      "     ╭───╮      │  │ \n",
-      "q2 : ┤ x ├──────┼──╳─\n",
-      "     ╰───╯      │    \n",
-      "q3 : ───────────┼────\n",
-      "     ╭───╮      │    \n",
-      "q4 : ┤ x ├──────╳────\n",
-      "     ╰───╯           \n",
-      "\n",
-      "Measurement results from sampling: { 10100:200 }\n",
-      "\n",
-      "For x = 1, 5^x mod 21 = 5\n",
-      "For x = 1, the computed result of the circuit is 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Demonstrate iterated application of 5y mod 21 where y = 1\n",
-    "@cudaq.kernel\n",
-    "def demonstrate_mod_exponentiation(iterations: int):\n",
-    "    qubits = cudaq.qvector(5)\n",
-    "    x(qubits[0]\n",
-    "     )  # initalizes the qubits in the state for y = 1 which is |10000>\n",
-    "    for _ in range(iterations):\n",
-    "        modular_mult_5_21(qubits)\n",
-    "\n",
-    "\n",
-    "shots = 200\n",
-    "\n",
-    "# The iterations variable determines the exponent in 5^x mod 21.\n",
-    "# Change this value to verify that the demonstrate_mod_exponentiation\n",
-    "# kernel carries out the desired calculation.\n",
-    "iterations = 1\n",
-    "\n",
-    "print(cudaq.draw(demonstrate_mod_exponentiation, iterations))\n",
-    "\n",
-    "results = cudaq.sample(demonstrate_mod_exponentiation,\n",
-    "                       iterations,\n",
-    "                       shots_count=shots)\n",
-    "\n",
-    "print(\"Measurement results from sampling:\", results)\n",
-    "\n",
-    "# Reverse the order of the most probable measured bit string\n",
-    "# and convert the binary string to an integer\n",
-    "integer_result = int(results.most_probable()[::-1], 2)\n",
-    "\n",
-    "print(\"For x = {}, 5^x mod 21 = {}\".format(iterations, (5**iterations) % 21))\n",
-    "print(\"For x = {}, the computed result of the circuit is {}\".format(\n",
-    "    iterations, integer_result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff07579c",
-   "metadata": {},
-   "source": [
-    "##### The case N = 21 and a = 4:\n",
-    "\n",
-    "The example below is one where the work register has been optimized to use fewer gates and qubits than would have been necessary through a straightforward implementation of modular multiplication as seen in the previous case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "69963075",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def modular_exp_4_21(exponent: cudaq.qview, work: cudaq.qview):\n",
-    "    \"\"\" Controlled modular exponentiation kernel used in Shor's algorithm\n",
-    "     |x> U^x |y> = |x> |(4^x)y mod 21>\n",
-    "     based off of the circuit diagram in https://arxiv.org/abs/2103.13855\n",
-    "     Modifications were made to account for qubit ordering differences\"\"\"\n",
-    "    swap(exponent[0], exponent[2])\n",
-    "    # x = 1\n",
-    "    x.ctrl(exponent[2], work[1])\n",
-    "\n",
-    "    # x = 2\n",
-    "    x.ctrl(exponent[1], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    x.ctrl([exponent[1], work[0]], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "\n",
-    "    # x = 4\n",
-    "    x(work[1])\n",
-    "    x.ctrl([exponent[0], work[1]], work[0])\n",
-    "    x(work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    x.ctrl([exponent[0], work[0]], work[1])\n",
-    "    x.ctrl(work[1], work[0])\n",
-    "    swap(exponent[0], exponent[2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84a197e2",
-   "metadata": {},
-   "source": [
-    "Now we are ready to define the `phase_kernel` to carry out the instructions in the circuit diagram drawn at the beginning of this section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "206bcac3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def phase_kernel(control_register_size: int, work_register_size: int, a: int,\n",
-    "                 N: int):\n",
-    "    \"\"\" \n",
-    "    Kernel to estimate the phase of the modular multiplication gate |x> U |y> = |x> |a*y mod 21> for a = 4 or 5\n",
-    "    \"\"\"\n",
-    "\n",
-    "    qubits = cudaq.qvector(control_register_size + work_register_size)\n",
-    "    control_register = qubits[0:control_register_size]\n",
-    "    work_register = qubits[control_register_size:control_register_size +\n",
-    "                           work_register_size]\n",
-    "\n",
-    "    h(control_register)\n",
-    "\n",
-    "    if a == 4 and N == 21:\n",
-    "        modular_exp_4_21(control_register, work_register)\n",
-    "    if a == 5 and N == 21:\n",
-    "        modular_exp_5_21(control_register, work_register, control_register_size)\n",
-    "\n",
-    "    inverse_qft(control_register)\n",
-    "\n",
-    "    # Measure only the control_register and not the work_register\n",
-    "    mz(control_register)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "id": "ca9b570a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮                                                                    »\n",
-      "q0 : ┤ h ├──●────●────●───●──●────────────────────────────────────────────────»\n",
-      "     ├───┤  │    │    │   │  │                                                »\n",
-      "q1 : ┤ h ├──┼────┼────┼───┼──┼───●────●────●───●──●───●────●────●───●──●──────»\n",
-      "     ├───┤  │    │    │   │  │   │    │    │   │  │   │    │    │   │  │      »\n",
-      "q2 : ┤ h ├──┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───┼────┼────┼───┼──┼───●──»\n",
-      "     ├───┤╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮»\n",
-      "q3 : ┤ x ├┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├»\n",
-      "     ╰───╯╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯»\n",
-      "q4 : ────────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼──────»\n",
-      "               ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      »\n",
-      "q5 : ──────────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────»\n",
-      "               ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         »\n",
-      "q6 : ─────────────────┼───┼────────────────┼───┼────────────────┼───┼─────────»\n",
-      "                    ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │         »\n",
-      "q7 : ───────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─────────»\n",
-      "                    ╰───╯                ╰───╯                ╰───╯           »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                                                                            »\n",
-      "────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                            »\n",
-      "────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                            »\n",
-      "──●────●───●──●───●────●────●───●──●───●────●────●───●──●───●────●────●───●─»\n",
-      "  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │  │ ╭─┴─╮  │    │   │ »\n",
-      "──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳──╳─┤ x ├──┼────┼───╳─»\n",
-      "  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │  │ ╰───╯  │    │   │ »\n",
-      "──┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼──┼────────┼────┼───┼─»\n",
-      "╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │  │      ╭─┴─╮  │   │ »\n",
-      "┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼──╳──────┤ x ├──┼───┼─»\n",
-      "╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │         ╰───╯  │   │ »\n",
-      "───────┼───┼────────────────┼───┼────────────────┼───┼────────────────┼───┼─»\n",
-      "     ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │              ╭─┴─╮ │ »\n",
-      "─────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳──────────────┤ x ├─╳─»\n",
-      "     ╰───╯                ╰───╯                ╰───╯                ╰───╯   »\n",
-      "\n",
-      "################################################################################\n",
-      "\n",
-      "                           ╭─────────────╮╭────────────╮╭───╮\n",
-      "───────────────────────────┤ r1(-0.7854) ├┤ r1(-1.571) ├┤ h ├\n",
-      "        ╭────────────╮╭───╮╰──────┬──────╯╰─────┬──────╯╰───╯\n",
-      "────────┤ r1(-1.571) ├┤ h ├───────┼─────────────●────────────\n",
-      "   ╭───╮╰─────┬──────╯╰───╯       │                          \n",
-      "─●─┤ h ├──────●───────────────────●──────────────────────────\n",
-      " │ ╰───╯                                                     \n",
-      "─╳───────────────────────────────────────────────────────────\n",
-      " │                                                           \n",
-      "─┼───────────────────────────────────────────────────────────\n",
-      " │                                                           \n",
-      "─╳───────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "─────────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "─────────────────────────────────────────────────────────────\n",
-      "                                                             \n",
-      "\n",
-      "Measurement results for a=5 and N=21 with 3 qubits in the control register \n",
-      "{ 000:2843 010:913 111:1850 001:1935 011:1830 100:2846 101:1861 110:922 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "control_register_size = 3\n",
-    "work_register_size = 5\n",
-    "values_for_a = [4, 5]\n",
-    "idx = 1  # change to 1 to select 5\n",
-    "N = 21\n",
-    "shots = 15000\n",
-    "\n",
-    "print(\n",
-    "    cudaq.draw(phase_kernel, control_register_size, work_register_size,\n",
-    "               values_for_a[idx], N))\n",
-    "\n",
-    "results = cudaq.sample(phase_kernel,\n",
-    "                       control_register_size,\n",
-    "                       work_register_size,\n",
-    "                       values_for_a[idx],\n",
-    "                       N,\n",
-    "                       shots_count=shots)\n",
-    "print(\n",
-    "    \"Measurement results for a={} and N={} with {} qubits in the control register \"\n",
-    "    .format(values_for_a[idx], N, control_register_size))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4718d04f",
-   "metadata": {},
-   "source": [
-    "### Determining the order from the measurement results of the phase kernel\n",
-    "\n",
-    "The measurement results from sampling the `phase_kernel` can be used to estimate the phase of the operator $U|x\\rangle = ax\\mod N$. We will only be interested in the most probable non-zero outcomes of the sampling.  Therefore, we'll create a function `top_results` to extract those values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "id": "9e96846f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def top_results(sample_results, zeros, threshold):\n",
-    "    \"\"\"Function to output the non-zero results whose counts are above the given threshold\n",
-    "    Returns\n",
-    "    -------\n",
-    "        dict[str, int]: keys are bit-strings and values are the respective counts  \n",
-    "    \"\"\"\n",
-    "    results_dictionary = {k: v for k, v in sample_results.items()}\n",
-    "    if zeros in results_dictionary.keys():\n",
-    "        results_dictionary.pop(zeros)\n",
-    "    sorted_results = {\n",
-    "        k: v for k, v in sorted(\n",
-    "            results_dictionary.items(), key=lambda item: item[1], reverse=True)\n",
-    "    }\n",
-    "    top_key = next(iter(sorted_results))\n",
-    "    max_value = sorted_results[top_key]\n",
-    "    top_results_dictionary = {top_key: max_value}\n",
-    "\n",
-    "    for key in sorted_results:\n",
-    "        if results_dictionary[key] > min(threshold, max_value):\n",
-    "            top_results_dictionary[key] = results_dictionary[key]\n",
-    "    return top_results_dictionary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b55ab5a",
-   "metadata": {},
-   "source": [
-    "Let's see how the `top_results` extracts the top results from the measurement results for a=4 and N=21 with 3 qubits in the control register that we computed above.  One of these top result is likely to be an estimate for the phase of $U$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "id": "cb717231",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'100': 2846,\n",
-       " '001': 1935,\n",
-       " '101': 1861,\n",
-       " '111': 1850,\n",
-       " '011': 1830,\n",
-       " '110': 922,\n",
-       " '010': 913}"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Example\n",
-    "top_results(results, '000', 750)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cda8137a",
-   "metadata": {},
-   "source": [
-    "The algorithm for translating the phase estimate of the operator $U|x\\rangle = ax\\mod N$ into the order of $a \\mod N$ involves continued fractions. The function `find_order_quantum` below carries out this computation.  To learn more about how the phase of $U$ relates to the period of $a \\mod N$, check out these three lectures by Scott Aaronson: lecture [19](https://www.scottaaronson.com/qclec/19.pdf), [20](https://www.scottaaronson.com/qclec/20.pdf), [21](https://www.scottaaronson.com/qclec/21.pdf)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "id": "a562fde9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_order_from_phase(phase, phase_nbits, a, N):\n",
-    "    \"\"\"Uses continued fractions to find the order of a mod N  \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    phase: int\n",
-    "        Integer result from the phase estimate of U|x> = ax mod N\n",
-    "    phase_nbits: int\n",
-    "        Number of qubits used to estimate the phase\n",
-    "    a: int\n",
-    "        For this demonstration a is either 4 or 5\n",
-    "    N: int\n",
-    "        For this demonstration N = 21\n",
-    "    Returns\n",
-    "    -------\n",
-    "    int: period of a mod N if found, otherwise returns None\n",
-    "    \"\"\"\n",
-    "\n",
-    "    assert phase_nbits > 0\n",
-    "    assert a > 0\n",
-    "    assert N > 0\n",
-    "\n",
-    "    eigenphase = float(phase) / 2**(phase_nbits)\n",
-    "\n",
-    "    f = fractions.Fraction.from_float(eigenphase).limit_denominator(N)\n",
-    "\n",
-    "    if f.numerator == 1:\n",
-    "        return None\n",
-    "    eigenphase = float(f.numerator / f.denominator)\n",
-    "    print('eigenphase is ', eigenphase)\n",
-    "    coefficients_continued_fraction = list(\n",
-    "        contfrac.continued_fraction(eigenphase))\n",
-    "\n",
-    "    convergents_continued_fraction = list(contfrac.convergents(eigenphase))\n",
-    "    print('convergent sequence of fractions for this eigenphase is',\n",
-    "          convergents_continued_fraction)\n",
-    "    for r in convergents_continued_fraction:\n",
-    "        print(\n",
-    "            'using the denominators of the fractions in the convergent sequence, testing order =',\n",
-    "            r[1])\n",
-    "        if a**r[1] % N == 1:\n",
-    "            print('Found order:', r[1])\n",
-    "            return (r[1])\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47511fbe",
-   "metadata": {},
-   "source": [
-    "We are now ready to combine all the elements above into a function to find the order of $a$ $\\mod N$ and test it in the factoring algoithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "7ad0abcf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_order_quantum(a, N):\n",
-    "    \"\"\"The quantum algorithm to find the order of a mod N, when x = 4 or x =5 and N = 21\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    a: int\n",
-    "        For this demonstration a will be either 4 or 5\n",
-    "    N: int\n",
-    "        For this demonstration N will be 21\n",
-    "    Returns\n",
-    "    r: int the period if it is found, or None if no period is found\n",
-    "    -------\n",
-    "    \n",
-    "    \"\"\"\n",
-    "\n",
-    "    if (a == 4 and N == 21) or (a == 5 and N == 21):\n",
-    "        shots = 15000\n",
-    "        if a == 4 and N == 21:\n",
-    "            control_register_size = 3\n",
-    "            work_register_size = 2\n",
-    "        if a == 5 and N == 21:\n",
-    "            control_register_size = 5\n",
-    "            work_register_size = 5\n",
-    "\n",
-    "        #cudaq.set_random_seed(123)\n",
-    "        results = cudaq.sample(phase_kernel,\n",
-    "                               control_register_size,\n",
-    "                               work_register_size,\n",
-    "                               a,\n",
-    "                               N,\n",
-    "                               shots_count=shots)\n",
-    "        print(\"Measurement results:\")\n",
-    "        print(results)\n",
-    "\n",
-    "        # We will want to ignore the all zero result\n",
-    "        zero_result = ''.join(\n",
-    "            [str(elem) for elem in [0] * control_register_size])\n",
-    "        # We'll only consider the top results from the sampling\n",
-    "        threshold = shots * (.1)\n",
-    "        most_probable_bitpatterns = top_results(results, zero_result, threshold)\n",
-    "\n",
-    "        for key in most_probable_bitpatterns:\n",
-    "            # Convert the key bit string into an integer\n",
-    "            # This integer divided by 8 is an estimate for the phase\n",
-    "            reverse_result = key[::-1]\n",
-    "            phase = int(reverse_result, 2)\n",
-    "\n",
-    "            print(\"Trying nonzero bitpattern from the phase estimation:\", key,\n",
-    "                  \"=\", phase)\n",
-    "            r = get_order_from_phase(phase, control_register_size, a, N)\n",
-    "            if r == None:\n",
-    "                print('No period found.')\n",
-    "\n",
-    "                continue\n",
-    "\n",
-    "            return r\n",
-    "            break\n",
-    "    else:\n",
-    "        print(\n",
-    "            \"A different quantum kernel is required for this choice of a and N.\"\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "id": "79927898",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trying a = 5\n",
-      "Measurement results:\n",
-      "{ 11010:452 11001:92 11101:47 00000:2485 01001:96 00100:131 01011:1747 00101:1654 10101:1736 00110:475 11111:23 01000:50 01101:38 01110:35 01010:420 11110:22 01111:26 00011:50 01100:120 10011:35 00010:25 00001:22 11011:1710 00111:79 11100:113 10001:14 10010:29 10100:129 10110:467 10000:2536 10111:81 11000:61 }\n",
-      "\n",
-      "Trying nonzero bitpattern from the phase estimation: 10000 = 1\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 01011 = 26\n",
-      "eigenphase is  0.8125\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (4, 5), (13, 16)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 5\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 16\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 10101 = 21\n",
-      "eigenphase is  0.65\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (1, 2), (2, 3), (13, 20)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 2\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 3\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 20\n",
-      "No period found.\n",
-      "Trying nonzero bitpattern from the phase estimation: 11011 = 27\n",
-      "eigenphase is  0.8421052631578947\n",
-      "convergent sequence of fractions for this eigenphase is [(0, 1), (1, 1), (5, 6), (16, 19)]\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 1\n",
-      "using the denominators of the fractions in the convergent sequence, testing order = 6\n",
-      "Found order: 6\n",
-      "The order of a =5 is 6\n",
-      "Found factors of N = 21: 7 and 3\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(7, 3)"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_integer = 21\n",
-    "initial_value_to_start = 5  # Try replacing 5 with 4\n",
-    "quantum = True\n",
-    "shors_algorithm(my_integer, initial_value_to_start, quantum)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c243a5ee",
-   "metadata": {},
-   "source": [
-    "### Postscript\n",
-    "Recent [work of Oded Regev](https://arxiv.org/abs/2308.06572) improves Shor's Algorithm by reducing the number of gates needed.  You can read more about it [here](https://www.quantamagazine.org/thirty-years-later-a-speed-boost-for-quantum-factoring-20231017/)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/trotter.ipynb b/pr-2308/sphinx/applications/python/trotter.ipynb
deleted file mode 100644
index b508dd155c..0000000000
--- a/pr-2308/sphinx/applications/python/trotter.ipynb
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "231113c7-9432-497b-91f1-c4d068dd61b8",
-   "metadata": {},
-   "source": [
-    "# Computing Magnetization With The Suzuki-Trotter Approximation\n",
-    "\n",
-    "A key application for quantum computers is the simulation of quantum systems. This tutorial will demonstrate a CUDA-Q implementation of a Suzuki-Trotter simulation of a Heisenberg Model Hamiltonian to compute the magnetization of a spin chain.\n",
-    "\n",
-    "This example takes advantage of CUDA-Q's state handling abilities to perform the recursive Trotter simulations. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ccd64e3-9e36-470b-9b15-2a7de6bf6ee3",
-   "metadata": {},
-   "source": [
-    "### Problem Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5c2633a0-979b-46b8-a7a6-44fa61bc62c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import time\n",
-    "import numpy as np\n",
-    "from typing import List"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbbf2909-2607-48d1-aa72-6594f09bbd88",
-   "metadata": {},
-   "source": [
-    "The goal of this problem is to estimate the time evolution of an initial quantum state, governed by a Hamiltonian, and then compute the average magnetization fo the final state.  The time evolution can be approximated using the Trotter method:\n",
-    "\n",
-    "$$  e^{-iHt} \\approx \\prod_{n=0}^N e^{\\frac{-iHt}{n}}$$\n",
-    "\n",
-    "The Heisenberg Hamiltonian is defined below, parameterized by these predefined constants: $g$, $Jx$, $Jy$, and $\\omega = 2\\pi$.   The time step $dt$ is selected for this problem as well as `n_steps` and `n_spins` ($N$), determining the size of the simulation.  The default setup considers 11 spins and 10 steps, which can easily be simulated on a CPU.  If you have access to at least one GPU, the problem can be increased to around 25 steps and 100 time steps.\n",
-    "\n",
-    "\n",
-    "$$ H = \\sum_{j=1}^{N}(J_x\\sigma^x_j\\sigma^x_{j+1} + J_x\\sigma^y_j\\sigma^y_{j+1} + J_x\\sigma^z_j\\sigma^z_{j+1} + \\cos(\\omega * t)\\sigma^x_j   )$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e409539e-e97b-4815-8021-477308726155",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 1.0\n",
-    "Jx = 1.0\n",
-    "Jy = 1.0\n",
-    "Jz = g\n",
-    "dt = 0.05\n",
-    "n_steps = 10\n",
-    "n_spins = 11\n",
-    "omega = 2 * np.pi\n",
-    "\n",
-    "\n",
-    "def heisenbergModelHam(t: float) -> cudaq.SpinOperator:\n",
-    "    tdOp = cudaq.SpinOperator(num_qubits=n_spins)\n",
-    "    for i in range(0, n_spins - 1):\n",
-    "        tdOp += (Jx * cudaq.spin.x(i) * cudaq.spin.x(i + 1))\n",
-    "        tdOp += (Jy * cudaq.spin.y(i) * cudaq.spin.y(i + 1))\n",
-    "        tdOp += (Jz * cudaq.spin.z(i) * cudaq.spin.z(i + 1))\n",
-    "    for i in range(0, n_spins):\n",
-    "        tdOp += (np.cos(omega * t) * cudaq.spin.x(i))\n",
-    "    return tdOp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "067ae183-8e36-4171-adaf-c8506ac2ac48",
-   "metadata": {},
-   "source": [
-    "Next, two CUDA-Q kernels are defined. The first, `getInitState`, prepares an initial state where an $X$ gate is applied to the first and then every other qubit to initialize a chain of alternating spins. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1b64301b-f967-4e69-95d7-64a0091054cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def getInitState(numSpins: int):\n",
-    "    q = cudaq.qvector(numSpins)\n",
-    "    for qId in range(0, numSpins, 2):\n",
-    "        x(q[qId])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0077726b-7c07-4699-8f05-b685d8a4d3a7",
-   "metadata": {},
-   "source": [
-    "The second, `trotter`, performs a single Trotter step given some provided initial state and then returns the resulting state.  Two notes should be made about this kernel. \n",
-    "1. It takes advantage of CUDA-Q's state handling abilities, allowing each step to proceed from the previous, still stored in GPU memory during the simulation.  This provides a significant speedup which is demonstrated in the plot at the end of this tutorial.\n",
-    "2. This kernel takes advantage of the `pauli_word` object which allows a list of Pauli words and their coefficients to be passed into the kernel and applied as an exponentiated matrix operation using `exp_pauli`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4bd5de1b-76c5-4dd7-844a-dfc598a78e6a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def trotter(state: cudaq.State, coefficients: List[complex],\n",
-    "            words: List[cudaq.pauli_word], dt: float):\n",
-    "    q = cudaq.qvector(state)\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(coefficients[i].real * dt, q, words[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74593ace-c2d5-4b4b-931f-54fc4f6ee855",
-   "metadata": {},
-   "source": [
-    "The functions below are used to strip the Hamiltonian spin operator into a list of coefficients and Pauli words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0aeba077-3a12-4c1d-919e-efc521f531fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def termCoefficients(op: cudaq.SpinOperator) -> List[complex]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.get_coefficient()))\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "def termWords(op: cudaq.SpinOperator) -> List[str]:\n",
-    "    result = []\n",
-    "    ham.for_each_term(lambda term: result.append(term.to_string(False)))\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02aa14dc-bf7a-40e2-b55c-77e4e1d13d33",
-   "metadata": {},
-   "source": [
-    "Finally, a second spin operator is defined which will be used to compute the expectation value corresponding to the average magnetization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e0be9949-0d1a-44f5-bdeb-6be751dfe136",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "average_magnetization = cudaq.SpinOperator(num_qubits=n_spins)\n",
-    "for i in range(0, n_spins):\n",
-    "    average_magnetization += ((1.0 / n_spins) * cudaq.spin.z(i))\n",
-    "average_magnetization -= 1.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5527c9b-86e0-4f3d-b5cf-db887d07b375",
-   "metadata": {},
-   "source": [
-    "### Running the Simulation\n",
-    "\n",
-    "Before looping through the Trotter steps, the initial state is constructed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "78348fa1-6f14-4238-a6ec-b0c8449892d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = cudaq.get_state(getInitState, n_spins)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "812b8f44-0a96-4d43-a622-8a4b4a59241d",
-   "metadata": {},
-   "source": [
-    "Next, the time steps are looped through. At each step, the time dependent Hamiltonian is defined. This Hamiltonian is used to construct the Trotter kernel for that time step, which is then used to compute the average magnetization expectation value.  The state is saved, and used as the initial state for the next time step. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "57e4929d-9b79-4d21-b48a-27d47d7e65a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 0: time [s]: 0.03444695472717285, result: -0.09042024163828166\n",
-      "Step 1: time [s]: 0.0026793479919433594, result: -0.08898564687193886\n",
-      "Step 2: time [s]: 0.002758026123046875, result: -0.08698024360923415\n",
-      "Step 3: time [s]: 0.002524852752685547, result: -0.08507694741170907\n",
-      "Step 4: time [s]: 0.0026259422302246094, result: -0.08394118068746997\n",
-      "Step 5: time [s]: 0.002542734146118164, result: -0.08394076573115139\n",
-      "Step 6: time [s]: 0.0027430057525634766, result: -0.08502222139504187\n",
-      "Step 7: time [s]: 0.0025305747985839844, result: -0.08677832064885871\n",
-      "Step 8: time [s]: 0.003045797348022461, result: -0.08863390649349775\n",
-      "Step 9: time [s]: 0.0025949478149414062, result: -0.09005513983609514\n",
-      "Step times: [0.03444695472717285, 0.0026793479919433594, 0.002758026123046875, 0.002524852752685547, 0.0026259422302246094, 0.002542734146118164, 0.0027430057525634766, 0.0025305747985839844, 0.003045797348022461, 0.0025949478149414062]\n",
-      "Results: [-0.09042024163828166, -0.08898564687193886, -0.08698024360923415, -0.08507694741170907, -0.08394118068746997, -0.08394076573115139, -0.08502222139504187, -0.08677832064885871, -0.08863390649349775, -0.09005513983609514]\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "times = []\n",
-    "for i in range(0, n_steps):\n",
-    "    start_time = time.time()\n",
-    "    ham = heisenbergModelHam(i * dt)\n",
-    "    coefficients = termCoefficients(ham)\n",
-    "    words = termWords(ham)\n",
-    "    magnetization_exp_val = cudaq.observe(trotter, average_magnetization, state,\n",
-    "                                          coefficients, words, dt)\n",
-    "    result = magnetization_exp_val.expectation()\n",
-    "    results.append(result)\n",
-    "    state = cudaq.get_state(trotter, state, coefficients, words, dt)\n",
-    "    stepTime = time.time() - start_time\n",
-    "    times.append(stepTime)\n",
-    "    print(f\"Step {i}: time [s]: {stepTime}, result: {result}\")\n",
-    "\n",
-    "print(f\"Step times: {times}\")\n",
-    "print(f\"Results: {results}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b46a3b14-31c0-4711-9064-51ca1f48e7d4",
-   "metadata": {},
-   "source": [
-    "CUDA-Q's state handling capabilities provide a massive performance boost for this algorithm. Rather than resimulate all previous operation at any given time step, saving the previous state in GPU memory allows completion of the simulation with fewer operations. The figure below demonstrates the 24X speedup realized by a 100 step Trotter simulation.\n",
-    "\n",
-    "![Htest](./images/statehandle.png)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f59cf33e-e939-4abe-ae48-2a77f25be5a0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/unitary_compilation_diffusion_models.ipynb b/pr-2308/sphinx/applications/python/unitary_compilation_diffusion_models.ipynb
deleted file mode 100644
index 0e5d788ac2..0000000000
--- a/pr-2308/sphinx/applications/python/unitary_compilation_diffusion_models.ipynb
+++ /dev/null
@@ -1,1062 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d60f1e23-2541-4b01-84c8-0aff758061b4",
-   "metadata": {},
-   "source": [
-    "# Compiling Unitaries Using Diffusion Models\n",
-    "\n",
-    "This notebook is based on the work presented in [Quantum circuit synthesis with diffusion models, Florian Fürrutter, Gorka Muñoz-Gil & Hans J. Briegel , Nat. Mach. Intell. **6**, 515–524 (2024)](https://doi.org/10.1038/s42256-024-00831-9)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b6a7722-f302-458b-9f90-8493e46d786d",
-   "metadata": {},
-   "source": [
-    "Quantum computing relies on efficiently translating quantum operations into viable physical realizations on existing quantum hardware. Recently, diffusion models — a powerful class of generative models in machine learning — have demonstrated exceptional performance on this task. \n",
-    "\n",
-    "In this tutorial notebook, we will demonstrate how to use this method to synthesize arbitrary unitaries into a `cudaq.kernel`, effectively decomposing them into sequences of quantum gates, a process commonly known as unitary compilation.\n",
-    "\n",
-    "Our focus will be on generating circuits using a pre-trained model. Specifically, we will illustrate how to compile 3-qubit unitaries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fef3bf4a-1008-4df8-b6e8-d5357db1f6d5",
-   "metadata": {},
-   "source": [
-    "### Diffusion model pipeline\n",
-    "\n",
-    "Generative diffusion models (DMs) have recently delivered remarkable results across a wide range of applications, from image generation to protein folding. In this work, we leverage DMs to generate quantum circuits based on a user specified unitary matrix and a text prompt that defines the allowed gate set, effectively using DMs as unitary compilers. The method is thoroughly explained in Ref. [(Fürrutter et al., 2024)](https://doi.org/10.1038/s42256-024-00831-9). The following figure  provides an overview of the proposed circuit generation pipeline:\n",
-    "\n",
-    "<figure>\n",
-    "  <img src=\"images/pipeline_simplified.png\" alt=\"Quantum circuit generation pipeline overview\" width=\"70%\"/>\n",
-    "  <figcaption><b>Quantum circuit generation pipeline</b>. Figure adapted from <a href=\"https://doi.org/10.1038/s42256-024-00831-9\">(Fürrutter et al., 2024)</a></figcaption>\n",
-    "</figure>\n",
-    "\n",
-    "The pipeline consists on 3 main components:\n",
-    "\n",
-    "**1) Circuit encoding:** Like any neural network, diffusion models operate with continuous inputs and outputs. However, since the circuits we consider are composed of discrete gates (i.e., with no continuous parameters), we develop a mapping that transforms each gate into a continuous vector. This allows us to represent a given circuit as a three-dimensional tensor, as illustrated. Crucially, this mapping is invertible: when the DM generates continuous tensors, we can apply the inverse map to convert them back into the circuit form. An overview of these steps is provided in the figure below:\n",
-    "\n",
-    "<figure>\n",
-    "  <img src=\"images/circuit_encoding.png\" alt=\"Quantum circuit encoding\" width=\"60%\"/>\n",
-    "  <figcaption><b>Quantum circuit encoding</b>. Figure adapted from <a href=\"https://doi.org/10.1038/s42256-024-00831-9\">(Fürrutter et al., 2024)</a></figcaption>\n",
-    "</figure>\n",
-    "\n",
-    "**2) Conditioning:** The user's input (the set of available gates and the unitary to compile) is also transformed into a continuous tensor by two neural networks. For the gate set description, where the input is a text prompt (e.g., \"Compile using ['x', 'h']\"), we utilize a pre-trained language model. For the unitary, we employ a neural network that is training jointly with the diffusion model.\n",
-    "\n",
-    "**3) Unitary compilation:** The generation procedure follows the typical DM process: the model is given a fully noisy tensor which is iteratively de-noised until reaching a clean sample based on the given conditioning (the desired unitary and gate set). The tensors generated by the DM are then mapped to circuits via the inverse encoding procedure. To learn more about the practical implementation of diffusion models we recommend [this tutorial](https://course.fast.ai/Lessons/lesson9.html). \n",
-    "\n",
-    "In the following we will use `CUDA-Q` and `genQC` to perform all these steps and go from a desired unitary matrix, $U$, to a quantum circuit that we can execute using CUDA-Q. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1453ad1-e4d4-4e38-9684-fd967bc1a8de",
-   "metadata": {},
-   "source": [
-    "### Setup and compilation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f1b7d96a-92d4-46f3-95a6-1d1e46d63240",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.037362Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.036980Z",
-     "iopub.status.idle": "2024-08-27T18:11:38.867336Z",
-     "shell.execute_reply": "2024-08-27T18:11:38.866445Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.037346Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install genQC==0.1.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "07d177c6-60fd-497d-ae2b-41e225b56a2d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.868456Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.868206Z",
-     "iopub.status.idle": "2024-08-27T18:11:38.872654Z",
-     "shell.execute_reply": "2024-08-27T18:11:38.871931Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.868440Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import genQC\n",
-    "from genQC.imports import *\n",
-    "from genQC.pipeline.diffusion_pipeline import DiffusionPipeline\n",
-    "from genQC.inference.export_cudaq import genqc_to_cudaq\n",
-    "import genQC.inference.infer_compilation as infer_comp\n",
-    "import genQC.util as util\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "\n",
-    "# Fixed seed for reproducibility\n",
-    "torch.manual_seed(0)\n",
-    "np.random.seed(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ccb97524-0f13-49ff-893d-983c572e66c6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:38.873619Z",
-     "iopub.status.busy": "2024-08-27T18:11:38.873403Z",
-     "iopub.status.idle": "2024-08-27T18:11:39.025224Z",
-     "shell.execute_reply": "2024-08-27T18:11:39.024476Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:38.873603Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\n",
-      "cuda\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = util.infer_torch_device()  # Use CUDA if we can\n",
-    "util.MemoryCleaner.purge_mem()  # Clean existing memory allocation\n",
-    "print(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acf7656c-be92-4b36-8c38-d3ff6c1912e3",
-   "metadata": {},
-   "source": [
-    "### Load model"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "0047cbe9-a21c-4c72-a144-7a2929752329",
-   "metadata": {},
-   "source": [
-    "First, we load the pre-trained model weights and setup the DM pipeline.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a6b18d03-6976-4eec-9ab0-7b763d73ad17",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:39.026541Z",
-     "iopub.status.busy": "2024-08-27T18:11:39.025956Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.879166Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.878426Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:39.026521Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pipeline = DiffusionPipeline.from_pretrained(\n",
-    "    \"Floki00/qc_unitary_3qubit\", device)  # Download from Hugging Face\n",
-    "pipeline.scheduler.set_timesteps(40)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc112d57-71a8-403c-a0a9-d2f7d0eb7492",
-   "metadata": {},
-   "source": [
-    "Set parameters the model was trained on. Note that these are fixed and depend on the pre-trained model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3037d98c-16fb-4a7f-bf37-7e9991b6603e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.880376Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.879966Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.883994Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.883336Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.880352Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "vocab = {\n",
-    "    i + 1: gate for i, gate in enumerate(pipeline.gate_pool)\n",
-    "}  # Gateset used during training, used for decoding\n",
-    "num_of_qubits = 3  # Number of qubits\n",
-    "max_gates = 12  # Maximum number of gates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d7cc1c9-7ddd-4464-98b8-8dbcaa02a53e",
-   "metadata": {},
-   "source": [
-    "### Unitary compilation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b962aea8-9c46-4795-8b74-bca35900f1ed",
-   "metadata": {},
-   "source": [
-    "Let's start by defining the unitary we want to compile. Note that this model has been trained to compile unitaries that arise from circuits composed of the gates contain in `vocab`. While this is a universal gate set (i.e. can perform universal computation), it can only do so with an infinite number of gates. Because the number of gates in the model is restricted to 12, we can only expect the model to generate unitaries under this constraints. We will consider here the compilation of one of such unitary. Nonetheless, stay tuned for bigger and better models!\n",
-    "\n",
-    "We start by defining our unitary as a `numpy.matrix`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5107c719-4220-4209-a657-4a9c0766b80a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.884880Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.884694Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.896385Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.895667Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.884863Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "U = np.matrix([[0.70710678, 0., 0., 0., 0.70710678, 0., 0., 0.],\n",
-    "               [0., -0.70710678, 0., 0., 0., -0.70710678, 0., 0.],\n",
-    "               [-0.70710678, 0., 0., 0., 0.70710678, 0., 0., 0.],\n",
-    "               [0., 0.70710678, 0., 0., 0., -0.70710678, 0., 0.],\n",
-    "               [0., 0., 0.70710678, 0., 0., 0., 0., 0.70710678],\n",
-    "               [0., 0., 0., 0.70710678, 0., 0., 0.70710678, 0.],\n",
-    "               [0., 0., -0.70710678, 0., 0., 0., 0., 0.70710678],\n",
-    "               [0., 0., 0., -0.70710678, 0., 0., 0.70710678, 0.]],\n",
-    "              dtype=np.complex128)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14954138-f16b-48f6-b35d-5b1ac00cff1e",
-   "metadata": {},
-   "source": [
-    "and check that the matrix is indeed unitary, i.e. $U^{\\dagger} U = U U^{\\dagger} = I$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "57164622-8359-4c58-b4d9-1b64ae603b43",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.897692Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.897368Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.904008Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.903243Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.897677Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "assert np.allclose(U.H @ U, np.identity(2**num_of_qubits)) and np.allclose(\n",
-    "    U @ U.H, np.identity(2**num_of_qubits))  #check if unitary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0ba07c5-075a-46ca-9a22-cc758ce33cb9",
-   "metadata": {},
-   "source": [
-    "The loaded model was trained on the gate set `['h', 'cx', 'z', 'x', 'ccx', 'swap']`. Specifically, it was trained to generate circuits using any arbitrary subset of this gate set. Therefore, during inference, we can instruct the model to compile the unitary using any of these subsets. However, it is crucial to follow the prompt structure `Compile using [...]`, as the model was trained with this specific format. For example, let's consider a scenario where we compile the unitary without using the `x` gate:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0f0d3e85-a22e-4a1b-ad17-b02417e56734",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.905214Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.904884Z",
-     "iopub.status.idle": "2024-08-27T18:11:40.912812Z",
-     "shell.execute_reply": "2024-08-27T18:11:40.911995Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.905195Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Notice how the x gate missing from the prompt since this is a restriction we set\n",
-    "prompt = \"Compile using: ['h', 'cx', 'z', 'ccx', 'swap']\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cab3a1e8-a404-4ebb-9a58-251d47eebb4a",
-   "metadata": {},
-   "source": [
-    "Now, we call the diffusion model pipeline to generate encoded circuits based on the specified conditions: `prompt` and `U`. One of the key advantages of this method is that, once the model is trained, sampling new circuits is very fast. Therefore, under the same conditions defined above, we will generate 128 circuits and analyze them in the following sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "56edb114-3ef7-455b-b4b8-0e262e7b6316",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:40.913747Z",
-     "iopub.status.busy": "2024-08-27T18:11:40.913584Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.824624Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.823799Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:40.913732Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Number of circuits to sample from the trained DM.\n",
-    "samples = 128\n",
-    "\n",
-    "# As the neural network works only with real numbers, we first separate\n",
-    "# the two components and create a 2 dimensional tensor for the magnitude\n",
-    "# of each component:\n",
-    "U_r, U_i = torch.Tensor(np.real(U)), torch.Tensor(np.imag(U))\n",
-    "U_tensor = torch.stack([U_r, U_i], dim=0)\n",
-    "\n",
-    "# Now we generate a tensor representation of the desired quantum circuit using the DM based on the prompt and U. This is also known as inference.\n",
-    "out_tensors = infer_comp.generate_comp_tensors(\n",
-    "    pipeline=pipeline,\n",
-    "    prompt=prompt,\n",
-    "    U=U_tensor,\n",
-    "    samples=samples,\n",
-    "    system_size=\n",
-    "    num_of_qubits,  # Max qubit number allowed by the model (this model is only trained with 3 qubits)\n",
-    "    num_of_qubits=num_of_qubits,\n",
-    "    max_gates=max_gates,\n",
-    "    g=10  # classifier-free-guidance (CFG) scale\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81f09d75-a960-4456-bdb9-57ef7668864e",
-   "metadata": {},
-   "source": [
-    "Below is an example of a generated tensor which represents a quantum circuit: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "13c1af73-11bc-46ca-99dd-bccea29f95c1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:41.825914Z",
-     "iopub.status.busy": "2024-08-27T18:11:41.825524Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.831202Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.830421Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:41.825898Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],\n",
-       "        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],\n",
-       "        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out_tensors[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5605d0a1-626d-4aa5-8721-0416470215dc",
-   "metadata": {},
-   "source": [
-    "### Convert tensors to CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b43d006-1e6a-4911-9283-b5a96eb5cf06",
-   "metadata": {},
-   "source": [
-    "Next, we convert the generated tensors into `cudaq.kernels`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "01efc6f7-8f0f-425f-be58-7fcaf880d0fd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:41.832305Z",
-     "iopub.status.busy": "2024-08-27T18:11:41.831956Z",
-     "iopub.status.idle": "2024-08-27T18:11:41.840723Z",
-     "shell.execute_reply": "2024-08-27T18:11:41.839940Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:41.832289Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "\n",
-    "cudaq.set_target('qpp-cpu')  # Note that cpu is faster for 3 qubit kernels\n",
-    "\n",
-    "# cudaq.set_target('nvidia') # Set to GPU for larger circuits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9d5b9ae5-c11e-413b-af00-4412dc86b48d",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply.started": "2024-08-27T18:11:41.841726Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7cdb66833df4aee8a75c3c7a3558ebc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model generated 0 invalid tensors that does not correspond to circuits.\n"
-     ]
-    }
-   ],
-   "source": [
-    "kernel_list = []\n",
-    "valid_tensors = []\n",
-    "\n",
-    "invalid_tensors = 0\n",
-    "for out_tensors_i in tqdm(out_tensors):\n",
-    "\n",
-    "    # Use a try-except to catch invalid tensors (if any)\n",
-    "    try:\n",
-    "        kernel = genqc_to_cudaq(out_tensors_i,\n",
-    "                                vocab)  # Convert out_tensors to CUDA-Q kernels\n",
-    "    except:\n",
-    "        kernel = None\n",
-    "\n",
-    "    if kernel:\n",
-    "        kernel_list.append(kernel)\n",
-    "        valid_tensors.append(out_tensors_i)\n",
-    "    else:\n",
-    "        invalid_tensors += 1\n",
-    "\n",
-    "print(\n",
-    "    f\"The model generated {invalid_tensors} invalid tensors that does not correspond to circuits.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a5823e6-9189-46b7-974d-43a2a0cad531",
-   "metadata": {},
-   "source": [
-    "For example, the following tensor encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dc7d35c7-a7cb-4663-8220-b1030e534deb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:43.439383Z",
-     "iopub.status.busy": "2024-08-27T18:11:43.438787Z",
-     "iopub.status.idle": "2024-08-27T18:11:43.443973Z",
-     "shell.execute_reply": "2024-08-27T18:11:43.443101Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:43.439367Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 5,  3,  0,  0, -5,  3,  3,  0, -5,  0,  0,  0],\n",
-       "        [-5,  0,  0,  6,  5,  0,  0,  3,  5,  0,  0,  0],\n",
-       "        [-5,  0,  1,  6, -5,  0,  0,  0, -5,  0,  0,  0]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "valid_tensors[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47116105-b1e1-454e-80fc-6a03061045f5",
-   "metadata": {},
-   "source": [
-    "corresponds to the following `cudaq.kernel`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4835c288-b018-49d7-b307-3ff05c2c8c56",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:43.444990Z",
-     "iopub.status.busy": "2024-08-27T18:11:43.444651Z",
-     "iopub.status.idle": "2024-08-27T18:11:43.569067Z",
-     "shell.execute_reply": "2024-08-27T18:11:43.568320Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:43.444975Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮╭───╮        ╭───╮╭───╮     \n",
-      "q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──\n",
-      "     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮\n",
-      "q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├\n",
-      "       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯\n",
-      "q2 : ──●──┤ h ├─╳───●──────────────●──\n",
-      "          ╰───╯                       \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Arbitrary input state to the circuit for plotting\n",
-    "\n",
-    "input_state = [0] * (2**num_of_qubits)\n",
-    "\n",
-    "print(cudaq.draw(kernel_list[0], input_state))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59809685-5d70-4612-8642-c4e924a1ab6c",
-   "metadata": {},
-   "source": [
-    "### Evaluate generated circuits\n",
-    "\n",
-    "As mentioned earlier, one of the key advantages of using diffusion models (DMs) as a unitary compiler is the ability to rapidly sample many circuits. However, as is common in machine learning, the model has a certain accuracy, meaning not all generated circuits are expected to exactly compile the specified unitary. In this section, we will evaluate how many of the generated circuits are indeed correct and then perform post-selection to identify (at least) one circuit that successfully performs the desired unitary operation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2685d88b-aceb-4a8c-8404-f744196d34c5",
-   "metadata": {},
-   "source": [
-    "### Simulate kernels"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f5d1e8ca-3abf-47b4-bbca-932bdff86f94",
-   "metadata": {},
-   "source": [
-    "First, we calculate the unitary matrix $U$ implemented by each of the kernels. The elements of this matrix are defined by the transition amplitudes between the basis states, which can be expressed as:\n",
-    "$$\n",
-    "\\begin{equation}\n",
-    "   \\langle i|kernel|j\\rangle = U_{ij},\n",
-    "\\end{equation}\n",
-    "$$\n",
-    "where $|i\\rangle$ and $|j\\rangle$ are computational basis states (typically in the $z$-basis), with $|n\\rangle$ representing the standard basis vector that has a 1 in the $n$-th position and 0 elsewhere."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "11143924-c33f-4fd5-8f04-422eba9ed5a0",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply.started": "2024-08-27T18:11:43.570011Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f3961ae5e254402a4dcd5124de3a5d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "N = 2**num_of_qubits\n",
-    "\n",
-    "got_unitaries = np.zeros((len(kernel_list), N, N), dtype=np.complex128)\n",
-    "\n",
-    "for i, kernel in tqdm(enumerate(kernel_list), total=got_unitaries.shape[0]):\n",
-    "    for j in range(N):\n",
-    "        basis_state_j = np.zeros((N), dtype=np.complex128)\n",
-    "        basis_state_j[j] = 1\n",
-    "\n",
-    "        got_unitaries[i, :,\n",
-    "                      j] = np.array(cudaq.get_state(kernel, basis_state_j),\n",
-    "                                    copy=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5eb62ba-40a6-486c-b307-47b11b9b3226",
-   "metadata": {},
-   "source": [
-    "For example, the circuit printed above corresponds to the following unitary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4a471612-a3e4-4bea-9f61-dc823283af95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.875219Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.874792Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.879735Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.878882Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.875200Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.set_printoptions(linewidth=1000)\n",
-    "print(np.round(got_unitaries[0], 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "203def54-bb97-4344-8544-6b1374b3f9a3",
-   "metadata": {},
-   "source": [
-    "### Compare unitaries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5241775-2692-4b10-b9ef-a75a3ddb0eb8",
-   "metadata": {},
-   "source": [
-    "Now that we have the unitaries for each of the kernels, we compare them to the user provided unitary matrix, `U`. To do so, we compute the infidelity between the exact unitary and the generated ones. The infidelity is a value between 0 and 1, where 0 indicates that the unitaries are identical (up to a global phase)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "bb4915e8-5a92-4038-ab49-fa67a0a5b668",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.880925Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.880574Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.891293Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.890501Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.880909Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def infidelity(want_unitary, got_unitary):\n",
-    "    return 1 - np.abs(\n",
-    "        np.trace(np.conj(want_unitary).T @ got_unitary) / 2**num_of_qubits)**2\n",
-    "\n",
-    "\n",
-    "infidelities = np.array(\n",
-    "    [infidelity(U, got_unitary) for got_unitary in got_unitaries])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2ae9d89-3aab-43db-85cb-2aa6fc494d87",
-   "metadata": {},
-   "source": [
-    "Let's now plot an histogram of the infidelities for our generated circuits:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c8313728-3a69-475b-bf3c-f0a8b8e8324a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.892419Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.892234Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.982604Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.981724Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.892404Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "plt.title(\n",
-    "    f\"Distribution of infidelities for {len(got_unitaries)} generated circuits\",\n",
-    "    fontsize=12)\n",
-    "plt.ylabel(\"Number of circuits\", fontsize=14)\n",
-    "plt.xlabel(\"Unitary infidelity\", fontsize=14)\n",
-    "plt.hist(infidelities, bins=30)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1912f6c-e685-4994-bf3a-fe9530dbd513",
-   "metadata": {},
-   "source": [
-    "As we can see, the model generated almost 30 circuits that compile the unitary with an infidelity close or equal to zero! The kernel with the lowest infidelity is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "656baf92-5a1e-4227-ac1f-67b5f9394310",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.983493Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.983309Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.988444Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.987556Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.983478Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The best kernel has an infidelity of 3.4e-09,\n",
-      "     ╭───╮╭───╮        ╭───╮╭───╮     \n",
-      "q0 : ┤ x ├┤ z ├─────●──┤ z ├┤ z ├──●──\n",
-      "     ╰─┬─╯╰───╯   ╭─┴─╮├───┤╰───╯╭─┴─╮\n",
-      "q1 : ──●────────╳─┤ x ├┤ z ├─────┤ x ├\n",
-      "       │  ╭───╮ │ ╰─┬─╯╰───╯     ╰─┬─╯\n",
-      "q2 : ──●──┤ h ├─╳───●──────────────●──\n",
-      "          ╰───╯                       \n",
-      "\n",
-      "with the unitary:\n",
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "min_index = np.argmin(infidelities)\n",
-    "\n",
-    "print(f\"The best kernel has an infidelity of {infidelities[min_index]:0.2},\")\n",
-    "\n",
-    "input_state = [0] * (2**num_of_qubits)\n",
-    "input_state[0] = 1\n",
-    "print(cudaq.draw(kernel_list[min_index], input_state))\n",
-    "\n",
-    "print(f\"with the unitary:\")\n",
-    "print(np.round(got_unitaries[min_index], 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e724b39-cc63-40c8-8ad0-9d90d94e23bd",
-   "metadata": {},
-   "source": [
-    "which, as we can see, exactly compiled our wanted unitary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "46d190d6-7bed-4490-a343-bf856190a101",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.989293Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.989114Z",
-     "iopub.status.idle": "2024-08-27T18:11:55.996387Z",
-     "shell.execute_reply": "2024-08-27T18:11:55.995546Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.989278Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [-0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]\n",
-      " [ 0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.    +0.j  0.7071+0.j]\n",
-      " [ 0.    +0.j  0.    +0.j  0.    +0.j -0.7071+0.j  0.    +0.j  0.    +0.j  0.7071+0.j  0.    +0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.round(U, 4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8bf2fe6-07ca-4bfa-a46b-96a91daeb185",
-   "metadata": {},
-   "source": [
-    "### Choosing the circuit you need"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "983dc8c1-f3ae-4d06-ad7a-e57addfc6e74",
-   "metadata": {},
-   "source": [
-    "As we have seen above, we now have almost 30 kernels that compile the desired unitary! This is particularly valuable when dealing with hardware constraints, where, for instance, we might want to avoid using certain qubits or specific gates. One practical example is finding the circuit with the fewest Toffoli gates (also known as `ccx`). In our `vocab` definition above, we identified that `ccx` corresponds to the label 5 in our tokenized tensors. Let's use this information to search for the circuit that minimizes the number of `ccx` gates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "acb0f4d8-2e8a-4b32-9e37-006571ca2aa5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:55.997443Z",
-     "iopub.status.busy": "2024-08-27T18:11:55.997033Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.005395Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.004630Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:55.997427Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model generated 16 distinct correct circuits.\n",
-      "These circuits have this number of ccx gates: tensor([1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 1, 1, 1, 1, 1])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, we remove possible duplicates and only pick distinct circuits\n",
-    "_, idx_unique = np.unique(np.array(valid_tensors), axis=0, return_index=True)\n",
-    "unique_tensors = torch.stack(valid_tensors)[idx_unique]\n",
-    "unique_infidelities = infidelities[idx_unique]\n",
-    "unique_kernels = [kernel_list[idx] for idx in idx_unique]\n",
-    "\n",
-    "# Then, find the correct circuits\n",
-    "idx_correct = torch.argwhere(torch.tensor(unique_infidelities) < 0.01).flatten()\n",
-    "correct_tensors = unique_tensors[idx_correct]\n",
-    "print(\n",
-    "    f\"The model generated {correct_tensors.shape[0]} distinct correct circuits.\"\n",
-    ")\n",
-    "\n",
-    "# Now let's flatten the last two dimensions (related to the actual circuit) and find out how many 5's (i.e. ccx) gates each circuit has:\n",
-    "num_ccx = (correct_tensors.flatten(1, 2) == 5).sum(1)\n",
-    "print(\"These circuits have this number of ccx gates:\", num_ccx)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7625aac1-78d8-4ce3-9765-88ae48c92dd3",
-   "metadata": {},
-   "source": [
-    "As we can see, it appears that the diffusion model (DM) requires at least one Toffoli gate to compile the unitary. We can now print a few of these circuits to select the one that best suits our needs, or to study whether there are any interesting patterns the model employs for this specific unitary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "535501a6-cafb-48e6-b53c-636d7ac5a815",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:56.006584Z",
-     "iopub.status.busy": "2024-08-27T18:11:56.006136Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.013882Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.013117Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:56.006569Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        ╭───╮╭───╮               \n",
-      "q0 : ───┤ x ├┤ z ├───────●───────\n",
-      "        ╰─┬─╯├───┤╭───╮  │       \n",
-      "q1 : ─╳───●──┤ h ├┤ z ├──┼───────\n",
-      "      │   │  ├───┤╰───╯╭─┴─╮╭───╮\n",
-      "q2 : ─╳───●──┤ h ├─────┤ x ├┤ h ├\n",
-      "             ╰───╯     ╰───╯╰───╯\n",
-      "\n",
-      "     ╭───╮╭───╮             \n",
-      "q0 : ┤ x ├┤ z ├──●──────────\n",
-      "     ╰─┬─╯├───┤╭─┴─╮        \n",
-      "q1 : ──●──┤ h ├┤ x ├─╳──────\n",
-      "       │  ├───┤├───┤ │ ╭───╮\n",
-      "q2 : ──●──┤ h ├┤ z ├─╳─┤ h ├\n",
-      "          ╰───╯╰───╯   ╰───╯\n",
-      "\n",
-      "     ╭───╮╭───╮             \n",
-      "q0 : ┤ x ├┤ z ├─────●───────\n",
-      "     ╰─┬─╯├───┤     │  ╭───╮\n",
-      "q1 : ──●──┤ h ├─╳───┼──┤ z ├\n",
-      "       │  ├───┤ │ ╭─┴─╮├───┤\n",
-      "q2 : ──●──┤ h ├─╳─┤ x ├┤ h ├\n",
-      "          ╰───╯   ╰───╯╰───╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the correct kernels\n",
-    "correct_kernels = [unique_kernels[idx] for idx in idx_correct]\n",
-    "\n",
-    "# Get the ones with only one ccx\n",
-    "correct_kernels_ccx1 = [\n",
-    "    correct_kernels[idx] for idx in torch.argwhere(num_ccx == 1).flatten()\n",
-    "]\n",
-    "\n",
-    "# Draw a few of this circuits\n",
-    "for kernel in correct_kernels_ccx1[:3]:\n",
-    "    print(cudaq.draw(kernel, input_state))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "dd32819f-8bb6-4af8-bf69-84b600ba7ad5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-27T18:11:56.020863Z",
-     "iopub.status.busy": "2024-08-27T18:11:56.020639Z",
-     "iopub.status.idle": "2024-08-27T18:11:56.027045Z",
-     "shell.execute_reply": "2024-08-27T18:11:56.026361Z",
-     "shell.execute_reply.started": "2024-08-27T18:11:56.020849Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n",
-      "genQC Version 0.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)\n",
-    "print(\"genQC Version\", genQC.__version__)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/applications/python/vqe.ipynb b/pr-2308/sphinx/applications/python/vqe.ipynb
deleted file mode 100644
index 207e195938..0000000000
--- a/pr-2308/sphinx/applications/python/vqe.ipynb
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Variational Quantum Eigensolver"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A common application of the Variational Quantum Eigensolver (VQE) algorithm is to compute the ground state energy of a molecular system. The code below demonstrates how to perform classical preprocessing for a $H_2$ molecule (i.e. obtain the integrals from a Hartree-Fock computation to build the molecular Hamiltonian), prepare the initial Hartree-Fock state on the quantum register, add the parameterized UCCSD ansatz to the kernel, and select the COBYLA optimizer.  We are then ready to call `cudaq:vqe` to estimate the minimum energy of the system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install openfermionpyscf==0.5 matplotlib==3.8.4 scipy==1.13.0 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import numpy as np\n",
-    "\n",
-    "# Single precision\n",
-    "cudaq.set_target(\"nvidia\")\n",
-    "# Double precision\n",
-    "#cudaq.set_target(\"nvidia-fp64\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The problem of interest here is a chain of hydrogen atoms seperated along the z-axis at a fixed interval called the bond distance. \n",
-    "\n",
-    "The interatomic electrostatic forces due to the electrons and protons and the shielding by the neutrons creates a chemical system whose energy can be minimised to find a stable configuration. \n",
-    "\n",
-    "Let us first begin by defining the molecule and other metadata about the problem.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of hydrogen atoms.\n",
-    "hydrogen_count = 2\n",
-    "\n",
-    "# Distance between the atoms in Angstroms.\n",
-    "bond_distance = 0.7474\n",
-    "\n",
-    "# Define a linear chain of Hydrogen atoms\n",
-    "geometry = [('H', (0, 0, i * bond_distance)) for i in range(hydrogen_count)]\n",
-    "\n",
-    "hamiltonian, data = cudaq.chemistry.create_molecular_hamiltonian(\n",
-    "    geometry, 'sto-3g', 1, 0)\n",
-    "\n",
-    "electron_count = data.n_electrons\n",
-    "qubit_count = 2 * data.n_orbitals"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now generate a Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz from the template provided by CUDA-Q.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(thetas: list[float]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    for i in range(electron_count):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_count, qubit_count)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using CUDA-Q Optimizers\n",
-    "\n",
-    "We use the builtin optimizers within CUDA-Q for the minimization procedure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-1.1371744305855906\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer = cudaq.optimizers.COBYLA()\n",
-    "\n",
-    "energy, parameters = cudaq.vqe(kernel,\n",
-    "                               hamiltonian,\n",
-    "                               optimizer,\n",
-    "                               parameter_count=parameter_count)\n",
-    "\n",
-    "print(energy)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Integration with Third-Party Optimizers\n",
-    "\n",
-    "We can also integrate popular libraries like scipy with CUDA-Q. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Define a function to minimize\n",
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, np.pi, parameter_count)\n",
-    "\n",
-    "# Use the scipy optimizer to minimize the function of interest\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 40})\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/applications/python/vqe_advanced.ipynb b/pr-2308/sphinx/applications/python/vqe_advanced.ipynb
deleted file mode 100644
index 3135b93829..0000000000
--- a/pr-2308/sphinx/applications/python/vqe_advanced.ipynb
+++ /dev/null
@@ -1,729 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# VQE with gradients, active spaces, and gate fusion\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial will explore the Variational Quantum Eigensolver, a hybrid quantum classical algorithm for determining the ground state energy of molecules. The first part of this tutorial will walk through the key aspects of the VQE algorithm and how to implement it with CUDA-Q.  The following sections explore advanced topics: parallel gradients, active spaces, and gate fusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The Basics of VQE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE algorithm is hybrid quantum-classical algorithm, meaning some subroutines run on a quantum computer or quantum simulator and others run on a traditional (super)computer.  \n",
-    "\n",
-    "The goal is to take a parameterized quantum circuit and a qubit form of the molecular Hamiltonian, measure an expectation value that corresponds to the ground state energy of the molecule, and then repeat the process to variationally minimize the energy with respect to the parameters in the quantum circuit. The optimization is performed on a classical device while the expectation values are determined on a quantum device.  See the figure below.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![VQE.png](./images/VQE.png)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next few cells will elaborate on each part of the VQE procedure and show you how to build a VQE simulation to compute the ground state energy of the water molecule."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installing/Loading Relevant Packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the relevant packages.\n",
-    "!pip install pyscf==2.6.2 openfermionpyscf==0.5 matplotlib==3.8.4 openfermion==1.6.1 -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openfermion\n",
-    "import openfermionpyscf\n",
-    "from openfermion.transforms import jordan_wigner, get_fermion_operator\n",
-    "\n",
-    "import os\n",
-    "import timeit\n",
-    "\n",
-    "import cudaq\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementing VQE in CUDA-Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Like most quantum chemistry programs, the first step is to specify a molecular geometry, basis set, charge, and multiplicity. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = [('O', (0.1173, 0.0, 0.0)), ('H', (-0.4691, 0.7570, 0.0)),\n",
-    "            ('H', (-0.4691, -0.7570, 0.0))]\n",
-    "basis = 'sto3g'\n",
-    "multiplicity = 1\n",
-    "charge = 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE procedure requires some classical preprocessing.  The code below uses the PySCF package and OpenFermion to compute the Hartree Fock reference state and compute the integrals required for the Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecule = openfermionpyscf.run_pyscf(\n",
-    "    openfermion.MolecularData(geometry, basis, multiplicity, charge))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the Hamiltonian is built using `get_molecular_hamiltonian`. The Hamiltonian must then be converted to a qubit Hamiltonian consisting of qubit operators. The standard Jordan-Wigner transformation is used to perform this mapping. \n",
-    "\n",
-    "Finally, the Jordan-Wigner qubit Hamiltonian is converted into a CUDA-Q spin operator which can be used to evaluate an expectation value given a quantum circuit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecular_hamiltonian = molecule.get_molecular_hamiltonian()\n",
-    "\n",
-    "fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)\n",
-    "\n",
-    "qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)\n",
-    "\n",
-    "spin_ham = cudaq.SpinOperator(qubit_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, the quantum circuit needs to be defined, which models the wavefunction. This is done in CUDA-Q by specifying a CUDA-Q kernel. The kernel takes as an input the number of qubits, the number of electrons, and the parameters of the circuit ansatz (form of the wavefunction) yet to be defined. \n",
-    "\n",
-    "The number of qubits corresponds to the potential positions of electrons and is therefore twice the number of spatial orbitals constructed with the chosen basis set, as each can be occupied by two electrons. \n",
-    "\n",
-    "The Hartree-Fock reference is constructed by applying $X$ bitflip operations to each of the first $N$ qubits where $N$ is the number of electrons. Next, a parameterized ansatz is chosen. Theoretically, any set of operations can work as an ansatz, however, it is good practice to use an ansatz that captures the underlying physics of the problem. The most common choice for chemistry is the Unitary Coupled Cluster Ansatz with Single and Double excitations (UCCSD).  This UCCSD hate operations are automatically added to the kernel with the `cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)` function. \n",
-    "\n",
-    "The STO-3G water molecuule UCCSD ansatz requires optimization of 140 parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "140\n"
-     ]
-    }
-   ],
-   "source": [
-    "electron_count = 10\n",
-    "qubit_count = 2 * 7\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_num: int, electron_num: int, thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    for i in range(electron_num):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)\n",
-    "\n",
-    "print(parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The classical optimizer requires a custom cost function which is defined below. The `cudaq.observe()` function computes an expectation given the Hamiltonian and the kernel defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, spin_ham, qubit_count, electron_count,\n",
-    "                            theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final step is to run the optimization using the scipy minimize function and a selected optimizer, in this case COBYLA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "UCCSD-VQE energy =   -70.21455023422772\n",
-      "Total number of qubits =  14\n",
-      "Total number of parameters =  140\n",
-      "Total number of terms in the spin hamiltonian =  1086\n",
-      "Total elapsed time (s) =  3.9171073289999185\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.set_target('nvidia')\n",
-    "start_time = timeit.default_timer()\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 50})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('UCCSD-VQE energy =  ', result.fun)\n",
-    "print('Total number of qubits = ', qubit_count)\n",
-    "print('Total number of parameters = ', parameter_count)\n",
-    "print('Total number of terms in the spin hamiltonian = ',\n",
-    "      spin_ham.get_term_count())\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result of this procedure is an estimate of the ground state energy of water. However, the convergence behavior is not perfect, more iterations would greatly improve the result, but would take a few minutes to run."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parallel Parameter Shift Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One way to accelerate VQE is to use an optimizer that accepts a gradient. This can drastically lower the number of VQE iterations required at the cost of computing the gradient on the quantum side of the algorithm.\n",
-    "\n",
-    "The parameter shift rule is a common technique to compute the gradient for parameterized circuits. It is obtained by computing two expectation values for each parameter corresponding to a small forward and backward shift in the ith parameter. These results are used to estimate  finite difference contribution to the gradient.\n",
-    "\n",
-    "![parametershift.png](./images/parametershift.png)\n",
-    "\n",
-    "This procedure can become cost prohibitive as the number of parameters becomes large."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each of the expectation values needed to evaluate a parameter shift gradient can be computed independently.  The CUDA-Q `nvidia-mqpu` backend is designed for parallel computations across multiple simulated QPUs. The function below uses `cudaq.observe_asynch` to distribute all of the expectation values evaluations across as many GPUs that are available. First, try it with `num_qpus` set to 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\u001b[38;2;255;000;000mwarning\u001b[0m] Target \u001b[38;2;000;000;255mnvidia-mqpu\u001b[0m: \u001b[38;2;000;000;255mThis target is deprecating. Please use the 'nvidia' target with option 'mqpu,fp32' or 'mqpu' (fp32 is the default precision option) by adding the command line option '--target-option mqpu,fp32' or passing it as cudaq.set_target('nvidia', option='mqpu,fp32') in Python. Please refer to CUDA-Q \u001b]8;;https://nvidia.github.io/cuda-quantum/latest/using/backends/platform\u001b\\documentation\u001b]8;;\u001b\\ for more information.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)\n",
-    "\n",
-    "cudaq.set_target(\"nvidia-mqpu\")\n",
-    "\n",
-    "num_qpus = cudaq.get_target().num_qpus()\n",
-    "\n",
-    "epsilon = np.pi / 4\n",
-    "\n",
-    "\n",
-    "def batched_gradient_function(kernel, parameters, hamiltonian, epsilon):\n",
-    "\n",
-    "    x = np.tile(parameters, (len(parameters), 1))\n",
-    "\n",
-    "    xplus = x + (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    xminus = x - (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    g_plus = []\n",
-    "    g_minus = []\n",
-    "    gradients = []\n",
-    "\n",
-    "    qpu_counter = 0  # Iterate over the number of GPU resources available\n",
-    "    for i in range(x.shape[0]):\n",
-    "\n",
-    "        g_plus.append(\n",
-    "            cudaq.observe_async(kernel,\n",
-    "                                hamiltonian,\n",
-    "                                qubit_count,\n",
-    "                                electron_count,\n",
-    "                                xplus[i],\n",
-    "                                qpu_id=qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1\n",
-    "\n",
-    "        g_minus.append(\n",
-    "            cudaq.observe_async(kernel,\n",
-    "                                hamiltonian,\n",
-    "                                qubit_count,\n",
-    "                                electron_count,\n",
-    "                                xminus[i],\n",
-    "                                qpu_id=qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1\n",
-    "\n",
-    "    gradients = [\n",
-    "        (g_plus[i].get().expectation() - g_minus[i].get().expectation()) /\n",
-    "        (2 * epsilon) for i in range(len(g_minus))\n",
-    "    ]\n",
-    "\n",
-    "    assert len(gradients) == len(\n",
-    "        parameters) == x.shape[0] == xplus.shape[0] == xminus.shape[0]\n",
-    "\n",
-    "    return gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The cost function needs to be slightly updated to make use of the gradient in the optimization procedure and allow for a gradient based optimizer like L-BFGS-B to be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gradient = batched_gradient_function(kernel, x0, spin_ham, epsilon)\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector: list[float], \\\n",
-    "                       gradient=gradient, hamiltonian=spin_ham, kernel=kernel):\n",
-    "\n",
-    "    get_result = lambda parameter_vector: cudaq.observe\\\n",
-    "        (kernel, hamiltonian, qubit_count, electron_count, parameter_vector).expectation()\n",
-    "\n",
-    "    cost = get_result(parameter_vector)\n",
-    "    exp_vals.append(cost)\n",
-    "    gradient_vector = batched_gradient_function(kernel, parameter_vector,\n",
-    "                                                spin_ham, epsilon)\n",
-    "\n",
-    "    return cost, gradient_vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the code below. Notice how the result is converged to a lower energy using only 10% of the steps as optimization above without a gradient. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "VQE-UCCSD energy=  -73.19471262288755\n",
-      "Total elapsed time (s) =  57.27010986900132\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "init_params = np.random.normal(0, 1, parameter_count)\n",
-    "\n",
-    "start_time = timeit.default_timer()\n",
-    "result_vqe = minimize(objective_function,\n",
-    "                      init_params,\n",
-    "                      method='L-BFGS-B',\n",
-    "                      jac=True,\n",
-    "                      tol=1e-8,\n",
-    "                      options={'maxiter': 5})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('VQE-UCCSD energy= ', result_vqe.fun)\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, run the code again (the three previous cells) and specify `num_qpus` to be more than one if you have access to multiple GPUs and notice resulting speedup.  Thanks to CUDA-Q, this code could be used without modification in a setting where multiple physical QPUs were availible."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using an Active Space"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Performing electronic structure computations with all electrons and orbitals is often prohibitively expensive and unnecessary.  Most of the interesting chemistry can be modeled by restricting simulations to the highest energy occupied molecular orbitals and lowest energy unoccupied molecular orbitals. This is known as the active space approximation. \n",
-    "\n",
-    "Below is an example of STO-3G water modeled with a 4 electron 3 orbital active space simulated with UCCSD-VQE. Using an active space means you can run VQE for the same molecule using fewer qubits and a more shallow circuit.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![cas.png](./images/cas.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The molecule is defined the same way, expect for you now include variables `nele_cas` and `norb_cas` to define the active space. The `ncore` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = [('O', (0.1173, 0.0, 0.0)), ('H', (-0.4691, 0.7570, 0.0)),\n",
-    "            ('H', (-0.4691, -0.7570, 0.0))]\n",
-    "basis = 'sto3g'\n",
-    "multiplicity = 1\n",
-    "charge = 0\n",
-    "ncore = 3\n",
-    "nele_cas, norb_cas = (4, 3)\n",
-    "\n",
-    "molecule = openfermionpyscf.run_pyscf(\n",
-    "    openfermion.MolecularData(geometry, basis, multiplicity, charge))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Hamiltonian is now constrcuted with the same steps, but only models the active space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "molecular_hamiltonian = molecule.get_molecular_hamiltonian(\n",
-    "    occupied_indices=range(ncore),\n",
-    "    active_indices=range(ncore, ncore + norb_cas))\n",
-    "\n",
-    "fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)\n",
-    "\n",
-    "qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)\n",
-    "\n",
-    "spin_ham = cudaq.SpinOperator(qubit_hamiltonian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, the kernel is defined only by the orbitals and electrons in the active space. Notice how this means you only need to optimize 8 parameters now. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8\n"
-     ]
-    }
-   ],
-   "source": [
-    "electron_count = nele_cas\n",
-    "qubit_count = 2 * norb_cas\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_num: int, electron_num: int, thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(qubit_num)\n",
-    "\n",
-    "    for i in range(electron_num):\n",
-    "        x(qubits[i])\n",
-    "\n",
-    "    cudaq.kernels.uccsd(qubits, thetas, electron_num, qubit_num)\n",
-    "\n",
-    "\n",
-    "parameter_count = cudaq.kernels.uccsd_num_parameters(electron_count,\n",
-    "                                                     qubit_count)\n",
-    "\n",
-    "print(parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, spin_ham, qubit_count, electron_count,\n",
-    "                            theta).expectation()\n",
-    "    thetas = theta\n",
-    "    return exp_val\n",
-    "\n",
-    "\n",
-    "exp_vals = []\n",
-    "\n",
-    "\n",
-    "def callback(xk):\n",
-    "    exp_vals.append(cost(xk))\n",
-    "\n",
-    "\n",
-    "# Initial variational parameters.\n",
-    "np.random.seed(42)\n",
-    "x0 = np.random.normal(0, 1, parameter_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The VQE procedure below is much faster using an active space compared to inclusion of all orbitals and electrons."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "UCCSD-VQE energy =   -74.96341992791962\n",
-      "Total number of qubits =  6\n",
-      "Total number of parameters =  8\n",
-      "Total number of terms in the spin hamiltonian =  62\n",
-      "Total elapsed time (s) =  1.754178541001238\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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OiIhIWgxLbk4hDvBmWCIiIpICw5KbU4ljltgNR0REJAWGJTcnTkrJbjgiIiJJMCy5OUc3HJ+GIyIikgbDkptTiS1L7IYjIiKSAsOSm3PM4M1uOCIiImkwLLm5qy/SZcsSERGRFBiW3JxjBm9OHUBERCQNhiU355jBm5NSEhERSYNhyc2J74bjAG8iIiJJeERY2rNnD2QyWaPLgQMHGhyfk5MDf39/aLXaFn8PvV6P6OhoyGQylJWVdVzx7eQYs8RuOCIiIml4RFhKTExEfn6+0zJnzhz069cPI0aMcDrWbDZjypQpSEpKatX3mD17NoYOHdqRZXcIlZzdcERERFLyiLCkVqsRHh4uLiEhIUhPT8fMmTMhk8mcjl26dCni4uLw8MMPt/j87777LsrKyvD73/++o0tvN4U4wJvdcERERFLwiLB0rW3btkGv12PmzJlO2zMyMrB582asXbu2xec6fvw4li9fjg8//BByufv9cYiTUrIbjoiISBJKqQtoi9TUVCQnJyM6OlrcptfrkZKSgg0bNiAgIKBF5zGZTJgyZQpWrVqF3r174+zZsy3+nMlkEteNRmPrLqAVHE/DcVJKIiIiaUjalLJ48eImB247lpMnTzp95tKlS9ixYwdmz57ttH3u3LmYOnUqxo4d2+Lvv2TJEsTHx+Oxxx5rVd0rV65EYGCguMTExLTq862h4NNwREREkpIJgiBZk0VxcTH0er3LY2JjY6FWq8X1FStWYM2aNcjLy4NKpRK3a7VaVFRUiOuCIMBms0GhUGDdunWYNWtWg3PfcMMNOHLkiDjuqf5nXnrpJfzxj39stKbGWpZiYmJgMBha3KrVUlt/voRnP/oFt/YPxYY5o5o9/thlA/7xzVk8f/dA9A317dBaiIiIuhOj0YjAwMBmf39L2g2n0+mg0+lafLwgCEhLS8P06dOdghIAZGVlwWq1iuvp6el44403kJmZiaioqEbP99///hfV1dXi+oEDBzBr1ix89913uO6665qsQ6PRQKPRtLju9rj6briWtSx9dOAiPvvlMvoE++D3yYM6szQiIqIewaPGLGVkZCA3Nxdz5sxpsC8+Pt5p/eDBg5DL5UhISBC3bd26FUuWLBG79q4NRCUlJeK5WjNHU2cSJ6Vs4QDvqlp7YNRX1nZaTURERD2J+z3+5UJqaioSExMRFxfXps8bDAZkZ2d3cFWdyzHA29zCAd4mi70FqqyKYYmIiKgjeFRY2rhxI77//vsWHZuSktJgJu6UlBS4GqJ1++23QxAEt2lVAq7O4G1tYTecyWxvWbrCsERERNQhPCos9USt7YartTpalsydVhMREVFPwrDk5hwDvFs6g7fJbD+ulGOWiIiIOgTDkptTid1wLR2zZO+GK6syu+xyJCIiopZhWHJzV98N17puuFqrTXwyjoiIiNqOYcnNqRStm2fJ0Q0HsCuOiIioIzAsuTllq7vhroYlDvImIiJqP4YlN3d1gHcLu+HqhSVOH0BERNR+DEtu7urUAS3shrNcHafEsERERNR+DEtuztENZ2lDN9wVjlkiIiJqN4YlN3d1gHdbuuE4ZomIiKi9GJbcnGPqAKtNaHbeJIvV5hSq+H44IiKi9mNYcnMq+dVb1FzrUu0145rYskRERNR+DEtuzjFmCWj+/XD151gCOMCbiIioIzAsuTlHNxwAmJuZmLJhyxLDEhERUXsxLLk5xwBvALC2tmWpkt1wRERE7cWw5OYUchlkdY1L9VuWymvMDWb1rj/HEsAB3kRERB2BYckDXJ2Y0h6OistNGPmn3Xjiw4NOxznmWPJS2W9rZa21QYAiIiKi1mFY8gCOV544WpJOFhhRbbbiSJ7B6ThHWAr108Ax1InvhyMiImofhiUP4Hgizlw3gFtfYe9eq6p1bjVytCJ5qxTQ+qgBcJA3ERFRezEseQCxG66uZamkwgQAqKq1OE1U6Zi9W62UQ+ujAsBB3kRERO3FsOQBlI5XntSNWdLXvfPNJji/C87xtUYpRzBbloiIiDoEw5IHUIktS45uOJO4r7peV9zVsMRuOCIioo7CsOQBFOKYpbqWpYqrAaiy1iJ+Xb8bLqiuG44DvImIiNqHYckDqK55Gq6k8mpYcm5Zsn+tUcoR5FvXslTJliUiIqL2YFjyAI6n4Sx1T8OVVl7thqv/RJxjBm+NSoGgum64UnbDERERtQvDkgdQ1LUsmW0Nu+HqhyXHu+HUCjmCfe3dcKVsWSIiImoXhiUPoKprWbLabKiqtTgFpKp6Y5autizJER3kAwC4oK/qwkqJiIi6H4YlD+CYZ8lsFZxalYBruuHqjVnqF+oLALhQWiVOZklEREStx7DkARyvO7FYBXGOJYfGpg5QK+UID/CCl0oOi03ApSvVXVcsERFRN8Ow5AHEAd42m9McS0DjUwdolArI5TL0DbG3LuWWVDQ4p80mNNhGREREDTEseYD6M3i3tBsOAGJ19rB0trjS6TN/3XUaw1fsxNnihiGKiIiInDEseQBlvRm8SyqdW5Yan8Hbflsd45ZyS5zD0u6ThTBUm/Hj+SudVjMREVF3wbDkAeq/SNdVy1Jtg7DkB6BhWCoy2gOXscYCIiIico1hyQOonLrh7EHH30sJ4JqpA+qNWQIab1my2QSU1J3DWM1XoRARETWHYckDKMSpA2zi03AxdfMoNTpmSVU3ZqkuLOUbasTuurJqMyx1g7uNNQxLREREzfGIsLRnzx7IZLJGlwMHDjQ4PicnB/7+/tBqtc2eu7Fz/uc//+mEq2i7MH8NAOBUYTlK6rrhYoK9ATTeDaeua4kK8lVDW/dC3XN6e+tScfnVMU/GanbDERERNccjwlJiYiLy8/Odljlz5qBfv34YMWKE07FmsxlTpkxBUlJSi8+flpbmdO7777+/g6+gfW4bpAMA7MkuFrvQegfbW5aqzY10w6mu3tZru+KKymvEfWxZIiIiap5S6gJaQq1WIzw8XFw3m81IT0/HwoULIZPJnI5dunQp4uLiMG7cOGRmZrbo/Fqt1un87mZkv2B4qxQoqtcqFFMXlipNjbxIt27MEmAPSz9fKBPDknPLEsMSERFRczyiZela27Ztg16vx8yZM522Z2RkYPPmzVi7dm2rzjd//nyEhoZi5MiReP/99yEIridsNJlMMBqNTktn0igVGNM/1GlbdJC9G666sRfpKq/eVse4JcdcS05hiU/DERERNcsjw1JqaiqSk5MRHR0tbtPr9UhJScH69esREBDQ4nMtX74cH3/8MXbu3IkHH3wQTz31FNasWePyMytXrkRgYKC4xMTEtPlaWuqOOJ34tb+XEoHeagBAVf1uOLPzpJRA/ekD7BNQFrFliYiIqFUkDUuLFy9ucuC2Yzl58qTTZy5duoQdO3Zg9uzZTtvnzp2LqVOnYuzYsa2q4eWXX8aYMWMwfPhwvPDCC/jDH/6AVatWufzMkiVLYDAYxOXixYut+p5tcfugMPHrUD8NfDX2rrbGJ6V07oYDgDPFlRAE4ZqWJYYlIiKi5kg6Zun5559HSkqKy2NiY2Od1tPS0hASEoJJkyY5bc/IyMC2bdvw1ltvAQAEQYDNZoNSqcS6deswa9asFtU0atQorFixAiaTCRqNptFjNBpNk/s6S5TWG4N6+SO7sBzBvmr4qOy3rv6YpVpLI91wOl/IZICh2gx9Za1TWKowWWCzCZDLncd9ERER0VWShiWdTgedTtf8gXUEQUBaWhqmT58OlUrltC8rKwtW69XgkJ6ejjfeeAOZmZmIiopq8fc4dOgQgoKCujwMtcTtcTpkF5ZD56eBt7quZclsFQPPta87AQAvlQLRQd64WFqNnKIKFNd7Ea8gAOUmCwK9nf8siYiI6CqPeBrOISMjA7m5uZgzZ06DffHx8U7rBw8ehFwuR0JCgrht69atWLJkidi199lnn6GwsBC33HILvLy8sHPnTrz22mv4/e9/37kX0kZzbo1FflkNZiT2gY/6aldbjcUKL6VCHOBdPywBQH+dnxiWiow1TvuM1WaGJSIiIhc8KiylpqYiMTERcXFxbfq8wWBAdna2uK5SqbB27Vo8++yzEAQB/fv3x+rVqzF37tyOKrlD6fw1eGfKcAD215Y4VNVaIa83hYL6mrA0oJc/vs4uxrHLBvEJOC+VHDVmG8ctERERNcOjwtLGjRtbfGxKSkqD8VDXbhs/fjzGjx/fQdV1LblcBm+VAtVmK6pMVvH9cYDzAG/A3rIEAD+cLQVgn+E7SuuNM8WVMPCJOCIiIpc8cuoAsnN0xVWZLeJ74WQyQKVwHrB9XZhj+gD7XEs6f43Y9cZXnhAREbnGsOTBfOqmD6iqtYqzd6sV8gazmvevC0sOOn8NAhxhid1wRERELjEseTDH9AFVJmuTg7sBINBbBZ3/1af7dP4aBHg5WpYYloiIiFxhWPJgjukDqmotV98Lp1I0eqxj3BLgaFmyBy2+8oSIiMg1hiUP5lNvriXHmCW1ovFbWr8rTufHliUiIqKW8qin4ciZj7quG67WKs7erVE1H5bCAjSoqGtR4pglIiIi1xiWPJijZanSZGn0vXD1DbimZckxLxOfhiMiInKNYcmDid1wtVYxLF07IaWDUzecvwZmq31SS7YsERERucYxSx5MHOBtrtcN10RY0vlrEKX1hkohQ58Q36sDvDlmiYiIyCW2LHkw37oxS/aWJfsA76bCkkwmw3+euAWGajOCfdXiAO9yPg1HRETkEsOSB/NudMxS042FMcE+iKn7WpyUki1LRERELrEbzoP5NNoN1/gA72sFeNlzcrnJAmu9l/ISERGRM4YlD9aabrhr+dd1wwEQpxEgIiKihhiWPFhjM3g39TTctdRKObzrZvvmE3FERERNa1NYqqys7Og6qA3Ebrh6Uwe0tGUJgPhEnIHjloiIiJrUprDUq1cvzJo1C3v37u3oeqgVvOuFJfFFuk28G64xfOUJERFR89oUljZs2IDS0lLceeedGDhwIF5//XVcvny5o2ujZjiNWTK7fjdcY8Qn4tgNR0RE1KQ2haX7778fn376KfLy8jBv3jxs3LgRffr0wa9+9Sts2bIFFgsHDHcFn/pjltrSDeflmJiy4f369Oc8rPzqBMx1LVZEREQ9VbsGeOt0Ojz33HM4fPgwVq9ejV27duGhhx5CZGQkXnnlFVRVVXVUndQIcZ6lFrxItzFNtSzZbAKWfnoU731zFlt+utRB1RIREXmmdoWlwsJCvPnmmxg8eDAWL16Mhx56CLt378af//xnbNmyBffff38HlUmN8anrhqu12FBV24ZuuCbGLJ0tqUSFyd7a9Pc9Z2Bh6xIREfVgbZrBe8uWLUhLS8OOHTswePBgPPXUU3jssceg1WrFYxITExEfH99RdVIjHN1wAFBYXgOgdQO8Q/00AIALpc4tgEfzDOLX5/VV+OJIPibfENWeUomIiDxWm1qWZs6cicjISHz//fc4dOgQFixY4BSUACAyMhIvvfRSR9RITdAo5egT4gMA+PlCmbitpYb31gIADp6/4rT9SF1Y8tfYs/TfMnJg4yzfRETUQ7UpLOXn5+O9997DzTff3OQx3t7eWLZsWZsLo+bJZDK8/cgNTl1vLX3dCWAPS3IZcOlKNfIN1eJ2R1h69u6B8PdS4nRRBb45XdxxhRMREXmQNoUli8UCo9HYYCkvL0dtbW1H10gu3Ng7CMsnXy+ut3QGb8D+ypP4iAAAwMFz9tYlm03A8ctGAEBi/xDcMSgMAHCmqKKjSiYiIvIobQpLWq0WQUFBDRatVgtvb2/06dMHy5Ytg83GgcFd4dGRvfG7uwZgaHQgRvYNbtVnb647/se6rrhcvX1wt5dKjv46P4T4qQEA+kqGYCIi6pnaNMB7/fr1eOmll5CSkoKRI0cCAPbv348PPvgAS5cuRXFxMd566y1oNBq8+OKLHVowNe53dw3E7+4a2OrPjegbhPWZ53DgXCmAq4O74yMCoFTIEexjD0ulFQxLRETUM7UpLH3wwQf485//jIcffljcdt9992HIkCF47733sHv3bvTu3Rt/+tOfGJbc3Ig+9palE/lGlNeYxbA0JCoQABDMliUiIurh2tQNl5mZieHDhzfYPnz4cGRlZQEAbr31Vly4cKF91VGnCw/0QkywN2yC/Yk6x+DuhLqwFOJrD0tXqhiWiIioZ2pTWIqJiUFqamqD7ampqYiJiQEA6PV6BAUFta866hI317Uuvf99Lo5cuqZlydc+F1MpW5aIiKiHalM33FtvvYXf/OY3+Oqrr8TpAw4ePIiTJ0/ik08+AQAcOHAAjzzySMdVSp1mRN9gbPk5D3uy7dMD+GmU6B/mBwAIrmtZ0leYJKuPiIhISm0KS5MmTUJ2djbee+89ZGdnAwAmTJiATz/9FH379gUAPPnkkx1WJHWuSTdEYn+uHmabgCitN8bFhUFVN3eTIywZaywwW23idiIiop6i1WHJbDZj/Pjx+Mc//oGVK1d2Rk3Uxfw0Svzl0YZj0ABA662CXAbYBOBKZS3CAry6uDoiIiJptbqZQKVS4fDhw51RC7khuVyGIMf0ARzkTUREPVCb+lQee+yxRgd4U/fk6IrjXEtERNQTtWnMksViwfvvv49du3bhpptugq+vr9P+1atXd0hxDnv27MEdd9zR6L79+/c3eEddTk4Ohg8fDoVCgbKysmbPv379eqxevRqnTp1CQEAAfvOb32Dt2rUdUXq3EOTLuZaIiKjnalNYOnr0KG688UYAwKlTp5z2yWSy9ld1jcTEROTn5ztte/nll7F7926MGDHCabvZbMaUKVOQlJSEzMzMZs+9evVq/PnPf8aqVaswatQoVFZW4ty5cx1ZvsdzzLXE6QOIiKgnalNY+vrrrzu6DpfUajXCw8PFdbPZjPT0dCxcuLBBOFu6dCni4uIwbty4ZsPSlStXsHTpUnz22WcYN26cuH3o0KEdewEeLphhiYiIerB2PQeek5ODHTt2oLq6GgAgCEKHFNWcbdu2Qa/XY+bMmU7bMzIysHnz5hZ3oe3cuRM2mw15eXmIj49HdHQ0Hn74YVy8eLEzyvZYbFkiIqKerE1hSa/XY9y4cRg4cCDuvfdesYts9uzZeP755zu0wMakpqYiOTkZ0dHRTjWlpKRg/fr1CAgIaNF5zp49C5vNhtdeew1/+ctf8Mknn6C0tBR33303amubDgYmkwlGo9Fp6c7YskRERD1Zm8LSs88+C5VKhQsXLsDHx0fc/sgjj2D79u0tPs/ixYshk8lcLidPnnT6zKVLl7Bjxw7Mnj3bafvcuXMxdepUjB07tsXf32azwWw245133kFycjJuueUWbNq0CadPn3bZ1bhy5UoEBgaKi+MVL93V1QHenMWbiIh6njaNWfrf//6HHTt2OLXsAMCAAQNw/vz5Fp/n+eefR0pKistjYmNjndbT0tIQEhKCSZMmOW3PyMjAtm3b8NZbbwGwdwnabDYolUqsW7cOs2bNanDuiIgIAMDgwYPFbTqdDqGhoS5fArxkyRI899xz4rrRaOzWgSmE74cjIqIerE1hqbKy0qlFyaG0tBQajabF59HpdNDpdC0+XhAEpKWlYfr06VCpVE77srKyYLVaxfX09HS88cYbyMzMRFRUVKPnGzNmDAAgOztbDH6lpaUoKSlBnz59mqxDo9G06jo93dVuOLPElRAREXW9NnXDJSUl4cMPPxTXZTIZbDYb3nzzzSbnQ+oIGRkZyM3NxZw5cxrsi4+PR0JCgrhERUVBLpcjISEBQUFBAICtW7ciLi5O/MzAgQMxefJkPPPMM8jMzMTRo0cxY8YMxMXFdep1eJoQP3tYulJVC5utawbxExERuYs2tSy9+eabGDduHA4ePIja2lr84Q9/wLFjx1BaWorvv/++o2sUpaamIjEx0SnwtIbBYBBf/Ovw4Ycf4tlnn8XEiRMhl8tx2223Yfv27Q1arnoyrY/9z8JqE2CsMUNb9/oTIiKinkAmtPF5f4PBgL/97W/45ZdfUFFRgRtvvBHz588XxwH1JEajEYGBgTAYDC1+Es/TDFm2A+UmC3Y/fxuu0/lJXQ4REVG7tfT3d5talgAgMDAQL730Uls/Th4m2E+NcpMFpZW1uK7lw8yIiIg8XpvDUllZGfbv34+ioiLYbDanfdOnT293YeRegn3VOK+v4hNxRETU47QpLH322WeYNm0aKioqEBAQ4PTKEZlMxrDUDQX7cGJKIiLqmdr0NNzzzz+PWbNmoaKiAmVlZbhy5Yq4lJaWdnSN5AYam8W7wFCDImONVCURERF1iTaFpby8PDz99NONzrVE3VNw3fQB+gp7WKoxWzHhr9/i3nf2wmK1ufooERGRR2tTWEpOTsbBgwc7uhZyY738vQAABUb7S5PP66twpcqMkgoT8g1Nty5xXiYiIvJ0bRqzNHHiRCxatAjHjx/HkCFDGsxJdO2rSMjz9Q21tyLmllQBAM7pK8V9eWXViAlu2Mp46UoVfvOPLCQNCMWbDw3rmkKJiIg6WJvC0ty5cwEAy5cvb7BPJpM5vXaEuoc+Ib4AgPP6SgiCgPP1wtKlK9WNfubtnaeRb6jBjmOFePOhLimTiIiow7UpLF07VQB1fzFBPpDLgKpaK4rLTTinrxL3XbpS1eD404Xl2PrzJQCAodqMGrMVXipFl9VLRETUUVo1Zunee++FwWAQ119//XWUlZWJ63q9HoMHD+6w4sh9qJVyRAc5uuIqcaFeWMprpGVp9c5TqD9cqZBPzRERkYdqVVjasWMHTCaTuP7aa685TRVgsVgavHuNuo8+IfawdE5f6TRm6dpuuKN5Bnx1tAAyGeCvsTdeFrgYBE5EROTOWhWWrn2NXBtfK0ceql+ofdzSqcIKXC67GpDyypzD0vajBQCACQnhiI+0v2ungC1LRETkodo0dQD1TH3rBnnvPV0CmwA4Jm6/XFYNa70+t0MXywAAY/qHIjzAPuUAu+GIiMhTtSosyWQyp1ebOLZRz+CYPiC7sBwAMDDMH0q5DBabIIYhm03AL3Vh6YYYLcIDHWHJ1PCEREREHqBVT8MJgoCUlBRoNBoAQE1NDebNmwdfX3uLQ/3xTNT9OFqWHGJ1vqg2W3GhtAp5ZdWI1HrjTHEFyk0WeKsUGNTLH70CHJNZsmWJiIg8U6vC0owZM5zWH3vssQbH8CW63Vd0kA8UcpnY5dYnxBdlVWZcKK3CpStVuLlvMH6ua1UaEh0IpUKOXgH2YF3IAd5EROShWhWW0tLSOqsO8gBqpRxRWm9cKLVPG9A3xAf6CntromP6gJ8vlAEAhsdoAeDqmKVyhiUiIvJMHOBNrdI39GpXXO8QH3HuJcf0AY7B3cN7awFA7IYrNJr49CQREXkkhiVqlb4hPvW+9kVUkDcAe1iqqrUgu8AIALghJggAEFbXDVdrseFKlbmLqyUiImo/hiVqFccgb7VSjvAAL0TXhaW8smocuWSATbB3vTmegtMoFQj2VQPgxJREROSZGJaoVfqH+QEAYkN9IZfLEKWtC0tXqrE3pwTA1S44h14ct0RERB6sTS/SpZ5rTP9QLEoehFH9ggEAEYFeUMhlqLXasCYjBwAwsm6fQ3iABify+UQcERF5JoYlahWFXIb5d/QX15UKOSK1XrhYWg2lXIZpo3rjsVv6OH3G0SXHuZaIiMgTMSxRu704IR7fnynBrDH9EKvza7A/zJ+vPCEiIs/FsETtNmFIBCYMiWhyP195QkREnowDvKnTOSam5NNwRETkiRiWqNNdnZiSYYmIiDwPwxJ1Osf74fSVtaiutUpcDRERUeswLFGnC/ZVI7Ju3NLfvj4tcTVEREStw7BEnU4mk+GV+64HAPzjm7M4mmcQ950rqcS9f/0Omw9elKo8IiIilxiWqEuMTwjHxCERsNoE/OGTwzBbbQCAf353Fsfzjfgg65y0BRIRETWBYYm6zKuTrofWR4Xj+Ub898dLMFms+OyXywCA7IJy1FpsEldIRETUEMMSdRmdvwYL6mb/fvebM/jfsUIYaywAALNVwKnCcinLIyIiahTDEnWpqaN6I8hHhfP6Kry67ZjTvvpjmYiIiNwFwxJ1KR+1ErNv7QfAPpUAAIyLCwMAHL3MsERERO7HI8LSnj17IJPJGl0OHDjQ4PicnBz4+/tDq9W6PO/69eubPG9RUVEnXQ09Prov/DX2N+0MjQ7E/cOjAABH84xSlkVERNQojwhLiYmJyM/Pd1rmzJmDfv36YcSIEU7Hms1mTJkyBUlJSc2e95FHHmlw3uTkZNx2220ICwvrrMvp8QK9VZh/p33s0qwx/ZAQFQgAOJFvhMVqw8cHLmLJliP4V9Y5nMhngCIiIml5xIt01Wo1wsPDxXWz2Yz09HQsXLgQMpnM6dilS5ciLi4O48aNQ2Zmpsvzent7w9vbW1wvLi5GRkYGUlNTO/YCqIHfjo3FIyNiEOSrhs0mwE+jRIXJgs8P5+OFLYchCFeP3TT3Foy+LkS6YomIqEfziJala23btg16vR4zZ8502p6RkYHNmzdj7dq1bTrvhx9+CB8fHzz00EMujzOZTDAajU4LtY5MJkOQrxoAIJfLMDgyAADw0tYjEARgWHQg+of5AQB2Hi+UrE4iIiKPDEupqalITk5GdHS0uE2v1yMlJQXr169HQEBAm887depUp9amxqxcuRKBgYHiEhMT06bvR1clRNq74iprrVApZHhnynD87q4BAIDMMyVSlkZERD2cpGFp8eLFTQ6wdiwnT550+sylS5ewY8cOzJ4922n73LlzMXXqVIwdO7ZNtWRlZeHEiRMNztuYJUuWwGAwiMvFi3xVR3slRF0NuI/d0gd9QnyReF0oAOBkQTmKymukKo2IiHo4mSDUHx3StYqLi6HX610eExsbC7VaLa6vWLECa9asQV5eHlQqlbhdq9WioqJCXBcEATabDQqFAuvWrcOsWbNcfp/Zs2fjp59+ws8//9zq6zAajQgMDITBYGhzq1ZPd66kEnf8eQ/8NEp8s+gOBNd10d371+9wPN+Ivz56AybfECVxlURE1J209Pe3pAO8dToddDpdi48XBAFpaWmYPn26U1AC7C1DVqtVXE9PT8cbb7yBzMxMREW5/iVbUVGBjz/+GCtXrmzdBVCH6Rvqi3/NGgWdv0YMSgBw64BQHM834vucEoYlIiKShEeNWcrIyEBubi7mzJnTYF98fDwSEhLEJSoqCnK5HAkJCQgKCgIAbN26FXFxcQ0++9FHH8FiseCxxx7r9Gugpt06IBSDwv2dto3pb++K23u6BBI2ghIRUQ/mUWEpNTUViYmJjQaeljAYDMjOzm70vA888ECzk1hS17u5bxBUChkuG2pwTl8ldTlERNQDSTpmqbvgmKXO9ch7WdiXW4rlk6/H9NF9pS6HiIi6iZb+/vaoliXqmcbF22dTX/ftWdSYrc0cTURE1LEYlsjtPXZLH4QHeOHSlWqk7s2VuhwiIuphGJbI7fmolVg8wT5Obe3XOSg0cs4lIiLqOgxL5BEm3xCJ4b21qKq14i+7TkldDhER9SAMS+QRZDIZFiUPAgB8dbQAVhufSyAioq7BsEQeY2TfYPhrlCirMuPYZYPU5RARUQ/BsEQeQ6mQI7F/CADgu9P2l+vuyS7Cu3vOoMJkkbI0IiLqxhiWyKMkDbC/HufbU8UoLjfht//6EW9sP4nxf/kWP5x1/Z5BIiKitmBYIo8yti4s/XThCv6WcRomiw0AcOlKNab88wfsY2AiIqIOxrBEHqV3iA/6hPjAbBXwQdZ5AMDbjwzDuLgwCAKwaf8FiSskIqLuhmGJPE7SgFDx6wFhfpg8LArz7+wPANh5vJCzfBMRUYdiWCKP4xi3BABP3XEd5HIZhsdoEaX1RmWtFXuyiyWsjoiIuhuGJfI4Y/qHIiLQCwlRAbhvaCQA+zxME4dGAAC+OJIvZXlERNTNKKUugKi1/DRKfPuHOyAI9ukEHCYOicC6b89i94lCVNda4a1WSFglERF1F2xZIo+kUsihVjr/9R0aHYjoIG9U1VrxdXaRRJUREVF3w7BE3Ub9rrgdxwokroaIiLoLhiXqVu4cFAbAPsO3je+PIyKiDsCwRN3KjX2C4KtWoLSyFsfzjVKXQ0RE3QDDEnUrKoUco6+zz8P0zSlOIUBERO3HsETdzm0D7WHpu9MMS0RE1H4MS9TtOCat/PH8FVSaLBJXQ0REno5hibqdvqG+6B1sf3/cD3yxLhERtRPDEnVLjvfHfduCcUs2m4DMMyW4XFbd2WUREZEHYliibum2gfauuK+OFsBkafrFul9nF+G+v+3F1H/uw9wPD3ZVeURE5EEYlqhbun1QGCICvVBUbsKWn/Ia7LfaBCxLP4qZaQdw7LJ9ioET+UaXwYqIiHomhiXqltRKOeYkxQIA/vHNGVisNnFfjdmKJzf8iA+yzkMmA2bf2g/+XkrYBCC3pFKqkomIyE0xLFG3NWVkDIJ8VDivr8KXR+2vPzFZrJj74UH873gh1Eo5/jblRrz8q8EYEOYHAMgpqpCyZCIickMMS9Rt+aiVmDmmHwBg1Y6T+Dq7CM99/Au+O10CH7UC/5o1UnyXXP+6sHS6kGGJiIicKaUugKgzzRjdFx9mncfF0mrMTDsAAFApZHjv8ZswKjZEPG5AmD8AtiwREVFDbFmibi3QR4XPF96K2bf2g49aAbkMePuRG8SJKx3692I3HBERNY4tS9TthQd64eVfDcbT4wagvMaM6CCfBsf019nD0tmSClisNigV/P8IIiKy428E6jECvVWNBiUAiNJ6w1ulgNkq4HxpVRdXRkRE7oxhiQiAXC4TB3nX74rLKarAos2/oNBYI1VpREQkMYYlojqNhaW/f52DzT9ewvt7c6Uqi4iIJOYRYWnPnj2QyWSNLgcOHGhwfE5ODvz9/aHVaps994EDBzBu3DhotVoEBQUhOTkZv/zySydcBbm7q9MHlIvbThbYvz6eb5SkJiIikp5HhKXExETk5+c7LXPmzEG/fv0wYsQIp2PNZjOmTJmCpKSkZs9bUVGB8ePHo3fv3ti3bx/27t0Lf39/JCcnw2w2d9blkJsSJ6YstrcsWW2C+LUjNBERUc/jEWFJrVYjPDxcXEJCQpCeno6ZM2dCJpM5Hbt06VLExcXh4Ycfbva8J0+eRGlpKZYvX45Bgwbh+uuvx7Jly1BYWIjz58931uWQm6rfDWezCTivr0Stxf6alOJyE0oqTFKWR0REEvGIsHStbdu2Qa/XY+bMmU7bMzIysHnzZqxdu7ZF5xk0aBBCQkKQmpqK2tpaVFdXIzU1FfHx8ejbt28nVE7urHewD7xUctSYbcgprsCpQufWpGy2LhER9UgeGZZSU1ORnJyM6OhocZter0dKSgrWr1+PgICAFp3H398fe/bswYYNG+Dt7Q0/Pz9s374dX331FZTKpqegMplMMBqNTgt5PqVCjht7BwEA9uWWIrvAeYLKExy3RETUI0kalhYvXtzkwG3HcvLkSafPXLp0CTt27MDs2bOdts+dOxdTp07F2LFjW/z9q6urMXv2bIwZMwY//PADvv/+eyQkJGDixImorq5u8nMrV65EYGCguMTExLTuwsltjepnfwXK/txSnCqytyQFeqsAcNwSEVFPJRMEQZDqmxcXF0Ov17s8JjY2Fmq1WlxfsWIF1qxZg7y8PKhUKnG7VqtFRcXVlgBBEGCz2aBQKLBu3TrMmjWrwblTU1Px4osvIj8/H3K5PTfW1tYiKCgIqampePTRRxutyWQywWS6On7FaDQiJiYGBoOhxa1a5J5+OKvHo+t+QJi/BgHeKuQUVWDKyBhs2n8RCVEB+Hxh8w8OEBGRZzAajQgMDGz297ekrzvR6XTQ6XTNH1hHEASkpaVh+vTpTkEJALKysmC1WsX19PR0vPHGG8jMzERUVFSj56uqqoJcLncaJO5Yt9lsTdah0Wig0WhaXDd5jhtitFAr5CgqN6Go3B6IJw2Lwqb9F3GqkK9CISLqiTzqp35GRgZyc3MxZ86cBvvi4+ORkJAgLlFRUZDL5UhISEBQkH0cytatWxEXFyd+5u6778aVK1cwf/58nDhxAseOHcPMmTOhVCpxxx13dNl1kfvwUilwQ4xWXPfXKDGyXzB81ArUWmw4p6+UrjgiIpKER4Wl1NRUJCYmOgWe1jAYDMjOzhbX4+Li8Nlnn+Hw4cMYPXo0kpKScPnyZWzfvh0REREdVTZ5mJH9gsWvB/Tyg0Iuw8Be/gCAE/kct0RE1NNIOmapu2hpnyd5hu9OF+Px1P0AgEdvjsHrDw7Fki2HsWn/Rcy/4zosSm5bWCciIvfS0t/fHtWyRNQVbuwdBIXcPo7N0aIUF27/R3Tw3BXJ6iIiImkwLBFdw1ejxJj+oZDJrnbJ3RkXBpnMPv/SBX2VxBUSEVFXYlgiasQ7j96ALxYmISEqEAAQE+yDW/uHAgA+OnhBytKIiKiLMSwRNULro8bgSOf+6ykjewMANh+8BIu16akliIioe2FYImqhu+J7IcRXjaJyE77OLpa6HCIi6iIMS0QtpFbK8eBN9vcR/vl/2fj88GVUmCwSV0VERJ2NYYmoFaaM7A0vlRwnC8qxYOPP+NU738FYY5a6LCIi6kQMS0St0C/UF9sW3Irf3haLIB8Vzumr8OXhfKnLIiKiTsSwRNRKA3v5Y8mEeDwx9joAwNaf8ySuiIiIOhPDElEbTb4hEoB97qW8smqJqyEios7CsETURpFab9wSa5+0Mv0QW5eIiLorhiWidnhguP3puK0/5YGvWSQi6p4YlojaYfyQcKiVcpwuqsCxy0apyyEiok7AsETUDgFeKtwxSAcA+PpkkcTVEBFRZ2BYImqnpAH2sLQ3p0TiSoiIqDMwLBG1U9IA+wt2f7pwBVW1nNGbiKi7YVgiaqfewT6IDvKG2SpgX26p1OUQEVEHY1giaieZTIZb+9tbl74/za44IqLuhmGJqAPcWtcV19y4pY8PXsRdq7/BuZLKriiLiIg6AMMSUQdIvM4elk4WlKO43NTkcf/+4Txyiirw358udVVpRETUTgxLRB0g2FeN6yMDAADfN9G6ZLUJyC4sBwAcOMexTUREnoJhiaiDjB1on0KgqVajC6VVqDHbAACHLpah1mIT9wmCgKWfHsFrX57o/EKJiKhVGJaIOsjUkb2hlMvw3ekS/HzhSoP9J/OvzvBdY7bh2GWDuH4834gNP1zAum/PoqyqtkvqJSKilmFYIuogMcE+uH94FABg7dc5AIDichMM1WYA9vFM9R08dzVQ7a33FN3F0urOLpWIiFqBYYmoAz11+3WQyYBdJ4rwxIcHMeq1Xfj137+H1SbgZIG9ZSky0AuA87il+k/RXbxS1bVFExGRSwxLRB0oVueHXw2NBAD873ghbAJwtrgSB86VIruuZWnKyN4AgIPnr0AQBNSYrdhfbzLLC6UMS0RE7oRhiaiDPXf3QMTqfHFXfNjVQd8/XsL5uhD00IhoaJRylFbW4mxJJX48fwWmeoO9LzIsERG5FaXUBRB1N/1CfZHx/O0AgK9PFuHbU8XY8nMeBAEI9VMjItAbw2K02J9bigO5pTint4cjb5UC1WYrLl7hmCUiInfCliWiTpTYPwT+XkpYbQIAIC7cPhfTLf2CAQCrdmTjs18uAwDuGxYBALjEliUiIrfCsETUiTRKBe6O7yWuDwr3BwDMHNMPCVEB0FfWIq/M3pL0aN1YpktXqmGrC1dERCQ9hiWiTjZhSIT4dVxdWAryVePj347GXXVB6vrIAAyNCoRCLkOt1YbC8hpJaiUiooY4ZomokyUNCIW/RolykwUJUYHidh+1Eu89fhO+PJKPhKhAKBVyRGm9caG0ChdLqxER6C1h1URE5MCwRNTJvFQK/HPGCFwsrUJ8RIDTPoVchvuGRYrrMcGOsFSFkXXjmoiISFoMS0Rd4JbYENwSG9LscTFBPgD04lxLgiBAJpN1cnVEROQKxywRuZGYYB8A9lm83/vmDOJf2Y7tR/MlroqIqGfziLC0Z88eyGSyRpcDBw40OD4nJwf+/v7QarXNnnv37t1ITEyEv78/wsPD8cILL8BisXTCVRA1zxGWfrlYhrd3nUKN2YbnP/4FZ4orJK6MiKjn8oiwlJiYiPz8fKdlzpw56NevH0aMGOF0rNlsxpQpU5CUlNTseX/55Rfce++9GD9+PH7++Wd89NFH2LZtGxYvXtxZl0LkUu+6sHSmuBI1Zvus3pW1Vjy14SdU11qlLI2IqMfyiLCkVqsRHh4uLiEhIUhPT8fMmTMbjOdYunQp4uLi8PDDDzd73o8++ghDhw7FK6+8gv79++O2227Dm2++ibVr16K8vLzZzxN1tJgg5yfg3pkyHDp/DbILyzFr/QEUcUoBIqIu5xFh6Vrbtm2DXq/HzJkznbZnZGRg8+bNWLt2bYvOYzKZ4OXl5bTN29sbNTU1+PHHH11+zmg0Oi1EHSHYVw0ftQKAfcqBScMi8bcpw+GjViDrrB73/nUv9p3VS1wlEVHP4pFhKTU1FcnJyYiOjha36fV6pKSkYP369QgICHDx6auSk5ORmZmJTZs2wWq1Ii8vD8uXLwcA5Oc3Pah25cqVCAwMFJeYmJj2XRBRHZlMhlH9guGlkuOF8XEAgFGxIdi24FYM6uWPkgoTfrvhRxiqzRJXSkTUc0galhYvXtzkwG3HcvLkSafPXLp0CTt27MDs2bOdts+dOxdTp07F2LFjW/z977nnHqxatQrz5s2DRqPBwIEDce+99wIA5PKm/2iWLFkCg8EgLhcvXmzFVRO59u5jN+G7P9zpNIFl/zA/pC8Yg/5hfiirMuMf35yRsEIiop5FJgiCZC+hKi4uhl7vukshNjYWarVaXF+xYgXWrFmDvLw8qFQqcbtWq0VFxdUnhgRBgM1mg0KhwLp16zBr1qwmv4cgCMjPz0dQUBDOnTuHwYMHY//+/bj55ptbdB1GoxGBgYEwGAwtbtUiaoudxwsx98OD0Cjl2LPodtSYbSguN3ECSyKiNmjp729JJ6XU6XTQ6XQtPl4QBKSlpWH69OlOQQkAsrKyYLVefVooPT0db7zxBjIzMxEVFeXyvDKZDJGR9lmUN23ahJiYGNx4442tuBKirnFXfBhu7huEA+eu4NdrM1FgtA/4XvnAEEypexEvERF1LI8as5SRkYHc3FzMmTOnwb74+HgkJCSIS1RUFORyORISEhAUFAQA2Lp1K+Li4pw+t2rVKhw5cgTHjh3DihUr8Prrr+Odd96BQqHokmsiag2ZTIbFE+IBQAxKAPDqtmM4VcgnOImIOoNHhaXU1FQkJiY2CDwtZTAYkJ2d7bTtq6++QlJSEkaMGIEvvvgC6enpuP/++zugWqLOcVOfILz54FA8M24Avl10B8YO1MFksWHBRs7FRETUGSQds9RdcMwSSam43IQJf/0OJRUm9A72wYv3xkOlkGFfbim0PircmxCBvqG+UpdJROR2Wvr7m2GpAzAskdQOnivF/I0/odBoanT/rf1D8d7jN8FXw3dnExE5tPT3t0d1wxFR40b0Dcbu52/HvNuug69agb4hPpgyMgZJA0KhkMuwN6cESz89Cv6/ERFR67FlqQOwZYnciSAITq8B2p9biin//AFWm4A3HxyKh2/mJKpERABbloh6rGvflziyXzCeu3sgAOCVbUdxNM8gRVlERB6LYYmoB3jytutw20Adasw2pKTtx7mSSqlLIiLyGAxLRD2AXC7D36YOx/WRASipqMXj7+/DvrN6mK02fH74Mn615js8nroPZ4srmj8ZEVEPwzFLHYBjlshTFJeb8NA/MnFeXwUAUCvlqLXYxP1qpRy/v2cg5ibFNujOIyLqbjhmiYga0PlrsHHuLXjwxmgEeqtQa7HB30uJp+/sj6QBoai12PDalyexYd8FqUslInIbbFnqAGxZIk9kttpwqrAcMcE+CPBSQRAErMnIweqdp6BWyLHlqUREBHrh2GUjhsVoEeitav6kREQehJNSdiGGJeouBEHA3A8PYteJIgR4KVFVa4XFJiDUT42lEwdj8g2RLe6es9oEvJx+FKcLy9E3xBc39wvGQzdGQy5n9x4RuQeGpS7EsETdSVlVLSa+sxd5ZdUAgAAvJYw1FgDA0OhATB/dF9fpfHEkz4CSilqE+Kqh9VFBEOwDyccOCIXWR42/7DqFv+w67XTu5+4eiKfHDejyayIiagzDUhdiWKLu5ry+EjuPF+L2QTr0DvbFum/PYE1GDkz1BoM3JdRPjamj+mBNxmkIArDgjv6oMFmwPvMc5DLgP0+Mxsh+wV1wFURErjEsdSGGJeoJSipM+OjARXx88CIqTRYkRAUiUuuN0opalFXXQiGX4dKVavFJOwCYMjIGKx8YCgB47uND2PJTHsIDvLAoeRCigrwRpfVGeKAXVAo+a0JEXY9hqQsxLBHZmSxWvLP7NP7xzVlcHxmAj387Gl4qBQCg0mTBr9bsRe41E2LKZUD/MD+M7BeMEF8NcooqUFxhQrCPGoHeKhhrzCirMmNQuD/uHRKBm/oEQdHKcU/XvgKGiAhgWOpSDEtEzsqqauGjVkKtdG4xulhahXe/OYPz+krkXanG5bIa1Fqb79qrT+evwfjrwzE4MgCXrlRBX2H/Xv5eSsTqfDGwlz/6h/lBpZAjp6gCL249gnMllXj7kRswpn9oR14mEXk4hqUuxLBE1DY2m4CichMOXSzDvlw9Kk0WDAjzR69ALxiqalFWZUagjwo+aiWyzuix83iBONjcFV+1AsNitDh4/oo46aZSLsOrk67H1JG9GzyRV1ZVCxlkCPTh9AhEPQnDUhdiWCLqGrUWGzLPlOCrIwUoMNYgJtgbYf5eqDZbcaWyFjlFFcguKEe56Wqgum2gDv5eSnx+OB+A/em+G3oHwVetgNkq4HRROc7rqyCTAcOitbglNgRh/hoEeKtgttpQa7FB569B72Af9A/zE7sVicjzMSx1IYYlIvdhswk4WVCOA+dKEan1xl3xYQCAv+85g7Vf56Cq1trmc3urFBjTPxT9w/xQVF6DIqMJhcYaVJgsuH1QGB6/pQ90/hoUl5tgtQnwVitwpaoWhy6UodBYgxF9g5HYPwRVJisuXanCpSvVyCurRq8AL/xqaIQYxKw2odXjsoio9RiWuhDDEpFnMFttOJFvxJE8A2w2AXK5DDFBPhgWrUW12YpvThXh2GUjSitrYayxQK2QQymXoai8Buf0VSitrO202oJ8VBjRNxjHLxtx2VCNfqG+SIi0P3EY4qtGiJ8awb5q+GqUsNkElFTU4vClMlwqq8at/UNx75AIp1nWzxZXoMZsQ1y4PycCJWoCw1IXYlgi6v4EQcCJ/HJknCxESUUtwgI06OXvhV4BXrAKAj4+cBHbjxXAJggI8VVDKZejqtYCL5UCN8RoEeqvQWZOCc7pq6CQyxAR6IXoIG9EBHpjf26pOAloW6mVcgzq5Y/ewT44UWDE2WL7U4ehfmqM6hdiH/ulUkCpkEMhBxRyexBUKeTwUsmhUSpc/tdLpUCgjwoBXq7HdQmCfRyaTRAQHuDFpxDJrTEsdSGGJSICgBqzFUq5DEoX80ZdqayFv5fS6RirTcDuE4U4r6/C9ZEB6B3ig9NFFTiRb0RxuQmllbUoraxFSUUtasxWyGWAn0aJhKhAhPppsP1oAbILy52+j0ohg1ohR2U7uh0b4++lRICXClW1FtSYbfBRK+CjUcBXrYS3WoGLpVUoqbC3wGl9VLhO5wd/LyV81ArYbIBVEKBWyuGjUsBiE1BeN2Bf66OC1lsFrY8KAd4qeKkU0CjlsFgFVJvt16BRyiEAqKixoNZqg59GiQBvFQK8lPD3UsFqE1BZa4FSLkOAlwoKuQzGGjOqTFYo5DLI5TIo5TIo6i2OdXldqHP8RhQgOK07CIJ939Xj7AHR1S/SxuLitSGy8WOuPU/Do1qSRRs75tpzNXpMWz/XgvNce1RLzhMd5NPgCdv2YljqQgxLRCQlQRCQW1KJ00UVuKCvQq9AL9wxSAeNUoGD50pxPN+ISpMVVWYLrFYBFpsAq83+X7PVhhqzFSZL3X/NNpgsVtSYbaix2NdrLFbUmO3bWkIht/9Ktdj464U6TsbztyFW59eh52zp729lh35XIiLqcjKZDLE6v0Z/kST2D0ViB80vVWmyIN9QDWONBX4aJTRKOarNVlSarKg0WVBVa0FYgBfiwwMglwOnCytwobQKFSYLqmutkMtlkMvsTzVW1VqhVsjh56WEIACGajPKqmthqDLDUG0Ww5lKKYdXXWuCY04ufy8VVAoZKk0WGKstMFSbUW4yQymXw1ulgE0QYKw2w2wTEOClhJ9GCZtgb8Gzh0Sb/WtBEMOjTRAAyMQWDkerxtV1mbgug3PLkEzm2N6weeTaNqfGWqqa01ibxrVbGpy3kbauhse0pJZm6m/sE9cc1Pgxrf+MXMIuXYYlIiJqEV+NEv3D/Ft8fEJUIBKiAjuxIqKuwRcyEREREbnAsERERETkAsMSERERkQsMS0REREQuMCwRERERucCwREREROQCwxIRERGRCwxLRERERC4wLBERERG5wLBERERE5IJHhKU9e/ZAJpM1uhw4cAAAcO7cuUb3//DDDy7PfeHCBUycOBE+Pj4ICwvDokWLYLFYuuKyiIiIyAN4xLvhEhMTkZ+f77Tt5Zdfxu7duzFixAin7bt27cL1118vroeEhDR5XqvViokTJyI8PByZmZnIz8/H9OnToVKp8Nprr3XsRRAREZFH8oiwpFarER4eLq6bzWakp6dj4cKFTm9+BuzhqP6xrvzvf//D8ePHsWvXLvTq1Qs33HADVqxYgRdeeAGvvvoq1Gp1h14HEREReR6P6Ia71rZt26DX6zFz5swG+yZNmoSwsDDceuut2LZtm8vzZGVlYciQIejVq5e4LTk5GUajEceOHevwuomIiMjzeETL0rVSU1ORnJyM6OhocZufnx/+/Oc/Y8yYMZDL5fjvf/+L+++/H59++ikmTZrU6HkKCgqcghIAcb2goKDJ728ymWAymcR1g8EAADAajW2+JiIiIupajt/bgiC4PlCQ0AsvvCAAcLmcOHHC6TMXL14U5HK58MknnzR7/scff1y49dZbm9w/d+5c4Z577nHaVllZKQAQvvzyyyY/t2zZsmbr5sKFCxcuXLh4xnLx4kWXeULSlqXnn38eKSkpLo+JjY11Wk9LS0NISEiTrUX1jRo1Cjt37mxyf3h4OPbv3++0rbCwUNzXlCVLluC5554T1202G0pLSxESEtJgDFV7GI1GxMTE4OLFiwgICOiw87qD7nxtQPe+Pl6bZ+rO1wZ07+vjtXUeQRBQXl6OyMhIl8dJGpZ0Oh10Ol2LjxcEAWlpaeITa805dOgQIiIimtw/evRo/OlPf0JRURHCwsIAADt37kRAQAAGDx7c5Oc0Gg00Go3TNq1W27KLaIOAgIBu9w/EoTtfG9C9r4/X5pm687UB3fv6eG2dIzAwsNljPGrMUkZGBnJzczFnzpwG+z744AOo1WoMHz4cALBlyxa8//77+L//+z/xmK1bt2LJkiU4efIkAOCee+7B4MGD8fjjj+PNN99EQUEBli5divnz5zcIQ0RERNQzeVRYSk1NRWJiIuLi4hrdv2LFCpw/fx5KpRJxcXH46KOP8NBDD4n7DQYDsrOzxXWFQoHPP/8cTz75JEaPHg1fX1/MmDEDy5cv7/RrISIiIs/gUWFp48aNTe6bMWMGZsyY4fLzKSkpDcZI9enTB19++WVHlNfhNBoNli1b1i1bubrztQHd+/p4bZ6pO18b0L2vj9cmPZkgNPe8HBEREVHP5ZGTUhIRERF1FYYlIiIiIhcYloiIiIhcYFgiIiIicoFhyY2tXbsWffv2hZeXF0aNGtVgtnFPsHLlStx8883w9/dHWFgY7r//fqfpGwDg9ttvh0wmc1rmzZsnUcUt9+qrrzaou/60FjU1NZg/fz5CQkLg5+eHBx98UJwh3t317du3wbXJZDLMnz8fgOfds2+//Rb33XcfIiMjIZPJ8OmnnzrtFwQBr7zyCiIiIuDt7Y277roLp0+fdjqmtLQU06ZNQ0BAALRaLWbPno2KioouvIrGubo2s9mMF154AUOGDIGvry8iIyMxffp0XL582ekcjd3v119/vYuvpKHm7ltKSkqDusePH+90jCfeNwCN/vuTyWRYtWqVeIy73reW/Nxvyc/HCxcuYOLEifDx8UFYWBgWLVoEi8XSlZciYlhyUx999BGee+45LFu2DD/99BOGDRuG5ORkFBUVSV1aq3zzzTeYP38+fvjhB+zcuRNmsxn33HMPKisrnY6bO3cu8vPzxeXNN9+UqOLWuf76653q3rt3r7jv2WefxWeffYbNmzfjm2++weXLl/HAAw9IWG3LHThwwOm6HK8N+s1vfiMe40n3rLKyEsOGDcPatWsb3f/mm2/inXfewT/+8Q/s27cPvr6+SE5ORk1NjXjMtGnTcOzYMezcuROff/45vv32WzzxxBNddQlNcnVtVVVV+Omnn/Dyyy/jp59+wpYtW5Cdnd3o66KWL1/udD8XLlzYFeW71Nx9A4Dx48c71b1p0yan/Z543wA4XVN+fj7ef/99yGQyPPjgg07HueN9a8nP/eZ+PlqtVkycOBG1tbXIzMzEBx98gPXr1+OVV16R4pIg6Yt0qWkjR44U5s+fL65brVYhMjJSWLlypYRVtV9RUZEAQPjmm2/EbbfddpvwzDPPSFdUGy1btkwYNmxYo/vKysoElUolbN68Wdx24sQJAYCQlZXVRRV2nGeeeUa47rrrBJvNJgiC594zQRAEAMLWrVvFdZvNJoSHhwurVq0St5WVlQkajUbYtGmTIAiCcPz4cQGAcODAAfGYr776SpDJZEJeXl6X1d6ca6+tMfv37xcACOfPnxe39enTR3j77bc7t7h2auzaZsyYIUyePLnJz3Sn+zZ58mThzjvvdNrmCfdNEBr+3G/Jz8cvv/xSkMvlQkFBgXjMu+++KwQEBAgmk6lrL0AQBLYsuaHa2lr8+OOPuOuuu8Rtcrkcd911F7KysiSsrP0MBgMAIDg42Gn7v//9b4SGhiIhIQFLlixBVVWVFOW12unTpxEZGYnY2FhMmzYNFy5cAAD8+OOPMJvNTvcwLi4OvXv39rh7WFtbiw0bNmDWrFlOL4r21Ht2rdzcXBQUFDjdq8DAQIwaNUq8V1lZWdBqtRgxYoR4zF133QW5XI59+/Z1ec3tYTAYIJPJGrzP8vXXX0dISAiGDx+OVatWSdbd0Vp79uxBWFgYBg0ahCeffBJ6vV7c113uW2FhIb744gvMnj27wT5PuG/X/txvyc/HrKwsDBkyBL169RKPSU5OhtFoxLFjx7qwejuPmsG7pygpKYHVanX6SwIAvXr1Et9r54lsNht+97vfYcyYMUhISBC3T506FX369EFkZCQOHz6MF154AdnZ2diyZYuE1TZv1KhRWL9+PQYNGoT8/Hz88Y9/RFJSEo4ePYqCggKo1eoGv5B69eqFgoICaQpuo08//RRlZWVOs9976j1rjON+NPbvzbGvoKBAfNm2g1KpRHBwsEfdz5qaGrzwwguYMmWK00tLn376adx4440IDg5GZmYmlixZgvz8fKxevVrCaps3fvx4PPDAA+jXrx/OnDmDF198ERMmTEBWVhYUCkW3uW8ffPAB/P39G3Tje8J9a+znfkt+PhYUFDT6b9Kxr6sxLFGXmT9/Po4ePeo0rgeA0/iBIUOGICIiAuPGjcOZM2dw3XXXdXWZLTZhwgTx66FDh2LUqFHo06cPPv74Y3h7e0tYWcdKTU3FhAkTEBkZKW7z1HvWk5nNZjz88MMQBAHvvvuu077nnntO/Hro0KFQq9X47W9/i5UrV7r1aygeffRR8eshQ4Zg6NChuO6667Bnzx6MGzdOwso61vvvv49p06bBy8vLabsn3Lemfu57GnbDuaHQ0FAoFIoGTwYUFhYiPDxcoqraZ8GCBfj888/x9ddfIzo62uWxo0aNAgDk5OR0RWkdRqvVYuDAgcjJyUF4eDhqa2tRVlbmdIyn3cPz589j165dmDNnjsvjPPWeARDvh6t/b+Hh4Q0errBYLCgtLfWI++kISufPn8fOnTudWpUaM2rUKFgsFpw7d65rCuwgsbGxCA0NFf8eevp9A4DvvvsO2dnZzf4bBNzvvjX1c78lPx/Dw8Mb/Tfp2NfVGJbckFqtxk033YTdu3eL22w2G3bv3o3Ro0dLWFnrCYKABQsWYOvWrcjIyEC/fv2a/cyhQ4cAABEREZ1cXceqqKjAmTNnEBERgZtuugkqlcrpHmZnZ+PChQsedQ/T0tIQFhaGiRMnujzOU+8ZAPTr1w/h4eFO98poNGLfvn3ivRo9ejTKysrw448/isdkZGTAZrOJQdFdOYLS6dOnsWvXLoSEhDT7mUOHDkEulzfownJ3ly5dgl6vF/8eevJ9c0hNTcVNN92EYcOGNXusu9y35n7ut+Tn4+jRo3HkyBGnsOsI+oMHD+6aC6mvy4eUU4v85z//ETQajbB+/Xrh+PHjwhNPPCFotVqnJwM8wZNPPikEBgYKe/bsEfLz88WlqqpKEARByMnJEZYvXy4cPHhQyM3NFdLT04XY2Fhh7NixElfevOeff17Ys2ePkJubK3z//ffCXXfdJYSGhgpFRUWCIAjCvHnzhN69ewsZGRnCwYMHhdGjRwujR4+WuOqWs1qtQu/evYUXXnjBabsn3rPy8nLh559/Fn7++WcBgLB69Wrh559/Fp8Ie/311wWtViukp6cLhw8fFiZPniz069dPqK6uFs8xfvx4Yfjw4cK+ffuEvXv3CgMGDBCmTJki1SWJXF1bbW2tMGnSJCE6Olo4dOiQ079BxxNFmZmZwttvvy0cOnRIOHPmjLBhwwZBp9MJ06dPl/jKXF9beXm58Pvf/17IysoScnNzhV27dgk33nijMGDAAKGmpkY8hyfeNweDwSD4+PgI7777boPPu/N9a+7nviA0//PRYrEICQkJwj333CMcOnRI2L59u6DT6YQlS5ZIcUkCw5IbW7NmjdC7d29BrVYLI0eOFH744QepS2o1AI0uaWlpgiAIwoULF4SxY8cKwcHBgkajEfr37y8sWrRIMBgM0hbeAo888ogQEREhqNVqISoqSnjkkUeEnJwccX91dbXw1FNPCUFBQYKPj4/w61//WsjPz5ew4tbZsWOHAEDIzs522u6J9+zrr79u9O/hjBkzBEGwTx/w8ssvC7169RI0Go0wbty4Btet1+uFKVOmCH5+fkJAQIAwc+ZMoby8XIKrcebq2nJzc5v8N/j1118LgiAIP/74ozBq1CghMDBQ8PLyEuLj44XXXnvNKXBIxdW1VVVVCffcc4+g0+kElUol9OnTR5g7d26D/6H0xPvm8N577wne3t5CWVlZg8+7831r7ue+ILTs5+O5c+eECRMmCN7e3kJoaKjw/PPPC2azuYuvxk4mCILQSY1WRERERB6PY5aIiIiIXGBYIiIiInKBYYmIiIjIBYYlIiIiIhcYloiIiIhcYFgiIiIicoFhiYiIiMgFhiUiog4gk8nw6aefSl0GEXUChiUi8ngpKSmQyWQNlvHjx0tdGhF1A0qpCyAi6gjjx49HWlqa0zaNRiNRNUTUnbBliYi6BY1Gg/DwcKclKCgIgL2L7N1338WECRPg7e2N2NhYfPLJJ06fP3LkCO688054e3sjJCQETzzxBCoqKpyOef/993H99ddDo9EgIiICCxYscNpfUlKCX//61/Dx8cGAAQOwbds2cd+VK1cwbdo06HQ6eHt7Y8CAAQ3CHRG5J4YlIuoRXn75ZTz44IP45ZdfMG3aNDz66KM4ceIEAKCyshLJyckICgrCgQMHsHnzZuzatcspDL377ruYP38+nnjiCRw5cgTbtm1D//79nb7HH//4Rzz88MM4fPgw7r33XkybNg2lpaXi9z9+/Di++uornDhxAu+++y5CQ0O77g+AiNpOktf3EhF1oBkzZggKhULw9fV1Wv70pz8JgmB/C/q8efOcPjNq1CjhySefFARBENatWycEBQUJFRUV4v4vvvhCkMvl4lvsIyMjhZdeeqnJGgAIS5cuFdcrKioEAMJXX30lCIIg3HfffcLMmTM75oKJqEtxzBIRdQt33HEH3n33XadtwcHB4tejR4922jd69GgcOnQIAHDixAkMGzYMvr6+4v4xY8bAZrMhOzsbMpkMly9fxrhx41zWMHToUPFrX19fBAQEoKioCADw5JNP4sEHH8RPP/2Ee+65B/fffz8SExPbdK1E1LUYloioW/D19W3QLdZRvL29W3ScSqVyWpfJZLDZbACACRMm4Pz58/jyyy+xc+dOjBs3DvPnz8dbb73V4fUSUcfimCUi6hF++OGHBuvx8fEAgPj4ePzyyy+orKwU93///feQy+UYNGgQ/P390bdvX+zevbtdNeh0OsyYMQMbNmzAX/7yF6xbt65d5yOirsGWJSLqFkwmEwoKCpy2KZVKcRD15s2bMWLECNx6663497//jf379yM1NRUAMG3aNCxbtgwzZszAq6++iuLiYixcuBCPP/44evXqBQB49dVXMW/ePISFhWHChAkoLy/H999/j4ULF7aovldeeQU33XQTrr/+ephMJnz++ediWCMi98awRETdwvbt2xEREeG0bdCgQTh58iQA+5Nq//nPf/DUU08hIiICmzZtwuDBgwEAPj4+2LFjB5555hncfPPN8PHxwYMPPojVq1eL55oxYwZqamrw9ttv4/e//z1CQ0Px0EMPtbg+tVqNJUuW4Ny5c/D29kZSUhL+85//dMCVE1FnkwmCIEhdBBFRZ5LJZNi6dSvuv/9+qUshIg/EMUtERERELjAsEREREbnAMUtE1O1xtAERtQdbloiIiIhcYFgiIiIicoFhiYiIiMgFhiUiIiIiFxiWiIiIiFxgWCIiIiJygWGJiIiIyAWGJSIiIiIXGJaIiIiIXPh/kqbqlMUxo1YAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.set_target(\"nvidia\")\n",
-    "\n",
-    "start_time = timeit.default_timer()\n",
-    "result = minimize(cost,\n",
-    "                  x0,\n",
-    "                  method='COBYLA',\n",
-    "                  callback=callback,\n",
-    "                  options={'maxiter': 500})\n",
-    "end_time = timeit.default_timer()\n",
-    "\n",
-    "print('UCCSD-VQE energy =  ', result.fun)\n",
-    "print('Total number of qubits = ', qubit_count)\n",
-    "print('Total number of parameters = ', parameter_count)\n",
-    "print('Total number of terms in the spin hamiltonian = ',\n",
-    "      spin_ham.get_term_count())\n",
-    "print('Total elapsed time (s) = ', end_time - start_time)\n",
-    "\n",
-    "plt.plot(exp_vals)\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Energy')\n",
-    "plt.title('VQE')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gate Fusion for Larger Circuits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CUDA-Q simulations take advantage of a technique called gate fusion. Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. \n",
-    "\n",
-    "![gate-fuse.png](./images/gate-fuse.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is particularly important for larger circuits and can have a significant impact on the performance of the simulation. Each system is different, so you should test different gate fusion levels to find out what is best for your system. You can find more information [here](https://developer.nvidia.com/blog/new-nvidia-cuda-q-features-boost-quantum-application-performance/)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/sphinx/conf.py b/pr-2308/sphinx/conf.py
deleted file mode 100644
index 94357aa479..0000000000
--- a/pr-2308/sphinx/conf.py
+++ /dev/null
@@ -1,219 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-
-import os
-import sphinx_rtd_theme
-
-# -- Project information -----------------------------------------------------
-
-project = 'NVIDIA CUDA-Q'
-copyright = '2024, NVIDIA Corporation & Affiliates'
-author = 'NVIDIA Corporation & Affiliates'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| used in various places throughout the docs.
-
-# The short X.Y version.
-version = os.getenv("CUDA_QUANTUM_VERSION", "latest")
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    # 'sphinx.ext.imgmath',
-    'sphinx.ext.ifconfig',
-    'sphinx.ext.autodoc',  # to get documentation from python doc comments
-    'sphinx.ext.autosummary',
-    'sphinx.ext.autosectionlabel',
-    'sphinx.ext.doctest',  # test example codes in docs
-    'sphinx.ext.extlinks',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.napoleon',  # support google/numpy style docstrings
-    #'sphinx.ext.linkcode',
-    'sphinx_reredirects',
-    'breathe',
-    'enum_tools.autoenum',  # for pretty-print Python enums
-    'myst_parser',  # for including markdown files
-    'sphinx_inline_tabs',  # showing code blocks in multiple languages
-    'nbsphinx',  # for supporting jupyter notebooks
-    'sphinx_copybutton',  # allows for copy/paste of code cells
-    "sphinx_gallery.load_style",
-    "IPython.sphinxext.ipython_console_highlighting",
-]
-
-nbsphinx_allow_errors = False
-nbsphinx_thumbnails = {
-    # Default thumbnail if the notebook does not define a cell tag to specify the thumbnail.
-    # See also: https://nbsphinx.readthedocs.io/en/latest/subdir/gallery.html
-    '**': '_static/cuda_quantum_icon.svg',
-    'examples/python/tutorials/hybrid_qnns': '_images/hybrid.png',
-    'examples/python/tutorials/multi_gpu_workflows': '_images/circsplit.png',
-}
-
-imgmath_latex_preamble = r'\usepackage{braket}'
-
-imgmath_image_format = 'svg'
-imgmath_font_size = 14
-#imgmath_dvipng_args = ['-gamma', '1.5', '-D', '110', '-bg', 'Transparent']
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-source_suffix = {
-    '.rst': 'restructuredtext',
-    '.md': 'markdown',
-}
-
-# The master toctree document.
-master_doc = 'index'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = [
-    '**/_*', '.DS_Store', 'examples/python/building_kernels.ipynb'
-]
-
-# Generate OpenAPI spec for the REST API
-import ruamel.yaml
-import json
-
-current_path = os.path.dirname(os.path.realpath(__file__))
-# YAML file defines the spec
-yaml_file = current_path + '/api/rest/.openapi.yaml'
-yaml = ruamel.yaml.YAML(typ='safe')
-with open(yaml_file) as fpi:
-    data = yaml.load(fpi)
-json_spec = json.dumps(data, indent=2)
-
-# Auto-generate the HTML file with the embedded OpenAPI spec.
-html_template_file = current_path + '/_templates/openapi.html'
-with open(html_template_file, 'r') as file:
-    filedata = file.read()
-# Replace the placeholder with the spec
-filedata = filedata.replace("##SPEC_JSON##", json_spec)
-html_output_file = current_path + '/Output/openapi.html'
-os.makedirs(os.path.dirname(html_output_file), exist_ok=True)
-# Write the file out again
-with open(html_output_file, 'w') as file:
-    file.write(filedata)
-# Add this to `html_extra_path` so that it is copied to the deployed docs.
-html_extra_path = ['Output/openapi.html']
-
-# The reST default role (used for this markup: `text`) to use for all documents.
-default_role = 'code'  # NOTE: the following may be a better choice to error on the side of flagging anything that is referenced but but not declared
-#default_role = 'cpp:any' # see https://www.sphinx-doc.org/en/master/usage/restructuredtext/domains.html#cross-referencing
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'lightbulb'
-
-# autosummary is buggy: this must be py instead of cpp so that the domain setting
-# can be propagated to the autogen'd rst files.
-# primary_domain = 'py'
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'sphinx_rtd_theme'
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-html_theme_options = {
-    "collapse_navigation": False,
-    "sticky_navigation": False,
-    "prev_next_buttons_location": "both",
-    "style_nav_header_background":
-        "#76b900"  # Set upper left search bar to NVIDIA green
-}
-
-html_css_files = ['_static/cudaq_override.css']
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'cudaqDoc'
-
-
-def setup(app):
-    app.add_css_file('cudaq_override.css')
-
-
-# -- Options for BREATHE -------------------------------------------------
-
-breathe_projects = {"cudaq": "_doxygen/xml"}
-
-breathe_default_project = "cudaq"
-
-breathe_show_enumvalue_initializer = True
-
-# -- Other options -------------------------------------------------
-
-autosummary_generate = True
-
-# This is unfortunately not sufficient for docs generation, due to the use of Union type;
-# see also https://github.com/sphinx-doc/sphinx/issues/11211. 
-# autodoc_mock_imports = ['cuquantum', 'cupy']
-
-intersphinx_mapping = {
-    'python': ('https://docs.python.org/3/', None),
-    'numpy': ('https://numpy.org/doc/stable/', None),
-}
-
-redirects = {"versions": "../latest/releases.html"}
-
-nitpick_ignore = [
-    ('cpp:identifier', 'GlobalRegisterName'),
-    ('cpp:identifier', 'CountsDictionary::iterator'),
-    ('cpp:identifier', 'CountsDictionary::const_iterator'),
-    ('cpp:identifier', 'State'),
-    ('cpp:identifier', 'pauli'),
-    ('cpp:identifier', 'Job'),
-    ('cpp:identifier', 'mlir'),
-    ('cpp:identifier', 'mlir::Value'),
-    ('cpp:identifier', 'mlir::Type'),
-    ('cpp:identifier', 'mlir::MLIRContext'),
-    ('cpp:identifier', 'mlir::ImplicitLocOpBuilder'),
-    ('cpp:identifier', 'BinarySymplecticForm'),
-    ('cpp:identifier', 'CountsDictionary'),
-    ('cpp:identifier', 'QuakeValueOrNumericType'),
-    ('cpp:identifier', 'cudaq::ctrl'),
-    ('py:class', 'function'),
-    ('py:class', 'type'),
-    ('py:class', 'cudaq::spin_op'),
-    ('py:class', 'numpy.ndarray[]'),
-]
-
-napoleon_google_docstring = True
-napoleon_numpy_docstring = False
-autosectionlabel_prefix_document = True
-autosectionlabel_maxdepth = 2
-copybutton_copy_empty_lines = False
diff --git a/pr-2308/sphinx/examples/cpp/basics/bernstein_vazirani.cpp b/pr-2308/sphinx/examples/cpp/basics/bernstein_vazirani.cpp
deleted file mode 100644
index a27a270b70..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/bernstein_vazirani.cpp
+++ /dev/null
@@ -1,79 +0,0 @@
-#include <cudaq.h>
-#include <iostream>
-
-// If you have a NVIDIA GPU you can use this example to see
-// that the GPU-accelerated backends can easily handle a
-// larger number of qubits compared the CPU-only backend.
-
-// Depending on the available memory on your GPU, you can
-// set the number of qubits to around 30 qubits, and set the
-// `--target=nvidia` from the command line.
-
-// Note: Without setting the target to the `nvidia` backend,
-// there will be a noticeable decrease in simulation performance.
-// This is because the CPU-only backend has difficulty handling
-// 30+ qubit simulations.
-
-std::vector<int> random_bitstring(int qubit_count) {
-  std::vector<int> vector_of_bits;
-  for (auto i = 0; i < qubit_count; i++) {
-    // Populate our vector of bits with random binary
-    // values (base 2).
-    vector_of_bits.push_back(rand() % 2);
-  }
-  return vector_of_bits;
-}
-
-__qpu__ void oracle(cudaq::qview<> qvector, cudaq::qubit &auxillary_qubit,
-                    std::vector<int> &hidden_bitstring) {
-  for (auto i = 0; i < hidden_bitstring.size(); i++) {
-    if (hidden_bitstring[i] == 1)
-      // Apply a `cx` gate with the current qubit as
-      // the control and the auxillary qubit as the target.
-      x<cudaq::ctrl>(qvector[i], auxillary_qubit);
-  }
-}
-
-__qpu__ void bernstein_vazirani(std::vector<int> &hidden_bitstring) {
-  // Allocate the specified number of qubits - this
-  // corresponds to the length of the hidden bitstring.
-  cudaq::qvector qvector(hidden_bitstring.size());
-  // Allocate an extra auxillary qubit.
-  cudaq::qubit auxillary_qubit;
-
-  // Prepare the auxillary qubit.
-  h(auxillary_qubit);
-  z(auxillary_qubit);
-
-  // Place the rest of the qubits in a superposition state.
-  h(qvector);
-
-  // Query the oracle.
-  oracle(qvector, auxillary_qubit, hidden_bitstring);
-
-  // Apply another set of Hadamards to the qubits.
-  h(qvector);
-
-  // Apply measurement gates to just the `qubits`
-  // (excludes the auxillary qubit).
-  mz(qvector);
-}
-
-int main() {
-  // Note: this algorithm will require an additional ancillary qubit.
-  auto qubit_count = 5; // set to around 30 qubits for `nvidia` target
-
-  // Generate a bitstring to encode and recover with our algorithm.
-  auto hidden_bitstring = random_bitstring(5);
-
-  auto result = cudaq::sample(bernstein_vazirani, hidden_bitstring);
-  result.dump();
-
-  std::cout << "Encoded bitstring = ";
-  for (auto bit : hidden_bitstring)
-    std::cout << bit;
-  std::cout << "\n";
-  std::cout << "Measured bitstring = " << result.most_probable() << "\n";
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/cuquantum_backends.cpp b/pr-2308/sphinx/examples/cpp/basics/cuquantum_backends.cpp
deleted file mode 100644
index 4d0c87ca49..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/cuquantum_backends.cpp
+++ /dev/null
@@ -1,43 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ cuquantum_backends.cpp -o dyn.x --target nvidia && ./dyn.x
-// ```
-
-// This example is meant to demonstrate the cuQuantum
-// GPU-accelerated backends and their ability to easily handle
-// a larger number of qubits compared the CPU-only backend.
-
-// On CPU-only backends, this seems to hang, i.e., it takes a long
-// time to handle this number of qubits.
-
-#include <cudaq.h>
-
-// Define a quantum kernel with a runtime parameter
-struct ghz {
-  auto operator()(const int N) __qpu__ {
-
-    // Dynamically sized vector of qubits
-    cudaq::qvector q(N);
-    h(q[0]);
-    for (int i = 0; i < N - 1; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    mz(q);
-  }
-};
-
-int main() {
-  auto shots_count = 1024 * 1024;
-  auto counts = cudaq::sample(shots_count, ghz{}, 28);
-
-  if (!cudaq::mpi::is_initialized() || cudaq::mpi::rank() == 0) {
-    counts.dump();
-
-    // Fine grain access to the bits and counts
-    for (auto &[bits, count] : counts) {
-      printf("Observed: %s, %lu\n", bits.data(), count);
-    }
-  }
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/cutensornet_backends.cpp b/pr-2308/sphinx/examples/cpp/basics/cutensornet_backends.cpp
deleted file mode 100644
index 9c3add9a72..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/cutensornet_backends.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-// Compile with:
-// ```
-// nvq++ cutensornet_backends.cpp -o dyn.x --target tensornet
-// ```
-//
-// This example is meant to demonstrate the `cuTensorNet`
-// multi-node/multi-GPU backend.
-// On a multi-GPU system, we can enable distributed parallelization across MPI
-// processes by initializing MPI (see code) and launch the compiled executable
-// with MPI.
-// ```
-// mpirun -np <N> ./dyn.x
-// ```
-
-#include <cudaq.h>
-
-// Define a quantum kernel with a runtime parameter
-struct ghz {
-  auto operator()(const int N) __qpu__ {
-
-    // Dynamically sized vector of qubits
-    cudaq::qvector q(N);
-    h(q[0]);
-    for (int i = 0; i < N - 1; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    mz(q);
-  }
-};
-
-int main() {
-  // Initialize MPI to enable `cuTensorNet` distributed parallelization (see
-  // Simulation Backends/Tensor Network Simulators section of the documentation
-  // for more info)
-  // ```
-  // cudaq::mpi::initialize();
-  // if (cudaq::mpi::rank() == 0)
-  //   printf("Number of MPI processes: %d\n", cudaq::mpi::num_ranks());
-  // ```
-
-  auto counts = cudaq::sample(100, ghz{}, 28);
-  counts.dump();
-
-  // Fine grain access to the bits and counts
-  for (auto &[bits, count] : counts) {
-    printf("Observed: %s, %lu\n", bits.data(), count);
-  }
-
-  // Finalize MPI if initialized
-  // ```
-  // cudaq::mpi::finalize();
-  // ```
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/expectation_values.cpp b/pr-2308/sphinx/examples/cpp/basics/expectation_values.cpp
deleted file mode 100644
index 41eea1f156..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/expectation_values.cpp
+++ /dev/null
@@ -1,33 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ expectation_values.cpp -o d2.x && ./d2.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-
-// The example here shows a simple use case for the `cudaq::observe`
-// function in computing expected values of provided spin_ops.
-
-struct ansatz {
-  auto operator()(double theta) __qpu__ {
-    cudaq::qvector q(2);
-    x(q[0]);
-    ry(theta, q[1]);
-    x<cudaq::ctrl>(q[1], q[0]);
-  }
-};
-
-int main() {
-
-  // Build up your spin op algebraically
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-
-  // Observe takes the kernel, the spin_op, and the concrete
-  // parameters for the kernel
-  double energy = cudaq::observe(ansatz{}, h, .59);
-  printf("Energy is %lf\n", energy);
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/mid_circuit_measurement.cpp b/pr-2308/sphinx/examples/cpp/basics/mid_circuit_measurement.cpp
deleted file mode 100644
index 7224fadccd..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/mid_circuit_measurement.cpp
+++ /dev/null
@@ -1,49 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ mid_circuit_measurement.cpp -o teleport.x && ./teleport.x
-// ```
-
-#include <cudaq.h>
-
-struct kernel {
-  void operator()() __qpu__ {
-    cudaq::qarray<3> q;
-    // Initial state preparation
-    x(q[0]);
-
-    // Create Bell pair
-    h(q[1]);
-    x<cudaq::ctrl>(q[1], q[2]);
-
-    x<cudaq::ctrl>(q[0], q[1]);
-    h(q[0]);
-
-    auto b0 = mz(q[0]);
-    auto b1 = mz(q[1]);
-
-    if (b1)
-      x(q[2]);
-    if (b0)
-      z(q[2]);
-
-    mz(q[2]);
-  }
-};
-
-int main() {
-
-  int nShots = 100;
-  // Sample
-  auto counts = cudaq::sample(/*shots*/ nShots, kernel{});
-  counts.dump();
-
-  // Get the marginal counts on the second qubit
-  auto resultsOnZero = counts.get_marginal({0});
-  resultsOnZero.dump();
-
-  // Count the "1"
-  auto nOnes = resultsOnZero.count("1");
-
-  // Will fail if not equal to number of shots
-  assert(nOnes == nShots && "Failure to teleport qubit in |1> state.");
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/multi_controlled_operations.cpp b/pr-2308/sphinx/examples/cpp/basics/multi_controlled_operations.cpp
deleted file mode 100644
index 6e820659eb..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/multi_controlled_operations.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ multi_controlled_operations.cpp -o ccnot.x && ./ccnot.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-
-// Here we demonstrate how one might apply multi-controlled
-// operations on a general CUDA-Q kernel.
-struct ApplyX {
-  void operator()(cudaq::qubit &q) __qpu__ { x(q); }
-};
-
-struct ccnot_test {
-  // constrain the signature of the incoming kernel
-  void operator()(cudaq::takes_qubit auto &&apply_x) __qpu__ {
-    cudaq::qvector qs(3);
-
-    x(qs);
-    x(qs[1]);
-
-    // Control U (apply_x) on the first two qubits of
-    // the allocated register.
-    cudaq::control(apply_x, qs.front(2), qs[2]);
-
-    mz(qs);
-  }
-};
-
-int main() {
-  // We can achieve the same thing as above via
-  // a lambda expression.
-  auto ccnot = []() __qpu__ {
-    cudaq::qvector q(3);
-
-    x(q);
-    x(q[1]);
-
-    x<cudaq::ctrl>(q[0], q[1], q[2]);
-
-    mz(q);
-  };
-
-  auto counts = cudaq::sample(ccnot);
-
-  // Fine grain access to the bits and counts
-  for (auto &[bits, count] : counts) {
-    printf("Observed: %s, %lu\n", bits.data(), count);
-  }
-
-  auto counts2 = cudaq::sample(ccnot_test{}, ApplyX{});
-
-  // Fine grain access to the bits and counts
-  for (auto &[bits, count] : counts2) {
-    printf("Observed: %s, %lu\n", bits.data(), count);
-  }
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/noise_amplitude_damping.cpp b/pr-2308/sphinx/examples/cpp/basics/noise_amplitude_damping.cpp
deleted file mode 100644
index 35c2bfffaf..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/noise_amplitude_damping.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ noise_amplitude_damping.cpp --target density-matrix-cpu -o dyn.x
-// && ./dyn.x
-// ```
-//
-// Note: You must set the target to a density matrix backend for the noise
-// to successfully impact the system.
-
-#include <cudaq.h>
-
-// CUDA-Q supports several different models of noise. In this case,
-// we will examine the modeling of energy dissipation within our system
-// via environmental interactions. The result of this "amplitude damping"
-// is to return the qubit to the |0> state with a user-specified probability.
-
-int main() {
-
-  // We will begin by defining an empty noise model that we will add
-  // our damping channel to.
-  cudaq::noise_model noise;
-
-  // We define an amplitude damping channel setting the probability of
-  // the qubit decaying to the ground state to `1.0`.
-  cudaq::amplitude_damping_channel ad(1.);
-
-  // We will apply this channel to any Hadamard gate on the qubit.
-  // In other words, after each Hadamard on the qubit, there will be a
-  // probability of `1.0` that the qubit decays back to ground.
-  noise.add_channel<cudaq::types::h>({0}, ad);
-
-  // The Hadamard gate here will bring the qubit to `1/sqrt(2) (|0> + |1>)`,
-  // where it will remain with a probability of `1 - p = 0.0`.
-  auto kernel = []() __qpu__ {
-    cudaq::qubit q;
-    h(q);
-    mz(q);
-  };
-
-  // Now let's set the noise and we're ready to run the simulation!
-  cudaq::set_noise(noise);
-
-  // Our results should show all measurements in the |0> state, indicating
-  // that the noise has successfully impacted the system.
-  auto noisy_counts = cudaq::sample(kernel);
-  noisy_counts.dump();
-
-  // To confirm this, we can run the simulation again without noise.
-  // The qubit will now have a 50/50 mix of measurements between
-  // |0> and |1>.
-  cudaq::unset_noise();
-  auto noiseless_counts = cudaq::sample(kernel);
-  noiseless_counts.dump();
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/noise_bit_flip.cpp b/pr-2308/sphinx/examples/cpp/basics/noise_bit_flip.cpp
deleted file mode 100644
index aa598b09f5..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/noise_bit_flip.cpp
+++ /dev/null
@@ -1,51 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ noise_bit_flip.cpp --target density-matrix-cpu -o dyn.x
-// && ./dyn.x
-// ```
-//
-// Note: You must set the target to a density matrix backend for the noise
-// to successfully impact the system.
-
-#include <cudaq.h>
-
-// CUDA-Q supports several different models of noise. In this case,
-// we will examine the modeling of decoherence of the qubit state. This
-// will occur from "bit flip" errors, wherein the qubit has a user-specified
-// probability of undergoing an X-180 rotation.
-
-int main() {
-
-  // We will begin by defining an empty noise model that we will add
-  // these decoherence channels to.
-  cudaq::noise_model noise;
-
-  // We define a bit-flip channel setting the probability of the
-  // qubit flipping 180 degrees about the X axis to `1.0`.
-  cudaq::bit_flip_channel bf(1.);
-  // We will apply this channel to any X gate on the qubit, giving each X-gate
-  // a probability of `1.0` of undergoing an extra X-gate.
-  noise.add_channel<cudaq::types::x>({0}, bf);
-
-  // After the X-gate, the qubit will remain in the |1> state with a probability
-  // of `1 - p = 0.0`.
-  auto kernel = []() __qpu__ {
-    cudaq::qubit q;
-    x(q);
-    mz(q);
-  };
-
-  // Now let's set the noise and we're ready to run the simulation!
-  cudaq::set_noise(noise);
-
-  // Our results should show all measurements in the |0> state, indicating
-  // that the noise has successfully impacted the system.
-  auto noisy_counts = cudaq::sample(kernel);
-  noisy_counts.dump();
-
-  // To confirm this, we can run the simulation again without noise.
-  // We should now see the qubit in the |1> state.
-  cudaq::unset_noise();
-  auto noiseless_counts = cudaq::sample(kernel);
-  noiseless_counts.dump();
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/noise_callback.cpp b/pr-2308/sphinx/examples/cpp/basics/noise_callback.cpp
deleted file mode 100644
index 9c8a9724dd..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/noise_callback.cpp
+++ /dev/null
@@ -1,67 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ noise_callback.cpp --target density-matrix-cpu -o dyn.x
-// && ./dyn.x
-// ```
-//
-// Note: You must set the target to a density matrix backend for the noise
-// to successfully impact the system.
-
-#include <cudaq.h>
-#include <iostream>
-
-// CUDA-Q supports several different models of noise. In this
-// case, we will examine the dynamic noise channel specified as a callback
-// function.
-
-int main() {
-
-  // We will begin by defining an empty noise model that we will add
-  // our channel to.
-  cudaq::noise_model noise;
-  //  Noise model callback function
-  const auto rx_noise = [](const auto &qubits,
-                           const auto &params) -> cudaq::kraus_channel {
-    // Model a pulse-length based rotation gate:
-    // the bigger the angle, the longer the pulse, i.e., more amplitude damping.
-    auto angle = params[0];
-    // Normalize the angle into the [0, 2*pi] range
-    while (angle > 2. * M_PI)
-      angle -= 2. * M_PI;
-
-    while (angle < 0)
-      angle += 2. * M_PI;
-    // Damping rate is linearly proportional to the angle
-    const auto damping_rate = angle / (2. * M_PI);
-    std::cout << "Angle = " << params[0]
-              << ", amplitude damping rate = " << damping_rate << "\n";
-    return cudaq::amplitude_damping_channel(damping_rate);
-  };
-
-  // Bind the noise model callback function to the `rx` gate
-  noise.add_channel<cudaq::types::rx>(rx_noise);
-
-  auto kernel = [](double angle) __qpu__ {
-    cudaq::qubit q;
-    rx(angle, q);
-    mz(q);
-  };
-
-  // Now let's set the noise and we're ready to run the simulation!
-  cudaq::set_noise(noise);
-
-  // Our results should show measurements in both the |0> and |1> states,
-  // indicating that the noise has successfully impacted the system. Note: a
-  // `rx(pi)` is equivalent to a Pauli X gate, and thus, it should be in the |1>
-  // state if no noise is present.
-  auto noisy_counts = cudaq::sample(kernel, M_PI);
-  std::cout << "Noisy result:\n";
-  noisy_counts.dump();
-
-  // To confirm this, we can run the simulation again without noise.
-  cudaq::unset_noise();
-  auto noiseless_counts = cudaq::sample(kernel, M_PI);
-  std::cout << "Noiseless result:\n";
-  noiseless_counts.dump();
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/noise_depolarization.cpp b/pr-2308/sphinx/examples/cpp/basics/noise_depolarization.cpp
deleted file mode 100644
index a6471c6d2e..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/noise_depolarization.cpp
+++ /dev/null
@@ -1,53 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ noise_depolarization.cpp --target density-matrix-cpu -o dyn.x
-// && ./dyn.x
-// ```
-//
-// Note: You must set the target to a density matrix backend for the noise
-// to successfully impact the system.
-
-#include <cudaq.h>
-
-// CUDA-Q supports several different models of noise. In this
-// case, we will examine the modeling of depolarization noise. This
-// depolarization will result in the qubit state decaying into a mix
-// of the basis states, |0> and |1>, with a user-provided probability.
-
-int main() {
-
-  // We will begin by defining an empty noise model that we will add
-  // our depolarization channel to.
-  cudaq::noise_model noise;
-
-  // We define a depolarization channel setting the probability of the
-  // qubit state being scrambled to `1.0`.
-  cudaq::depolarization_channel depolarization(1.);
-  // We will apply the channel to any Y-gate on qubit 0. In other words,
-  // for each Y-gate on our qubit, the qubit will have a `1.0`
-  // probability of decaying into a mixed state.
-  noise.add_channel<cudaq::types::y>({0}, depolarization);
-
-  // Our kernel will apply a Y-gate to qubit 0.
-  // This will bring the qubit to the |1> state, where it will remain
-  // with a probability of `1 - p = 0.0`.
-  auto kernel = []() __qpu__ {
-    cudaq::qubit q;
-    y(q);
-    mz(q);
-  };
-
-  // Now let's set the noise and we're ready to run the simulation!
-  cudaq::set_noise(noise);
-
-  // With noise, the measurements should be a roughly 50/50
-  // mix between the |0> and |1> states.
-  auto noisy_counts = cudaq::sample(kernel);
-  noisy_counts.dump();
-
-  // To confirm this, we can run the simulation again without noise.
-  // Without noise, the qubit should still be in the |1> state.
-  cudaq::unset_noise();
-  auto noiseless_counts = cudaq::sample(kernel);
-  noiseless_counts.dump();
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/noise_modeling.cpp b/pr-2308/sphinx/examples/cpp/basics/noise_modeling.cpp
deleted file mode 100644
index eb61cb9512..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/noise_modeling.cpp
+++ /dev/null
@@ -1,56 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ noise_modeling.cpp --target density-matrix-cpu -o noise.x && ./noise.x
-// ```
-
-#include "cudaq.h"
-
-int main() {
-  // Define a  kernel
-  auto xgate = []() __qpu__ {
-    cudaq::qubit q;
-    x(q);
-    mz(q);
-  };
-
-  // Run noise-less simulation
-  auto counts = cudaq::sample(xgate);
-  counts.dump();
-
-  // Create a depolarizing Kraus channel made up of two Kraus operators.
-  cudaq::kraus_channel depol({cudaq::complex{0.99498743710662, 0.0},
-                              {0.0, 0.0},
-                              {0.0, 0.0},
-                              {0.99498743710662, 0.0}},
-
-                             {cudaq::complex{0.0, 0.0},
-                              {0.05773502691896258, 0.0},
-                              {0.05773502691896258, 0.0},
-                              {0.0, 0.0}},
-
-                             {cudaq::complex{0.0, 0.0},
-                              {0.0, -0.05773502691896258},
-                              {0.0, 0.05773502691896258},
-                              {0.0, 0.0}},
-
-                             {cudaq::complex{0.05773502691896258, 0.0},
-                              {0.0, 0.0},
-                              {0.0, 0.0},
-                              {-0.05773502691896258, 0.0}});
-
-  // Create the noise model
-  cudaq::noise_model noise;
-  // Add the Kraus channel to the x operation on qubit 0.
-  noise.add_channel<cudaq::types::x>({0}, depol);
-
-  // Set the noise model
-  cudaq::set_noise(noise);
-
-  // Run the noisy simulation
-  counts = cudaq::sample(xgate);
-  counts.dump();
-
-  // Unset the noise model when done. This is not necessary in this case, but it
-  // is good practice in order to avoid interference with future simulations.
-  cudaq::unset_noise();
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/noise_phase_flip.cpp b/pr-2308/sphinx/examples/cpp/basics/noise_phase_flip.cpp
deleted file mode 100644
index 3926644d4e..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/noise_phase_flip.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ noise_phase_flip.cpp --target density-matrix-cpu -o dyn.x
-// && ./dyn.x
-// ```
-//
-// Note: You must set the target to a density matrix backend for the noise
-// to successfully impact the system.
-
-#include <cudaq.h>
-
-// CUDA-Q supports several different models of noise. In this
-// case, we will examine the modeling of decoherence of the qubit phase.
-// This will occur from "phase flip" errors, wherein the qubit has a
-// user-specified probability of undergoing a Z-180 rotation.
-
-int main() {
-
-  // We will begin by defining an empty noise model that we will add
-  // our phase flip channel to.
-  cudaq::noise_model noise;
-
-  // We define a phase-flip channel setting the probability of the
-  // qubit undergoing a phase rotation of 180 degrees (π) to `1.0`.
-  cudaq::phase_flip_channel pf(1.);
-  // We will apply this channel to any Z gate on the qubit.
-  // In other words, after each Z gate on qubit 0, there will be a
-  // probability of `1.0` that the qubit undergoes an extra
-  // Z rotation.
-  noise.add_channel<cudaq::types::z>({0}, pf);
-
-  auto kernel = []() __qpu__ {
-    cudaq::qubit q;
-    // Place qubit in superposition state.
-    h(q);
-    // Rotate on Z by 180 degrees.
-    z(q);
-    // Apply another Hadamard.
-    h(q);
-    mz(q);
-  };
-
-  // Now let's set the noise and we're ready to run the simulation!
-  cudaq::set_noise(noise);
-
-  // With noise, our Z-gate will effectively cancel out due
-  // to the presence of a phase-flip error on the gate with a
-  // probability of `1.0`. This will put us back in the |0> state.
-  auto noisy_counts = cudaq::sample(kernel);
-  noisy_counts.dump();
-
-  // To confirm this, we can run the simulation again without noise.
-  // Without noise, we'd expect the qubit to end in the |1> state due
-  // to the phase rotation between the two Hadamard gates.
-  cudaq::unset_noise();
-  auto noiseless_counts = cudaq::sample(kernel);
-  noiseless_counts.dump();
-}
diff --git a/pr-2308/sphinx/examples/cpp/basics/static_kernel.cpp b/pr-2308/sphinx/examples/cpp/basics/static_kernel.cpp
deleted file mode 100644
index c8743414e6..0000000000
--- a/pr-2308/sphinx/examples/cpp/basics/static_kernel.cpp
+++ /dev/null
@@ -1,40 +0,0 @@
-
-// Compile and run with:
-// ```
-// nvq++ static_kernel.cpp -o ghz.x && ./ghz.x
-// ```
-
-#include <cudaq.h>
-
-// Define a CUDA-Q kernel that is fully specified
-// at compile time via templates.
-template <std::size_t N>
-struct ghz {
-  auto operator()() __qpu__ {
-
-    // Compile-time sized array like std::array
-    cudaq::qarray<N> q;
-    h(q[0]);
-    for (int i = 0; i < N - 1; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    mz(q);
-  }
-};
-
-int main() {
-
-  auto kernel = ghz<10>{};
-  auto counts = cudaq::sample(kernel);
-
-  if (!cudaq::mpi::is_initialized() || cudaq::mpi::rank() == 0) {
-    counts.dump();
-
-    // Fine grain access to the bits and counts
-    for (auto &[bits, count] : counts) {
-      printf("Observed: %s, %lu\n", bits.data(), count);
-    }
-  }
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/builder/builder.cpp b/pr-2308/sphinx/examples/cpp/other/builder/builder.cpp
deleted file mode 100644
index bdcb3c4c21..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/builder/builder.cpp
+++ /dev/null
@@ -1,175 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ builder.cpp -o builder.x && ./builder.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/builder.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-
-static bool results_are_close(const cudaq::sample_result &f1,
-                              const cudaq::sample_result &f2) {
-  // Stub for a fancy comparison.
-  f1.dump();
-  f2.dump();
-  return true;
-}
-
-// This example demonstrates various uses for the `cudaq::builder`
-// type. This type enables one to dynamically construct callable
-// CUDA-Q kernels via just-in-time compilation. The typical workflow
-// starts by creating a `cudaq::builder` and any CUDA-Q kernel runtime
-// arguments via the `cudaq::make_kernel<ParameterTypes...>()` function.
-// Programmers get reference to the builder and the concrete runtime
-// parameters for the kernel function, and can then begin building up
-// the CUDA-Q kernel. Once done adding gates, the builder itself is
-// callable, and can be used just like any other CUDA-Q kernel.
-
-int main() {
-  {
-
-    // Create a Hamiltonian as a `cudaq::spin_op`.
-    using namespace cudaq::spin;
-    cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                       .21829 * z(0) - 6.125 * z(1);
-
-    // Build a quantum kernel dynamically
-    // Start by creating the Builder, the kernel argument types
-    // should be provided here as template parameters.
-    auto [ansatz, theta] = cudaq::make_kernel<double>();
-
-    // Allocate some qubits
-    auto q = ansatz.qalloc(2);
-
-    // Build up the circuit, use the acquired parameter
-    ansatz.x(q[0]);
-    ansatz.ry(theta, q[1]);
-    ansatz.x<cudaq::ctrl>(q[1], q[0]); // Need to get rid of ::
-
-    // The buildable kernel can be passed to CUDA-Q functions
-    // just like a declared kernel type.
-    ansatz(.59);
-    double exp = cudaq::observe(ansatz, h, .59);
-    printf("<H2> = %lf\n", exp);
-  }
-
-  {
-    // Build up a 2 parameter circuit using a vector<double> parameter
-    // Run the CUDA-Q optimizer to find optimal value.
-    using namespace cudaq::spin;
-    cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                       .21829 * z(0) - 6.125 * z(1);
-    cudaq::spin_op h3 = h + 9.625 - 9.625 * z(2) - 3.913119 * x(1) * x(2) -
-                        3.913119 * y(1) * y(2);
-
-    // Create the kernel with signature void(std::vector<double>)
-    auto [ansatz, thetas] = cudaq::make_kernel<std::vector<double>>();
-
-    // Allocate some qubits
-    auto q = ansatz.qalloc(3);
-
-    // Build the kernel
-    ansatz.x(q[0]);
-    ansatz.ry(thetas[0], q[1]);
-    ansatz.ry(thetas[1], q[2]);
-    ansatz.x<cudaq::ctrl>(q[2], q[0]);
-    ansatz.x<cudaq::ctrl>(q[0], q[1]);
-    // Can do fancy arithmetic with Parameter types.
-    ansatz.ry(-thetas[0], q[1]);
-    // -or- `ansatz_builder.ry(-1.0 * thetas[0], q[1]);`
-    // -or- `ansatz_builder.ry(thetas[0] * -1.0, q[1]);`
-    // -or- `ansatz_builder.ry(-1 * thetas[0], q[1]);`
-    ansatz.x<cudaq::ctrl>(q[0], q[1]);
-    ansatz.x<cudaq::ctrl>(q[1], q[0]);
-
-    // You now have a callable kernel, use it with the VQE algorithm
-    cudaq::gradients::central_difference gradient(ansatz);
-    cudaq::optimizers::lbfgs optimizer;
-    auto [opt_val_0, optpp] = cudaq::vqe(ansatz, gradient, h3, optimizer, 2);
-    printf("<H3> = %lf\n", opt_val_0);
-  }
-
-  {
-    // Make a kernel for sampling, here the GHZ state on 8 qubits
-    int n_qubits = 8;
-    auto ghz_builder = cudaq::make_kernel();
-
-    // Allocate the qubits
-    auto q = ghz_builder.qalloc(n_qubits);
-
-    // Build the GHZ state
-    ghz_builder.h(q[0]);
-    for (int i = 0; i < n_qubits - 1; i++) {
-      ghz_builder.x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    ghz_builder.mz(q);
-
-    // You can get the MLIR representation
-    auto mlir_code = ghz_builder.to_quake();
-
-    // Sample and get the counts
-    auto counts = cudaq::sample(ghz_builder);
-    counts.dump();
-  }
-
-  {
-    // In a simulated environment, it is sometimes useful to be able to
-    // specify an initial state vector. The initial state vector is 2 to the
-    // power `n` where `n` is the number of qubits.
-
-    // In this example, we create a kernel template `sim_kernel` that captures
-    // the variable `init_state` by reference.
-    auto sim_builder = cudaq::make_kernel();
-    std::vector<cudaq::complex> init_state;
-    auto qubits = sim_builder.qalloc(init_state);
-    // Build the quantum circuit template here.
-    sim_builder.mz(qubits);
-
-    // Now we are ready to instantiate the kernel and invoke it. So we can set
-    // the `init_state` to a vector with 2 complex values (1 qubit) and
-    // get the results.
-    init_state = {{0.0, 1.0}, {1.0, 0.0}};
-    auto counts0 = cudaq::sample(sim_builder);
-
-    // Now suppose we have a different initial state with 4 complex values (2
-    // qubits). Let's rerun the kernel with the new `init_state`.
-    init_state = {{1.0, 0.0}, {0.0, 1.0}, {0.0, 1.0}, {1.0, 0.0}};
-    auto counts1 = cudaq::sample(sim_builder);
-
-    // Finally in this wholly contrived example, we test the results to make
-    // sure they are "close".
-    if (results_are_close(counts0, counts1)) {
-      printf("The two initial states generated results that are \"close\".\n");
-    }
-  }
-
-  {
-
-    // Let's do a final sampling task. Let's
-    // build up an element of the Toffoli truth table.
-    auto ccnot_builder = cudaq::make_kernel();
-    auto q = ccnot_builder.qalloc(3);
-    ccnot_builder.x(q);
-    ccnot_builder.x(q[1]);
-    ccnot_builder.x<cudaq::ctrl>(q[0], q[1], q[2]);
-    ccnot_builder.mz(q);
-
-    auto mlir_code = ccnot_builder.to_quake();
-    printf("%s\n", mlir_code.c_str());
-
-    auto counts = cudaq::sample(ccnot_builder);
-    counts.dump();
-  }
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/builder/qaoa_maxcut_builder.cpp b/pr-2308/sphinx/examples/cpp/other/builder/qaoa_maxcut_builder.cpp
deleted file mode 100644
index 3815310d2f..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/builder/qaoa_maxcut_builder.cpp
+++ /dev/null
@@ -1,97 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ qaoa_maxcut_builder.cpp -o builder.x && ./builder.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-#include <cudaq/spin_op.h>
-
-// This example demonstrates the same code as in `qaoa_maxcut.cpp`,
-// but with the use of dynamic kernels. Here we have QAOA with `p` layers,
-// with each layer containing the alternating set of unitaries corresponding
-// to the problem and the mixer Hamiltonians. The algorithm leverages the
-// CUDA-Q VQE support to compute the Max-Cut of a rectangular graph
-// illustrated below.
-//
-//        v0  0---------------------0 v1
-//            |                     |
-//            |                     |
-//            |                     |
-//            |                     |
-//        v3  0---------------------0 v2
-// The Max-Cut for this problem is 0101 or 1010.
-
-int main() {
-
-  using namespace cudaq::spin;
-
-  cudaq::set_random_seed(13); // set for repeatability
-
-  // Problem Hamiltonian
-  const cudaq::spin_op Hp = 0.5 * z(0) * z(1) + 0.5 * z(1) * z(2) +
-                            0.5 * z(0) * z(3) + 0.5 * z(2) * z(3);
-
-  // Specify problem parameters
-  const int n_qubits = 4;
-  const int n_layers = 2;
-  const int n_params = 2 * n_layers;
-
-  auto [kernel, theta] = cudaq::make_kernel<std::vector<double>>();
-
-  auto q = kernel.qalloc(n_qubits);
-
-  // Prepare the initial state by superposition
-  kernel.h(q);
-
-  // Loop over all the layers
-  for (int i = 0; i < n_layers; ++i) {
-    // Problem Hamiltonian
-    for (int j = 0; j < n_qubits; ++j) {
-
-      kernel.x<cudaq::ctrl>(q[j], q[(j + 1) % n_qubits]);
-      kernel.rz(2.0 * theta[i], q[(j + 1) % n_qubits]);
-      kernel.x<cudaq::ctrl>(q[j], q[(j + 1) % n_qubits]);
-    }
-
-    for (int j = 0; j < n_qubits; ++j) {
-      // Mixer Hamiltonian
-      kernel.rx(2.0 * theta[i + n_layers], q[j]);
-    }
-  }
-
-  // Instantiate the optimizer
-  cudaq::optimizers::lbfgs optimizer;                    // gradient-based
-  cudaq::gradients::central_difference gradient(kernel); // grad vector
-
-  // Set initial values for the optimization parameters
-  optimizer.initial_parameters = cudaq::random_vector(
-      -M_PI / 8.0, M_PI / 8.0, n_params, std::mt19937::default_seed);
-
-  // Optimization using gradient-based L-BFGS
-  auto [opt_val, opt_params] =
-      cudaq::vqe(kernel, gradient, Hp, optimizer, n_params);
-
-  // Print the optimized value and the parameters
-  printf("Optimal value = %.16lf\n", opt_val);
-  printf("Optimal params = %.16lf %.16lf %.16lf %.16lf\n", opt_params[0],
-         opt_params[1], opt_params[2], opt_params[3]);
-
-  // Sample the circuit using optimized parameters
-  auto counts = cudaq::sample(kernel, opt_params);
-
-  // Dump the states and the counts
-  counts.dump();
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/builder/vqe_h2_builder.cpp b/pr-2308/sphinx/examples/cpp/other/builder/vqe_h2_builder.cpp
deleted file mode 100644
index 94a0459c44..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/builder/vqe_h2_builder.cpp
+++ /dev/null
@@ -1,124 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ vqe_h2_builder.cpp -o builder.x && ./builder.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-
-// This example demonstrates the utility of the builder pattern
-// for a common chemistry example. Here we build up a CUDA-Q kernel
-// with N layers and each layer containing an arrangement of
-// random SO(4) rotations. The algorithm leverages the CUDA-Q
-// VQE support to compute the ground state of the Hydrogen atom.
-
-namespace cudaq {
-
-// Define a function that applies a general SO(4) rotation to
-// the builder on the provided qubits with the provided parameters.
-// Note we keep this qubit and parameter arguments as auto as these
-// will default to taking the qubits and variational parameters (`QuakeValue`s)
-void so4(cudaq::kernel_builder<std::vector<double>> &builder, QuakeValue &&q,
-         QuakeValue &&r, QuakeValue &parameters) {
-  builder.ry(parameters[0], q);
-  builder.ry(parameters[1], r);
-
-  builder.h(r);
-  builder.x<cudaq::ctrl>(q, r);
-  builder.h(r);
-
-  builder.ry(parameters[2], q);
-  builder.ry(parameters[3], r);
-
-  builder.h(r);
-  builder.x<cudaq::ctrl>(q, r);
-  builder.h(r);
-
-  builder.ry(parameters[4], q);
-  builder.ry(parameters[5], r);
-
-  builder.h(r);
-  builder.x<cudaq::ctrl>(q, r);
-  builder.h(r);
-}
-
-} // namespace cudaq
-
-int main() {
-
-  // Read in the spin op from file
-  std::vector<double> h2_data{0, 0, 0, 0, -0.10647701149499994, 0.0,
-                              1, 1, 1, 1, 0.0454063328691,      0.0,
-                              1, 1, 3, 3, 0.0454063328691,      0.0,
-                              3, 3, 1, 1, 0.0454063328691,      0.0,
-                              3, 3, 3, 3, 0.0454063328691,      0.0,
-                              2, 0, 0, 0, 0.170280101353,       0.0,
-                              2, 2, 0, 0, 0.120200490713,       0.0,
-                              2, 0, 2, 0, 0.168335986252,       0.0,
-                              2, 0, 0, 2, 0.165606823582,       0.0,
-                              0, 2, 0, 0, -0.22004130022499996, 0.0,
-                              0, 2, 2, 0, 0.165606823582,       0.0,
-                              0, 2, 0, 2, 0.174072892497,       0.0,
-                              0, 0, 2, 0, 0.17028010135300004,  0.0,
-                              0, 0, 2, 2, 0.120200490713,       0.0,
-                              0, 0, 0, 2, -0.22004130022499999, 0.0,
-                              15};
-  cudaq::spin_op H(h2_data, /*nQubits*/ 4);
-
-  int layers = 2, n_qubits = H.num_qubits(), block_size = 2, p_counter = 0;
-  int n_blocks_per_layer = 2 * (n_qubits / block_size) - 1;
-  int n_params = layers * 6 * n_blocks_per_layer;
-  printf("%d qubit Hamiltonian -> %d parameters\n", n_qubits, n_params);
-
-  // Create the builder with signature void(std::vector<double>)
-  auto [kernel, params] = cudaq::make_kernel<std::vector<double>>();
-
-  // Allocate the qubits, initialize the HF product state
-  auto q = kernel.qalloc(n_qubits);
-  kernel.x(q[0]);
-  kernel.x(q[2]);
-
-  // Loop over adding layers of SO4 entanglers
-  int counter = 0;
-  for (int i = 0; i < layers; i++) {
-    // first layer of so4 blocks (even)
-    for (int k = 0; k < n_qubits; k += block_size) {
-      auto subq = q.slice(k, block_size);
-      auto sub_p = params.slice(p_counter, 6);
-      cudaq::so4(kernel, subq[0], subq[1], sub_p);
-      p_counter += 6;
-    }
-
-    // second layer of so4 blocks (odd)
-    for (int k = 1; k + block_size < n_qubits; k += block_size) {
-      auto subq = q.slice(k, block_size);
-      auto sub_p = params.slice(p_counter, 6);
-      cudaq::so4(kernel, subq[0], subq[1], sub_p);
-      p_counter += 6;
-    }
-  }
-
-  // Run the VQE algorithm from specific initial parameters.
-  auto init_params =
-      cudaq::random_vector(-1, 1, n_params, std::mt19937::default_seed);
-
-  // Run VQE
-  cudaq::optimizers::lbfgs optimizer;
-  cudaq::gradients::central_difference gradient(kernel);
-  optimizer.initial_parameters = init_params;
-  optimizer.max_eval = 20;
-  optimizer.max_line_search_trials = 10;
-  auto [opt_val, opt_params] =
-      cudaq::vqe(kernel, gradient, H, optimizer, n_params);
-  printf("Optimal value = %.16lf\n", opt_val);
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/compute_actions.cpp b/pr-2308/sphinx/examples/cpp/other/compute_actions.cpp
deleted file mode 100644
index 1ad73d8bfc..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/compute_actions.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ compute_actions.cpp -o gs.x && ./gs.x
-// ```
-
-// The algorithm leverages the VQE support to compute ground
-// state of the Hydrogen atom.
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-
-#include <map>
-#include <vector>
-
-struct ansatz_handcoded {
-  void operator()(double theta) __qpu__ {
-    cudaq::qvector q(4);
-    x(q[0]);
-    x(q[2]);
-    rx(M_PI_2, q[0]);
-    h(q[1]);
-    h(q[2]);
-    h(q[3]);
-    x<cudaq::ctrl>(q[0], q[1]);
-    x<cudaq::ctrl>(q[1], q[2]);
-    x<cudaq::ctrl>(q[2], q[3]);
-    rz(theta, q[3]);
-    x<cudaq::ctrl>(q[2], q[3]);
-    x<cudaq::ctrl>(q[1], q[2]);
-    x<cudaq::ctrl>(q[0], q[1]);
-    h(q[3]);
-    h(q[2]);
-    h(q[1]);
-    rx(-M_PI_2, q[0]);
-  }
-};
-
-// The kernel defined by `ansatz_compute_action` is
-// equivalent to the one defined in `ansatz_handcoded`.
-struct ansatz_compute_action {
-  void operator()(std::vector<double> theta) __qpu__ {
-    cudaq::qvector q(4);
-    x(q[0]);
-    x(q[2]);
-
-    cudaq::compute_action(
-        [&]() {
-          rx(M_PI_2, q[0]);
-          h(q[1]);
-          h(q[2]);
-          h(q[3]);
-          x<cudaq::ctrl>(q[0], q[1]);
-          x<cudaq::ctrl>(q[1], q[2]);
-          x<cudaq::ctrl>(q[2], q[3]);
-        },
-        [&]() { rz(theta[0], q[3]); });
-  }
-};
-
-int main(int argc, char **argv) {
-
-  std::vector<double> h2_data{0, 0, 0, 0, -0.10647701149499994, 0.0,
-                              1, 1, 1, 1, 0.0454063328691,      0.0,
-                              1, 1, 3, 3, 0.0454063328691,      0.0,
-                              3, 3, 1, 1, 0.0454063328691,      0.0,
-                              3, 3, 3, 3, 0.0454063328691,      0.0,
-                              2, 0, 0, 0, 0.170280101353,       0.0,
-                              2, 2, 0, 0, 0.120200490713,       0.0,
-                              2, 0, 2, 0, 0.168335986252,       0.0,
-                              2, 0, 0, 2, 0.165606823582,       0.0,
-                              0, 2, 0, 0, -0.22004130022499996, 0.0,
-                              0, 2, 2, 0, 0.165606823582,       0.0,
-                              0, 2, 0, 2, 0.174072892497,       0.0,
-                              0, 0, 2, 0, 0.17028010135300004,  0.0,
-                              0, 0, 2, 2, 0.120200490713,       0.0,
-                              0, 0, 0, 2, -0.22004130022499999, 0.0,
-                              15};
-  cudaq::spin_op H(h2_data, /*nQubits*/ 4);
-
-  const auto param_space = cudaq::linspace(-M_PI, M_PI, 10);
-  printf("Using the hand-coded kernel\n");
-  for (const auto &param : param_space) {
-    // `E(params...) = <psi(params...) | H | psi(params...)>`
-    double energy_at_param = cudaq::observe(ansatz_handcoded{}, H, param);
-    printf("<H>(%lf) = %lf\n", param, energy_at_param);
-  }
-
-  printf("Using the kernel written with cudaq::compute_action\n");
-  for (const auto &param : param_space) {
-    // `E(params...) = <psi(params...) | H | psi(params...)>`
-    double energy_at_param =
-        cudaq::observe(ansatz_compute_action{}, H, std::vector<double>{param});
-    printf("<H>(%lf) = %lf\n", param, energy_at_param);
-  }
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/distributed/mpi.cpp b/pr-2308/sphinx/examples/cpp/other/distributed/mpi.cpp
deleted file mode 100644
index d786fd9d2b..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/distributed/mpi.cpp
+++ /dev/null
@@ -1,68 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ mpi.cpp -DCUDAQ_ENABLE_MPI_EXAMPLE=1 -o mpi.x && mpiexec -np 4 ./mpi.x
-// ```
-
-// This example demonstrates CUDA-Q MPI support.
-
-#ifndef CUDAQ_ENABLE_MPI_EXAMPLE
-#define CUDAQ_ENABLE_MPI_EXAMPLE 0
-#endif
-
-#include <cudaq.h>
-
-int main(int argc, char **argv) {
-#if CUDAQ_ENABLE_MPI_EXAMPLE == 0
-  return 0;
-#else
-  // Initialize MPI
-  cudaq::mpi::initialize();
-
-  if (cudaq::mpi::rank() == 0)
-    printf("Running MPI example with %d processes.\n", cudaq::mpi::num_ranks());
-
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-  auto ansatz = [](double theta) __qpu__ {
-    cudaq::qubit q, r;
-    x(q);
-    ry(theta, r);
-    x<cudaq::ctrl>(r, q);
-  };
-
-  // In addition to the built-in `MQPU` platform, users can construct MPI
-  // application directly using CUDA-Q MPI support.
-  const auto allParams =
-      cudaq::random_vector(-M_PI, M_PI, cudaq::mpi::num_ranks());
-
-  // For example, each MPI process can run `cudaq::observe` for a different
-  // parameter.
-  const double rankParam = allParams[cudaq::mpi::rank()];
-  const double rankResult = cudaq::observe(ansatz, h, rankParam);
-  printf("[Process %d]: Energy(%lf) = %lf.\n", cudaq::mpi::rank(), rankParam,
-         rankResult);
-  // Then, using `cudaq::mpi::all_gather` to collect all the results.
-  std::vector<double> gatherData(cudaq::mpi::num_ranks());
-  cudaq::mpi::all_gather(gatherData, {rankResult});
-  if (cudaq::mpi::rank() == 0) {
-    printf("Gathered data from all ranks: \n");
-    for (const auto &x : gatherData)
-      printf("%lf\n", x);
-  }
-
-  // Verify that the data has been assembled as expected.
-  if (std::abs(gatherData[cudaq::mpi::rank()] - rankResult) > 1e-12)
-    return -1;
-  cudaq::mpi::finalize();
-  return 0;
-#endif
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/gradients.cpp b/pr-2308/sphinx/examples/cpp/other/gradients.cpp
deleted file mode 100644
index ad910ec6a8..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/gradients.cpp
+++ /dev/null
@@ -1,86 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ gradients.cpp -o gs.x && ./gs.x
-// ```
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-
-struct deuteron_n3_ansatz {
-  void operator()(double x0, double x1) __qpu__ {
-    cudaq::qvector q(3);
-    x(q[0]);
-    ry(x0, q[1]);
-    ry(x1, q[2]);
-    x<cudaq::ctrl>(q[2], q[0]);
-    x<cudaq::ctrl>(q[0], q[1]);
-    ry(-x0, q[1]);
-    x<cudaq::ctrl>(q[0], q[1]);
-    x<cudaq::ctrl>(q[1], q[0]);
-  }
-};
-
-int main() {
-  using namespace cudaq::spin;
-
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-  cudaq::spin_op h3 = h + 9.625 - 9.625 * z(2) - 3.913119 * x(1) * x(2) -
-                      3.913119 * y(1) * y(2);
-
-  // Default here is COBYLA
-  // Should see many more iterations
-  printf("Optimize with no gradients.\n");
-  cudaq::optimizers::cobyla optimizer;
-  auto [opt_val, opt_params] = optimizer.optimize(
-      2, [&](const std::vector<double> &x, std::vector<double> &grad_vec) {
-        double e = cudaq::observe(deuteron_n3_ansatz{}, h3, x[0], x[1]);
-        printf("<H>(%lf, %lf) = %lf\n", x[0], x[1], e);
-        return e;
-      });
-
-  auto argMapper = [](std::vector<double> x) {
-    return std::make_tuple(x[0], x[1]);
-  };
-
-  // Change the default to L-BFGS which requires gradient calculation
-  // Should see fewer iterations
-  printf("\nOptimize with gradients.\n");
-  cudaq::gradients::parameter_shift gradient(deuteron_n3_ansatz{}, argMapper);
-  gradient.shiftScalar = 1e-1;
-  cudaq::optimizers::lbfgs optimizer_lbfgs;
-  optimizer_lbfgs.max_line_search_trials = 10;
-  auto [opt_val2, opt_params2] = optimizer_lbfgs.optimize(
-      2, [&](const std::vector<double> &x, std::vector<double> &grad_vec) {
-        double e = cudaq::observe(deuteron_n3_ansatz{}, h3, x[0], x[1]);
-        gradient.compute(x, grad_vec, h3, e);
-        printf("<H>(%lf, %lf) = %lf\n", x[0], x[1], e);
-        return e;
-      });
-
-  printf("\nOptimize with gradients, change a few options.\n");
-  cudaq::gradients::central_difference gradient2(deuteron_n3_ansatz{},
-                                                 argMapper);
-  optimizer_lbfgs.initial_parameters = std::vector<double>{.1, .1};
-  optimizer_lbfgs.max_eval = 10;
-  auto [opt_val3, optp3] = optimizer_lbfgs.optimize(
-      2, [&](const std::vector<double> &x, std::vector<double> &grad_vec) {
-        double e = cudaq::observe(deuteron_n3_ansatz{}, h3, x[0], x[1]);
-        gradient2.compute(x, grad_vec, h3, e);
-        printf("<H>(%lf, %lf) = %lf\n", x[0], x[1], e);
-        return e;
-      });
-
-  printf("Opt loop found %lf at [%lf, %lf]\n", opt_val3, optp3[0], optp3[1]);
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/cpp/other/remote_sim/simple.cpp b/pr-2308/sphinx/examples/cpp/other/remote_sim/simple.cpp
deleted file mode 100644
index 30492c7d50..0000000000
--- a/pr-2308/sphinx/examples/cpp/other/remote_sim/simple.cpp
+++ /dev/null
@@ -1,45 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// clang-format off
-// ```
-//  nvq++ --target remote-mqpu --remote-mqpu-auto-launch 1 simple.cpp -o simple.x &&
-// ./simple.x
-// ```
-// clang-format on
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-
-// The example here shows a simple use case for the remote simulator platform
-// (`remote-mqpu`). Please refer to the documentation for more information about
-// its features and command line options.
-
-int main() {
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-
-  auto [ansatz, theta] = cudaq::make_kernel<double>();
-  // Allocate some qubits
-  auto q = ansatz.qalloc(2);
-  // Build up the circuit, use the acquired parameter
-  ansatz.x(q[0]);
-  ansatz.ry(theta, q[1]);
-  ansatz.x<cudaq::ctrl>(q[1], q[0]);
-
-  // Observe takes the kernel, the spin_op, and the concrete
-  // parameters for the kernel.
-  // This will be delegated to the remote simulator via HTTP requests.
-  // Use `export CUDAQ_LOG_LEVEL=info` to inspect the server-client
-  // communication.
-  double energy = cudaq::observe(ansatz, h, .59);
-  printf("Energy is %lf\n", energy);
-  return 0;
-}
diff --git a/pr-2308/sphinx/examples/images/Bloch_sphere.png b/pr-2308/sphinx/examples/images/Bloch_sphere.png
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diff --git a/pr-2308/sphinx/examples/images/mqpumgpu.png b/pr-2308/sphinx/examples/images/mqpumgpu.png
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diff --git a/pr-2308/sphinx/examples/python/building_kernels.ipynb b/pr-2308/sphinx/examples/python/building_kernels.ipynb
deleted file mode 100644
index b847b4936e..0000000000
--- a/pr-2308/sphinx/examples/python/building_kernels.ipynb
+++ /dev/null
@@ -1,420 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13858fc6-edc7-49cb-8b56-152ebec2f729",
-   "metadata": {},
-   "source": [
-    "# Building Kernels\n",
-    "\n",
-    "This section will cover the most basic CUDA-Q construct, a quantum kernel. Topics include, building kernels, initializing states, and applying gate operations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb9f7912-cb38-463c-b604-4657848b6f68",
-   "metadata": {},
-   "source": [
-    "### Defining Kernels\n",
-    "\n",
-    "Kernels are the building blocks of quantum algorithms in CUDA-Q. A kernel is specified by using the following syntax. `cudaq.qubit` builds a register consisting of a single qubit, while `cudaq.qvector` builds a register of $N$ qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0c312467-3ba7-43fb-b820-d1ee2ced7fc3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "d9af7484-a69b-4239-b8b9-1abb47ee9421",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    A = cudaq.qubit()\n",
-    "    B = cudaq.qvector(3)\n",
-    "    C = cudaq.qvector(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83459bfa-b0b8-4d6b-bb31-2e708da5a30e",
-   "metadata": {},
-   "source": [
-    "Inputs to kernels are defined by specifying a parameter in the kernel definition along with the appropriate type. The kernel below takes an integer to define a register of N qubits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "58af9585-a2c7-4059-983b-670962bdf65c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N = 2\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(N: int):\n",
-    "    register = cudaq.qvector(N)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7866f4c5-4b8c-408b-8980-8ebca60f28b8",
-   "metadata": {},
-   "source": [
-    "### Initializing states\n",
-    "\n",
-    "It is often helpful to define an initial state for a kernel. There are a few ways to do this in CUDA-Q. Note, method 5 is particularly useful for cases where the state of one kernel is passed into a second kernel to prepare its initial state.\n",
-    "\n",
-    "1. Passing complex vectors as parameters\n",
-    "2. Capturing complex vectors\n",
-    "3. Precision-agnostic API\n",
-    "4. Define as CUDA-Q amplitudes\n",
-    "5. Pass in a state from another kernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "57b5d6c5-b0b7-449d-ac0c-7166e2127c00",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Passing complex vectors as parameters\n",
-    "c = [.707 +0j, 0-.707j]\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(vec: list[complex]):\n",
-    "    q = cudaq.qubit(vec)\n",
-    "\n",
-    "\n",
-    "# Capturing complex vectors\n",
-    "c = [0.70710678 + 0j, 0., 0., 0.70710678]\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    q = cudaq.qvector(c)\n",
-    "\n",
-    "\n",
-    "# Precision-Agnostic API\n",
-    "import numpy as np\n",
-    "\n",
-    "c = np.array([0.70710678 + 0j, 0., 0., 0.70710678], dtype=cudaq.complex())\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    q = cudaq.qvector(c)\n",
-    "\n",
-    "# Define as CUDA-Q amplitudes\n",
-    "c = cudaq.amplitudes([0.70710678 + 0j, 0., 0., 0.70710678])\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    q = cudaq.qvector(c)\n",
-    "\n",
-    "# Pass in a state from another kernel\n",
-    "c = [0.70710678 + 0j, 0., 0., 0.70710678]\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel_initial():\n",
-    "    q = cudaq.qvector(c)\n",
-    "\n",
-    "state_to_pass = cudaq.get_state(kernel_initial)\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(state: cudaq.State):\n",
-    "    q = cudaq.qvector(state)\n",
-    "\n",
-    "kernel(state_to_pass)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d5f68d7-80cf-4e2d-861c-7f945e17b6de",
-   "metadata": {},
-   "source": [
-    "### Applying Gates\n",
-    "\n",
-    "\n",
-    "After a kernel is constructed, gates can be applied to start building out a quantum circuit. All the predefined gates in CUDA-Q can be found [here](https://nvidia.github.io/cuda-quantum/latest/api/default_ops).\n",
-    "\n",
-    "\n",
-    "Gates can be applied to all qubits in a register:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "8b93d86d-f6a1-4fb8-9efb-c7039ff4b383",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    register = cudaq.qvector(10)\n",
-    "    h(register)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "490cb6c7-d9a2-4861-bd6d-8fddd764039c",
-   "metadata": {},
-   "source": [
-    "Or, to individual qubits in a register:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "5491190b-fdaa-4203-9143-fbeb1519b074",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    register = cudaq.qvector(10)\n",
-    "    h(register[0])  # first qubit\n",
-    "    h(register[-1])  # last qubit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22c0e379-80fc-43b0-bee0-41d1c6492585",
-   "metadata": {},
-   "source": [
-    "### Controlled Operations\n",
-    "\n",
-    "Controlled operations are available for any gate and can be used by adding `.ctrl` to the end of any gate, followed by specification of the control qubit and the target qubit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "68b48107-e326-4a8a-986b-eb2df6207538",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    register = cudaq.qvector(10)\n",
-    "    x.ctrl(register[0], register[1])  # CNOT gate applied with qubit 0 as control"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b88044d5-f0d2-429a-824e-6e11617d5e75",
-   "metadata": {},
-   "source": [
-    "### Multi-Controlled Operations\n",
-    "\n",
-    "It is valid for more than one qubit to be used for multi-controlled gates. The control qubits are specified as a list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "03843595-5b4b-4684-ad41-974f7c84470a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    register = cudaq.qvector(10)\n",
-    "    x.ctrl([register[0], register[1]], register[2])  # X applied to qubit two controlled by qubit 0 and 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66044f3f",
-   "metadata": {},
-   "source": [
-    "You can also call a controlled kernel within a kernel: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "032e4bb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 101:1000 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "@cudaq.kernel\n",
-    "def x_kernel(qubit: cudaq.qubit):\n",
-    "    x(qubit)\n",
-    "    \n",
-    "# A kernel that will call `x_kernel` as a controlled operation.\n",
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    \n",
-    "    control_vector = cudaq.qvector(2)\n",
-    "    target = cudaq.qubit()\n",
-    "    \n",
-    "    x(control_vector)\n",
-    "    x(target)\n",
-    "    x(control_vector[1])\n",
-    "    cudaq.control(x_kernel, control_vector, target)\n",
-    "\n",
-    "# The above is equivalent to: \n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qvector = cudaq.qvector(3)\n",
-    "    x(qvector)\n",
-    "    x(qvector[1])\n",
-    "    x.ctrl([qvector[0], qvector[1]], qvector[2])\n",
-    "    mz(qvector)\n",
-    "\n",
-    "\n",
-    "results = cudaq.sample(kernel)\n",
-    "print(results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47d6a1de-f659-4c58-b2ed-91d1682dad18",
-   "metadata": {},
-   "source": [
-    "### Adjoint Operations\n",
-    "\n",
-    "The adjoint of a gate can be applied by appending the gate with the `adj` designation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "74db71e1-4046-454e-a434-2bd27fda2336",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    register = cudaq.qvector(10)\n",
-    "    t.adj(register[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e61ba842-c969-4ad0-95ee-0916ec753b4f",
-   "metadata": {},
-   "source": [
-    "### Custom Operations\n",
-    "\n",
-    "Custom gate operations can be specified using `cudaq.register_operation`. A one-dimensional Numpy array specifies the unitary matrix to be applied. The entries of the array read from top to bottom through the rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "d51340c1-d211-4ddc-aaf4-ba59a91ba9cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "cudaq.register_operation(\"custom_x\", np.array([0, 1, 1, 0]))\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    h(qubits[0])\n",
-    "    custom_x(qubits[0])\n",
-    "    custom_x.ctrl(qubits[0], qubits[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef38ddbb-433c-49d6-a124-8da3ef034c04",
-   "metadata": {},
-   "source": [
-    "### Building Kernels with Kernels\n",
-    "\n",
-    "For many complex applications, it is helpful for a kernel to call another kernel to perform a specific subroutine. The example blow shows how `kernel_A` can be caled within `kernel_B` to perform CNOT operations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "7d298eaf-08a4-4905-89d2-f52b79dc1484",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel_A(qubit_0: cudaq.qubit, qubit_1: cudaq.qubit):\n",
-    "    x.ctrl(qubit_0, qubit_1)\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel_B():\n",
-    "    reg = cudaq.qvector(10)\n",
-    "    for i in range(5):\n",
-    "        kernel_A(reg[i], reg[i + 1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7b3cb6b-74ca-49e2-9ea5-c9f69b0316ed",
-   "metadata": {},
-   "source": [
-    "### Parameterized Kernels\n",
-    "\n",
-    "It is often useful to define parameterized circuit kernels which can be used for applications like VQE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d05544d4-fb57-49d7-84ff-d9dd090f49fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel(thetas: list[float]):\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    rx(thetas[0], qubits[0])\n",
-    "    ry(thetas[1], qubits[1])\n",
-    "\n",
-    "thetas = [.024, .543]\n",
-    "\n",
-    "kernel(thetas)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/examples/python/building_kernels.py b/pr-2308/sphinx/examples/python/building_kernels.py
deleted file mode 100644
index 35583fbb9b..0000000000
--- a/pr-2308/sphinx/examples/python/building_kernels.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# ### Defining Kernels
-#
-# Kernels are the building blocks of quantum algorithms in CUDA-Q. A kernel is specified by using the following syntax. `cudaq.qubit` builds a register consisting of a single qubit, while `cudaq.qvector` builds a register of $N$ qubits.
-
-# In[14]:
-
-import cudaq
-
-# In[15]:
-
-
-@cudaq.kernel
-def kernel():
-    A = cudaq.qubit()
-    B = cudaq.qvector(3)
-    C = cudaq.qvector(5)
-
-
-# Inputs to kernels are defined by specifying a parameter in the kernel definition along with the appropriate type. The kernel below takes an integer to define a register of N qubits.
-
-# In[16]:
-
-N = 2
-
-
-@cudaq.kernel
-def kernel(N: int):
-    register = cudaq.qvector(N)
-
-
-# ### Initializing states
-#
-# It is often helpful to define an initial state for a kernel. There are a few ways to do this in CUDA-Q. Note, method 5 is particularly useful for cases where the state of one kernel is passed into a second kernel to prepare its initial state.
-#
-# 1. Passing complex vectors as parameters
-# 2. Capturing complex vectors
-# 3. Precision-agnostic API
-# 4. Define as CUDA-Q amplitudes
-# 5. Pass in a state from another kernel
-
-# In[17]:
-
-# Passing complex vectors as parameters
-c = [.707 + 0j, 0 - .707j]
-
-
-@cudaq.kernel
-def kernel(vec: list[complex]):
-    q = cudaq.qubit(vec)
-
-
-# Capturing complex vectors
-c = [0.70710678 + 0j, 0., 0., 0.70710678]
-
-
-@cudaq.kernel
-def kernel():
-    q = cudaq.qvector(c)
-
-
-# Precision-Agnostic API
-import numpy as np
-
-c = np.array([0.70710678 + 0j, 0., 0., 0.70710678], dtype=cudaq.complex())
-
-
-@cudaq.kernel
-def kernel():
-    q = cudaq.qvector(c)
-
-
-# Define as CUDA-Q amplitudes
-c = cudaq.amplitudes([0.70710678 + 0j, 0., 0., 0.70710678])
-
-
-@cudaq.kernel
-def kernel():
-    q = cudaq.qvector(c)
-
-
-# Pass in a state from another kernel
-c = [0.70710678 + 0j, 0., 0., 0.70710678]
-
-
-@cudaq.kernel
-def kernel_initial():
-    q = cudaq.qvector(c)
-
-
-state_to_pass = cudaq.get_state(kernel_initial)
-
-
-@cudaq.kernel
-def kernel(state: cudaq.State):
-    q = cudaq.qvector(state)
-
-
-kernel(state_to_pass)
-
-# ### Applying Gates
-#
-#
-# After a kernel is constructed, gates can be applied to start building out a quantum circuit.
-# All the predefined gates in CUDA-Q can be found here:
-# https://nvidia.github.io/cuda-quantum/api/default_ops.
-#
-#
-# Gates can be applied to all qubits in a register:
-
-# In[18]:
-
-
-@cudaq.kernel
-def kernel():
-    register = cudaq.qvector(10)
-    h(register)
-
-
-# Or, to individual qubits in a register:
-
-# In[19]:
-
-
-@cudaq.kernel
-def kernel():
-    register = cudaq.qvector(10)
-    h(register[0])  # first qubit
-    h(register[-1])  # last qubit
-
-
-# ### Controlled Operations
-#
-# Controlled operations are available for any gate and can be used by adding `.ctrl` to the end of any gate, followed by specification of the control qubit and the target qubit.
-
-# In[20]:
-
-
-@cudaq.kernel
-def kernel():
-    register = cudaq.qvector(10)
-    x.ctrl(register[0],
-           register[1])  # CNOT gate applied with qubit 0 as control
-
-
-# ### Multi-Controlled Operations
-#
-# It is valid for more than one qubit to be used for multi-controlled gates. The control qubits are specified as a list.
-
-# In[21]:
-
-
-@cudaq.kernel
-def kernel():
-    register = cudaq.qvector(10)
-    x.ctrl([register[0], register[1]],
-           register[2])  # X applied to qubit two controlled by qubit 0 and 1
-
-
-# You can also call a controlled kernel within a kernel:
-
-# In[22]:
-
-
-@cudaq.kernel
-def x_kernel(qubit: cudaq.qubit):
-    x(qubit)
-
-
-# A kernel that will call `x_kernel` as a controlled operation.
-@cudaq.kernel
-def kernel():
-
-    control_vector = cudaq.qvector(2)
-    target = cudaq.qubit()
-
-    x(control_vector)
-    x(target)
-    x(control_vector[1])
-    cudaq.control(x_kernel, control_vector, target)
-
-
-# The above is equivalent to:
-
-
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(3)
-    x(qvector)
-    x(qvector[1])
-    x.ctrl([qvector[0], qvector[1]], qvector[2])
-    mz(qvector)
-
-
-results = cudaq.sample(kernel)
-print(results)
-
-# ### Adjoint Operations
-#
-# The adjoint of a gate can be applied by appending the gate with the `adj` designation.
-
-# In[23]:
-
-
-@cudaq.kernel
-def kernel():
-    register = cudaq.qvector(10)
-    t.adj(register[0])
-
-
-# ### Custom Operations
-#
-# Custom gate operations can be specified using `cudaq.register_operation`. A one-dimensional Numpy array specifies the unitary matrix to be applied. The entries of the array read from top to bottom through the rows.
-
-# In[24]:
-
-import numpy as np
-
-cudaq.register_operation("custom_x", np.array([0, 1, 1, 0]))
-
-
-@cudaq.kernel
-def kernel():
-    qubits = cudaq.qvector(2)
-    h(qubits[0])
-    custom_x(qubits[0])
-    custom_x.ctrl(qubits[0], qubits[1])
-
-
-# ### Building Kernels with Kernels
-#
-# For many complex applications, it is helpful for a kernel to call another kernel to perform a specific subroutine. The example blow shows how `kernel_A` can be called within `kernel_B` to perform CNOT operations.
-
-# In[25]:
-
-
-@cudaq.kernel
-def kernel_A(qubit_0: cudaq.qubit, qubit_1: cudaq.qubit):
-    x.ctrl(qubit_0, qubit_1)
-
-
-@cudaq.kernel
-def kernel_B():
-    reg = cudaq.qvector(10)
-    for i in range(5):
-        kernel_A(reg[i], reg[i + 1])
-
-
-# ### Parameterized Kernels
-#
-# It is often useful to define parameterized circuit kernels which can be used for applications like VQE.
-
-# In[26]:
-
-
-@cudaq.kernel
-def kernel(thetas: list[float]):
-    qubits = cudaq.qvector(2)
-    rx(thetas[0], qubits[0])
-    ry(thetas[1], qubits[1])
-
-
-thetas = [.024, .543]
-
-kernel(thetas)
diff --git a/pr-2308/sphinx/examples/python/dynamics/cavity_qed.py b/pr-2308/sphinx/examples/python/dynamics/cavity_qed.py
deleted file mode 100644
index 20ee0e5d81..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/cavity_qed.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import cudaq
-from cudaq import operators, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# This example demonstrate a simulation of cavity quantum electrodynamics (interaction between light confined in a reflective cavity and atoms)
-
-# System dimensions: atom (2-level system) and cavity (10-level system)
-dimensions = {0: 2, 1: 10}
-
-# Alias for commonly used operators
-# Cavity operators
-a = operators.annihilate(1)
-a_dag = operators.create(1)
-
-# Atom operators
-sm = operators.annihilate(0)
-sm_dag = operators.create(0)
-
-# Defining the Hamiltonian for the system: self-energy terms and cavity-atom interaction term.
-# This is the so-called Jaynes-Cummings model:
-# https://en.wikipedia.org/wiki/Jaynes%E2%80%93Cummings_model
-hamiltonian = 2 * np.pi * operators.number(1) + 2 * np.pi * operators.number(
-    0) + 2 * np.pi * 0.25 * (sm * a_dag + sm_dag * a)
-
-# Initial state of the system
-# Atom in ground state
-qubit_state = cp.array([[1.0, 0.0], [0.0, 0.0]], dtype=cp.complex128)
-
-# Cavity in a state which has 5 photons initially
-cavity_state = cp.zeros((10, 10), dtype=cp.complex128)
-cavity_state[5][5] = 1.0
-rho0 = cudaq.State.from_data(cp.kron(qubit_state, cavity_state))
-
-steps = np.linspace(0, 10, 201)
-schedule = Schedule(steps, ["time"])
-
-# First, evolve the system without any collapse operators (ideal).
-evolution_result = cudaq.evolve(
-    hamiltonian,
-    dimensions,
-    schedule,
-    rho0,
-    observables=[operators.number(1), operators.number(0)],
-    collapse_operators=[],
-    store_intermediate_results=True,
-    integrator=ScipyZvodeIntegrator())
-
-# Then, evolve the system with a collapse operator modeling cavity decay (leaking photons)
-evolution_result_decay = cudaq.evolve(
-    hamiltonian,
-    dimensions,
-    schedule,
-    rho0,
-    observables=[operators.number(1), operators.number(0)],
-    collapse_operators=[np.sqrt(0.1) * a],
-    store_intermediate_results=True,
-    integrator=ScipyZvodeIntegrator())
-
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-ideal_results = [
-    get_result(0, evolution_result),
-    get_result(1, evolution_result)
-]
-decay_results = [
-    get_result(0, evolution_result_decay),
-    get_result(1, evolution_result_decay)
-]
-
-fig = plt.figure(figsize=(18, 6))
-
-plt.subplot(1, 2, 1)
-plt.plot(steps, ideal_results[0])
-plt.plot(steps, ideal_results[1])
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Cavity Photon Number", "Atom Excitation Probability"))
-plt.title("No decay")
-
-plt.subplot(1, 2, 2)
-plt.plot(steps, decay_results[0])
-plt.plot(steps, decay_results[1])
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Cavity Photon Number", "Atom Excitation Probability"))
-plt.title("With decay")
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig('cavity_qed.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/cross_resonance.py b/pr-2308/sphinx/examples/python/dynamics/cross_resonance.py
deleted file mode 100644
index 29a83920bf..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/cross_resonance.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import cudaq
-from cudaq import spin, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# This example simulates cross-resonance interactions between superconducting qubits.
-# Cross-resonance interaction is key to implementing two-qubit conditional gates, e.g., the CNOT gate.
-# Ref: A simple all-microwave entangling gate for fixed-frequency superconducting qubits (Physical Review Letters 107, 080502)
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Device parameters
-# Detuning between two qubits
-delta = 100 * 2 * np.pi
-# Static coupling between qubits
-J = 7 * 2 * np.pi
-# spurious electromagnetic `crosstalk` due to stray electromagnetic coupling in the device circuit and package
-# see (Physical Review Letters 107, 080502)
-m_12 = 0.2
-# Drive strength
-Omega = 20 * 2 * np.pi
-
-# Qubit Hamiltonian (in the rotating frame w.r.t. the target qubit)
-hamiltonian = delta / 2 * spin.z(0) + J * (
-    spin.minus(1) * spin.plus(0) +
-    spin.plus(1) * spin.minus(0)) + Omega * spin.x(0) + m_12 * Omega * spin.x(1)
-
-# Dimensions of sub-system
-dimensions = {0: 2, 1: 2}
-
-# Initial state of the system (ground state).
-rho0 = cudaq.State.from_data(
-    cp.array([[1.0, 0.0], [0.0, 0.0]], dtype=cp.complex128))
-
-# Two initial states: |00> and |10>.
-# We show the 'conditional' evolution when controlled qubit is in |1> state.
-psi_00 = cudaq.State.from_data(
-    cp.array([1.0, 0.0, 0.0, 0.0], dtype=cp.complex128))
-psi_10 = cudaq.State.from_data(
-    cp.array([0.0, 0.0, 1.0, 0.0], dtype=cp.complex128))
-
-# Schedule of time steps.
-steps = np.linspace(0.0, 1.0, 1001)
-schedule = Schedule(steps, ["time"])
-
-# Run the simulation.
-# Control bit = 0
-evolution_result_00 = cudaq.evolve(hamiltonian,
-                                   dimensions,
-                                   schedule,
-                                   psi_00,
-                                   observables=[
-                                       spin.x(0),
-                                       spin.y(0),
-                                       spin.z(0),
-                                       spin.x(1),
-                                       spin.y(1),
-                                       spin.z(1)
-                                   ],
-                                   collapse_operators=[],
-                                   store_intermediate_results=True,
-                                   integrator=ScipyZvodeIntegrator())
-
-# Control bit = 1
-evolution_result_10 = cudaq.evolve(hamiltonian,
-                                   dimensions,
-                                   schedule,
-                                   psi_10,
-                                   observables=[
-                                       spin.x(0),
-                                       spin.y(0),
-                                       spin.z(0),
-                                       spin.x(1),
-                                       spin.y(1),
-                                       spin.z(1)
-                                   ],
-                                   collapse_operators=[],
-                                   store_intermediate_results=True,
-                                   integrator=ScipyZvodeIntegrator())
-
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-results_00 = [
-    get_result(0, evolution_result_00),
-    get_result(1, evolution_result_00),
-    get_result(2, evolution_result_00),
-    get_result(3, evolution_result_00),
-    get_result(4, evolution_result_00),
-    get_result(5, evolution_result_00)
-]
-results_10 = [
-    get_result(0, evolution_result_10),
-    get_result(1, evolution_result_10),
-    get_result(2, evolution_result_10),
-    get_result(3, evolution_result_10),
-    get_result(4, evolution_result_10),
-    get_result(5, evolution_result_10)
-]
-
-# The changes in recession frequencies of the target qubit when control qubit is in |1> state allow us to implement two-qubit conditional gates.
-fig = plt.figure(figsize=(18, 6))
-
-plt.subplot(1, 2, 1)
-plt.plot(steps, results_00[5])
-plt.plot(steps, results_10[5])
-plt.ylabel(r"$\langle Z_2\rangle$")
-plt.xlabel("Time")
-plt.legend((r"$|\psi_0\rangle=|00\rangle$", r"$|\psi_0\rangle=|10\rangle$"))
-
-plt.subplot(1, 2, 2)
-plt.plot(steps, results_00[4])
-plt.plot(steps, results_10[4])
-plt.ylabel(r"$\langle Y_2\rangle$")
-plt.xlabel("Time")
-plt.legend((r"$|\psi_0\rangle=|00\rangle$", r"$|\psi_0\rangle=|10\rangle$"))
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig('cross_resonance.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/gate_calibration.py b/pr-2308/sphinx/examples/python/dynamics/gate_calibration.py
deleted file mode 100644
index 5e6a645b96..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/gate_calibration.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import cudaq
-from cudaq import operators, Schedule, ScalarOperator, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# This example demonstrates the use of the dynamics simulator and optimizer to optimize a pulse.
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Sample device parameters
-# Assuming a simple transmon device Hamiltonian in rotating frame.
-detuning = 0.0  # Detuning of the drive; assuming resonant drive
-anharmonicity = -340.0  # Anharmonicity
-sigma = 0.01  # sigma of the Gaussian pulse
-cutoff = 4.0 * sigma  # total length of drive pulse
-
-# Dimensions of sub-system
-# We model `transmon` as a 3-level system to account for leakage.
-dimensions = {0: 3}
-
-# Initial state of the system (ground state).
-psi0 = cudaq.State.from_data(cp.array([1.0, 0.0, 0.0], dtype=cp.complex128))
-
-
-def gaussian(t):
-    """
-    Gaussian shape with cutoff. Starts at t = 0, amplitude normalized to one
-    """
-    val = (np.exp(-((t-cutoff/2)/sigma)**2/2)-np.exp(-(cutoff/sigma)**2/8)) \
-           / (1-np.exp(-(cutoff/sigma)**2/8))
-    return val
-
-
-def dgaussian(t):
-    """
-    Derivative of Gaussian. Starts at t = 0, amplitude normalized to one
-    """
-    return -(t - cutoff / 2) / sigma * np.exp(-(
-        (t - cutoff / 2) / sigma)**2 / 2 + 0.5)
-
-
-# Schedule of time steps.
-steps = np.linspace(0.0, cutoff, 201)
-schedule = Schedule(steps, ["t"])
-
-# We optimize for a X(pi/2) rotation
-target_state = np.array([1.0 / np.sqrt(2), -1j / np.sqrt(2), 0.0],
-                        dtype=cp.complex128)
-
-
-# Optimize the amplitude of the drive pulse (DRAG - Derivative Removal by Adiabatic Gate)
-def cost_function(amps):
-    amplitude = 100 * amps[0]
-    drag_amp = 100 * amps[1]
-    # Qubit Hamiltonian
-    hamiltonian = detuning * operators.number(0) + (
-        anharmonicity / 2) * operators.create(0) * operators.create(
-            0) * operators.annihilate(0) * operators.annihilate(0)
-    # Drive term
-    hamiltonian += amplitude * ScalarOperator(gaussian) * (
-        operators.create(0) + operators.annihilate(0))
-
-    # Drag term (leakage reduction)
-    hamiltonian += 1j * drag_amp * ScalarOperator(dgaussian) * (
-        operators.annihilate(0) - operators.create(0))
-
-    # We optimize for a X(pi/2) rotation
-    evolution_result = cudaq.evolve(hamiltonian,
-                                    dimensions,
-                                    schedule,
-                                    psi0,
-                                    observables=[],
-                                    collapse_operators=[],
-                                    store_intermediate_results=False,
-                                    integrator=ScipyZvodeIntegrator())
-    final_state = evolution_result.final_state()
-
-    overlap = np.abs(final_state.overlap(target_state))
-    print(
-        f"Gaussian amplitude = {amplitude}, derivative amplitude = {drag_amp}, Overlap: {overlap}"
-    )
-    return 1.0 - overlap
-
-
-# Specify the optimizer
-optimizer = cudaq.optimizers.NelderMead()
-optimal_error, optimal_parameters = optimizer.optimize(dimensions=2,
-                                                       function=cost_function)
-
-print("optimal overlap =", 1.0 - optimal_error)
-print("optimal parameters =", optimal_parameters)
diff --git a/pr-2308/sphinx/examples/python/dynamics/heisenberg_model.py b/pr-2308/sphinx/examples/python/dynamics/heisenberg_model.py
deleted file mode 100644
index a094ca0daa..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/heisenberg_model.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import cudaq
-from cudaq import operators, spin, Schedule, ScipyZvodeIntegrator
-
-import numpy as np
-import cupy as cp
-import matplotlib.pyplot as plt
-import os
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# In this example, we solve the Quantum Heisenberg model (https://en.wikipedia.org/wiki/Quantum_Heisenberg_model),
-# which exhibits the so-called quantum quench effect.
-# e.g., see `Quantum quenches in the anisotropic spin-1/2 Heisenberg chain: different approaches to many-body dynamics far from equilibrium`
-# (New J. Phys. 12 055017)
-# Number of spins
-N = 9
-dimensions = {}
-for i in range(N):
-    dimensions[i] = 2
-
-# Initial state: alternating spin up and down
-spin_state = ''
-for i in range(N):
-    spin_state += str(int(i % 2))
-
-# Observable is the staggered magnetization operator
-staggered_magnetization_op = operators.zero()
-for i in range(N):
-    if i % 2 == 0:
-        staggered_magnetization_op += spin.z(i)
-    else:
-        staggered_magnetization_op -= spin.z(i)
-
-staggered_magnetization_op /= N
-
-observe_results = []
-for g in [0.0, 0.25, 4.0]:
-    # Heisenberg model spin coupling strength
-    Jx = 1.0
-    Jy = 1.0
-    Jz = g
-
-    # Construct the Hamiltonian
-    H = operators.zero()
-
-    for i in range(N - 1):
-        H += Jx * spin.x(i) * spin.x(i + 1)
-        H += Jy * spin.y(i) * spin.y(i + 1)
-        H += Jz * spin.z(i) * spin.z(i + 1)
-
-    steps = np.linspace(0.0, 5, 1000)
-    schedule = Schedule(steps, ["time"])
-
-    # Prepare the initial state vector
-    psi0_ = cp.zeros(2**N, dtype=cp.complex128)
-    psi0_[int(spin_state, 2)] = 1.0
-    psi0 = cudaq.State.from_data(psi0_)
-
-    # Run the simulation
-    evolution_result = cudaq.evolve(H,
-                                    dimensions,
-                                    schedule,
-                                    psi0,
-                                    observables=[staggered_magnetization_op],
-                                    collapse_operators=[],
-                                    store_intermediate_results=True,
-                                    integrator=ScipyZvodeIntegrator())
-
-    exp_val = [
-        exp_vals[0].expectation()
-        for exp_vals in evolution_result.expectation_values()
-    ]
-
-    observe_results.append((g, exp_val))
-
-# Plot the results
-fig = plt.figure(figsize=(12, 6))
-for g, exp_val in observe_results:
-    plt.plot(steps, exp_val, label=f'$ g = {g}$')
-plt.legend(fontsize=16)
-plt.ylabel("Staggered Magnetization")
-plt.xlabel("Time")
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig("heisenberg_model.png", dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/landau_zener.py b/pr-2308/sphinx/examples/python/dynamics/landau_zener.py
deleted file mode 100644
index 3f94b7c7c8..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/landau_zener.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import cudaq
-from cudaq import spin, operators, ScalarOperator, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# This example simulates the so-called Landau–Zener transition: given a time-dependent Hamiltonian such that the energy separation
-# of the two states is a linear function of time, an analytical formula exists to calculate
-# the probability of finding the system in the excited state after the transition.
-
-# References:
-# - https://en.wikipedia.org/wiki/Landau%E2%80%93Zener_formula
-# - `The Landau-Zener formula made simple`, `Eric P Glasbrenner and Wolfgang P Schleich 2023 J. Phys. B: At. Mol. Opt. Phys. 56 104001`
-# - QuTiP notebook: https://github.com/qutip/qutip-notebooks/blob/master/examples/landau-zener.ipynb
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Define some shorthand operators
-sx = spin.x(0)
-sz = spin.z(0)
-sm = operators.annihilate(0)
-sm_dag = operators.create(0)
-
-# Dimensions of sub-system. We only have a single degree of freedom of dimension 2 (two-level system).
-dimensions = {0: 2}
-
-# Landau–Zener Hamiltonian:
-# `[[-alpha*t, g], [g, alpha*t]] = g * pauli_x - alpha * t * pauli_z`
-g = 2 * np.pi
-# Analytical equation:
-# `P(0) = exp(-pi * g ^ 2/ alpha)`
-# The target ground state probability that we want to achieve
-target_p0 = 0.75
-# Compute `alpha` parameter:
-alpha = (-np.pi * g**2) / np.log(target_p0)
-
-# Hamiltonian
-hamiltonian = g * sx - alpha * ScalarOperator(lambda t: t) * sz
-
-# Initial state of the system (ground state)
-psi0 = cudaq.State.from_data(cp.array([1.0, 0.0], dtype=cp.complex128))
-
-# Schedule of time steps (simulating a long time range)
-steps = np.linspace(-2.0, 2.0, 5000)
-schedule = Schedule(steps, ["t"])
-
-# Run the simulation.
-evolution_result = cudaq.evolve(hamiltonian,
-                                dimensions,
-                                schedule,
-                                psi0,
-                                observables=[operators.number(0)],
-                                collapse_operators=[],
-                                store_intermediate_results=True,
-                                integrator=ScipyZvodeIntegrator())
-
-prob1 = [
-    exp_vals[0].expectation()
-    for exp_vals in evolution_result.expectation_values()
-]
-
-prob0 = [1 - val for val in prob1]
-fig, ax = plt.subplots(figsize=(12, 8))
-ax.plot(steps, prob1, 'b', steps, prob0, 'r')
-ax.plot(steps, (1.0 - target_p0) * np.ones(np.shape(steps)), 'k')
-ax.plot(steps, target_p0 * np.ones(np.shape(steps)), 'm')
-ax.set_xlabel("Time")
-ax.set_ylabel("Occupation probability")
-ax.set_title("Landau-Zener transition")
-ax.legend(("Excited state", "Ground state", "LZ formula (Excited state)",
-           "LZ formula (Ground state)"),
-          loc=0)
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-plt.savefig('landau_zener.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/pulse.py b/pr-2308/sphinx/examples/python/dynamics/pulse.py
deleted file mode 100644
index d77d84b18b..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/pulse.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import cudaq
-from cudaq import spin, operators, ScalarOperator, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# This example simulates time evolution of a qubit (`transmon`) being driven by a pulse.
-# The pulse is a modulated signal with a Gaussian envelop.
-# The simulation is performed in the 'lab' frame.
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Device parameters
-# Strength of the Rabi-rate in GHz.
-rabi_rate = 0.1
-
-# Frequency of the qubit transition in GHz.
-omega = 5.0 * 2 * np.pi
-
-# Define Gaussian envelope function to approximately implement a `rx(pi/2)` gate.
-amplitude = 1. / 2.0  # Pi/2 rotation
-sigma = 1.0 / rabi_rate / amplitude
-pulse_duration = 6 * sigma
-
-
-def gaussian(t, duration, amplitude, sigma):
-    # Gaussian envelope function
-    return amplitude * np.exp(-0.5 * (t - duration / 2)**2 / (sigma)**2)
-
-
-def signal(t):
-    # Modulated signal
-    return np.cos(omega * t) * gaussian(t, pulse_duration, amplitude, sigma)
-
-
-# Qubit Hamiltonian
-hamiltonian = omega * spin.z(0) / 2
-# Add modulated driving term to the Hamiltonian
-hamiltonian += np.pi * rabi_rate * ScalarOperator(signal) * spin.x(0)
-
-# Dimensions of sub-system. We only have a single degree of freedom of dimension 2 (two-level system).
-dimensions = {0: 2}
-
-# Initial state of the system (ground state).
-psi0 = cudaq.State.from_data(cp.array([1.0, 0.0], dtype=cp.complex128))
-
-# Schedule of time steps.
-# Since this is a lab-frame simulation, the time step must be small to accurately capture the modulated signal.
-dt = 1 / omega / 20
-n_steps = int(np.ceil(pulse_duration / dt)) + 1
-steps = np.linspace(0, pulse_duration, n_steps)
-schedule = Schedule(steps, ["t"])
-
-# Run the simulation.
-# First, we run the simulation without any collapse operators (no decoherence).
-evolution_result = cudaq.evolve(hamiltonian,
-                                dimensions,
-                                schedule,
-                                psi0,
-                                observables=[operators.number(0)],
-                                collapse_operators=[],
-                                store_intermediate_results=True,
-                                integrator=ScipyZvodeIntegrator())
-
-pop1 = [
-    exp_vals[0].expectation()
-    for exp_vals in evolution_result.expectation_values()
-]
-pop0 = [1.0 - x for x in pop1]
-fig = plt.figure(figsize=(6, 16))
-envelop = [gaussian(t, pulse_duration, amplitude, sigma) for t in steps]
-
-plt.subplot(3, 1, 1)
-plt.plot(steps, envelop)
-plt.ylabel("Amplitude")
-plt.xlabel("Time")
-plt.title("Envelope")
-
-modulated_signal = [
-    np.cos(omega * t) * gaussian(t, pulse_duration, amplitude, sigma)
-    for t in steps
-]
-plt.subplot(3, 1, 2)
-plt.plot(steps, modulated_signal)
-plt.ylabel("Amplitude")
-plt.xlabel("Time")
-plt.title("Signal")
-
-plt.subplot(3, 1, 3)
-plt.plot(steps, pop0)
-plt.plot(steps, pop1)
-plt.ylabel("Population")
-plt.xlabel("Time")
-plt.legend(("Population in |0>", "Population in |1>"))
-plt.title("Qubit State")
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig("pulse.png", dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/qubit_control.py b/pr-2308/sphinx/examples/python/dynamics/qubit_control.py
deleted file mode 100644
index 1d0122f7ea..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/qubit_control.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import cudaq
-from cudaq import spin, ScalarOperator, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# This example simulates time evolution of a qubit (`transmon`) being driven close to resonance in the presence of noise (decoherence).
-# Thus, it exhibits Rabi oscillations.
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Qubit Hamiltonian reference: https://qiskit-community.github.io/qiskit-dynamics/tutorials/Rabi_oscillations.html
-# Device parameters
-# Qubit resonant frequency
-omega_z = 10.0 * 2 * np.pi
-# Transverse term
-omega_x = 2 * np.pi
-# Harmonic driving frequency
-# Note: we chose a frequency slightly different from the resonant frequency to demonstrate the off-resonance effect.
-omega_drive = 0.99 * omega_z
-
-# Qubit Hamiltonian
-hamiltonian = 0.5 * omega_z * spin.z(0)
-# Add modulated driving term to the Hamiltonian
-hamiltonian += omega_x * ScalarOperator(
-    lambda t: np.cos(omega_drive * t)) * spin.x(0)
-
-# Dimensions of sub-system. We only have a single degree of freedom of dimension 2 (two-level system).
-dimensions = {0: 2}
-
-# Initial state of the system (ground state).
-rho0 = cudaq.State.from_data(
-    cp.array([[1.0, 0.0], [0.0, 0.0]], dtype=cp.complex128))
-
-# Schedule of time steps.
-t_final = np.pi / omega_x
-dt = 2.0 * np.pi / omega_drive / 100
-n_steps = int(np.ceil(t_final / dt)) + 1
-steps = np.linspace(0, t_final, n_steps)
-schedule = Schedule(steps, ["t"])
-
-# Run the simulation.
-# First, we run the simulation without any collapse operators (no decoherence).
-evolution_result = cudaq.evolve(hamiltonian,
-                                dimensions,
-                                schedule,
-                                rho0,
-                                observables=[spin.x(0),
-                                             spin.y(0),
-                                             spin.z(0)],
-                                collapse_operators=[],
-                                store_intermediate_results=True,
-                                integrator=ScipyZvodeIntegrator())
-
-# Now, run the simulation with qubit decoherence
-gamma_sm = 4.0
-gamma_sz = 1.0
-evolution_result_decay = cudaq.evolve(
-    hamiltonian,
-    dimensions,
-    schedule,
-    rho0,
-    observables=[spin.x(0), spin.y(0), spin.z(0)],
-    collapse_operators=[
-        np.sqrt(gamma_sm) * spin.plus(0),
-        np.sqrt(gamma_sz) * spin.z(0)
-    ],
-    store_intermediate_results=True,
-    integrator=ScipyZvodeIntegrator())
-
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-ideal_results = [
-    get_result(0, evolution_result),
-    get_result(1, evolution_result),
-    get_result(2, evolution_result)
-]
-decoherence_results = [
-    get_result(0, evolution_result_decay),
-    get_result(1, evolution_result_decay),
-    get_result(2, evolution_result_decay)
-]
-
-fig = plt.figure(figsize=(18, 6))
-
-plt.subplot(1, 2, 1)
-plt.plot(steps, ideal_results[0])
-plt.plot(steps, ideal_results[1])
-plt.plot(steps, ideal_results[2])
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Sigma-X", "Sigma-Y", "Sigma-Z"))
-plt.title("No decoherence")
-
-plt.subplot(1, 2, 2)
-plt.plot(steps, decoherence_results[0])
-plt.plot(steps, decoherence_results[1])
-plt.plot(steps, decoherence_results[2])
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Sigma-X", "Sigma-Y", "Sigma-Z"))
-plt.title("With decoherence")
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig('qubit_control.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/qubit_dynamics.py b/pr-2308/sphinx/examples/python/dynamics/qubit_dynamics.py
deleted file mode 100644
index 69dab0958e..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/qubit_dynamics.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import cudaq
-from cudaq import spin, Schedule, RungeKuttaIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Qubit Hamiltonian
-hamiltonian = 2 * np.pi * 0.1 * spin.x(0)
-
-# Dimensions of sub-system. We only have a single degree of freedom of dimension 2 (two-level system).
-dimensions = {0: 2}
-
-# Initial state of the system (ground state).
-rho0 = cudaq.State.from_data(
-    cp.array([[1.0, 0.0], [0.0, 0.0]], dtype=cp.complex128))
-
-# Schedule of time steps.
-steps = np.linspace(0, 10, 101)
-schedule = Schedule(steps, ["time"])
-
-# Run the simulation.
-# First, we run the simulation without any collapse operators (ideal).
-evolution_result = cudaq.evolve(hamiltonian,
-                                dimensions,
-                                schedule,
-                                rho0,
-                                observables=[spin.y(0), spin.z(0)],
-                                collapse_operators=[],
-                                store_intermediate_results=True,
-                                integrator=RungeKuttaIntegrator())
-
-# Now, run the simulation with qubit decaying due to the presence of a collapse operator.
-evolution_result_decay = cudaq.evolve(
-    hamiltonian,
-    dimensions,
-    schedule,
-    rho0,
-    observables=[spin.y(0), spin.z(0)],
-    collapse_operators=[np.sqrt(0.05) * spin.x(0)],
-    store_intermediate_results=True,
-    integrator=RungeKuttaIntegrator())
-
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-ideal_results = [
-    get_result(0, evolution_result),
-    get_result(1, evolution_result)
-]
-decay_results = [
-    get_result(0, evolution_result_decay),
-    get_result(1, evolution_result_decay)
-]
-
-fig = plt.figure(figsize=(18, 6))
-
-plt.subplot(1, 2, 1)
-plt.plot(steps, ideal_results[0])
-plt.plot(steps, ideal_results[1])
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Sigma-Y", "Sigma-Z"))
-plt.title("No decay")
-
-plt.subplot(1, 2, 2)
-plt.plot(steps, decay_results[0])
-plt.plot(steps, decay_results[1])
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Sigma-Y", "Sigma-Z"))
-plt.title("With decay")
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig('qubit_dynamics.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/silicon_spin_qubit.py b/pr-2308/sphinx/examples/python/dynamics/silicon_spin_qubit.py
deleted file mode 100644
index 677c0087b5..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/silicon_spin_qubit.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import cudaq
-from cudaq import spin, operators, Schedule, ScalarOperator, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# This example demonstrates simulation of an electrically-driven silicon spin qubit.
-# The system dynamics is taken from https://journals.aps.org/prapplied/pdf/10.1103/PhysRevApplied.19.044078
-
-dimensions = {0: 2}
-resonance_frequency = 2 * np.pi * 10  # 10 Ghz
-
-# Run the simulation:
-evolution_results = []
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-# Sweep the amplitude
-amplitudes = np.linspace(0.0, 0.5, 20)
-for amplitude in amplitudes:
-    rho0 = cudaq.State.from_data(cp.array([1.0, 0.0], dtype=cp.complex128))
-    t_final = 100
-    dt = 0.005
-    n_steps = int(np.ceil(t_final / dt)) + 1
-    steps = np.linspace(0, t_final, n_steps)
-    schedule = Schedule(steps, ["t"])
-    # Electric dipole spin resonance (`EDSR`) Hamiltonian
-    H = 0.5 * resonance_frequency * spin.z(0) + ScalarOperator(
-        lambda t: 0.5 * np.sin(resonance_frequency * t) * amplitude) * spin.x(0)
-    evolution_result = cudaq.evolve(H,
-                                    dimensions,
-                                    schedule,
-                                    rho0,
-                                    observables=[operators.number(0)],
-                                    collapse_operators=[],
-                                    store_intermediate_results=True,
-                                    integrator=ScipyZvodeIntegrator())
-    evolution_results.append(get_result(0, evolution_result))
-
-fig, ax = plt.subplots()
-im = ax.contourf(steps, amplitudes, evolution_results)
-ax.set_xlabel("Time (ns)")
-ax.set_ylabel(f"Amplitude (a.u.)")
-fig.suptitle(f"Excited state probability")
-fig.colorbar(im)
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-# For reference, see figure 5 in https://journals.aps.org/prapplied/pdf/10.1103/PhysRevApplied.19.044078
-fig.savefig("spin_qubit_edsr.png", dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/tensor_callback.py b/pr-2308/sphinx/examples/python/dynamics/tensor_callback.py
deleted file mode 100644
index b0592b0774..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/tensor_callback.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import cudaq
-from cudaq import ElementaryOperator, spin, operators, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# This example demonstrates the use of callback functions to define time-dependent operators.
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Consider a simple 2-level system Hamiltonian exhibits the Landau–Zener transition:
-# `[[-alpha*t, g], [g, alpha*t]]
-# This can be defined as a callback tensor:
-g = 2.0 * np.pi
-alpha = 10.0 * 2 * np.pi
-
-
-def callback_tensor(t):
-    return np.array([[-alpha * t, g], [g, alpha * t]], dtype=np.complex128)
-
-
-# Analytical formula
-lz_formula_p0 = np.exp(-np.pi * g**2 / (alpha))
-lz_formula_p1 = 1.0 - lz_formula_p0
-
-# Let's define the control term as a callback tensor that acts on 2-level systems
-ElementaryOperator.define("lz_op", [2], callback_tensor)
-
-# Hamiltonian
-hamiltonian = ElementaryOperator("lz_op", [0])
-
-# Dimensions of sub-system. We only have a single degree of freedom of dimension 2 (two-level system).
-dimensions = {0: 2}
-
-# Initial state of the system (ground state)
-psi0 = cudaq.State.from_data(cp.array([1.0, 0.0], dtype=cp.complex128))
-
-# Schedule of time steps (simulating a long time range)
-steps = np.linspace(-4.0, 4.0, 10000)
-schedule = Schedule(steps, ["t"])
-
-# Run the simulation.
-evolution_result = cudaq.evolve(hamiltonian,
-                                dimensions,
-                                schedule,
-                                psi0,
-                                observables=[operators.number(0)],
-                                collapse_operators=[],
-                                store_intermediate_results=True,
-                                integrator=ScipyZvodeIntegrator())
-
-prob1 = [
-    exp_vals[0].expectation()
-    for exp_vals in evolution_result.expectation_values()
-]
-
-prob0 = [1 - val for val in prob1]
-fig, ax = plt.subplots(figsize=(12, 8))
-ax.plot(steps, prob1, 'b', steps, prob0, 'r')
-ax.plot(steps, lz_formula_p1 * np.ones(np.shape(steps)), 'k')
-ax.plot(steps, lz_formula_p0 * np.ones(np.shape(steps)), 'm')
-ax.set_xlabel("Time")
-ax.set_ylabel("Occupation probability")
-ax.set_title("Landau-Zener transition")
-ax.legend(("Excited state", "Ground state", "LZ formula (Excited state)",
-           "LZ formula (Ground state)"),
-          loc=0)
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig('tensor_callback.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/dynamics/transmon_resonator.py b/pr-2308/sphinx/examples/python/dynamics/transmon_resonator.py
deleted file mode 100644
index 9cea953bfb..0000000000
--- a/pr-2308/sphinx/examples/python/dynamics/transmon_resonator.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import cudaq
-from cudaq import operators, spin, Schedule, ScipyZvodeIntegrator
-import numpy as np
-import cupy as cp
-import os
-import matplotlib.pyplot as plt
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# This example demonstrates a simulation of a superconducting transmon qubit coupled to a resonator (i.e., cavity).
-# References:
-# - "Charge-insensitive qubit design derived from the Cooper pair box", PRA 76, 042319
-# - QuTiP lecture: https://github.com/jrjohansson/qutip-lectures/blob/master/Lecture-10-cQED-dispersive-regime.ipynb
-
-# Number of cavity photons
-N = 20
-
-# System dimensions: transmon + cavity
-dimensions = {0: 2, 1: N}
-
-# See III.B of PRA 76, 042319
-# System parameters
-# Unit: GHz
-omega_01 = 3.0 * 2 * np.pi  # transmon qubit frequency
-omega_r = 2.0 * 2 * np.pi  # resonator frequency
-# Dispersive shift
-chi_01 = 0.025 * 2 * np.pi
-chi_12 = 0.0
-
-omega_01_prime = omega_01 + chi_01
-omega_r_prime = omega_r - chi_12 / 2.0
-chi = chi_01 - chi_12 / 2.0
-
-# System Hamiltonian
-hamiltonian = 0.5 * omega_01_prime * spin.z(0) + (
-    omega_r_prime + chi * spin.z(0)) * operators.number(1)
-
-# Initial state of the system
-# Transmon in a superposition state
-transmon_state = cp.array([1. / np.sqrt(2.), 1. / np.sqrt(2.)],
-                          dtype=cp.complex128)
-
-
-# Helper to create a coherent state in Fock basis truncated at `num_levels`.
-# Note: There are a couple of ways of generating a coherent state,
-# e.g., see https://qutip.readthedocs.io/en/v5.0.3/apidoc/functions.html#qutip.core.states.coherent
-# or https://en.wikipedia.org/wiki/Coherent_state
-# Here, in this example, we use a the formula: `|alpha> = D(alpha)|0>`,
-# i.e., apply the displacement operator on a zero (or vacuum) state to compute the corresponding coherent state.
-def coherent_state(num_levels, amplitude):
-    displace_mat = operators.displace(0).to_matrix({0: num_levels},
-                                                   displacement=amplitude)
-    # `D(alpha)|0>` is the first column of `D(alpha)` matrix
-    return cp.array(np.transpose(displace_mat)[0])
-
-
-# Cavity in a coherent state
-cavity_state = coherent_state(N, 2.0)
-psi0 = cudaq.State.from_data(cp.kron(transmon_state, cavity_state))
-
-steps = np.linspace(0, 250, 1000)
-schedule = Schedule(steps, ["time"])
-
-# Evolve the system
-evolution_result = cudaq.evolve(hamiltonian,
-                                dimensions,
-                                schedule,
-                                psi0,
-                                observables=[
-                                    operators.number(1),
-                                    operators.number(0),
-                                    operators.position(1),
-                                    operators.position(0)
-                                ],
-                                collapse_operators=[],
-                                store_intermediate_results=True,
-                                integrator=ScipyZvodeIntegrator())
-
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-count_results = [
-    get_result(0, evolution_result),
-    get_result(1, evolution_result)
-]
-
-quadrature_results = [
-    get_result(2, evolution_result),
-    get_result(3, evolution_result)
-]
-
-fig = plt.figure(figsize=(18, 6))
-
-plt.subplot(1, 2, 1)
-plt.plot(steps, count_results[0])
-plt.plot(steps, count_results[1])
-plt.ylabel("n")
-plt.xlabel("Time [ns]")
-plt.legend(("Cavity Photon Number", "Transmon Excitation Probability"))
-plt.title("Excitation Numbers")
-
-plt.subplot(1, 2, 2)
-plt.plot(steps, quadrature_results[0])
-plt.ylabel("x")
-plt.xlabel("Time [ns]")
-plt.legend(("cavity"))
-plt.title("Resonator Quadrature")
-
-abspath = os.path.abspath(__file__)
-dname = os.path.dirname(abspath)
-os.chdir(dname)
-fig.savefig('transmon_resonator.png', dpi=fig.dpi)
diff --git a/pr-2308/sphinx/examples/python/executing_kernels.ipynb b/pr-2308/sphinx/examples/python/executing_kernels.ipynb
deleted file mode 100644
index 0d3250c8c1..0000000000
--- a/pr-2308/sphinx/examples/python/executing_kernels.ipynb
+++ /dev/null
@@ -1,284 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Executing Quantum Circuits \n",
-    "\n",
-    "In CUDA-Q, there are 3 ways in which one can execute quantum kernels: \n",
-    "\n",
-    "1. `sample`: yields measurement counts \n",
-    "2. `observe`: yields expectation values \n",
-    "3. `get_state`: yields the quantum statevector of the computation \n",
-    "\n",
-    "## Sample\n",
-    "\n",
-    "Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q `sample` call enables this: \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮     \n",
-      "q0 : ┤ h ├──●──\n",
-      "     ╰───╯╭─┴─╮\n",
-      "q1 : ─────┤ x ├\n",
-      "          ╰───╯\n",
-      "\n",
-      "{ 11:506 00:494 }\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np \n",
-    "\n",
-    "qubit_count = 2\n",
-    "\n",
-    "# Define the simulation target.\n",
-    "cudaq.set_target(\"qpp-cpu\")\n",
-    "\n",
-    "# Define a quantum kernel function.\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_count: int):\n",
-    "    qvector = cudaq.qvector(qubit_count)\n",
-    "\n",
-    "    # 2-qubit GHZ state.\n",
-    "    h(qvector[0])\n",
-    "    for i in range(1, qubit_count):\n",
-    "        x.ctrl(qvector[0], qvector[i])\n",
-    "\n",
-    "    # If we dont specify measurements, all qubits are measured in\n",
-    "    # the Z-basis by default or we can manually specify it also \n",
-    "    # mz(qvector)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, qubit_count))\n",
-    "\n",
-    "result = cudaq.sample(kernel, qubit_count, shots_count=1000)\n",
-    "\n",
-    "print(result)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that there is a subtle difference between how `sample` is executed with the target device set to a simulator or with the target device set to a QPU. In simulation mode, the quantum state is built once and then sampled $s$ times where $s$ equals the `shots_count`. In hardware execution mode, the quantum state collapses upon measurement and hence needs to be rebuilt over and over again.\n",
-    "\n",
-    "There are a number of helpful tools that can be found in the [API docs](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api) to process the `Sample_Result` object produced by `sample`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Observe\n",
-    "\n",
-    "The `observe` function allows us to calculate expectation values. We must supply a spin operator in the form of a Hamiltonian, $H$,  from which we would like to calculate $\\bra{\\psi}H\\ket{\\psi}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<H> = 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from cudaq import spin\n",
-    "\n",
-    "# Define a Hamiltonian in terms of Pauli Spin operators.\n",
-    "hamiltonian = spin.z(0) + spin.y(1) + spin.x(0) * spin.z(0)\n",
-    "\n",
-    "# Compute the expectation value given the state prepared by the kernel.\n",
-    "result = cudaq.observe(kernel, hamiltonian, qubit_count).expectation()\n",
-    "\n",
-    "print('<H> =', result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Get state\n",
-    "\n",
-    "The `get_state` function gives us access to the quantum statevector of the computation. Remember, that this is only feasible in simulation mode. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.70710678+0.j 0.        +0.j 0.        +0.j 0.70710678+0.j]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute the statevector of the kernel\n",
-    "result = cudaq.get_state(kernel, qubit_count)\n",
-    "\n",
-    "print(np.array(result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statevector generated by the `get_state` command follows Big-endian convention for associating numbers with their binary representations, which places the least significant bit on the left.  That is, for the example of a 2-bit system, we have the following translation between integers and bits:\n",
-    "$$\\begin{matrix} \\text{Integer} & \\text{Binary representation}\\\\\n",
-    "& \\text{least signinificant bit on left}\\\\\n",
-    "0 =\\textcolor{red}{0}*2^0+\\textcolor{blue}{0}*2^1 & \\textcolor{red}{0}\\textcolor{blue}{0} \\\\\n",
-    "1 = \\textcolor{red}{1}*2^0 + \\textcolor{blue}{0} *2^1 & \\textcolor{red}{1}\\textcolor{blue}{0}\\\\\n",
-    "2 = \\textcolor{red}{0}*2^0 + \\textcolor{blue}{1}*2^1 & \\textcolor{red}{0}\\textcolor{blue}{1} \\\\\n",
-    "3 = \\textcolor{red}{1}*2^0 + \\textcolor{blue}{1}*2^1 & \\textcolor{red}{1}\\textcolor{blue}{1} \\end{matrix}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Parallelization Techniques\n",
-    "\n",
-    "The most intensive task in the computation is the execution of the quantum kernel hence each execution function: `sample`, `observe` and `get_state` can be parallelized given access to multiple quantum processing units (multi-QPU). \n",
-    "\n",
-    "Since multi-QPU platforms are not yet feasible, we emulate each QPU with a GPU.\n",
-    "\n",
-    "\n",
-    "### Observe Async\n",
-    "\n",
-    "Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result. \n",
-    "\n",
-    "`observe` can be a time intensive task. We can parallelize the execution of `observe` via the arguments it accepts. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.220446049250313e-16\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set the simulation target to a multi-QPU platform \n",
-    "# cudaq.set_target(\"nvidia\", option = 'mqpu')\n",
-    "\n",
-    "# Measuring the expectation value of 2 different hamiltonians in parallel\n",
-    "hamiltonian_1 = spin.x(0) + spin.y(1) + spin.z(0)*spin.y(1)\n",
-    "# hamiltonian_2 = spin.z(1) + spin.y(0) + spin.x(1)*spin.x(0)\n",
-    "\n",
-    "# Asynchronous execution on multiple qpus via nvidia gpus.\n",
-    "result_1 = cudaq.observe_async(kernel, hamiltonian_1, qubit_count, qpu_id=0)\n",
-    "# result_2 = cudaq.observe_async(kernel, hamiltonian_2, qubit_count, qpu_id=1)\n",
-    "\n",
-    "# Retrieve results \n",
-    "print(result_1.get().expectation())\n",
-    "# print(result_2.get().expectation())"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Above we parallelized the `observe` call over the `hamiltonian` parameter however we can parallelize over any of the argument it accepts by just iterating obver the `qpu_id`."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sample Async\n",
-    "\n",
-    "Similar to `observe_async` above, `sample` also supports asynchronous execution for the [arguments it accepts](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.sample_async(kernel%3A%20object%2C%20%5C*args%2C%20shots_count%3A%20int%20%3D%201000%2C%20qpu_id%3A%20int%20%3D%200)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.AsyncSampleResult). One can parallelize over various kernels, variational parameters or even distribute shots counts over multiple QPUs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get State Async\n",
-    "\n",
-    "Similar to `sample_async` above, `get_state` also supports asynchronous execution for the [arguments it accepts](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.sample_async:~:text=cudaq.get_state(arg0%3A%20object%2C%20%5C*args)%E2%86%92%20cudaq.mlir._mlir_libs._quakeDialects.cudaq_runtime.State)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUDA-Q Version  (https://github.com/NVIDIA/cuda-quantum 0eb6b444eb5b3a687e6fd64529ee9223aaa2870e)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.__version__)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/sphinx/examples/python/intro.py b/pr-2308/sphinx/examples/python/intro.py
deleted file mode 100644
index 68b4b901ab..0000000000
--- a/pr-2308/sphinx/examples/python/intro.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import cudaq
-
-
-# We begin by defining the `Kernel` that we will construct our
-# program with.
-@cudaq.kernel
-def kernel():
-    '''
-    This is our first CUDA-Q kernel.
-    '''
-    # Next, we can allocate a single qubit to the kernel via `qubit()`.
-    qubit = cudaq.qubit()
-
-    # Now we can begin adding instructions to apply to this qubit!
-    # Here we'll just add every non-parameterized
-    # single qubit gate that is supported by CUDA-Q.
-    h(qubit)
-    x(qubit)
-    y(qubit)
-    z(qubit)
-    t(qubit)
-    s(qubit)
-
-    # Next, we add a measurement to the kernel so that we can sample
-    # the measurement results on our simulator!
-    mz(qubit)
-
-
-# Finally, we can execute this kernel on the state vector simulator
-# by calling `cudaq.sample`. This will execute the provided kernel
-# `shots_count` number of times and return the sampled distribution
-# as a `cudaq.SampleResult` dictionary.
-result = cudaq.sample(kernel)
-
-# Now let's take a look at the `SampleResult` we've gotten back!
-print(result)
diff --git a/pr-2308/sphinx/examples/python/measuring_kernels.ipynb b/pr-2308/sphinx/examples/python/measuring_kernels.ipynb
deleted file mode 100644
index 40fa54bff8..0000000000
--- a/pr-2308/sphinx/examples/python/measuring_kernels.ipynb
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13858fc6-edc7-49cb-8b56-152ebec2f729",
-   "metadata": {},
-   "source": [
-    "# Measurements\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ec9af8fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa410efc-fcb1-43e1-ab99-25a7fcbab868",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Kernel measurement can be specified in the Z, X, or Y basis using `mz`, `mx`, and `my`. If a measurement is specified with no argument, the entire kernel is measured in that basis. Measurement occurs in the Z basis by default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7f45a62e-48cb-4705-8081-e59782c91b5f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    mz()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39efaac5-5400-4df6-ac5d-80637d9d3082",
-   "metadata": {},
-   "source": [
-    "Specific qubits or registers can be measured rather than the entire kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "16a14177-fa79-4eb3-8fc8-f3e937bc21b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    qubits_a = cudaq.qvector(2)\n",
-    "    qubit_b = cudaq.qubit()\n",
-    "    mz(qubits_a)\n",
-    "    mx(qubit_b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb5dd767-5db7-4847-b04e-ae5695066800",
-   "metadata": {},
-   "source": [
-    "### Midcircuit Measurement and Conditional Logic\n",
-    "\n",
-    "In certain cases, it it is helpful for some operations in a quantum kernel to depend on measurement results following previous operations. This is accomplished in the following example by performing a Hadamard on qubit 0, then measuring qubit 0 and savig the result as `b0`. Then, an if statement performs a Hadamard on qubit 1 only if `b0` is 1. Measuring this qubit 1 verifies this process as a 1 is the result 25% of the time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "44001a51-3733-472c-8bc1-ee694e957708",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel():\n",
-    "    q = cudaq.qvector(2)\n",
-    "    h(q[0])\n",
-    "    b0 = mz(q[0])\n",
-    "    if b0:\n",
-    "        h(q[1])\n",
-    "        mz(q[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/examples/python/multi_controlled_operations.py b/pr-2308/sphinx/examples/python/multi_controlled_operations.py
deleted file mode 100644
index 54beb1071e..0000000000
--- a/pr-2308/sphinx/examples/python/multi_controlled_operations.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import cudaq
-
-# Here we demonstrate how one might apply multi-controlled
-# operations on a general CUDA-Q kernel.
-
-
-# [Begin OptionA]
-# A kernel that performs an X-gate on a provided qubit.
-@cudaq.kernel
-def x_kernel(qubit: cudaq.qubit):
-    x(qubit)
-
-
-# A kernel that will call `x_kernel` as a controlled operation.
-@cudaq.kernel
-def kernel():
-    control_vector = cudaq.qvector(2)
-    target = cudaq.qubit()
-    x(control_vector)
-    x(target)
-    x(control_vector[1])
-    cudaq.control(x_kernel, control_vector, target)
-
-
-results = cudaq.sample(kernel)
-print(results)
-# [End OptionA]
-
-
-# [Begin OptionB]
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(3)
-    x(qvector)
-    x(qvector[1])
-    x.ctrl([qvector[0], qvector[1]], qvector[2])
-    mz(qvector)
-
-
-results = cudaq.sample(kernel)
-print(results)
-# [End OptionB]
\ No newline at end of file
diff --git a/pr-2308/sphinx/examples/python/noise_callback.py b/pr-2308/sphinx/examples/python/noise_callback.py
deleted file mode 100644
index 8eba41deeb..0000000000
--- a/pr-2308/sphinx/examples/python/noise_callback.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import cudaq
-import numpy as np
-
-# Set the target to our density matrix simulator.
-cudaq.set_target('density-matrix-cpu')
-
-noise = cudaq.NoiseModel()
-
-
-# Noise model callback function
-def rx_noise(qubits, params):
-    # Model a pulse-length based rotation gate:
-    # the bigger the angle, the longer the pulse, i.e., more amplitude damping.
-    angle = params[0]
-    angle = angle % (2 * np.pi)
-    # Damping rate is linearly proportional to the angle
-    damping_rate = np.abs(angle / (2 * np.pi))
-    print(f"Angle = {angle}, amplitude damping rate = {damping_rate}.")
-    return cudaq.AmplitudeDampingChannel(damping_rate)
-
-
-# Bind the noise model callback function to the `rx` gate
-noise.add_channel('rx', rx_noise)
-
-
-@cudaq.kernel
-def kernel(angle: float):
-    qubit = cudaq.qubit()
-    rx(angle, qubit)
-    mz(qubit)
-
-
-# Now we're ready to run the noisy simulation of our kernel.
-# Note: We must pass the noise model to sample via keyword.
-noisy_result = cudaq.sample(kernel, np.pi, noise_model=noise)
-print(noisy_result)
-
-# Our results should show measurements in both the |0> and |1> states, indicating
-# that the noise has successfully impacted the system.
-# Note: a `rx(pi)` is equivalent to a Pauli X gate, and thus, it should be
-# in the |1> state if no noise is present.
-
-# To confirm this, we can run the simulation again without noise.
-noiseless_result = cudaq.sample(kernel, np.pi)
-print(noiseless_result)
diff --git a/pr-2308/sphinx/examples/python/noisy_simulations.ipynb b/pr-2308/sphinx/examples/python/noisy_simulations.ipynb
deleted file mode 100644
index 38187dc845..0000000000
--- a/pr-2308/sphinx/examples/python/noisy_simulations.ipynb
+++ /dev/null
@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Noisy Simulation \n",
-    "\n",
-    "Quantum noise can be characterized into coherent and incoherent sources of errors that arise during a computation. Coherent noise is commonly due to systematic errors originating from device miscalibrations, for example, gates implementing a rotation $\\theta + \\epsilon$ instead of $\\theta$.\n",
-    "\n",
-    "Incoherent noise has its origins in quantum states being entangled with the environment due to decoherence. This leads to mixed states which are probability distributions over pure states and are described by employing the density matrix formalism. \n",
-    "\n",
-    "We can model incoherent noise via quantum channels which are linear, completely positive, and trace preserving maps. These maps are called Kraus operators, $\\{ K_i \\}$, which satisfy the condition $\\sum_{i} K_i^\\dagger K_i = \\mathbb{I}$. \n",
-    "\n",
-    "The bit-flip channel flips the qubit with probability $p$ and leaves it unchanged with probability $1-p$. This can be represented by employing Kraus operators: \n",
-    "\n",
-    "$$K_0 = \\sqrt{1-p} \\begin{pmatrix}\n",
-    "    1 & 0 \\\\\n",
-    "    0 & 1\n",
-    "\\end{pmatrix}$$\n",
-    "\n",
-    "$$K_1 = \\sqrt{p} \\begin{pmatrix}\n",
-    "  0 & 1 \\\\\n",
-    "  1 & 0\n",
-    "\\end{pmatrix}$$\n",
-    "\n",
-    "Let's implement the bit-flip channel using CUDA-Q:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "# To model quantum noise, we need to utilize the density matrix simulator target.\n",
-    "cudaq.set_target(\"density-matrix-cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮\n",
-      "q0 : ┤ x ├\n",
-      "     ├───┤\n",
-      "q1 : ┤ x ├\n",
-      "     ╰───╯\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's define a simple kernel that we will add noise to.\n",
-    "qubit_count = 2\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(qubit_count: int):\n",
-    "    qvector = cudaq.qvector(qubit_count)\n",
-    "    x(qvector)\n",
-    "\n",
-    "\n",
-    "print(cudaq.draw(kernel, qubit_count))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11:1000 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "# In the ideal noiseless case, we get |11> 100% of the time.\n",
-    "\n",
-    "ideal_counts = cudaq.sample(kernel, qubit_count, shots_count=1000)\n",
-    "ideal_counts.dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{ 11:844 10:88 01:65 00:3 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, we will define an out of the box noise channel. In this case,\n",
-    "# we choose depolarization noise. This depolarization will result in\n",
-    "# the qubit state decaying into a mix of the basis states, |0> and |1>,\n",
-    "# with our provided probability.\n",
-    "error_probability = 0.1\n",
-    "depolarization_channel = cudaq.DepolarizationChannel(error_probability)\n",
-    "\n",
-    "# Other built in noise models \n",
-    "bit_flip = cudaq.BitFlipChannel(error_probability) \n",
-    "phase_flip = cudaq.PhaseFlipChannel(error_probability)\n",
-    "amplitude_damping = cudaq.AmplitudeDampingChannel(error_probability)\n",
-    "\n",
-    "# We can also define our own, custom noise channels through\n",
-    "# Kraus operators. Here we will define two operators representing\n",
-    "# bit flip errors.\n",
-    "\n",
-    "# Define the Kraus Error Operator as a complex ndarray.\n",
-    "kraus_0 = np.sqrt(1 - error_probability) * np.array([[1.0, 0.0], \n",
-    "                                                     [0.0, 1.0]],\n",
-    "                                                    dtype=np.complex128)\n",
-    "\n",
-    "kraus_1 = np.sqrt(error_probability) * np.array([[0.0, 1.0], \n",
-    "                                                 [1.0, 0.0]],\n",
-    "                                                dtype=np.complex128)\n",
-    "\n",
-    "# Add the Kraus Operator to create a quantum channel.\n",
-    "bitflip_channel = cudaq.KrausChannel([kraus_0, kraus_1])\n",
-    "\n",
-    "# Add noise channels to our noise model.\n",
-    "noise_model = cudaq.NoiseModel()\n",
-    "\n",
-    "# Apply the depolarization channel to any X-gate on the 0th qubit.\n",
-    "noise_model.add_channel(\"x\", [0], depolarization_channel)\n",
-    "# Apply the bitflip channel to any X-gate on the 1st qubit.\n",
-    "noise_model.add_channel(\"x\", [1], bitflip_channel)\n",
-    "\n",
-    "# Due to the impact of noise, our measurements will no longer be uniformly\n",
-    "# in the |11> state.\n",
-    "noisy_counts = cudaq.sample(kernel,\n",
-    "                            qubit_count,\n",
-    "                            noise_model=noise_model,\n",
-    "                            shots_count=1000)\n",
-    "noisy_counts.dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.8666666666666666"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can also use noise models with the observe function\n",
-    "\n",
-    "hamiltonian = spin.z(0)\n",
-    "\n",
-    "noisy_result = cudaq.observe(kernel,\n",
-    "                             hamiltonian,\n",
-    "                             qubit_count,\n",
-    "                             noise_model=noise_model)\n",
-    "\n",
-    "noisy_result.expectation()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pr-2308/sphinx/examples/python/operators.ipynb b/pr-2308/sphinx/examples/python/operators.ipynb
deleted file mode 100644
index 801b2357d2..0000000000
--- a/pr-2308/sphinx/examples/python/operators.ipynb
+++ /dev/null
@@ -1,99 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9a37411d-09c5-42f4-9d62-132715cead3e",
-   "metadata": {},
-   "source": [
-    "# Operators\n",
-    "\n",
-    "Operators are important constructs for many quantum applications.  This section covers how to define and use spin operators as well as additional tools for defining more sophisticated operators."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3695762-03b5-46d6-9aac-853d1ebca9d1",
-   "metadata": {},
-   "source": [
-    "### Constructing Spin Operators\n",
-    "\n",
-    "The `spin_op`  type provides an abstraction for a general tensor product of Pauli spin operators, and their sums.\n",
-    "\n",
-    "Spin operators are constructed using the `spin.z()`, `spin.y()`, `spin.x()`, and `spin.i()` functions, corresponding to the $Z$, $Y$, $X$, and $I$ Pauli operators. For example,  `spin.z(0)` corresponds to a Pauli $Z$ operation acting on qubit 0.  The example below demonstrates how to construct the following operator $2XYX - 3ZZY$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "29ca747b-ee90-4a83-a41b-6480e0c7b6c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "\n",
-    "operator = 2 * spin.x(0) * spin.y(1) * spin.x(2) - 3 * spin.z(0) * spin.z( 1) * spin.y(2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "222ea088-ea18-46cf-872d-5e76d25e5e93",
-   "metadata": {},
-   "source": [
-    "There are a number of convenient methods for combining, comparing, iterating through, and extracting information from spin operators and can be referenced [here](https://nvidia.github.io/cuda-quantum/latest/api/languages/python_api.html#cudaq.SpinOperator) in the API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "970e6f09-f0db-4aa3-b611-c33e8436aa55",
-   "metadata": {},
-   "source": [
-    "### Pauli Words and Exponentiating Pauli Words\n",
-    "\n",
-    "The `pauli_word` type specifies a string of Pauli operations (e.g. ‘XYXZ’) and is convenient for applying operations based on exponentiated Pauli words.  The code below demonstrates how a list of Pauli words, along with their coefficients, are provided as kernel inputs and converted into operators by the `exp_pauli` function.\n",
-    "\n",
-    "The cell below applies the following operation: $e^{i(0.432XZY +0.324IXX)}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3fe49bcb-0fe1-48e3-aee2-bff882f03fe6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words = ['XYZ', 'IXX']\n",
-    "coefficients = [0.432, 0.324]\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(coefficients: list[float], words: list[cudaq.pauli_word]):\n",
-    "    q = cudaq.qvector(3)\n",
-    "\n",
-    "    for i in range(len(coefficients)):\n",
-    "        exp_pauli(coefficients[i], q, words[i])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/examples/python/optimizers_gradients.ipynb b/pr-2308/sphinx/examples/python/optimizers_gradients.ipynb
deleted file mode 100644
index 35b7442af7..0000000000
--- a/pr-2308/sphinx/examples/python/optimizers_gradients.ipynb
+++ /dev/null
@@ -1,303 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e466cb3d-c036-45f7-af9f-b18b9aa22e8a",
-   "metadata": {},
-   "source": [
-    "# Optimizers and Gradients\n",
-    "\n",
-    "Many quantum algorithms require the optimization of quantum circuit parameters with respect to an expectation value.  CUDA-Q has a number of tools available for optimization techniques.  This example will demonstrate how to optimize the variational parameters of a circuit using:\n",
-    "\n",
-    "1. Built in CUDA-Q optimizers and gradients\n",
-    "2. A Third-Party Optimizer\n",
-    "3. A Parallel parameter shift gradient.\n",
-    "\n",
-    "First, the kernel and Hamiltonian and specified below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a77a963a-6f5c-4751-93c8-b3ccbb5921f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "from cudaq import spin\n",
-    "import numpy as np\n",
-    "\n",
-    "hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(\n",
-    "    0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: list[float]):\n",
-    "\n",
-    "    qubits = cudaq.qvector(2)\n",
-    "    x(qubits[0])\n",
-    "    ry(angles[0], qubits[1])\n",
-    "    x.ctrl(qubits[1], qubits[0])  \n",
-    "\n",
-    "initial_params = np.random.normal(0, np.pi, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c3de68e-754a-4412-8063-78ee9646b4b0",
-   "metadata": {},
-   "source": [
-    "### Built in CUDA-Q Optimizers and Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eea5d9f2-ade2-4411-88a7-b8c5505c29c3",
-   "metadata": {},
-   "source": [
-    "The optimizer and gradient are specified first from a built in CUDA-Q optimizer and gradient technique. An objective function is defined next which uses a lambda expression to evaluate the cost (a CUDA-Q `observe` expectation value). The gradient is calculated using the `compute` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "87387c0a-86cf-4dfb-9130-b1c3054e43d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = cudaq.optimizers.Adam()\n",
-    "gradient = cudaq.gradients.CentralDifference()\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector: list[float],\n",
-    "                       hamiltonian=hamiltonian,\n",
-    "                       gradient_strategy=gradient,\n",
-    "                       kernel=kernel) -> tuple[float, list[float]]:\n",
-    "\n",
-    "        get_result = lambda parameter_vector: cudaq.observe(kernel, hamiltonian, parameter_vector).expectation()\n",
-    "\n",
-    "        cost = get_result(parameter_vector)\n",
-    "    \n",
-    "        gradient_vector = gradient_strategy.compute(parameter_vector, get_result, cost)\n",
-    "\n",
-    "        return cost, gradient_vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b98f7129-d360-4e9c-9ea5-d4173ecb5ef1",
-   "metadata": {},
-   "source": [
-    "Finally, the optimizer is called to return the optimal cost and parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "713d7619-7b62-42b7-bb46-601ed9883e6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "minimized <H> = -1.748382901613712\n",
-      "optimal theta 0 = 0.58409164053813\n"
-     ]
-    }
-   ],
-   "source": [
-    "energy, parameter = optimizer.optimize(dimensions=1,function=objective_function)\n",
-    "\n",
-    "print(f\"\\nminimized <H> = {round(energy,16)}\")\n",
-    "print(f\"optimal theta 0 = {round(parameter[0],16)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a36a46fc-b04b-43e3-ac10-3b2ee59c3418",
-   "metadata": {},
-   "source": [
-    "### Third-Party Optimizers\n",
-    "\n",
-    "The same procedure can be accomplised using any third-party such as SciPy. In this case, a simple cost fucntion is defined and provided as an input for the standard SciPy `minimize` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0541fa5c-3c4e-44e7-bd13-52d3b07af50e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " message: Optimization terminated successfully.\n",
-      " success: True\n",
-      "  status: 1\n",
-      "     fun: -1.7488646919931474\n",
-      "       x: [ 5.944e-01  1.288e+00]\n",
-      "    nfev: 33\n",
-      "   maxcv: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.optimize import minimize\n",
-    "\n",
-    "def cost(theta):\n",
-    "\n",
-    "    exp_val = cudaq.observe(kernel, hamiltonian, theta).expectation()\n",
-    "\n",
-    "    return exp_val\n",
-    "\n",
-    "result = minimize(cost, initial_params ,method='COBYLA', options={'maxiter': 40})\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d73755a8-2c45-41c8-8d9b-d88a0b411869",
-   "metadata": {},
-   "source": [
-    "### Parallel Parameter Shift Gradients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b01d93b-0953-4139-8a90-44414327f726",
-   "metadata": {},
-   "source": [
-    "CUDA-Q's `mqpu` backend allows for parallel computation of parameter shift gradients using multiple simulated QPUs. Gradients computed this way can be used in any of the previously discussed optimization procedures.  Below is an example demonstrating how parallel gradient evaluation can be used for a VQE procedure. \n",
-    "\n",
-    "The parameter shift procedure computes two expectations values for each parameter shifted forwards and backwards. These are used to estimate the gradient contribution for that parameter.\n",
-    "\n",
-    "The following code defines a function that takes a kernel, a Hamiltonian (spin operator), and the circuit parameters and produces a parameter shift gradient with shift `epsilon`. The first step of the function builds `xplus` and `xminus` , arrays consisting of the shifted parameters. \n",
-    "\n",
-    "Next, a for loop iterates over all of the parameters and uses the `cudaq.observe_async` to compute the expectation value.  This command also takes `qpu_id` as an in out which specifies the GPU that will be used to simulate the ith QPU.  In the example below, four GPUs (simulated QPUs) are available so the gradient is batched over four devices. \n",
-    "\n",
-    "The results are saved in the `g_plus` and `g_minus` lists, the elements of which are accessed with commands like  `g_plus[1].expectation()` to compute the finite differences and construct the final gradient. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7508c92a-ff80-4a92-9396-691825b1cabb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import  numpy as np\n",
-    "# cudaq.set_target('nvidia', option = 'mqpu')\n",
-    "\n",
-    "num_qpus = 1\n",
-    "epsilon =np.pi/4\n",
-    "\n",
-    "\n",
-    "def batched_gradient_function(kernel, parameters, hamiltonian, epsilon): \n",
-    "\n",
-    "    #Prepare an array of parameters corresponding to the plus and minus shifts\n",
-    "    x = np.tile(parameters, (len(parameters),1))\n",
-    "    xplus = x + (np.eye(x.shape[0]) * epsilon)\n",
-    "    xminus = x - (np.eye(x.shape[0]) * epsilon)\n",
-    "\n",
-    "    g_plus = []\n",
-    "    g_minus = []\n",
-    "    gradient = []\n",
-    "\n",
-    "    qpu_counter = 0 # Iterate over the number of GPU resources available\n",
-    "    \n",
-    "    \n",
-    "    for i in range(x.shape[0]): \n",
-    "\n",
-    "        g_plus.append(cudaq.observe_async(kernel,hamiltonian, xplus[i], qpu_id = qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1 \n",
-    "\n",
-    "        g_minus.append(cudaq.observe_async(kernel, hamiltonian, xminus[i], qpu_id = qpu_counter%num_qpus))\n",
-    "        qpu_counter += 1 \n",
-    "        \n",
-    "    #use the expectation values to compute the gradient    \n",
-    "    gradient = [(g_plus[i].get().expectation() - g_minus[i].get().expectation()) / (2*epsilon) for i in range(len(g_minus))]\n",
-    "\n",
-    "    return gradient\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a317073-0e29-40d1-bf09-d168e7227f14",
-   "metadata": {},
-   "source": [
-    "This function can be used in a VQE procedure as presented below. First, the gradient is computed using the initial parameters, then the standard VQE construction is used, but the `batched_gradient_function` is used to evaluate the gradient at each step. This objective function will return the cost and gradient at each step and can be used with any SciPy optimizer that uses a gradient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6313f4ed-a620-4982-b587-42e19267ef84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gradient = batched_gradient_function(kernel, initial_params, hamiltonian, epsilon)\n",
-    "\n",
-    "\n",
-    "def objective_function(parameter_vector,\n",
-    "                       hamiltonian=hamiltonian,\n",
-    "                       gradient=gradient,\n",
-    "                       kernel=kernel):\n",
-    "\n",
-    "    cost=cudaq.observe(kernel,hamiltonian,parameter_vector).expectation()\n",
-    "   \n",
-    " \n",
-    "    gradient_vector = batched_gradient_function(kernel, initial_params, hamiltonian, epsilon)\n",
-    "\n",
-    "    return cost, gradient_vector\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "edd702a6-213d-469e-861b-9beee6207284",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " message: Optimization terminated successfully.\n",
-      " success: True\n",
-      "  status: 1\n",
-      "     fun: -1.7488646919931474\n",
-      "       x: [ 5.944e-01  1.288e+00]\n",
-      "    nfev: 33\n",
-      "   maxcv: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "result_vqe=minimize(objective_function,initial_params, method='L-BFGS-B', jac=True, tol=1e-8, options={'maxiter': 5})\n",
-    "print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/examples/python/performance_optimizations.ipynb b/pr-2308/sphinx/examples/python/performance_optimizations.ipynb
deleted file mode 100644
index db8a563f80..0000000000
--- a/pr-2308/sphinx/examples/python/performance_optimizations.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf94a9f4-6be0-4eda-b441-ecc85f89e0c7",
-   "metadata": {},
-   "source": [
-    "# Optimizing Performance\n",
-    "\n",
-    "Performance is a key focus for the CUDA-Q design.  This section highlights some features that advanced users can take advantage of to increase performance in certain situations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dcc9c85-780b-495e-aebe-8cd9c0012792",
-   "metadata": {},
-   "source": [
-    "### Gate Fusion\n",
-    "\n",
-    "Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. A full command line example would look like `CUDAQ_MGPU_FUSE=4 python c2h2VQE.py --target nvidia --target-option fp64,mgpu`\n",
-    "\n",
-    "<img src=\"../images/gate-fuse.png\" style=\"width:400px;\">\n",
-    "\n",
-    "The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation.  See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.\n",
-    "\n",
-    "<img src=\"../images/gatefusion.png\" style=\"width:300px;\">\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pr-2308/sphinx/examples/python/random_walk_qpe.py b/pr-2308/sphinx/examples/python/random_walk_qpe.py
deleted file mode 100644
index af28ddd100..0000000000
--- a/pr-2308/sphinx/examples/python/random_walk_qpe.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Compile and run with:
-# ```
-# nvq++ random_walk_qpe.cpp -o qpe.x && ./qpe.x
-# ```
-
-import cudaq
-from typing import List
-from cudaq import spin
-
-# Here we demonstrate an algorithm expressed as a CUDA-Q kernel
-# that incorporates non-trivial control flow and conditional
-# quantum instruction invocation.
-
-
-# Define the random walk phase estimation kernel
-@cudaq.kernel
-def rwpe_kernel(n_iter: int, mu: float, sigma: float) -> float:
-    iteration = 0
-
-    # Allocate the qubits
-    number_of_qubits = 2
-    qubits = cudaq.qvector(number_of_qubits)
-
-    # Alias them
-    aux = qubits[0]
-
-    target = qubits[number_of_qubits - 1]
-
-    x(target)
-
-    while iteration < n_iter:
-        h(aux)
-        rz(1.0 - (mu / sigma), aux)
-        rz(.25 / sigma, target)
-        x.ctrl(aux, target)
-        rz(-.25 / sigma, target)
-        x.ctrl(aux, target)
-        h(aux)
-        if mz(aux):
-            x(aux)
-            mu = mu + sigma * .6065
-        else:
-            mu = mu - sigma * .6065
-
-        sigma *= .7951
-        iteration += 1
-
-    return 2.0 * mu
-
-
-# Main function to execute the kernel
-def main():
-    n_iterations = 24
-    mu = 0.7951
-    sigma = 0.6065
-
-    phase = rwpe_kernel(n_iterations, mu, sigma)
-    print(f"Phase = {phase:.6f}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/pr-2308/sphinx/examples/python/visualization.ipynb b/pr-2308/sphinx/examples/python/visualization.ipynb
deleted file mode 100644
index 76347fe487..0000000000
--- a/pr-2308/sphinx/examples/python/visualization.ipynb
+++ /dev/null
@@ -1,394 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization\n",
-    "\n",
-    "## Qubit Visualization\n",
-    "\n",
-    "What are the possible states a qubit can be in and how can we build up a visual cue to help us make sense of quantum states and their evolution?\n",
-    "\n",
-    "We know our qubit can have two distinct states: $\\ket{0}$ and $\\ket{1}$. If these were the only two states, we could represent them as two vectors on a one-dimensional line (i.e., the z-axis in the image below). We also know that qubits can be in an equal superposition of states: $\\ket{+}$ and $\\ket{-}$. In order to capture all of the states in equal superposition, we will need a 2D plane (i.e., the $xy$-plane in the image below). If you dive deeper you will learn about the existence of other states that will call for a 3D extension. \n",
-    "\n",
-    "In general, a quantum state can be written in the form $\\ket{\\psi} = \\cos(\\frac{\\theta}{2})\\ket{0}+e^{i\\varphi}\\sin(\\frac{\\theta}{2})\\ket{1}$ where $\\theta$ is a real number between $0$ and $\\pi$ and $\\varphi$ is a real value between $0$ and $2\\pi$.  For example, the minus state, $\\ket{-} = \\frac{1}{\\sqrt{2}}\\ket{0}- \\frac{1}{\\sqrt{2}}\\ket{1}$, can be rewritten as\n",
-    "$$\\ket{-}  = \\cos(\\frac{\\theta}{2})\\ket{0}+e^{i\\varphi}\\sin(\\frac{\\theta}{2})\\ket{1}\\text{ with }\\theta = \\frac{\\pi}{2}\\text{ and }\\varphi = \\pi.$$ \n",
-    "This can be visualized in the image below as a unit vector pointing in the direction of the negative $x$-axis.\n",
-    "\n",
-    "Using spherical coordinates, it is possible to depict all the possible states of a single qubit on a sphere. This is called a Bloch sphere. \n",
-    "\n",
-    "<img src=\"../images/Bloch_sphere.png\" alt=\"Bloch Sphere\" width=\"300\" height=\"300\">\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us try to showcase the functionality to render such a 3D representation with CUDA-Q. \n",
-    "First, let us define a single-qubit kernel that returns a different state each time. This kernel uses random rotations.\n",
-    "\n",
-    "Note: CUDA-Q uses the [QuTiP](https://qutip.org) library to render Bloch spheres. The following code will throw an error if QuTiP is not installed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# install `qutip` in the current Python kernel. Skip this if `qutip` is already installed.\n",
-    "# `matplotlib` is required for all visualization tasks.\n",
-    "# Make sure to restart your kernel if you execute this!\n",
-    "# In a Jupyter notebook, go to the menu bar > Kernel > Restart Kernel.\n",
-    "# In VSCode, click on the Restart button in the Jupyter toolbar.\n",
-    "\n",
-    "# The '\\' before the '>' operator is so that the shell does not misunderstand\n",
-    "# the '>' qualifier for the bash pipe operation.\n",
-    "\n",
-    "import sys\n",
-    "\n",
-    "try:\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    import qutip\n",
-    "\n",
-    "except ImportError:\n",
-    "    print(\"Tools not found, installing. Please restart your kernel after this is done.\")\n",
-    "    !{sys.executable} -m pip install qutip\\>=5 matplotlib\\>=3.5\n",
-    "    print(\"\\nNew libraries have been installed. Please restart your kernel!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudaq\n",
-    "import numpy as np\n",
-    "\n",
-    "## Retry the subsequent cells by setting the target to density matrix simulator.\n",
-    "# cudaq.set_target(\"density-matrix-cpu\")\n",
-    "\n",
-    "\n",
-    "@cudaq.kernel\n",
-    "def kernel(angles: np.ndarray):\n",
-    "    qubit = cudaq.qubit()\n",
-    "    rz(angles[0], qubit)\n",
-    "    rx(angles[1], qubit)\n",
-    "    rz(angles[2], qubit)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we instantiate a random number generator, so we can get random outputs. We then create 4 random single-qubit states by using `cudaq.add_to_bloch_sphere()` on the output state obtained from the random kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.default_rng(seed=11)\n",
-    "blochSphereList = []\n",
-    "for _ in range(4):\n",
-    "    angleList = rng.random(3) * 2 * np.pi\n",
-    "    sph = cudaq.add_to_bloch_sphere(cudaq.get_state(kernel, angleList))\n",
-    "    blochSphereList.append(sph)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can display the spheres with `cudaq.show()`. Show the first sphere:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jcIdEx3Xheh50XUe5WGxKKXC4rouvv/4aa+vrOLq8jBfOn8c0ixLUGw14vp86HmraHhFIkrS8kldzZKoxNJaO0VUVCtNzkE5RDkYSYu6nMAKCMMSjR4/w+eefA4qCM2fPCvdHXn6aLxRQmZrCzNwcLMsS/yQkDjskOZB4ZkEphe/7qFeraFSr8Fy3aUHhvgCu76eLk6ZhqlzuKfDreV7sCQ0pWwizyxgPpw+cTtj3g/YfbdfzYDuOOFc+l0MuS3ZaTYqwRw6EX0GhkLoh8tez16ysrODbb76Bqml47Qc/wNEjRwB2vbxiod5oQNc0FPL5pnOJPgtMnKl1S8twDwX2dR8R4MSrnxvXA7bj4KOPPoJt23jt9deRy+WE7oE7TyqqilK5jMr0NKamp1EqlZDL5aSgUeLQQpIDiWcOQRCgXquhXq3Cs+2mhVDTNBi6DtMwEEYR6rYNyioQKqVS18melyuGUSRC5BH7Pg7DNCUBpItZxpiImxrxnXP257z7ocpsi8Vr+1x0OPngOXqOOI5hOw6CMASlFJqqolAoCEOlVh0BBRD4PnbrdQDA9NRU0xgajQa++vJLPNrcxPMnTuDll16CoeupGyRLy4A5RTZcF7quY2ZqKiUCjBQMs9NvV8VB2f3l0YVsembYWEIQhvjkk0+wubmJH/7whzh69GhqWMVSEfz9DoMAmmmiNDWFmZkZTDOiIPtBSBw2SHIg8UwgjmO4rovdnR249Toi3xeGQoauwzAMGIYBlS14NnPbo0kCXdeRt6x0Acgs+nH2/3G8Zw2ccSMUUYFMCV62t4FYrHiYnFcpsL8RoHs2yMIAqcVmWNM0mIYB0zDENe39OW06Dz+v7/vpdbI0Ro4ZDWW1CHys9Xodnu/D0HWUikVxrOvXr+PSpUvI5XJ47bXXMDMzk+buw7DJG0FhxMt2HKiKso9gdERm7DxKwMfG0y8kc12UO0GynwuL6cxxhFEU+3k/UZo4SfDll1/izp07ePHFF3Hu3Dlx3iiOEYYhgiCA63kIggCEEOQKBVRmZjAzM4OpqSnkZP8HiUMCSQ4knlpwG2PbttHY3UXgukjCEIQQmEy1nxWTBVGERqOBre1tOK6LOI6hahoMvutjC7+CPeGfIAEMfFevsdy4ytoUE9Y/AEBzLwRKQVlpXcJC1FyRny1l5N/z6wLalx7yUDqlaX8GI2OKZDIClN2hc1JgOw68IAAohaooqRbBMISZEQWwtb2NJElSu2LDQLVaxZdffYXdnR2cOn0ap06fFqWC4v4wMyjTMMTueYf5QpRKJZitTodtUhq90LFSIQN+D7uZUHFip7RLq2SIxeVLl3Dp8mWcPHECr/7gB/sITpIk8IMAjuPAcV1EUQQzl0OpUsHU9DRmZ2dR6hGFkpB43JDkQOKpQxiGqR9/vQ7XcRB5XhoB0DRYliWcC6MoguO6cFwXtusi8Dz4YYiEhapN00zz6iw0z4VxKgvz83C4+HmmSRIHF8f1+zHbV6HQ+vuMViH7lac0/DBEGAR7boyKkhKR9I+BTKSERxi4UVIQBGg4ThrhoGkzpXw+D4WkDZWqu7sghKBUKuHa1au4euUKCuUyLly4gEI+v1cpQYggIrqmNXVxBFJRoh8E+2yXh0U/5CB7/zoZIzWBa0FarahZdOLO3bv44osvMDc/j7feeqtjY6coiuC6LhrMslnTdRRKJZSnpjA9PY2ZmZmmCI+ExJMCSQ4kngokSQLHceCyZj2h4yAKAhAAlmUJwZ3jurDZjs73fRBCRCrB8zwRKZiqVGDlck1WxoOCInVSHOQD1steeZDjBEGQ/mPh7pARBpG+wF7kgGstDF1HxMiBpmkAISjk82mfAteFbdu4dOkSbNvG8ydO4MTzz4sdsJ6JTvDywnbggkjDMFAek2fAIAQhZk6Ww4Iw/cfDjQ387ne/Qz6fx9vvvLOXMujgrRAEAeq2Ddu2kQAoFIsolEqoVCqYmZlBpVIZekwSEuOGJAcShxpxHIuGOhFrgZxEUboz1nXElArS4HleWifPc/VsF22ZZipsY+VppWJxLAKyaICIQfZ6RG+CPgV6Is+e0SW0hud5Pj6IItE+OQgChMxlkL+SpyXiKIIXhlCQagVcx8HDhw+xvbWFSqWCcy+8gEIuJ3QOhq53DJO36gCiOE77LDCHxXbXyd8f/l4B2KeByF5v3Kb8shNEtGUMU1+tVsOHH34IwzTxwfvv7yOR7XpXJEkC27axW6/DDwLkWHfJYrmMo0ePolAoSN8EiccOSQ4kDiUipg9wXTe1NW40oCLd1YXMHdBxXSE244uNqqp73f1yOei6nu6IWWlfuVQauVQRgOikmBUcAi2LW8v3nfonTAJZ8WPIejP4vi9MgPwgSEWcvo/q7i7ura2BAliYncXRI0dQKpVQzOdF1GXQc/Oqh3Kx2DP3zttT846L2f9z8kC51oKTpMz9bhV38v9HUdQ3AeuGarWKX/3ylzh15gxeeemltPNm9v3OlKtyPQhf/D3Pw26tBttxkM+kG+bm5pBjwlAJiccBWV8jcagQRRHq9Tpc1wUAOLUanFpNmPzwjogKIdAUBYqqImeaMBkhMLmJTeZ4juOAAiiwNMLAaF34+1DAkzbfj2sZ4OWLQvTIwuj8K7dlphkLYm7wpLNOiFEYomHbaTlnrYbpfB5hkmC6UoGmKHAcB4HvI29ZaTvllvuaHQtfrHnkAAAMXRfCRa3NPc82WOKaik7gZAEs0tBKILjQkINrIzRmwgT033yqHaampvDSyy/jm2++weLCAhYWFprG1gqSIQqWZcE0TYRhiM3tbWxtbAidQrlcRrlcRj6flyRB4sAhyYHEoUAYhiJSAKQufNsbGwhcF4qmpVEBQqAZRlqJYFnI5XKwDCPdybUBTRI0Gg1QALqm7SMObdFCBNphGN1APwG8VsOkJLPIx8zWOc5UQvTjsEiAvYoKACAEYRCAKAqCIMD9u3cxOzWF50+cwBdffYXFubmUQFEKVddTrUejAd/3UcjnUSgUkGf3sVvPBU3TEARBauPcwVFQEBneZTHToZL3RuD3oZPTJADhE8EjDdnvE2Cv+gT7Izr94uyZM9h4+BCff/45/n9/5+/AYPcg+ywIcWOGkIGdzzQMLC0soOE4qO/sIMqkfizLQrFYlCRB4kAhyYHEE40gCNBoNOB5HgDAcRxUd3bgNxowNA2GacLUdeSYy98grnS26yJiu9diobD/BX0QgVZ08vfv+XedFjakoXJk2hGHzGeh34ygmtlNK9nwPC/dU5Q0hcDr8xUF6w8e4Mb16zj+3HM4c/o06o0GVEJQLpVgWdaeNiGO4XkeEkrRaDRQq9WgKIogCjnLgqooYqHl2geejkh42WO7cRMCMMfE1ivl4XmaIQu8H0O2zTXYa6IOhI1Xh/BGWZqui7ENRBgIwRs//CH++he/wOdffIF33nln30toC2FTmLCRH1dRFJSLRViGgVq9jka1ijiOkc/nRcSsWCxKTYLEgUCSA4knElEUYXd3F77vI2E7fC461ADkDAM5y0KlUklV4gPWx/u+L/on8GZAwOih/UGjBsLBEHv5cf5VOCwy18V2UAkBUVXxNduUiIfaWxeS7K41SRK4jgPX98W137p1C3fu3MGF8+dx5tw5OLad7rYJEakZl5G1fC6H+dlZ2KxKxPW8VHDnOKg3GlAUBfl8HsV8HnlG3CjSVtYgJI10cHOjLvep9XeCNGXKR7nFczaFwIlCu8hDEscglKZdGVllBwDhU9FKFrLjaKcZsSwLb/zwh/joww9x8+ZNnD59ussV7T0rlL2PvAzUMAxMTU2hYdtwazWoioIkSZDP51Gr1dBoNCRJkJg4JDmQeKJA2Q600WgIguC6LhRKQaIIlqqikM+jVC7vefunf9j3Obh1MADkLAtGhxr1YcffC2JCzyzSEaUiIsAdF9v9He/+yP0Vsk6MvcaVNW7iBlGu54lwt6oouHz5MtZWV/H6a6/h1MmT4j6prHqDMIJAmO6Ak7dSoYBSoZCSDd+HY9twPQ9xksCxbTQaDRBFQd6yUCwUUvMppKkdBRACwsyAe9/Hdj9jCzm/XoURJqqqMNrcoyRJ4AUBQlbqGbNyz6CFLOi6nt5zRhbaaUYApC2ez5zBt999h7m5OZQrlZ6Ekzs9ih4TjNhVSiU4joPa9jaK5bI4v67rqNVqcF0XU1NTHT0WJCRGgSQHEk8MgiBAtVqF53nY2dmB67owNA16koBQivLUFMql0n5jmn6Q2aHbrJ+CpmnI5/NjvYYsOciSgNbFO9uHIWCVAa1QMmRAZ0ZLTcK6fggBabZxTijdi5qwY+isv8Fnn32GarWKH7/9No4sL4voBYDm0k5CYLF0QcO2EYYh6o0GioUCVEbe8rmcICCO66ZVJczC2nZdkQbI53IoFQr7S0dJptsjK1nsZ5EVNsrZlBBbzLPlj1wcqSgKTF2HzqIPCaWI2fuSJQu+78Pn7wsnC+y9aU1DvPzyy9jc3MSnn32Gn3/wgSBW7bpdtgOPKBCkkRlN01KCVaulvgjMOClkXSOLxSJK7HMhITEuyFJGiceOJElQq9Wwvb2NnZ0d2LaNvK5DUxRoqpqqtplZzqCPK/fP5xMnt7RNKMVUpTIWC1uKVNQG5iTYbpKOeWMeFhlIMmSAaxRUFs4Wiw7LtQs73wF8D/i4st/7LFLA76GmachZFoIowke//S2CMMRP3nkH09PTIj++W6uJv//lL36Bn3/wAaampsTCzUtKE96ZsFhMNQYZ22U+BjdDFMIwRK3RQELTrpCFfB6lYhHFfL7te5J917stsgNrPihFzAhBOySs5DHKkIVWZMmCzjpJ1up1/PKXv8Tx48fxg9deE+fi72G/RCE7Rt/3RTqnUCxiem4OOoueqaqKSqUi20VLjA0yciDxWOG6LlZXV7G1tQXXdWGqKsrMmKhcLqNQKIidY7/EQJQG8sWUfQ3DUHgfFAuF0YkB351mdQOKArBdcVYz0E6LoGoadFYpoLGvYtfL6vVbr6EdOpVN8t1ywBaVhJUuqowUmIaBnWoVH334ITRNwwfvv5/eb6T3LgjDdJyquj+ywd4TTdNQKhbRsG3EcZyK5rKRAF5WSdOmTjnLAqan4QcBlI0NuJ4HTVEQ+D4eeh62VBVT5fK+DphNV9diZ9w0rI53qQOY2HOfLgMs5cGMsnjqSZCFMEzJAhNAco8IYI8sXLx4Ed98+y0WFxawfPTo/nLKAcaoMC2Coihp9KVeh12vp1bM09Owcjlsb28jl8uhPIa24hISkhxIPBaEYYi7d+/i4cOHCIIAWpKgZJooFYtCES9K9tCDGGQW1GyUIIskSdCwbVBAdGAcCoTsn9jZ4ucHgdgVtxtvVuSmqSoI22FzvwF+7H4IAbCXNujkpxCw8cQ8MsEMoEzWfGmN2f9WymW88847QsPBd/sRIwd6xg9g75L3BKCqpqFUKqU6EUYQCoVCc5Mn9np+bNMwMD01hVIUQdd1RGGIOiMYWzs72KlWUWEkoTXl0EoUWishktbX9EC796pJn5G+CAAGIguzs7OYn5vDZ198gXdzOZRKpT19QLv3rMszzktOQSkKhUJaWeL7aNTrCFwXVj4Pg6VyfN8X/ggSEsNCkgOJA8fdu3dx7949xEEAkiTIGwZmZmcxMz0tJkCOpsUzi2zNf1aD0GGhdFmXRUKI2B33AqVU+Oh38ssPM3bEwJ5lMiEkJQKZf+2MePg59n4xQOqgHVHBnuCSixoVQtJIgWmK+3R7ZQVffvkljiwv40c/+lHTTpMfm1dIaLoO+H7rafYqRFg5ZLlUQsO2RflpvlAQ5kitokmaJFBVFVEUiehDZWoK9Xod9UYDYRhip1bDTrWKUqmE6XK5I6HLRop4ioenRfrJw3csI22tImHHz6YEepGFFy5cwKe/+x2++/57vPTSS6nGgTX02he56lFxQ5ASMRpFyDPDLod10ySqCpokqFeryJdKovnYNP9MSUgMCEkOJA4Mruvi2rVr2N3ZAcIQlmFgbmEBM9PTwiUvG37nbXab8rQZItCvAIsr6BMApT52U1miQfZ+KH4fRRF8pmbPLiyqqsIyTRCmlWhaDNmC0q2ssCdaCEq7v/I8D7brioiCZZowTVMsRBTA5UuXcPnKFZw+dQqv/uAHTccRVs4ZHwW9Q58JgpYqA0JQLBRgEyJaFidJ0tR5Ubya+S0Ae++5Qggq5TJKpRIc1nsgCEPYtp1GI/J5TPfIq2fvsdBbdFl0+05VZY6Zfb8SQLS15teQJQslSnHh/Hl8/c03cB0HuXw+bQ7muqJRVbZteGvKpDVywSMIcRyLjqFcxwEAlmnCazSwu72N8tQUwjDE7Oys7PwoMTAkOZA4EDx8+BA3btxA5LpQAcwvLmJpYWFv0eIRAgYuLMsq7puqFAZQZnu+nzr6sVxwR7QsItkziLxyEOxzveM7Qb5DyzYy4q/pRAD6IgbZtEkHcG+BMIpAABiahnyh0FSjTwF89eWXuL2ygpdfeglnz51r6x/QFDVoiXi0Gxu7EPG1wKoWHMcRBknFFlJGsGct3CryUwhBsVhEsViE47qo1eui7NK2bViWhempKRQ6ED1esYAsSWh5b/l3o3bBVNASVWjRLyiE4NSpU7h27RrWVlfx8iuvwGcttcMwRBiGUBRFtNBu2uVnKzYy16AQAigK4iSBaZqglAqxJ2HkRNd11KtV2I0GfM/D4tLSXtdICYk+IMmBxEQRBAHu3LmDjbU10CCApet47vhxFLOteluJAScFwF5Yf0gkSZIq9AHkW3ecHfQD2XHwevdWEyI+mXOvgezfKKq6V43Qz8La9leZcrweBCIIAtiOI3bBhUw4P4vvvvsOt+/cwZtvvIHnnnuu6zF5CWOvGvpsx8QsLMuCQkiaZvB9uITsW5xUVd3bGXdAPpdDPpeDHwTY3d2FwzQdaxsb0HUd01NT+yocSKf7mo26sKhUFMdde2D0A9JCQrgeg49CVRScO3sW33z7LV68eBHlUglxHMNnDa6SJIHnefA8Dzpz/TSy0YTs2DlBUBRBni3LQkIpAt+H4zgg+TwMw0Ahn4fjebi/sgK7VsOxEydkW2iJviHJgcTEsL29jTt37qBRrYIkCWbKZRw5ckSUXwFIy7SYFgBAxzz6sPB9P93NAc0CuQ6gLGfsZ0xwOHRNE2LGbpEAAgBs8u51rnY/410keyGhaTtqPwhASFqFUCwUhN0wD/sTSnH95k1cvXoVr776Kp577rmOCzLfdYdhiCRJ0mOxRYi3Oc5el9ilI0Ni2K7dME3kkfpKOJ4HoigwLUu4IRJFASUkPSa/dx3umWkYWJifT0sg63XUbRtJkuDh5mbbCofsuNqBIiWOwtp5AI1CN7QKMPmifuLkSVy5ehXXrl/Hqz/4AVRVTTuDWhbCMITP2mdzfwWH+S8Yprm/MRU7pqqqqa6ElYOCiWJtx0k1L7qOfC4H1/OwvbUF23Hw3PPPY3F5eeTrlHj6IcmBxNgRRRHu37+PRw8fwqvXYWgaFhYXMTs9ndrmctC0854o3WM/jgepAe8CHm6lSHeg3SZEsZPz/X3+9yZr5jRI6aOiKE1eBm3H1zLW1soDHq4GpXutiNnvQuYvwMdqsWZThC224viU4v6DB/j9V1/h3Asv4MyZM22JQfaaY9a9kbCulkDn6EX275oWY/a9aRhIWN8G27ZT0qDre/4HvNUyrzRoIR5ZEAC6YWB2ZgaVSiXtP9BotK1wUDWta0SCZho1ibQDae4cOSqy4zd0HWfOnMHlK1dw/oUXYLIoCu+GaRgG4iRBkEldeb4Pz/ehaRrMNqSUu1ry7pU5Vq0QsI6apWIxNfrKiBdvXr2K2u4uzrzwwlg8PiSeXkhyIDFWVKtVPHjwQHSWqxSLmJudRblU2ie2itmOnkyAGPBdVMJ24u06LiZJIiIE2VI9QoiYjPc596F/EaGqqmk+vd1rM776/B6IkryWc/Hr4Yu947rwPQ8gRDgSthsnAGw+eoTPPvsMzz3/PF5+6aWu4xVWziylwMP+/aLTfcnlckKzYTcaUFh5IiEkjR7w9tEtaYbWRbqppFBRUKlUUCmX04ZPmQqH7WoVZVYSa3R43+M2WoN94tMxkQSO06dP49q1a7h56xZefPFFQfo4VEVJO4laVuqc6fsIMl4Zjus2pbOAVNMgrkRRUCgUQJn1eL3RQLlUgqqqojrCtm2sr66iXqvhhYsXUSyVxnqNEk8PJDmQGAuiKMLq6ip2trfh7O6CUIrFuTlUKpW29dYxM5hpVcq3CroGQVNLY0JEY6VcJmrAQ+a+7+/TEei6DtM0obfoCLLja1dx0Am89EwQD17nTwgoD3u3LBDdlqMoimDbtmguZDFToU7jqdVq+OjjjzE3N4cf/vCHfYs4eQnkoCVw3e5LPp/ft6vlnSFjsBA/j1LsHTCNYHQ6H3tNqVRKxYuOg916HWEQoGHb2KlWUSgWUSmV0m6dhCBmBlWtzpn7jsuODTSTklFgGgZOnz6NWzdv4twLL+w9Z5mIUHravYoHTmD9IEASx8JsSVVVmKYpjJESRrS5IJSLUxu2LWyt9Yxhld1o4Jsvv8Tpc+ewuLw88rVJPH2Q5EBiZNRqNdy/fx+h48B3HOSZu2G5WExzzBnwBVFtt/gOmfNt8iNgf+/7vlgETNNEGIYI2CSbhcZ0BGYvPUKXfDj/fbsFhCBt/tOkq0gHvfd3vS8wVeu7buopwKIF3cSCjuvitx9+iEKhgLd//OPuIeSW+86rB/blukcA95dIGg1EnCBkokmd7qzSQzvQenxuEFSr1US1hO04aQfPclncs2xEplVQ2OnnrR4Hw+DMmTO4ceMGbjOCkC1RBPbfB0VRhLMk1yYEYYg4juE4DlzXTc21WFMm/jfFQgG1ej31PmARBN6qu1Qspj4YUYRrly+jsbuL46dOyXJHiSZIciAxNJIkwYMHD7C9uQlEEWgUYapSgWkYKBQKzcSAT7ad6s3Z8QYOY2erGTJ/67puqupWVdTq9aa0AdcRGKbZVOq379hdxpLtGcD+oONrFQBQ1bT/APtZvwV0cRTB4YZGLD+dz+e7KuyDMMRHv/0tFELw7rvvikWj47VkIjiUKfgBdExVDAtCUh+Eer2OmLXh5uftVlLYznyoG/hi6noedut1uLYN13VRZ7vo6Uql6dqyfSCaSlkzkSge8clGoIYhCTnLwonnn8eNGzdw+syZfdEZIaTMiBk5dFaimCQJAhb9iuM4Jb5hCOJ5yOVyIiJRKhZRbzSQxDEato1CPp9Gaxh5qDO9xsbGBlzXxZHjxzEzNzfEVUk8jZCKFImh0Gg0cPXqVWw/fAj4PvKmKYiBZVl7RjV0z7ioq3p8kKgB323xhb3l78IggOt5wmmPEwPTMFAqFjFVqSCXy7UlBpQdb99YWsbe78LA/0pBmlMWYeQ+dsO+56FWr4sISLFQSHtCdLlPSZLgk48/hud5ePfdd3s24mklOXHGVXESznqKoqDImjPFUSQEo7QLOQAb46Alh5ZlYW5mBvMLC2mkRVHgOg4erK2hVq/3/HtOSHjJYJIkSLg+Aimp4q8ZBGfPnUMQhlhZWWlz0kxrbfb/ViiKAss0USmXUS6VUrEs0veu0WiIPheKooj0TZIkcBxnj2QRgkI+D6IoafOseh33V1awcv06gnZumBLPHCQ5kBgIPFpw88YN+I0G1CTB4sJC6gzIyqe4OQ0hpMnkaJS8Lc3mZRWlYzg+DAJsPHoEh4VbeVh2emoKhUJhfyieqdSbJvkx5JebxstPhXSBI13GDyDd6dXrcJjrnaZpqHSxD86e87PPP8fO9jbe+clP0rB9D7SOQugNhowatLvuVqiqikKxmGoKWI3/IHec7+R7IUkShMyeeW5uDgsLC0KYt1WtYnV9HR7rcjgI+LPCKx4S9rOEXXuvsRULBTx3/DiuXbvWkVjw90VEKzq8TtM0FPJ5VBgxByDKPR3HEecjhKSpCFYxAqQko5DPAyRtsuX5Puq1Gm5cvozq9vZA90Ti6YMkBxJ9I0kS3Lp1C1sbG4DnoZzL4djRo6LroKqqKBaLovogG6ruteB28gQQIkO+6+5CCnZ3d7FdrQpdQblUwvTUVJMgsfXYTYZLZDweCyL6gPYRBl6CpqSDaPpdEASo1etCLJnP50W+uPtJKb759ls8ePAAP3rrLczOzvYeZ5sIRpypVBgG/d5DjeW+CSEIwxAuW8j6OX7byE4G3NwobklTWZaFxcVFTJXL0FUVETNT2tzaQjSCU2K2/JFHGGLmCcHJQuvTfe6FF+B7Hu7du9f7BJnoW6er5qmCcqa5k8+epSAMUcjnQRkJ4A21QNL+HzkWXfI8T1Tu3Lt9G3du3hSVKxLPHqTmQKIvRFGE27dvw9ndhZIkWFhYQLFYTPPHLOxdLpX2QucZcBOiTtiXv0eGFPQIJYdBAMd1xSQW+D5MwxD2u9lz7DvfmMhA0zl6CReBprSICgjjJdd1U+MlQsSOsN9F+tqNG7h54wZef+01HDlypO8xt97faExixH7SRLquo1gowGfWyJ7vt3V2bAcC7FV5ZBbmmGbcNtvk7nkJZLFQwHa1CtdxYLsuHNdNu0RmnTuHRFPvhWzJKiFiN1Yul7F85AiuXL2K48eP95dS489NNjWV+TvCUkHFQkGUPsZxDM/zoLBeFjSO4bguypmKHMs0QZmvgu26UBQFmqahVq3CdV2cOH0alrRefuYgIwcSPRFFEW7fvAmPEYPlpSUUi0WxmHFi0M5KOOlBDPjrmtBHpCAKQ9RqtTQnz7wEDNOElculor1cruOujWT+jQPZUHpPYoA29fs0dToM2I7OsiyRK+4H9+7dw3fffosL58/j5KlTA409i2z9/6hixH71I6ZpwrSs1LAqcw/6Ogf2UgxxHKcdMVv6XnQah6ppmJ+bw/zCAkxmVby5s4PV9fV9FS2joCmKxvQLCftcnDlzBo1aDZubm4MdNEMSSMv/OTRNQ7lUQoFZSydJIsyoQqb1yCKXy4mIQyNjxR36Pm5duYJarTbE1UscZkhyINEVURTh5vXr8Gs1gBEDy7JAkwSu64omOe3y4f3Wh/PwK7qQATEeRgp2azWEYQjC6v2nKhWh0tZ0vcmJMbsYj5MUAL1TCL0QRVFaTZEk6YReqaRCsT4X142HD/H5F1/gxIkTuHjx4gAD37+z52JENdMxcdIQlSPs+bGZiLQXeI4/imMkcTyQZiGLnGVhaXERU6WSSDWsrq9ja3t75KZMAh1SAjOzs8jl87j/4EEqdBxS69JaRZGFYRgol0rIWRYU1njMcRxUd3fhtwgPC/l8aifN+pFwxEmCO9evY+vhw6HGJ3E4IcmBREdwYhDV66CUCmIAmtoSc5OfdqHg1i6LnUD7yCED7UmBaZqoVCqiCyBvoWy0iA5bNRDjAI9IDHNcvuhEYYh6vY4kjqEqCsrFomi4oyiK6LHQacmoVqv43SefYHFhAa+99lrfJkcAmlstM4iUwphLGLuBv++GrqcEgRDYTG3fDpSmzppRFKVmUBmfgGHNsxRFQWVqCsuLi6KqoWHbeLC2hkajMdQx24I/5xn/hCPLy1hbXRVpgmxFxEBUIVsVtO+0KYEul0pClBtFER6xfgv8eSSECP1Ba/dRAHhw9y5W+9FISDwVkORAoi2CIMDNa9dSYkAIji4vi7I4Smlq34u0Z0E79KoDz+64u5WohVGEeoYUIEMKipnQe5IkCMMQFKn/ftOiMdil9wTNTO5DgRD4vo96vQ4KQNN1lEql5r4T7HVCvNhCoBzbxm8//BClUglvvfXWQDv9dhoPIBM5eAzkgJC0a6OmaaCUotFoiN10wghBGMeIWeqgrdCTH68lD98vdF3H/Pw85ubmYBgGKKV4tL2NtY2NfU24RgEfJwWwfOQIXNdFdXe36TW8vFOQhT6jCiJC0aZ0WFEU5HM5zM/OCr+EeqOBWr2elpRSCl3XU3JIadrqHHvRCEIIth4+xO3r1/e12ZZ4+iDJgcQ+BEGAW9euIWw0QAnBkaUl0UmRACLkqPHdXgu6VSdkSQFHu+mbh9t3d3fhZ0jBVAspEGNmxEBli+m4xYZ87BSprmCUY7uum7ZYJkR4L/Rc3Bk54PnjDz/+GLqm4Z133hl4p99p7MPaJndCP2WNvL8CB8+RR3GcWiGzCAFNkr70HATppDbK+5PP57G8uJg6KioKwjDEg/V1bO3sjC/VgJQUz8/NQTcMrK2tdX1t31EFXnnDIgntXqfrOmZnZlDI5xFFkRAt1up1+EEgIoE+q1xoLaWs12q4cfXqvrSExNMFSQ4kmuB5HlauXkXkOAAjBtxJUGF16SJq0KZnAhddtQNXbANo2t1lXx1FEer1Oqq7u0LsyA1f2pECjiAIRNTgSUohtB6n3mik5IpSWJaFAqtBHwTfffstXNvGO++8I9I8tOVfxzF0+H0cx2LhG5dtcj9ljdl20GEUgVKaPleEIGK190Ofm5CuRLUbFEXB9NQUlpeXkTPNNNXQaODB2hrsPssu+zqPqmJ5aQmrq6t9/002qhBn9Bat7ysnSu1+xzuNcg8ETjodxxEpw6boAfY+pwSA77q4efky7D7MpCQOJyQ5kBBwHAd3r11D4LogioJjR47ANM2mCZ4bq+iGAb3NjjXBfsW+mFTaLYJsV5IlBVwtniUF3XbHvJkSKBVGMOPGqGQjoTTdmfk+QCkKhULHlEw3PHjwADdv3sSrr76KcrnMBkf26uDbkC5h2tNG1c6RjRoMm7vvhCxp4RURvLqAV0ckXKNCWO8I5k3hue6+Bln9gmD0KIiu61hcXMTs7CwMlvJ4uLmJ9YcPEYwptH70yBHU63W4rjuwsJIQ0kwU2hAh4eHR8r7mcznxO9M0hR8If29cz4PnecKhU/iWsL+J4xi3rl1DrVod/KIlnnhIciABII0Y3LtxA4HnQVHVNJXAxHF8UomiSORe2y1slFLRipj/n7aIsFoRxzHqtt1ECrLpg66kgP0Lw1DU1Y9LTEcxBm0BQ5QkqHEhJYASs7wdFI5t44svvsDRY8dw4sSJjq8jLePOLg7CCpgv2GClgOwe8qhBUyQCzfX6wlegzev4sbMle5wECGOglnQDH2824qSznS1vMxwPq+QH9ms5hkChUMDy8jIqpRJ0RUHg+1hdW8POGFINi0tLUFUVa6ur6YQ8JDnj72/WgEn8DhApBw5VVWFxEub7MHRd2DFz3YntONipVjtGoyiluHv7NlzbHmrMEk8uJDmQgOM4WLl+HaHnQdU0LC8twWjTpZDnGA3T3LcI81Bn9v+9wsqe72N3dxdhCyko9REp4AY3BBC18foYogZN4dMhF6QsIiaojFhFQjHjYDfQuJIEn33+OXRNG7gyYe8gVIg/SeZnoLQpcrBvIWD/5/luvuhz+2Dxjwvnsv+AnnoKUYbX8vNcLifG44yw+HRS8Q8KRVEwPT2NxaUl5CwLGiHYrdexur6+zzdgEGiahvn5eayurTVpZdpVlPSLJqfGFvLCBYtAml5QVVVUIHHRYrlYRKlQAJBqZHYzzctaR5XEMVZu3hSfY4mnA5IcPONwHCf9YLsuVFXF0aUlUU7XCh7ebS0VBPaIgRAcdosWJAlq9Toato2EUmi63hcpyB4/W/4Y8hLGEclBNqc6jsC6zzzu4ySBzkxphg1zX758GZubm3jzrbeGvs5uiyMP70+i2VJ68s5nF4tha86cEKHJiMJwqD4IwB7JG7RxUyeYhoHFxUXMzMzAZKr/9Y0N7LRUHAyCI0eOYGtrS0TmuJviICmeblofHrlpJb6EEBEFDIJAeEyoqopSqYRioQAQgjAIUG809lVt8A1AFIZYuXmzYwmqxOGDJAfPMBzHwc3r1xFzYnDkCLQOxIBSKsqXWrUGPOxMWwlBm8mKRwsC5lVQyOXSUG2vtsIdShPDKEoXNkLaaiD6gUh/YDykAEjTNI16HQnzXSgVi02q/EHw6NEjXLl8GS9evIjZmZmhx9QpNJxkdpeTMD/q5UEgftdmfKqqIs9MoRyW/x4U2R34ONUUxWIRS4uLKObz0FUV1VoN6w8fDtWnYXl5GaAU6+vr+37XT+SDu0T2QjaawJ93TddTbREhcFy36TmxTBNFJhCllMJ2nLS7Y+Y1nLC7to37t2/3e8kSTzgkOXhG0Wg0cPP6dVDfh65pOHbkSNddO48aEEXZ5z6YxHFqd4yWUHxmUo6TBPVstIB1GuQiKKEsb4FojNRhceE7GUPXh5r4RaRjDCkEDpuVKgJscmXEYJjxBb6Pzz77DHMLCzh37txI4+p0D0Wb5gN0RmwCTyt0eA9M04TB2hLz52egw7eca1wNtoDUE2Jubg7T09PQCUHg+1hbWxs4ymFZFqZnZrpWLXSLfAzU8hx70YSIGUnlLAuEVSxkUyQqcx3lrdiBtMSx3mjspRmYqJgQgmq1irX79/seh8STC0kOnkEEQYDbN28iCQIYhpFGDHrsunknt+wOP+F5/+xk22aC8ljHRL8lWpAtmWvdXTb1K+gy6XEx4lB5/My5xwFeqsgXhlwu11SqODD9oBRffPkl4jjGG2+8MXTkIT1U57NPPKWA/iIH3caYz+WgqCooK7cbw4A6EtJhUCqVsLi4CN0wkFCKtYcPsTtgP4KjR4/i4cOHXSMAnfpFDCuKJKw8mVKKHBPJer6/r6w1TpKU6BYKIIqSCok7pBk2Nzaw9ejRUOOReHIgycEzhiRJcOfOHdAggKVpOLa83NeiEGZSCsJuFb3z2PV6PXW7axMtaEXrItorHB3HMaIoSv0NBiQH4yYGvFQxCAKAUhSLReRG7GR38+ZNrK6u4o033hjpWL3MiLI9FR4H+iFPRFEE0QrCcGzNkRRFGRtBME0TS/PzKOZy0BUF29UqHj561PfCfeTIEcRxjId99DAQmp4+vC36hXBHREoQQClUVU2jSZQijmPouo4y0wa1SzPwz9PqvXuoy2ZNhxqSHDxj2NjYgF+rQaEUi/PzfYWReUthinR3ya18u+1kW6MF+TbRgk7n6kUKOPiuRdf1vsVm4xYdAqlHQNbeuVQqtRcNDjCBV6tVfPvttzhz+jSWlpZGHmO3a+VakknbJnd6T4UgscciqmmaKL2zbbtv/QEv4+sEvviNAyqrPKhUKtAVBa7nYXV9vS/75WKxiHK53NMtkYMgJU3jIjcAxHPr+z5C5pvANw+894aiKCgWCh3TDAAASnHv1i24YzSMkjhYSHLwDKHRaGB7fR00jjE3Owujz1r7KIoAJmAyDEOQgnY7ok7RgnyHaAEHL30cJOfNd+ntqic6nWPcCJl5UxzHUFU1tdwdIsWRRRxF+OzTT1Eql/HSyy+PaaRdzjdmZ8RO6PTuD5Irt0xTdN90HKfv97TXOYiijPX5qFQqWFhYgKFpSOIYqxsbqPfRxGl5eRlra2t9L/jcV0IhZCwER9c0KCzdEjHHSpXdmzCz+PMmTd3SDNwkaSxpIIkDhyQHzwiiKMLaygpoHKOQz++563UBn6AoALQRIrZORsNEC3jIm+/kWxsMdQK320361BuMy9AoiyAMUa/VRKliZYRSxSy+/v3v4TgO3nzzzZEFgr1CznGmqc9BiBE7umSid+SA/32hUICiqojjGK7r9nvini8ZV6kjh8XaQecsCyoh2NzexqOtra5phuXlZQRhiGofroO8QyUftaIoI7tbcrdEENJUVgnmY5F9XgC0TzNkKh6iKMKNK1fgjLO7pcSBQJKDZwRrKyuIfR8qSZu9dEOrEJDnopvKlzLfjxItyJq+cPQzwWUV9t1y5aIvAh2tWVIrwihKSxWRRlNKpVJPwWA/u8F79+5hZWUFr772Gkql0ljG2lW3wVMKE7BN7hf8vvW771UUBQVW3uh5Xl82xv1e27jvAE8zlFkTJ9u2u1ovT09NQWGq/17o1Ddi1PfR0HWhMeDRg+xxuQMjx740g++LNINCUnvn61eujLf9tcTEIcnBUw5KKbbu34fXaCCMYywuLHTc3Wbz8dkJoVVNTjPfD60tyJyrFTyC0A0839zLXrnTOUYB7wPBiUGxz+ZJvciBbdv48ssv8dzx43j++PExjbY7OMmadEqBo9P7DQyW9tF1HRavzR/BXnn/YMbfzZM3cZqbm4OhaWkUr0MDJ0VVUSoWe5ID7lLZCh6BG6WyRVEUmMwh1WcNzYA9wyO+4PNmXbyMslOagTdsu3n5MhqyUdOhgSQHTzGSJIGzs4OdzU34YYiZqSnB7rPIWgYDezXTwqO/tcSQhReHiha0pBE6oVctetwHOUCPcwyDKI5RY9ds6HrfxACUdg1tJ0mC3/3udzBNEz/4wQ+G9tdvOiV6ExKuNxhH/4F+0Xq/+illbAcrl4OuaaBI+050rXYYbIATiaLk83ksLi7CYgvvw83Ntm2gp2dmupID7i3SDYTpEIa9Ct77I2Btm/efYK8UVJhosRRfuzSDoiiIkwQ3r1xBfQQnSYmDgyQHTymSJEHUaODR2hqiOIZpGJienm56TXYy5ou16ImQmRyzuegkSYTL4VDRggEmrG67H6Gcbjlnkz9Cn+fpFxEjRAnTGBSLxb4XkV7L3qVLl1Dd3cWP3nwT2oiCxix6je9xlTG2PnvshwMRBEII8oWCsO/1uxgPDRNXGEc/hlbouo6FhQWUCgXoioJ6vZ66KmbSDJVKBbVarWuzo37RzWCsG1RVTVNNiiLKRrPPEv+O3yOuf0iSBISQfWmGRqORujNGEW5evYqaJAhPPCQ5eAqRJAli28b2o0cp86cUC/PzTa+hmZ1sdnLuVEbIzVJq9TrqA0YLgOFC/ATtRXKU5UMTSpsIyaDkYxDESSLEh5qmpXbIA+wuuzkwbmxs4NrVq3jx4kVMj2CPvA89FgR+H4HJlzFm0fqMNZHAIRaxQtZeeUxtlDmUIcbU85iKgtnZWczMzEDTNERh2NS8aWpqCgkjoq1o10ipFwiGI3/Z6EE3B8YsieKRBAD7TJNcx0EQRWmK4epV7MpWz080Dm5GkDgQcGJg12qo1+sI4hgLs7NC0Z/9kItcL08j9NipN2wb+VwOqqYhZ1mp5WovUsBIyLALNicI2QmR961XCNkTS3IL1yHP0w0JpWm5Iqv5HqZPQqflJfB9fP7ZZ1hYWMC5s2dHHqvQhSQJkr0f7kVUMrvzKIrSHhdInSYDJnBrqlJhf+O4LijSDn16pmMnAfbe3zapqCwREM8K/8qfjRZDn0HfQ8M0EUYRAt9Hw3FQLpX2aVaGfi74zpv17xgnisUidMNIGy6FIdYfPsRUpYKpSgWEEOxWq01VRTHrhDksBk3fiLLGJEEURV19KlqjE8JhUdNQLhZhOw6iKILv+4iTBDnTxK1r13Dy7FlMtUQ0JZ4MSHLwFCFJEsSNBkLPw/b2NsIoQrlYRLFYFK9pnahb9QXt4Lgu3Ex5UqVU6pnrB8a3k+cEgbKuctn2wvw84+yNkAUnBrzlcrlUGq7kr0Md+jfffouEUrzxxhttFx8u+BJfWb5ZNLtiyvHWksWmBaBV78D+H7BOh6qqdq1Fp9jrfOmzCX7f5aFF0Jr5HmgmRwRAgpTA8Ocuq2TXNC3t88AIK/e/IIqSdipsU7KXz+UQRRHiOIbneaLT4LhACEGCEUhGB5iGgaXFRWxtb8N1HFRrNfi+j5xlobq7Cy5LTUYkBkBm7CwK2NP7gaQ9Feq2LRql9fiDJjEzkBIaXl3i+z5c34fveSBIIwucIIw1YiYxFkhy8JSAE4MkDEXrV03TMDMzIwSAraBA14ZAlFLYtg3P99MJW1VhWVb/xKDDeYcBAQBFAVjeEkj1Bp2ubRzgvRKiKIKiKCgNSwyAJmLAF/pHjx7hzsoKXn7lFcRJAttxmhb8bLSk3SLLr11MxhkSIHbgfIHl32e1JGEIVVVh6npavpaJAIhzsr/hhMDSdZiWtXc92UhDRu+RHROPWLS+hi9SYLvOmBnv9IMsUeBEQlEUuL6PgL1fuq6npIJfSw9RaPcTpi2U+1lUB4WiKJifm0OtVkO1WoXv+9BzOWzv7ABo7po5DvA0Qz8NrFTWz4Jm/BS6HRdgRIqnKLHnX2FZFhRVRb1eh+d5aT8Hy8Lt69dBz5zBzOzsCFclMW5IcvAUgIsPEUWo1evwfB8xpVheWOis+qe08+/YMeuNhujvXq5U4LquMB7qVGo4ahqhGzhBiFhaQdO0yRMDdv2lYrFvgyNezcEFWDELy2bzxTRJ8NXXX6NULmNmdjb1skebBR8QKR8l+5UthkpmZ81fCzSnjzqhTghUTUMhnxf55U4wma2uYZrItal4GQacBAFpeVwUxyjkclA0TSxIPCqS8O+5CQ+l6U6+ZdGMowhhGCL0fSFWFKI8pIuioqriq8JTGn1C4QvfBKoZyuUyTNPE5uYmSoUC7j94gN3dXRQKhbGfC4BwQuxGEaI4hsb6qQRh2Lf7J08zZO8SF/Lm8vlUf8CEjjnLwsqNG6BJgtkWbZTE44MkB4ccSZIgqteBOE6rCKpV+FGEuZkZGIYhdmYc/egL4jgWoXQQglKxCF3XRVlTEASw2iwmkyQGTedJEiBJJlabTwHYGWJU6pBGSVitdxzH6cIfRYg6hH/jKBK6CBCCe/fuodFo4O0f/xiGYYgdbuuC3yvl02n8/fyFMJI6wDLGJrBnhV+3yghfrwWIR1W4riJLJAqsAoA/p5qm7YW7kwT74hKKApWQlDCwJkOif0i7zwghom5/EgTBNE0sLS6iVqth5c4drG5sYHF+HuUxGWK1gm8Q4g7XE4Zh2myNUoRBgMSy+oqetdMhAOkcYRoGaJKkBlYZgnDn1i1QSjG3sDCOS5MYEZIcHGJkiUGcJNje2kIYxyjmcqhUKiI6wEEp7ZpGAFKRWo2V6yksx65pGmJWDum4bltyMMlKgSxi1gBK4R3kJqA1sG0bfhgClIpyxSAIRBQgZqSg7bm5jwMh0FRV7FKBvRC453m4ffs2zp45gyNHjox9/P3ck2zaYpKtmrtBPJtZrUQff6ewxRwdxm0aBhzHSUvqmN4mYe9bFEVI4hgx9wqIY8SEIE6SlAxmIzbsvdsXbWD6BzohgkAUBcePHcM333wDz3Wxs7sLSikqfVieD4t23SkjNq9wDUicJOlnf5DIURsdAgBoug4TgOe6CFjULGdZuMsIwvzi4ohXJDEqJDk4pMgSAwDY2dmBFwQgJLVH5qIvjn6Ige/7qNs2wEoES6xXAP9gG4wcZFMLk/IUaAeK5iZBfELrZCM7CPii32g04DgOkiiClct1tHzlERhu36xmiEA7K+I4E0349ttvoes6Lly4MNKYR0GS8TcYd0+BYTDOEZiGAZ+ROd/3kc/n07p9SqFnIkBc1JkkCZI4RsS+8p+ROAaN4zTakL1HhEDlKR72HGrs+1HIAn+WkySBmcvBMk0kUQRVUVCt1ZBQiulKZejjd4PY6WNvIQ8zXU91TYPtuvCDACZzpuz3uOk3zWkGwo5LKYXneSKtlrMs3Lt9G0mSYHF5eVyXJzEEJDk4hEjiONUYsAk+DMO0hW2S4Mji4l7NekYU1stx0PU82LYNIP3QlopFET7kk4WmqmldNpt0+Q7ioIgBsGd+xHe7rZGRfo8VRxEi9i9ktde+78Njtea5XE6Qj9YogNoSERgE6+vruH//Pn705psjd29sh37vAY8aHESzpW4QJJZrJcZxTEKQtyzUmZjWMIy2aSEuzFMVBdA0ZGNhvCokzpAHoRlJEsSEAEkCGoZNVuOqpkFVVfFZ6ff+ZitS+NgqU1PwPA+Vchm7rDQ5SRLMTrD0j4tQKaUIWFqNkwPF80TDs347obY7LgFE5MUwDFAAvucJ0WvOsvDgzh2AUixOIrIm0RckOThkEOLDjKVpdXcXcZIgn8shn88D2JtseAi0E7jFKV8ULdNEIWMJ3FoiZ+p6Ezk4SGIAsLQCbTY/aqqTb5fvTxJErIkML3fj0QbKJvkgCNIctaqiUCymu80OUYChroG9H7//+msszM/j6LFjIx9zFLSWgz42EJI2xWqTYhgFuq6LCILjuqk3BfonHwohUDRt3wTZLtoQZ8SmNAxTt0b2PCqKIogCJw6tz1OnaoSpqSncvXsXlXIZhBBUd3dhOw5okmBmenpixI4AouESAUSLbF3X4fs+oiHIAT8u1yEo2ItsmoYBJAm8IGgmCHfvAoAkCI8JkhwcMsS2LYgBRWob6zoOoiTB0tSUeB2lNA1zdjkWZTX8fIeQz+f314e3TGQ8tRDHcUoQeqjcR0HWxZGDVyq0c/TjJCGMIkRhiCiOEYbh3sTLd3tId8yarkNTVeELb+g68vn8YDnVPkEIwbWrV2HbNt5+552J5KoHWVZF5OBxkwO0RH/GeFwrl0MYhoijCEEQQGcVF6OgU7SBAqKLYRxFKRlNElCWpuCfsWx0gZPPTqhMTcG9ehV+EAhjp+1qFY7nIdnextzMzEQIAqWp6RUBoGdSCJqmwQ+C0V0oucg2Q4hMZqjmtkYQ7t1DrlBAeULpFInOkOTgECF2XVA2yXBFeq1eR5QkyFvW3qJGe7vM7atIKBTalrNld+KccOTzeTisoYrBasnHjXbEgJezARkDJBbm5FGBKI4FAciGaVVVha5p0Ng//ve+76cGQITAyt7DMaPRaODKlSs498ILY2vFvA8D7LqflLTCPoxRYKqy99RxXbie15c/x7DgpEExDICREJoke5GFOEbE9QxBgAB77wHvYyBSEYzUT01NAYSgtruL+fn5VByrKKlhku/j0dYW5mdnx/4eeix9oCgK8mzRphm9BtdlDHteHsFpjeQYGQ1CliDcuXEDL7z8MowxkDuJ/iHJwSFBEoaIXRfA3gcqDEPYjUYaNeB5SF5O2KaMiCNbkUBYRYLeYeLMGtxw5bFlWQjYDqLhOChnHBjHAV750IqY7cpiZs7EledZ90EuvNSzodw2jnoAEDCtBgiBaZpjd9XL4uuvv4aVy+H8+fMTO8cgOOhWzV0xQUGkaRjiWfU8b2LkD9hfukcUBbqiiM8WZSZPIYtqAdgrhY1jBJnj8MiCpqrY2dnBPKv/L+TzIAC2t7fhBwEebm5iYW5ubAQhYiXRQOo6yUXHInqgqoiQziEjLdZ8Psn6LChK+v4Q0iRSBICVGzdw7uLF4c8nMTAkOTgESJIEERMLchBC9kcNMgt4Ry//IEC90UhD86qKcrHYsemOUC6zcH1WaVwoFLBbqyEMglTBPCZWv2/cTBgVhCHq9Tocx0l3/5nSK5URAZHbbbfgUdrkCBdGERr1OijSHUuBaTUmgQcPHmB9YwPvvP32xHL82VbYvcAjKjwH/CRg3JqD7HHzuRxqjQb8IICqaR2J8LjOB+wXhvKuhYqiwDRNkY7IemVwXQxNEkSUIkL6Odvc2sKRRgOarkNnqS9FUbC5uYkwDLHx6BEW5+dHJgiUUmGjbRiGEMxmKw40TUvTdaOSg8xz10SqKIXJzssJAr+Ta/fvY/kxa3WeJUhycAgQ27YIlQPphykMguaoQR8GRJ7nodGhIqEjSMZKNwNNVZHL5eA6DmzHSa1qR1xo+PUlTCAYhiHCMNxXqWAYhrBx1lhb2Z4gqf0t7xPQYJ0lTcNo6j0xbkRxjK9+/3scWVrC8oRLs/q9+1nzoyeGHPDKmAkcW9M0mKYJz/Pgui60UmliQtpWYp7tUtjunNz0Klu5EsWx0C3k83k0HCclDqxvhEIINF3H9PQ0dqpVRKxp08Lc3EipE8/3EccxiKK0dcHk5YceEyWOgqyFNq+mokwgzHs6AGhKMaw9eIBCqST1BweEJyzhKNGK2HWRBIH40PDJvClqYJr7iUHLzqVh24IYmKbZVwOhbpMakOYDufe63RLZGBRhFMFxHOzWatipVmE7DgJWJqawsH8hn0e5VEK5VEIul4Ou64N1R2QTkeO6SCiFoWkTJQYAcOXyZQRBgFdefXWi5xkkNC9y3U+Q3oATt0mYWgHps6oQgiSOhenOpMAjMjHLzQ8KTVXF814sFhEx0zFVUURJZRAECKMIlmkKcfDGo0dDL9pxNp3QxQVRZ+6RlBuBDYmmpzVDEACkzwAj7pZlCR2C47q4c+OGcFWUmCxk5OAJRhKGiFx33+4uDALha7DIREv7lgZe2kcpGiykCqCp3LEb+ETdbckhAIqFAnbrdQQDphcoUs1EyEoI46zNM6tGMHUdumGIvHiVCbuGjlBQioZtC9e3UqmU1luPwUSpHer1Oq5fv47z589PzB9fYIDxc0OmJ6FSQYDs9T+YBBRCkGOmVo7rQtP1sZMjnobjIXKFEMR9iIO7wbIs+MyrwTCMPd0Cq8hRNA3lchk71Srq7NqWFhbS1ur99gKhFLbrApRC1/Wu6QLu+RGycYwtTZaJUPKNELdaBliKgXVzvHPzJs4+RgOxZwWSHDyhSJIEoW03dcnjqDHv+LxlIZfLdW2eVKvXxW6iVCikHfV6oF8jHSAN2VqWBc910bDtNETaIbTJS7pCpiFAZiIFITA0DTrLdbabuCkrORwoWpCB63lp61mgKaUi8sSA6CY3Dnz51VcoFAo4d/bsmI44HvAd35MUOQAmb6ZlGgY8XUfIWlWPS2fCScG+fgs8lTUC8bQsS1TiaBm/AV3XQXO5tNFUFIEQIuzTV9fXMV2piNSboesd/Tq4z0nMjtGPKFdnRmhRHMOgwzWh6nlPOGFvSTG4rouHGxsolkpSfzBhSHLwhCK27bTjXIeoQZgkWJie7jihxlGEXUYiCCFpRUIfxiXDTGT5XA5xHCMMAtTq9abqhyiOU0LQpj6aECJ2RNxopRsSJrobRngVBgFcxwEFUGzXYZFFX1Se+xwxknDv3j1sbm7ip+++C1VVm+yTHzeeyDJG9t6PszVxu3PkcjlEzPeAP3fDgEfWsiLXds9vJ4Fiv7BMEwRpyW2rnoAAojQ3Z1koFgpY39iAFwTYqlZTgmAYaRqFkXadNbbiO3PbcdKeEoSgwISOvaDpOojnpZ9n7no4Lu1KVvicESq2EoTbN29K/cGEIcnBE4jYdZGEYdsPXDZqkO8QBYjjWBCDXhUJWWQFj+2cBjuB78TrjQbCIEC1WoVhmqIeOgtVVaEbxt5ups9zJJnQ/6BphThJ0GDEwLKsnirrrLaDsgVgkDOGYYhvvvkGx48dw/zCwsTy6ByDHJ1Suhc5eILSCjxCNkmBJK/QsUwTLsthlwcUJ7amDiYN07JAWWlfr9SUrus4sryMza0t+L6PBqvsUZnRFw0ChEy/pKnqnguioqCYz/dt560qCqAoAHOJ1FjXxrG/c5noAdBMEBzXxfXLl/Hy669L/4MJ4QnaOkgAzM/A89pOklmtwUwHf/U4SbBbr6d5e1VFpVzujxiwDyI/bzLE5GfoOjwWPdja2hLCIV5+NVWpoFKpCCHjIJOJEEcqymB18ZTCbjSQJAk0XUdhQC8DQtImO8oAC9f333+PKIrw8iuvDHSuYTCoiE/cR0KeqMhBtlxu0ucwmbiPJgl8ZhveCSJlwEoRs8ZafZ93BNLDLcq9PkWUqqpibnY2JcG6joZtp/qaQiFdRBUFSZKg3mig1mig4TipedMAzwLXHRBCRDQw24StH/S8h4QAbfxJTLaxAFLb+FvXrg1wVolB8OTMDhJt/Qyy4FGDnGW1LTVKmB0yZfXUU5VKXx96HhYcJgQaxTFsx0krDGwbJmulzLs5lkollEolWJYFJdPhcdCpkmZsjweB7TgiJ1ssFIZbfLjPA1tQFT5ptTnWTrWKm7du4eKLLwrDnYnuMgc8Nk9vPElRgyZM8F4RropXFKHVcT1vX8onSwi4K+eo7yF/dgY9js4cSHuRmCxUVcX83BxyjARtMcOkXC6HUrGYpid4ySCrdqjV66gz4XI/Y9Q1DQRplRFH302m0H/6iL9nWVismoJSivX1dTy4d6+vY0kMBplWeIIQ23bHHvE8ahAnCRbbRA0oUnU8r1Mul8t9s/lBdzUUaQ7Ub6l3JooCyzAwVanAdhxEUYR6o4FyqQRN0/YqIIZYoLnqe5CUQrbLYl+eDgMg2+WSZtIwX3/9NabKZZw5fXps5+oxkMEiB0+oGHGSEYN259ENA1oYIgoCuK4rQvaTThkorPS3XxBCYOVy4jke5O/mZmdTq2XPw9bOjrA8juIYlmkiNzUFhRD4rCySCx/djBaok4OmpmkpgWK9TgTxIqRniqGdNXrH15K0AVaSKZvkwskGc0lduXULpXJZ6g/GjCdshnh2ETlOR50BANRqNSRJAqtN1IACqDcaQiBULhY7WgY3/2H7SbDT5BVGERq2LaIEURSJ8qdisYipSiXtZsjKBDVNAyhFrV5Pe8N3ID79gEcO+q1UiKIIjm2DAsjl89Am0B4ZgBBQKYqC+/fvY2t7G6++9tqBLHbDLGTxkyhGxOQrFfZOtFcua5mmILqe7w+VMhj49Nkx9AnLsgYmB+lpCGZnZtKyRgCr6+uo7u6CIG2yZrLKoGKhgEqpBNM0QRQFMaXwfR/1eh21eh2+7++7L6qqimeoVWjci8APeof5fEQyY1BVVcyDruvi+pUr0v9gzHiyZohnFHEXnQHAlPauiyiO22oNbNtOF19gb1Fm6Pgx7cDeuQ0vR0IpXM9DdXcXtVotnSjYDiSXy2FqagqlUgmGYTSNnxAixkIpRa3R6Dtv2g68c2I/kQOaJGjYdmp0ZBhtUzDjBqUUly9dwrEjRzA/N5dqFICm9MMkFp5BF9UnUYzYiol0rOTPNU3beieUQlUU8dwOErYfFYMKai3THPqzQwjB9NRUmgpDmpoEsK/lssL6GpRLJRQZmeadJh3XxS5rF50lAry0sp3xEiEkbYfeAorOm48uF5ESkRZCm7V4rlarUn8wZsi0wmNGHMdp2WKHCYMAontiu6iB47rCXrRYLMLoIT4UIcAeE1TIGtVk2TilNPWFN82+lM2EEBSLRdiNBoIoguO6CMMQhUJh4LB20m/kgBsdxTEUVU11BgeAO3fuoG7beOvHPxY/ayVLIn/K0j20R/i1JwZMKQBPaBljK3h53AiHoGgj1mTEANgjVdxkKGbtvftV7I+KQQySLMvC5tbWUOcJwxAOS5skSYKI6QusXA5WB5U/91HgviRBEAhL8yAMBanin+EwitBO5itcLzNo3Xz0i6xgOkuyc5YlnCjX19ZQqlRw9PjxIc4g0YoneIZ4NpA4TtcJPqGp3W8Ux5iemmr6nef7cFmnxgILE7Yiu0D1mnBpksB1XREl4MRAVRTk83nMTE+jWCz2PYHybm6lUkm4MoZRlB6btZ7uF6JVc49FrdXo6CB6ByRJgstXruDY0aOo9Mp7kj0nuGwFRDbc3HeEYVBiQPd8/p8od8Q26Pdd4yQgAbs+VlXQ0fWy5XlQCEm9BBRlqND90GAVMP3AsqyBIxsUabjddhxQSqGpKpaXllAsFKASgq2trZ5Wy4qiwGJW68VCARojDFEcw3XdtBW268Jn5KH9ZbZsRIaNnhEiNgZZkWLWuCmKY6zcvIlatTrcOSSaIMnBY0TkOKCdPqBsIXddV9QSZ93LfCZQBJDuArqEzgm6RwxoksCxbWzv7IhdN5CWDZXLZVSmptKSqgF2m4Lps//nTDMtq1SUtKqi0YDtun0vhNkSvE4QRkeUIl8oHFjo/M7KChzHwYUhW8ry94WnIQRpaCmBoy274EHBRV2KoozcJGvsaB1Py/959QDXWSQt/8/ej0EJocnKBeM4PtC8db8ljqZppum8Po8bx3GTZbplmiiwKoWZ6enUXTFJ8HBzs++qAU3TUGDlyDk2F1BCkCQJPM9DtVZLzZTaXSe7Vu5AOiyyf0kURbznTfoDz8O1y5dHbgwlIcnBY0Mcx0i65BH54uA4DmJKm4gBFwYC6cTRq3a/k4NZlhS4ngeK9IOWz+cxPTUlJpRB0alcUWO+C1kzk916vS/3QG6d3Ckc3mp0NK4W0r0gogbHjqFcKo3noNloAvYWEV5CKXoQ8EgDex1PVXQbK/DkpRSaFviMyDJm2oA405FUpAvGiKbowYSbMg0D3nyon4ZRAes4GscxFEVBsVAQXglA+vmenZ2FpmmI4xiPtrYGcqXk5Y/lUgmlYhGGaYJSmnY6dZzUCK1LVHCk966VBGae46zb5W6thvsrK6OcSQKSHDw2UJYOaAceYuYmLXEci1KrKIpSURGrEujZ0IctqNmFuh0pUJiTYoV5sg/bv6DpGtr9nPkNFAsFsVvb3d0Vu5z2l0DFwtd2xzui0dEouHX7NlzXxcUDbgRDgL3QauZnbUkg+xpnIgetvxf/BtzZtS7s2QU+W02RXdzb/b51rMhcy0Gkhsxs18YnLHrAu676XcgBt0J2XBcUqQ9BqQO51zUNczMzUAlBEATY2d0dauyaqqKYz6ebCFUFkgRRFHUkCTRJRtbYtM5L2fkgl8sJ/4O7d+6IlKvEcJDk4DEgjiLEHSagbO7ZY8SAK4njJEGt0QAo3esq2OtkPJXAyIbjOPtIQYmVIY5DjNWvkIynLFRVBUXaUrreaLSNIvDwMWkzOQBjMjoaAnEc4+qVK3j++ecn3vo5C0opBtF787uRsHvYmm4h2X8t0YjsOff9y7xXtOVf698iQyL6pR8HQQo4eFtwQlKr4oOyRwZ6GySZlgVQ2nGxC8JQLMYEqUivUCh0vX+GYWBmZgYq0mqn3VptqLHzFJVhGCiXSjB1PSUJzByt3mggZDbNXIM0TmSPmdUfBGEoqxdGhCQHjwFJh7LF1hpo13WRUCrEfLVaTZQR9kUM2PGSJIHDhIYu21lkScG4vMkHVZhrqooKc08E0g90lZVMZXOTfMfRjhgIoyNKUSoUDjRkfuvmTXi+j/Pnzx/YOYFmA6ZBwMPH/VSKtB5faXfOJ0230Ae6jdg0TSjMXrhbaHxS6CQStSwrLbdsiRxwkzHHcZAkCVRFQbFYhGmafZ0vn8uhUqlAUxTs1mqwHWfwMbPcf5wpby6XSjA0DZSRhEajgd1arclqeegnp53QtMX/gM8na6ur2NneHvZMzzwkOThgxGGIpM+wJfc2yOdyqNfraUvYjPthLyQsUrBTrcLrgxSMslfqqA7vAUIICrkcKhl/Bs/3Ua1W0+gGpYjZcVuv+cCMjtogiiJcvXoVJ0+c6J3aGTOGfZ+SDmmFJwHtnubWsrVxoNvxuPZAeQzRA6CzQZKqqjAMQ1RTxHEM27aFeJgQAsuyUCyVBhbhlkolUVq8tb0Nb8CUCn+WaMZASlEU5DMkIc6QBO5q2M4WuR/QdtHDlsoP0zCERfS1K1cO/H18WvDkzRJPOWLX3R81aFNJwFMKAOu50OJ+2A2CFOzupjlISlNSUChgempqnwHKqOAfvVFChpqmocJETqqigCL1cKjWavBZCiQ7KTwOo6Msbt68iTAM8cILLxzoeYEhTGSAJqX4k0gOOmHsqYUexzNNE4R1MXws0YMO4zNZJ0nXdUWonrCfl0sl0dp5GExVKrBMs+8Sx6bxZpxYW4WNPB1aKhRS/xVKEYWhIAnDtudut9S3pixyLNpS393FXSlOHAqHZ5Z4ChAHAZDxCOdoNwG6rpuG6rBnT1pucT9sBV9Qd3Z34Xie8FIvFQpppIDlVDuJDYdh2K0li6PC0HVUymXk8/lUIMZIQKPR2PNXf0xGRxxRGOLq1as4cfKkSPl0xRh3LkObyGQm4ieaHLTcq4PUHfDzWSy98DiiB8D+DoeUaYy4pwCQfk5KpZJYBEcBIWSvxDGOBypxBCDcC1v/hiKNVvF0Q6lUSjcmGZLgcBv2QdBlbPxO8OgFCMFdVmosMRie4Fni6UKSJIjbCIo6hRJdx4HreaLdcrFYFKU6raBI63u3d3ZS0xNOCnj6oM8c5KAYtsNiLxBCkDPNvZpqStPabSZarDvOgRsdZXH9+nUkcXzgWgNgeL3Bk9pT4aDRl6+AYQjtwaBmXWMBC5NTmvY4qNVqopJEZWQ4n8+P9b0UJY66PnCJI9cdtL4+SZKmuS1LEnROEqIItm2nTeXabJw6nLDjr7LpCk3TYDGPiJXbt6X3wYB4tmeKAwRljYf6QcCcD33fh5nLdXQ/BNKowu7uLhwWplNVVTRB4urrVnQqnxpmlzTJZZmrj4uVikiFuK6Lzc1NOK4LwzQPvEdAEAS4dv06Tp4+3dV4alIYdif7JOsNuoEbeB3oOVnlAm+VfNDn51bFjUYjFRCzEl6V6YWG8R7pB8OWOPI0Z5YcdGsKxnf1rSShUa/3Ha3pRPJa04+maUJTVTxcX8fW1pbUHwyAwzVTHFIkSYK41f6Uh+LbPOS7tRpcz4NpmsLEpBUUaQlftVZDFMeghIhIQSdSkEXbHegAO/CR+wIMAJUQ5HI5FAoFRGEo2lrzXZUfBGMN3XfD9evXkQA4d+5c33/TrgHNsBhajMjd5A4ZOWh1iRwZfT4npmGk7ZUpPTDfgziO4TgOavV6WqmUJICiwMrlJkYIWjFMiaOiKKIqCkif0biP+9xEEti99n1fiBa7odfRm/QHuRziKML66ipqQ5ZsPouQjZcOAEmbxauTlXEURahWq4iSBHPT020NfcIoSl3QmGugbhgo5vMD++VzC9JBKw0OkhiIcxKCOIpgWBYM04RpGAjDEFEcI7JtOCxXzHd8k4AfBLh+4wbOnDqVGtMcMPhubKgyRhY5GNXc6nFgElUL/ZzTNE24cQzf9/d1HR0XKKUIWXOj7IKoKApMy4LOupoSRRlKiDoM8rkc4koFu7Uadms1YZ3cCfzzxntaxAPOJ6qioFAoQA/DtIyZpRoMwxBlnKOApzO2Hz3C/OIi8vn8gTXYOsyQ5OAAQFudzSjt2OOgtruLKIqgKAoW5uaafp9QCsdxREkTYR8q0zBGqjkn6H9HKojEAeb5ubAp8H2YpinCkZRSeL4P3/eRJEmq5vY86LoOq8/OkYPg2tWrAICzZ88OeAHjW9iGvesicvCEN1x6koK+pq7DV1XEUYQgCPr2D+gHosthS9MiXddhmGaTvojStL30sOr+YVAqldLyQ9vG1vY2VFZ50A5ZcjDKGA1dh1Yqpd1b2T2Pogi5DpGTXqRRIUQ897qmIVQUVLe3YVkWZmZmhh7nswJJDiaM2PeRsFpkAGk6ocPujXc6o0mCSrnc9IEIwhAN2xa7B9M0kc/lRu6uJ3z6+1jAxlGyOAy4s6OqKE3togkhyFkWcqYpWkyHUYQwDBGGIVRVhWkYfaVZesHzPNy8eRNnz54deJEYhHx1wyjHENbJh9S4iLs7jn6w/uvreeWCE0XwxhQ9iNiiF4Thnv21osDQ9e5Rr8xCd1CYqlQQsc/V1s4OFufn2y7SWUEiN0MaFoqioJDPIwhDuKziyrZt6KxcuWMjsjagaP7smaaJRw8fojI9jSAIxmb+9rRCkoMJglcoZB/oTpNLGATwgwC+50EzDFGel1AK27YRBIHwKyjyuuExgRCS2vF2iQhMqjKhH7i+jziORVOofSBE9KCPWRjYD4I0h8vayhqmCVPXhTnKoLh27RoURRk8avAEINsz4UmPHLTFuHUHA8DQdXiahjiK4Pv+UCLUJEnSnTB7jjlUVRVkt9v1EUKgqSrCA+z5wM87Mz2NR5ubCMIQDzc3sbSwsG/x5yOngCifHvW8hq5DU1V4vo8gCBDyKIJl7UUEBzRSUlUVJAiwtbkJ0zQx1xKZlWiGJAcTBG3RGnQSIPKdMWXd5wghKBaLaVtm/nNKYVkW8vn8RCZK7qtP2xCEx0kMojiGz0pA+7l2TiByuVxKttiE7LP0A594RBe3Pu6l67q4dfMmXjh/fqhUxViiBiPoDbK56n6eHcr+Jk4SJHGc7t5YnbjPJmtVVVMb3HFpGPoQ0I5FdzDgMXh0ym40hPagn8WPmygFjKRmDiiev0FEhq3eBwcFXuL4aHMTYRTh0dYW5mdn9+4BpSKqwyM84wJRlJQMaFrqlsrmSV3XYVmWaCjX9YxsXPzZMQ0Dmw8fYmZ2Fp7nPZaKo8MCSQ4mhCRJkLRWKHSAw3oJ+EEATdNAkYaxgyBILY+ZkdEkRTRZh7F2H7bHQQwoMztKAGi6PpCzIw8JW6aJKAzhs4masvvsBwEIWI7XMLru3q5cvQpV03DmzJnxXNiQyO7QAtZTgveWiKIoFYQlCeI4TluCs+/5rjVhuWu+6Iuvmdfy71sRxzESSvHb3/626VkgigJVUaBoGlT+vaqm5EFRoKqq+Bn/v8J+pqqq+HsgFdpapokgDNP3zrJEpGNs5GAI215d06BqGiL2DHVaUOI4RsBSWklLzT63QNZ1faidNVEUEEYOD5ok8BLHh48eiRLH2elpACkZyEYLxqWLEK6LzACqWCikz3oQpELkKIJlWX3PCfz5UVQ1dYJ8+BAme8Yk2kOSgwmB+n7Tbq9TdULg+6mhD/ud7/tQVRU+W8jyuRxyuVyTZmGSYkBBENgE+jgqEzg830/FmWCd6Ya8bk3XRSvnKIoQhCGCMBQmN9xQiROQbG7ZcRzcvn0bL168OHw5WT/vGaVCN+F5HnzPgx8E4nvbcVLxJfs90JxT5aFnQsjeYqyq0FiZGUW6QOmaBt0woBDStEgrmUV8388UBbZt49PPPsMrr76KXC63n1QwMVrCSAonE0kcizbIraSE56h5gx6xoLIFnIAZ2VgWTMuCwfQjlmWJn1mmCdOyYJrmxMo0ee8CO1O5wBfDKKNxaVoYCYGmaWm6S9NGD7UzS/GDrFrIgpc4bm9twW40oDHPBX7NreWM4wABBJnl74Gu60KX5bouwjBEjkUReh6PEQTTNLH56BFm5ufhui5yB9ji/TBBkoMJIEkSJKxCQfgJtEsnxLFow6prGh41GrBdF/Nzc6LDWtOCNCFBUuuOjI+3SUh5wIhYzTcohZnLjW3XyIlCHnsTexCG6Q6bTfK246SLqK7j0qVLMHQdp06dGuqUvCOm2OWz73mVBScDHhNfZRd8LsC0TBOGaWJ6ampvYWSLpMkWR62LlsBxXfieB8M0u5akdcPOzg4AYG52FlNTU0MdoxuCMITdaIidos/vESdLvi/q7jlxAtBEJPi9EsShhUDwSNIwDboMXYenquL5UBUFURTtIwT8udHGQAiyIEifJW5g9jjMfLIljjvValMFAxe6TqKiQskQIu4QyVOGURShbtvI9RNFYM+JqijCGIl/jh7XPPckQ5KDCYC2phM6PHi8NbGiKHBsG4HvQyEEU1NT+z3T+WRwQA+xiHY8JjiOk3rK63raJa+1HHQM0DQNmqalJilsd8u9E8IoQq1ex8qdOzh79iw8zxOvbxdB4O1z67Ua6vU6arVa2mCm0UDAmuRwZBf8YrGIubm55h0x+17TNLEQ8IVhGAzSqvmxguXjS8UiUC7v+zVl+W0gJdZ+EMDj6ZUWMmE3Gtja2oLP3lN+fBrH0A0D5XIZJdboq1QqoVQuI5+N0DEklCKOIkRxjJjV3ydJgkKxmGoumBiWRwkm9ZnJEoJBKozGjWKpBM/3EcYxtqtVIVDsZKE8DhAAyBAEnjLkWoSERRGSJOnuP5J5bwzDwPbWFuYWFuA4zoF3Vj0MkORgzEiSZK8lc5fyK9/3EfKdB6XwWWphemoqbRiSxYSJwb4phtKm1qiU0tT6+YDIguf7YkIv5PMiujLJs6uqihxL4fAa9Js3bkDTNCwvLwudQsBC/a7rwnUcOLaNWqORhvpZUyTu+jY3P4+Tp07ByuXErpU3vxoII5bxNYV+DzMyegGiqul97SMkHMdxU3qmxgjc9vY27ty500SeiqUSisUiCvk8LPY85PJ5qIwIKJoGGkVQCEGhUBAEbtJQFKWJHCgYr/ivH3ByNjU1lc5fcSz0B0JzwFOpY74nPJqZjZioqopCPi8qGrjfSa9mVDz1Zug6Nh8+nKjQ+zBDkoMxg7JFTaj+2zxwCUsnRKzWWdF10CSBYVntJ7uD9hVoOR8hBJTtDCYNbvSUICUGTeHyA7oPCkmtmVdXVzEzPY2V27fTBYW1yk2HwiydczkszM+jWCyiMjWFcrm8Lwca9dtQpgNGveuCHDzBZYx9X+MQmhtVVVEoFFAoFDA1PY0jR46I38VxjEa9jt1aDbVaDTbrAPro4UOhRQF7r4ulEor5PAzTRJIkB7ugELKv6uQg0wuUUpH7VxQF09PT2Hz0CK7rwmbpKhHlohTqJCqqWvRQ/GecDPBNRZIkqQdMJzLM/t7QddR2dxHHMWzbRrFYHPuYDzMkORgzaKYWue3EwXwLfN9HHIbI5fPQDQOarsMLw8djzNFno5ODCGXatp3mnVV1n5J4EtNwo9HAzs4OGvU66o2GSAdwsdzW1hZK5TLK5TKOHDmCYqmEQqEAwzCQJAmiKGqarHjJpKIo0DMpCMJDrwcMSqlYVJ5oA6Q+ymVFue1Qh0/vQxRFCKMIcRQJ0SQAlNl7DKRkQtU0UOaTYdu2SBWtr63B8TxhXW7lcigVi3tpilIJ06z98TjRrpSRKAroiMSzF3hZa7aaCQAsy0KxVEK90UB1d7e5Mdwk54gO1SamaUJRVbiOgxhpyjafz7dPpfGoaJJAVRQ06vW0z8OYO10edkhyMEYkSZJ+WLso/F3XRaPRQBRFKLCmSjnLQrVaBShtFtUcsM4gPWXnnRmvuJhUmiFgpWKgFMV8vql0bxxIkgQ7OzvY3trC1tYWtra34TMtg2lZKBWLmJ2dxfPPP49r165hanoaP37rrZ67wziOEUWRWHgSptznqYiE+VQQknbWayrvY993gqh4GfJe81bNj4ucDIwR/Q64PoNXRcSsCiJmCxySpCkcTwCojMTx9yZ7nwrFIubn55vOYTca2KpW4ToOwiBAvV7Ho81N3L59W2hDKpUK5ubmMDMzg9nZ2fbmXQNAaVMJQDDZ6gVuatQJ5XI5JcNhiJ1qNXUOnfDmoVV/IH7OxKBKoQDHdZGwaEC+k/UyIAh8o15HsVRCo9EQBFFCkoOxgmYV1G0QhiF2dnYQhaHoMmiapijtArA/cnAQxICxcdrn+SaRZuBOkBTo3IVuwHvhB8EeEdjaws7OjmhrPTM9jZMnT2JmZgbTMzMwM6RsbX0druvizR/9qK9FmS8q3FY5juO0IRTbnXKrXEqpIBHNl9W+rFBV1X07tkHBJ/enJp/Kn9WMSRMvo+SllZ2eSgIAqgqDkzRGCAa9N7lcDqUoQiGfR6lYbHKdbDQa2NzcxPb2NtbX13Hjxg2ApK3HZ2dnMTs7i5mZGVQqlYHIGsloDrJQCEEygfRCNo3QCYqiYHpqCg8fPYLPhLy5A/ANIGCpzzbXzHUIDmt1bdt2+0ZLmb8PMq8tFAqH00V0ApDkYIyg3C+9g85g89EjRGEIXdcxlQk9hmGYNldp2bUcWMSAE4MBMO40g+O6wpd9nyCTn7PHMer1Ora2trC9vY3NzU00Gg0AQM6yMDM3h5eOHsXs7Gw6MXe5tzdv3sT01BRmh2zOIsgCI3pxZuHKGg1FcSzcL+Mowr4AMYvUcFU8V4Xz/xP2fbdFRojtnvQJjz9HLGdNuW8C/z7zNQzDrmI8fk9azZn4AjvqQqqoKkxdh8cEqtk6+WKxiGKxiBMnTgAAgiAQ5HRrexsPHjwATRKomiaiCrOzs5iZnobeJaXYLWJCSNp3YRyzRac0QicYpolyuYzdWg12oyEaok0aCoAY7ecE3p/BcV3EUQTHcUQpqwCbpxV2X33XhZXPo9FooFKpTHz8hwGSHIwJIqXQRigURRG2trYQMAfEuYWFpq5rIXOvE+z2oNMJdLhOi01phhEmhDAM4TO1f7FQ2PeBbzfZxHGMnWo1JQObm9ja3k5TEgAqU1OYX1jAhfPnMTs315FstEO90cD6+jp++MMfjnW3zRf21ogIZUZBURtDIXAToV4HbyEP/HtCCHzPQxTH0BgZ4QI7nr+fVBtiYC8dlH0+kpbFn2sBPN+H67pQVVWIPnsdnxs5aS2mT90snccVgjeYkyPvt9DpPhqGgeXlZSwvLwPIpLa2t7G5tYVbN2/i8pUrAKWYqlQww8gCT0Xw4/YiB8qY0gvDlCKWy+X0/fM81Ot1lA6iLJAv7Hy8rV4tLFrTsZKBv1/s73zPg5XPi7LGoQ3PniLIOzAmUN9vu8CGvo/dWg2e60LVNMzNzTURAyC1jaVZcgA8MTqDftAkEhtQi0ApRcNxQJGKilrvDUcQBKjXatjd3RUpAh5tmZmZwalTpzA7N5fuwEb4YN+4eROmaeLYsWNDH2MQEELS8HYb0hBHEUKmYRG5dPaV7+4oIxE8DNwafXBcV6Q3WtMZmUE0LW5ZR09OJnhvBYc9x00EgC3+/PthaGLMiIIgLlmSoyhQAPE9gPH2dRgCXKPAbc+71tdnoCiKWPx5E68G82TY3t7Go0ePcOvWLYCktfxzc3OYnZ2F4zjNPRpa0C3U3gvZaMGwmJ6aQq1WQ8hMiWYPQFidvWbKnrvWJne8csjzPFHJUMjomfjz1qjVMLe4iDAMUa/XMc3soZ9lSHIwJvASxuzD6XkeXNuG67rQNA2Vqam27X7DKEKCtLRm0vbIbTEG/YAoM1LVlCD0CcfzEMcxFEL2ibZ2qlWsra3h7t27qNZqIJQin89jbnYWx597Ls3dlstjU+EHUYSV27dx9uzZxx6G56ShV/khJw1Z8pDdlSusckLVtL28det73RJqb/ck8IWpnWai63Ug85nIpElEhIMt8noYQmP9B0o9Ssp6CeUOCqZhIGLmWf2Sg3bgqYjnn38eQEqEt7e3RTriu+++S7UVlOLjTz7B0SNHsLS01KRPGsb7gGLv+Rk1gqTrOkqlEqrVKuqNBkqsomfSUAhBTPcaP7W7epPZXXPH1YZto9ASTYxYBQuQisaLxeJEe9kcBkhyMAYkcSxSChye48Bl7m2ariOfz6PYQbEchWHqc3DQZYwTyA0KNXEfqYYoiuC5LkAp8mxBePjoEVZXV7G2ugrbdWHoOqZnZnD02DEsLy9jaoJq4jsrK0iSBCeHtEoeN/p5d3gFRCdwXUOpVGoKlWZz7zS728/+LPM9T9nkLAsFnvrJpCfA/8/D4Jnx9QOaJAgPoKJinE+8bhhQWdomZFqiccAwDCwtLWFpaQlRFOHTTz/FxsYGFELgOA4++/xzKIRgbm4Oy8vLOHLkiEhB9ON9wCM8nEiMK7WUZ2F5CmC7WsXC3NyBVMj0k1LRWfMm23GQxDHqrJKBj09RFNSrVcwtLcF1XdTrdcwMqTl6WiDJwRhAg6BpYuQd86IogqJpMHQ99bRv8yHkYjUgZd8HHjUAJkMSeugReDohjCLUdndx6+ZNrK2vIwxD5HM5LLFJb3Z2VvQGaBd1GRcSSnHj5k0cO3Zs7Irroc1qRnxfsgt86wIwqN6AE1d9wO6YA6OfaplRDo/xEQRCCAzTRMx6P4x7p+l5Hj766CPYto2jR49ip1rFBx98ANd1sba2hrXVVXzz7bf45ptvUKlUcOTIESwvL6eRlw73cZJRFwKgUiqhYduI4xi79TqmD0Lcx9NRhHQlCbwvg+04oEnahrxYKKSfA0qxs7OD4ydPil4nQRA8Ht+ZJwSSHIwBWeOjiKljufDKYL3HOwlceKWCQsjBimAOQFEMtNcjuK6LO3fv4sH9+9hm/g5TU1M4c+YMlpeXUalU9uXAJz3mjY0N2I0G3njjjbEfe1j1NsVoC2GSIWaHwuNgAAy7yI/7CTIMAy6LHsRxPLZ01O7uLj766CMoioL3338fN27eFFqaXC6HU6dO4dSpUwjDEBsbG1hdXcW169dx6fJl5CwrFUEeOYK52VnxWUro6JUa3UCQVnKUy2XU6nU0Gg3kDqAtskhp9kF6eCVDg/XIcFxX9NRImFtmLpeD4zio1+uYnZ2d6NifZEhyMCISJiYkhCCJItFZLo5jGLoOTVW77kR5WZb2OFIK2VzwhCeNeq2G1dVV3F9dxfb2NhIAU+UyXrx4EcePH+/a+ER8+Cc2wlSIODU9PXT5YjcMEzkYR6tsETVg+f0nGfzu9DvKx9WZsBWKonQsaxwWDx8+xO9+9zsUi0W8/fbbsCwLURS17Sap6zqOHTuGY8eOIUkSbD56hLX1ddy/fx83bt6EpqppimJ5GQuLiyOJdfuFZVlpkyrXxU61ikXWnGmS4Gmufp4J7oZYr9XS1Kbvp1UMALYePcKZF16A4zipi+0YCd9hgyQHI4JHDZIkScNp7OHkUYBcodA1VBoxcpA7KHJwQGWSNEmwvb0t9AMN24bKJqqjR4+iWC6jkM+jXCr1PhgnBxNaDGr1OjbW1ycSNRgW47hSbqz1tEUNAEyc0A4CwzRFh8hR2/+urKzg66+/xuLSEn705ptiYQqDoOfCrigKFhYXsbC4iFdeegmbW1t4sLaGtbU1rNy9C1VRsDA/j2WWfhj7jj5z3eVKBT5rgc6bM00ahBCQJNnXG6YdVEVBLpeD67rwgyBtxGQYqFWroJTCMAwEQSDEic8iJDkYEQlTuDq2jThJ0oYjhCBmAkOtB+vk3QcnmsfthTFNsjRJsL6xgbXVVayurSHwfZimieXlZbz86qtYWFhAEseo1WpIKEUhnxfiqK5ObFy/MKFc6c2bN2FaFo5OqnxxiPtLhiwJzCI5TCmFYUrwejw37V4/CfA23jSKBiprbMV3332H69ev49SpU3j11VebfhdFUX9RCfbcJACmZ2ZQmZ7GxYsX4dg21tbWsLq6iq+++gpfffUVZqansby8jOPHjyM/Bm+CrN25qqqYqlSwubWVdi81zZEtpPsag6r2NU9wXxluc+56XupDoqrY3tpCoVgUHVglOZAYGEkYAqyXOF/kTZav4l37uoLStIyx1eNgUugwOY46ZbqOg5WVFaysrMB1XZRKJTz//PM4cuQIZqanm3YUjUYjtUjO6DCyZjntJnsuGJrE1B5EEVZWVnD23LmuPQ5GwhC73LFEDg5DwyWGgR06kaZLBrmvk7wLo5Q1JkmCzz//HA8ePMArr7yC06dP73tNGIYodajUaVt1wj0q2O/yhQJOnzmD02fOIPB9bKyv48HaGq5cuYLvL13C0uIiTpw8iaXl5dGfF3ZOi/UrqTcaqNZq6WZpwmkNAmYp3eu5YL/n9vVRFAmB4tajR5idm8Pu7i6CIEhTOs+gKdKzd8VjBA0CBL4P3/NAkdYre54HgNXW9viQUSCtraV0okr89GTjXVppkmDj4UPcvn0b62trUBQFzz33HE6cPImpqam2fxMGgSBRWR1GKyFoJQnC0WwCO787rFnOqZMnx37sUTBKsyUOQQ4OQ+SAYZArHlSUOEmNwrBljUEQ4JNPPkG1WsWPf/xj4aTYinYLVGtJ4j4QApX5AGRhmCaOP/88jj//POIowr3793H79m188vHHsPJ5nDxxAidOnBhcP9HmeS2XSqlLIUsvzE9Y4Ef6IAatv81ZVmoyxQSK2NqC67owTRM+c+4s9ZP+fMogycGQoFGE0HXhui4oIOqMoygCISTN5/VY0IIgAEU6eR8W0YvneSJKYNs2pioVvPrqqzh+/HhbwVQWDiNRlmW1NfdptYoV7nsZtfU4kVCKG7du4fjx45NVVA+4yI+DGAB76u0nXYzYhAHGOnDFwgR1CsOUNTYaDXz88ceIogg/+9nPOpJqII0cCM0BTx3040aqKCBx3PE+qZqGE4wMVKtVrNy6hWvXruHylStYXlrCyZMnsbC4OFA0IXsuwpozPXr0CL7vo9FoTDRMn3VJ7EQGW3/Oo7w2Iwie7+P+ygqeO3VKkgOJwRE6DmzbRkIpLMOAaZqo1+sA0qhBV2tX9nDylMLEQ1ajToiU4uHGBm7fvo1VFiU4duwYfvTmm6nNaB8Thx8EiMJQWJr2QtbxTMlYpI4T6xsbaDQaeHPCQkTyGFIKwOGMHAwCMuBiP2mKNEhZ49bWFj755BOYpon33nuvZz4+DEMoqtrcdbLPBVtRFNG6uxumpqbwg9dfx0svv4z79+7h9soKPvzwQxQKBUEgupLoDhE+wzBQLpWwW6+n6QXLgjHp9EIHnZKwHG8Bb/hm2zaiKML6+jqWjh4VG75xmlwdFkhyMATiIECjWkXMFvZcPo8wDJujBt3APtQh0ywYk9y19jN5drBs9j0Pd+7cwcrt22jYNkrlMl555RUcP368awe5dscXERbmdd4vREMhQtIGRGPc/d28eRPT09MTd0J7XJr6wyRIHLSUcahzTLi6IVvW6AdBx4Zf9+/fxxdffIGZmRn8+Mc/7rno8BbgGmvhPTD6dE7k0HQdJ06dwomTJ1Hd2cGt27dx5coVXL58GUeWl3Hy5EnMLyzsN9YCOn42i8UiXNYEqbqzg7nZ2Yk8l9z4ixACQikSND9T3Z4vVVWRy+fhOg78IMCtGzdw4vTp1AbfdSU5kOgOSinqW1uIoih13GJuZEJrYJrNUYMui1nEeiocRO1xV2Q/5JTi0eYmVm7dwv3VVRAAx44dww/ffHOfuLBfeEGAOIqgKMpQ4XvRBIjV/tOW8Q4DXr745ptvDvX3g2DQsP44PA54V0fgcJCDYd/HgfwOFAXo0rxoHOBljT6vnW95769du4bvv/8ezz33HF5//fWOzwbve0ApRcB0Or3Sdt3AexAMBEIwNTOD12dm8Morr+Du3bu4vbKC33z4IYqFAk6eOIHnn38eJv9Md3nOiaJgZmoKG5ubaXMj20ZlAqF67nfAx6O0RA96pSZ1TUNiWfB9H1ubm5hdWICu63BdF+UJWrc/iZDkYEA4tRoi3wchBIViEYSQ5qjBAMJCnlefmN6gj8mAvyLwfdy5excrt26ljVNKJbz00kt4/rnnBosStBmD57qglCI/ZA14tkQq+39geKJw88YNGKaJI0ePDjyeQTHIjlXYTY8qRjxEBkhZDDrWgXQHB+CLoGkaNF0HDcOmskZKKb7++musrKzgwoULOH/+/L6x8Q6DSYYcEqReKABG27kOGD1ohabrOHX6NE6dOoXt7W3cunUL31+6hO8vXcLRI0dw8tQpsVHqdJ81XUelXE6bMzEnwkmkF7LPhNhMZCo6esE0DCRJgjAMcW9lBafOnkXMKlGeJTtlSQ4GQJIk8Go1IUDki7rnugDaRA36ON6o7ZI7os9JYHt7G9evX8f9Bw9AKcWxo0fxg9dfx9zs7FjG5bEcrKqqQ1dkEEUR+cNWS+G2RKHHAuuHIVbu3MG5F16YXPniCHgmxYgHsHAf1J0wDQNRFImyRt486eHDh/jhD3+I5557DgATz7HQN6VUaFNax8krfEbVJg0VPWgFIZiZncXM7CxeffVV3L17F7du3cK93/wGxUIBR44cwXPPPYdO8cFioZA2pQsC1Go1zE0gpdd6hU2ljX1eP3/fHNvGzs4Opqen4bquJAcS7dGoVoEkEW5aQPrBjeK4t9agy2L1OCbwnWoVly9dwur6OnKWhYsXLuD5EyfG+vDTJIHreQClyDH/8mGQrWJI2P1v+7q9P+hKFO6srCChFCdPnBhqPINiIKOeMZ0zPmRixEFFdgIDaFAOSvuh6zpURUEURajVavjss8/gOA7effddzM3NiZRB1ruj21XzFtmjpBXSk4wWPWiFbhipd8Lp09jc3MSNmzdx/cYN3F5Zwflz53Dy1Km2UdFypQLv0SO4rpu6So65jLtdNKlfUaY4BosCu56HjdVVVCoVeJ6Hcrl8eAj3iJDkoE/EcYzAtgGkjU/4JNYUNej00HTKK07KpKbLh79Wr+Py5cu4f/8+isUifvTmm1haWhoo4tEvXM9DkiTQNG1k0sEbOPU7sXUiCkmS4OatWzh+7NjEG8IMhXG5VXK9wSGZyMT7OkRa4UkDIQSGYaC6u4tvfv97EEXBe++/j2KhMFRHRB45GIc2SVGU1FtlnM8FIZibn0exWMTuyZO4e+cOvv3+e1y/fh3nz5/HiRMnmuYXwzDSygDXRa1eHzs56EQYB733uq6nFtBBgJ2dHczOziIIgsl70jwhkOSgT9R3d4EkgaaqgsH3HTUA0ge2n7rkCcF2HFy+fBl379yBlcvh9ddfx3PPPQeFEEQTEGklSZKaQ40YNeDgpY1DWRHvHQSPHj1Co9HAG2++OTY/gXGiNW0yLA5rX4Vhrn1gv4MJg1KKne1tfPnFFzBzObzz9tsdKxf6QRRFoBg9rcChKMrYPUOAVOdimiZefvllXLhwAZcvXcJXX3+Na9ev48KFCzh+/Lj4vJVLJTiuiyAIRGfEsaHDtfXlnNgCyzDgOA52d3YwNTUlzJGeBUhy0AeiKIJn29CRRg34BNZX1CCLFrtX0TVvnAtUSxjddV1cuXIFKysr0E0TL7/6Kk6cOAGttaJizHA9T3g4jCNVwXfAo05p9+/fR7lUatsIZiLvxwAYR5VC9lgAJhIRmgTEeIf428feoTEjJqSU4vbt2/j973+P+fl5vPDCCyNHb6IoAsH4hMtkQhsVUUKoKCgUCnjjzTfxwvnzuHTpEj7//HNcvXoVFy5cwLFjx6DpurBW5tGDSRJZkVIYsBRa03WomgaX+drouv5EbiomAUkO+kCtVoNC0/4HmqYhoVREDXqW52Xz3S0Ppcg3jvtDQQi8IMC1K1dw89YtqKqKCy++iNOnTrVtBDXunVecJPB5hcK4mq30cD3rBxGzij137lyHUzR/4LlD48hRjz5fN+73ADh8kYOhFqwJOh+2A21D8Pn5v/32W9y4cQNnzpzBCy+8ANt1EYThSCmskLdrHteCxLxDxh094NVX2VGWSiW89dZbqO7s4PvLl/HZp5/i6rVrePHCBcwtLMBxHIRxDMdxJuacKCqAMET0gFJYpgnbcbC7u4tcLgefdd982iHJQQ8EQQDPtmGx8Dh/rPqOGmR/14EkjI2Dspro69ev4/qNGwCAc+fO4ezZs93zlWOeXF3HSf0bDGNsxiH8Ho8yoa2trSGOIhw/fry/c6YnbvrZJHcN4+jEyCE0B4eEHIxqgjTJ96UtGWhBHMf44osvmpsnMfOviDX2GTYtEEXR2L1QBnWX7AeUeWu0e+ampqfxk3fewdbWFr6/dAkfffwxZmZmcOLkSWiahlqjgXw+P57nNXttlCJOkubnahARK7seXdPgseiB67qSHEhkogaGAZWx7TAIRNSga/6ph8hqnF3zojjG9evXcePaNURxjDNnzuDs2bMwD7j0JopjBL4PUDrWPKKY+Edo23z3/n1Mz86iMEJ7WuHbDhZxGWMqYpzEYGJi10lhhPtIgP46NPazIGQIGo8c9UIQBPj444+xu7vb3DyJEOiGgYQ1HhqWHIQj/G1HMIOgcUYPkh7zHQDMzs7iZ+++i41Hj3D50iV8+cUXmJqawtKRIyjk86iMwWgoGwnNekZwDHLd/Hk0TRO1eh12sYharYapqamnPrUgyUEXuK6LwPNgUgqLLXQUaHZD7Ddq0PpzHuoa0ecgjuPU3vTyZURRhBMnT+L8uXMDMdtxphV41MAwzbFOaApGG6fn+1hfXcUrP/jBWMYjTGo6VaK0LHb95MXHtftNMvnVwxI5GHUXO+izse+9GDJq06jX8dHHHyOOIvzspz/FVIuWxdA0+KwbKR3SBGxSLYO5xfBYPvuZ+9nzGgnB4sICFufnsba+ju+++w5Xr1zB6toafvDyyyPbmYsW1mjzPmOIOYR9jizTxM7WFvL5PFzXHV/K9AmFJAcdQClFvV6HGkWwcjmorJRuXFGDLIaZMJIkwcqdO7hy+TJcz8Nzzz2HixcuDLdbH1OIMWJlP+OOGgBIxZwYPq1w/8EDUEJw7AAcEYH97ylB89h7takeBY9bWDkMhvY54H+G9pM+v6+Ups6Dw5QSdsLW1hY+/vhjWJaFn37wQdsWx5qmQVUUhHGcpgeGSLOFQTAZX/8x+h5kj9F3tIoQLC8vY3lpCd9fuYK7d+7g13/7tzi6vIwLFy+iPKS9MvdDaddgCWDEXlE6/n4f2LUZhoFGtYpGvY6dnR1JDp5VOI6DKAhgECK8wweKGgDdJzpKh26Ms7u7i88/+wy7u7s4duwYzl+8iNIIofJxwWHNlSzTHLsltMJsgPv+QLfg3t27WFxcfHwOZ2wi3ge2c0syoqlRF/WYlTE+ie6PHTHCAiVEcNldcEYcJ3aSYyQG9+/dwxdffonZ2Vn8+K23OhsUEQJd01KflCE7+03S139c2gMRvh9SUHr65ElUymVsPHqE1dVVrP71X+OFF17A+fPnB54facvXtqcEmmyVe42PaynyuRwePXqEYqmEpaWlyVnfPwGQ5KADbNuGFsfI53Lgj+bAUYNuGDJacPXqVVy5cgXFUgnvf/ABprv0gO8X49hfhix0CqDtDmpU8N3IIC5nHA3bxtbWFt44gCZLA4P5N2Tfg2xZH22NQGRd9XqlNA4ROWi9puwnKLvAt/s7HjVozS+33p1xpc6uXr2KS5cu9WyexGEYBnzfRxSGSCgdWAdiOw4WlpZGGXJnEAKVjG6rLCoVhiS2lmXBsiwsLCzg6NGj2Hz4EFeuXcPa2hreeOMNVCqV/seC/gyP+o6aZMo+c5aFWqOBWrWKarWK2dnZvsd12CDJQRtEUZRGDZipB5A+cO4gvgY9fs8niX7DybXdXXz++eeo7u42M+rHWd+dgeN5adTAsqBMgE1zhj7M7u/evXtQVRVHuFDsMaDne9wh5bDvr7gZFNC2LwfhoXNKBTnYJ6zL7JgmkXpoGhf7nnT4OX+92N3T/W12e+0AxfcTLmmklOKrr77CnTt32jdP6gBVVaGqqiiBHkQkHMcxfN8ff5ouizFoDyirCBjleSqVSvB8H4Hv4+Tp0zhy5Ai++OIL/PJXv8L5F15IPSP6ILz9zhEE2PestX9h5vNICHKmiXqjAdu2JTl41uD7PhSaGvjwxyKMovFFDQDRRIirhjshSRJcu3YNly9fRqlYxPvvv49pLnp6QoiBHwSIwjD94EyoxEchBOC5RAzmHXD37l0cPXr08YYAJyBGbPt6do/4jhBoX5KZ/X+n3VN2Z0VZk7CmhRztF3Ee4s+coCncj5bvaQsxGnZ56SZKHPWTEoUhfvfpp3j06BHeeOONvsthOXRdRxTHA5MDviGZaH6bCe6Gicpx8MjBKNUxpmEgl8vBdRzUajUszM3hg/ffx5UrV3D5yhWsrq72jCJQDLaBUPrVHmSIp65paLCSxqcZhyfueIAIggCE0qY8YhAEANIQYc8KhQE+IEqnXDTSaMGvf/1rXPr+e5w7exbv//zne8SAn+txg9Vyi6jBhELZ2WjEIKKynZ0d1Or1gSfzsaPLezVKOLbt8QaMChD2DLb+a/od64zZ9Hetx2nzs0Exyt9PSoDpui7+5m//Fjs7O3j33XeHepb4vBGy1EK/cNgCNIlUXRO6zEP9gKd0Rn0PysViep+YrTJRFFy4eBHvv/cekiTBL3/1K1y5cqXtHEApTfU2E54XDcNAFMewbVukUp9GSHLQAkopfN8HSRLRa5xXKQCA0U1QlBGV9XMegTbK9StXruAXv/wl4jjG+x98gIsvvrhfYDamyMG+nd4A8IIAcRRBmWDUgINf/yDk4N69e7BME/Pz85MaVl/oOl2NOQIk/DMOi+bgCa6uqFar+PWvfpV+Dt97D3Nzc0MdR1EUaKoKRVHEXNIPXMcBxQGQA4zoiTGGyAGQRliKhQIUQlCr18WzPDU9jQ9+/nOcO3sWly5fxq9//Wvs7u7unR7Yb3bUBwgG1+aoqgpNUdCo1+E4zoBnPDw4JLPHwSGKIiRRBJUQqIwchGGYNj5RVfGzthggatBaqcDzpbVaTUQLzp49iw9aowUc404pDPOhphQes0keR3OlXlAGJAcJpbh37x6OHTv22Beeg0wAHbZSxlH6KjRhzNe7vr6O3/zt3yKXz+O9995DccjSOg4ePQgGIAeO40xMx7MPI0QPkjE+c8ViESrrHulkQvdKJooQxzF+9atf4crVq6JEdZQz9/x8tqS+VF2HY9tPNTmQmoMWCL2BrouHLQhDUEqhdcsVDmNmlMkzU0px7fp1fPf99ygWCqm2oJMZyBOiNQjCEHEcQ1HVA+lUpihKKiLqkxw8fPgQrufh+HPPTXZgI2KcTYOymoBDEzngGHFhGWd05tatW/jmm2+wvLyMN954Yyx6FZ3NKVGSIE6SvkpNHdedeEQui2F9D8ZZIaOqatr+uVZDvV5PK8Yyx+VRhCtXruD777/H3Xv3Ui3CsL4I6EN70DK/G7oOx3HgOM5ErbsfJyQ5aAFPKeiZKoUoozfoiAEfjuxuqVav44vPP8fOzg7Onj2LC+fPd5+MJqDK7ibm6gTh+dBLhzEmDBo5uH/vHkrF4ljKPSeJcU4uWQvbw0IOxhY5QJfFrd/7Sym+/e470Tzp5ZdeGltEghACXdeRBAHCIIDax6LvOs5kKxVaMOxzyJsbjWsWKBSLsFlTpnqjsc9WWVEUXLx4EYtLS/j8s8/wq1/+EucvXMC5s2eHSm30rFxoOaahaWgkCeq1GoIgeCrbOB+O2eOAQClFEARQAWFWwlMKKiGdF+xhmDbbPayuruIXf/3XCIMA7733Hl5+6aW0c2KnYw6gaxgIA36goihCxCIq1gF9MFRmhNQPOYiiCPcfPMCxxy1EZOh0d8fdapjvfg7lPmYMi/AoO9c4jvG7Tz/FzZs38eqrr+Lll18ee6rCMAwQRUHQp5DNcZwDd+IbZnHl1VfjIqQKISiVSiCUot5oIIqifa9JkgRTlQp+/vOf4/Tp07j0/ff49a9/jXqtNtw5Bxi7qqpQVFWQg6cRkhxkEAQBaBxDIUSE/Pgbr4/ZWa9h2/jiyy9x5+5dLCws4N2f/nTPU7xT7u8JSScAaYSF91A4kHwoAMJIUz8lV2tra4jC8PFXKfTAOBTeWQzruvk4cRBPdS8S5vs+fvOb3+DhxgZ+/NZbOHXq1ETGoWsaFKQLW7sFrxWO6yJ3wORgGL+IfpouDYpCPg9d16EQgnq93nw+VloLpM/6iy++iJ+99x6iKMJf//VfY2VlZeDzDeJ3wAWmjXpdRFCfNhyeGeQA4Ps+lCQRUYMEELvjruRgUMcz28bnn38OgpR0bKyv4//+3/+Lv/n1r3H1yhVUd3fb1/JPMHQ/yJFpkojOiwcVNQDS6A1IfxbKd+/fx9TsLIpPgK10N4x7YRSRg17kIDP5txsD1y4k7Gscx8LrIPt3/YyfP8vtSh+bxvyYnu9GvY6/+Zu/ges4+OnPfoalSZplkbRTo9JH9MAPAsTMpfVAMWhKij8TE8i9V8plEKQmayEjU1ldTRYz09P44IMP8NyJE/jyq69w6fLlgSNzXT83mWNxcuC6LmzbHmvPjicFUnOQARcjipRCEKShMuZw1hYDChF3trfx0ccfQ1VVvPzSS1A0DUuLi1hfX8fGxoawZrVyubRz2dIS5ufnx97PfR8G2C34QYCEmURNpCFMB/AIRcIWrU7hT8/3sba+jldefvnAxjYsOtoC9zHR8n4M2ddlTaJ6Tow9CAKwt6gSXolDyL7X9vPUtJs6acvX7Pck8/9BlhuunWn9m05j3NzcxCeffIJcLoef/vSnB1IyaGgaAt/v2anRZUr4g44cAGyRZD06ekHs4nsYug0Dbqvseh5qtRpmZma6LsSqquK1H/wAhXwe333/PRzbxuuvvz6eSFrLXK9pGgghqO3uIgiCgTrhHgZIcsCQJAlC34dJaVMJI9BDiDgA1tfW8Lvf/Q6VqSn8+K23sPHwIYIggGEYOHnyJE6ePIk4jrG1tYWN9XWsb2zgzp07ACGYnp7G8vIylpaW0lzcWEY0BCiF53kHqjXgIGxHw0uXOqUz7j94ACTJgXVg7BuENPm0t/O3aPd9x8O1OYYoKTtEaYV2pLQdYWiHVjIgDKX6sAO+f+8ePv/iC8zPz+OtH/2oc/OkMUPTtNSRMIq6dmo8MAOkDui3cmHcJl6t4LbKnufBc92+5uOz587ByuXw5RdfwPM8vPXWW31tZAgAqijp57QVLXb1qqJAVRTs7u7C931JDp5WxHGc7sJUFSohIqUAdDE+GiBqcPvWLXz91VdYPnoUb7zxhjBEASGIo0hEJlRVxcLCAhYWFvAy0hTE+sYGVldXcen77/Hdd98hn8thcXERi0tLWJifn0iv904Q5YuKAuMxKHQVtqNJkgToQA7u3b2LxaWlJ0ZBLBYvPuFkwrCtzYI4hlaNcwOkQ1RaNYovQ7voQ+v/Ka+Dz0Q9rly+jMuXLuH5Eyfw2muvHWwpGqtaiJOka6dG13FACHlsz7FC+mvINI6+Ct1gGgYsw4DNQvg9yQEjh8ePH4dlWfjdJ5/gb3/zG/zk7bdh9UG0FLSPdDURezBRoqLArtdFJ9SnCZIcZKBkFnueUsg2TRkKlOLSpUu4cuUKTp0+jVdeeUVM3Fx9H8UxOj3uhUIBp0+exKmTJxFHER5tbmJ9YwPr6+u4vbICRVEwNzeHJUYWSsXicOPsE77vA0iFiI+jtpf08Dp43B0YKdqnBCiwL3UzaMi8H4zTjOagIFIJ4xpzy33mFtCcJHz19de4u7KCCxcv4vwLL4gdYdZzZNLQdb1np0ZRqfCEv5dRHIu5ctzgn6dCsQjbdeH6PkpR1H1DlHn/5ufn8bOf/Qwfffwxfv3rX+Odd99FuR8/hE7Rg6aXpNG5p1WQKMlBB4gqhRGiBjRJ0oqEO3fwyssv48yZM00TIA+L92Kd/FFXNQ1LS0tYWlwEffllNGw7TT+sr+O777/HN99+i0KxiMXFRSwtLmJubi4ti+wDCtjC0uWa4jhOSROlyD2m3UwvC+V79+5BVRQsT6rFLUMnEgB0XuSG8ZIYFIfOOhnj9Tngx9kXSaBpR8RPfvc7bG1t4c0339wrc2XnbyIF7L2lJNMIqo9URb/QNK1np0bX8x6L3kCAp/F6LJL89/2YOg0Cmjm2YRgwdB1+FMH1vIE2QeVKBe+99x4++vBD/M2vf4233367pw122+hBy+eaf8biOIbjOHvVZk8JJDnIQEkSQFGQAIiZMrZjCKsHMYjYRLT56BF+9KMf4fixY/teo7K0Qj8lTa3nJgBKxSJKZ87gzJkziKIIDx89wsb6OtYePMCtmzehKArmFxYEoSj0mGiIonTdNXm+D4qDLV9shdLF64Ai7cB45OjRsaRaRMh6QBLQEa2RgwnsUA+lO+KYy+DaHcVxHHz00UdwPQ/v/uQnvXsk8BREO0JAWjpHDkkaenVqdBwHhcdcbUOAnhuhhKVkx/nMZYkBRz6fR7C7C9txupKDdu9FLpfDz957D5988gl++9vf4s033sDRNnNy63Fa3+fsfeAaKABoDOmt8CRDkgMGSmmaVkBLSkFR2n/wu3xgPNfFRx99BNu28e6773aciFRNS/N6XSIHHRcQvtCwr5qm4cjyMo4sL4MCqNVqWN/YwMb6Or75/e/xe0pRKhaxuLSEpeVlzM7Oipa+/YCXLz4OIWIW3VwSq9Uq6vV6al4zIPhC0O49nVSIfuxljNkSryc8FJ3FRNIKGexsb+O3H34ITdPwwRh6JHQjA4SFo/t5bw3DgMeqFtqlFhzHGbrR09iQEQG3BfMd4VVdo4KXx7YrV87n86jV64iTBJ7nDSwA1HUd7/7kJ/j8iy/wu88+w8uui7Nnz3Z8vdIiyGyXBhTkoNEYaCyHAZIccGQexqCXXXIXYlCv1fDhhx8CAN57//2u+S3eKyDqRA64G2KnSbOFIIgfI60PrpTLeOHsWQRRhIcbG1jf2MC9+/dx48YNaJqGhfl5LC4tYXFpCXlWUtWJjPhBkPrBq+qBli+2gos425GDu3fvwjRNLCws9HWspDUiMOEFNSuIAzB2U6vsBH4oBYljPCZ/ltfW1vDpp5+iWCzi7bffnri4j2ZEp9mKkXafK14rnyQJwhYL3oSmTc0ea1qBoZspUvZzOHJagZGuTnOQoijI5XKwbRs2a0g1KIii4M033kA+l8O3334Lx3XxyssvtyembebVfWNihKguycFTDPaQU0pFmJ8vgu3Ciu2wubmJjz/6CLl8Hj95552eJUiaqqbiug7kQIjYuiFbFtfhQ2VoGo4dPYpjR4+CAtjd3cU60yp8/dVXoEjzcosLC5hfWMDszMy+UjiPmx495nIdlYnHEjaRZEvYVldXceTIkbYf9Ha+6Y9btDf2hi0ZMeLjvrZBMKkukjdv3MDvv/kGR44cweuvvz4RwVxHtBJtTuJbPqe6riOMIoRR1EQOPM8DBQ7eAKkdWBql3ezCo57j0Bv0UxlRLBTgOA4830fUTpjYj18LIXjxpZeQz+fx+2++gee6/TXXavN88uirY9s9x37YIMkBA2HkIIwi6Ews1PGBb7ObX33wAJ9+9hnmZmf7rqlVVTUtZRyXu1YfHwwCYKpSwVSlgvMvvAA/DPGQpR/u3LmDK1euQNd1LCwupuWSi4tQVRVxFIEoymMvDyQscgCmPOfvUa1WQ9228QoTIrbehSdtqZxEJze+iztMxKAJYxo3TRJ88803uH79Os6ePYuXXnrp8TvY8WvLlLFCUdLUguftSy1wA6SD7qvQCaRD10I+d42SUqBISUY/z62u66kwMQxhO86+hkyDiH5PnjoFy7Lw2Wef4be//S3eeeedpnmbAKA95lROKHzfF541TwskOeBgD3kUhtA1rXtKoQXb29v49LPPcGR5GT98442+WbSiqiCECFvQXueZBExdx/Fjx3D82DFQAJvcgGl9HV9+8QUAoFQuo1KpYHl5GVOVymNdfIQRUpKkRkhME7K2vg6FEMzNzR2IV/9Q6MORcBQcxkoFYLyRgyiK8Plnn2F1dRU/ePVVnDp9+kBKEwcGW3T486woCqIogmkYoJTCdhxQPD4DpFYQtF94kxEiB1l9wSDvfaFQgF+tiqqFUZ735SNH8NOf/hQffvghPvv8c/z4xz9uSskphHQtYxfOoQB2q1XM95nSPAyQ5IAjSZBQKqoUOvZSaHmIPdfFJx9/jOnp6YGIAQAREqOUph+QbI5ywOE3jW/IyZAAmJ6ZwfT0NM5fuADf97G2toa79+7h/r17uHPnDkzDSEWNS0tYWFg40EhC1qKVItVqcDfLjfV1zM/Pi/8/kcg+O5OsVDhskYMxiSg9z8PHH3+Meq2Gt99+e2+ifsLvh6ZpCOMYURQJw7VGowHTMPbmiMc5QCBNLbTRJCVJklYqDBg5oNiz/x4UlmVBZUJuz/NGjq5Mz8zgR2++iY8++QSXL13Ciy++2DTO5oE3R40pUmKUUCrJwdMI/oBzYaCmaX0p+eM4xicffwxFUfDWW28NzJ5VRRETeRT//9n70xg5sjU7EDzXdl/DY/OIIGPhElySTDKTmUkmmZWZzHyqBtTAtICSBKEHI2h+CChhGpA0rVKVBOmHRgJUgy4JKEFQT6GAgiCgJXT/6HkaVAElQa/qvcyqVyQzk/uWXINkcHOPPcI32+/8sHstzD18MXc39wiScQCCZIS7mbn5tXvP/b7znc+BzN/f7cLRxSIUnAC4uC+VSkEURdiW5WkV8nk8f/4chBAMZjJeqeTEBAZ6EFXg10IB36FMFEUQ5tQIeDbXi4uLHVUp9BNckBgmXNkJ3kjrZEQjSORCYJdSfHn+PDKZDFxmztPKzGa7IUsSDMOoiiCur60hnU5XVZ8E9TXbAUJItUsgr1Roo4yRX3vYNEI9CIKAeDyOQrGIcqUSSeolOz6O48eP4/bt2xhIp30PjC3Pap1r5tGF0lsmStwlB4DfYIT3UmhqfAT4u/NrV69ibWMDX331Vcflfdzxz3acxgLIblCb62z18uD5KYXByhdjsRiUgQEMj4zg+Pvvo1KpIM+aRT14+BB3f/wRmqZ5OoVsFtmxsY7zbz4hqL1m9lnEGvOoxYUFOJRirMfGR1HA9/2n9W2Tu8GbaJ0MBEoZO3z/4sICLn33HeKxGD777LNNhT+/1z0gYlGCN/ChjgPHcSCKItY3NjAeHM/B8slAJURfP1VNWaOvlWI9Bloh6F3Q7SYiHo+jWCrBsCyYlrVpcd/F93xodhbra2u4cvUqkqkUMpmMf61NU1Pss5TeMlHiLjkAANeF47pwbBuCIGxNKQQHBhsIDx8+xPz8PM6cOYPMwEDHp5a4KJHvGlqVL/YYwbMapumJ/kRxy0Ifi8Wwb/9+7Nu/H67jYHllxessyYSNhBAMDw/7bo3pEFGFRvXNtRBZlQcnB7lcDqlEAskeW0dHhXr10lHgTbROBrrTHMw/fYqr165hdHQUZ2qEwJzohhlT2wpCIEkSHNeFZduglKJYKiFdI7YLvp7WzEn9iioEyxq5+VErlX83KYRGkGUZmqqiousolctQ2Bzc1RkIwalTp1AoFnHx4kV8/fXX0DStpciR6zEqrFHW24JdcgCAOg7guhAI2VTlBga8v/Ngk1c+n8etW7dw5OhRTLZw2WqFoAWnj15M7h2kGrgjotqifFEQRYyOjmJ0dBQnTpxAqVRCnvkq3Lt3D3fu3EGMNYviWgVJkjYd6NqcNLgxleu6cB0HuVwOe3ZaB8ZG6OEu1t+VvUFpheB33w45oJTi3r17+PHHH7FvZganPvwQpHaR4qI/7ICcfQvIsgzTsmBbFsqsUmEg7KYjGFUgpHHjoCgQKGt0mJCwUUqBCw7dLlIIzRBPJLxOjboON5WKRIgrShLOnj2Lb37xC3x36RI+/+ILv6qsnqeMw1JXhImk/X4YbwF2yQHgRQ44A2aq4S09CdiAKBaL+P677zAxMYFj773X9alFSQJh5wT6NIk1WaD4I2yziYoQUtfatRkSiQQOHDiAAwcOwHUcr1kUS0E8efIEhDWLGmPWzqlksi1CRATB96VfXV1FuVLB+NhYW9e4XSBA5028WoELEt8gclAvKtfyLY6DK1evYv75cxw/dgyHjxypb2/NXx/BZfYaPLVg2TbW19YASpHqxMmRUo8YsFLJXtwD3q3Rr1SoEzmgzIsE6F0kK6ZpECUJlm2jXKkgmUhE8jljsRjOnj2LP/vzP8f169fx0UcfbVYtBCuOAp443H+lWCjskoO3Ba7rAizXJ8kyKDztQZWGgA0Iy7Jw4eJFaJqGTz75JJJBL7IH2GGiyL6h2Q6WED9qoClKV4sNEQRkx8Ywms3ihOuiWC57WoVcDnfu3sXtW7cQTyQwPj6OsbExr+IghPJZFATYhOBVLgdREDA8PNzxNb4teFPTChxhrtsyTVz67jusLC/jzJkzzSN3wQl9h98TQRAgCQJcQcDa2hoSiUR3pk1cbxH4P8FmxU9XIASEiRFdSrfoDZyIUwjNEI/FfGFiMsI+FINDQzj14Ye4cvUqMpkMDh486P0iMG9y22iuxaCAJ0p8QzYqrfDOkwOuZHZsG6IogrJyxtq8MAXw/Q8/wNB1fP3115FZCIui6D1oXFndTzQSK7I+CqC041LFKlFhQMiZTCSQPHgQBw8ehOM4frOoXC7nNYsSRYyOjHhkYXy8YeMZSRRhWhby+TyyY2M7u4QxCEJ68j0HTX7eJEFiO5UKpWIRFy5ehKnr+PzzzzEcou+AQAicN+R+8JLG9Y0NpLvQMdUFJwrMW6Hb3Hyw+Zkgil7EgpC+6zsSiQQKxSJs24YRsQnR9MwM1tfXcePmTaRSKYyOjlbdN64T86PMb1nFwhsyo/YQTFTjOM5mxz9KYdk2FL7gUIrbt28jn8vhVz7/PFLhG08rOLa9fTucGpLAowayLLfd3ZDySShY8tQAoihiYnzca69MKQqFAnJMq3Dz1i3QGzeQ5C2oJyYwMjLiRzFESYJt21hdXcWpU6fa/MDbB757i7xSIRA1eJMiB2EbRa2urODixYuQJAnnv/469DP45twJQJJlwDRRLBaxf9++3pwkKGbsQsjIy7+5GK+f0YIgRFFETNNQrlRQLpUidyh8/8QJbGxs4Pvvv8dXX32FeGCzYrNNpCSKsNnm7m2yUX7nyQFllQqAFwJXZBmGYcC2LJ8cPJufx8MHD3Dy5EmMRWxyIXKXRMfZ/tAnY/6GrsOltKUQkcMXFrK/+bHaPXcqnUYqncahQ4dgWRaWFheRy+Xw8tUrPH78GKIoYnRsDOPZLEZGR7GyvAyXUoy9SWG8HoW5e9G8qB/wyxib3I9XL1/i8g8/YCCTwdmzZ0OPS3bg7i6wj5BEEY5lwTQMJPpReRNIO/D7H3Z5dxj5py0cBPuBZDLpkQNdR7Jev4UuQAjBmTNn8Itf/AIXLl7EV+fPe2JqSmG7LqjrQlJV3yPHNM23xkb5nScHXG8AeAu1LEkwdN03JFlZW8PlK1cwMzOzmXeKEDxf59j2FpfE7YDtOF4ekRAoTIPRaHr1SUGIKEG7kGUZE3v2YGLPHoBSrBcKyL9+7TWLunED1HUhSBJkWUahUICmqpG0jO01KHqjCXgTKxWA5mWMlFI8fvwYN2/exOTevfj444/fnPRRJyAEZVYO129RW200oeFyz17nC/F2APlSFAWKLMO0bd9SOUrIioJz587hm2++wQ8//ICzZ8/6n59bXwOb97BUKEB5CzRQb/GTFhLM3wDwmLvEtASubcNxXVy5cgWDAwM4depUTyZ1SZIgEM8SWDdNxLa566FpmgAhkFV1czcRmMD9SaRG7NRTEOK3oD585Ags00R+YQHfff89qOvil7/8JRTeLIpVQGg7xJO+X2jlFUBZyWgwuhMsJa1XVsp/TilFRdcBeA1mdPZvwsPSgb/5v/lVVKU56qU8GkSaqOvi1s2beDQ3hyOHD+PY8eNvVLqkU5TLZYjNerv0GjXRBF/AyMeM64ISz7qYYOdUxsTjcViFAsrlcuTkAPD6y3xy+jQuXriAly9f+noX7lMTRKFQwOAuOXizUWubzEP8kizDtm08ffYMa6urOP/115G0JG0EWVGgWxZ0w9gZ5IDSqvLFoKufLzjaxolaVhQk4nE4joMPP/wQsVgMa6urXrOoa9cAeDXi4+PjGB8bw+DQ0I6ZxLq5b5R6nShr/3ZdFxVW7y1J0qZtMOq4THYI7h5qmqZPDroBX+htNu5lZllOBAGu4+D6jRtYWFjABydP4sCBAx0TgzeNUBSLRSSTSU+gvM2RRE4oCdhcycgfAH+MRRnC7wbxeBwbhQJs9iz0Yh6dmJjA+Pg47ty9i3Nnz4JSCklVNyOr7FnjPhVvOnbGN7tdYKSAaw546ZDEDEnu37uHPXv3YnhwsHfXwBbicrkMQ9eBqFXKbcCyLN/YhKcU+E6Cd06rUjpvY4lYLp+HLEkYyGSgyDLGx8f9ZlH5fN7zVJibw/3796HIsmfrPD6OsWy2vZx11Kij5q636Dssn+kGftZsobdZpEsM1Jc3QtVun/87GAWo/R3g29PKsuztaoMRB2xGIRCINlRFl+p8ZsATsrlM92NaFkxdx41bt1CpVHDyxAkMj4xgfWMDgLdLJawfCTcsC/7Nf19zoqb3YqehsLGBVCIBQRA8UfR2RhCC3yUDF9TyngqSKO4IHwlBEBCPxVAslVAul3u2yXr//ffxJz/7GV68eoWJiQnPb4U90/w+lN+SioV3mxywXRdf+Hh0QJYkvH71CqVyGZ+dO9dTVzsKQGEhfNM0e3KOsDAty3NEZMQgWIYY3IFVKZy3iSDkcjlks1mvVC3gLqmqKqanpzE9PQ3Xdb2IQj6PXC6H55cvAwCGBgc9p8axMQwODvZ8d0mZYYzturBME5ZlhV7068FfEFl1DY+KCKKIuKZ5lq/B7ysQzu/0s/JogaZpHeXDg2Mp+Jl1XYdAPPtgy7Jw+do1gFKcPn0ayUTCu0fsGK7remnAFufi9yRIcrhx1o6OJFCKjY0NjM7Oev1WLKu/5ICTAfbvRr+3bNtLHbH7bPfIAbEdEEL8fgu6rntGdj2IagwMDGDP5CSezs1hz5491Wkzds8Mw4j8vNuBd5scYNO2WBQEf5GjAJ48eYLx8XHE+6AaVhk5sEzTb7yyHTBN09sNyLIXIWhRsxycEPq5ezAMAyvLy/jwo48AwN/F1E5QgiBgaHgYQ8PDOHbsGHRd9zwV8nk8ZM2iVFX1+z9ks1koXbSgDpIAx3G8fztOlQcBDxfX3q/gZBtc9Ov9rB64W10n5af9QLDvffBbEkQRkiShUCzi+rVrSMTjOPfZZ4gFNCM8neXyv4P/ZkSDEy3+ev5cu9xDhP2ORxhEUfT/3imkoVgqwXEcZDIZEEI80Vs/yDe7N2GrDvi9FSQJRBQhotpnY7vAo1qGZaFULmOgUW+KDsHnmIOzs3idy+Hl8+c4GnDJjTqVt93YebNIP+G6m3qDwIQ69/gxLMvC/v37YRrGZpe3iMGHkCgI3s7JtqEbBhLbYL9p2bY/GXVi8LSlXrqHk1o+lwMFMDE+7qm72aLcSsmuaRpm9u3DzL59cB0HK6uryL1+jVw+j/n5eRBCMDQ05PeAaNSCOgwJqAVhoXApsBCFWfTDYrvLyToGpXj1+jUe3LuHbDaL02fObBl/hBAQUUSrO7SFRDC/Etu24bC0i8u/O6c6/rATSEOhUAAFkMlkYLOW5Lbj9I7stUkKOLiAW5YkP03GTZG2i2TxTxCPxWCaJoweRGEF9hk1VcX4+DgePnqEgwcPQgyMV05U3wa82+QAm4yXl8GZpon79+9j//79iKkqDMOAoqq9ESQGHkpFUVAxDBh9Jgc8hOgbHylKVQi6XRbsk4QeThL5hQUMZjLQNM3rHMlEpe2UuQmiiJGREYyMjOD9EydQLpc9rUIuh/v37+Pu3bvQVBXZbBaj2awnaiQkFAkQBcHbEbMFhi82voAwKOyMCP443inCyxCglOL+gweYm5vD1NQUPvn4464EeFtIBKWQJclP4YDlyl1G6hz2Xe4U0rC2tgZFlhGLxVBhEY+ehMdZeqCTxZz3E3Ap9a+L8NQejw5tB1Fl59Q0DVhfh2WaPbl3Omthf2h2Fq9evsSDhw9x7NixLa/bzghwVHjnyQFnwfyLvP/gAVxKcfS99zxDEstCJWLfbh+BB0lRFIhAJErwMOCkwGVqZItXKQRb3nZADvz38vPUlEJ1G02grotXuRwO7t8PwFNL27Zd3dWyA8TjcczMzGDv3r0wTRNLi4tYWFzE4tISns7PQyAEA5kMhoeGMDwygkQiAUkU65KAhtfO/m5aR94puDbkDSEHLmue9Pz5c8zOzuLI4cM9u/aguJIv7lXRiVrSwLp9NiUNguB/75IkVaUlO0WhUEAqlQKBN64NtsBFAb/qICgu7OB6/ZRCQKPFSxqrNEq8u22fwOcp/t1SFj2IkhzYtg3LskBdFwPpNGZnZ/H48WMcYHMRAN8XhjpOVWffNxHvNDkI5iYlUUSlUsHjx49xeHYWmqrCkWVYjIUastx2d8IWJ6/6r6ooEESx56LEqp4H8AZycHGVA5+RixK72SUFa9yDHgn8Z+1iZXUVlmFgbHwcwCapczqYiHjY1rYsX+3Pkc5kkM5kcOjwYRi6jtWVFSwtL+Ppkyd4/PgxEvG4Vyo5Po6R0dFQkxAnBb2IqbxJDomWaeLipUtYXVnByZMnq2yxowQNjL2m6IQ0uC5M1wVYiSdhKSNRkjy/lDr1762wvr6OEV4/L0n+s9lxSWNN2iAKQsrJSu14r91I0EBUotc5+Npzq4oCy7Iij8LyVIUsyxBFEYcPH/Yqoh48wAFukMc2Q7brvvGL65t+/V3BZg+2wMKE9378EaIo4tChQwA8LYAai0GvVKDrOhQm1IsCtCb8pqiqn9Pqhf1mM7EMr1KQaz4fz4VH9XDXJQreRYWeSHO5HFRFwWAmA6DaYbIVeJiW56HrpQcEpv+QJcnbETLyMc7IiGPbWFxa8oWNc0+eQCAEI6OjGGdahWSDVrs0wkm69rhB45qdjFKxiAsXLsA0DHz++edQVHUzJB01goSpk3B3E9Jgc2LJiDVl+gburEqATaLA/m72GR3XRaFQ8BcZPg5d14VlWeGFsnXKD6ME7yFQm8JrpM3hv2tYAdEDqJqGUqnkpRxdNxLiaTENiOu6UGMxzyhOknD4yBHcvXsXe/bsqTZee1M1QAG80+SALyiCIKBQLOLJ06c4ceKE75IIAJqqwjQMOI4DwzSrWzl3g5rBIxACWZZhMMYbFTmojRTUA69SqHvOHuUQG1Y6tCAKuVwOY+Pjmw2YApGD2onAYSTAJwN1PgcPC3My0GoiESXJjxicpF4XthzrKnn79m3cvHULyUTC91UYHRnxJ9Lg2buNyARR1ZFxB6cVVpeXceHiRciKgq++/hqJZBIb6+sAek9qIk3lsNJLSZIAVfUjDLZtw3YcOGys2Vzky0rbgkRBlKSqz8zFiAMBYinJsj92W5GDehUwUYPrDWhAb8DBq23qzTN1HVYjvq4gVKabopTCtKyu52zKNFkupX4Le37OgwcO4PHjx5ibm8Ox48c3/Tu6THPuBLzT5MAOpBRu3bqFmKZh/4EDVa8hhEDTNL9+VlGUnrXElRUFgq5DNwykGuw+20GtXW49cGFW0Oim3whLFCqVClbX1jA7O+v/jCv+HRZxAeBPyvXIgBTczYUgA62uO5lKYTaVwuyhQ7BtG4sLC8jl83j1+jUez81BEASMZbMYGxvD6Ogo4olEdW10BOCfcie3an754gWuXL6MTCaDs+fO+YtdT9MhNfn1noW3AxEGvgw5zJadEwaXVUYFtQuSIHjRBUnC+toaQClSARM0WZKgw6sk2kKaA892v/aoNpsniCDUFWgTQfBy7Q3gOy5GPE5rhZWEECiqClfXYRhG1+TANE0vTRLYQHEyJIoi3jt6FDdv3cL0zIyfxtglB284eOSgWCzi+fPnXmOXOoNeUVVIug7bcWDoelUNdido9DCrigIB3Zto+JNGiMnQYCkFSZbr5jW5iKlf4epmRCGfzwN0swujy3ZrhmGgrOte+LXG9pk30+JkoJefQ5Ikv1kUpZ6hTT6XQ35hATdu3IDtOEin0z5RGBoejqQKZidYWjcCpRSPHj7Erdu3MTU5iY8//tivDAqmQ3ZyxKMTiIIAUVH88cjHKk9tOa4L23VhM+Hc4vIyYrEYHNv2xH4BkSsnFlKd+9ZP8BbFcgN9DQkx3wR1CFGkLBsRbU1Voet61yWNruvCYBUKsTrmYpRSTM/M4MGjR3gyN4cs69r7NngdvNPkwGZOX69fvYKmaZienvZ+UcPSCYBYPI5CoeBFD7otbWwwcBRVBREETwPQgQiJD0hegRAGFhv4DaMGEe9y20EtUcjn8xjIZOBSikKx6CmHA2VqrutCluUqvcB2XvvAwAAGBgZw+MgRmIax6dT4/Dnu378PURSRZY2ixsbGOiadQYOfnQTqurhx4wbmnjyp3zwp+Bz04NqrbH+3+d4IglBFXHmInnswFNbXkUgmYbByZiII3iLM8vWWaULY5r4rvhixgQqfO1KGWRarIjk9SF1GVdLIyYXANAZB+H4lhGBmehr3HzyAbZqQYjHflOxNxjtLDrjhDyEES0tLGB0drRLM1UKWZciyDKuHpY2KLEMUBBBK2+7QGBQhhZ0GuVqfAE0rMfpdllQLl1KYhoGFxUWMZrMolUr+7wRBQCIe97QDstyTjmxRQFIU7N27F3v37gWlFKurq3jNDJiuXbsGSikGBgY8A6aJCQwNDobeTfvf+w7afdu2je+//x75fB6nTp3C/kC5F0evF++qEPYO28kRpjGSZRmu62J9fR2zhw75ZmguT0lYFnTThClJfgpiO0BZxQZF82ZLPMXX7rE7jSI0ekcUJY2O48BiPiqJOsQ9KPIeHBwEKMXKygr27N37VhghvdvkAF7YaHVtDft5KUoTxGIxWJYF0zRhq2pnD2qLB0BW1bY6NIYRHDaC2SKlwFHVbKlPcJhK2zRNWLYNg3UdHEinPQEhKy3ljU8sFrLl+cft3iluQc1CmBkcxMDAAI4ePQrTNLGwsIBcLoenT5/iwYMHkBUFY9kssszaWWsyFtw2SWGvoVcquHDhAoqlEj47d84vO61FqzbT3WBLbpvtVHfcuACwurYGy7YxOjqKWCwGjUUVeE09KIWu616qQRAgMVLRz8gY30RwU6iG6OB6glGEtrUhTV7bbUmjXiMkrT0XCcy9mqZB1TQsLS3tkoM3HZwcrK2tAYCfK2oEAo+NqooCwzRRqVQ6Eg3WljDWQpVllAkJ1aGxVee7VvCrFEIIEfsxsTqsMZHBfAeCKBQKAKWYmpzc0vhHYCFYi+VzFUXZIsTcaYtCMPyqKAomJycxOTnpRxXyLAVx9coVAMDg4KBfATGYyVRN0DspcrCxtoYLFy8CAL768kukWclpPfS0/LLmmHxh2wnxgyChJ4RgeWkJoiB4u09URxVisRgogLKue5bQxGvQZpqm9zpJgqQoVbbcvUAjf4NadFw2yuC6LgRRDB2pbEYkeEmj2UFJI0/3uK6LeIt0HyfnQ4ODWFhc9H62Sw7eXPBBtby8jEQy2XIA8LC9pmn+blbvRAnb4qFRNc3THbQQ0nTSzS8IXnoVtkqBCEJPQrO268IyDJi87CsASZKgyDJkRcHz58+RTCYbdgSUJMmvO1cUZXOSYqi6V4T0fZcd9rvi/R2GhobwHmtBnWOixsdzc7h37x4URfF1Ctls1p9It1tzsJDP49J33yGZSGxpnlQPvYwc1EWPynJboZYMbF6O9++l5WVkMpm63x8hxGvMRilkTYMoCLAtCxYrKTSZiysBa7rFGm9FfU8b+RvUg0BIx70+eMO3MBGEVvoGXtLoUM+DIqyJHWWRGgqPuDcjFUQQQJlfzvDwMF6+eAG9UtmtVnjTQeCRg1ZRA/5aEAJBFKHFYqhUKqiUy57QKMISQL6wcdONWjvebtIIQVim6ecPhQYCoyCiLAVzXBemYcBgnzEISZKgMkIQFH2uLC1heHi44TElWQYMo6HdbO1k2W+yQNFZ2F9VVczMzGBmZgau6/rNovILC36zqPTAAIaGhrB3zx6/w2e/8fTpU1y7dg1jY2M4ffp0qOZd/SYHPS1nDKAVGah97fLSEqZnZhoeTxRFQBDg2ja0RAKyLEOjnrurZVmwWQtwThQAr6JAVpSOmqhtuUbeoI7ShmLEIKL4Nvnmp1kUodU3GSxp1HU9FDmglKJcqfgiZ19E2oBYBi2ph4aHQQEsLi7uViu8yaCUwjAMlEoljI6OhnoPH/SapsFlpkjlYhFCOh3qoQkzXERB8ISPtg1d15EICB+7jRYEYTClfz/7xdss2mIyYgLA95FX2ERWrwrEdhysra9jkleT1AEPd9rMra7VgtOQLHDFdYQLF/e133L+2tr1FhAEASPDwxgZHsb78Hwf8vk8Xrx4gWfPnmFubg4xTfPTD9lstufeFZRS3L1zB/cfPMD+/fvx4QcfhE5v9MvjgCPS87Dvrsr1MgQZqEWpVIJhGM2JryiCoLp2ngSMmKimbSEK3K2RV0koLXRFzWDbNgTAa2oV4hhc8xNFmWK3Quh2Sxp1XffTCYl4PFQ0jl9jTFWRTqWwuLS0Gzl4k0GZshQARoORgyYTdjCMFYvHvYfQslAsFpFKpVqXN4Z8WBRFQYWZISUSicj7hDuuC5uFIsMuHoQQuGh/gqWUeq6PzCeCQ5IkqKwOvNUDuLa6Csd1MdJkAhWZGRJvktOoFrsRaifyWl/42skuCtJAuqzzjsVi2LdvHwaHhjwnPdPE0tIScrkcnj17BkEQMDQ05Kcg0ul0pLt013Fw5coVPH/xAifefx+zhw61dfxtSSt0CE7wuMcAqdlJ1qaxwmJ5eRmUUgwPDTV8TdAFtF6Jc5AoIBaDzVT2Fsu162znrLDnrd1ugTyl0M4zFXWUpp4vQpjjt1PSqBsGLMvydQZV96kBSQlGDoggYDSbxevXr3c1B28yKKVYW1lBOp2uDjc1ecCDYkJCCBLxOIrFImzHQalUQjKZjCTvqygKREI88w0AiNga1WIsWmRdBcNCaOOBd5h5iMFsRwHvQVIUBVqblR4rKysQBaGlAFRmXvS2bbdNDhqh0U6QMhIZjDLUvqbqtT0EpdQTtI2OYnx8HO+//z5KpZLXgjqfx48//ojbt28jFo9jjPkqZLPZrsriTMPApUuXsLa6ik/PnMHeycmOrhvoETnosCwu+H3WRqAoEHkKanl5Gal0umn4nxACMTi2W0T7JFGEFItB0zRf4Os6ji9k5JG6sNoEbn4UJjoavOYowclGu6QjbEmjaVkwDQOu60LTtK2va5BWoECV5md0dBSPHz/2HC/fcLyz5MC1baysrWHPxETo99QKYIggIJFIoFAswrZtlEslJJg97ha0MaBVZobEW7ZG4aIXhJ9SaFdMGULQZbEyTJ77BFgDK0WBqmkdkafllRUMDQ+H6nuACNvcNkM90lDVACnw736QA3Yh/s8SiQQOHDiAAwcOwHEcP6KQX1jA06dPvRTFyIjnqzA+jmQyGXpCLxWL+IsLF2CZJj7/4gsMNYnotLhw7+9edGRskFbwF3xKfbIf1INUPd99iGgsLy83TSlwSKIIm3jd/sIminjOXVFV2LYN0zD8Xg02c2JUVLVpyoF3oQTQVsSBoLVgsBPwZ0xo49itShptlsJ1WZq1bjS1wTNMAPAYAREEjI6MgAB4/uIFPvjoo5BXuDPxzpKD1dVVGLrut0jtFIIoIplMorCx4amGK5W6A7BVCWMQvDsiDwlG2XbUpRQ28zdQOgi91/sElFIYpunl6wLhNFmSoGoalC7U02EEW8HzAegLOaiH2rr6qgWn3mLFVN0k8Jp271NV06UG7xVF0dMhMNvpYqnkEYVcDnfu3MGtW7eQYM2ixsfHMTo62nAhWF9bw+3bt6EoCr766iskujCdilRzwBZ7QqnXhCgYfkb1QuX/rpeahxAwTBMbGxt+F9hmEJlbYpjuo/XA0w6u43i7ZGbu46ccZNlzfq353rm/AU/btYNemaf5C3LIOVVlvXHqlTQ6joNyueylTUSxbvVZs/FBKfU3AgIhIJKEzOAgXrx40ean2nl4Z8nBixcvQIC2dz31xG6iKCKRSKDIxEUCIVvLuEISA/4qLRaDadsolUqRkgMuBuSd4dpCzWewHcdPHQQFhoqqeqmDNnOb9VAql6HreqjviZvCuKzpTdufr0cgYOSwFnSrT0VtFYX//sDfQRIRXGDDEotkIoHZgwcxe/AgHMfBwuKi14I6l8Pc3BwEUcToyIjffTIoir127RqGhoZw9uzZ9iNPNeDppqbRpMCiD6BqnAXFgCQQBYAgAEHxXs3fOwUrTG8QZmxLoggB4QW3jSCIIjRRhKqq3m7aNDcJg2X5Xi4S26BwMtJJCqpX97u2C2mryJzKdE12TUmj67ooVyr+wt7QaKwJCXFd198I8O8km83i5atXffGG6SV2xuy5DXj56hWGM5m2Sn2aTcCyoiBOKcrlMiq6DoE9ZO2Ch83i8TjKxaJXVkNpZDXsFi916qRKgamQeZTACuxiREGAxkKYUdbbrywvgwJNBVubl+cJs1xmorRTyEHHodWaBbEeiXBZBQsRhKqUBniUJ5hyqCOuFEQR48yF8YMPPkChUEAun8dCPo9bt27hxo0bSCaTPlEZHRnBuc8+26JV8Ssy6oTqt7gTsgWfV99Q1JCiRot+8Hz8XKizCDVZLPpVzhgGy8vLUDWtoXdHEIIggIgiiOvCcd2uiTchxBco2o7jpRxYaXG5UoGg65AVxXdR7eRZ6tXCKAqCTyy5ULGZALBeSSMvWaSu6/XOicU6ul6f4AaiEaOjo3g8N4d8Po/xBu6gbwJ2xuy5DVhbXcX+0dH22W0TFqmqqp8KKJdKXrMOWQ61OFBsslDAK8ERRBHUcaDrekuTprDgPSU6EewZhoFypVIVtld46qBHJXMrKytIJpOhSy4lSYLFTGK629dGiDZLFts7dEDUxwkDPycCC2etyjsQeQheWYK1oD40OwvLtrGQy+HO3btYWVmBSykWFhdx6dIljI+NYWxiwo+QCYHj+eepPXdwkad0U6hae28aLfph0eRe7zRyMDw8HPpzSqIIhzn3RRGVCx5XisdBXReGZcFiIuJKpYJSuQxRkqqiR+2g1cLdCWpTtC6r4GiWwlBZSSN3ha1woyJKm5Ys+pG6RtfCyEVwDA9mMqCUYmFhYZccvImgHU7YrUQ2sVgMruvCNE2vgiFEiSMFY6BBkZsgVKUWoiAHjuv6D2o7YULLslAul/3GKwReHk9VVUg9tuwNK9jiCPod7AS0GmdVO+xOwL5PEowU1Crs619Y3dfU6iSePnuGb0p/huvqTWi2hv/7xN+EWbFw/cYN0Bs3kE6lMD4xgbGxMQwPDTWtpd+y+2fagHqeD1XPGQ2UD4bATln8m8F2HKysrODY8eOh3yOK4qbuoMuUTj0QFv1TFQU2K9EmAOC6KJZK0HjpcRvPfNSU2A2M9yBauSrGNA3r6+swTXNzLuNeBk0+T6tP6rKxKdYIlN/cZMIm3llywCekTr7EVrmkeDzulx0Vi0Ukk8mGBKE2YlB7nFKxiFK5jOEIUgu8JClswxbbtr1IAa88IAQxTYPWJ+Mk27axur6OmX37Qr+H76hcx6nrMLmjUGcSaaScbxR9cGonw4giFLx50svSK1wmV+FSBzrR8RfmJfyLL/9fsEzT1yo8Y82iJEnCWDaLMear0KxZFFDdMKpWtNlI+Mr1G0EdQUPNxg7G2toaHNdti/iKdcyQegFCCGRFgaQo0AJznc5KIRVGILYjnx5MKdSiWamjKIqQJQlmpYJSqQRFURCr9TKoB0Fo6HEAbEYOtpSEv8FaA453lhwALDzV5pcYRvhFCEEykUChUIDjOCgWCnUjCBTebr7R8TSmHrYiSi349cotogYuyzsG+zuoqop4LOYb9/SjhfPK6mpowRaHIAh+asHeAeSgKrReEw6laLAI1jtQo7Bno9B8F1gPNE8aPzYB3N783YvycwCeZoW3oAalWFtbQy6fRz6Xw9WrVwEAmUzG91UYHBra6hPRwbVzElFPh+G/ho/NwP3mpGunCMSWl5chCgIyLZqrBSGKIoggwGG+Be14lLQL13G8c0gS0skkHMeBbhhwXNd3OdVUta8Oq0Drqq9mBIEQ4pUtEoJ0KhWJFwonKlGXm+8EvNPkwBdQtf3G1iU0RBCQTCaxUSjAcV0UCgUkEgl/QPJUQrPJKurUQiu9AXVdz5lR1/2fyYqCuKZVCZL61cJ5ZXkZsiQh3Wb3S0mS/FruTkSh3aB2saaUbppY1Rkz3TSpCSKqRS+fy+H7779HMpnEuc8+w7XVa1W/r9sDlHgtqDODg14LasPwDJgWFvDkyRPcf/AAiiz77afHxsY2hcCE9KSbZL373WkqsRdYXl7eQphagRCvXTIhBE6PyYHJqxSYZTIn3SZzO3UpRZnl8OuaBgWuOSqdR6OUQi2qvmcmetV1HZRSv6V7GFJDvZM2jS7717RLDt4+dDKpttIdcAiiiFQqhRJzUSwWi4jFYlBUNfTDwlML3VYtUDRuu8r7TJQrFX9ClSQJ8Xi8cZQhZI1xN1hZWcHg4GDb35EsSdCBjmvCQyNQCVDbECv4d9O71OV99KsOIlj0njx5guvXr2N8bAynz5yp+92HuVJFVTE1PY2p6WlQSrG6suIbMF1m9d+ZTAaDg4MYGRlBJp2ObtFudr/ZOeqR8i3pnR6WoXHvjv3797f9Xt8MyXFCmyF1Aot7oQSExoQQz/JclmGYJgzDgO26KJbLkETRIwl1CEtU5KBZSmELWGSKwiuHdmwbhDXJE1i5cyvtRKvlPtjrZrs7ovYCu+Sgw/eFnTwEZvtbKpe9Rk/MTTEWi4WaEHnVgmUYXaUWeEpBEISqHQcnBb4FqCAgHo+3ZNa9Vn1TSrG0vIwDBw60/V5flMja2nY7ydeW4W2ZoFqEOZuh2ymlKoTe8UEobt+5g4cPHuDAgQM4efKkvxMSSE0qrM3vnBCCoeFhDA0P49jx4zAMA/lcDq9ev8b88+d48uQJbt644RkwTUwgOzraWZnt5glbki2hTuRryztqRJL891EsAcViEYZptqU34OCWx70U3DpMr0MprUsQCSFeSkGWPZ8TlsIrlkp+9VIvwuztGMkBXsq2XC77osFUKuXZuTNhdkthZSu9QeBagvNh1yLjHYJ3nhx0grZzl4T4qthKuQyDOQkm4vGW4SgiCIjFYrC6TC1YNVGDYAWCfx5WgRDms/nufj1izMViEYZhdGTNKwgCRFH03CBtu+22tb7wLfD/jolQj6Mr3boM8uZJL168wPsnTuDQ7GxN1UCNKrzLhJKqqpiemcHExASKpRKKxSI2NjaQY2SBwDMmG2edJQeijCpwhFxk6hlXRTHxLy8vgwAYDOHdUQteseB2aYbUDBZryibJcnMlP5ubFFYmaNk2TNuGWSxWVTZEcY1uE21Wo8/AWy8TQvy+N5IowmTzQje9RYDqFKIgCJtzKS/FfcOjCe8kOQh+qR1/gW2yWApvYhQEAeVSCZZpouA4SCSTLUVz8VgMpUKhq9QC1xsQABuFQlUFgqZpiGla2/cijDtZp1heXQUoxeDgYEfvlyQJjuOEIgfBSZaLLes6GnaAlotJl/fP39F2cL2mYeDipUtYW1vDp59+ij179255zZZ6ioi+b37Ph4aGMDU1hePHj6NSLvvNou7dv487d+9CYy2oJ8bGMDo21vWEHsm1V/2n/QV6aXkZ6XS6I0GcIAheeJ3t7ntxP3hKIez1iYKARDwOmwmnbcfZWtmAcCmpRgg71/AUqcHsoWVJghaL+b0eREkCWDvrZrU07egNejkPbie2/0nbBtT9ItsUK4UZ7EEPd/5vWZaRTKdRKhbhOA4KGxueULFJKFXTNAiS1FVqwbIsVCoV2I7j5wWDFQgdocamNkqsLC0hPTDQsbmSJIow0KTPQlDxXhsZiIoYcEV+s+MRUtWkqaNzoH1yUCwWcfHCBZimiS+++AJDjXaxtdygk4usg2AZI0csHse+/fuxb/9+uI6D5eVlX6vw7NkzEEI2m0WNjXldOmvr3UOcO9KUWG2KIkg0UZ8YLi0tITs62vEpRVGEZds9IQdOoNFSuxE3SRSRTCT85mu8ssEyTa/pWo9Fe9zcyLQsuJRCU5QtkVBZln0C0wycTDRDI/vvNz1iwLFLDro8TsuBQCkcWu2nwNsPl0olWJaFUrEILRaD1mDR7za1YJimX1apaVrdCoSOwBbVXjwMKysrjResEAiaIfH8ol8G1y+W301kKiQ6IQfLS0u4eOkSVFXFV181b55UGzlwaTQlrP51N1gwBFHEaDaL0WwWJwCUWbOoXD6PH+/exe3btxGPxTDG+j+Mjox443m7d3A1ZKH2+dB1HaViEcNHj3Z8ClEUIZDemCGZLKUgdtEsTZblqsoGh1KUymXIstyRP0KY59V1XZTKZS/d4rqebqqG3ATLuFuJlYOOo43A0whbSM92j8GI8E6SAx8BM5Ve+B0A8LvubT21lwcrVyowdB0Vlh+Lx+P1DZECqYV2oOs6VtfXvV0Gq56IqjaZEOJZPEfseWBaFtbW1nDg4MGOj8GNnnhJ43aEo0OTkC4qFuq1a26GFy9e4PLlyxhmzZNaif+i1hz4x2mT1MQTCRw4eBAHWLOopaUlv1nUkydPIBCC0dFRjI6Oes2imhGefu7sasjC0tISXAAjIyMd6xd4GrIXokTbNOECiHVph84rG2RJgq7rMFjLZNu2EY/F2ooitLJg5mZt/DXJJqlaiXW3bHnvQvgpcHIg1RCp3cjBG4zIQshNBhCvqW2FOHPpqpRKMA0DjuMgmUiA1AzuYGqhrOuIt3CfcxiTtizL71A4mMlEb1rSgwdhdXUVFOgochBMEUiSBMd1YW0TOQibLhAI2ep0GBKhS6koxYMHD3Dnzh1MTU3ho48+ClUnX1utEBW6MW8KtqA+efIkiqUS8rkcXudyuHX7Nm7evIlEMonx8XGMjY9jZHi4erHYxp3d61wOA6kUtFhsM6rASuvC3gluhkRtO1IzJNu2/c1MVM8Lr36STBOFUskzhSsWoWlaJHORaZrQKxU4lEIURcSb9EkA2Odiz2WzTYPruk1LGd2A9bcgCFVj6u2IG7zD5IACvvVlp19m0/e2MQGpigJJEFAsleDYNjaKRSQTiWrjoWBqoVhsSg4My0KpVKrSOWii2BM3M0IpXERburOyvAxFlr1ugCHQqJpAliSYpgnLshBrQaZ6gbAjoOPJpKaUqhFc18WN69fx9OlTHD16FO+99154UrdFcxDN1BeqXXMYsAhccnYWB2dnq2ydX718icePHkGUJIyOjPgpiFiELdDbAqXI5/OYnppil17TOZP9rJUynxACURDgRGyGFBQiRr37VRQFaUJQYg2PKroO27a9TU+zfhwNnm1ubGRYFiilkGU5lKjar1hgYuVGpZqkxfzNIw88Qhl8tes4fUkp9hrvJDkQBAGZwUFvh9qmELEW9XLuLY1v6kCUJF+H4Ng2CoUCYrEYVFX1r69VaoHCy83qzPZYEkXE4nEUNja8B6gXu2dCvLrxCHdjyysrGBoebvhw1RoNNYIsy14I0bY9IrhDXcy68drwj9HgXtmWhe+++w6Li4v46OOPMTMz0+a19Sat4C+IEX8noiRhYmICExMTAKXYKBQ8UWM+j5s3b+LG9etIpdMYHhnB+NgYhoeHe+o0GMTa2hr0SgVjdTr1BUWMwf83IgqiJIHYdmRmSJTS7tq5h4AoSUjE495u3zBg2TbsYhGxeLzh3FQvpeC6rt8dllLqNYtqQ3shShIEx2mcWgiR5vMN5fjYCXxHK6urECXpje7ICLyj5EAURezZswdPfvwROvMI7wT1dAfd9B0QAkJF0zRRKZdhmibi8ThESWqaWrBt24s8sHNrmoZ4LOb3YxcEoWeLY9SGSCsrK1vMjzoREwqCAEkUvYnPtvvvAx/2WsNUNbQ6fp336pUK/uIv/gLlSgW/8iu/gtFstq3jA3VMkKLSHPAysF7urghBOp1GOp3G4cOHYVkWFhYWkMvl8OL5czx6+BCSJCHLmkWNj401FAVHgVwuB0mWMdwiXUa4eNb/GFsjDFLETZhsx9lMKfSILPH5UlVVSJLkGxSVy2UoigKthcdKsEyRsv8nmrm4NoAsSTAMw3NNrDd3tXhug3qDLaJuQrCQzyOTyXRkcrWT8E6SAwCYnp7G7atXsZjPY2p6uqPJuRY+MeDH6XDBTCQSkGQZOmPHhY0NqKoKLRbbklqgACpM0Ah4k20ymfSZOGe4PYkacLDJK4pp3jBNGKaJVDodSXWBLMuwbRuWZfWdHIQGyzu3C78csM57efMkAuD8l18i3UaDn2aIigRy+hwlNQgTSeLNoj788EOsrq35osbr166BUoqBgQGvVHJ8HEMtWlC3i1wuh2w229YxtxAFlt8WBME3Q4oCNos2yrLcu3A4W4gJIRBFEclkErquw7QsmKbpixW5PiSYUrAsCzrr6eC6rudf0KLdciOIAVEipdS7x4ENnUtpU71B0OyIO0GSwLO4tLyME6dO7aYV3lRkMhlAlrGwuOiRgw6/SM48a2vau51EuYd5pVKBaRheJzSWIxMBlFnurlgu+wRAlWXEE4mq3RgPvfVakBdVaqFQKMB1XSQSiUiqIGRJQoUQL5/ao7LLRmiHMHWlfan5TLlcDj8Emie1ap3c/Lqiv19VHRm3K9VDCDKZDDKZDI6wZlELCwvI5fN4ylpQy7KMLOsqOTY2BrWL+2joOpZXVvDxRx91dc2+AJU1Q3LCWgE3AU8pULTvbdAOCDbJDeCN21gsBkmS/GqtYqmEGBMr8gZTuq771wd46dVurlOSpKrdv+u6/vzVjt6g3pxqGgaKpVJHfTN2Gt5ZciCKIsbGx5HL57teNIKe2lGCEIJ4PA5VUfwcGyiFYZqwbBv5xUXf5CPBXld1XWjcbClydGuIxISNa2trIEBoMWIrSJLkhV/ZZNCvqgVuwRwWnaRm6in+n8zN4fqNGxgfH8fp06e7/ry90BwE+0FEStbauH+191tRVUxOTWFyagrUdb0W1EyrcOXKFQDA4OCgXyUxODjYFrFZWFgAAIyNjYV+T9PrZ7bEBN5OV+yCnPt9V4CetzivN85lWYYoir4Fc4V1exRFEYZhgBIC6rpQmbag2zEjszmhygMlWBIcUm8QvFf8HQuLixAlqaOeMDsN7zQ52Lt3L368dg3lSgWJDhXMfJj20liHixWDfghlpi/Yu2cPUqlU3UYnDrdMJqR7w6MW6GTnW49UFYtFxBOJSBu3yLIMl1KYlrUj7HejQhU5oBS3b9/Gw4cPceDgQZw8cSKSXflWTU330Rz+fUcZM2hXBNxseSGCgMGhIQwODeG9Y8dg6Dry+TxyuRweP36Me/fuQVEUjLGIQjabbSmIe/36NTIDA11FH2ohiCKIZXnGP5LkLfB19Amt4AsRe5lSYBAIQb0tBC95NE0TxWIRJcPwCYGiKIilUpE5LHITKRfeQs/TjWGMj3haA6ivzVhYWMA4m5PfdLw9M2Wb4KLE7y9cwOLiIhJtqrg5+lnTqmoaTNuGqqooFYt+OZBt2xDr5NMttiPoqd4ggNrcXUOwibxeCWShUIj8wVJkGaZlef0keig4C6LdcRFmYtpyDnavXcfB999/j5cvX+LEyZOYnZ1t8+xNrmtL5KB78Mk1ypRCL7uEqpqG6ZkZTM/MgLouVngL6nwez+fnQQjB4OCg76uQGRioSlNS10U+n498NykQArDUAhAQLiJQ/RBCXOenFLZZk+O6LmwuEgRbiCn1e9JECUmS4FhWFTkIE0EOuiLWu6al5WWc+/zzSK91u/BOkwNVVRFLJrGQz2Nfh+SAox/57FKxCMe2Pb0EgGKphEKhAEIIDEVBIiDmAfqYUmCorfetBf8d7+ZY725tbGxgYs+eSK+L5yeDYcReo+1+CR0sbBSeCczt27dRKBRw9uzZyO9dvWqcrhEM4W4XOtUYCQKGR0YwPDKC4++/7zWLWlhAPpfDgwcPcPfuXaiahjGmVchms9jY2IBpWZGXtomsYqGeKLG22qFRSSRvwEZY/X+v0eium6bpCw4pIUgnk3Bd12+7HO+gKqEZJEmCwUpB/WsLEMxG17mlhJG/F958bJjmW5FSAN5xcgAA4xMTePH6NU6jfeU0xWYdci87c1FKUSqV/IGZSCR8y07dMLyQuWnCMk3E4nHPDAT9EyMGLrTutfP2zhyNSJTDekdEHTngE5/L3BJrtRm9QNsjgRCvd3wbi1ahWMSVK1fgOA6+/OKLjloAt7ysHmgOIjNACqCTZy8KQh+Lx7Fv3z7s27dvs1lUPo98Lod5FlXQNA0i22lGuYng/gxNnVh5JZEgbKb+AveKRw3UHgoRaxH0LnAcx28IRyn1Ozzye8W1VuVyGVos1nEjtlpIsgzCdVw1IILgPYt10LCEEV5KQZZl7Nu3L5Jr3G688+Rg7969uDI/j431dQx0UO5V90GPkCRQSlFkHRwJIT4xkCQJ6+vrkFzXq2AQBJiW5XkjGAZUVfUtWftGDpjALJj/dYGW6l+OQrEISmlP8nVKoKSxH+SgI7SxaCwtLeHSxYuQZRnnzp3rCTGoe0kRjO1urJOjAkG0ZZRATbOoEydQLpWQz+dx+/ZtOK6Ln//854jxZlFjYxjNZrt6NkV2/xzXDWXytcVgyXFg8g1EH8kBt4s2DAMm6+UASj1LZVn205OEEMRjMVRYtQIv146CIPil3jxyEIgaUNf1BZpBuKwyBKivN1hcXMTM/v1tGTLtZLyz5IDX2o6Pj6Nimsjn822Rg2DUgB/PH1wd5I/rwXVdFItF/zzBhiL8//bqKorFIib37oVpmiiXy7AdB+X1ddiWhXg83t9JmCmLm6UOGqFQKABAT8iB33ClTyWNnXz7YUWdz58/x5UrV5DJZHD8+HEkEokOzhb2mmpNkLpHT8hBJ89bjSd+1IgnEpiYmMDVa9fw8UcfQYvFvM6Sr1/j6ZMnEATBa0HNyEIimWzrnhBBgMh24Y7rQgqZLuOf2OKqe2YW1g9wjUOpVPLFfYosV9koB3VLhBDf+pwTBC5U7AZ8HnVYqlEUxaqxLdSZw2stk6s+FzxnxI8//bSr69pJeGfJAeB9yaIoYnBkBAsLCzh8+HBb768dIFGKohzXRbFQ8BeyZJ2KhGQqhfWNDe+hKZd9G9Iyq2owDQMAfN/xXkcQuAkURWcTf6FQ8NTJPdjFSJIEgRDYlMK27Z7WcwPoaNFpOX4oxf3793H37l1MT0/j0JEjcFlEqVeoPbaLCKoVAgK6qNBLMtYN8vk8AC99qSgKstms1yyqUPC0Cvk87ty+jVs3byKRSPgGTCOjo6HKCgVRBNgC1y4M0wQB+mIORlm1kMGay9muC5FFQoOC6XrfCY8g6ITAYNbLlEUaOgWvzKCuC7uODqme9sCpU8LIsb62Btd13xq9AbBLDgAAU1NTmH/8GI7rhiqhq40aVP0uAnLAO5dR5oSWTCbriuhEQUAikUBhYwPrGxuIxeMgLGdnmSZsx4EgCF74zjAgKwpimhb9wsh2AISlFQSgSmMQFsVCAal0OtprC0DmXRotq+fkoCPHyCYRJ9d1cf36dTx7+hRH33sP7x096kWV0GML4l6YILG/e3vdIRBRhK8ZXudyGBoc3LIAJ1MpJFMpHGQtqBeZAVMul8Pc3BxEUcTI6CjGGVmIN4gOCUxL0C45sFlHR0qpT8br2TR3C9d1PQdEpo3i4zXGShSDcyhl1QmNSKPGGivp3EKZEYROSaYkSX7XWjRowBS8F775UR1ykFtYgBaPY3JysqNr2YnYJQcADhw4gIe3b+Ppkyc4ePBgy/c1CksT4jUh6mRh5HBYjwRKvRakiUSiqbo+nUp5ZY2VCkzT3JyECEEyHkcsFoPjODBN0xctSpLkkYSah7MtMF0BN/upF2Zr98jrGxsdtWkOC1lRYDCb1p4j4JYZFo1ebbHmSUuLi/j4448xzSpr+pG7F7ZcVTTpMiDCUsYOn7deRw5cx8FCPo9Dhw41fZ0oihifmMD4xISnMQo2i7p1Czdu3EAqlUJ2bAwT4+NVzaKEJhULzcCjiqqi+N9DlIJqx3Vh1mgKBEFATFGgKArqtbQPM45VVQURBG++Y2LKMN0Y60GSJJi27UcEahGMHjiMSAFbIwe26+LJ3BzeP3Wq5yZS/cQuOQAwMDCAg7Oz+PHHHzEzM9My/N6rnLVt2ygWi961SRKSiUTL88iyjFgsBttxsL6+jtHRUQCbk4WiKBCZUr+i6zCYN0KhWIQoitA0rX3XMUrrht04CCEQ2S4hLFzXRaFQaLtrYDuQue4gAsvZZuh4fNSZnCvlMi5cuOA1T/r8c//75ecB0NOSwFrVSDfElyNyUtOHCEAnWFlZgWXbdbswNgIhBKl0Gql0GocOH4ZtWVhYXEQul8PLFy+8FtSi6DeLGhkeBgjxvQ7CwHUcmKyEUWmSuyeCAMraD4eF7TgeKWALN6UUkiBAYRFL3zSu5n3tEBMe6dArFc/AiVLEYrG2x5MkSRAohdWEWPHKhWZ6g7m5Oei6jl/91V9t6/w7HbvkAB4rPHP2LP7bH/4hHjx8iGPvvdfwPS5aTGodTlSmZaFcKgHwBm079sHpVAoVXUepWMRgJuN9LrZz5WkSgaUb4rEYdF2HrutwHAelUgnlSgUxTfO6ojVbMHkVQpjPx/J5YR/YcrkMx3V76ixGCIEsiqAstbDjVMU1Y2dtbQ0XL1yAIAj46vz5LSmXVjXZ0VzSzi9l7PSKei1KfZ3LQVXVjqqgOCRZxp49e7Bnzx5QSlHY2PDSD69f4/r16wClSCaTyGQymJyexsjQEEiL3SvXGoii2JQgB22uw1gKc1t3sPSAxLxk6pmw1d75dvVaiixDIATlctkze2Oaq3bGlECIb83cCPx3vISxduNoWBbu3buHQ4cPv/FdGGuxSw7gffGpwUEcPHgQDx48wMH9+xvanIZZ8NoNVzq27RMDWZbbVp+rmgaVleptFApIs46GhJAtrJ83O4lpGnTDQEXX/f7olUoFmqp6yuGaCYaLDcNOqO16P/SyUiEIWZZh8ZLGHpGDbpZPPnZe53L44bvvkEqnce7cubriq9rStF4gap8D6rr+QrPdaYVeI5fLYXxsLLLvhxCC9MAA0gMDXgtq08TCwgKev3iBl7kcnj57BkmWPQOmsTGMjY9vmccopbBYy+NQJb1BYlCHJFhMZGiz79WlFLIsI64ozSsgIrgnkiQhHo97XgjML6FtG/wwZmWENDQ/enD/PlzHwUfdNNTaodglB/BC2pQQHDlyBE+fPsWP9+7hww8/3PL6llEDwK/1573RW4FSimIXxIAjmUpBNwxsFAqIx+OgaNGXnZmzaKoKw7K89tCOg4phQNd1KKqKmKb5KQkuNmwH7ewGCoUCZFFErMf2xrIsg7AGL71KD3WTyyaE4PHjx7hx4wYmWPOkln0xelqtsPVn3dy3YCoksnu/A8lBuVRCYWMD7x092rNzyIqCvZOTSGcyOMRC+ctLS16zqKtXAXjdZ8fGxjA+NobBoSG/O6lASPveBrxUG4Blml7lAatOoq4LRVWhKkpHfVE61TtIkoREPO6bxBnM4yUM+Aao1blt2/bGK6r1BuVKBY8eP8ahQ4cwwFxr3ya80+SA+2NzcwtFVXHkyBHcvnMHs7OzVaF9/gC0nNBYOF8IaZ9bYuJDgVUedApuL2qbpmeOJEnhcuqEQFUUqIrilUTqOmxWLlTRdaiKAk3TOurP0K7HQbKHlQocwZxhz0oaO5zoKKW4efMmHj58iIMHD+JEs+ZJQcfJjs4WFvWO3lEtBoAeuCOGfM76jVwuBwBVGpFeQRIEWIKAdCqF0dFRHH3vPZiGgXw+j3w+jydzc7h//z4UWcbwyAgGBwexZ2Ki7fPQOpUHgOeuqKhq599pl+ROFEVosRgqlQp0w4AoiuHLtkOmTPh5grj744+QJAlHDh/uqqxyp+KdJgcA/J2x4ziQBAEHZ2fx+PFj3Ll7F5+eOVP12rYMSkLsmnnjJABIdNmimBCCVCoFe3UVG4UCBgcH22bwsix7YXdGEkxWMmQGKxza7NwWtnpjo1BAKqI2za0gy3JPSxo7meocx8EPly/j1cuXOHnyJA62aJ4UHFs9rVYgW8eQFzno7HiRexzsUDFiLp/H0PBwX5oZ+RULgdy5oqqYmp7G1PQ0qOtidWUFL1+/Rj6fx6tcDnfu3MHg0JBXKjk25vVrafCduK7rVztx91MCjxSozKq9XZDA3907Z1Q7oJYrFSRbVHn51xAQV9dDVWOqwFyxXihgfn4eJ06cgMTuw9uGXXIgil6tq+NAYnnyY8eO4fKVKzg0O+uX1kU9/ViWBUPXASCyFsWJZBIbGxu+F3knbah5L4ZUMglH07wKB8Pw8vTFIkRBgMLKkcJGE1rtM7nQaiybbft6O4Eiy5viqR0AwzBw8eJFrG9s4Oy5cxgbG2v5nqoJrc8+By5ox+2W/chBH5pftUKv7ppj21jI5/FeE2FzlBAEAYRV4dQDEQQMjYxAjcUwMzUFx3Wxtr6OhXweDx8+xI8//giVt6AeH8dYNgtJFH1tDt/AuNTrfaCy598/fhtp1KrrQnRusoBX0ug6jtesiekPGpHQYJq02dl5+lEQhKpoxO3btxGLxbB/3z6A0l1y8DYiKEokLOQ8NTWFhw8f4vbt2/jiyy99htkWWI6qHivlncYAj+FH5QgoMsOk9UIBxVIJ42EX20Ausep4rGoiFotBZzXLjuOgwqodBD5RqGpDfQM3RWrGzrnHer96oMuyDFAKh0WMtrM2uVAo4C8uXIDLmycNDoYqS+tVk69aNNIcdIqoyxh3XszAa8DjOE7kXRgbwddONSnJcx0HtmXBpRTpdBqZTAb7Zmbg8hbUrAKCN4saGBjA8PAwBoeG/H4uCVWtG66nHRADAH5FU1QghCAej6NYKsFxHOi6XlfD1E4JsGWaAJhWiZU1Li0vI5fL4ZNPPvH0QLvk4O0Ef7Bs2wa4U5gg4Pj77+PChQvIv36NMWZO0rYgD/UnrzI3OZIkxCMW4CWTSUiEoGiaMC2rtegoRM5WFEW/DNKyLC/EaFmwHQeOrqOi6xAlyc891kZBmrWMBVilAu1Nw6VGkGUZ1DRhmGbk30HYBWtpaQmXLl2Cpmk498UXfqQnjKCxH5UKQD0TpODZ20fkHgc7MKXw/PlzpJlXQT/g74Iphes4WyqNAK98EfDGfTBqIxCCzMAA4vE4pqemUKlUsLS8jOWlJcw9fQrn0SOomoYJFlHItmoWxdKpYb7dYHfGqCAIAmKahjIzSRIlacvmi1dRCbyaq8EYclgUAtjcUFAAt27d8kpHuRsiE3e/bXjnyQEPj5mmCTBBICHE65o2Oorbd+4gOz7e2WRWZ+CVy+XNDosdhP1bQRAEKKoK2bJQYJULdcGEk83sSmtBCPFTCpRSnySYhgGbhR/LlQokSfKUy2wiImA1xQ2OWygUAEHoWnfRDlRV9cOmiLpCIsSCNT8/j6tXr2JkZASffvppVT6znSqPXhsQ12ud5XZBDvhiEFVaodsISpT9UAAvpfD69WscPnIksmO2Ai8b5mZIW8qQmVthsHzRtm3YlgWTRRO8F1JoLFR++NAhgBAsLy8jn8shl8/j6ZMnIIRUNYvaQug5MQiRLuhV9EuWZaiO4/VhqFQgNfJz4ISqwXHMIKFi89fLV6+wsrqKX/mVX/H0F29p1ADYJQc+k3ZdF5bjQAT8hfP948fxi2++wfyzZ75lbUvUPBRBQR4X9wFoaYvcKVzXRTyRQEXXvfrfetGDgJlRpzs4QghUVfVaQ8fjm/bMluXnKcvw7q+iKE2Ff4VCwbsfPd4FB6EwtzbHcWDbdqRNqZppLCjdbJ40MzODU6dObR0HYfKwUe/AGyLa4/MFIZLvegdGDV6/fg3bcTDVZ499URQh2HbdnTifcwCPFFQqFa98G5tlqYosQ5LlLenB7OgosqOjOHHiBErlMnK5HBbyedy9exe3b91CPB73m0WNBptFUeq3Xt4OqKrqPduOg1K5XDW/hDEPcyn1NUk88uC6Lu7cvo1sNovs6KiXZqB055mpRYR3nhzwRa7CrDj9R4NSDA4NYXLvXty5cwd7JyfD5aabhKgqTGeg9bBDosNboKoqXABrGxuexWrg+iht3/e/GQRB8DwTNM3r48C7r9m2F1mwLK+uWhQhM6IQXNQ2CgWk+5hS4JBlGRTwqzEiQ4P767ourl27hmfPnuHY8eM4cvhw134BPU8r1Dl+V5oDvlhEQYwjELNFHTl4/vw5BjOZho2SegUuSqwlBw6zSrcMA5Ise5+VjU9ZkiDLcuixn4jHcfDAARw8cMBrFrW05JOFJ0+egAgCsqOjGGMGTMlEwvuea66pH5SOEIJYPO63vNd1HfFYrG4qo973bzF7ad65FwCePX2KQrGI06yKjaf/mllQv8l458kBAJ8cGIYBTVGqJvZjx4/jv/23/4YrV67g9OnT7U/GbPIpcaMjSeppfoq6LiilSKXTKBWLKJZKVZbKlF1TryCKImKiiJimwbFtGCzt4DgODEo961ZBgMIiCpIkobC+jr1TUz27pkZQFcUnL7GIDJEaaVP85klLS/jkk08wPT3d8BgkxGLlv2IbIgftdc2oRuQ+BzsIpmEgl8/j+PHjfT83jz65juP7EfCKKF3XAUqhMMW90gYhaARRFP0ySAAoFovIsfTDrdu3cfPmTSSSSYyPj2NsbAyjIyP+NfJFtdcjQGCp21Kp5GmlRLGqyqJR5Ja7SAKb5Yvr6+u4eesWpqenkWF22PwZ3I0cvMXgX65p26CKUpUzSyYSOP3JJ/j+hx+QSqU6Kk/iYTxCSM93FJwVJxMJz9aUuSZmuvB37xSiJCHORJe2bXutVg0DruPAcF3v36xyoxf6i1bgjWAcSntniASgVC7j4oUL0HUdn3/+OUZGRpq/YRvztVsvJbrIAc/RAtGQgyjuQZQL1KvXr0EpxeTevREeNTwsRnSDJbqGYUAQBMTj8VCN3DpFMpnE7OwsZmdnYds2FpeWkM/l8PLlSzx+9AgCbxY1NoaxbNabB1kev5ckgTeXq+i6b5BULwIcbMRmO46vxZIlCbqu4+LFi0gmEpvOuYFndJccvMUQRdE3/7FtG7IkgbBdNgUwOTmJQqGAu3fuIJVKtdWz27Ztn4Umk8meh4G5upYIAlKpFAzTxNraGlKJRGsb3h5CkiQkGVHwhYym6ZUduS5ACFbX1rzUA9vZ9Cr1EoSiKKBMK9ELcrC6uoqLFy9CEEWcP38+dEVGq3B3v9IKpJ4JUqcH65s3w/bgxfPnGBkehtZjC3DAW8xs1m7Ytm3YjGxbtg1JliFLkudJoKqe2LdJzX/UkCQJE+PjmBgfx4fw0oY8/XDjxg24rot0Ou1FFEZHMTQ83NNIkqIosJm2SNd134m2kftoUIjoui4uXroEx3XxxblzEEURFKjy+VD78H1vB3bJAYOqqh7zdhy/tS8CTWKOvvceisUiLv/wAxLxOAaZOVIrVCoV//j9qKd3Wd9xkU0I6+vrcBwHq+vr1dqDbYJAiO/EmIjFvBbVhECLxbxwaEAIRAiBJEmQGVHoBVlQFcUXinbS9rUZXr9+je9/+AEDrHlSOzuMsP0ZtkOO2GnbZjdAaLq9z7wRWNckI6LvW69UkF9YwKlTpyI5Xi0oIwN8kXMCngZcXCyIIhRBQDKRgCzLqJTLEJiV8HaaTqVTKaRTKRw+dAiWbSPPPBWeP3+Oe/fuQZQkjLGowvj4eE/SrpqqosjKr7kA2SckrGJBAHzvE8AjB5cvX8b62hq++PJL3zPBJxVsPGu7kYO3G6qqolgsekIU/mUHanYJgI8++gjFUgkXLlzAT37yE8RahMJtxuoJq4ONWvxUD36NMQvZZTIZLC0uYmNjA+l0OjLDpY7Byq5cNrE7jgMBniqaUuqXRFq2XZU79d66SRZkWY6EbPFJwgZgW1b3drfs+330+DFu3byJiT17cPqTT9q/1laphX5FDuqfvKNjRVnG6Lvr7RC8fPkSAiHYu2dPJMejrusvZFvIAItqcsIscQIgijAty5tn2LNDKd3+Zz4AWZIwuXcv9u7dC1CKldVVvHr1CgsLC7h27Zo/Z41lsxgfH8fg0FDXUQVKqddvgUWHjYAAmfASa9cFBMGP8kqShAcPHuDlixc48+mnGBwcDB6wKh2yW8r4lkNRFBBWJ+y75tWYeYiiiHNnz+IX33yDCxcu4PxXXzXdzfKogaIo/vFc9NC2lYkRg3KxWCwGVdPgVCpYWVnxBUQ7BYauQw4YJwVLqfjEaLG/q8hCpeLlBFkKQpakjsmCoihwdB1GBOSAArh58yYePXqEQ4cO4f333+/JAt6/aoU6C3mnBDfKa95hPRWeP3+O0Wy24/FDme7FCRACvnBRVnbISYDExnptWJxwV1bXhek4nr9Ije3vTgHfwAwNDSGdTuPo0aMwLQsL+TxyuRyePn2K+w8eQJFlT6fA/nSS3xdEEdR1oamqv/moKl/mDrGBqOXC4iJ+vHsX7x07hj01hM83UIKnq9qJ9zcKvJ2fqgPwkkad2YyKoljX7UvTNJw7dw5/9u23+OGHH3D27Nm6k13wAVcDkYhWVsLdgLouXOYDHsTQ4CAM5ntQLpcbGyP1CSTgTKYbRsO+8nwi1ODdMz5xWkx0RV3XTwkQeOE+WZL8nGvYHaoiy749NO0iN+s4Dr777ju8evUKH3z4IQ4eONDRcYDNpjCNwt7tWMB2gyhNkNw+EZp2wC3Ou7mmUrGIlZUVfPzJJ6HfExzPfmQgoHOilEJiCzsnBQ07dDIILF3jUArLMEAp3TnOfQ3IHL/3PMIxOTmJyclJuJRibW3NM2DK5fD8xQsAwGAm41VAjI9jMJNp+b1RSv1SSkEQtkQPePqOd+allKJQLOL6tWuYnJrC4cOHtxxTYP4GwNsrRgR2yUEVVFWFXi7Dsm3wr7ze0MsMDOD0mTO4eOECbt++jRMnTmx5zZaoQeB4vdrv8N21WBNGlGQZyVQK6xsbWFldhaZp2974hptDGboeKizHUwoSKwWllHphV0YUbNsGdRzojgPCyAKfDLhmodFnliQJIiFwCYFlWVXlTmFhGAYuXLiA1bU1nDt3LhJffT/kWQf92zPXK2Xc/rRClAS7W6ry4sULCIKAiSZtkCllvTy4gJCVHAZ/LzByKzJCUM8GuRk4ebBM0yNiLLK2I9Dg+2q0uAuEYGhwEEODg3jvvfeg6zoWFhaQy+fx+PFj/HjvntcsiukUsmNjddMn3CqZozZ6ECTXpmVBNwxcu3YNAwMD+OijjxpW6/Cf7hjy1QPskoMAOAu0bbtqMNcrt5kYH8eJEydw69YtpFIp7Nu3z/9d3agBB4sedCrqagSX7USA+pPvwMAASqUSTNPcttLGIHj0QDeMjh4wXmYkSxJi2AzL8qiCY9veZKzrMFi4FcyISWQTL/83IQSKqsKuVGCaZtvkYGNjAxcvXoTjODh//nx097ZJ6DyMy1s0lxAdOehXKqRtBHaCneD58+cYn5iAJEl+uSYff7wdPHckBOBHCARCfCLANQPdgPdX0A0Diiy/cbvaZposTdMwPT2N6elpuJRidWUFuVwO+YUFzD9/DgJgeHjYJwvpdLru8WqjBzzaYjkOLMvCzZs3IQoCzp49W3cerR27yi45eDcgSRJEWQZlC4wkSf4DVw+zhw6hUCzi6tWrSCQSGB0dBQDPdARbowa9Ap94eFisnoBHEAQMpNNYWV3F+vr6tpc2At7kqFcqSEbQU4HrD/hOiZd6WcGwLauGIJbl7xh4OoKAdYdkhM7/7ltgcXERly5dQjwexxdffBHphBwmytQq1OyjwzREvbQCwES6gchGbfqt3nVH5nEQVaUCQ6fRPNd1sba6io2NDRw8eBAF5saHABEI3h9BEDxCykhtt2SgFgIhHjF2HEBR3ghywEk7AfMaYPqAZhAIwfDwMIaHh3H8+HFUKhXk83nk83ncf/AAd+7ehaZpvknTaDZbFUEJRg94GbVpGLhz5w7KxSLOnz9f997xFEhwTXgT7nGn2CUHNVBVFSazUg4KTepFDwiADz74AMVCAZcuXcLXX38NVVFgMcVww4FDGrdzbhcu3dzH8bBtI0KSSqVQLJWgG8aOKG0khMAwzZ6UAgmC4DeJAjZzvP4fRh6o64I6DihLKTjMAEVVFIiC4Jum8D/B3QRvnjQ6OoozZ85AluUqVXnXaCa6q40ctNAnVL2uS/AxVzt+Wx2Zj88tpYz82mm4mESvXT63nC8wdngkgI+TJ8+eQZAkJNNp2KyqBoR440UQIAgCROY5EJrIdQjCxjB13Tdq0eLkTGC9GNqt6orFYti3bx/27dsHl1IsLy3hNfNVePbsGQgjE8FmUVLA1waE4NHjx1haWsLpTz5p2E1TYKLyIPrZLK7f2CUHNdA0DbogeA86r2ttMlhFQcCnZ8/im2++wcULF/AJEyW1ihrwqa2bqboqVIkQgi9CqkobU8nktk4ivA9DP0qBgpqFIFzX9XPBoBSlchmOZYGKIixWTuYL1tjuTxAEPH36FI8ePcL0zAxOffih/11H6fjWcAdFKVz2h/+/6u8adHM99Vo2d1yswDUHorj1eaolGjTQUhfwjWd8UtKEDLULvjhRSqvSAEFCUPuR+TXkcjmMZbOIxWI+meQdEvsNTgwANBT59hNhF/ng6/i/O23nLBCC0dFRZLNZUEpRLpc9X4VcDvd+/BF3bt9GPBbD6NiYl/4jBMsrK3jx8iWOHD6MiSalqNwYj49VURQxkMm0fY1vCnbJQQ0URQEVRTim6YW5ePi5yS5OVRR89tln+PnPf46r167h5MmTrRfdLqMHTrP3NZmYYrEYtFgMTrmM1dXVSIRzncI0TQjYXlEPX+y5dwLgEYZUKlUdbQgsFrfv3EHu9WscOHgQ+2ZmsL6+DpH5JVDA3yUKzNPBb6kbEr7NMKtXrxvYp5uh/Z6iruag/Uk7WGIbJq3gpywC49wN/G7LdfEFpkUEhZcG8rJf7idgWRZcx6n6ZPz83FRMYNEATgA21tdRLpXw0YcfbrswjZf5AujeqyMihJ7bal7HCUKnvjDBeTUej2P//v3Yv38/XNfFEmsWlcvnMf/0KQRJAoXns3KgSXURCQob2VqQGhjYdmF3L7FLDmogCAJkVYVrmrADyvVWeclUMolTp07h8pUruHrlCj7/4ovWJYMd1mq7fBKsQVjB12AmA71SQVnXvXam21TaqOs6KDzC0m05WRQQRRGyJMFkTmqaqlblKk3TxKVLl7CysoIPT53CWDbric6YrsEN5sIDEwkBKzNjREFkO0tOTEiARPj3oAUp7V8pYx10QmiDY7bLa663YPilaK4LF8wbwHXhUArqOHD4z+q816EULksH8SoXngrg/65HaF68fAlN03yt0XbCCFToiGwh2+7nKSxoqzEeEQRB8NotZ7M4QSnu3LmDx3NzIIKAmZmZppUd9eb/sC65byp2yUEdqKrqdfKy7dDKdZdSpFIpnP7kE9y6dQs//8Uv8Nm5cxhqMYDaDZ/Vy/X6vwv5MPHSxo2NDayuriK2TaWNhmEAlEJTVb83+nZDURRPuVyjhSiXy/iLCxdgGsaW5klVHgy2XVUzTbliHd7iRCiFxSfDOpM3YQtSMOrAJ/1gNKJfqGeC1Em1QrAbYzuLln8P2d88ghP8uRtc9APH5tGVKtGkIEAA0z2w+8ndREVRDH19lFI8f/HCa7K0zbtH6rowGNHm7YO3lRy0uenxw/VbDtN+5CDMe1xKceP6dTx58gQj2SwGBwehqmrD+8WNpWqf16EdYEffS+ySgzrQNA1FQfAEazUPWaOcss2ctVKpFH7y9de4cPEivv32W5w+fbplo6Z21NKu47TceYWZFAYGBlAul2FsY2kjr+rQNM1TK++A3Y4sy0Cl4nVmY53aePMkSZLw5ZdfbmmexPUMoiRB5gtX4PdugCTwvHbwZ3yBA9vluoxE8O/ZJ49c90AICqwFOGHit+B944SCh9+DinD/9zW/A1CVpuCvqWuC5EscqB92BzaJq58O4D+nnvOcaZoQRBEV1kJ4y3sDx+S6iuBEzxd7J7jos/MRQvyFPUiigpEZTgT4Z/SfZUp9bUnYqMbS0hJ0XW+rCVuvYBgGAEASBAiyDJMR1Dcm5N2ETPjfVQiSUFsxUw+8dfri4iKOHz8Ol333UrOoQY1XAo92RtVga3JyEi9fvsTXX3+Nn//8501fe/v2bXz44YdwHAf/+l//a/zGb/xGJNdQD7vkoA5kWQYRRVCmXg/6cAOoO5A5OZAlCaqm4csvvsCVq1fx3XffoVgs4uiRIw0nHkI8Q6BWA9upw16rwNMKLY4DeDvRdDqN1ZWVbStt1HUdsixv9nnfAdEDURShSJLvvLi6uooffvgBAwMDOHv27KaWJLCQ8ImjSiQYgEAI0KBVbBDB3TEnEdRxYPNweaBk1bFtUEJ8wVxXqDM587FuOiaGMQQHLigoCAiKhSLWrM0dqv/6wO49OJkSQmBYFkxdhyjLvlV2U1Dqh5v5gs939hLgL/YkQADaJZZbRMEBUsXvc6NjPn/+vK0GbL2C6zjQGTlQNc1r0YzuhM5doZNUaRgNCqtkaH6YxqZhAFAulfAXFy6gous4d+4cZFnG0tISJEVp6pRZOw4IEKkQ8dNPP8VPf/pTXL16tWXE5x/8g38Ax3Fw8OBB/N2/+3cju4Z62CUHDaDGYjANY0tJI1A/esBL2PgCK0oSTp8+jWQyidu3b6NYLOLUqVP1FwhCIDQIrXG0XAA6WFRTySSKxSJ0Xcfq2lpVqLwfMGoMkAjQ094TYSErCkzbxsNHj/DowQPsnZzExx9/vFmRwO911DnRBiSiSmNCPac9m0Ua4tzumYvt+HUFdvDBSIa/Uw+8hvKFOPCZ/ImYUu9HLgV1vZSC67hbF05OlvgCy7wjeBTDdhyIsgxFlqGoalU0oyp6EYhqCNhMs1Tdj27JUAP4ZKFOCoQy4kcIges4ePniBfbv378tVQlBGIbh61hkSfLaDTMh67agUz1Kq5e0KHFstaguLy/j0qVLECUJX50/D1GSUCwWIcoyJDa+ghtBjkbHjJIUnj17Fj/96U+xvr6OBw8e4MiRI3Vf90d/9Ef42c9+BgD4nd/5nY6cXNvBLjloAFVVoQsCTMvyW3UC9RcuCnilcEDV4CKE4NixY0imUrhy+TJKpRLOnTtX90ttOvDRo50ACZQ2FgpIpVJ9LW00DKPqfIT0tvdEWEiiiAf372P++XMcmp317bG367qq0k4sfM4bVKmKEv0CFQjnG46JNazBJRQu8QhwLKVhIDHALifkjr1chigI0DStK2V/r4hBIwS/cy5KzC8swLQsTE1N9fVaauE6DgzWRZDfU36N/b5PADoWWIc7dPNj19okBzH//DmuXrmCTCaDs2fP+sTAdd2qnhW15CDY4bb2WgaCXRq7xNmzZ/1/X758uS45sCwL//Af/kMAwJdffom/+lf/amTnb4Q3JCnVf2iaBogiXFbm1Ay2bft5z3qRgempKXzxxRdY39jAz3/+cxQKha0HaTLJciV1UwR3l22A585EllvvJ/SQfRX6BZ4bv/Tdd3jx4gWOHDmCA7Oz4d/fo+vq+96UheuJIEASRDhw4MBmfzsgBFX5/FAIGCB1ilotR2Ro85rmnjzB4OAg0gMD3mK8TaRR13UIgN+cCdiM2GzHFfWcPLNxWe88jX5298cfcfmHHzA5OYkvvvgCiqp6mhfAJ9kyIwR2zTwrsOZ7tUil05F2YvzkbXe3gQAAjCVJREFUk0/84/3www91X/Pv/t2/w4MHD0AIwe/+7u9Gdu5m2CUHDSAIAuLJJCg2hXMctbktOxA1CNbYBjEyMoKvv/oKgiDgF7/4BRYWFra8pt4U1VJnwFB1xjYnu8FMBqIg+KWN/YKu61vcEevWsPcQQb1ApVLBN998g8WFBZz99FPs3bMHpmGE74PRq8mx2f3o8b0idasV2odfrdCFSI6nKKJGO8cslctYyOX8mngezhbQ38nUsW2YlgUXqHqG+PPT97RCN99NO2OYbu0620gn8MPly7j34484duyY10RJEGCZJhzbBoE3X1Ng09+Ei1ID56qHTIRRA8DboJ08eRJAfXKwvLyMf/Ev/gUA4G/9rb+Fjz76KNLzN8IuOWiCZCoFiKLfRS2I4HC0a1MKDQZ7MpnE+fPnMTg4iF/+8pd48vRp9QtqogcdhQY7WCx4aaMkCFhZWelbSFLX9boh5lbCom7BTYZ4HtlxXayvr+MX33wDwzDw5fnz2LN3L0RG9iwWum2FnlVa1ExS/fI4AOovnJ00DfM7Mm5zjr4e2tnxPn3yBBJrLVx1DGxqkQT0Ptqj6zoIPAG0FIhWbktaodvoSZvvrdUX1H5WQ9fxZ3/2Z3j18iXOnDmDw0wMTl3X3+ipmuZpSCj1+lywOYdrxxo9y5RSZHogQuWphevXr2+xYP9n/+yfYW1tDYlEAr/9278d+bkbYZccNIEoilCYQRAvF+LgC1hQbyCHCDUpzE1x3759uHr5Mm7dvFnF8rlAq90QKn9tpwvUwMAAJEmCbdvY2Njo6BjtwHEcmKZZV+NA0LtFhKvQg5UFCwsL+Pbbb6HIMr766itkmBJZVVWAEOghyUG/Igf9DBlH0ZXRd3wEet5foBOEJaOO6+LJ06eYmZ5uWnniP4vBPxGODd71lWKruyi/v31NK3T52dquMgmQkVrNy8bGBn7xzTcolkr48osvsGfvXv93BmtlLQoCVEXxKn248yX7PluRA1VVt5QyRwFODsrlMu7cueP//O7du/j93/99AMBv/dZvYU8Te+eosfOe1B2GGBsIpml6If4ACDxiwHcM/oTR4mERBAEffvghTn7wAR48fIhL333nRx8QKKPqJ3hpoyQIWFtf9wlPr2CyBbepADKCCdUT2nvRAd5CN7jYzs/P48KFCxgeGsKXX35ZJT5VWbiRMoOjMOfqFepNVv3Yg9cNFrf5vQTHcjdphV7mtMPcy1cvX8IwDOxrYrPb+ATRpR783W+dslCeeulXWiGKaFlH3ypLnwTHVj6fxzfffANRkvD1V19V7fBdx4HJNniapvnvJYws+OSgRVVElELEIIKixGBq4Td+4zdg2zYmJyd9QWK/sEsOWiAWi0FUFIBSf3AFYTWoUmgFQghmZ2dx7tw55PN5fPvNN1hbW9v8fZuTaFjr5GZIJZO+L/vi0lLHxwkDwzDq7nw4eG17J+BRl6BTYb3X3L17F5cvX8bM9DTOnTu3xQiFCIJPXvQ6330/sW3B+Hr53A4iB0D3UYNtUeAHMPfkCUZGRpDqsBNfFKkH27Lgsg1JPWLNjZ5qDaR6hSjO0cl9cFnPEUEQ4FKKe/fu4S/+4i8wPDyM819+iViNJTy3apckCZIse34ijuM19JKkLZGDRhjsETk4dOiQ76Z7+fJlAMAf//Ef47/+1/8KAPjt3/7t1nb8EWOXHIRALJEA4C1owXwrIcQfTJ2qVycmJnD+/Hk4rouf/+mf4tbNm7CZYKYtRDEREK+1qSQIqOg61tbXuz9mA+jMJS/S0knqNffxLXUbvMx1XVy5cgX37t3D+8eP48NTpxouXLy7nWVZHeXae4EoiGA72BI96DBy0G2qqKeft8Wx1zc2sLS0hAP790dyOn4HhTaFfLquw4U3LutFYYLumNtdEhwWnVwnX8yXl5fxp3/6p7h79y4OHz6Mc2fPbiH53NacAIixzYifPoDnLMldRrnZWCP00vTq008/BeBFDmzb9t0PP/nkE/zNv/k3e3beRtglByGgJZN+m1mzJv9ss1a/3ZS2ZDIZ/OQnP8F7x4/j3oMH+NnPfoZ8Pt9RmVS306eiKBjIZCCJIlZXV7doLaJC0Dq5EcLW0AeFha3CqZZp4pe//CVeBsRKzc7B2zwToG7kqOZCWl5rx9hGIV/tot6J5qDecd4kPJmbg6ppked8/XvD/jQbQ5Zp+otas5bMpE7IPXJE+F22TfoIgWkYuH79updGEAT85Cc/wbHjx7f2uaDUn2sUVfXKEwHPRIwQ//+ARzgIs82vh2Qq1dOOlzy1cOvWLfybf/NvcO/ePQDA7/7u7/ZtIxDErglSCBBJgqooqFQqXm2+olR/WV2EwDlEUfT6iY+P49q1a/jzX/4SMzMzOHHiRCgnLB5Kj2IQpdNpv2vj4tIS9kxMRO7TbhhGlXVyI7TaAXFxYZgSSG6fapomPv/8cwyHbJyiaRqKpRJ004TK+kDUQ6PucpGDRw56fyaGGnJA21t03AjSCr3eBTcbZ5Zt49n8PGYPHuyZoDIYSQCwtSk2pX5qS1PVps+NwJpL9eyOsXu1HVSPUopXr17h+vXrsC0LJ0+exIGDBxu+3mCESiCkilC5jAAE76MAeA3S+lTCWAtODkzTxD/5J/8EAPDX//pfx+eff97T8zbCLjkIAUEQoCWTXt7KdWEFWjlHCdd1kUwm8fnnn+P58+e4efMmcq9f4/0TJzAzPd3X3ePw8DCM169h2TZWV1dDL6Rhoet66JRC0FedN8epUsCHuC8rKyu4ePEiZFnG+fPnkWwjbyzLsh9ytGwbSqMoUb8XsH6lFbqslvDTCjuwUsFHk+/u+fPncGwb+yJKKTS9DPY3r3LgJME0Td8MTWnSJAjog9dBxMQg7HiqlMu4fv06XuVyGB8bw4cffripLahT3UVd1498qppWRewcx/FE5IGfEVEEmmgOet1H49NPP/WfccuyoKoqfud3fqen52yGXXIQEoIkeZbKug7DMKrIAc/xBf/fbofB2kY109PTyI6N4datW7h8+TLm5+dx6tSptha1biBKEoaHhrC8vIz1jQ3E4nHEI+pCBngPZ5jSTwC+4xtFdVlcWLx69QqXf/jBs09tYF/d9Pxs16HrOkxdh9LoO+hx5IB0QIqiwNa0Qnvgi1TH19sPcV2D745Sirm5OYxNTFRVsvQDXLwIoGqRaxltA3p3z3owxluVebquiydPnuD27dsQJQlnTp/Gnr17t0Rva69LNwxQSr2S9Jpn3nFdUBJwtA2SqTrjVBTFSJst1cPAwACOHj2KH3/8EQDw9/7e3/P6d2wTdjCV31kgsuxZ/RLi1xlXoZa5tjkR1hO7aaqK0598gi+++AKlchk/+5M/wf179+ruCHoxEcQTCcQTCUiiiKWlpUjLG8OSJ787IaW+ujgsKKV49PAhLl26hPGJCXz+xRcdR3y454Fl21tKWgMn7OjYobFNArPab8ndGvRuim7dEWkfSFCjM6ysrmJ9fR0Ht3GSrug6KHMGbKY18NErl9FeiRybXOv6+jq+/fZbXLt2DZNTU/jvfvVXsXdycms0q2Y+4T4qAOqSOsdxQNg9pZR6C2GT6+h1SoEjm80CAEZHR/FP/+k/7cs5G2GXHISEKMsQAgyU+3MHhxPf4QJoayKnlDYNAY5ls/jvfvVXcejAAdy5cwd/+qd/ipWVlbqvjXo3OTg4CEWW4ToOlhqcsyO4bsv8rVuzS27V8jgI6rq4ceMGbt66hSNHjuD06dNdhbVF5sFOCPGb3Ww5Z8dHbw/9pwhbJ+J2QLusVuhHfKTRJ5p7/BiJeBzZsbE+XMVWOI4Dk5GDWCzm97xo9h3w+xXlOKFA5OmEZnAcB3fu3MHPf/5zmKaJ8+fP49SpUw0FgbXzHp+fFVmuO2+4rkdxRVGEKAib3UwboB+tuX/44Qd8++23AIB//s//OQYGBnp+zmbYTSu0ASLL0FQVpmHAtqwtNbGtOoc1Qqt2o4DXKfDEiROYnJrClatX8c3Pf44Ds7N4//hxr243eA0RQhAEDA0NYWFhAaVyGQXWvbFbuJRWKYWD6CR1EIRt2/j++++Rz+dx6tSpyEJzqqrCsm2YhoFYHWFizydO/t320T4ZAMiWPUSbxHcHuyNyVBF7BsM08fLlS7x37Nh2XBIogHKlAhDi2SRzUy54+XECz9yn4TMf4S6/07mtEywsLODatWsol8s4cvQojrSoKOLgIkkz0D+hUTUUjxz4xCCA2jOJgoDh0dFOPkpb+K3f+i0AwPvvv49f//Vf7/n5WmGXHLQBQdMgGAYkWYZtWVU7yC0DLOTEzcPmoV5PvBbLX3/1Febm5nDn7l28evUKpz74AMMjI218kvagahpS6TTW19extLwMTdP8ZiWdoi4hYj4FjZTQvulJkyiLruu4cOECisUiPvvsM4xFuOPj1RW8U2dQGNbP3XxQtNYP1Fbit0PconBH7Ee9fr17+ezZM1AAMzMzPT9/PZiG4Rse1QuNU3idA3vuhtgnYmCYJm7fuoWnT59ieHgYZ8+dQzqVCu0vwjU5wf4J9Qipb4BESKgS9OzERNfzXSv8wR/8Ab755hsAXgfGdqKkvcIuOWgDoijCVRRoqoqiZdX1APB92kMO6HaFi4QQCIKAg7OzmNizBzdu3MCFS5cwMT6OA/v392wQZwYGYOg6KoaBhaUlTIyNdRWmd13XDzMHOyNylXYjEHjh6XoTxsbGBi5cuABKKc6fPx95WI4LEytclNrjCaMetmPvvYXDtfHerqMG/XL6q5PDfjI3h7179/akMqkVXNYkiBODRvePAgBv00ypN+9EHVHqg5Zmfn4eN2/dgus4+OijjzA9M+Mv9u3Mj7quV/VPqAfHccCb3LVahAVCsHdqqq2PEwblchmvXr1CoVDAH/7hH+Jf/st/CQD4O3/n7+D8+fORn68T7JKDNiFqGiTThChJftvU2kWiXoiyHiilXp/7Dh/meDyOc2fP4uXLl7h+7RryuRz2HTiA9957L/oJjbknvn79GqZhYH19vSsrUf7QV4WdQ1/K1p3MwsICvvvuOyTicZz77LOeKct5xYpt23AcJ3Q/jSjR98hBTdvmTiIHtT0A2jh5X+5t7b1cWFhAqVTCx5980vNz10NF1wG2eCkhSn4p4M8jkY6LHt5/CmBxYQF3f/wR+YUF7N2zByc/+MBPBRC0J0a1LAumaUIgxO+fUA+26wKsiqH2emoxNDra1KitU/yn//SftqQOzpw5g9/93d+N/FydYpcctAlBkgBJgqaqKNk2TNNsuFsPxXo7IAbcIpU3Edo7OYlMJoM7d+7gydwcnj59igMHDuDQoUORDmxJljE4NISVlRWsrq0hFot1fHyXsfdOd4XB9MLTp09x/fp1ZEdHcebMmS32qVFCEATIsgzTNGGY5mZ5Zz8dzPquOag5fRvVCm63ZYx9Qu0onJubQzqd9v3u+wnLtmFblidCbNdPn1UqCBFUFvTKgpkCyL1+jXv372NleRkDAwP47Nw5jI2Pb54b7UWoXMdBpVIB4DkhNpsDLMMAEQSIjVIKnGQRgskeRA0A4OrVqwC8zca+ffvwN/7G38Bv/uZv9r1cthl2yUEHkGMxUMvyQn3MsKKWXQuEoHkLj+5zqcFWs4qq4tChQ9h/4AByuRzmHj/G48ePsW/fPhw5fLj9SaYBkskk9EoFxUoFi0tL2LtnT9vpBUopbNftynKah1F/vHsX9+7fx/79+/HhBx/0RfSmqipM04Rpml7IF/3JiwOoIgTbpzkI/17fAKnDHGq/4jHBT1iuVJDL5XDygw/6dPZNUEr9RU7VtMYLWDMEFrdudv48zRcVXErx6uVL3Lt3D2vr6xgZHsZnv/IryGazVeXBtE3ySylFuVLxPQ1alXsapgm4bsPX8bOmMxkke9CeGQB+7/d+D7/3e7/Xk2NHhV1y0AEEWQYRRaiqigKwpd8CByGkadqg24kvyOx5/l6SJBw/fhyHDx/G3OPHePjoER7PzWHfzAwOHz4cSaXB0PAwzNevYVgWlldWMBpSDMl7IICVbnbjtW87Dq5cuYL5+Xm8//77OHToUN92p7Isb6aVTDNc7XmU6PMufKtDYhvVCl2WMfLue/3E06dPQUQRUz3aNTaDbhigrMxX67ApWXDXzb+7tskrIS3NicLCcV08n5/Hg/v3sVEsIpvN4ssvv8QImzdqr00IbHrCQNd13yI5EY83LfWkrutFZQjZqhmqcR+dnJxs4yrePuySgw4hxmJQbBtEEOA4DnTDQLKG5RMAYOK5ugtXBA8fnwgIEyVxoZ4syzhy9ChmZ2fx9OlTPHjwAM+ePsXk1BQOHz6MTBduX4IgYHBoCEuLiygUi4hpWkvnxiAxALvOTnf5hmHg0qVLWFldxdmzZyNvhhMGqqKg7DgwDKP/5IChb10Zt5CD8GkFp0sDpH4TA5dSPHnyBNNTU11FtjqBzcpkKaWIN8mZtw2WZgjdiCkinYHtOHj29CkePHyIUqmEiYkJfPzJJ1s9A2qiYe2c2WIRPADQGDEAGouWeXdVQRC26LKCr07E4xiM2DL+TcMuOegQkqrCLpUQj8dRKBRQKpUQi8WqhVdNbG6jCkMTzvB5CNFx/J0H4NkgH5ydxf79+zE/P4/7Dx7g53/6pxifmMDRo0c7zqnGYjEkUylsbGxgeWUFqqo21V7UTkw0bPlmDYrFIi5cvAjTMPDFF19geHgYtEl75l6BN+JyHAe2bXccNu8IfdYcbDFBauOdlHmB7PSOjPzqXr9+DV3XI2vNHBYUmyJEWZYj0c3Qmn+3KgMGvPmk23SCZdt4MjeHhw8fQjcM7N27F+fOnUM6nW5woZtX2s7Ycmx7MwWjqlV27I2Oo7N7vIUYMAdWwKtK60WFwpuGXXLQBaRYDDFdR6lUgmVZqFQqSCYS/u9JgL3WihMjXcxqcoz1fAIEUcS+/fsxMzODFy9e4N79+/jFL36BbDaLo0ePeqmBNifwwUzG7zWxtLyMiYCgCIC/aNcjQm4HaYWl5WVcungRsqLgq6++2oxWBBoz9QtEEKCoKgzDgG4YiEek6diJEGoLKMOW6QZIW1/JUweg7Ll5+PAhhoeHke6zO51pGL5IN9atiJjUqkQ88AgjDUTwtrymC2JgmiYeP36MR48ewbJtzExP49Dhw62jitg6P7YCdV1PZwBAlqQtwuh6c47A3U0p3RLtcwIkNh6LYYTZGL/L2CUHXUBgu+WYpqFcLvsNmYK5LH+416mjjvRasBmhcF234WRMBAFT09OYmprCq1evcO/ePfzZn/0ZhoaHcfTIEW+BD/uQsvLGfC4HXdextrbmpyv8NEIDtJtWePHiBX64fBlDg4M4e/ZsVUdHgt4pq5tBU1UYug7LsjxHtj45AAbL1vqB2jOFjdMEGy51EiUKa34TFZaWl7G8vIxz58719bzc0wDwxlS344inMxv+vlMdQgPouo5HTNvkuC4O7N+PQ7Oz4UXQlLatM6hUKt48Jwh1Ff618wEhXk8cy7JAgS2RA5tHDSQJE5OTO7uDaJ+wSw66gCAIkBMJSIUCZEWB4zgol8uQ0unNXTHL323JpUU98bGaaJ5TC/P6PXv3Ys+ePcgvLOD+vXu4eOECBgYGcOToUUzu3RtqAVIUBZlMBqtra355o6woLXfyYQWJlFI8ePgQd27fxuTUFD766CNIdYgP11v0s2SOd3szLQsV0/TyxP0A391tVyljGwZfQBetmvtMDu7fu4eBgQGM10TAeo0Ks0gWWDQqErS6d4ywdRNxK5XLePjgAZ48eQJBFHHgwAHMzs6GbsXuXyrai6Qaug6LNYGLNzGI8gkCmxe4NkGUpC16Ese2vYiCqmJsYqKt639bsUsOuoTIyo1URYFl26Cui0qlgkRNn3E+wfKJtd0wWhgIrClLW5MqIRgbG8PY2BiWl5Zw7/59fP/dd7ibSuHw4cOYnp5uObmn0mlUdB2lchn5hQWMj421dB5z0VpQ57LmSU+ePMHhw4dx/Pjxhu/hbmehRVcRQdM0mKYJyzThKspbuePYaoIU7h67XVYq9BOrKyvILyzgzOnTfT2vZVmwbdsTIQZSkl2BRwZCvbT9KEKhWMSD+/fxbH4ekiThyNGjOHjgQMOmSC3Rxrlt24bOnGljLUo9g2JtCkA3TYAQqDV6DpdSOEyntXdysu9C1J2K3bvQJQRBgJpIVJkhmcxaV5blqvBWy+qFLkEEoUrn0C6GR0bwKyMjWFtdxb3793Hl8mXcvnULeycnMTMzg6HBwYa71aHBQRiGAdOysLi0hLFstvlnbCFItCwL3//wAxbyeZz66CPs37ev5fVz6+V+7jclSYIsy3BN09Me9NHEpG9LbhdliABAdrjeAPCiBslkEnv27u3bOSmlfvdAVdMi02X4UaUQ84D/ihavN00TL16+xPyzZ1hm/VWOHz+O/fv3d72Y0pCWz9R1USmXAXjdFltFWWo3Yiazo64lMbxJkyiKmNqmPho7EbvkIAJo6TQ2WDtjWVFgWxbKlQrSrMXvlmHfo3BpsFdBNzn4DMvrFwoFzD97hvn5eczNzSGZTGJ6ehrT09NIBHc5lEKQJIyMjCCfz0NnAsVm/gc8X1gP5UoFFy9eRKmD5kmENUbqZ3qBRw8MXYemaX3bKfetlLHDagXfOrmD6+ynfmRjYwOvcjmcPHmyr+NGZ2WLQheeBt2A8lw/tzCvIQiO4yCXz2N+fh6516/hUopsNotPTp/2zM+6IDNVkdOQ6cVyueyXIWohSDjF5jPi2DZsx/H0BjX32nYcuADGx8a2pY/GTsUuOYgAkixD0jTYug5RFOE4DlzHga7rW8Qy/mMQki23A+51EBSCdTPJplIpHH//fRw/fhyLS0teKeT9+7h79y6Gh4cxMz2NvXv3QlIUP183PDyMpeVllEolyLKMTAPVd6NOlGvr67hw4QIIgC/Pn2/4/kYg8Hz8+ylm802RmLCsX9GDfi1jtWSnXc1BJwK70J1KI8C9+/cRj8Uw3cfyNdu2YbCdbDyZjF4/QltbVQUtloNzxfLyMuafP8eL589hmCYGMxkcf/99TE5ORmLHTl3Xiya18YwahgHbcUDgeRCEGRsCS7FS9n4QAkUUIdWUm/P2zfv6XL6607FLDiKCkkjA1nXYloVYLIZyqQTdMCArCiRRrKodJoT4pVNRToACK2Xk5+LMuetdGCEYHR3F6OgoPvzwQ7x+/Rrz8/O4ev06rly7honxcUxPTWF8YgKJRAK2bWNtfR2r6+uQJKmqvJOj3uSfy+Xw/fffI5FM4rPPPuu8pCsiE5d2ENuO6ME2+Ry4IU2QfM1BB+SgX8SgVCrh5YsXeP/Eib5Wm1R0HYS59EWd4w5z72rFiMViEc+fP8f8/DyKxSJUVcXMvn2YmZ5GqpE/QQegvEopEPJvBSvQATcWi4WKWBDv4D5B4joFuSZqQCmF6zhIZzKRd3F907FLDiJCLJFAeXUVtuMgLkmQZNlLL5TLSNWr821Qi9wN+OTmRw7Yz2mEi6UoipicnMTevXtRqVTw4sULPJ+fx8VLl6AoCiYnJzE9PY1kIoFioYClpSVIori1DhnVte9PnjzxmieNjeHTM2e67rsgEuK78/UDMpvkbcuCUSdi1BPwevUeL6R1LLxavocyi2ygi2qFPuD+/fuQFQX79u3rWyrDNAy/aVovOv5xNPo0BB5xswI6gpWVFUiShD179uDURx9hZGSkJ/dji2C6lSg52FBJUUKJHv2NUeBnhmmCAlvEiLzqYe/U1I4ep9uBXXIQERRFAVEUuLoO13G8ro2s/l03DG8SqH3YIt7hCswtsXZfJwBtGN6GA3VdaKqK2YMHMXvwIArFIubn5zE/P48nc3OIxeMYGRnBwMAAFhYXMTE+XuWgSJlnPqUUt+/cwcMHD7B//3588MEH0TykEZRqtQtN01ByHBim6QnMer37JaSpj3xUEGqrFUKQg2DVSLtRgH4t0pVKBc+ePcOx48chiiJstlD0Er6nAaVVdr9RI9iUrfb8r1+98nQEuRwAIJvN4vTp05iYmKiuMorYO6RuFLPJ8WsbKoUlUrXEwLIs32CKawp46bNj24gnEn6fh11sYpccRARCCJR4HIZpwrIsaJoGNRaDXqlA13UvL8139j28BhACuO6WlEWUJkGus7XfZCqZxPFjx3Ds2DEsLS35Icr5+XnEEwnkFxZw/L33fCdBno/+/ocf8PLFC5w4cQKzs7ORhpM5+ehX+F2RZVRYrw2TE8Ieox/mT1tMkMKo4AN6g7a+U2a13Y+0wsOHDyFKEg6EqISJAhSeNwD3JOmZ+I0Tg0Dofnl5Gc/n5/HixQuYloXBTAYnTpzA5ORkQ18CAibwjYJgNxinzUZSsKFSPBZrOSa4uNP/3Oznvt5Alv2NB0+92o6DPWNjbXszvAvYJQcRQlVVGLLsdepTVSiKAss04TgOKuUyEsnk1nI7Hj2IYBELOtE5rltlFsTLm2iTLpFhUI8YVF0DgNGREYyOjODkBx/g1cuXePToEZ49fYqnT59iz8QEZmZmQF0Xj+fmoFcqOHPmTE86oBF4qQunX9ULxLO+LTG3TFVVe3Lenppp1UUH7oYdphQ6dVNsF4Zh4MmTJzh06BAkWe5L+avOooogpPd225SiVC7j+fPneM50BPFYDPsPHMD01FT47qxswe2aIDQapw1+XtVQKaTOQKiNxLI51WDloj4ZY8TAsiyosRiGR0Ya9oV5l7FLDiJEPB5HsViEw2w6FUVBLBZDsVj01MmGUb9kqaYEsRsIogjbdes+dP5OoMMFpRUxqIUkCJiemsLY2BhevnyJhcVFrK6u4rtLl+C6LsqlEg6xDpG9MIUC+l+9oCgKKmzHw0nim46tJkjh0wrtplb6pRJ59PgxAODAwYN9OZ/FOi66lCLZo3SCY9tYXl5GPp/H69evsb6+DlGSsHfPHnz88ccYGh7uTOfUZWl0s2e73hG3NFQKsXDXPTrbDJmWBUppld6AUgrTsnDwyBEkEom+lrC+KdglBxFCEAQkk0ls2DYqLJUgiCJUVYVuGH56oe5A5DumLhcxHploxvQ7edC7CV2rioLx8XGIkoTs6ChkWcbFixchyzLu37/vG9CMZrPIZrMYHRmJPCTft94LTGRWLpehs14bPZt4WBrJT5/0ApRCoAQEBAIEUNBQLZs7jRz0gxxYloXHjx9j//79mw14ejg2OBEGvN4JUXRcZAfG2vo6FhYWsLCwgKWlJbiOA0XTMJjJYHp6Gvv274ckin3vOwLA37k3Hf811+UyC3oKQGJzZ6hToT5BsCzLS1MJgnff2fksy8JINotkMtmyMdS7il1yEDESiQRKpRIc1/UiBZoGRVVhMlFMmaUXemaExHNqzcgB0J7YKKA87xTxWAyZgQGsra/DtG0oqor9rEHL0vKyN8Hl85ibmwMBMDg46BGF0VGMjIx0V73ASFdPp8cAsVNZ9MANRJB6emo0XlSp68Jl358b/De/HyylRQP/973u2edJ0zSGMeyXMFolG2tra5tOfIygkMC/K5WKV5dOiCeUFQTP3psQ/+96cJlQtZd4PDcHx3FwaHbW/1mvCBwFUGaufkIborr6B6MolUpYXFhAfmEBi4uLME0ToihidGQEx48fRzab9YSx5TIIIZERA/79uiHTkrUliw1fF/i36zgolUqe0ZEoIh7Cz4AbOTW6Zq43UBWlyiROkCSM79mDVCq1GzVogF1yEDEIIUilUlhzHL9LoyAIiMfjKBQKsCwLpmE03j10GT3g5CDMhBDWajiqfgUD6TRs20ahUAAhBJVKBbIsY2J83G/3XNF1bxe0uIhn8/O4/+ABBEHA8PAwsqOjGBsbQyaT6SiXLcDTYvRkMqgpz9JUFRVmhNUwWhTBuVzXhe04cFhfD4eJUX0iEAgJV0Wm+PUESJNf+srPwV7fyCGRAiCcXLD/8x2caVmgrgvbslAJeHz452IkoZY48B4ZvZqwbdvGo4cPMTMzE8plr1twUR2l1DPHavNzGbqOxaUlLObzyC8soMwW/aGhIRw4cADZbBaDg4NVz4NpmpuLOe28BXM9hKmO2TLWmr/Y82apIQZhUi/NiAHgjTXf3yCwubBsG9PM9vltbrXeLXbJQQ/g6wxcF4Zpeg1CWIisousol8tIp1JAs8HfoUAx2La56evg+R+QZjtq2rztcicYHByE7ThQZBmFQgGmZVW1uI5pGmampzEzPQ1KKQrFIhYXFrCwuIj79+/jzp07kBUF2dFRZMfGkM1mkQyZMySE9E1/oCqKL0CzbLvqM3YCl5m1OI6Diq57zZ5sG7IkeWkk9joaXIgDY0jkC3BwIQaqdv1+tQs2d4oAUBALWMISXLggIBBjQDqVqlok/KgDG088z6tpmh+lcl3Xq6Rhr3WZba3/GfnPKYUkCBAkCaIgQBRFiKIYSYnr06dPYZomjhw+vOV3jULTncKybd/PP5FIhBLV2baN5aUlLC4uIp/PY319HaAUqYEBTExMIMsjaU3GE6XUe7YRvYtmqw0FrxgIHa3gxCBgjRxWkyG0iEzYlgWT+Rtwy2RKKQYGBxFPJJBOp3ejBk2wSw56AEII0uk0VlZWYDgOVNZHgHvw25aFYqmEZCPFcGBX1y5B8C2UQ0YOmqUX+GQdJQRCPE2BqsKwLM8DoUEXR0II0qkU0qkUDh48CJdSrK6uYmFhAYsLC7h+/Tqo6yIejyM7NoYxloZoFboNGzHpBoT55VdYKWs75CAYDXBcF67j+NaxgLcztB3HCxljc+EXCIHAdt1iiBB+6M8SeD/vMdqs66bruv7nrSf2clmKg1JaHelwXa91LivFs20bDjaJD2G7SkkQIEoSBEYcwn4+13Xx4MEDTE1N9XzHSClFpVwGZSHthou563pjenERC/k8lpaXQV0XaiyGsWwWs4cOITsy0laUg1IKuC6EHinwm1UvtEUMGEEslct+r5VkIhGKGBD2/mYolsuglEJRFCgsckBYOkGW5b6UGr/J2CUHPYKmaVAUxbPUtW3EmDAtkUhgY2MDhmlCLJcRazRJdVjBIIoiQEhbhi4C2drJkTvc9YJXC4QgkUyivLQEx3GwsLiIsbGxlsp2gRAMDw1heGgI7x09Ctu2sbS05OkVFhfx5MkTEEIwkE5jbGwMo9kshgYHq0RNfHcctTlSvWmKR4oc294SIQnCpRS2bcNm7XuDRMA/NsvhiqIIVZYhM1tqjdV/BzUCUaN2/LVSb/jdGBsQE4GRGQDgd8TXPGCTPDgsUuI4DhyW63ZdF5brwrRt//OKkgRJkiCxCEOj5+XZs2fQdR1Hjhyp/zmbfqrwoPBsmSmw1bzHdVEoFLC4tISFfB6LS0uwLAuSJGF0eBgnT57E6OgokslkR6SO8mhfj6p/2Enqzhn8/GHhuG7nxCDMsYtFgBAk2BxLAeyZmPBTv7tRg+bYJQc9RCqVwvLyMgxK/egB7zWwsbHh5Q9ZRKEuSPsWy3zHyCfXZju8IKoe9h4SAw6FCYREQmCYJpZbdHGsB0mSMD4+jnGmV9B1HYuLi1hYXMTz589x/8EDEEIQY2LI9MCA93c6jVQ63XvzIBY90HW9KnrgkwH2xwkQOX5Foij6IXVBFCGJYtXEadl2T3PzVZ9jS2+F5vfN4d0Y20kDBHLUnDwERag8usDJAo8y8HSL6TiosEWLEwVJkvzxTynFwwcPsIeJ0HoJg5eyWhZc20Yul8PG+jrWVlexUSj4Jk9DQ0M4NDuLUaYb6Pa79DcSNfbpvUIwgtBuNRBlizcnBomQqQQe9Wv62QhBqVTyzKYC82tmeBixRAKKouxGDUJglxz0EKqqesZIhgEDAA8MKqqKWDyOcqWCUrEIkZAtDUF8tClQ5Aplk4Wmw5IDgBEEJmTrNRRFgWXbGBkZweLSEkqlEiRZxmAXzU80TcPU1BSmpqZAqWcCs7a2hvX1dayvr2P+2TPcZ/XTgiginUphgJGGAfYn6soClZEDyzBQAPxwea34jy9mkixvIQKhEBAfRo+aY7YSpAUiB+2codlRuVCxdjw7juOTLNg2XLD2vLYNV9chMrKQz+VQLJVw+syZ0NcUFpRSFItFrK+vY3V1FasrKygUi75NsiAI3hjLZDA9M4OBdBqZwcHIGy4JggDK0jQUvavA4KjSnLRJDIqlEqwAMWin/XMrYuA6DorlMsBFoAAUTcPQ8DAAIB1hI6m3GbvkoMdIpVIwDAO6bXt++2zi1GIx2Mwop1AsIiUIkbl0SZIEk4Wo1TYXO8pC7r3edciyDMs0EY/H/RLHtfV1P1zeLQghSCYSSCYSmNy71/+5aVlYX1vDxsaG1zlydRXzz575TZpisRgGBgaQYV3a0uk0UslkR6Y1tm3DMk0Ypun5XJimP1nxHS5v2NStKU4vFwJhS7VCi8gBM8tqJ3LQKR3lhEFVVU/DwPQZPCLDxZEPHj7E0PAwJFmGYZqeB0kH98yyLI9srq1hfWPDH0sOMx5TNQ2JRAJ79+zB0PAwBgYGOk4RtAOCTVLmt8ruwzn5+cKeixMDh80xoYlBq3PwcmVKPSGwbYMQ4leITOzdC0KIn+7dRWvskoMeg7skVioVVFwXCTaIuV84dV1Yto1ioYBUKlVfuMQfipA1xpIkgRBSFa4OjWDpW/vvDg1FUXzh3cDAgFfiWCxicWnJK/3sUZmZIst++2nAW5Rc10WxWMTa+jo22KT/7NkzlCsV32ExNTCAwUBqYmBgoG6HOJcRPsM04ToOKCMBhNVbS5KEeEg72JaoGQu9MnraojlocQ6+OIWtLqAhjhkGhKcUJAkIkIW5uTmUKhUcO3bMb3xUKZchKQoUpt+o9xmLpZK3+DPiur6+jgrzLCCCgIF0GulMBlNTUxgYGPBFkoSQvhCC4OcO+kP4JaW97jLI0p5iA/1BLajrosirEghBPGQFh3eq5sQgOH6KLKWgxWIQBAHZsTGILErT65TS24RdctAHpFIp6Kz8TIvHITJWSwQBiUQChWIRjuOgWCwilU57O656DwNPMbSYdCRJAgkowUOzemxO0lG4NTYDZ++WaUKKxTA4OAjHcVCuVLCwsIBsNtszghAEgbeIpVIpb+II9HgwDMOPMGywKMOzZ898A6FEPO5HF1RWripLEhRV9SdqWZYhx+PQYjGYuu67tb1JqB09rUYFjxyEJQe9qh7h4/7hw4eYZm3GTcuCZZqw4aUfNsplGKYJ27ZRKhZRKBSwsbGB9fX1zWhALIZMOo2pyUkMZDIYSKe9xT/w+QzmgEqB0KW1UWGLcRS77l5ege+hEHKeoEHxIbwW96HP1fxCqoiBaRhe+SIhSMRiUGMxDAwNwXEcxGKx3R4KbWCXHPQBkiQhkUigWCyiUKkgraoQmCiJsvB3oViEw3awyWSyvlYgZAUDIQSiJPm517APxBb230OCwK/JsizEGMPn+oNKpYL84iKyw8NIRJBiaIVGi5OqqlVRBsCb5Aosv7yysoLVtTXMzc15Oc7A8VRVhaZpSMTjiMVi0DTNq7dmjbkGBgb8CE+7aPqNdOiP0RRt9FagTCkPtBc5iBo8vHz37l1YloVEIoG7d+96UYNKxbO31nWvVJQtrpbrIplIYCCdxp49e5DJZJDOZFqm5mzb9ogBpf5Y7gdqIwYctKf6E/jVMf7xW8wTnBg4jBgkmF1xmGhDs09Qz7GxUCyCEAKV2VSP7dnjdXYUhF2tQZvYJQd9QiqVgmVZMAwDBcNASpYhgBkRiSKSySSKhQJs20a5XEY8kYBIyNaJPmQFgyhJIG2QAwrUL+/jE0HEJIFHDkzL8n8mCAJGR0awvLyMUrmMheVljAKRaBBaIXRInng94VOpFOLJJCaZ+NFhboC248BgCxBfiJaWl1GpVLzGO4FDyaKIGCMPMU2DFot5/+b/j8ehKUp7kYYQlrXtYqtDYuMyUB418C4lJDlo83pt1piH31/+b71SQalSQYV1xeTHppTi3r17iMViiMfj0DQNAwMDiMdi0NhuUpIkz3shkMKTFKWlbsJ1Xd8eWVaUvuaz62mDeFSrF90tuSVyvW+rkfdBLTGIJxIQBAF2iCZuAjyPi0afwq0hwg5zJAWARCyGzNCQL/ocHBxsS5y9i11y0DcQQjA4OIilpSUvhGmaSEiSr1YXRdGLLpRKME0TAsuZNYwg8B1agwlAYs55ToiHUDcM/P2///exsbGB0dFR/Kt/9a+qzstdFE3Lwv/7t38bDx89gixJ+Ef/6B/hvaNHO7of3FLYYm1ZObhVMgCUymUsLi2BUopUj5ujtDSEYr0ydMPwPSAgCFA1zd9ZttoJ8a555XIZtuOAMrfDSqWCQrGIhcVFVHTddxHkLoVaPI6YpvmLGWFlfvFYzEtnBFwEeXmjGHAX7HYn247mwG+41GAipsCWckQ76GcQ+MOJMl/4y4wI2La96f5IqWdoE4shHothIJ3G+NgYNE3D8+fPsb6+jq+//jpUBMpiIl6DuU867DkVZRmqotQl2ZVKxb+WfpXHkWY+HYHvJkpy4FIKsQEx8E5bx/OgDjEQRTEUeRVa6J7qHaHIfA1kWUYsmcTA4CAAYGBg4K3ojtpv7JKDPkIQBAwNDXkEwXVh2DZUSfL3YZIsIxaL+R39BFH0/ABEcetD0kp3wMyQLKbYbvZqVVXxV/7KX8F//I//EYuLi/jzP/9zfPXVV9XnohT/n//1f8XDR48gEIL/x//0P3VMDABvlwVK/Z7tQXCCQAQBxWIRSysroJR6ltM9RD2CwHcjBrNhhet6DXQSCSiBnglh+k+IkoTBoSEoigLqukgPDGzZmVJKveoGthj6u2P29/r6OkqVSrXYlBA/ssOrTfj4IJSCBDwT6v2RmJdC0KY4+DuT7cY4cvk87pbv+pUBvHcAN3IyWZ8HyjwIbEYEbNuGwwyefOvuwE7XN3vC5uTPUzKxWAyjo6P+/zlR0jTNG+s1WF1bw7Vr1/Dhhx+GIgZ8gZdkGZIsw2G9UUzT9LQwpZL3PKqq/70bhuGVTFKKVCLRUfVDJ2jWnKpKMxQhmhEDfj4/zM+uo1Sp+FUJPjFA6zRSU4fXBgSeuq5vPKVpGkZGRyGy5k39SE2+jdglB32GJEnIZDJYWVmB7roAK6viUFUV1HX9xUAgBDKbOIXaNEOTCIIoihAIgcOMY+pNoD4oxV/6S38J/+W//BcsLy/jD//wD/HFF19URQ/+4//2v+Hq1auA6+L/9rf+Fj49fbq7+8C884NphSAEQcAIjyAUi1heXQV1XQx04YMQBvwuWiyPbJim76goSRK0WAxSnUZKYYVZMlt8LMtCpVLZkjIhxGvapKkqMplM3WMUi0XopgmNRQ38nTjfcfMFOWDBXOs26Nh2lamQZRj+e93AaxzXRcEtwqEOXJeCgOJ1/jWeLD2pMmgSRdFLZQmCZxcsSVAUxScYYg0B4SSEezrU+7nIol/tggK4desWUqkU9u3b18ER4C8smqr61Sdcx2DoOkRJgmUYfrlcP0LWPGLQ7J74aYWodA9c5xTipVxvQuF1ouREMBEgBv7rGkBosPj7pKDBe0vMKlkQBAwODSGRTEJRlJ7PF28zdsnBNkDTNKTTaWxsbEC3bQi27ZfaAJ4HguO6ME0TpXIZKTZpOpRCQM2uoMkOQZIkLxdu203JgcMWvl/7tV/DH/zBH2BxcRF/9u23+PonPwEA/Jc//mP8t5/9DADwf/kf/gf85b/8lyPJaytskWyGkeFhCISgUChgZX0dFECmhw+8y0KhhmH41RuyJEFLJJqa1rSTN4/FYt4Om4Wx2zbDIZ7zmyxJdcVyUZUGcvz5L3+JV8uv4S03BCfeP4H/ft9/X/e1xVIJtmUhHo+3zL/7UYPIrtTD69evsbS0hM/Onet6By2IIrRYzDMzYx1VXdfF+toaKLxW5P3QGQR9DJohyshBu98NIQQQBJSKRd8CvJYYAPXTb5SnLRqM21bjucCiBjFNw8TevRBFMRLXyXcZb1ZN1VuEZDKJWCwGSBKKlQrcGm1APB6HLEm++5rLw7G0TqdE/gDU/Jw/lM38DoLv+PLLL30r4j/8oz+C4zj47rvv8L//H/8HAODcuXP4v/6P/6N/zm6XH0VRYDLhWDMMDQ156n5B8I2LogalFJVKBWvr67AsC5QQyIqCNCtba7mAtzEJiTxdRAjKzLGxo2uO4FrCoB3NQVvuiEKtvVL3cCnF7du3MTo6ijE2lqMAt8JOJZN+OSr/hEW2GPYS7TQzIuiecHUiaHRdF+VSCRZzAI3H46EiKtR16xMDNse0+uSVSgUuS1nN7NsHSZIwNDS0K0DsErvkYBuRyWQ8YZ4so1gqVT0cvEmTKIq+SY+/c2DmLlUPU53KBm7u0qwJUzBXTgjBX/vrfx0AsLS0hP/wH/4Dfv/3fx+UUhx77z38+q//etU5SJcEgVsoh0GG1ZdLgoD1jQ2sREgQTMvCxsaG1ySJWboOpFJI1dn1RAXeZY978HeERguG26r7QbsIV63AqzaAcO6IvdjTzc3NoVAs4sT770d/cEpRrlQ88XA8jszQEARRhEspSoUCypVK5NbjhJ03/CVGUMYYtnInAMe2PZJk2xAJQTwer0uoa4/bsMVzG9fAyVkilcLQyIg/r+6iO+ySg20Eb74iaRpceJ3cqn7POpUJguB1GQsQCF56VyWEq9UdMDMk3uWuFvXCz2fPnMH09DQA4JtvvoFlWZiamsL/83/+n+s+7N1MQoqitEwrBJHJZHwR38bGBpZWVjo+N+ClUwrFIorFIiy2oCYSCaRSKZ8UCAi3a2u31FMQBL+joq94D4sW95yELHcNf7pw9sl+TjjkrjNqN0fLtnHvxx8xMz2NgQZ6jW4QrJRIJBJedUQq5QkURdFrxV4oeCY8EXy2Thwv/fmhQ82Br51p47k2TdPb3LiuX5bdKNIW/DScGNRcQFtpMdMwvO/FsrD/wIHNiOwuusYuOdhm8NyYqGmwTdO3Z+UQ2MNGWOVBueb3AKqjCIEIAiEEoiyDtogeVIEQfP311/5/BwYG8Ju/+ZvNH7gOIwhyyLRCEJmBAe9+sdzm0vJy2+elbAe4vr7udc6jFKqiYCCd3prDZ/n9luiAJGmq6tWNswqFKBFlrnVrb4X64FEDbiHcDLwyIUrcu3cPtuvi+PHjkR4X8Mp9DcvynTH54kcEAfF4HMlEwq8Qqui6p73oxL6cIazGoBZd9VXgz3HI93KBZoU1OeIdZ8Vm2pzAZxKCIl42b7VLhgosWjEwOIg9e/bsGh1FiF1ysAOgKAoyw8MgigLdMLYsmKIo+pasZh0CURVFCD5s2PQ7CJsTzefz+OlPf+r/3zCMUIK5TiYjRZY7mkDT6TSGhoYgCAJKpRIWFhdDh3Nt2/aEoLoOh/lLpFMpxNkuvhF6ImsiBDFWG19pIyTtX0vEi2vrE/LTNkgrBMhB62NGe0dL5TIePXqEw4cORe43YJmmb64T07S6PTUkSUIymURc0/znsVwsth0V4rX9HUceWPSm3bvbqb7AYPdF1TTf4Kj55dW5Pna/OjHDWltfh0MpDh0+3LC6ZxedYZcc7BDE43EkBwchACiXSlsIAvdAALxdTKlGo8DhuK43SfO67SZmSLXvLxQK+J3/5X9BoVBAkpkO6bqOP/qjPwr3IdqcYBRZ9p3s2kUqlcLw0BBEQUBF17G4tNRycTUMAxuFAix2L5LxOFKNrKprEHWonoP7WIAQfwEKi6Z1591dVs2xQkYO2ujGGLXj5u3bt6GqKg4fOtTR+xtdjW3bKDHRqKoozc10CIGiqhhIpaDKMiCKsGwbpVIplA8GpbRxKV9ItNuumUcK2jmjY9ubkRFCEGOuk8Fz1vUiqI1qEM8+vpPPS10Xi4uLgOticHAQs4cO9c22+l3B7t3cQRgYHISSSACEoMSc4YJQVRXxeByAl+crBUWKDFwk6DCGLvPIATNDCiL4UBqGgX/9r/4VFhYXoWka/vE//sf4+OOPAQB/8id/Er5CoA2CEE8kYBhGW7qDIJLJJIaGhyGyvP1igwgCpRSlUslr/EI9R71UKtV2CVoUKvCtB/Xq5Am87yBUhKdBdcrWl0VztULtNNHgvH5IO8QkHSU1WF5ZwcuXL3HsvfeahrSbod6dchzH6/AHT9wbCxmRIIKAWDyOJHtWXUpbpxkiIAYA2xwgXPSmHRLBYVmWpy9wHF8TVe85akQQ+fn8tEkHn9d1HKytrXnznyDg1Mcft18OvIuW2CUHOwiEEAxPTEBlXf0q5fKWCIGqqn6KwWJtjmvLIAHv4XOYU57ALEuDkxPFZhjYdV3823/7bzH35AkEQcDf+7t/FzMzM/hrf+2veeexLPz//vN/bvfDtHwJzw8WCoX2jh1AMpHAyMgIJBZBqE0xOK6LjUIBhmnCcV3ENM0XebYNFj2o98m6WYa5Kx8JGT0Ie66oyEFt5KBRLYTbRuQgqpQIBXDz1i2k02lMz8x0dZyq/zMBMIHnNpqIx9uOjEmyjHQyCYndj1KpVDdSxqsLuiUGLkstUlZx0wydEANd11EulTb1BclkYzJWxyQsGKVw67wmDHj32tW1NRBRxL59+zAZ6KS6i+iwSw52GERJwsDICGJsN2nxCEFg4pBZ4x+BuePxls+14BOOIAighMCpjR6wh/Pf//t/j5s3bwIA/vbf/ts4cfIkAGBqagqffPIJAOCbb7/FwsJCuA8RDC82eVkqmQQBsNEFOQC8lMzI6CgkQYCh68jn83AphWlZWF9f9/oYsPN1nY+uUzIKYLNhT4fgKSPLsjqOpNSCh5i7xhbNQf2j+mWMrdI0EeoNXr58idWVFZw4caI7MhT8TCzS5LLdfCfEgIMLihVZBmHjs8wiWN6pookYAIG+FoLQlBy062HgMqLE9QWKqrbUFwSP7hMfSjsSWXJYto1yqYSNYtEzoEok8BGbn3YRPXbJwQ6EHI8jFo8jnkwCLEJQrIkQiKLo5ctZN7RCobCliRGHJMsApdW7FjYZ/eef/hTffvstAOCv/tqv4csvv6x676/92q/5nun/3//z/2zvg7BJqNG0J0kSEskkChsb7R23DuKxGLLZLERBgGlZmJ+fx/r6ujfBCwJSqVRktc8E2Oqj3+XkLjLffsLU7q3OD4TzqI9iGQ5TyugGdC4tKxW6WCCCcFwXt2/dwtjYGLLZbCTHBKUolcuweXvhRKJ7K2JW9x/XNIB1JOSeAFFEDDhcxwGYyLbxpZC2vBgcx0GJuV4C8JqAtRDvAltdEF1u3tYhyTINAxUWebEMA4qq4vDhw7t9E3qIXXKwAyGIImRmy8pD4LZloVgTIRBE0Vv0mJNiKdCqNgiFtZ61HQeWacKl///23uzJjuu+8/zmvtylVqA2VGEjKa4iCFISRYUkOqy2FG63NdZY7nl02BF+8UxEd8fMy8S8+E+YDj9MTzvGdtvR7QjLbluOaDvstmW3SYoSTQKkSVGUAJAACBBLrXfJPfPMQ55zKvPuW4EE+PsoSgXWcjPz3lt5vue3fH8MSZriH/7hH/CnPF3wla98Bb/0rW91/e7m5iY+x+covPrqq7h+/frY1zPoRjI3N4fGDMQBkNtSHz9+HEkco91u4+7ODlRFkSJq1hR3fLMI4Tu2DSj5PIxwRtGDWZxXVylmj26FYjHioGPO0tvgypUr8HwfTz311Mwe0/d9aczlVip9p0tOgskdFoWw9KZsd+xkUL2BSCOMk06I+T0nE/UF1WpPu+5+iONM+5oHQQA/CPKZDb4P07ZRr9Xw6GOPTfW4xGBIHHxC0V03H0BjGHluT9OQZhlazWbphqKoqhwywhiD53k9Wx1FTltMQXzzzTfx//7H/4g0y/DZp57Cr//6r/c9l29961tQ+Y7jO3/8x5NdkOhj7vhyrVabmTgA8kVKpFyE3fI0FsXDKN2Ip7wJKqqaV31jSGujuOlOdbQxzmuEboVho5rlY81IpAVBgB+/+y5Onz6N2gymdTLkXUBRHAOMlbwMZonGBb2u6/kcgnZ7pLHqo5Bxv5NexkIKxnu/FOsLtGH1BQVkLVNBOHeZtY0IYwy+5yEKQyjIowe6rkNVVTz2+ON5RJQ4MqjE8xOKqqqw5+fh7+3JG4qoeG61WvnAF95WJa2WeVFeEIbIGIPrulK9W5YlR9A6rotz587hD//gD+Qf86Cb9sbGBn7/P/2nmVyX3L3wG0e9XpfOc9PejD3e4aFrGjY3N9FoNBBGEe5ubyOq17FwBH3QCgqh2hns0m3LksN9fN/P8909jglgJDEyagpi4GN0pRW6Kea7BzGLyAEDcOHixXyRmNHuMSp6GTjO0dnv8oW66rr53zNyj4bKiHMIBiEiB8XRyCJtMWq0QLzvRBrBsCw4HW2KvRCvKsuyQ5fFKWBZBo9HcTQAqq4j5sOVTp46hZOnT0/1+MRwKHLwCUYzDFhiV8TD44Zh5JXUnicLhAS246DCBUEURaV5DLquy8U34mOIAZ4/V1UwbrF8L3ajxduM6FiYNnrg+X4eemQsr9dwXaysrOSObYqCxsEBbt+5M7NdWiczcyRUFLg8jzpNm2f5Iac7t+5uhQFphWEL3AzEwY0bN/DRzZt4+ty5wb4DI5IkCXzPgwLAHuZlMCligS6YlFVcN+9k4K3Lk+yuBb06FWTb4Iivf1d9geMMNQcDNy8SbYm9fnbceRNZmqLleUiSJN8kuS5arRagKJibm8O5Z54Z6/GIySBx8AnHcByYIgKgKKhWKvLm5fk+PD7HXGDyVkdVyS2Tm82mvHFblgUoSk+rXvH4UiQctfseP94sOhZ830fg+2BZBoeP180PoWBpaSm3W9Y0hEGAW7duTWy8NIhZeiDoug7TsqCqKrxeDnv3Oq0wQvHlSAOXZiCgwijCxYsXsba+jo2NjakfTwwMgqLko7mPwpefMaDHcCFFVVGpVKRAaI1oltSLYuRmkghEZ31BpVodLJL4tQy0axYdChMUQKZpmtdUuS58z0OaptB1Hc9/6UvkaXCPIHFwH2BWq9BFIRDfWbquKzsQvA4vBN0wUK3VoPGBTaJOweR1B2mS9BzjLELkwGGe8KhFgm4YU3UsBEEA3/dzDwPHgdWjVbFWq+HYsWPQdR1pmuLW7dto95hRMS2zdFF0bFvOXejbvXCP7JO7aw7Kx5U7RwxOK8zivfTWW2+BZRmeOXdu6sfKsgwt/j7QNE0ajM2cAWF2IRA0IRAmjCCIegNN08aajwAgb6/sqC8YNDiJIRcFog2zL2N2YsRxjHarhYwxmLqOquvmnR3tNqCqOHf+/EzqS4jRIHFwH6AoCqy5OaiaJv/YLMvKBzIhHzlcjBAA5cKnjLF88mAcwzTNUmFiv+MJGG9BkhP3joC5uTk0m82xF48wivKe8QHCQGDbNlZWVnKDKUXB3e1t7O3vT3nmZYriaurHUlVZbxCE4dRV7dOcV1daoeN1Eu87ZUCnAsP04uDWrVu4fv06nvrsZwe+1qMgevfBGFQhDGbovwBg5McTToMa//m2540dFZKdCmNEDdI0RbvVkrUWhmmi0se/QLx+Mn0gig0HPD5jLHdqHYIY4OTx6zYMQ9ZL7e3tAYqS1xmcOjXytRHTQ+LgPkFVVdhcIAgM00S1VoOqKLkZUrMJv5BmEO1HpmHIVkdRSTzKWNmSeckRioR6vY5Go5EXRY742GmaSjMZy7ZHMjfSdR0rKyt5a6OioNFo4Pbt2zOvQ1CAmTxHumHAME2oioJ2Mb0w60VsTDqvLBsxpTDNWcdJggsXLuD48eM4OYUTIgDZ1SPOu+K6g3fAkzBmUZ6IIIj22HHmbCiqKj1QRrJN5o8vO58UJa8vKBQwF364FBmS359R14mIDIRhCIZ801PhnVrNRgNJmmJ+fp7qDD4GSBzcR2iGAatjJKmu66jX67KVMQjD/I+K7zQVnoawef4wThIpDAYVu8lipo6bQEkkzIhix0I/B8JOPN9HmqbQNG1kz3sgv67FxUUs8qFNQRjOrg6hUACmqupMhgu5jiNv/mLBGKdbQTLFuajK4NkKoxQjTvtcvP3224iTBM9MuUiIiEHKDYOqlcphKH4WdBYejoGqaXAdJxfvI0SLGCBrGVL+Nzms3kB0O4liZmGDXKwvEBMSxd86MFnkaVByRKTL2rzOQVVVVF1XFkCGYYhmqwXLtnGOZid8LJA4uM8wLAtmpVJanMWuQxQiptwxURQrKooiK/gFfo/BTr2Qt4TOdjZekzALoSDyiK1m8/A4A5wVRUsmgInDwdVqFcePH4fB6xA+un07DzPPkHEiIYMeQ9ww/SDIBzNNcL3jWuaWfrer5qB82x+ljXGaZ+Hu9jbef/99PPHEE1PVBRSFAWMMlULv/izErgKMnWfvRDcMWIYBRdPgceOfnscSryVfwPt6HHAy3hooFmPw4VCV4lRSxmRNQa/jjEs/J0whUMTkWdM08wgnr6vKsgx7e3vQDQOfefxxLC0tjX1sYnpIHNyHWNUq9B67ZcM0UeNRBICPKG40ZGuSGNpk6no+kOjgYKQdsyi061eRzBjLc54TCoVarZZ3LHQUJfY6XsLTCYwx2LY9VW+4ZVlYXV2FbVlQkS9Ce3t7E1eM91p6ZzFG1jDNPL2gqvA8T96oj7yjhNPlc9Bx2GHigPX6pRFJkgSvv/46lpaWcObMmYkeAzgUBuJca4Wiu5kIgxnaINu2nac5ehSjMuSCseh0mIkBa31mKohOhJj/rRumiZowTkOhngDIIx+9L3Csa+g5spl7KIjXQVUUVKtVuK5bSkk1Gw2kjGHj5EmcJj+Djw0SB/cpdr0OtUeoTeWFbNVqFaqYu9Bq5dMdswyGaWJufh6WaSLlCn2sHus+NwkFh+FI2eUw4s1S13VUKpXeXgeixZI/ls/PVdO06YcoIQ+FH19ZQb1eh6aqaDSbuHv37kzrENQJw8xFXMcB+KAtIegmecRJfmfQbAUhDIERPA4m4Efvvgs/CHD+/PmJH0OMXhaLaLVSmXi0cy+kMJhhMaqIiEVhKO2cpTDoeC+Jv7fOmg/RFujxv31F01CpVuE4Tu5tUigw7AXrJxRGoHNSrJgPIyJ+pq5L6/fiMUQ64fjqKs6cOUPphI8REgf3KaJAsecNifds12s1mUuMoggNPpxJ0zQsLi3Btm3EcYwwCNDk3QzDEEcb6pgmwp0jtkPWecdC3+OqKoIgQBzH+US2GbadKYqChYWFUh3CRzP2Q5jWNlhRVbjcqS7i46fHXehn5cVQTCsUBy7NIkpSZGd3F5cuXcITjz+OarU60WOIBZIdhTA4wgiOruuwTBOqpsH3/VwYFI5ZJE0S2XUhzicMw3y4k4ga2raMlhQjfIP+jtUBqb2BFIQSyzL4ngdPTLnkmxeXb146r2Nvbw/zy8vYOHECi4uLkxydmBEkDu5jNF2HMz/fVyCIHUi1ML2x1W6j3W7D0HW4fMIaA3cl4xGGYVGEcZ3XGGN5nnfAgiY6FgY8CEJuC+1MmU7oR7EOIeN1CK0x6hAGPR9yYZ5iITEtS87ICIJgoumGk9QdDJqtMKxTYdLuljTL8Mbrr2Nhfh4PPfTQ2L8PHEYMRN2NKD6cCSMWzk6DY9uyvTEMw77HE86mqqoiTRK0220Evi99Cyq1mhyYNI6V8qTv1DRNkWWZTGdEXKCI2gLDNLtEapZl2N7ZgVOtzszgipgOEgf3Obppwp6fH3gDNngIzy5EEZrNJjRNg6ZpsExThujF9wb5IBQZ1fhHzFRgWSbrE4rU63V43DK1F0IYaJo2dY/7IDrrELa3t7E7RR1CEVEUOM2SItrN0jTt6XQ5FNZr4PJgOrsViq/dsE6FSSMmP/7xj9FqtXD+2WcnEjTS+ZAx2dLb7xwn2vnPaOx0X/gibvNJnVEc9z3PlBcGi8mtne2JwmBplGPOgizL4HmebDVWecF0hZ9LT2GwvQ3TtrG2sYFTp07Juini44PEwQOAYZqw5uf73+T4ouS4Lmq1GjRNQ8YY4iRB6PuIogiWZR1+jxdvjRJFKD7+qIj6hKwwz6FWq4GBdyz0IIoiZGkKi+86RGX4UdBZh9BsNnHn7l3EM6hDGFjcOQKqqsKtVGT0IB1zkVIUZeqe/qK8GFqMOMGCs7+/j/feew+PPvaYnL0xDmmSoNlqyfOq9jH2mQT5uh1h1EBamSM3BBLeB3EP4ZwxhjiK0G61EPKUm2hPFIZnIx0zP/DU5x6FIZrttjxXUQRt9RlklWUZtnd3oRkG1k6cwOnTp4/OqZIYCxIHDwimbcOemxsoEMAYdB5FcGwbuqYBioJ2u41mqyVdFR3eOieiCKPuUCe9wbAsk3bQu7u7XTuYhNs9M0BOogSm750fRGcdQhRFuHnjxsAZECNXrE8pECzThKZpUPhrNwmdLWusx0eG3kNzZPEdj2AwRZFeAUzkqcXnMV+jjDG8fuEC6vU6HnnkkbGvK0kStNrtXARpWl/Hv7G5hx0ipWMoyqGraUcNTMat0YWzoMbTiJNc87RXJdIZLV7foWoaqtWqTAH2enzRsqiqKtY3N3H69OmJa0uI2UPi4AHCdBzY9Xr/3b6oFQAvUKrV4LguGGNoHBzg4OCgZxTBa7fzoSzDdqli0Ssca1QMw8Dy8jI+unWrVMgoiqtSxmBbVldIcpYDj3oh6hBMw4ACYHd3F7du3+5bvDnOuSgTVoOLcLOCvAo8GFA4WTK0AZAW6z9E0WjhZ4ofYkFUO24TDIeueYmw0xWLUeciqii5m96Ioe2f/OQnONjfx7PPPjv2Aie8+Rny6I9w2hvKoAV/jBz9xAw5hmWaAH/dkjQFQ24d3mg288FcyNsfq3xq66jM4qqkKOAFzYwxmLYt6zsGXdf+/j4yRcEGFwaTRImIo4P6RB4wTNdFlmUIm83+xjd8EddUFYsLC9KKOOAjj4MwhMWLh8Sc+ziO0Wg0SlMP+yLy6qJiecTd1uraGt55553cMY3PkWBZlofP0xQG78vudbspfm3WeztRh9BoNNBoNBCGIW589BEW5uZQq9VKC5CorRgJRRnJo77X72mals+JQD6V0jAMaKI9DfnzkaHjuS8u2OMcrrMgkT9OWuhUGGid3LET7vf9RrOJd3/8Yzz88MOYn58f6xzjOJZRFJ1HDEZd1AdJ3lm3KfY4wGDjJC68TMNAGEXwfR8AZG0OY7l9+NipE8bAprimlIvSpCCSVU2TKa9hqau9/X0kaYqNkydx8tQpzM3NTXwuxNFAkYMHELtahVWr5TvCQT/I28/m5+dlKJKx3G3N933ZWigGOAlP+larNVaue9Sb9NraGrIsw93tbfm1kFs964Yhz08UYA0SHbO+lStKPkt+dXUVjmVBA7B/cIDbd+6UWh7HFiZcwI1dB6AouTESb8trtdtIsgwpjwSkw56fcWpEunwOckQkSR+14K3/AcAAvP5P/wTXcfDYo4+O9etRFElhYBjGWMIAQHfkoNiKN9aZjI6oK0BnN0fh30LoMd51EEUR9vf3EQuvANPM2x0VZbgfQGeUasLXqxgpEMLA4BuJCi+WHfZePuDma+ubm9ja2sLCwsJE50IcLSQOHlDsahVWpSJtVQeh6zosy4JhGNB1HW6lApWnFHzfR6vVgmEYshYhjmM0Gw0ZaRiVYTfsGr/B3L51S34t5uLAKlQvK4Ubd6eXQldkYcY7PsMwsLK6igVeixDHMW7duoW9/X1kWTZxCHqcFrPCL8HSdaS8WC3gu8pRjzdqPUDnWcnIAd+99jLjGpe33noLB40GPve5z+WtbiPWb4RhmLtGKkppmt9UiEX7KChGCnqIAulWyP87yzL4fCy56EJQFCWPWPGaIV3Xh0cNJvUs4CR9REGtVpMFhAwYKgwazSZ838fG1hY2t7awvLw8xVkRRwmJgwcYp16H6bo9WwclPHrg8PysaE2r12pdIiEMQximKaMIQjiM4iYovRHQXyQoioLV1VV8dOuWvEGKKMHAnRG/8XXWKgBHV7RYq9WwtrYGl7vNNRoNfHT7thxoMy4jdTHwBSVLU7A0RcYYFF3PRzvzdFAygpFV8Zgj/mD5NHggvq8z4hjCAwA+vH4dVy5fxtNPP43FxUX52qm87a3fWYZhiMD3oQAwpxAGYqGedGDSiAc5fHwRdSvWeAClv1HxN9dsNuXEQsMw4PAJpJqqIkmSPKo24G9jWmksREG7IArMgihQNQ1pmkJR1YHCgDGGFi9YXN/awsaJEzh+/PiUZ0ccJSQOHnDc+XlYjpMbk/S58Sl89+HwiXBi7LNlml0iIQrD3BedLwhJkqDZbI4eRRAioU+udW1tTe5QsizLK5+V0d33pJ8CDq19j2LMNJBHXI4dO4alpSXomoY0SXDrzh3s7u5ONZ+h5wKXZUjTVDojFgsATdPMizUVBS3P67Ku7X+wUdvcOsVBoeYAPQyQRMh8BJrNJt64cAEnNjdx+tSp3ueodPtDhHw3nSEfRjaNMFB4WuSoagsUQE5PLAqBnhX8aQpPiIIoKnUh1Ot1GIYh39NJkoBl2UBxMOm7PkkStFutnqLA4aJAXJsw9xr09+/5PhqNBtY3N7G+sYG1tbUJz4y4V1BB4qcAh3sgRL4P9BnOoiLvYIiiCHEcIwgCecO1TBOmYSDiXy8uPhljUHgUIYwi2JY1cn+1AsjJheK2sry8DF3TcPv2bZzc2gIwnWd/MQUhbmCqopSK0KZpKwSASqUC27axu7eHdquFVrsNz/exuLiYz0QY95z5+Qhr4pRb/xYXrs5pebbjIElTWVxarVaHLnSjpoS6TJD472Z9DJBGTQkkcYxXX30Vruvmo5iHvQZ85x0GgZwoatv2WCO7S9fBxcBM2xN5hIDJ/8xrQIa9v7I0RRCG0k0QyJ9Xm6f7gMMixDRNZdRAUdVDcdBx7ElIkgRhEJTMyEzThGVZUhAIFEU5fG8OEgaeh/2DA6xvbmJtbY3cD+8TKHLwKUBRFDjz8zBsu7ybLv+Q3KGIeerFdIEQCfVaLS9e5DcKlfe6R3GMNEngeR4aIhQ66k230FWh6zqOr6zg1q1bedsWGz6jfhyKN89OM6bOlr5x0DQNx5aXsXzsGHRNA8sy3L1zB9vb2zI3PyrFuRSJuPkO+FlxXaIgLOG7z5GYaLedyfeG2tmuxjtMRngQvHHhAgLfx/Nf+MJIA3YYY2h7Xu67wds5JxUGxR3vVIhagUJUQBQCjzK/IONirlGwGdZ1HdVKBbWO1sRiwXCcJECWQTeMUhfKTCIF/P3aK1IA5IuGgkPBDfSOUCiKgsD3sX9wgLWNDayuruLEiRMTniFxr6HIwacEVVXhLiygvbODNI6RZlme0y0szAoOixODIIDneajVanJ3JXa0pmnCMAzEcQw/CKCDh/DFYs4YvDRFEASweCRhnDar1dVVXLx4EVEhrDprxO26lN7oDPWKCu9CSHvYDtB1XRiGgf39/XwinufBDwIsLiygUqkMPa+MMTDeyy6O16tYUUZECoubaCVr8+l3uqaVTKN6MsLi2OuapW1yr5TCCFy6fBk3PvwQX/jCF1Ct1Yb+fJqmaHuenM9RqVRgGkZXrn5UhFHTuOJA/B10vk/E98QCPuxx0zRFGASICsJR13XYltVXKKmqCk1VkTImxbdpGJOnQnhqIgjDkoDtFykAcmEgWiDTNB1Y0xAEAXb39nB8bQ0rq6vY2tqa+XAu4uggcfApQlVVVJaWpEAQux0hEsDbkBzHQRhFeYgxiqRlcZFeIkEsZEmSIObiQ4wYHkUkiEVobXUVFxjDnTt38q6AIxzbKq+rx2ILFNIR8guHggGFRbsosDRVxdLiIqqVCnZ2dxEnCbZ3dtD2PCwsLMDocT3SqKhj1y0EWedSU0qXFDB0HbZt5+IuCKDp+tDIS9di10GnG76IPgGTpXx2dnbw9ttv46GHH8b6CCHmOI7R9ry8TY779GvFnDd/300kEjp/p7gLLyz6RYHUeRQR3heRqEH0EgUGF+SjRE9UTUOcJEiSBKqm5S6nY5ImCeI4zuc1FN5vA0VBoZYHOBRJAE8toiCOsgxRFGFnZwfLq6tYWVvDqVOnSBjcZ5A4+JTRKRCA/IalapqsAVAZg+s4aHseAt/P3QEHdBiYpgnTNBFFEaIoKokEz/dly5+ISliWNfBGUalUUK/Xsb29jfmFhelb00ZgmEgoUgqnFuoYRIRB5JhNy8La2hoO9vfRaDYR+D4+CgIszM/nERn5gGy0jg+glBLpd562bSPhC0i73UatWh08AGnYDrrj+c/AkPHFrSQORijoC4IAP/zBD7C4uIgnn3xy4M+CMfhBgDAM86JZ7nrY61qEuM3StOv90tneKgsPC6kAIQCVwx8qP3Yv+Os9SqQgSRKEYViaj2DqOqwxJ4xqfPJilqb5cKIR/zZYliGKY8Q8/ScvgRssmXw8dCeiw6Lz6ooiVil8Fl0JjUYDSysrWF1bw5kzZ0gY3IeQOPgUIgRC0Ggg4jsyEa5VecGiZVmyONH3/dIwFFnh3XFDFCIh4zuHMIqg6zpSXmyFIEAQhjANAzZvyep301hfW8OVK1egPPLIkdojdzKOSDj8UaVrgSn6LtTn5mA7Th5FiCLs7O6i3W5jaXExf35GyNGXbsCF8+u3KFVcF03e8eH5/sCURuc13vFu47Z3O7dJBnDXu1v6/l3vLt7afjtvMfU1LDtLODt3tud7ovM4r/3wh2AAPv/5zw8UfYwxtNttGbq2bFt2ZPRDAR9bnGWynkARYX7+3yINIR4nKyzsI7/PCs89w+BUkwjbJ1OKguIinXAhOYrxUZIk8u+4iG4YsHhbci+BUYpYdXy/+H4V3h5CJO/v7iKKY6xxDwOKGNy/kDj4lKKqKtz5eeimCb/RkOFFGUVQFLiuK+2CLcs6DOUWblS9bouqqsrFX6QmRPohjKLcuMb3YZkmXNeF7ThduevV1VW89957aLVaqPfKSY+wS52GTpHQa/fU/5eVrgXHNE2srqyg2WjggD+n12/eRMVxpAPl4a8r8nGkT0NnzQH/3O+cFFVFxXXRaLVyg6QgkGO5e/48f6w/eO8P8Uc/+aOBl/fP2/+MD7c/BAPDDnYAAC+e+Cr+j2f+94G/984772B7extf/vKXB55LwgtbxfvLrVRgCBMs8ToUd+2FKIBw6BNtu+J9XXyeOgsoR6IgiEfqxOglCgyj9Hc0FEWBioK9MxfxSZpC07Se6Smgf9pA03UYhpFHAvss2DJlht7vrWLUrFhIHEcRdvb2YNs2tjY3sbq2hpWVFRIG9zEkDj7lmK4LzTTh7e0hjePDKAJjULl/f6k4scAoS7Ou67lpkuPkNyw+qyHgAsH3fZimCcd1Ua1WpUhYWlqCruvY3d7G2upq9wPfg1QDUBYJYgFlQzoI+uXwFUVBfW4Ojuvi7vY2Em5F3Wq3UeWpFI3PlABQEiRM7IaVw7kVojJeCCXWIUhUTYNr2/ncDN+Xr0UvGIAoDfGdn/7J0OdEQ764pThMhfz9h/+AX3vs17BkL/b8nY9u3sRPfvITPPnkk1g+dqzvY4dhCC8I8op4UV+gqjKvfXjC5ToQoFD3IQTCCKmaUbwNpHfGEFHAGJMCuJgmMg0Ddp9cfo+D5UV/4jFR/jsTw410VS093rC0gWEYg2t3ehS99iLjxbKiS0lRFLRaLTQaDSweO4ZjKys4ceIEDVF6ACBxQEDT9TzN0Gwi4h71QiRYto2Q74BEcWIvZKi2z822WJvgui6iKJKV/CFPQTQbDbiui1qtBsMwsLS8jO3d3b7mTfKxAVksddSSQQHyCYMYsFgMyeFrmobjx47B930cNBqIwlC6x1VrNdSr1Z7eAQKZuii0YBaPVzyyYZowkgRRHKPZaslpefJawKvPswyaYsDUTIRRCAYGBQo/FkPGMvk6qFCRsgyhEiFBHvY3NRNVvVJKpygsH+7Tajbx2muvYW1tDQ899FDP540xBs/3kfCaFc0wpPtk8VxHReHPsxwO1YsBwqAo8AbWn/B6ERG6L9aEGKYJu08uv+cxB+zYBRH3NzB4C+OkaYPScQH5nh6EEFIKv+YsTbF/cIAkSbC+tYXlY8dw8uTJkQoriU8+9CoSAHiaYW4OumXB39/PnQlVFeACwff9gcWJxVA4gIE33s60g+d5aLfbcgFrtlqwbRsL8/O4efMmwiDIW7b60FWkh6MVCV11CR2RhEHHFgs6ADiOA8dx4LXbOGg0EEcRWs0m2q0WatVqHknps7DI51vkfAdEM1zukJkmCXxukFR8DRVx01cU/KtTv4D/8pM/kq6Ih/9/uHPXYUBRgBSJ7GT4xubXYWqF+Rf8+cnSFD/4wQ9gWRaeffbZvm2Rnuflz4uiyPfGSLDBxj+igK/Xc9Or60C0Ig5LHBQFQVG8qko+EMsasX133GLbJI7l6OZGszl22qATdcBz14kUoYzJNILjuljjrocrKytjXQvxyYYSQkQJ07ZRW16GxhdjVVXz4inu5d5sNkdrGRvxpqfrOur1OlZXV3H82DFpahMEQb4wKgquXb0qbZtHoVSZPtJvTIYiPkQr6BCKLYBF3EoFa2trWOLukFmW4YDPajgo1IMUEQuPuGEP60aoVCqAkjva+QPmP/zSmW+iort9vw8AekdawVAMfPuhX+55vRcuXEC71cIXnn++ZOgjiJNEWmWL8xxFGLA+kZJeDGuFFVGvUvFiD4SLYbPZRLPVQhhFMt1hGoZMDTkDCm0BXjipdJpSDz6/hD9PbT5aXViLK7x4uMrFpGVZQ4WBwlMXI7sp8hSCSDc2Wi3c3d3FwvIyNk+dwsOPPELC4AGEIgdEFypPM4TNJsJ2G7qioFKtotlsIoqiPDxdrY49ZriXmY9AURS5k47jGM1mE2mSwHUcfHjzJupzczJUajsOTD5Bchii6nqaiYmjHENcA5AXkPW61mHiplKpwHHd3DGPz7tvNBp5USa/+YsbvwxBF4b4iK/3WuBUVUW1UkGr1UIUhtB1vTsawxiqRhW/dPZ/wh++9597nqPK/wcACfLc9r/Y+hdYdrqn6129ehXXrl3Dc88+i7m5ua7vh2EI3/fzxYq3KY5arNcVqRr287z/vggrREz6iYIsy5DEMSLeGlrE1HXopglD14e+t6SfCHAYaRjwO8IiOYlj6RTq+35ejMhTdNIwadTnoJDuYqP8juhC4PUTWZZhb3cXaZZhY2sLx48fx+bmJqURHlDoVSV6oqoqnLk5aDzNYAK5QGg0coHQbKJWq6FzIM4gRC/6sApxwzDyuQSui2aziXd//GN4vg+HhzPbnpcvbroO07bzm7Rh9F1Y5M4QRxtJEKhKbkVdLF4cZXS2+N1qpQLXdfN0w8EB0jTFfqOBZquFWq2GaqVSyk+LHaRs3+uTZtB5C6koMFUrle4bu6LgF0//Iv7synfRiltd56cjFxQpUjAw6IqOX+kRNdjZ2cHFixdx+vRpbJ08WfoeYwye58k8+SijlgcJy1EQ8zSKrXmlyvuOY8VJgiSKSmZFAOT7TjeMkdIGoniv+Hr1uo6MdyEkcYwkSXoKyTiOoWsa6vPz+eyMoQdn3SJqDFEgfSBY7si4s7MDt1rF+toa1tfXaariAw6JA2Igpm1DX16Gt78PmzEwvvuMowi+58HiIdSRRUJx1zRksbQsC5ubm7h69Sr2dnaw+fTT8D1P7uLiKIIXBKUbtmEY+eceu7lSOxvyHf5R1SbIXRqv2xhWVNmJqiioVqtwKxW0ms08kpJl2D84QLPZzKvBRZcCOq5D9Pf3EAi2bSPJMiRRlBdAVqtllz3GUDEq+KWzv4Q/+PEfdJ2Xzm8ZMfKF/WubX8Nxt7xI7O/v45WXX8bS0hI++9nPlr5XtEEGjxbZAyyepbCbQdRH7ShQ7BRrxeK+4nc0VYVhmjB1feTiQtFVMuis0ySRboedUQngsNNH13VEUQRN02CZZj49dRCidmLc54yLAoUdOh6yLEOz2cRBo4FjKytY5tGCkcQJcV9D4oAYiqrrqC4vI2g0wHjNQZt3GiiqCss0kQGTiQTxn+i+kSqKAsuycPLUKbz77rv47Gc/i6XlZaRJgjCOEQYB4ig6HDRUEAqqpkHnBVqGrnflncXxip0Oo7ZzjYJ4XAVAiu7d4qhpDlVRUK/XUa3VDkVCmmJvbw9hHMN1HFRdF2pnF0m/glBFQdV10eJ57DYXCEWfCUVR8M3T/wp/duXP0IyapV83eOQgQQJN0btqDZrNJl566SVUazV88fnnS9GcMAzz9ww/j1qvyEXX6U73ehSnV8oOCv7fAOTQsJjXDwhURcmL+0xz/FRHH7IsQ8wjA2KqYhGVT1jUDQO6ph3WlXBTsTiOc8vyAc9Zp83xiCdeEgXAYVvm7u4uMgCbJ0/KNkVKI3w6oFeZGBm7XodumsDeHjLG4PMuA1VVYeh6XomPMUUCR5qvdNwwdcPA+vo6Ll++jJ9euoSnn34amq7D1XW4jpPv9uIYEW+3TLjhD0O+8yoKA4OLhWJImHUenzN1xwO/ll7zEgAcpgGA7h7+HhRFQpOnGLIgQKPRQJqmWFxcRMUtFxKK3V9XFEHJJzi2uAOh8FkQAoFlGVzdwS+f/RZ+993fLz3mYeQgwc9u/gzWKoceFJ7n4aWXXoJt23jhhRfyiYHg3Qi+jzRJ8jZFXUfFdXuG5adNH+SXp8jHKr6fFOTRA2Fl7AdBqUBUAUqCYJQ0R68BWMWfEe/JmFsed56nrmm5GBgwAyPmwkDhEYxOoVtMmfVyNOxLD1Eg8DwPu7u7cKtVHOdjlo8N8KcgHjxIHBBjods26seOQVVVZHyoUou33um6fth/jwlEQo8brMFvmlsnTuDy++/jsUcfLU0aFGFXKRT4jVS0e4VxDMYHEKW6nnvb+36+S+M7NI1/FOk850kXrb4OhoV/i8LOfjlwdPzs3NwcatUqbt+5g0ajgSSOsbuzg0ajgbl6vWR1LdIbiqLkYWZRuMgLFIXFsoggFPPjv3DqX+JPLv9XNKIGAECDXliEGH7loV+RxwnDEC+99BIURcGXvvQlWJYFxhiCMMxnI/BzsW07r6jvTPlMmT4oRgg63SkZY3k+ny/UostAiDYhGo0BM0S6jtPj31mWj7JOC8fqROOCVYiBUQoZgzBEFMcybVY6fsFXYmQGiAI/CLC3v484inBsZQXHVlawtbVVek8Rnw5IHBBjo+k6aseOAaqKve1tRFGULy61mtx9TiUSisfi6YGNzU28f/WqFAi9KDoAxtwtTjjKiT7/KEmgIo9IZFmGqONYGp90p2matJEGUDr/bAShIP3mR2y/BMrRC+HI1+8oqqahXq/DMk254CVxjJ2dHewfHKAiHCcLoqcUUuete9VqVbYStlstVEQXiqLA0R388kP/M/6/H/0uAMDgt4sECX7mxItYr6wBAKIowksvvYQkjvHVF1/MO064p4JYhHTDgOM4XSJsUlFQivR0mkAVduxCFJR+V1Ggqyp00xxeWKiUHQsFRSGQcSHaK3qg8PSEeG+OayccRVFerJgksG07FzAd5zcyA0RBGIZoNBp5sa+mYX1zExsnTmBtbY3SCJ9S6FUnJkJVVcwdOwZN17H90UeI4jiPINRqpRbHaUWCmFkfmyZOnDiBS5cu4ZGHHhrau24UdlkxzylHvDo+40ZEURznOVrG5KKVpmlJMKiqCk1V5ehjrZALlueI3imIQS1ywxA7bXmMPguPoqqo1etYNk0c8LbHlBvkNBoNWLaNaqUCx7YPUxkFh0cxArnVbiMVEYRKRV7PL5z6l/jP7/0XtJN2qRjxf3no2/L5euX734fvefjyV74Ch0/zjLnTIZR8RofZw1mzM5UzynNSSj3xz2LxFEKg14RLITJ1TZN+Bj2LREUxn/xPHnUQQiDLkCZJ3526qqpSYA7qoBmVUEQNuLAYp3VRMoIoCMIQLMtgGAYeffJJnDp1qudrRnx6IHFATEV1YQGqpuHWtWt5/poLhK7iQkBOfizuFEfZMVqWhTAMZefCB1ev4qGzZ0e2TBZCwQUQxXHeotZhNytu+vykSueVZVlp1K6iKLlQEIKBLwhdKKO7z41CZ35bKdQIqJqGhYUFzM3Pw+NWzFEUIQwChEEARdNQcRxUKhV50xcpAk3T8hQDL3Zse54cjWxrFr668RX8t6t/KYsR16tr2KidQJqm+P6rr+Jgfx9f/vKX4TgOms2mfGzTNEuTNztTM6M8N70iBDJsz6NBvYyldCEG+Efp9QRKr7/4XsqNhaIoQsIjA/3mMyjIC3XFay8+ZlnQmvBrC8IQruP0TMcM+n02SBREERoHBwiDIH+fZhkWFhfx3PPPUycCAQBQ2KRbG4Io4DWb+OjqVaRpCkPXUalWhy7aDJDGMMNuemEYot1u4+2338bu/j6+8Y1vyHCvzC2Pec6RqE1IktKwGoHISXeeW79z1ToWC6YoSJLkyNolwyhCu9WCYZqo9hjJHMUx2q0WPM/Ld9P8vA3DQKVSyWcXaJosXEy4uBM/4zoOFABhFuN//d7/hnbTQ0Wv4v/6yv+JNXcVr732Gj766CN88YtfRKVaRcIHd6mqCtd1y5MmMfrr0ymCsjRFzF+jJEm6dvwK8udeioEBi7TwhQiiCKkQAEmChAuDfudTFAAiejQrIcDArWo7CnKbrRba7TaSNIVl26h12F73OdmBdSudogAAHNvG+uYmHn3iCUohEBISB8TM8Npt3LxyBVmWweQL0DgoA4QCYwzNZhO7e3t49dVX8bnPfQ6bJ070faxJ3tTFYjLx717n0VmINtBdL03lQiJ2loqqQuWL6DRFeDFP5ei6jlqt1jWlUJ4HYwh8H612G0GHdbLDowmObYMh9+5v8eFbpmnC5T31d7a38b2//x6+8LnPY2NjA6+/8QauX72K888+i7n5ebn4O6LgMH9yRr8YUXHPmBQBIjrQ+fyK57IUGSh8X9hUCwOfjP87yzJkaYo0yxBGEXrRmUZS+0WFZoAyYCFPkgQHzSYaBwdwKhVU+qRmxOOIa+33XgqjKB+/zh0pgfy1r9VqWNvYwNaZMzMTO8SDAclEYma4lQrWz5zBRx98gCiKoKgqHL77HIVBaQdR6T5Xq2F5eRk/ee89nDhxou9ji8VKhFdlMd4AxK5Q3ISLhYxCNHTuLcXCJYr4Ct9AhsP6BgBdk/PkufIFCcgXp86PfgJC2jV3VOd3LsoqY3BdF67rIkvTwx0pLxr0PS9vD3VdVPhHu91GxLsMHMdBliRQkC/K77z9Nq5dvYonnngiFwa84NC27ZF3nuJ5yXgeX3ykfCRw53WKiIAQWCICkCSJLDoVjzGs60OkIUTRqc7FgHi+j4quV3DAOYouD4N31PSaS1EUBeK/OxGiIPB92YliOw7m5+ag6zrWNjawtrk5zWURDygkDoiZ4lYqWDl5ErevXUMYhkjTFK7rHprsDAl7ApA3vKL1rKLkfvKhYeDUyZP4p9dfx507d7AywMJV3CqVHmHokfLdYlEqLHid7Wop39l2jk1WlHw6n/AwKB5PXh9fyFiWIRnS1SCeA01V88gDfz7F8dM0lQWAndGX4iAeTdcxNz+Pubk5BGEIr9XKPQjSFM1GIy9itCyYlgXGW1UVVZU78Rs3buDatWt45JFHsLqaexy4rltqLwUgOwSyNM3bBjs+F5Fjngu7X1VEV0ShKBcC8v0j8ul9ni9VRGgKAkvjn9Mhu+xZUEoVFK5xFBL+vAdBkDtI2vbhuRbbF4ekD5rNJgLPy5/PgigweFHj1pkzWCLvAqIPlFYgjgTP83Dnww8R+T4U5KJBDPqZ1GBI4eN3m80mfvDDH8IwDHzly1+e+BxlZGHiR8gpVsuLSIMg6nDeEygAFE07dDLsaMXj/xh4bowxNBoNQFG6ikBLXSEdoqH0PVUFGIPn+/DabSRxLG130zSFqmlwLAstz8OFCxcAAFtbW/nAHb6jFeIoS9N84S2kAsQuXn4uXGdx4Zafi/n8PiJg0MJf/Fo/oj6zC6al9HxPcVtt+z729/aQZhmq1SoqfJbGKGZZYRii2Woh8Dz5mktRYBi554Wm4czDD6M+Pz/xORIPPhQ5II4E13WxefYsbt+4gfb+PlqtFmzb7plmGFUsiAFDhmni1KlTePPiRezs7WFpYWGic1Q6Pkt3ucJNeBQBoaoqTNNEMSMsUhG6rudGTHy+AuNhcwbIaXeTIM5VhJYzETkoiozSL7CSTXTXnoAx2JaFWNMQBgF8PhY4ShJsxzG2d3bAAMzVaqjVagh8H5quH45/FiKHo6Cw+Kuq9IwQEQEZzeDnJaMdfJcr0yoF4SBrNqbY8Yt6hEkfoev3ekUwphAGaZahxaefWrYt5070i5JkWSanW/phCCQJGH9uHcfBXK0mozqMMTiVCk6fPQubTI2IIVDkgDhSGGPY393F7u3bSOMYmqahUrDqnegxATQODvDSK6+AMYavfe1rMI6oaKx4zH7CYdAAJ8YYoo7itwy8voILhmLBXPGzSDsMot1qIWUMruNA1/WuxV9O1+s4J/G5M0pRTIP4QYBWs4lbH32E3f19AMDK6irm6nUYhgHLsmDZNhzTzBdyXYfGd/9iJy8EQ3HxL/679L17QMZYVxtrJ70EwDQL/iiIR9/b25PRoMWlJVg9ihCzLIMfBAh8X/oTFN+bdocoAGOAqmJ1YwOr6+tUeEiMBIkD4p4QhiFu37iBsNkEA/J++x5FVp2tif1uY2mW4ebNm3j11VdxYnMTz54/LyvklcKicy8o/gHJQkh+s86QpxYmWVzETl+Me5aige9+kWVotVpIkgSWbeeFlB3XLiIL0phKpBTE88SYNAUShX4Zd1yM4xhXrlzB7du3Ua/XUavVcpc+04RpmoePraowLUt2KliF+oPi7YWNOGzqKIl56ufjEACddEal2u02dnZ3kaQp5ufmSn4DaZrm0QEuCISgEh4Xjm3DdRzYjlN+jhmDU63i1JkzFC0gxoLEAXHPSNMUO9vbaO7sIOUT5lzX7b/rxuAFPkkS/Ojdd/Gjd9/F5557DpuFqmtRzCh3qrO7jLFggKxFKKUnCjn14mfRXdFZi9CzZZIxeEGAKAhg2zZsxyn/jjheR8i/8zGEUIiTBIHv510ZaSo9JT7zyCNYX1+HoqqIguDwehSlq6ZCuE0KkeBwy98ShesrRl5k6Lx4/f3Ov/A1hbFyR0qPnxVnGERRV4HqvUKIxs6BUEDuSXHn7l3EcQy3UsH83FzeTeL78IMAURRBxtoYg6brcBwHjuvC4kKtE1VVscKjBQQxLiQOiHsKYwytVgu7d+4gbLehKko+EXCEtIB4oxZvg2EY4pXvfx+7e3t48cUXURvg7lZqkZziGsYl5X4HR/GnFoYhfM+DaVkjD8fptGVO4hgBX/ShKAiDAP/81lvwwxBf+PznYVsWGIBqrYY0SdBqtXJnRVWF47pIkyQPc/MR2qLTRAgTXddhWhZs/tGrLe9ekPKRyfcSKVoKtRW9zuvOnTsIowgMeb1OGIZIeDuwEE06FwSu43R1h5QPyuDW6zh55gxs2z6aCyMeeEgcEB8LYRhid3sb3v4+0iSBy3dAo1K80e7u7+Mf/8f/gFup4EsvvDC0116MMe6cAXFUiDRAMkUBYj9i7oKo6zqqtVrfn1N4V0LxnGIuCooFeoHv44033oCu6/jiCy/Atix4ngeVT74E8khIu9WSEYMqn8gJQE7qDMMwnyYYRaVx0YxxTwQhFmz7yEyGijDGEA+wQ57ZcXAYsRnUailI0hS37tzJXQuj6FAo8zSPaZq5IKhUoI/wPKmqirUTJ3B8bW0GV0N8miFxQHxsZFmG/f19NPf2ELbbMHUdjuuWFu1RYIzhg6tX8cPXXsPDDz+MRz/zmbFtYGV7X7HNb4akWYa0YIg0s8fl/gSKqmJubg4ADkPzPSrpRYGk8KAQXQWmZWF7extvvP465ufn8fwXvwjLNOUQJcuyYBTEW5ZlaHue9DPo2s1yQcAYQxAE0tQnCsOu4ki9w/pYjDM2ZjC4SD5PInoz4+dfRrN4/r8XGRdicRwfOj9y86a250l761qtBovPo7B5hGCc66/W69g8dSpPLxHElFArI/GxoaoqFhcXYZom9vf2EDSbaDYasG0bprDgHQFFUXDy5EncvXsXly5fxly9jpXV1bzgkeevmaJAKexgOxFh32KOX5lhZEFBXgMhhuHMAtHfD0UpLVKSYmcFtwwOwxAZLwwUtQGmYeCnly7hnbffxubWFp49f16aD4nFvygMGCDHPXvtNqI4hud5yBg7DGMXRnc7jgOHL1hpmuZCIQgQhiHiOEbKfQfCMCycOuMPk7dBFoWD+DBGHIEsTI+mFQadxbLFNkwxpyGOY8RCAHAxIA2qOCKy4HkeAt+HwhiWl5awvLTUXVA4AqqqYn1rC8dWVqa6PoIoQpED4hNBFEXY29uD324j9jwoWQaTF7WNGklIkgR/+7d/iyTLcP78eVRctyvn2pX3FTu+ESIGvSZJjnobFy2LAHI3xGF/djz1Ic95QDX9wcEBsixDvV7v2mkWRYG4TlVVcwFmmmBZhjcuXMC1a9fw2GOP4dFHH5XXJ6ISUBTMzc31nDUBQLbVAZBFpj1FWI/zT9M0n4JYsKiW0xb58Tp/q2SkJMYj8ymZQkSIqAND/r4Qg6VGpTh0SxYQ8msq7v6LH9KvAd2dCGLcshA0URQh4NGbarWK+YWFsQWooihYWl7G6sYGjEE1CAQxASQOiE8MjDF4nodWq4Wg1ULkeWBZJkf/juKNcHBwgL/9u7/D6toazvKCrJL97IBjF9v7Sja13T8s/1kSDIOKHUX7IX88sfCJ3xslP92PZqOBhC8ywrEwiWNEcZzPuODnpXFRYPDq9igM8eqrr2J3bw/nz5/H1tbW4XUh9/f3PQ+GaaJSqcgogrykwrVGUYS25wHIxyVXKpWSbXPnczEKjDEZfi8KhphP05RzFMR58Oex2KEhagxESqffUC/xGMIdcpA7pVj0xdUVnwdd16EbBgxdh6brcly4eO+maYpWuw3f9xEGAUzbxsL8/FjpA0VVsbi8jJX19VLbKEHMEhIHxCcOkadut9toHxwg8n1kaQpT12HZdu4NP4BLly/j4sWLeOqppzC/sJCPKHbde9ZjL3P+KIgHdEcM0hnlv8WQJF3XoWpaSRCItjcRKRC0mk28/MorSOIYz3/xi1haWup63FarhSSO5eyETnFQas0EECcJ2ryTQVWU3Oyq87Uq+ClM6ytQjDQUQ/hFQSGjIEIcoNAqCkh/h+I1DUNOhOQiQOciYFidS8w7PZI4hh8EMC0LtUplcOdBAVVVsUCigLhHkDggPtFEUYRWq4XG3h5i3wdLErnYGYbR82bOAHz/lVdwd2cHn3vuORiGAVVVUalWxy52nCWigh2A7MnvNXdh5MfLMkRxjGazCc/zYJomLJ5G0VU137WaZteidffuXfzgBz+AZVl44YUXukdrKwqQZdg/OAAYQ42nKzrFgbim4jOacWMmIXyqlQr0fq2LnV4GM0A8p6xQ/ClSC+g413tpyCQiK0L4iojCsLHmjDGomoal5WWsbGz0HdtMELOGxAFxX5AkCdrtNva3t/NIQpJAU1U5QbBz0Q/DEH/z3/87HMfBk08+mReuKQpcx/nY+uzBGNKOPzexg84KFrhFF0N07GxFDYGofFeQG+iEQQDLtlGv12GaZs8wdRzH+NGPfoTLly7h2PHj+MIXvtB3sUmSBK1ms9QF0Usc9L5MhhYfCQ0ABh8H3TfN0P0Avb/c47+FeZI4bq/Cw2K9x72GMZanEMIw7xSJYynW6vV6f4HCa0MWFhexsrFBkQLinkPigLivyLIM7XYbe9vbCFstZFkGTVFgWla+KBYWoJ3dXbz88sswDANPP/00TNNEmqaHzowfQxShmCcXCIHQWdVe/J0oihBFkfwZ8XO6rucCIYpgGAbqc3NdoXIAuHnzJi5euIA4SfDE44/j7NmzPRdrkSoIeJGhaZpw+e52VHEgringxkgCYa9cKlYs3n4K4oghj65k/GeGjViWsyh63M4yPsPiXhPHMTyeEkt4jYSu60izDLVara9vgappuShYX5eRIIK415A4IO5LxI5s9+5d+M1mXnjGGDReFS565f0gwMsvv4wwDPHcc8/Btm0Z8nZdt+d8hyM9b6DvLpYBMhwuBIEovhMLI2MMpkgX8EK3NMvQODiAwrsKZIW9oiDwPFx8803cvHkTa+vrePrpp1HhTopdi20hF99sNJCmaT5q2zTlbIfOQs3OuoNO0iSBxy2ZGV8cHdfNJyxO+Px1IsZF90sx9euyOCoYy0dgR7w1U0QuVC56qtVqd2SHt6XOLS5ilUQB8QmAxAFx3+P7Pg52dtBqNJDyCYii6E1FXoz25sWL2NvfxzPPPIPFxUXEfH6AwaMI96oWobjIilHL0iCJ9/snfKep4HABF/UD/Xr7D/b3kTGW70h1HVmW4f3338fb77wDQ9Pw9Llz+XyEIbtvRVGQZhmaBweyhVEOZJpwkWWMIYwiBL4vd/aW6CKZ4PGEIEl5geegW9i9TikIzwcxUVNVVaTcOltVVdnFId5vCnIPiYXlZSwuL5MoID4xkDggHhiyLEPo+2gdHKDdbCIKgsMRxFmGd3/0I3x05w4eOnsWGxsbec6e36grfId8FDCeMsiEUQ7vi8/4LIMevwAoSt4CN6LZT6vVyof2uC6CIMCFN97A7t4eTp8+jSeffHJgnUVnyF6kFEQLI4CpxIEgzTL4nifnG6iaJkdNj4uYUNmZoijOMLiXUYOMt+HGcSyHT6mqKo2dRPGhnGSpaajPz2NxaQn1+fl7co4EMQ4kDogHljgM0drfh9dqIeZGOz/96U9x5f33sb6+jocfekgOHFIUJbeudZxyLrhHm1uXGVKHv0FRCKRZ1tOZT6Q2hFOhqqrQNA2aqkpXwGIlf5amAwv6fN+H53m4du0aPvjgA9RqNTzzzDM9WxQ76UwNNBoNZGkKp1KR8y5GEgfCiXLI8Tp318OmcxbJhkQKyqdT8JYQdQsj/eboCDtqz/elN4LDU1dRFCHLspLXhlOpYGFpCQtLSxOJIoK4V5A4IB54sixD6Hm5sVIQ4MqVK7h48SKWlpbw+OOPI0mSfCIeL1wzDKOruFFSdCrs51rY4+tFASCsiTVVlTbDQxGFen2iDTdu3sSbFy4gjCI8/thjePiRR0ayFu6k0xVR1jpg+shBkYy39IVBIH0RbMfpO3xrHFEgSNO0p7tisRMEKNRO8AmKPV9jYcONQzEV8eFSaZYBWZYPlLJthEGAiEeGXNeFywXB4vIynBEnZxLExw2JA+JTRRLHCFotXL1yBa+8/DJsx8EXn38euq6j3W7nYWHkC4thGLBME5quHy5Mhba54uciKt/9CyEg5h8UYYxNXkHP7Xwzvmt96623cP3aNcwvLOAzjz6K9dXV0dsGO+iVUgBmLw4ECS9YTLn9sME9LHRdn0gQCI4qpSAHV/FplkAubIQ5l2jhzBjD6toaVtfXUZ+fn0ioEcTHCYkD4lPLjevX8d+++12ojOHcuXNYW1tDlmUIgiAfNQyU7JvVMSxuhyFb7yYphGQMOzs7uHLlCj788EPoui7dILM0Ra1a7W88NASRUnA7ajDGEQfSinrEYzLGEIZh3vbHGJBlUDUtn61hGBMJnVm3LzLGEEeRHHHNeGeMZdswDSOfXxEE0AwD9YUFnD59Gs4QgyOC+CRD4oD4VNNqtfBf//RP8dH161heXMRnHn4YJ0+eBIAjFwnjRg/iMMTV69fx/pUraDWbcCsVnD59GidPnoRlmmjx8cqObU9U9d4vpQBMFjkYxZtAkAHIkiRPNfCOExHGNy0LVg+nx76Pi9lFDQaJAkPXEYYhwiiC47qYX1xEfWEBi4uLMxs1TRAfFyQOCALAjRs38MpLL+HKpUuouy4efvhhPHT2LHRd7ykSTO4zMC1Zmg6OHjCG7Z0dvP/++7jx4YdgjGF9fR2nT5/GsWPHuroMfN+HYRioViqlVIaYyCjo5U9w584dXHn/fXz44Yd4/8oVXL5yBa1WCwDwpRdewG/8xm8MvZ7/5z/8B7z00kt49tln8W//zb8pXAaTx5Qf/QYhcVvoYuieZRk0XR8pmjCLqEHGBzyFYYgky6DwVkTLtqFpmhw3Xa3XMbe0hGq1ikqlAmeCkcsE8UmEymUJAsDGxga+/a//NXZ3d/Hqq6/i9TffxDvvvouzp07h4YcfRq1WQxAEiOMYcZIgiiKoqiqFwqTRBFXTeg5g6hUlePzxx7F18iTsPla6Ymed9ihaFIvpd77zHfzJn/wJ/u9//+9xbHm59DO/9Vu/BUVV4bVaiMZwQyzy7Pnz+Md//Ef881tvSavgYnth6Zz6LKIKt8W2+LCniO/OsyxD4Hnw0T+aIPwPJlmg5TTLKEIUx1CQF0JqigLLdaGpKvwgAGMMtbk5zC0toVKpoFqtkr0x8cBB4oAgCiwuLuLnf/7n8eKLL+K1117DP/3wh/jJ5cs4ubWFRx56CHNzc4jiGHEUyVx5EATQ+a7WNIze3gUDUBVF5tq3d3e7ogRPP/10V5SgF5qmQVEU2UbZGdoWQUJV07q6KaIogqKqcte+tLiItY0NvP3WW+KXDx9D1BOww7HGYvF/6qmnoPNw+ztvv41z58717+oYAeF26ThOKZoQRxGiIMijCaYJnXeDZAVr6VFJ0xRRGOath+I6efeBZZpQ+OusKArmFhcxt7gI13XliGyCeBAhcUAQPXBdF1/96lfxwgsv4M0338T3v/99XPngA6wfP465uTm4lQosy4LBFydFUeC12/AVBYZIO/TJkUdRhFaziVarhWaziWajgQP+3yzLRooS9EJRFOi6jpjvfh3Hkd8b1uMfBAF+5sUXsbW1hUceeQRzc3O4e/cu/u2/+3fiwUsdG51LvXhsy7Lw5BNP4OLFi3j9jTdw7tw52To4TQZzYDQhCADG8pHYyLtFhFhQVRWarkNVlFIqQqYuwhBJHCPjRlUApAV3mqaIPQ+mZWFpZQXVeh3VarW3/TFBPGCQOCCIARiGgeeeew7nz5/He++9h39+6y18uLOD5pUrSOM4X3y4m2HFceA4DmzHgW1ZqLguNF1HwA2KWs0mDlotJLx+AQAcx0GtWsXx48dx6tQpzM/PY2lpaeK8tWmaXeJAFAb2W5zF/Iaf/dmfRb1en7rt7pnz53Hx4kVceOMNsF/7NRllGHQO49AZTYiTJH8t2OFUxrhofiSOzyMjaZrmvhbscNiVbdtwq1XYtp17XHCvC50LvUqlkttsU0si8SmBxAFBjICqqnjsscfw2GOPAcgXnSAIsLe3h/39ffl5d3sbH965g3arBaQpNFWFaRioV6uo1mo4ffIk6nNzqPNdaNGNcZq+foHBi/WyLEMSx9B49GLQ44rJiZZlzWTxO3/+PH73d38X+/v7uHL5cj4BckbCoIiMJvBpm2KoVZpl+STEJMlrRHgaKBHFpJaFqutKQSDmUYhhXZqmlf5NUQLi0wiJA4KYAEVR4PBIwfr6etf30zTFzs4OoiiCpmmIwhBxECD0/TxywPPYma5DNww5hlnscqc5L0PXc6OeKII7pP0vjuN8xoSizKyobmF+HmfPnMHly5fxxhtv4OzZs/LchMXyNNfYSSZsm4WLJB9eFXNxtFCvw6lUYFgWNE2DYRhwuTgQiz91GBBEGRIHBHEEaJqG48ePA+C98nGc9/CHYd4GF4YIPQ9t3wdrt6FrmhyypPC5CnK54gvpqAuYZVly3PMwrwExGMg0zZmGzJ85fx6XL1/G62+8gW9/+9vy69KOeVyBUPxZIaKQOywmXOAkfKql5Tio1utwajXYti1rFSwuDgiCGA6JA4I4YhRFkS2PQB5VECIhDMO8Uj4IEPk+Qj6QSNU0qIoCTYS3uQWzKO5Tygc4/Ddj0HVddi3Ecdx32qQIuYuc+yx59tln8Z0//mN8eP067ty9i+PHjh2eLv+A6NIonHux5bEoHYRhVJqmSEXKIEnA+Nht23UxV6nA5hEBIQYMHpUhCGI8SBwQxD1G07R8IA8fwlOMKsRxnO+EoyiPLgQBvHYbiqLIyntN0/JIg6rmFfhi4mAhr28YBoIggOd50DUt/zm+GGd8WmTb88CAvF1P7Og7xyD3oTiASC7mPLTPAJw4cQLLx47h9u3beP311/GNb3yjp/FSrzSKcDgsffCogKKqeeFgpZK3jvICQtM0pSCgokGCmB4SBwTxMWMYBgzDQK1WkxX0YlcfRREOdnfR2t3NF3ZeXCcEgcqHOymKAk1RoIiIA/9amqZotFqoVatSAKiqCq/dhsGL8CzLkiONAcjFWvglAEBSdB0UuX1+/nJp72hxPH/+PP7qr/4Kb7z+Or7+9a+Xfi5jLHcy7PhI0zTvKOjRemnYNlY3NuByfwHxQWKAIGYPiQOC+AQhvApEqx4ALC8v5739IpoQx4jDEEmS5H7/WYY0jvPFlbf2ZWmKJE3hex4AIAwCmV5YWFiQ9QiWbct2vlK6ggsJKSiK0YTC+OKiqGB8WqT4+rmnn8b3vvc9fHD1Kra3t2FbljxfRVXziZWGAd0wYPCaC03XofAIiWkYMEwzn47JowKUIiCIewOJA4K4DxCCATwVAUDWFIgdd6+PIAiwv7ub1zmkKeI0RcIYMgCGZSHiIkEs7FlhlkGr1cL8/DzAIxT7+/v5gUUEgXdYiA/w1If499bp04iSBC3fx807d/DM+fPQeHeG6BIQo607P0TdBEEQHw8kDgjiPkXlff79ECmKpaUlNJvN3CaYMbx76RJWt7awsbkphUHnBwBkN27grffeg6qq2HroIaxubgKqmkcRCkKgJBAA+e9XXnkFP/3gA6iqii+/+CLm5uZKYoAgiE8uJA4I4gFFpCjm5+fzCADn6tWrWFhYwHLH4KUiwuTp1q1bAIAwjrF+4kSXABi0u//rv/5r7O3t4etf/zpWVlZmc1EEQdwTSBwQBNGF6I4QBYmMt0iOw1/8xV8AAH7xF39x5udHEMTRQmW+BEHMnAsXLuDatWsASBwQxP0IiQOCIGbOn//5nwMAnnnmGZw4ceJjPhuCIMaF0goEQQAAXnrpJVy6dEn+9/b2tvz3pUuX8Hu/93uln//VX/3Vvo/13e9+FwDwzW9+c6bnSBDEvYHEAUEQAIDf+Z3fwe///u/3/N7LL7+Ml19+ufS1fuLg+vXruHDhAgBKKRDE/QqJA4L4FPHiiy8CQKl7YdaIqMHm5iaeeeaZIzsOQRBHh8JmPWSdIIhPNT/3cz+Hv/mbv8Fv/uZv4rd/+7c/7tMhCGICqCCRIIiZ0Wg08Pd///cAKKVAEPczJA4IgpgZf/mXf4k4jlGv12UKgyCI+w+qOSAIYmb83d/9Hebm5vDNb35TDnoiCOL+g2oOCIIgCIIoQWkFgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKIEiQOCIAiCIEqQOCAIgiAIogSJA4IgCIIgSpA4IAiCIAiiBIkDgiAIgiBKkDggCIIgCKLE/w9l88NEAIl3NAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also show multiple Bloch spheres side by side - simply set the `nrows` and `ncols` in the call to `cudaq.show()` accordingly. Make sure to have more spaces than spheres in your list, else it will throw an error! Let us show two spheres in a row:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:2], nrows=1, ncols=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can show them in a column too, if we want! Simply set the `nrows = 2` and `ncols = 1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:2], nrows=2, ncols=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Can we show the entire list of 4 Bloch spheres we created? Absolutely!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cudaq.show(blochSphereList[:], nrows=2, ncols=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if we had to add multiple vectors to a single Bloch sphere? CUDA-Q uses the [QuTiP](https://www.qutip.org) toolbox to construct Bloch spheres. We can then add multiple states to the same Bloch sphere by passing the sphere object as an argument to `cudaq.add_to_bloch_sphere()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qutip\n",
-    "\n",
-    "rng = np.random.default_rng(seed=47)\n",
-    "blochSphere = qutip.Bloch()\n",
-    "for _ in range(10):\n",
-    "    angleList = rng.random(3) * 2 * np.pi\n",
-    "    sph = cudaq.add_to_bloch_sphere(cudaq.get_state(kernel, angleList), blochSphere)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This created a single Bloch sphere with 10 random vectors. Let us see how it looks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "blochSphere.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unfortunately, there is no such handy visualization for multi-qubit states. In particular, a multi-qubit state cannot be visualized as multiple Bloch spheres due to the nature of entanglement that makes quantum computing so powerful. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Kernel Visualization\n",
-    "\n",
-    "A CUDA-Q kernel can be visualized using the `cudaq.draw` API which returns a string representing the drawing of the execution path, in the specified format. ASCII (default) and LaTeX formats are supported."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@cudaq.kernel\n",
-    "def kernel_to_draw():\n",
-    "    q = cudaq.qvector(4)\n",
-    "    h(q)\n",
-    "    x.ctrl(q[0], q[1])\n",
-    "    y.ctrl([q[0], q[1]], q[2])\n",
-    "    z(q[2])\n",
-    "    \n",
-    "    swap(q[0], q[1])\n",
-    "    swap(q[0], q[3])\n",
-    "    swap(q[1], q[2])\n",
-    "\n",
-    "    r1(3.14159, q[0])\n",
-    "    tdg(q[1])\n",
-    "    s(q[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     ╭───╮                  ╭───────────╮       \n",
-      "q0 : ┤ h ├──●────●────╳───╳─┤ r1(3.142) ├───────\n",
-      "     ├───┤╭─┴─╮  │    │   │ ╰───────────╯╭─────╮\n",
-      "q1 : ┤ h ├┤ x ├──●────╳───┼───────╳──────┤ tdg ├\n",
-      "     ├───┤╰───╯╭─┴─╮╭───╮ │       │      ╰┬───┬╯\n",
-      "q2 : ┤ h ├─────┤ y ├┤ z ├─┼───────╳───────┤ s ├─\n",
-      "     ├───┤     ╰───╯╰───╯ │               ╰───╯ \n",
-      "q3 : ┤ h ├────────────────╳─────────────────────\n",
-      "     ╰───╯                                      \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.draw(kernel_to_draw))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\documentclass{minimal}\n",
-      "\\usepackage{quantikz}\n",
-      "\\begin{document}\n",
-      "\\begin{quantikz}\n",
-      "  \\lstick{$q_0$} & \\gate{H} & \\ctrl{1} & \\ctrl{2} & \\swap{1} & \\swap{3} & \\gate{R_1(3.142)} & \\qw & \\qw \\\\\n",
-      "  \\lstick{$q_1$} & \\gate{H} & \\gate{X} & \\ctrl{1} & \\targX{} & \\qw & \\swap{1} & \\gate{T^\\dag} & \\qw \\\\\n",
-      "  \\lstick{$q_2$} & \\gate{H} & \\qw & \\gate{Y} & \\gate{Z} & \\qw & \\targX{} & \\gate{S} & \\qw \\\\\n",
-      "  \\lstick{$q_3$} & \\gate{H} & \\qw & \\qw & \\qw & \\targX{} & \\qw & \\qw & \\qw \\\\\n",
-      "\\end{quantikz}\n",
-      "\\end{document}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cudaq.draw('latex', kernel_to_draw))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copy this output string into any LaTeX editor and export it to PDF.\n",
-    "\n",
-    "<img src=\"../images/circuit_pdf.png\" alt=\"Circuit in PDF format\" width=\"500\" \n",
-    "height=\"250\">\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pr-2308/sphinx/index.rst b/pr-2308/sphinx/index.rst
deleted file mode 100644
index 2b6837424c..0000000000
--- a/pr-2308/sphinx/index.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-************
-CUDA-Q
-************
-
-Welcome to the CUDA-Q documentation page!
-
-CUDA-Q streamlines hybrid application development and promotes productivity and scalability
-in quantum computing. It offers a unified programming model designed for a hybrid
-setting |---| that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for 
-programming in Python and in C++.
-
-You are browsing the documentation for |version| version of CUDA-Q. You can find documentation for all released versions :doc:`here <versions>`.
-
-**CUDA-Q** is a programming model and toolchain for using quantum acceleration in heterogeneous computing architectures available in C++ and Python.
-
-.. toctree::
-   :caption: Contents
-   :maxdepth: 2
-
-      Quick Start <using/quick_start.rst>
-      Basics <using/basics/basics.rst>
-      Examples <using/examples/examples.rst>
-      Applications <using/applications.rst>
-      Backends <using/backends/backends.rst>
-      Dynamics <using/backends/dynamics.rst>
-      Installation <using/install/install.rst>
-      Integration <using/integration/integration.rst>
-      Extending <using/extending/extending.rst>
-      Specifications <specification/index.rst>
-      API Reference <api/api.rst>
-      Other Versions <versions.rst>
-
-.. |---|   unicode:: U+2014 .. EM DASH
-   :trim:
\ No newline at end of file
diff --git a/pr-2308/sphinx/releases.rst b/pr-2308/sphinx/releases.rst
deleted file mode 100644
index de3e12d78b..0000000000
--- a/pr-2308/sphinx/releases.rst
+++ /dev/null
@@ -1,153 +0,0 @@
-************************
-CUDA-Q Releases
-************************
-
-**latest**
-
-The latest version of CUDA-Q is on the main branch of our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__ 
-and is also available as a Docker image. More information about installing the nightly builds can be found 
-:doc:`here <using/install/install>`
-
-- `Docker image (nightly builds) <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/latest>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/main/docs/sphinx/examples>`__
-
-**0.9.0**
-
-We are very excited to share a new toolset added for modeling and manipulating the dynamics of physical systems. 
-The new API allows to define and execute a time evolution under arbitrary operators. For more information, take 
-a look at the `docs <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/dynamics.html>`__.
-The 0.9.0 release furthermore includes a range of contribution to add new backends to CUDA-Q, including backends 
-from `Anyon Technologies <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#anyon-technologies-anyon-computing>`__, 
-`Ferimioniq <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/simulators.html#fermioniq>`__, and 
-`QuEra Computing <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#quera-computing>`__, 
-as well as updates to existing backends from `ORCA <https://nvidia.github.io/cuda-quantum/latest/using/backends/hardware.html#orca-computing>`__ 
-and `OQC <https://nvidia.github.io/cuda-quantum/0.9.0/using/backends/hardware.html#oqc>`__.
-We hope you enjoy the new features - also check out our new notebooks and examples to dive into CUDA-Q.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.9.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.9.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.9.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.9.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.9.0>`__.
-
-**0.8.0**
-
-The 0.8.0 release adds a range of changes to improve the ease of use and performance with CUDA-Q. 
-The changes listed below highlight some of what we think will be the most useful features and changes 
-to know about. While the listed changes do not capture all of the great contributions, we would like 
-to extend many thanks for every contribution, in particular those from external contributors.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.8.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.8.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.8.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.8.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.8.0>`__.
-
-**0.7.1**
-
-The 0.7.1 release adds simulator optimizations with significant performance improvements and 
-extends their functionalities. The `nvidia-mgpu` backend now supports user customization of the 
-gate fusion level as controlled by the `CUDAQ_MGPU_FUSE` environment variable documented 
-`here <https://nvidia.github.io/cuda-quantum/0.7.1/using/backends/simulators.html>`__.
-It furthermore adds a range of bug fixes and changes the Python wheel installation instructions.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.7.1>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.7.1>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.7.1>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.7.1/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.7.1>`__.
-
-**0.7.0**
-
-The 0.7.0 release adds support for using :doc:`NVIDIA Quantum Cloud <using/backends/nvqc>`,
-giving you access to our most powerful GPU-accelerated simulators even if you don't have an NVIDIA GPU.
-With 0.7.0, we have furthermore greatly increased expressiveness of the Python and C++ language frontends. 
-Check out our `documentation <https://nvidia.github.io/cuda-quantum/0.7.0/using/quick_start.html>`__ 
-to get started with the new Python syntax support we have added, and `follow our blog <https://developer.nvidia.com/cuda-q>`__
-to learn more about the new setup and its performance benefits.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.7.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.7.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.7.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.7.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.7.0>`__.
-
-**0.6.0**
-
-The 0.6.0 release contains improved support for various HPC scenarios. We have added a
-:ref:`plugin infrastructure <distributed-computing-with-mpi>` for connecting CUDA-Q 
-with an existing MPI installation, and we've added a :ref:`new platform target <remote-mqpu-platform>` that distributes workloads across multiple virtual QPUs, 
-each simulated by one or more GPUs.
-
-Starting with 0.6.0, we are now also distributing 
-:ref:`pre-built binaries <install-prebuilt-binaries>` for using CUDA-Q with C++.
-The binaries are built against the `GNU C library <https://www.gnu.org/software/libc/>`__ 
-version 2.28.
-We've added a detailed :doc:`Building from Source <using/install/data_center_install>` guide to build these binaries for older `glibc` versions.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.6.0>`__
-- `C++ installer <https://github.com/NVIDIA/cuda-quantum/releases/0.6.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.6.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.6.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.6.0>`__.
-
-**0.5.0**
-
-With 0.5.0 we have added support for quantum kernel execution on OQC and IQM backends. For more information, see :doc:`using/backends/hardware`.
-CUDA-Q now allows to executing adaptive quantum kernels on quantum hardware backends that support it.
-The 0.5.0 release furthermore improves the tensor network simulation tools and adds a matrix product state simulator, see :doc:`using/backends/simulators`.
-
-Additionally, we are now publishing images for experimental features, which currently includes improved Python language support.
-Please take a look at :doc:`using/install/install` for more information about how to obtain them.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.5.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.5.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.5.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.5.0>`__.
-
-**0.4.1**
-
-The 0.4.1 release adds support for ARM processors in the form of multi-platform Docker images and `aarch64` Python wheels. Additionally, all GPU-based backends are now included in the Python wheels as well as in the Docker image.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.4.1>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.4.1>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/releases/v0.4.1/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/0.4.1>`__.
-
-**0.4.0**
-
-CUDA-Q is now available on PyPI!
-The 0.4.0 release adds support for quantum kernel execution on Quantinuum and IonQ backends. For more information, see :doc:`using/backends/hardware`.
-
-The 0.4.0 PyPI release does not yet include all of the GPU-based backends.
-The fully featured version is available as a Docker image for `linux/amd64` platforms.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Python wheel <https://pypi.org/project/cuda-quantum/0.4.0>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.4.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/0.4.0/docs/sphinx/examples>`__
-
-The full change log can be found `here <https://github.com/NVIDIA/cuda-quantum/releases/tag/0.4.0>`__.
-
-**0.3.0**
-
-The 0.3.0 release of CUDA-Q is available as a Docker image for `linux/amd64` platforms.
-
-- `Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__
-- `Documentation <https://nvidia.github.io/cuda-quantum/0.3.0>`__
-- `Examples <https://github.com/NVIDIA/cuda-quantum/tree/0.3.0/docs/sphinx/examples>`__
diff --git a/pr-2308/sphinx/snippets/cpp/quick_start.cpp b/pr-2308/sphinx/snippets/cpp/quick_start.cpp
deleted file mode 100644
index 0457eda5de..0000000000
--- a/pr-2308/sphinx/snippets/cpp/quick_start.cpp
+++ /dev/null
@@ -1,27 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with: `nvq++ quick_start.cpp && ./a.out`
-// [Begin Documentation]
-#include <cudaq.h>
-
-__qpu__ void kernel(int qubit_count) {
-  cudaq::qvector qubits(qubit_count);
-  h(qubits[0]);
-  for (auto i = 1; i < qubit_count; ++i) {
-    cx(qubits[0], qubits[i]);
-  }
-  mz(qubits);
-}
-
-int main(int argc, char *argv[]) {
-  auto qubit_count = 1 < argc ? atoi(argv[1]) : 2;
-  auto result = cudaq::sample(kernel, qubit_count);
-  result.dump(); // Example: { 11:500 00:500 }
-}
-// [End Documentation]
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/nvqc/nvqc_intro.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/nvqc/nvqc_intro.cpp
deleted file mode 100644
index d5c19253e4..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/nvqc/nvqc_intro.cpp
+++ /dev/null
@@ -1,29 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// [Begin Documentation]
-#include <cudaq.h>
-
-// Define a simple quantum kernel to execute on NVQC.
-struct ghz {
-  // Maximally entangled state between 25 qubits.
-  auto operator()() __qpu__ {
-    constexpr int NUM_QUBITS = 25;
-    cudaq::qvector q(NUM_QUBITS);
-    h(q[0]);
-    for (int i = 0; i < NUM_QUBITS - 1; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  auto counts = cudaq::sample(ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/nvqc/nvqc_mqpu.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/nvqc/nvqc_mqpu.cpp
deleted file mode 100644
index 0c26774f93..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/nvqc/nvqc_mqpu.cpp
+++ /dev/null
@@ -1,51 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// [Begin Documentation]
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-#include <iostream>
-
-int main() {
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-
-  auto [ansatz, theta] = cudaq::make_kernel<double>();
-  auto q = ansatz.qalloc();
-  auto r = ansatz.qalloc();
-  ansatz.x(q);
-  ansatz.ry(theta, r);
-  ansatz.x<cudaq::ctrl>(r, q);
-
-  // Run VQE with a gradient-based optimizer.
-  // Delegate cost function and gradient computation across different NVQC-based
-  // QPUs.
-  // Note: this needs to be compiled with `--nvqc-nqpus 3` create 3 virtual
-  // QPUs.
-  cudaq::optimizers::lbfgs optimizer;
-  auto [opt_val, opt_params] = optimizer.optimize(
-      /*dim=*/1, /*opt_function*/ [&](const std::vector<double> &params,
-                                      std::vector<double> &grads) {
-        // Queue asynchronous jobs to do energy evaluations across multiple QPUs
-        auto energy_future =
-            cudaq::observe_async(/*qpu_id=*/0, ansatz, h, params[0]);
-        const double paramShift = M_PI_2;
-        auto plus_future = cudaq::observe_async(/*qpu_id=*/1, ansatz, h,
-                                                params[0] + paramShift);
-        auto minus_future = cudaq::observe_async(/*qpu_id=*/2, ansatz, h,
-                                                 params[0] - paramShift);
-        grads[0] = (plus_future.get().expectation() -
-                    minus_future.get().expectation()) /
-                   2.0;
-        return energy_future.get().expectation();
-      });
-  std::cout << "Minimum energy = " << opt_val << " (expected -1.74886).\n";
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/get_state_async.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/get_state_async.cpp
deleted file mode 100644
index 8fb882e2f0..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/get_state_async.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ get_state_async.cpp -o get_state_async.x --target nvidia
-// --target-option mqpu
-// && ./get_state_async.x
-// ```
-#include <cudaq.h>
-#include <cudaq/algorithms/get_state.h>
-int main() {
-  // [Begin Documentation]
-  auto kernelToRun = [](int runtimeParam) __qpu__ {
-    cudaq::qvector q(runtimeParam);
-    h(q[0]);
-    for (int i = 0; i < runtimeParam - 1; ++i)
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-  };
-
-  // Get the quantum_platform singleton
-  auto &platform = cudaq::get_platform();
-
-  // Query the number of QPUs in the system
-  auto num_qpus = platform.num_qpus();
-  printf("Number of QPUs: %zu\n", num_qpus);
-  // We will launch asynchronous tasks
-  // and will store the results immediately as a future
-  // we can query at some later point
-  std::vector<cudaq::async_state_result> stateFutures;
-  for (std::size_t i = 0; i < num_qpus; i++) {
-    stateFutures.emplace_back(
-        cudaq::get_state_async(i, kernelToRun, 5 /*runtimeParam*/));
-  }
-
-  //
-  // Go do other work, asynchronous execution of tasks on-going
-  //
-
-  // Get the results, note future::get() will kick off a wait
-  // if the results are not yet available.
-  for (auto &state : stateFutures) {
-    state.get().dump();
-  }
-  // [End Documentation]
-  return 0;
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/observe_mqpu.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/observe_mqpu.cpp
deleted file mode 100644
index 7c3674d2bd..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/observe_mqpu.cpp
+++ /dev/null
@@ -1,40 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ observe_mqpu.cpp -o observe_mqpu.x --target nvidia --target-option mqpu
-// && ./observe_mqpu.x
-// ```
-#include <cudaq.h>
-
-int main() {
-  // [Begin Documentation]
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-
-  // Get the quantum_platform singleton
-  auto &platform = cudaq::get_platform();
-
-  // Query the number of QPUs in the system
-  auto num_qpus = platform.num_qpus();
-  printf("Number of QPUs: %zu\n", num_qpus);
-
-  auto ansatz = [](double theta) __qpu__ {
-    cudaq::qubit q, r;
-    x(q);
-    ry(theta, r);
-    x<cudaq::ctrl>(r, q);
-  };
-
-  double result = cudaq::observe<cudaq::parallel::thread>(ansatz, h, 0.59);
-  printf("Expectation value: %lf\n", result);
-  // [End Documentation]
-  return 0;
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/observe_mqpu_mpi.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/observe_mqpu_mpi.cpp
deleted file mode 100644
index 82d1a49fe0..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/observe_mqpu_mpi.cpp
+++ /dev/null
@@ -1,37 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ observe_mqpu_mpi.cpp -o observe_mqpu_mpi.x --target nvidia
-// --target-option mqpu mpirun -np <N> ./observe_mqpu_mpi.x
-// ```
-#include "cudaq.h"
-
-int main() {
-  // [Begin Documentation]
-  cudaq::mpi::initialize();
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-
-  auto ansatz = [](double theta) __qpu__ {
-    cudaq::qubit q, r;
-    x(q);
-    ry(theta, r);
-    x<cudaq::ctrl>(r, q);
-  };
-
-  double result = cudaq::observe<cudaq::parallel::mpi>(ansatz, h, 0.59);
-  if (cudaq::mpi::rank() == 0)
-    printf("Expectation value: %lf\n", result);
-  cudaq::mpi::finalize();
-  // [End Documentation]
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/sample_async.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/sample_async.cpp
deleted file mode 100644
index a5cfaeda9e..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/sample_async.cpp
+++ /dev/null
@@ -1,50 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ sample_async.cpp -o sample_async.x --target nvidia --target-option mqpu
-// && ./sample_async.x
-// ```
-#include <cudaq.h>
-
-int main() {
-  // [Begin Documentation]
-  auto kernelToBeSampled = [](int runtimeParam) __qpu__ {
-    cudaq::qvector q(runtimeParam);
-    h(q);
-    mz(q);
-  };
-
-  // Get the quantum_platform singleton
-  auto &platform = cudaq::get_platform();
-
-  // Query the number of QPUs in the system
-  auto num_qpus = platform.num_qpus();
-  printf("Number of QPUs: %zu\n", num_qpus);
-  // We will launch asynchronous sampling tasks
-  // and will store the results immediately as a future
-  // we can query at some later point
-  std::vector<cudaq::async_sample_result> countFutures;
-  for (std::size_t i = 0; i < num_qpus; i++) {
-    countFutures.emplace_back(
-        cudaq::sample_async(i, kernelToBeSampled, 5 /*runtimeParam*/));
-  }
-
-  //
-  // Go do other work, asynchronous execution of sample tasks on-going
-  //
-
-  // Get the results, note future::get() will kick off a wait
-  // if the results are not yet available.
-  for (auto &counts : countFutures) {
-    counts.get().dump();
-  }
-  // [End Documentation]
-  return 0;
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/sample_async_remote.cpp b/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/sample_async_remote.cpp
deleted file mode 100644
index 18371c8677..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/cudaq/platform/sample_async_remote.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with:
-// ```
-// nvq++ sample_async_remote.cpp -o sample.x \
-//   --target remote-mqpu --remote-mqpu-auto-launch 2
-// ./sample.x
-// ```
-#include <cudaq.h>
-
-int main() {
-  // [Begin Documentation]
-  // Define a kernel to be sampled.
-  auto [kernel, nrControls] = cudaq::make_kernel<int>();
-  auto controls = kernel.qalloc(nrControls);
-  auto targets = kernel.qalloc(2);
-  kernel.h(controls);
-  for (std::size_t tidx = 0; tidx < 2; ++tidx) {
-    kernel.x<cudaq::ctrl>(controls, targets[tidx]);
-  }
-  kernel.mz(controls);
-  kernel.mz(targets);
-
-  // Query the number of QPUs in the system;
-  // The number of QPUs is equal to the number of (auto-)launched server
-  // instances.
-  auto &platform = cudaq::get_platform();
-  auto num_qpus = platform.num_qpus();
-  printf("Number of QPUs: %zu\n", num_qpus);
-
-  // We will launch asynchronous sampling tasks,
-  // and will store the results as a future we can query at some later point.
-  // Each QPU (indexed by an unique Id) is associated with a remote REST server.
-  std::vector<cudaq::async_sample_result> countFutures;
-  for (std::size_t i = 0; i < num_qpus; i++) {
-    countFutures.emplace_back(cudaq::sample_async(
-        /*qpuId=*/i, kernel, /*nrControls=*/i + 1));
-  }
-
-  // Go do other work, asynchronous execution of sample tasks on-going
-  // Get the results, note future::get() will kick off a wait
-  // if the results are not yet available.
-  for (auto &counts : countFutures) {
-    counts.get().dump();
-  }
-  // [End Documentation]
-  return 0;
-}
diff --git a/pr-2308/sphinx/snippets/cpp/using/first_observe.cpp b/pr-2308/sphinx/snippets/cpp/using/first_observe.cpp
deleted file mode 100644
index 2c3977c2e0..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/first_observe.cpp
+++ /dev/null
@@ -1,47 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with: `nvq++ first_observe.cpp && ./a.out`
-// [Begin Observe1]
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-
-#include <iostream>
-
-using namespace cudaq::spin;
-
-__qpu__ void kernel() {
-  cudaq::qubit qubit;
-  h(qubit);
-}
-
-int main() {
-  cudaq::spin_op spin_operator = z(0);
-  // Prints: [1+0j] Z
-  std::cout << spin_operator.to_string() << "\n";
-  // [End Observe1]
-
-  // [Begin Observe2]
-  auto result_0 = cudaq::observe(kernel, spin_operator);
-  // Expectation value of kernel with respect to single `Z` term
-  // should print: 0.0
-  std::cout << "<kernel | spin_operator | kernel> = " << result_0.expectation()
-            << "\n";
-  // [End Observe2]
-
-  // [Begin Observe3]
-  auto result_1 = cudaq::observe(1000, kernel, spin_operator);
-  // Expectation value of kernel with respect to single `Z` term,
-  // but instead of a single deterministic execution of the kernel,
-  // we sample over 1000 shots. We should now print an expectation
-  // value that is close to, but not quite, zero.
-  // Example: 0.025
-  std::cout << "<kernel | spin_operator | kernel> = " << result_1.expectation()
-            << "\n";
-}
-// [End Observe3]
diff --git a/pr-2308/sphinx/snippets/cpp/using/first_sample.cpp b/pr-2308/sphinx/snippets/cpp/using/first_sample.cpp
deleted file mode 100644
index 69aa9bfc23..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/first_sample.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// [Begin Documentation]
-#include <cudaq.h>
-#include <iostream>
-
-__qpu__ void kernel(int qubit_count) {
-  // Allocate our qubits.
-  cudaq::qvector qvector(qubit_count);
-  // Place the first qubit in the superposition state.
-  h(qvector[0]);
-  // Loop through the allocated qubits and apply controlled-X,
-  // or CNOT, operations between them.
-  for (auto qubit : cudaq::range(qubit_count - 1)) {
-    x<cudaq::ctrl>(qvector[qubit], qvector[qubit + 1]);
-  }
-  // Measure the qubits.
-  mz(qvector);
-}
-// [End Documentation]
-
-// [Begin Sample1]
-int main() {
-
-  int qubit_count = 2;
-  auto result_0 = cudaq::sample(kernel, /* kernel args */ qubit_count);
-  // Should see a roughly 50/50 distribution between the |00> and
-  // |11> states. Example: {00: 505  11: 495}
-  result_0.dump();
-  // [End Sample1]
-
-  // [Begin Sample2]
-  // With an increased shots count, we will still see the same 50/50
-  // distribution, but now with 10,000 total measurements instead of the default
-  // 1000. Example: {00: 5005  11: 4995}
-  int shots_count = 10000;
-  auto result_1 = cudaq::sample(shots_count, kernel, qubit_count);
-  result_1.dump();
-  // [End Sample2]
-
-  // [Begin Sample3]
-  std::cout << result_1.most_probable() << "\n"; // prints: `00`
-  std::cout << result_1.probability(result_1.most_probable())
-            << "\n"; // prints: `0.5005`
-}
-// [End Sample3]
diff --git a/pr-2308/sphinx/snippets/cpp/using/time.cpp b/pr-2308/sphinx/snippets/cpp/using/time.cpp
deleted file mode 100644
index ed810b1d77..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/time.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// Compile and run with: `nvq++ time.cpp && ./a.out`
-
-#include <chrono>
-#include <cudaq.h>
-#include <iostream>
-
-__qpu__ void kernel(int qubit_count) {
-  // Allocate our qubits.
-  cudaq::qvector qvector(qubit_count);
-  // Place the first qubit in the superposition state.
-  h(qvector[0]);
-  // Loop through the allocated qubits and apply controlled-X,
-  // or CNOT, operations between them.
-  for (auto qubit : cudaq::range(qubit_count - 1)) {
-    x<cudaq::ctrl>(qvector[qubit], qvector[qubit + 1]);
-  }
-  // Measure the qubits.
-  mz(qvector);
-}
-
-// [Begin Time]
-int main(int argc, char *argv[]) {
-  auto qubit_count = 1 < argc ? atoi(argv[1]) : 25;
-  auto shots_count = 1000000;
-  auto start = std::chrono::high_resolution_clock::now();
-
-  // Timing just the sample execution.
-  auto result = cudaq::sample(shots_count, kernel, qubit_count);
-
-  auto stop = std::chrono::high_resolution_clock::now();
-  auto duration = std::chrono::duration<double>(stop - start);
-  std::cout << "It took " << duration.count() << " seconds.\n";
-}
-// [End Time]
diff --git a/pr-2308/sphinx/snippets/cpp/using/two_qubit_custom_op.cpp b/pr-2308/sphinx/snippets/cpp/using/two_qubit_custom_op.cpp
deleted file mode 100644
index a11644bdaa..0000000000
--- a/pr-2308/sphinx/snippets/cpp/using/two_qubit_custom_op.cpp
+++ /dev/null
@@ -1,38 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                  *
- * All rights reserved.                                                        *
- *                                                                             *
- * This source code and the accompanying materials are made available under    *
- * the terms of the Apache License 2.0 which accompanies this distribution.    *
- ******************************************************************************/
-
-// [Begin Docs]
-#include <cudaq.h>
-
-CUDAQ_REGISTER_OPERATION(MyCNOT, 2, 0,
-                         {1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0});
-
-CUDAQ_REGISTER_OPERATION(
-    MyXY, 2, 0,
-    {0, 0, 0, {0, -1}, 0, 0, {0, 1}, 0, 0, {0, -1}, 0, 0, {0, 1}, 0, 0, 0});
-
-__qpu__ void bell_pair() {
-  cudaq::qubit q, r;
-  h(q);
-  MyCNOT(q, r); // MyCNOT(control, target)
-}
-
-__qpu__ void custom_xy_test() {
-  cudaq::qubit q, r;
-  MyXY(q, r);
-  y(r); // undo the prior Y gate on qubit 1
-}
-
-int main() {
-  auto counts = cudaq::sample(bell_pair);
-  counts.dump(); // prints { 11:500 00:500 } (exact numbers will be random)
-
-  counts = cudaq::sample(custom_xy_test);
-  counts.dump(); // prints { 10:1000 }
-}
-// [End Docs]
diff --git a/pr-2308/sphinx/snippets/python/quick_start.py b/pr-2308/sphinx/snippets/python/quick_start.py
deleted file mode 100644
index 657a086bd8..0000000000
--- a/pr-2308/sphinx/snippets/python/quick_start.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# [Begin Documentation]
-import sys
-import cudaq
-
-print(f"Running on target {cudaq.get_target().name}")
-qubit_count = int(sys.argv[1]) if 1 < len(sys.argv) else 2
-
-
-@cudaq.kernel
-def kernel():
-    qubits = cudaq.qvector(qubit_count)
-    h(qubits[0])
-    for i in range(1, qubit_count):
-        x.ctrl(qubits[0], qubits[i])
-    mz(qubits)
-
-
-result = cudaq.sample(kernel)
-print(result)  # Example: { 11:500 00:500 }
-# [End Documentation]
diff --git a/pr-2308/sphinx/snippets/python/using/backends/dynamics.py b/pr-2308/sphinx/snippets/python/using/backends/dynamics.py
deleted file mode 100644
index 8984078ad2..0000000000
--- a/pr-2308/sphinx/snippets/python/using/backends/dynamics.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# Made up values - not sure what values are reasonable here.
-omega_z = 6.5
-omega_x = 4.0
-omega_d = 0.5
-
-#[Begin Transmon]
-import numpy as np
-from cudaq.operator import *
-
-# Qubit Hamiltonian
-hamiltonian = 0.5 * omega_z * spin.z(0)
-# Add modulated driving term to the Hamiltonian
-hamiltonian += omega_x * ScalarOperator(lambda t: np.cos(omega_d * t)) * spin.x(
-    0)
-#[End Transmon]
-
-# Made up values - not sure what values are reasonable here.
-t_final = 1.0
-n_steps = 100
-
-#[Begin Evolve]
-import cudaq
-import cupy as cp
-
-# Set the target to our dynamics simulator
-cudaq.set_target("dynamics")
-
-# Dimensions of sub-systems: a single two-level system.
-dimensions = {0: 2}
-
-# Initial state of the system (ground state).
-rho0 = cudaq.State.from_data(
-    cp.array([[1.0, 0.0], [0.0, 0.0]], dtype=cp.complex128))
-
-# Schedule of time steps.
-steps = np.linspace(0, t_final, n_steps)
-schedule = Schedule(steps, ["t"])
-
-# Run the simulation.
-evolution_result = evolve(hamiltonian,
-                          dimensions,
-                          schedule,
-                          rho0,
-                          observables=[spin.x(0),
-                                       spin.y(0),
-                                       spin.z(0)],
-                          collapse_operators=[],
-                          store_intermediate_results=True)
-#[End Evolve]
-
-#[Begin Plot]
-get_result = lambda idx, res: [
-    exp_vals[idx].expectation() for exp_vals in res.expectation_values()
-]
-
-import matplotlib.pyplot as plt
-
-plt.plot(steps, get_result(0, evolution_result))
-plt.plot(steps, get_result(1, evolution_result))
-plt.plot(steps, get_result(2, evolution_result))
-plt.ylabel("Expectation value")
-plt.xlabel("Time")
-plt.legend(("Sigma-X", "Sigma-Y", "Sigma-Z"))
-#[End Plot]
-
-# Made up values - not sure what values are reasonable here.
-omega_c = 6 * np.pi
-omega_a = 4 * np.pi
-Omega = 0.5
-
-#[Begin Jaynes-Cummings]
-hamiltonian = omega_c * operators.create(1) * operators.annihilate(1) \
-                + (omega_a / 2) * spin.z(0) \
-                + (Omega / 2) * (operators.annihilate(1) * spin.plus(0) + operators.create(1) * spin.minus(0))
-#[End Jaynes-Cummings]
-
-# Made up values - not sure what values are reasonable here.
-omega = np.pi
-
-#[Begin Hamiltonian]
-# Define the static (drift) and control terms
-H0 = spin.z(0)
-H1 = spin.x(0)
-H = H0 + ScalarOperator(lambda t: np.cos(omega * t)) * H1
-#[End Hamiltonian]
-
-#[Begin DefineOp]
-import numpy
-import scipy
-from cudaq.operator import *
-from numpy.typing import NDArray
-
-
-def displacement_matrix(
-        dimension: int,
-        displacement: NumericType) -> NDArray[numpy.complexfloating]:
-    """
-    Returns the displacement operator matrix.
-    Args:
-        displacement: Amplitude of the displacement operator.
-            See also https://en.wikipedia.org/wiki/Displacement_operator.
-    """
-    displacement = complex(displacement)
-    term1 = displacement * operators.create(0).to_matrix({0: dimension})
-    term2 = numpy.conjugate(displacement) * operators.annihilate(0).to_matrix(
-        {0: dimension})
-    return scipy.linalg.expm(term1 - term2)
-
-
-# The second argument here indicates the the defined operator
-# acts on a single degree of freedom, which can have any dimension.
-# An argument [2], for example, would indicate that it can only
-# act on a single degree of freedom with dimension two.
-ElementaryOperator.define("displace", [0], displacement_matrix)
-
-
-def displacement(degree: int) -> ElementaryOperator:
-    """
-    Instantiates a displacement operator acting on the given degree of freedom.
-    """
-    return ElementaryOperator("displace", [degree])
-
-
-#[End DefineOp]
-
-#[Begin Schedule1]
-import cudaq
-
-# Define a system consisting of a single degree of freedom (0) with dimension 3.
-system_dimensions = {0: 3}
-system_operator = displacement(0)
-
-# Define the time dependency of the system operator as a schedule that linearly
-# increases the displacement parameter from 0 to 1.
-time_dependence = Schedule(numpy.linspace(0, 1, 100), ['displacement'])
-initial_state = cudaq.State.from_data(
-    numpy.ones(3, dtype=numpy.complex128) / numpy.sqrt(3))
-
-# Simulate the evolution of the system under this time dependent operator.
-cudaq.evolve(system_operator, system_dimensions, time_dependence, initial_state)
-#[End Schedule1]
-
-#[Begin Schedule2]
-system_operator = displacement(0) + operators.squeeze(0)
-
-
-# Define a schedule such that displacement amplitude increases linearly in time
-# but the squeezing amplitude decreases, that is follows the inverse schedule.
-def parameter_values(time_steps):
-
-    def compute_value(param_name, step_idx):
-        match param_name:
-            case 'displacement':
-                return time_steps[int(step_idx)]
-            case 'squeezing':
-                return time_steps[-int(step_idx + 1)]
-            case _:
-                raise ValueError(f"value for parameter {param_name} undefined")
-
-    return Schedule(range(len(time_steps)), system_operator.parameters.keys(),
-                    compute_value)
-
-
-time_dependence = parameter_values(numpy.linspace(0, 1, 100))
-cudaq.evolve(system_operator, system_dimensions, time_dependence, initial_state)
-#[End Schedule2]
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/nvqc/nvqc_intro.py b/pr-2308/sphinx/snippets/python/using/cudaq/nvqc/nvqc_intro.py
deleted file mode 100644
index 91b9ef5360..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/nvqc/nvqc_intro.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-# [Begin Documentation]
-import cudaq
-
-cudaq.set_target("nvqc")
-num_qubits = 25
-# Define a simple quantum kernel to execute on NVQC.
-kernel = cudaq.make_kernel()
-qubits = kernel.qalloc(num_qubits)
-# Maximally entangled state between 25 qubits.
-kernel.h(qubits[0])
-for i in range(num_qubits - 1):
-    kernel.cx(qubits[i], qubits[i + 1])
-kernel.mz(qubits)
-
-counts = cudaq.sample(kernel)
-print(counts)
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/nvqc/nvqc_mqpu.py b/pr-2308/sphinx/snippets/python/using/cudaq/nvqc/nvqc_mqpu.py
deleted file mode 100644
index 38454615ef..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/nvqc/nvqc_mqpu.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-# [Begin Documentation]
-import cudaq
-from cudaq import spin
-import math
-
-# Use NVQC with 3 virtual QPUs
-cudaq.set_target("nvqc", nqpus=3)
-
-print("Number of QPUs:", cudaq.get_target().num_qpus())
-# Create the parameterized ansatz
-kernel, theta = cudaq.make_kernel(float)
-qreg = kernel.qalloc(2)
-kernel.x(qreg[0])
-kernel.ry(theta, qreg[1])
-kernel.cx(qreg[1], qreg[0])
-
-# Define its spin Hamiltonian.
-hamiltonian = (5.907 - 2.1433 * spin.x(0) * spin.x(1) -
-               2.1433 * spin.y(0) * spin.y(1) + 0.21829 * spin.z(0) -
-               6.125 * spin.z(1))
-
-
-def opt_gradient(parameter_vector):
-    # Evaluate energy and gradient on different remote QPUs
-    # (i.e., concurrent job submissions to NVQC)
-    energy_future = cudaq.observe_async(kernel,
-                                        hamiltonian,
-                                        parameter_vector[0],
-                                        qpu_id=0)
-    plus_future = cudaq.observe_async(kernel,
-                                      hamiltonian,
-                                      parameter_vector[0] + 0.5 * math.pi,
-                                      qpu_id=1)
-    minus_future = cudaq.observe_async(kernel,
-                                       hamiltonian,
-                                       parameter_vector[0] - 0.5 * math.pi,
-                                       qpu_id=2)
-    return (energy_future.get().expectation(), [
-        (plus_future.get().expectation() - minus_future.get().expectation()) /
-        2.0
-    ])
-
-
-optimizer = cudaq.optimizers.LBFGS()
-optimal_value, optimal_parameters = optimizer.optimize(1, opt_gradient)
-print("Ground state energy =", optimal_value)
-print("Optimal parameters =", optimal_parameters)
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/platform/get_state_async.py b/pr-2308/sphinx/snippets/python/using/cudaq/platform/get_state_async.py
deleted file mode 100644
index ccb9aa85b3..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/platform/get_state_async.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-# [Begin Documentation]
-import cudaq
-
-cudaq.set_target("nvidia", option="mqpu")
-target = cudaq.get_target()
-qpu_count = target.num_qpus()
-print("Number of QPUs:", qpu_count)
-
-
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(5)
-    # Place qubits in GHZ State
-    h(qvector[0])
-    for qubit in range(4):
-        x.ctrl(qvector[qubit], qvector[qubit + 1])
-
-
-state_futures = []
-for qpu in range(qpu_count):
-    state_futures.append(cudaq.get_state_async(kernel, qpu_id=qpu))
-
-for state in state_futures:
-    print(state.get())
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/platform/observe_mqpu.py b/pr-2308/sphinx/snippets/python/using/cudaq/platform/observe_mqpu.py
deleted file mode 100644
index dd66535e3f..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/platform/observe_mqpu.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-# [Begin Documentation]
-import cudaq
-from cudaq import spin
-
-cudaq.set_target("nvidia", option="mqpu")
-target = cudaq.get_target()
-num_qpus = target.num_qpus()
-print("Number of QPUs:", num_qpus)
-
-
-# Define spin ansatz.
-@cudaq.kernel
-def kernel(angle: float):
-    qvector = cudaq.qvector(2)
-    x(qvector[0])
-    ry(angle, qvector[1])
-    x.ctrl(qvector[1], qvector[0])
-
-
-# Define spin Hamiltonian.
-hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(
-    0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-exp_val = cudaq.observe(kernel,
-                        hamiltonian,
-                        0.59,
-                        execution=cudaq.parallel.thread).expectation()
-print("Expectation value: ", exp_val)
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/platform/observe_mqpu_mpi.py b/pr-2308/sphinx/snippets/python/using/cudaq/platform/observe_mqpu_mpi.py
deleted file mode 100644
index 3b4368a8f6..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/platform/observe_mqpu_mpi.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-# [Begin Documentation]
-import cudaq
-from cudaq import spin
-
-cudaq.mpi.initialize()
-cudaq.set_target("nvidia", option="mqpu")
-
-
-# Define spin ansatz.
-@cudaq.kernel
-def kernel(angle: float):
-    qvector = cudaq.qvector(2)
-    x(qvector[0])
-    ry(angle, qvector[1])
-    x.ctrl(qvector[1], qvector[0])
-
-
-# Define spin Hamiltonian.
-hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(
-    0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-exp_val = cudaq.observe(kernel, hamiltonian, 0.59,
-                        execution=cudaq.parallel.mpi).expectation()
-if cudaq.mpi.rank() == 0:
-    print("Expectation value: ", exp_val)
-
-cudaq.mpi.finalize()
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/platform/sample_async.py b/pr-2308/sphinx/snippets/python/using/cudaq/platform/sample_async.py
deleted file mode 100644
index da27ff80d3..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/platform/sample_async.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-# [Begin Documentation]
-import cudaq
-
-cudaq.set_target("nvidia", option="mqpu")
-target = cudaq.get_target()
-qpu_count = target.num_qpus()
-print("Number of QPUs:", qpu_count)
-
-
-@cudaq.kernel
-def kernel(qubit_count: int):
-    qvector = cudaq.qvector(qubit_count)
-    # Place qubits in superposition state.
-    h(qvector)
-    # Measure.
-    mz(qvector)
-
-
-count_futures = []
-for qpu in range(qpu_count):
-    count_futures.append(cudaq.sample_async(kernel, 5, qpu_id=qpu))
-
-for counts in count_futures:
-    print(counts.get())
diff --git a/pr-2308/sphinx/snippets/python/using/cudaq/platform/sample_async_remote.py b/pr-2308/sphinx/snippets/python/using/cudaq/platform/sample_async_remote.py
deleted file mode 100644
index 763879ab9c..0000000000
--- a/pr-2308/sphinx/snippets/python/using/cudaq/platform/sample_async_remote.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-import argparse
-import cudaq
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--backend',
-                        type=str,
-                        required=False,
-                        default="tensornet")
-    parser.add_argument('--servers', type=str, required=False, default='2')
-    args = parser.parse_args()
-
-    # [Begin Documentation]
-    # Specified as program input, e.g.
-    # ```
-    # backend = "tensornet"; servers = "2"
-    # ```
-    backend = args.backend
-    servers = args.servers
-
-    # Define a kernel to be sampled.
-    @cudaq.kernel
-    def kernel(controls_count: int):
-        controls = cudaq.qvector(controls_count)
-        targets = cudaq.qvector(2)
-        # Place controls in superposition state.
-        h(controls)
-        for target in range(2):
-            x.ctrl(controls, targets[target])
-        # Measure.
-        mz(controls)
-        mz(targets)
-
-    # Set the target to execute on and query the number of QPUs in the system;
-    # The number of QPUs is equal to the number of (auto-)launched server instances.
-    cudaq.set_target("remote-mqpu",
-                     backend=backend,
-                     auto_launch=str(servers) if servers.isdigit() else "",
-                     url="" if servers.isdigit() else servers)
-    qpu_count = cudaq.get_target().num_qpus()
-    print("Number of virtual QPUs:", qpu_count)
-
-    # We will launch asynchronous sampling tasks,
-    # and will store the results as a future we can query at some later point.
-    # Each QPU (indexed by an unique Id) is associated with a remote REST server.
-    count_futures = []
-    for i in range(qpu_count):
-
-        result = cudaq.sample_async(kernel, i + 1, qpu_id=i)
-        count_futures.append(result)
-    print("Sampling jobs launched for asynchronous processing.")
-
-    # Go do other work, asynchronous execution of sample tasks on-going.
-    # Get the results, note future::get() will kick off a wait
-    # if the results are not yet available.
-    for idx in range(len(count_futures)):
-        counts = count_futures[idx].get()
-        print(counts)
-        # [End Documentation]
-
-        assert len(counts) == 1 << (idx + 1)
-        for key in counts:
-            assert len(key) == 3 + idx
diff --git a/pr-2308/sphinx/snippets/python/using/examples/cnot_gate.py b/pr-2308/sphinx/snippets/python/using/examples/cnot_gate.py
deleted file mode 100644
index 7cc193a966..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/cnot_gate.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-#[Begin Docs]
-import cudaq
-
-
-@cudaq.kernel
-def kernel():
-    # 2 qubits both initialized to the ground/ zero state.
-    qvector = cudaq.qvector(2)
-
-    x(qvector[0])
-
-    # Controlled-not gate operation.
-    x.ctrl(qvector[0], qvector[1])
-
-    mz(qvector[0])
-    mz(qvector[1])
-
-
-result = cudaq.sample(kernel)
-print(result)
-#[End Docs]
diff --git a/pr-2308/sphinx/snippets/python/using/examples/hadamard_gate.py b/pr-2308/sphinx/snippets/python/using/examples/hadamard_gate.py
deleted file mode 100644
index 3cd1c1b9f6..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/hadamard_gate.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-#[Begin Docs]
-import cudaq
-
-
-@cudaq.kernel
-def kernel():
-    # A single qubit initialized to the ground/ zero state.
-    qubit = cudaq.qubit()
-
-    # Apply Hadamard gate to single qubit to put it in equal superposition.
-    h(qubit)
-
-    # Measurement operator.
-    mz(qubit)
-
-
-result = cudaq.sample(kernel)
-print("Measured |0> with probability " +
-      str(result["0"] / sum(result.values())))
-print("Measured |1> with probability " +
-      str(result["1"] / sum(result.values())))
-#[End Docs]
diff --git a/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py b/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
deleted file mode 100644
index 00cdc6b0b7..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# [Begin prepare]
-import cudaq
-from cudaq import spin
-import numpy as np
-
-if cudaq.num_available_gpus() == 0:
-    print("This example requires a GPU to run. No GPU detected.")
-    exit(0)
-
-np.random.seed(1)
-cudaq.set_target("nvidia", option="mqpu")
-
-qubit_count = 5
-sample_count = 10000
-h = spin.z(0)
-parameter_count = qubit_count
-
-# Below we run a circuit for 10000 different input parameters.
-parameters = np.random.default_rng(13).uniform(low=0,
-                                               high=1,
-                                               size=(sample_count,
-                                                     parameter_count))
-
-kernel, params = cudaq.make_kernel(list)
-
-qubits = kernel.qalloc(qubit_count)
-qubits_list = list(range(qubit_count))
-
-for i in range(qubit_count):
-    kernel.rx(params[i], qubits[i])
-# [End prepare]
-
-# [Begin single]
-import timeit
-
-timeit.timeit(lambda: cudaq.observe(kernel, h, parameters),
-              number=1)  # Single GPU result.
-# [End single]
-
-# [Begin split]
-print('We have', parameters.shape[0],
-      'parameters which we would like to execute')
-
-xi = np.split(
-    parameters,
-    4)  # We split our parameters into 4 arrays since we have 4 GPUs available.
-
-print('We split this into', len(xi), 'batches of', xi[0].shape[0], ',',
-      xi[1].shape[0], ',', xi[2].shape[0], ',', xi[3].shape[0])
-# [End split]
-
-# [Begin multiple]
-# Timing the execution on a single GPU vs 4 GPUs,
-# one will see a 4x performance improvement if 4 GPUs are available.
-
-asyncresults = []
-num_gpus = cudaq.num_available_gpus()
-
-for i in range(len(xi)):
-    for j in range(xi[i].shape[0]):
-        qpu_id = i * num_gpus // len(xi)
-        asyncresults.append(
-            cudaq.observe_async(kernel, h, xi[i][j, :], qpu_id=qpu_id))
-
-result = [res.get() for res in asyncresults]
-# [End multiple]
diff --git a/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/hamiltonian_batching.py b/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/hamiltonian_batching.py
deleted file mode 100644
index 614a3048a3..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/hamiltonian_batching.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-#[Begin Docs]
-import cudaq
-from cudaq import spin
-
-if cudaq.num_available_gpus() == 0:
-    print("This example requires a GPU to run. No GPU detected.")
-    exit(0)
-
-cudaq.set_target("nvidia", option="mqpu")
-cudaq.mpi.initialize()
-
-qubit_count = 15
-term_count = 100000
-
-kernel = cudaq.make_kernel()
-qubits = kernel.qalloc(qubit_count)
-kernel.h(qubits[0])
-for i in range(1, qubit_count):
-    kernel.cx(qubits[0], qubits[i])
-
-# We create a random Hamiltonian
-hamiltonian = cudaq.SpinOperator.random(qubit_count, term_count)
-
-# The observe calls allows us to calculate the expectation value of the Hamiltonian with respect to a specified kernel.
-
-# Single node, single GPU.
-result = cudaq.observe(kernel, hamiltonian)
-result.expectation()
-
-# If we have multiple GPUs/ QPUs available, we can parallelize the workflow with the addition of an argument in the observe call.
-
-# Single node, multi-GPU.
-result = cudaq.observe(kernel, hamiltonian, execution=cudaq.parallel.thread)
-result.expectation()
-
-# Multi-node, multi-GPU.
-result = cudaq.observe(kernel, hamiltonian, execution=cudaq.parallel.mpi)
-result.expectation()
-
-cudaq.mpi.finalize()
-#[End Docs]
diff --git a/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py b/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
deleted file mode 100644
index 70b18a96cb..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# [Begin state]
-import cudaq
-
-
-@cudaq.kernel
-def ghz_state(qubit_count: int):
-    qubits = cudaq.qvector(qubit_count)
-    h(qubits[0])
-    for i in range(1, qubit_count):
-        cx(qubits[0], qubits[i])
-    mz(qubits)
-
-
-def sample_ghz_state(qubit_count, target):
-    """A function that will sample a variable sized GHZ state."""
-    cudaq.set_target(target)
-    result = cudaq.sample(ghz_state, qubit_count, shots_count=1000)
-    return result
-
-
-# [End state]
-
-# [Begin CPU]
-cpu_result = sample_ghz_state(qubit_count=2, target="qpp-cpu")
-cpu_result.dump()
-# [End CPU]
-
-# [Begin GPU]
-if cudaq.num_available_gpus() > 0:
-    gpu_result = sample_ghz_state(qubit_count=25, target="nvidia")
-    gpu_result.dump()
-# [End GPU]
diff --git a/pr-2308/sphinx/snippets/python/using/examples/pauli_x_gate.py b/pr-2308/sphinx/snippets/python/using/examples/pauli_x_gate.py
deleted file mode 100644
index af56fbabb5..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/pauli_x_gate.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-#[Begin Docs]
-import cudaq
-
-
-@cudaq.kernel
-def kernel():
-    # A single qubit initialized to the ground / zero state.
-    qubit = cudaq.qubit()
-
-    # Apply the Pauli x gate to the qubit.
-    x(qubit)
-
-    # Measurement operator.
-    mz(qubit)
-
-
-# Sample the qubit for 1000 shots to gather statistics.
-result = cudaq.sample(kernel)
-print(result.most_probable())
-#[End Docs]
diff --git a/pr-2308/sphinx/snippets/python/using/examples/two_qubit_custom_op.py b/pr-2308/sphinx/snippets/python/using/examples/two_qubit_custom_op.py
deleted file mode 100644
index d42feb6e34..0000000000
--- a/pr-2308/sphinx/snippets/python/using/examples/two_qubit_custom_op.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# fmt: off
-#[Begin Docs]
-import cudaq
-import numpy as np
-
-# Create and test a custom CNOT operation.
-cudaq.register_operation("my_cnot", np.array([1, 0, 0, 0,
-                                              0, 1, 0, 0,
-                                              0, 0, 0, 1,
-                                              0, 0, 1, 0]))
-
-@cudaq.kernel
-def bell_pair():
-    qubits = cudaq.qvector(2)
-    h(qubits[0])
-    my_cnot(qubits[0], qubits[1]) # `my_cnot(control, target)`
-
-
-cudaq.sample(bell_pair).dump() # prints { 11:500 00:500 } (exact numbers will be random)
-
-
-# Construct a custom unitary matrix for X on the first qubit and Y
-# on the second qubit.
-X = np.array([[0,  1 ], [1 , 0]])
-Y = np.array([[0, -1j], [1j, 0]])
-XY = np.kron(X, Y)
-
-# Register the custom operation
-cudaq.register_operation("my_XY", XY)
-
-@cudaq.kernel
-def custom_xy_test():
-    qubits = cudaq.qvector(2)
-    my_XY(qubits[0], qubits[1])
-    y(qubits[1]) # undo the prior Y gate on qubit 1
-
-
-cudaq.sample(custom_xy_test).dump() # prints { 10:1000 }
-#[End Docs]
-# fmt: on
diff --git a/pr-2308/sphinx/snippets/python/using/first_observe.py b/pr-2308/sphinx/snippets/python/using/first_observe.py
deleted file mode 100644
index a0b1d3caf1..0000000000
--- a/pr-2308/sphinx/snippets/python/using/first_observe.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-#[Begin Observe1]
-import cudaq
-from cudaq import spin
-
-operator = spin.z(0)
-print(operator)  # prints: [1+0j] Z
-
-
-@cudaq.kernel
-def kernel():
-    qubit = cudaq.qubit()
-    h(qubit)
-    # [End Observe1]
-
-
-#[Begin Observe2]
-result = cudaq.observe(kernel, operator)
-print(result.expectation())  # prints: 0.0
-#[End Observe2]
-
-#[Begin Observe3]
-result = cudaq.observe(kernel, operator, shots_count=1000)
-print(result.expectation())  # prints non-zero value
-#[End Observe3]
diff --git a/pr-2308/sphinx/snippets/python/using/first_sample.py b/pr-2308/sphinx/snippets/python/using/first_sample.py
deleted file mode 100644
index 1e60b16e32..0000000000
--- a/pr-2308/sphinx/snippets/python/using/first_sample.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-# [Begin Documentation]
-import cudaq
-
-
-# Define our kernel.
-@cudaq.kernel
-def kernel(qubit_count: int):
-    # Allocate our qubits.
-    qvector = cudaq.qvector(qubit_count)
-    # Place the first qubit in the superposition state.
-    h(qvector[0])
-    # Loop through the allocated qubits and apply controlled-X,
-    # or CNOT, operations between them.
-    for qubit in range(qubit_count - 1):
-        x.ctrl(qvector[qubit], qvector[qubit + 1])
-    # Measure the qubits.
-    mz(qvector)
-    # [End Documentation]
-
-
-#[Begin Sample1]
-qubit_count = 2
-print(cudaq.draw(kernel, qubit_count))
-results = cudaq.sample(kernel, qubit_count)
-# Should see a roughly 50/50 distribution between the |00> and
-# |11> states. Example: {00: 505  11: 495}
-print("Measurement distribution:" + str(results))
-#[End Sample1]
-
-#[Begin Sample2]
-# With an increased shots count, we will still see the same 50/50 distribution,
-# but now with 10,000 total measurements instead of the default 1000.
-# Example: {00: 5005  11: 4995}
-results = cudaq.sample(kernel, qubit_count, shots_count=10000)
-print("Measurement distribution:" + str(results))
-#[End Sample2]
-
-#[Begin Sample3]
-most_probable_result = results.most_probable()
-probability = results.probability(most_probable_result)
-print("Most probable result: " + most_probable_result)
-print("Measured with probability " + str(probability), end='\n\n')
-#[End Sample3]
-
-# Parallelize over the various kernels one would like to execute.
-
-
-#[Begin SampleAsync]
-@cudaq.kernel
-def kernel2(qubit_count: int):
-    # Allocate our qubits.
-    qvector = cudaq.qvector(qubit_count)
-    # Place all qubits in a uniform superposition.
-    h(qvector)
-    # Measure the qubits.
-    mz(qvector)
-
-
-num_gpus = cudaq.num_available_gpus()
-if num_gpus > 1:
-    # Set the target to include multiple virtual QPUs.
-    cudaq.set_target("nvidia", option="mqpu")
-    # Asynchronous execution on multiple virtual QPUs, each simulated by an NVIDIA GPU.
-    result_1 = cudaq.sample_async(kernel,
-                                  qubit_count,
-                                  shots_count=1000,
-                                  qpu_id=0)
-    result_2 = cudaq.sample_async(kernel2,
-                                  qubit_count,
-                                  shots_count=1000,
-                                  qpu_id=1)
-else:
-    # Schedule for execution on the same virtual QPU.
-    result_1 = cudaq.sample_async(kernel,
-                                  qubit_count,
-                                  shots_count=1000,
-                                  qpu_id=0)
-    result_2 = cudaq.sample_async(kernel2,
-                                  qubit_count,
-                                  shots_count=1000,
-                                  qpu_id=0)
-
-print("Measurement distribution for kernel:" + str(result_1.get()))
-print("Measurement distribution for kernel2:" + str(result_2.get()))
-#[End SampleAsync]
diff --git a/pr-2308/sphinx/snippets/python/using/time.py b/pr-2308/sphinx/snippets/python/using/time.py
deleted file mode 100644
index 08c3a93f6a..0000000000
--- a/pr-2308/sphinx/snippets/python/using/time.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-import cudaq
-
-
-@cudaq.kernel
-def kernel(qubit_count: int):
-    qvector = cudaq.qvector(qubit_count)
-    h(qvector[0])
-    for qubit in range(qubit_count - 1):
-        x.ctrl(qvector[qubit], qvector[qubit + 1])
-    mz(qvector)
-
-
-#[Begin Time]
-import sys
-import timeit
-
-# Will time the execution of our sample call.
-code_to_time = 'cudaq.sample(kernel, qubit_count, shots_count=1000000)'
-qubit_count = int(sys.argv[1]) if 1 < len(sys.argv) else 25
-
-# Execute on CPU backend.
-cudaq.set_target('qpp-cpu')
-print('CPU time')  # Example: 27.57462 s.
-print(timeit.timeit(stmt=code_to_time, globals=globals(), number=1))
-
-if cudaq.num_available_gpus() > 0:
-    # Execute on GPU backend.
-    cudaq.set_target('nvidia')
-    print('GPU time')  # Example: 0.773286 s.
-    print(timeit.timeit(stmt=code_to_time, globals=globals(), number=1))
-#[End Time]
diff --git a/pr-2308/sphinx/specification/cudaq.rst b/pr-2308/sphinx/specification/cudaq.rst
deleted file mode 100644
index e0e87a44e4..0000000000
--- a/pr-2308/sphinx/specification/cudaq.rst
+++ /dev/null
@@ -1,41 +0,0 @@
-Language Specification 
-***********************************
-
-NVIDIA CUDA-Q is a programming model in C++ for heterogeneous 
-quantum-classical computing. As such, the model provides primitive types, concepts, syntax, and
-semantics that enable the integration of quantum processing units (QPUs) with
-existing NVIDIA multi-node, multi-GPU parallel programming models and architectures. 
-The language is designed for C++. Extensions to other languages is fully supported 
-via appropriate bindings. 
-
-CUDA-Q implementations are intended to be enabled via library-based language 
-extensions that define quantum code as callables in the classical language being extended. 
-These callables are meant to be compiled with standard compilers and runtimes 
-and run via quantum computer simulators or available physical quantum architectures. The 
-language callables intended for quantum coprocessing must be annotated for 
-compilers and runtimes to process. These annotations should directly enable quantum-specific 
-optimization and transformation for optimal execution on simulators and QPUs. 
-
-The rest of this document will detail all aspects of the language specification - its machine model, 
-primitive types and operations, core concepts and abstractions, and algorithmic primitives. All code 
-specification and demonstrative examples are provided in C++ (the native CUDA-Q classical language). 
-Where possible, bindings for higher-level languages (e.g. Python) will be displayed to aid in understanding.
-
-.. toctree::
-  :numbered:
-  :caption: CUDA-Q
-  :maxdepth: 1
-
-  Machine Model <cudaq/machine_model.rst>
-  Namespace and Standard <cudaq/namespace.rst>
-  Quantum Types <cudaq/types.rst>
-  Quantum Operators <cudaq/operators.rst>
-  Quantum Operations <cudaq/operations.rst>
-  Quantum Kernels <cudaq/kernels.rst>
-  Sub-circuit Synthesis <cudaq/synthesis.rst>
-  Control Flow <cudaq/control_flow.rst>
-  Just-in-Time Kernel Creation <cudaq/dynamic_kernels.rst>
-  Quantum Patterns <cudaq/patterns.rst>
-  Platform <cudaq/platform.rst>
-  Algorithmic Primitives <cudaq/algorithmic_primitives.rst>
-  Example Programs <cudaq/examples.rst>
diff --git a/pr-2308/sphinx/specification/cudaq/algorithmic_primitives.rst b/pr-2308/sphinx/specification/cudaq/algorithmic_primitives.rst
deleted file mode 100644
index 7e8b8240f6..0000000000
--- a/pr-2308/sphinx/specification/cudaq/algorithmic_primitives.rst
+++ /dev/null
@@ -1,637 +0,0 @@
-Quantum Algorithmic Primitives
-******************************
-**[1]** The general philosophy of the CUDA-Q specification is that quantum 
-device code should be encapsulated as stand-alone callable instances of generic 
-signature, and that operations or primitive algorithms targeting a quantum 
-coprocessor be implemented as adaptors on those callable instances. Adaptors, by 
-definition, are generic functions that take any quantum kernel as input along with 
-the runtime arguments for that kernel. Runtime arguments passed to adaptor functions 
-flow through the adaptor to the provided kernel. This pattern allows general pre- 
-and post-processing around concrete kernel execution. 
-
-.. _cudaq-sample-spec:
-
-:code:`cudaq::sample`
--------------------------
-**[1]** A common task for near-term quantum execution is to sample the state
-of a given quantum circuit for a specified number of shots (circuit
-executions). The result of this task is typically a mapping of observed
-measurement bit strings to the number of times each was observed. This
-is typically termed the counts dictionary in the community. 
-
-**[2]** The CUDA-Q model enables this functionality via template functions within the
-:code:`cudaq` namespace with the following structure:
-
-.. code-block:: cpp
-
-    template <typename ReturnType>
-    concept HasVoidReturnType = std::is_void_v<ReturnType>;
-
-    // Kernel only
-    template<typename QuantumKernel, typename... Args>
-      requires HasVoidReturnType<QuantumKernel, Args...>
-    sample_result sample(QuantumKernel&& kernel, Args&&... args);
-
-    // Specify shots
-    template<typename QuantumKernel, typename... Args>
-      requires HasVoidReturnType<QuantumKernel, Args...>
-    sample_result sample(std::size_t shots, QuantumKernel&& kernel, Args&&... args);
-
-    // Specify sample options (including shots and noise model)
-    template<typename QuantumKernel, typename... Args>
-      requires HasVoidReturnType<QuantumKernel, Args...>
-    sample_result sample(const sample_options &options,
-                         QuantumKernel&& kernel, Args&&... args);
-
-**[3]** This function takes as input a quantum kernel instance followed by the
-concrete arguments at which the kernel should be invoked. CUDA-Q kernels 
-passed to this function must be entry-point kernels and return :code:`void`. 
-
-**[4]** Overloaded functions exist for specifying the number of shots to sample and the
-noise model to apply.
-
-**[5]** The function returns an instance of the :code:`cudaq::sample_result` type which encapsulates
-the counts dictionary produced by the sampling task. Programmers can
-extract the result information in the following manner: 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      auto bell = []() __qpu__ { ... };
-      
-      // Sample the state generated by bel
-      auto counts = cudaq::sample(bell)
-  
-      // Print to standard out
-      counts.dump();
-  
-      // Fine-grained access to the bits and counts
-      for (auto& [bits, count] : counts) {
-        printf("Observed: %s, %lu\n", bits, count);
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def bell(): 
-       ... 
-    
-    # Sample the state generated by bell
-    counts = cudaq.sample(bell) 
-
-    # Print to standard out
-    counts.dump()
-
-    # Fine-grained access to the bits and counts 
-    for bits, count in counts:
-        print('Observed: {}, {}'.format(bits, count))
-
-
-**[6]** CUDA-Q specifies the following structure for :code:`cudaq::sample_result`:
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      using CountsDictionary = std::unordered_map<std::string, std::size_t>;
-      inline static const std::string GlobalRegisterName = "__global__";
-      class sample_result {
-        public:
-          sample_result() = default;
-          sample_result(const sample_result &);
-          ~sample_result();
-          
-          std::vector<std::string> register_names();
-          
-          std::size_t count(std::string_view bitString,
-                      const std::string_view registerName = GlobalRegisterName);
-          
-          std::vector<std::string>
-          sequential_data(const std::string_view registerName = GlobalRegisterName);
-          
-          CountsDictionary
-          to_map(const std::string_view registerName = GlobalRegisterName);
-          
-          sample_result
-          get_marginal(const std::vector<std::size_t> &&marginalIndices,
-                 const std::string_view registerName = GlobalRegisterName);
-
-          double expectation(const std::string_view registerName == GlobalRegisterName);
-          double probability(std::string_view bitString, const std::string_view registerName == GlobalRegisterName);
-          std::size_t size(const std::string_view registerName == GlobalRegisterName);
-          
-          void dump();
-          void clear();
-
-          CountsDictionary::iterator begin();
-          CountsDictionary::iterator end();
-      };
-    }
-
-**[7]** The :code:`sample_result` type enables one to encode measurement results from a 
-quantum circuit sampling task. It keeps track of a list of sample results, each 
-one corresponding to a measurement action during the sampling process and represented 
-by a unique register name. It also tracks a unique global register, the implicit sampling 
-of the state at the end of circuit execution. The API gives fine-grain access 
-to the measurement results for each register. To illustrate this, observe 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-    auto kernel = []() __qpu__ {
-      cudaq::qubit q;
-      h(q);
-      auto reg1 = mz(q);
-      reset (q);
-      x(q);
-    };
-    cudaq::sample(kernel).dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def kernel():
-       q = cudaq.qubit()
-       h(q)
-       reg1 = mz(q)
-       reset(q)
-       x(q)
-    
-    cudaq.sample(kernel).dump()
-
-should produce 
-
-.. code-block:: bash 
-
-    { 
-      __global__ : { 1:1000 }
-      reg1 : { 0:501 1:499 }
-    }
-
-Here we see that we have measured a qubit in a uniform superposition to a 
-register named :code:`reg1`, and followed it with a reset and the application 
-of an NOT operation. The :code:`sample_result` returned for this sampling 
-tasks contains the default :code:`__global__` register as well as the user 
-specified :code:`reg1` register. 
-
-The contents of the :code:`__global__` register will depend on how your kernel
-is written:
-
-1. If no measurements appear in the kernel, then the :code:`__global__`
-   register is formed with implicit measurements being added for *all* the
-   qubits defined in the kernel, and the measurements all occur at the end of
-   the kernel. The order of the bits in the bitstring corresponds to the qubit
-   allocation order specified in the kernel.  That is - the :code:`[0]` element
-   in the :code:`__global__` bitstring corresponds with the first declared qubit
-   in the kernel. For example,
-
-.. tab:: C++ 
-
-   .. code-block:: cpp
-
-       auto kernel = []() __qpu__ {
-         cudaq::qubit a, b;
-         x(a);
-       };
-       cudaq::sample(kernel).dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel 
-    def kernel():
-        a, b = cudaq.qubit(), cudaq.qubit() 
-        x(a) 
-    
-    cudaq.sample(kernel).dump() 
-
-should produce 
-
-   .. code-block:: bash 
-
-       { 
-         __global__ : { 10:1000 }
-       }
-
-2. Conversely, if any measurements appear in the kernel, then only the measured
-   qubits will appear in the :code:`__global__` register. Similar to #1, the 
-   bitstring corresponds to the qubit allocation order specified in the kernel.
-   Also (again, similar to #1), the values of the sampled qubits always
-   correspond to the values *at the end of the kernel execution*. That is - if a
-   qubit is measured in the middle of a kernel and subsequent operations change
-   the state of the qubit, the qubit will be implicitly re-measured at the end
-   of the kernel, and that re-measured value is the value that will appear in
-   the :code:`__global__` register. For example,
-
-.. tab:: C++ 
-
-   .. code-block:: cpp
-
-       auto kernel = []() __qpu__ {
-         cudaq::qubit a, b;
-         x(a);
-         mz(b);
-         mz(a);
-       };
-       cudaq::sample(kernel).dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel
-    def kernel():
-        a, b = cudaq.qubit(), cudaq.qubit()
-        x(a)
-        mz(b)
-        mz(a)
-
-    cudaq.sample(kernel).dump() 
-  
-should produce 
-
-   .. code-block:: bash 
-
-       { 
-         __global__ : { 10:1000 }
-       }
-
-.. note::
-
-  If you don't specify any measurements in your kernel and allow the :code:`nvq++`
-  compiler to perform passes that introduce ancilla qubits into your kernel, it
-  may be difficult to discern which qubits are the ancilla qubits vs which ones
-  are your qubits. In this case, it is recommended that you provide explicit
-  measurements in your kernel in order to only receive measurements from your
-  qubits and silently discard the measurements from the ancillary qubits.
-
-**[8]** The API exposed by the :code:`sample_result` data type allows one to extract
-the information contained at a variety of levels and for each available 
-register name. One can get the number of times a bit string was observed via 
-:code:`sample_result::count`, extract a `std::unordered_map` representation via 
-:code:`sample_result::to_map`, get a new :code:`sample_result` instance over a subset of 
-measured qubits via :code:`sample_result::get_marginal`, and extract the 
-measurement data as it was produced sequentially (a vector of bit string observations 
-for each shot in the sampling process). One can also compute probabilities and expectation 
-values. 
-
-**[9]** There are specific requirements on input quantum kernels for the use of the
-sample function which must be enforced by compiler implementations. The kernel 
-must be an entry-point kernel that returns :code:`void`.
-
-**[10]** CUDA-Q also provides an asynchronous version of this function 
-(:code:`cudaq::sample_async`) which returns a :code:`sample_async_result`. 
-
-.. code-block:: cpp 
-
-    template<typename QuantumKernel, typename... Args>
-    async_sample_result sample_async(const std::size_t qpu_id, QuantumKernel&& kernel, Args&&... args);
-
-Programmers can asynchronously launch sampling tasks on any :code:`qpu_id`. 
-
-**[11]** The :code:`async_sample_result` wraps a :code:`std::future<sample_result>` and exposes the same 
-:code:`get()` functionality to extract the results after asynchronous execution. 
-
-**[12]** For remote QPU systems with long queue times, the :code:`async_sample_result` type encodes job ID 
-information and can be persisted to file and loaded from file at a later time. After loading from file, 
-and when remote queue jobs are completed, one can invoke :code:`get()` and the results will 
-be retrieved and returned. 
-
-:code:`cudaq::observe`
--------------------------
-**[1]** A common task in variational algorithms is the computation of the expected
-value of a given observable with respect to a parameterized quantum circuit
-(:math:`\langle H \rangle(𝚹) = \langle \psi(𝚹)|H|\psi(𝚹) \rangle`). 
-
-**[2]** The :code:`cudaq::observe` function is provided to enable one to quickly compute
-this expectation value via execution of the parameterized quantum circuit
-with repeated measurements in the bases of the provided :code:`spin_op` terms. The
-function has the following signature:
-
-.. code-block:: cpp
-  
-    // Kernel only
-    template<typename QuantumKernel, typename... Args>
-    observe_result observe(QuantumKernel&&, cudaq::spin_op&, Args&&... args);
-    
-    // Specify shots
-    template<typename QuantumKernel, typename... Args>
-    observe_result observe(std::size_t shots, QuantumKernel&&, cudaq::spin_op&, Args&&... args);
-
-    // Specify sample options (including shots and noise model)
-    template<typename QuantumKernel, typename... Args>
-    observe_result observe(const cudaq::observe_options &options,
-                          QuantumKernel&&, cudaq::spin_op&, Args&&... args);
-
-**[3]** :code:`cudaq::observe` takes as input an instantiated quantum kernel, the
-:code:`cudaq::spin_op` whose expectation is requested, and the concrete
-arguments used as input to the parameterized quantum kernel. 
-
-**[4]** :code:`cudaq::observe` returns an instance of the :code:`observe_result` type which can be implicitly 
-converted to a :code:`double` expectation value, but also retains all data directly
-generated and used as part of that expectation value computation. The 
-:code:`observe_result` takes on the following form:
-
-.. code-block:: cpp
-
-    class observe_result {
-      public:
-        observe_result(double &e, spin_op &H);
-        observe_result(double &e, spin_op &H, MeasureCounts counts);
-
-        sample_results raw_data() { return data; };
-        operator double();
-        double expectation();
-        
-        template <typename SpinOpType>
-        double expectation(SpinOpType term);
-
-        template <typename SpinOpType>
-        sample_result counts(SpinOpType term);
-        double id_coefficient() 
-        void dump();
-    };
-
-**[5]** The public API for :code:`observe_result` enables one to extract the 
-:code:`sample_result` data for each term in the provided :code:`spin_op`. 
-This return type can be used in the following way.
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-      // I only care about the expected value, discard 
-      // the fine-grain data produced
-      double expVal = cudaq::observe(kernel, spinOp, args...);
-
-      // I require the result with all generated data 
-      auto result = cudaq::observe(kernel, spinOp, args...);
-      auto expVal = result.expectation();
-      auto X0X1Exp = result.expectation(x(0)*x(1));
-      auto X0X1Data = result.counts(x(0)*x(1));
-      result.dump();
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    # I require the result with all generated data 
-    result = cudaq::observe(kernel, spinOp, *args)
-    expVal = result.expectation()
-    X0X1Exp = result.expectation(x(0)*x(1))
-    X0X1Data = result.counts(x(0)*x(1))
-    result.dump()
-
-
-Here is an example of the utility of the :code:`cudaq::observe` function:
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      struct ansatz {
-        auto operator()(double theta) __qpu__ {
-          cudaq::qarray<2> q;
-          x(q[0]);
-          ry(theta, q[1]);
-          x<cudaq::ctrl>(q[1], q[0]);
-        }
-      };
-    
-      int main() {
-        using namespace cudaq::spin; // make it easier to use pauli X,Y,Z below
-    
-        spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                    .21829 * z(0) - 6.125 * z(1);
-    
-        double energy = cudaq::observe(ansatz{}, h, .59);
-        printf("Energy is %lf\n", energy); 
-        return 0;
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def ansatz(theta : float): 
-       q = cudaq.qvector(2)
-       x(q[0])
-       ry(theta, q[1])
-       x.ctrl(q[1], q[0])
-    
-    h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                    .21829 * z(0) - 6.125 * z(1)
-    energy = cudaq.observe(ansatz, h, .59).expectation() 
-    print('Energy is {}'.format(energy))
-
-
-**[5]** There are specific requirements on input quantum kernels for the use of the
-observe function which must be enforced by compiler implementations. The
-kernel must be an entry-point kernel that does not contain any conditional
-or measurement statements.
-
-**[6]** By default on simulation backends, :code:`cudaq::observe` computes the true
-analytic expectation value (i.e. without stochastic noise due to shots-based sampling). 
-If a specific shot count is provided then the returned expectation value will contain some 
-level of statistical noise. Overloaded :code:`observe` functions are provided to 
-specify the number of shots and/or specify the noise model to apply.
-
-**[7]** CUDA-Q also provides an asynchronous version of this function 
-(:code:`cudaq::observe_async`) which returns a :code:`async_observe_result`. 
-
-.. code-block:: cpp 
-
-    template<typename QuantumKernel, typename... Args>
-    async_observe_result observe_async(const std::size_t qpu_id, QuantumKernel&& kernel, cudaq::spin_op&, Args&&... args);
-
-Programmers can asynchronously launch sampling tasks on any :code:`qpu_id`. 
-
-**[8]** For remote QPU systems with long queue times, the :code:`async_observe_result` type encodes job ID 
-information for each execution and can be persisted to file and loaded from file at a later time. After loading from file, 
-and when remote queue jobs are completed, one can invoke :code:`get()` and the results will 
-be retrieved and returned. 
-
-:code:`cudaq::optimizer` (deprecated, functionality moved to CUDA-Q libraries)
-------------------------------------------------------------------------------------
-The primary use case for :code:`cudaq::observe` is to leverage it as
-the core of a broader objective function optimization workflow. 
-:code:`cudaq::observe` produces the expected value of a specified 
-:code:`spin_op` with respect to a given parameterized ansatz at a concrete
-set of parameters, and often programmers will require an extremal value of that expected value 
-at a specific set of concrete parameters. This will directly require
-abstractions for gradient-based and gradient-free optimization strategies. 
-
-The CUDA-Q model provides a :code:`cudaq::optimizer` data type that exposes
-an :code:`optimize()` method that takes as input an 
-:code:`optimizable_function` to optimize and the number of independent
-function dimensions. Implementations are free to implement this abstraction
-in any way that is pertinent, but it is expected that most approaches will
-enable optimization strategy extensibility. For example, programmers should
-be able to instantiate a specific :code:`cudaq::optimizer` sub-type, thereby 
-dictating the underlying optimization algorithm in a type-safe manner. 
-Moreover, the optimizer should expose a public API of pertinent optimizer-specific 
-options that the programmer can customize.
-
-CUDA-Q models the :code:`cudaq::optimizer` as follows:
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      // Encode the optimal value and optimal parameters
-      using optimization_result = std::tuple<double, std::vector<double>>;
-      // Initialized with user specified callable of a specific signature
-      // Clients can query if the function computes gradients or not
-      class optimizable_function {
-        public:
-          template<typename Callable>
-          optimizable_function(Callable&&);
-          bool providesGradients() { return _providesGradients; }
-          double operator()(const std::vector<double> &x, std::vector<double> &dx);
-      };
-      class optimizer {
-        public:
-          virtual bool requiresGradients() = 0;
-          virtual optimization_result optimize(const int dimensions,
-                                              optimizable_function&& opt_function) = 0;
-      }; 
-    }
-
-Here, :code:`optimization_result` should encode the optimal value and optimal
-parameters achieved during the optimization workflow
-(i.e. a :code:`tuple<double, std::vector<double>>`). The optimize method takes
-as input the number of parameters (or dimensions of the objective function),
-and a function-like object (i.e. :code:`std::function` or a lambda, something 
-:code:`optimizable_function` can be constructed from) that takes a 
-:code:`const std::vector<double>&` and :code:`std::vector<double>&` for the
-function input parameters and gradient vector, respectively. The objective
-function must return a double representing the scalar cost for the
-objective function (e.g. the expected value from :code:`cudaq::observe()`).  
-
-Here is an example of how the :code:`cudaq::optimizer` is intended to be used: 
-
-.. code-block:: cpp 
-
-    auto ansatz = [](double theta, double phi) __qpu__ {...};
-    cudaq::spin_op H = ... ;
-  
-    cudaq::optimizers::cobyla optimizer;
-    optimizer.max_eval = 200;
-  
-    auto [opt_energy, opt_params] = optimizer.optimize(
-          2, [&](const std::vector<double> &x, std::vector<double> &grad_vec) {
-            return cudaq::observe(ansatz, H, x[0], x[1]);
-          });
-
-:code:`cudaq::gradient` (deprecated, functionality moved to CUDA-Q libraries)
------------------------------------------------------------------------------------
-Typical optimization use cases will require the computation of gradients for the specified
-objective function. The gradient is a vector over all ansatz circuit
-parameters :math:`∂H(𝚹) / ∂𝚹_i`. There are a number of potential strategies for
-computing this gradient vector, but most require additional evaluations
-of the ansatz circuit on the quantum processor. 
-
-To enable true extensibility in gradient strategies, CUDA-Q programmers can
-instantiate custom sub-types of the :code:`cudaq::gradient` type. The :code:`cudaq::gradient`
-type defines a :code:`compute(...)` method that takes a mutable reference to the 
-current gradient vector and is free to update that vector in a strategy-specific way. 
-The method also takes the current evaluation parameter vector, the :code:`cudaq::spin_op` used 
-in the current variational task, and the computed expected value at the given parameters. 
-The gradient strategy type takes the following form:
-
-.. code-block:: cpp
-
-    namespace cudaq {
-      class gradient {
-        public:
-          gradient(std::function<void(std::vector<double>)> &&kernel);
-  
-          template <typename QuantumKernel, typename ArgsMapper>
-          gradient(QuantumKernel &&kernel, ArgsMapper &&argsMapper);
-  
-          virtual void compute(std::vector<double>& x, std::vector<double> &dx,
-                            spin_op& h, double exp_h) = 0;
-          
-          virtual std::vector<double>
-          compute(const std::vector<double> &x,
-                  std::function<double(std::vector<double>)> &func) = 0;
-
-      };
-  
-      // gradient is intended for subclassing
-      class central_difference : public gradient {
-        public:
-          void compute(std::vector<double>& x, std::vector<double> &dx, spin_op& h,
-                  double exp_h) override { ... }
-      };
-    }
-
-The :code:`compute` function can make use of the quantum kernel parameterized ansatz, the 
-:code:`spin_op` for which the expected value is being computed, the
-pre-computed expected value at the current iteration's parameter, and the
-concrete arguments for the given quantum kernel at this iteration.
-
-A non-trivial aspect of the computation of gradients (in an extensible manner)
-is that we model the gradient as a derivative over concrete parameters for the
-circuit ansatz represented as a :code:`std::vector<double>` when the actual
-quantum kernel may be defined with general variadic :code:`Args...` types.
-To address this issue, programmers can provide a default translation
-mechanism for mapping common quantum kernel ansatz functional expressions to a :code:`vector<double>` representation - the 
-:code:`ArgsMapper` callable template type. This type must implement the 
-:code:`std::tuple<Args...>(std::vector<double>&)` callable concept. 
-
-The overall CUDA-Q workflow for leveraging the :code:`cudaq::optimizer`
-will work as follows (here we demonstrate with an ansatz without the
-default :code:`std::vector<double>` signature):
-
-.. code-block:: cpp
-
-    auto deuteron_n3_ansatz = [](double x0, double x1) __qpu__ {
-      cudaq::qarray<3> q;
-      x(q[0]);
-      ry(x0, q[1]);
-      ry(x1, q[2]);
-      x<cudaq::ctrl>(q[2], q[0]);
-      x<vctrl>(q[0], q[1]);
-      ry(-x0, q[1]);
-      x<cudaq::ctrl>(q[0], q[1]);
-      x<cudaq::ctrl>(q[1], q[0]);
-    };
-
-    cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-              .21829 * z(0) - 6.125 * z(1);
-    cudaq::spin_op h3 = h + 9.625 - 9.625 * z(2) - 3.913119 * x(1) * x(2) -
-                3.913119 * y(1) * y(2);
-
-    // The above ansatz takes 2 doubles, not a single std::vector<double>, which 
-    // the gradient type is expecting. So we must provide an ArgsMapper callable type
-    auto argsMapper = [](std::vector<double> x) {return std::make_tuple(x[0],x[1]);};
-
-    // Create the gradient strategy
-    cudaq::gradients::central_difference gradient(deuteron_n3_ansatz, argsMapper);
-
-    // Create the L-BFGS optimizer, requires gradients
-    cudaq::optimizers::lbfgs optimizer;
-
-    // Run the optimization routine. 
-    auto [min_val, opt_params] = optimizer.optimize(
-        2, [&](const std::vector<double>& x, std::vector<double>& grad_vec) {
-          // Compute the cost, here its an energy
-          auto cost = cudaq::observe(deuteron_n3_ansatz, h3, x);
-          
-          // Compute the gradient, results written to the grad_vec reference
-          gradient.compute(x, grad_vec, h3, cost);
-
-          // Return the cost to the optimizer
-          return cost;
-        });
-
-    // Print the results
-    printf("Optimizer found %lf at [%lf,%lf]\n", min_val, opt_params[0], opt_params[1]);
-
diff --git a/pr-2308/sphinx/specification/cudaq/control_flow.rst b/pr-2308/sphinx/specification/cudaq/control_flow.rst
deleted file mode 100644
index da3120dd74..0000000000
--- a/pr-2308/sphinx/specification/cudaq/control_flow.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-Control Flow
-************
-**[1]** All classical control flow expressions are inherited from the host language
-(e.g. C++, Python). 
-
-**[2]** Programmers can define quantum kernels using standard loop
-constructs and conditional statements. 
-
-**[3]** Conditional statements on quantum memory and measured :code:`cudaq::qudit` 
-results are permitted. 
diff --git a/pr-2308/sphinx/specification/cudaq/dynamic_kernels.rst b/pr-2308/sphinx/specification/cudaq/dynamic_kernels.rst
deleted file mode 100644
index 8681ad6a62..0000000000
--- a/pr-2308/sphinx/specification/cudaq/dynamic_kernels.rst
+++ /dev/null
@@ -1,163 +0,0 @@
-Just-in-Time Kernel Creation
-****************************
-
-**[1]** CUDA-Q provides a set of programming abstractions for dynamically constructing
-quantum kernel code at runtime. 
-
-**[2]** The callable :code:`cudaq::kernel_builder` abstraction
-facilitates the dynamic definition of quantum kernels that can optionally
-be parameterized by user-defined input arguments. 
-
-The :code:`kernel_builder` takes the following structure
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      // Type wrapping a value in the kernel IR
-      struct Value;
-      template <typename... Args>
-      class kernel_builder {
-        private:
-          std::vector<Value> arguments;
-
-        public:
-          std::vector<Value>& getArguments();
-          std::string name() const;
-          std::size_t getNumParams() const;
-
-          Value qalloc(std::size_t nQubits = 1);
-          Value qalloc(Value size);
-
-          void h(Value& qubit);
-          //... all other quantum operations ... 
-
-          // if (mz(q)) thenFunctor()
-          void c_if(Value& result, std::function<void()>&& thenFunctor);
-          
-          // Invoke a predefined kernel
-          template<typename OtherKernelBuilder, typename... Values>
-          void call(OtherKernelBuilder&& kernelToCall, Values&... args);
-          
-          // General multi-control on a predefined kernel
-          // models cudaq::control(...)
-          template<typename OtherKernelBuilder, typename... Values>
-          void control(OtherKernelBuilder&& kernelToControl, Value& ctrl, Values&... values);
-          
-          // General adjoint on a predefined kernel
-          // models cudaq::adjoint(...)
-          template<typename OtherKernelBuilder, typename... Values>
-          void adjoint(OtherKernelBuilder&& kernelToAdjoint, Values&... values);
-       
-          // The constructed kernel is callable
-          void operator()(Args... args);
-          void operator()(void** argsArray);
-
-          // Enable structured bindings
-          template <std::size_t N>
-          decltype(auto) get() {
-            if constexpr (N == 0)
-              return *this;
-            else
-              return arguments[N - 1];
-          }
-      };
-    }
-
-    /// Enable structured bindings on the kernel_builder type.
-    /// auto [kernel, theta, phi] = std::make_kernel<double,double>();
-    namespace std {
-      template <typename... Args> 
-      struct tuple_size<cudaq::kernel_builder<Args...>>
-        : std::integral_constant<std::size_t, sizeof...(Args) + 1> {};
-
-      template <std::size_t N, typename... Args>
-      struct tuple_element<N, cudaq::kernel_builder<Args...>> {
-        using type = std::conditional_t<N == 0, cudaq::kernel_builder<Args...>,
-                                  cudaq::QuakeValue>;
-      };
-    } // namespace std
-
-    namespace cudaq {
-      kernel_builder<> make_kernel();
-      template<typename... Args>
-      kernel_builder<Args...> make_kernel();
-    }
-
-**[3]** The structure above allows one to leverage the provided factory functions (:code:`make_kernel`)
-to construct an empty CUDA-Q kernel with defined argument signature. 
-
-**[4]** For each type in the signature, the programmer is returned a new :code:`Value` instance which 
-can be leveraged in the construction of the kernel. The intended mechanism for 
-kernel creation and argument value extraction is via standard C++ 
-`structured bindings <https://en.cppreference.com/w/cpp/language/structured_binding>`_.
-
-**[5]** Once the kernel is created, the programmer is free to build up the kernel expression 
-using the exposed API. There are methods for qubit allocation, quantum operation 
-invocation, control and adjoint synthesis, and conditional branching based on 
-`boolean` values. 
-
-Here is a simple example how one might build a CUDA-Q kernel dynamically. 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    auto kernel = cudaq::make_kernel();
-    auto qubits = kernel.qalloc(2);
-    kernel.h(qubits[0]);
-    kernel.x<cudaq::ctrl>(qubits[0], qubits[1]);
-    kernel.mz(qubits);
-    
-    // See algorithmic primitives section for more on sample
-    auto counts = cudaq::sample(kernel);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    kernel = cudaq.make_kernel()
-    qubits = kernel.qalloc(2)
-    kernel.h(qubits[0])
-    kernel.cx(qubits[0], qubits[1])
-    kernel.mz(qubits)
-
-Here is an example demonstrating how one may build a dynamic set of 
-CUDA-Q kernels for executing the standard Hadamard test. 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      auto [xPrep, qubitIn] = cudaq::make_kernel<cudaq::qubit>();
-      xPrep.x(qubitIn);
-
-      // Compute <1|X|1> = 0
-      auto hadamardTest = cudaq::make_kernel();
-      auto q = hadamardTest.qalloc();
-      auto ancilla = hadamardTest.qalloc();
-      hadamardTest.call(xPrep, q);
-      hadamardTest.h(ancilla);
-      hadamardTest.control(xPrep, ancilla, q);
-      hadamardTest.h(ancilla);
-      hadamardTest.mz(ancilla);
-
-      // See algorithmic primitives section for more on sample
-      auto counts = cudaq::sample(hadamardTest);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    xPrep, qubitIn = cudaq.make_kernel(cudaq.qubit)
-
-    # Compute <1|X|1> = 0
-    hadamardTest = cudaq.make_kernel()
-    q = hadamardTest.qalloc()
-    hadamardTest.call(xPrep, q)
-    hadamardTest.h(ancilla)
-    hadamardTest.control(xPrep, ancilla, q)
-    hadamardTest.h(ancilla)
-    hadamardTest.mz(ancilla)
-
-    # See algorithmic primitives section for more on sample
-    counts = cudaq.sample(hadamardTest)
diff --git a/pr-2308/sphinx/specification/cudaq/examples.rst b/pr-2308/sphinx/specification/cudaq/examples.rst
deleted file mode 100644
index c67874b4c1..0000000000
--- a/pr-2308/sphinx/specification/cudaq/examples.rst
+++ /dev/null
@@ -1,270 +0,0 @@
-
-Example Programs
-****************
-Hello World - Simple Bell State
--------------------------------
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      #include <cudaq.h>
-
-      struct bell {
-        int operator()(int num_iters) __qpu__ {
-          cudaq::qarray<2> q;
-          int nCorrect = 0;
-          for (int i = 0; i < num_iters; i++) {
-            h(q[0]);
-            x<cudaq::ctrl>(q[0], q[1]);
-            auto results = mz(q);
-            if (results[0] == results[1]) 
-              nCorrect++;
-            
-            reset(q[0]);
-            reset(q[1]);
-          }
-          return nCorrect;
-        }
-      };
-
-      int main() { printf("N Correct = %d\n", bell{}(100)); }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq 
-
-    @cudaq.kernel()
-    def bell(num_iters : int) -> int:
-        q = cudaq.qvector(2)
-        nCorrect = 0
-        for i in range(num_iters):
-            h(q[0])
-            x.ctrl(q[0], q[1])
-            results = mz(q)
-            if results[0] == results[1]:
-               nCorrect = nCorrect + 1
-            
-            reset(q)
-        return nCorrect 
-
-    counts = bell(100)
-    print(f'N Correct = {counts}')
-    assert counts == 100
-
-GHZ State Preparation and Sampling
-----------------------------------
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-  
-      #include <cudaq.h>
-
-      __qpu__ ghz(const int n_qubits) {
-        cudaq::qvector q(n_qubits);
-        h(q[0]);
-        for (int i = 0; i < n_qubits - 1; ++i) 
-          // note use of ctrl modifier
-          x<cudaq::ctrl>(q[i], q[i+1]); 
-        
-        mz(q);
-      }
-
-      int main() {
-        // Sample the state produced by the ghz kernel
-        auto counts = cudaq::sample(ghz, 10);
-        for (auto [bits, count] : counts) {
-          printf("Observed %s %lu times.\n", bits.c_str(), count);
-        }
-        return 0;
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq 
-    
-    @cudaq.kernel
-    def ghz(numQubits:int):
-        qubits = cudaq.qvector(numQubits)
-        h(qubits.front())
-        for i, qubit in enumerate(qubits.front(numQubits - 1)):
-            x.ctrl(qubit, qubits[i + 1])
-    
-    counts = cudaq.sample(ghz, 10)
-    for bits, count : counts:
-        print('Observed {} {} times.'.format(bits, count))
-
-Quantum Phase Estimation
-------------------------
-
-.. tab:: C++ 
-
-  .. literalinclude:: ../../applications/cpp/phase_estimation.cpp
-      :language: cpp
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq, numpy as np
-
-    # Compute phase for U |psi> = exp(-2 pi phase) |psi> 
-    # This example will consider U = T, and |psi> = |1>
-    # Define a Inverse Quantum Fourier Transform kernel
-    @cudaq.kernel
-    def iqft(qubits: cudaq.qview):
-        N = qubits.size()
-        for i in range(N // 2):
-            swap(qubits[i], qubits[N - i - 1])
-
-        for i in range(N - 1):
-            h(qubits[i])
-            j = i + 1
-            for y in range(i, -1, -1):
-                r1.ctrl(-np.pi / 2**(j - y), qubits[j], qubits[y])
-
-        h(qubits[N - 1])
-
-
-    # Define the U kernel
-    @cudaq.kernel
-    def tGate(qubit: cudaq.qubit):
-        t(qubit)
-
-
-    # Define the state preparation |psi> kernel
-    @cudaq.kernel
-    def xGate(qubit: cudaq.qubit):
-        x(qubit)
-
-    # General Phase Estimation kernel for single qubit 
-    # eigen states. 
-    @cudaq.kernel
-    def qpe(nC: int, nQ: int, statePrep: Callable[[cudaq.qubit], None],
-            oracle: Callable[[cudaq.qubit], None]):
-        q = cudaq.qvector(nC + nQ)
-        countingQubits = q.front(nC)
-        stateRegister = q.back()
-        statePrep(stateRegister)
-        h(countingQubits)
-        for i in range(nC):
-            for j in range(2**i):
-                cudaq.control(oracle, [countingQubits[i]], stateRegister)
-        iqft(countingQubits)
-        mz(countingQubits)
-
-    # Sample the state to get the phase. 
-    counts = cudaq.sample(qpe, 3, 1, xGate, tGate)
-    assert len(counts) == 1
-    assert '100' in counts
-
-
-Deuteron Binding Energy Parameter Sweep
----------------------------------------
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-      #include <cudaq.h>
-
-      struct deuteron_n2_ansatz {
-        void operator()(double theta) __qpu__ {
-          cudaq::qarray<2> q;
-          x(q[0]);
-          ry(theta, q[1]);
-          x<cudaq::ctrl>(q[1], q[0]);
-        }
-      };
-
-      int main() {
-        using namespace cudaq::spin;
-        cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                    .21829 * z(0) - 6.125 * z(1);
-    
-        // Perform parameter sweep for deuteron N=2 Hamiltonian
-        const auto param_space = cudaq::linspace(-M_PI, M_PI, 25);
-        for (const auto& param : param_space) {
-          // KERNEL::observe(...) <==> 
-          // E(params...) = <psi(params...) | H | psi(params...)>
-          double energy_at_param = cudaq::observe(deuteron_n2_ansatz{}, h, param);
-          printf("<H>(%lf) = %lf\n", param, energy_at_param);
-        }
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    import cudaq 
-    from cudaq import spin 
-
-    @cudaq.kernel
-    def ansatz(angle:float):
-        q = cudaq.qvector(2)
-        x(q[0])
-        ry(angle, q[1])
-        x.ctrl(q[1], q[0])
-
-    hamiltonian = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(
-        0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-    # Perform parameter sweep for deuteron N=2 Hamiltonian
-    for angle in np.linspace(-np.pi, np.pi, 25):
-         # KERNEL::observe(...) <==> 
-         # E(params...) = <psi(params...) | H | psi(params...)>
-         energyAtParam = cudaq.observe(ansatz, hamiltonian, .59)
-         print('<H>({}) = {}'.format(angle, energyAtParam))
-
-
-Grover's Algorithm
-------------------
-
-.. tab:: C++ 
-
-  .. literalinclude:: ../../applications/cpp/grover.cpp
-      :language: cpp
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel 
-    def reflect(qubits: cudaq.qview):
-        ctrls = qubits.front(qubits.size() - 1)
-        last = qubits.back()
-        cudaq.compute_action(lambda: (h(qubits), x(qubits)),
-                              lambda: z.ctrl(ctrls, last))
-
-    @cudaq.kernel
-    def oracle(q: cudaq.qview):
-        z.ctrl(q[0], q[2])
-        z.ctrl(q[1], q[2])
-
-    @cudaq.kernel
-    def grover(N: int, M: int, oracle: Callable[[cudaq.qview], None]):
-        q = cudaq.qvector(N)
-        h(q)
-        for i in range(M):
-            oracle(q)
-            reflect(q)
-        mz(q)
-
-    counts = cudaq.sample(grover, 3, 1, oracle)
-    assert len(counts) == 2
-    assert '101' in counts
-    assert '011' in counts
-
-
-Iterative Phase Estimation
---------------------------
-
-.. literalinclude:: ../../applications/cpp/iterative_qpe.cpp
-    :language: cpp
-    :start-after: [Begin Documentation]
-    :end-before: [End Documentation]
diff --git a/pr-2308/sphinx/specification/cudaq/kernels.rst b/pr-2308/sphinx/specification/cudaq/kernels.rst
deleted file mode 100644
index a0c104bb3d..0000000000
--- a/pr-2308/sphinx/specification/cudaq/kernels.rst
+++ /dev/null
@@ -1,362 +0,0 @@
-Quantum Kernels
-***************
-**[1]** To differentiate between host and quantum device code, the CUDA-Q programming
-model defines the concept of a **quantum kernel**. A quantum kernel is any callable 
-in C++ annotated to indicate compilation and execution on an available quantum coprocessor. 
-
-**[2]** All quantum kernels must be annotated to indicate they are to be compiled for, and executed
-on, a specified quantum coprocessor. CUDA-Q requires the :code:`__qpu__` function
-attribute for quantum kernel declarations. Other language bindings may opt to use other language 
-features to enable function annotation or decoration (e.g. a :code:`@cudaq.kernel()` function 
-decorator in Python). 
-
-**[3]** CUDA-Q specifically differentiates between kernels invoked from host code and those invoked
-from within another quantum kernel. The former are denoted **entry-point**
-quantum kernels, the latter are **pure device** quantum kernels. Entry point kernels can 
-only take classical types as input and return classical output. Pure-device kernels 
-can take quantum types as input.
-
-.. tab:: C++ 
-  
-  .. code-block:: cpp
-  
-      // Entry point lambda kernel
-      auto my_first_entry_point_kernel = [](double x) __qpu__ { ... quantum code ... };
-      
-      // Entry point typed callable kernel
-      struct my_second_entry_point_kernel { 
-        void operator()(double x, std::vector<double> params) __qpu__ { 
-          ... quantum code ...
-        }
-      };
-
-      // Pure device free function kernel
-      __qpu__ void my_first_pure_device_kernel(cudaq::qview<> qubits) {
-         ... quantum code ... 
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def my_first_entry_point_kernel(x : float):
-       ... quantum code ... 
-    
-    @cudaq.kernel()
-    def my_second_entry_point_kernel(x : float, params : list[float]):
-       ... quantum code ... 
-
-    @cudaq.kernel()
-    def my_first_pure_device_kernel(qubits : cudaq.qview):
-       ... quantum code ... 
-    
-**[4]** Quantum kernel function bodies are programmed in a subset of the parent classical language. 
-Kernels can be composed of the following: 
-
-  * Quantum intrinsic operations and measurements
-  * In-scope kernel calls with or without any kernel modifiers (e.g. :code:`cudaq::control`)
-  * Classical control flow constructs from the classical language (:code:`if`, :code:`for`, :code:`while`, etc.)
-  * Stack variable declarations for supported types. 
-  * Arithmetic operations on integer and floating point stack variables
-  * Coherent conditional execution - :code:`if ( boolExprFromQubitMeasurement ) { x (another_qubit); }` 
-  * Syntax for common quantum programming patterns (e.g. compute-action-uncompute).
-
-
-**[5]** CUDA-Q defines a set of allowed types that can be leveraged in quantum kernel 
-function signatures (input and return types), in-function variable declaration or construction, 
-and in variable capture from parent scope. 
-
-The allowed types are as follows: 
-
-  * :code:`T` such that :code:`std::is_arithmetic_v<T> == true`
-  * :code:`std::vector<T>` such that :code:`std::is_arithmetic_v<T> == true`
-  * :code:`std::span<T>` such that :code:`std::is_arithmetic_v<T> == true`
-  * :code:`std::vector<V>` such that type :code:`V` is a valid :code:`std::vector<T>` (possibly recursively)
-  * :code:`struct` types composed of any valid CUDA-Q type.
-  * :code:`std::tuple<Ts...>` such that T is any allowed type.
-  * :code:`std::pair<T,U>` such that T is any allowed type.
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-    struct MyCustomSimpleStruct {
-       int i = 0;
-       int j = 0;
-       std::vector<double> angles;
-    }; 
-
-    // Valid CUDA-Q Types used in Kernels
-    auto kernel = [](int N, bool flag, float angle, std::vector<std::size_t> layers,
-             std::vector<double> parameters, std::vector<std::vector<float>> recursiveVec, 
-             MyCustomSimpleStruct var) __qpu__ { ... }
-    
-    __qpu__ double kernelThatReturns() { 
-       ... 
-       return M_PI_2;
-    }
-    
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @dataclass
-    class MySimpleStruct:
-        i : int
-        j : int
-
-    @cudaq.kernel
-    def kernel(N : int, flag : bool, angle : float, layers : list[int], 
-                parameters : list[float], recursiveList : list[list[float]], 
-                var : MySimpleStruct): ... 
-    
-    @cudaq.kernel 
-    def kernelThatReturns() -> float:
-        ... 
-        return np.pi / 2.0
-
-**[6]** Any variable with an allowed CUDA-Q type can be allocated on the stack within 
-CUDA-Q kernels. Variables of type :code:`std::vector<T>` for any allowed type 
-:code:`T` can only be constructed with known size. Vector-like variables cannot be 
-default constructed and later filled with type :code:`T` data (i.e. no dynamic memory allocation). 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-    // Valid CUDA-Q Types used in Kernels
-    auto kernel = []() __qpu__ {
-      
-      // Not Allowed. 
-      // std::vector<int> i; 
-      // i.push_back(1);
-
-      // Valid variable declarations
-
-      std::vector<int> i(5);
-      i[2] = 3; 
-
-      std::vector<float> f {1.0, 2.0, 3.0};
-
-      int k = 0;
-
-      double pi = 3.1415926
-    };
-    
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel
-    def kernel(): 
-       # Not Allowed 
-       # i = [] 
-       # i.append(1)
-
-       i = [0 for k in range(5)]
-       i[2] = 3 
-
-       f = [1., 2., 3.]
-
-       k = 0 
-
-       pi = 3.1415926
-
-**[7]** All entry-point kernel arguments adhere to pass-by-value semantics. 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    auto kernel = [](int i, std::vector<double> v) __qpu__ {
-       // i == 2, allocate 2 qubits 
-       cudaq::qvector q(i); 
-       // v[1] == 2.0, angle here is 2.0
-       ry(v[1], q[0]);
-
-       // Change the variables, caller does not see this
-       i = 5; 
-       v[0] = 3.0;
-    };
-
-    int k = 2;
-    std::vector<double> d {1.0, 2.0};
-    
-    kernel(k, d);
-
-    // k is still 2, pass by value 
-    // d is still {1.0, 2.0}, pass by value 
-  
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel 
-    def kernel(i : int, v : list[float]): 
-        # i = 2, allocate 2 qubits 
-        q = cudaq.qvector(i)
-        # v[1] == 2.0, angle here is 2.0 
-        ry(v[1], q[0])
-
-        # Change the variables, caller does not see this 
-        i = 5 
-        v[0] = 3.0 
-
-    k, d = 2, [1., 2.]
-    kernel(i, d)
-
-    # k is still 2, pass by value 
-    # d is still {1.0, 2.0}, pass by value 
-
-
-.. FIXME Pass by value vs reference, should we mandate pass by reference for inter-kernel calls
-
-**[8]** CUDA-Q kernel lambdas in C++ can capture variables of allowed type 
-by value. CUDA-Q kernels defined as custom callable types can define non-reference type 
-class members of any allowed type. These member variables can be set with 
-pass-by-value semantics at kernel construction. 
-
-.. tab:: C++ 
-
-  .. code-block:: C++ 
-
-    struct kernel {
-      int i; 
-      float f; 
-
-      void operator()() __qpu__ {
-         cudaq::qvector q(i); 
-         ry(f, q[0]);
-      }
-    };
-
-    kernel{2, 2.2}();
-
-    int i = 2; 
-    double f = 2.2;
-
-    auto kernelLambda = [=]() __qpu__ {
-      // Use captured variables 
-      cudaq::qvector q(i); 
-      ry(f, q[0]);
-      i = 5; 
-      return i; 
-    };
-
-    auto k = kernelLambda(); 
-
-    // Pass by value semantics 
-    assert(k != i);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    i = 2 
-    f = np.pi / 2. 
-
-    @cudaq.kernel
-    def kernel() -> int:
-       # Use captured variables 
-       q = cudaq.qvector(i) 
-       ry(f, q[0])
-
-       i = 5 
-       return i 
-    
-    k = kernel() 
-
-    # Pass by value semantics 
-    assert k != i 
-
-**[9]** All quantum kernel invocations are synchronous calls by default. 
-
-**[10]** CUDA-Q kernels can serve as input to other quantum kernels and invoked by kernel 
-function body code. To support CUDA-Q kernel parameterization on callable quantum kernel 
-code, programmers can leverage standard C++ template definitions or dynamic typing in language bindings such as Python:
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-      __qpu__ void MyStatePrep(cudaq::qview<> qubits) {
-          ... apply state prep operations on qubits ...
-      }
-
-      struct MyGenericAlgorithm {
-        template<typename StatePrep>
-        void operator()(const StatePrep& statePrep) __qpu__ {
-          cudaq::qarray<10> q;
-          statePrep(q);
-          ...
-        }
-      };
-
-      // -or- with placeholder type specifiers
-      struct MyGenericAlgorithm2 {
-        void operator()(const auto& statePrep) __qpu__ {
-          cudaq::qarray<10> q;
-          statePrep(q);
-          ...
-        }
-      };
-
-      void callKernels() {
-
-        MyGenericAlgorithm algorithm;
-        algorithm(MyStatePrep);
-
-        MyGenericAlgorithm2 anotherVersion;
-        anotherVersion(MyStatePrep);
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def MyStatePrep(qubits : cudaq.qview):
-        ... apply state prep operations on qubits ... 
-    
-    @cudaq.kernel()
-    def MyGenericAlgorithm(statePrep : typing.Callable[[cudaq.qview], None]):
-        q = cudaq.qvector(10)
-        statePrep(q)
-        ...
-    
-    MyGenericAlgorithm(MyStatePrep)
-
-CUDA-Q kernel inputs can also be `constrained <https://en.cppreference.com/w/cpp/language/constraints>`_. 
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-
-      // Generic constraint on Kernel Function Signatures
-      template <typename Kernel, typename Signature>
-      concept signature = std::is_convertible_v<Kernel, std::function<Signature>>; 
-
-      // Specialized for taking a single qubit
-      template<typename Kernel>
-      concept takes_qubit = signature<Kernel, void(qubit&)>;
-    }
-
-    struct MyGenericAlgorithmOnQarray {
-      void operator()(cudaq::signature<void(cudaq::qarray&)> auto&& statePrep) __qpu__ {
-        cudaq::qarray<10> q;
-        statePrep(q);
-        ...
-      }
-    };
-
-    struct MyGenericAlgorithmOnQubit {
-      void operator()(cudaq::takes_qubit auto&& statePrep) __qpu__ {
-        cudaq::qarray<10> q;
-        statePrep(q[0]);
-        ...
-      }
-    };
-
-This approach enables the development of generic libraries of quantum 
-algorithms that are parameterized on sub-units of the global circuit representation. 
diff --git a/pr-2308/sphinx/specification/cudaq/machine_model.rst b/pr-2308/sphinx/specification/cudaq/machine_model.rst
deleted file mode 100644
index e2389c6dfc..0000000000
--- a/pr-2308/sphinx/specification/cudaq/machine_model.rst
+++ /dev/null
@@ -1,59 +0,0 @@
-Machine Model
-*************
-
-.. role:: raw-html(raw)
-    :format: html 
-
-:raw-html:`<a name="mm1"></a>`
-
-**[1]** CUDA-Q presumes the existence of one or more classical host processors, 
-zero or more NVIDIA graphics processing units (GPUs), and zero or more quantum processing units (QPUs). 
-
-**[2]** Each QPU is composed of a classical quantum control system (distributed FPGAs, GPUs, etc.) 
-and a register of quantum bits (qubits) whose state evolves via signals from the classical control system.
-
-**[3]** The machine model allows for three modes of quantum process parallelism: concurrent execution
-of quantum circuits on independent QPUs, dependent quantum parallelism via
-quantum message passing and inter-QPU entanglement, and QPU thread-level
-parallelism or the ability for concurrent execution of independent quantum
-circuits on a single QPU qubit connectivity fabric. 
-
-**[4]** The model assumes a
-classical memory space for the host processor which inherits the native language memory
-model semantics (e.g. C++ or Python). 
-
-**[5]** The model assumes a classical memory space for each control
-system driving the evolution of the multi-qubit state. This control system
-memory space should enable primitive arithmetic variable declarations,
-stores, and loads, as well as qubit measurement persistence and loading
-for fast-feedback and conditional circuit execution. 
-
-**[6]** The quantum memory
-space on an individual QPU is modeled as an infinite register of 
-qubits and physical connectivity constraints are hidden from the
-programmer. Note that compiler implementations of the CUDA-Q model
-are free to enable developer access to the details of the QPU
-qubit connectivity for the purpose of developing novel placement strategies. 
-
-**[7]** CUDA-Q considers general :math:`D`-level quantum information systems, e.g. qudits. Qudits
-are non-copyable, and can be allocated in chunks, via instantiation of 
-user-level quantum container types. Quantum containers come in two flavors - 
-quantum memory owning and non-owning (views). Moreover, the size of a quantum container 
-can be specified at either compile-time or dynamically at runtime. 
-As all qudits are non-copyable, qudits and containers 
-thereof can only be passed by reference. 
-
-**[8]** Each allocated qudit is unique and if  
-deallocated, is available for subsequent allocation. Deallocation occurs implicitly
-when the qudit goes out of scope. Uncomputation of qudit state should occur 
-automatically via compiler implementations of the CUDA-Q model.
-
-**[9]** The CUDA-Q model considers both remotely hosted QPU execution models as well as
-tightly-coupled quantum-classical architectures. Remotely hosted models
-support batch circuit execution where each circuit may contain simple
-quantum-classical operation integration. Tightly-coupled execution models provide
-streaming instruction execution, measure-reset, and fast-feedback of
-qubit measurement results. This multi-modal execution model directly
-influences the syntax and semantics of quantum kernel expressions
-and their associated host-code context. 
-
diff --git a/pr-2308/sphinx/specification/cudaq/namespace.rst b/pr-2308/sphinx/specification/cudaq/namespace.rst
deleted file mode 100644
index f20c0c8b94..0000000000
--- a/pr-2308/sphinx/specification/cudaq/namespace.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-Namespace and Standard
-**********************
-
-**[1]** The CUDA-Q language is a library-based extension for C++. As such, all
-pertinent data structures and library functions exist within the CUDA-Q
-namespace. This namespace is denoted :code:`::cudaq`. 
-
-**[2]** CUDA-Q is a C++ library-based language extension adherent to the `C++20 <https://en.cppreference.com/w/cpp/20>`_
-language specification. Any implementations that require backwards compatibility to previous 
-standards must retain the semantics of the user-facing API (e.g. `SFINAE <https://en.cppreference.com/w/cpp/language/sfinae>`_ 
-instead of `concepts and constraints <https://en.cppreference.com/w/cpp/language/constraints>`_).
\ No newline at end of file
diff --git a/pr-2308/sphinx/specification/cudaq/operations.rst b/pr-2308/sphinx/specification/cudaq/operations.rst
deleted file mode 100644
index 09497945f3..0000000000
--- a/pr-2308/sphinx/specification/cudaq/operations.rst
+++ /dev/null
@@ -1,127 +0,0 @@
-Quantum Intrinsic Operations
-****************************
-**[1]** To support low-level quantum programming and build foundational
-quantum kernels for large-scale applications, CUDA-Q specifies the *quantum
-intrinsic operation* abstraction for device-specific single-qudit unitary operations. 
-
-**[2]** A quantum intrinsic operation is modeled via a standard free function in the native classical language. It 
-has a unique instruction name and general specified function operands. 
-
-**[3]** These operands can model classical rotation parameters or units of quantum information (e.g. the :code:`cudaq::qudit`).
-The syntax for these operations is 
-
-.. code-block:: cpp 
-
-    void INST_NAME( PARAM...?, cudaq::qudit<N>&...);
-
-where :code:`INST_NAME` is the name of the instruction, :code:`qudit<N>&...` indicates one many
-:code:`cudaq::qudit` instances, and :code:`PARAM...?` indicates optional parameters of 
-floating point type (e.g. :code:`double`, :code:`float`). Quantum intrinsic operations return :code:`void`.
-
-**[4]** All intrinsic operations should start with a base declaration targeting a single :code:`cudaq::qudit`, and overloads
-should be provided that take more than one :code:`cudaq::qudit` instances to model the application
-of that instruction on all provided :code:`cudaq::qudits`, e.g. :code:`void x(cudaq::qubit&)` and
-:code:`x(cudaq::qubit&, cudaq::qubit&, cudaq::qubit&)`, modeling the NOT operation on a single 
-:code:`cudaq::qubit` or on multiple :code:`cudaq::qubit`. 
-
-**[5]** The specific set of quantum intrinsic operations available to the programmer can 
-be platform specific and provided via platform specific header files. 
-
-**[6]** Implementations should provide overloads to support broadcasting of single-qubit
-intrinsic operations across containers of :code:`cudaq::qudit`. For example,
-:code:`x(cudaq::qvector<>&)` should apply a NOT operation on all
-:code:`cudaq::qubit` in the provided :code:`cudaq::qvector`. 
-
-**[7]** Programmers can further modify quantum intrinsic operations via 
-an extra specified template parameter, and CUDA-Q leverages this syntax 
-for synthesizing control and adjoint variants of the operation. Specifically CUDA-Q 
-provides the `cudaq::ctrl`, and `cudaq::adj` type modifiers for synthesizing control and 
-adjoint forms of the operation. For language bindings that do not support template 
-parameterization, implementations may rely on static method calls (e.g. :code:`x.ctrl(q, r)`)
-
-Here is an example of how one might modify an intrinsic operation for multi-control and adjoint operations.
-
-.. tab:: C++ 
-  
-  .. code-block:: cpp 
-
-    cudaq::qubit q, r, s;
-    // Apply T operation
-    t(q);
-    // Apply Tdg operation
-    t<cudaq::adj>(q);
-    // Apply control Hadamard operation
-    h<cudaq::ctrl>(q,r,s);
-    // Error, ctrl requires > 1 qubit operands
-    // h<cudaq::ctrl>(r);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    q, r, s = cudaq.qubit(), cudaq.qubit(), cudaq.qubit()
-    # Apply T operation
-    t(q)
-    # Apply Tdg operation
-    t.adj(q)
-    # Apply control Hadamard operation
-    h.ctrl(q,r,s)
-    # Error, ctrl requires > 1 qubit operands
-    # h.ctrl(r);
-
-Operations on :code:`cudaq::qubit`
-----------------------------------
-The default set of quantum intrinsic operations for the cudaq::qubit type is as follows: 
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      struct base;
-      struct ctrl;
-      struct adj;
-  
-      // Single qubit operations, ctrl / adj variants, and broadcasting
-      template<typename mod = base, typename... QubitArgs>
-      void NAME(QubitArgs&... args) noexcept { ... }
-  
-      template<typename mod = base>
-      void NAME(const qvector& qr) noexcept { ... }
-  
-      template<typename mod = ctrl>
-      void NAME(qvector& ctrls, qubit& target) noexcept { ... }
- 
-      // Single qubit rotation operations and ctrl / adj variants
-      template <typename mod = base, typename ScalarAngle, typename... QubitArgs> 
-      void ROTATION_NAME(ScalarAngle angle, QubitArgs &...args) noexcept { ... }
- 
-      bool MEASURE_OP(qubit &q) noexcept;
-      std::vector<bool> MEASURE_OP(qvector &q) noexcept;
-      double measure(cudaq::spin_op & term) noexcept { ... }
-  }
-
-**[1]** For the default implementation of the :code:`cudaq::qubit` intrinsic operations, we let 
-NAME be any operation name in the set :code:`{h, x, y, z, t, s}` and :code:`ROTATION_NAME` be any 
-operation in :code:`{rx, ry, rz, r1 (phase)}`. Implementations may provide appropriate 
-function implementations using the above foundational functions to enable other 
-common operations (e.g. :code:`cnot -> x<cudaq::ctrl>`).
-
-**[2]** Control qubits can be specified with positive or negative polarity. 
-By this we mean that a control qubit can specify that a target operation is 
-applied if the control qubit state is a :code:`|0>` (positive polarity) or :code:`|1>` (negative polarity). 
-By default all control qubits are assumed to convey positive polarity. The 
-syntax for negating the polarity is the not operator preceding the control 
-qubit 
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    x<cudaq::ctrl>(!q, r);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    x.ctrl(~q, r)
-  
-The set of gates that the official CUDA-Q implementation supports can be found in the :doc:`API documentation </api/api>`.
diff --git a/pr-2308/sphinx/specification/cudaq/operators.rst b/pr-2308/sphinx/specification/cudaq/operators.rst
deleted file mode 100644
index 2645b861fe..0000000000
--- a/pr-2308/sphinx/specification/cudaq/operators.rst
+++ /dev/null
@@ -1,81 +0,0 @@
-Quantum Operators
-*****************
-
-:code:`cudaq::spin_op`
-----------------------
-**[1]** CUDA-Q provides a native :code:`spin_op` data type in the :code:`cudaq` namespace for the
-expression of quantum mechanical spin operators. 
-
-**[2]** The :code:`spin_op` provides an abstraction for a general tensor product of Pauli
-spin operators, and sums thereof:
-
-.. math:: 
-
-    H = \sum_{i=0}^M P_i, P_i = \prod_{j=0}^N \sigma_j^a
-
-for :math:`a = {x,y,z}`, :math:`j` the qubit index, and :math:`N` the number of qubits.
-
-**[3]** The :code:`spin_op` exposes common C++ operator overloads for algebraic expressions. 
-
-**[4]** CUDA-Q defines convenience functions in :code:`cudaq::spin` namespace that produce
-the primitive X, Y, and Z Pauli operators on specified qubit indices
-which can subsequently be used in algebraic expressions to build up
-more complicated Pauli tensor products and their sums.
-
-.. tab:: C++ 
-
-    .. code-block:: cpp
-
-        using namespace cudaq::spin;
-        auto h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) + \
-                 .21829 * z(0) - 6.125 * z(1);
-
-.. tab:: Python
-
-    .. code-block:: python 
-
-        from cudaq import spin 
-        h = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(0) * spin.y(1) + \
-                 .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-
-**[5]** The :code:`spin_op` also provides a mechanism for the expression of circuit
-synthesis tasks within quantum kernel code. Specifically, operations
-that encode :math:`N`\ :sup:`th`\ order trotterization of exponentiated :code:`spin_op`
-rotations, e.g. :math:`U = \exp(-i H t)`, where :math:`H` is the provided :code:`spin_op`.
-
-**[6]** The :code:`spin_op` can be created within classical host code and quantum kernel
-code, and can also be passed by value to quantum kernel code from host code. 
-
-The :code:`spin_op` should take on the following structure: 
-
-.. code-block:: cpp
-
-    namespace cudaq {
-    class spin_op {
-      public:
-        spin_op();
-        spin_op(const spin_op&);
-        bool empty() const;
-        std::size_t num_qubits() const;
-        std::size_t num_terms() const;
-        std::complex<double> get_coefficient();
-        bool is_identity() const;
-        void for_each_term(std::function<void(spin_op &)> &&) const;
-        void for_each_pauli(std::function<void(pauli, std::size_t)> &&) const;
-        spin_op& operator=(const spin_op&);
-        spin_op& operator+=(const spin_op&);
-        spin_op& operator-=(const spin_op&);
-        spin_op& operator*=(const spin_op&);
-        bool operator==(const spin_op&);
-        spin_op& operator*=(const double);
-        spin_op& operator*=(const std::complex<double>)
-    };
-
-    namespace spin {
-      spin_op x(const std::size_t);
-      spin_op y(const std::size_t);
-      spin_op z(const std::size_t);
-    }
-  }
-
diff --git a/pr-2308/sphinx/specification/cudaq/patterns.rst b/pr-2308/sphinx/specification/cudaq/patterns.rst
deleted file mode 100644
index 0f96c164b6..0000000000
--- a/pr-2308/sphinx/specification/cudaq/patterns.rst
+++ /dev/null
@@ -1,50 +0,0 @@
-
-Common Quantum Programming Patterns
-***********************************
-
-Compute-Action-Uncompute
-------------------------
-**[1]** CUDA-Q specifies syntax and semantics to support the compute-action-uncompute
-pattern, :math:`W = U V U \dagger` (here :math:`U` is the compute block, and :math:`V` is the action block).
-
-**[2]** Via special syntax for this pattern CUDA-Q enables compiler implementations to 
-key on circuit optimizations via programmer intent. Specifically, controlled versions of :math:`W` do not produce controlled
-operations of all instructions in :math:`U`, :math:`V`, and :math:`U \dagger`, but instead we are free to only
-control :math:`V`. 
-
-**[3]** The CUDA-Q specification requires the following syntax for this pattern:
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      // Will invoke U V U^dag
-      cudaq::compute_action (
-          [&](){ 
-            /*U_code*/ 
-          }, 
-          [&]() { 
-            /*V_code*/ 
-          } 
-      ); 
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    def computeF():
-       ... 
-    
-    def actionF():
-       ...
-
-    # Can take user-defined functions
-    cudaq.compute_action(computeF, actionF)
-
-    # Can take Pythonic CUDA-Q lambda kernels
-    computeL = lambda : (h(q), x(q), ry(-np.pi, q[0]))
-    cudaq.compute_action(computeL, actionF)
-
-
-**[4]** Compiler implementations must add the uncompute segment
-and optimize on any controlled versions of this block of code. 
\ No newline at end of file
diff --git a/pr-2308/sphinx/specification/cudaq/platform.rst b/pr-2308/sphinx/specification/cudaq/platform.rst
deleted file mode 100644
index fc44e7f12f..0000000000
--- a/pr-2308/sphinx/specification/cudaq/platform.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-
-Quantum Platform
-****************
-
-**[1]** CUDA-Q provides an abstraction describing the underlying quantum computing resource(s). 
-The underlying quantum platform can contain one or many quantum processing units (QPUs) each with 
-its own qubit connectivity and noise configuration.
-
-**[2]** CUDA-Q defines a :code:`cudaq::quantum_platform` in an effort to expose relevant system 
-information and enable asynchronous quantum kernel invocations.
-
-**[3]** The :code:`cudaq::quantum_platform` provides an API for querying the number of available 
-quantum processing units (QPUs), with each QPU assigned a logical integer index (:code:`{0,1,2,...}`). 
-
-**[4]** Programmers can specify the ID of the desired QPU and all subsequent CUDA-Q 
-kernel executions will target that QPU.
-
-The :code:`cudaq::quantum_platform`  should take the following structure
-
-.. code-block:: cpp
-
-  namespace cudaq {
-    class quantum_platform {
-    public:
-      quantum_platform();
-      ~quantum_platform();
- 
-      using QubitEdge = std::pair<std::size_t, std::size_t>;
-      using QubitConnectivity = std::vector<QubitEdge>;
-      std::optional<QubitConnectivity> connectivity();
-
-      std::size_t num_qpus() const;
-      std::size_t get_num_qubits(std::size_t qpu_id = 0) const;
- 
-      bool is_simulator(std::size_t qpu_id = 0) const;
-      bool is_remote(std::size_t qpuId = 0);
-      bool is_emulated(std::size_t qpuId = 0) const;
-      std::string name() const;
- 
-      std::size_t get_current_qpu() const ;
-      void set_current_qpu(const std::size_t device_id);
-
-    };
-  }
-
-CUDA-Q provides the following public functions to interact with the current
-:code:`cudaq::quantum_platform`
-
-.. code-block:: cpp
-
-  namespace cudaq {
-    quantum_platform &get_platform() ;
-  }
-
diff --git a/pr-2308/sphinx/specification/cudaq/synthesis.rst b/pr-2308/sphinx/specification/cudaq/synthesis.rst
deleted file mode 100644
index 038151ab8d..0000000000
--- a/pr-2308/sphinx/specification/cudaq/synthesis.rst
+++ /dev/null
@@ -1,106 +0,0 @@
-Sub-circuit Synthesis
-*********************
-**[1]** Execution of pure device quantum kernels can be modified via explicit intrinsic
-library calls that provide multi-qubit controlled application and adjoint,
-or circuit reversal, semantics. 
-
-**[2]** CUDA-Q defines the following library functions
-for enabling multi-controlled and adjoint operations on a general pure
-device kernel:
-
-.. code-block:: cpp
-
-    template <typename QuantumKernel, typename... Args>
-    void control(QuantumKernel &&kernel,
-                 cudaq::qubit& ctrl_qubit, Args &... args);
- 
-    template <typename QuantumKernel, typename QuantumRegister, typename... Args>
-      requires(std::ranges::range<QuantumRegister>)
-    void control(QuantumKernel &&kernel,
-                 QuantumRegister& ctrl_qubits, Args &... args);
- 
-    template <typename QuantumKernel, typename... Args>
-    void adjoint(QuantumKernel &&kernel, Args &... args);
-
-These functions can be leveraged in quantum kernel code in the following way
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      __qpu__ void x_gate(cudaq::qubit& q) { x(q); }
-      
-      struct kernel {
-        void operator() () __qpu__ {
-          cudaq::qarray<3> q;
-          ...
-          // Create Toffoli gate
-          auto ctrl_bits = q.front(2);
-          cudaq::control(x_gate, ctrl_bits, q[2]);
-          ...
-        }
-      };
-      
-      void rx_and_h_gate(double x, cudaq::qubit& q) __qpu__ { rx(x,q); h(q); }
-      
-      __qpu__ kernel(int N) {
-        cudaq::qvector q(N);
-        ...
-        // apply h(q[2]); rx(-pi, q[2]);
-        cudaq::adjoint(rx_and_h_gate{}, M_PI, q[2]);
-        ...
-      }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    @cudaq.kernel()
-    def x_gate(q : cudaq.qubit):
-        x(q)
-    
-    @cudaq.kernel()
-    def kernelTestControl():
-        q = cudaq.qvector(3)
-        ...
-        ctrl_bits = q.front(2)
-        cudaq.control(x_gate, ctrl_bits, q[2])
-        ...
-    
-    @cudaq.kernel()
-    def rx_and_h_gate(x : float, q : cudaq.qubit):
-        rx(x, q)
-        h(q)
-    
-    @cudaq.kernel()
-    def kernelTestAdjoint(N : int):
-        q = cudaq.qvector(N)
-        ...
-        # apply h(q[2]); rx(-pi, q[2])
-        cudaq.adjoint(rx_and_h_gate, np.pi, q[2])
-        ...
-
-
-**[3]** The :code:`cudaq::control(...)` function takes as input an instantiated pure
-device quantum kernel, a std::range of control qubits (:code:`cudaq::qvector`
-or :code:`cudaq::qview`), and the remaining arguments for the kernel itself.
-
-**[4]** Compiler implementations are free to synthesize multi-controlled operations
-using any pertinent synthesis strategy available. Qubits may be aggregated into
-a range of control qubits with or without the use of the :code:`operator!`
-:doc:`negated polarity operator <operations>`.
-
-.. tab:: C++ 
-  
-  .. code-block:: cpp
-
-      cudaq::control(kernel{}, {qubit0, !qubit1}, kernel_arg);
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    cudaq.control(kernel, [qubit0, ~qubit1], kernel_arg)
-  
-**[5]** The :code:`cudaq::adjoint(...)` function takes as input an
-pure device quantum kernel and the remaining arguments for the kernel.
diff --git a/pr-2308/sphinx/specification/cudaq/types.rst b/pr-2308/sphinx/specification/cudaq/types.rst
deleted file mode 100644
index 2a61b8576c..0000000000
--- a/pr-2308/sphinx/specification/cudaq/types.rst
+++ /dev/null
@@ -1,451 +0,0 @@
-Quantum Types
-*************
-The CUDA-Q language extension provides certain fundamental types that are pertinent
-to quantum-classical computing. These types are provided via defined classical library
-types in C++ in the :code:`cudaq` namespace. 
-
-:code:`cudaq::qudit<Levels>`
-----------------------------
-**[1]** The :code:`cudaq::qudit` models a :math:`D`-level unit of quantum information. The state of
-this system (for :math:`N` qudits) can be described by a :math:`D`\ :sup:`N`\-dimensional vector in
-Hilbert space with the absolute square of all elements summing to 1. 
-
-**[2]** The :code:`cudaq::qudit` encapsulates a unique :code:`std::size_t` modeling the index of the
-qudit in the underlying quantum memory space (assuming an infinite register
-of available qudits). 
-
-**[4]** To adhere to the no-cloning theorem of quantum mechanics,
-the :code:`cudaq::qudit` is non-copyable and non-movable. Therefore, all :code:`cudaq::qudit` 
-instances must be passed by reference, and the no-cloning theorem is satisfied
-at compile-time. 
-
-**[5]** :code:`cudaq::qudit` instances can only be allocated within CUDA-Q quantum
-kernel code and can never be allocated from classical host code.
-
-**[6]** All instantiated :code:`cudaq::qudit` instances start in the :code:`|0>` computational basis 
-state and serve as the primary input argument for quantum intrinsic operations 
-modeling typical logical quantum gate operations.
-
-**[7]** :code:`cudaq::qudit` instances can be negated when leveraged as controls in 
-quantum operation invocations. The mechanism for negation should be via overloading of :code:`qudit<N>::operator!()`.
-
-The :code:`cudaq::qudit` takes on the following structure
-
-.. code-block:: cpp
-
-    template <std::size_t Levels>
-    class qudit {
-      protected: 
-        const std::size_t idx = 0;
-      public:
-        qudit();
-        qudit(const qudit&) = delete;
-        qudit(qudit &&) = delete;
-        std::size_t id() const;
-        static constexpr std::size_t n_levels();
-        qudit<Levels> &operator!(); 
-    };
-
-:code:`cudaq::qubit`
---------------------
-**[1]** The specification provides a primitive :code:`cudaq::qubit` type which models a
-single quantum bit (:math:`2`-level) in the discrete quantum memory space.
-:code:`cudaq::qubit` is an alias for :code:`cudaq::qudit<2>` 
-
-.. code-block:: cpp
-    
-    namespace cudaq {
-      using qubit = qudit<2>;
-    }
-
-.. tab:: C++ 
-
-  .. code-block:: cpp
-
-      {
-        // Allocate a qubit in the |0> state
-        cudaq::qubit q;
-        // Put the qubit in a superposition of |0> and |1>
-        h(q); // cudaq::h == hadamard, ADL leveraged
-        printf("ID = %lu\n", q.id()); // prints 0
-        cudaq::qubit r;
-        printf("ID = %lu\n", r.id()); // prints 1
-        // qubit out of scope, implicit deallocation
-      }
-      cudaq::qubit q;
-      printf("ID = %lu\n", q.id()); // prints 0 (previous deallocated)
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    # Allocate a qubit in the |0> state
-    q = cudaq.qubit()
-    # Put the qubit in a superposition of |0> and |1>
-    h(q)
-    print("ID = {}".format(q.id())) # prints 0
-    
-    r = cudaq.qubit()
-    print("ID = {}", r.id()) # prints 1
-    # qubits go out of scope, implicit deallocation
-    
-Quantum Containers
-------------------
-**[1]** CUDA-Q specifies abstractions for dealing with groups of :code:`cudaq::qudit` instances in the
-form of familiar C++ containers. 
-
-**[2]** The underlying connectivity of the :code:`cudaq::qudit` instances stored in these containers is opaque to
-the programmer and any logical-to-physical program connectivity mapping should be done by compiler implementations. 
-
-:code:`cudaq::qview<Levels = 2>`
-++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qview<N>` is a non-owning reference to a subset of the discrete quantum memory space, 
-and as such, it is a :code:`std::span`-like C++ range of :code:`cudaq::qudit`.
-
-**[2]** The :code:`cudaq::qview` does not own its elements and can therefore be passed by value or reference. 
-
-**[3]** The :code:`cudaq::qview` is templated on the dimensionality of the contained quantum information unit, 
-and defaults to :math:`2` for qubit systems.
-
-**[4]** The :code:`cudaq::qview` provides an API for individual qubit extraction and sub-register slicing. 
-Programmers can extract the front :math:`N` :code:`qudits`, the back :math:`N` :code:`qudits`, and the 
-inner slice starting at a given index and including user-specified :code:`count` :code:`qudits`.
-
-The :code:`cudaq::qview` should take on the following structure:
-
-.. code-block:: cpp
-    
-    namespace cudaq { 
-      template <std::size_t Levels = 2>
-      class qview {
-        private:
-          std::span<qudit<Levels>> qudits;
-        public:
-          // Construct a span that refers to the qudits in `other`.
-          template <typename R>
-          requires(std::ranges::range<R>)
-          qview(R&& other);
-          qview(const qview& other);
-
-          // Iterator interface.
-          auto begin();
-          auto end();
-
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
-
-          // Returns the `[0, count)` qudits.
-          qview<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qview<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
-
-
-          // Returns the `[start, start+count)` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-      };
-    }
-
-:code:`cudaq::qvector<Levels = 2>`
-++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qvector<Levels>` is a container of elements from the discrete quantum memory space - a C++ container of :code:`cuda::qudit`.  
-
-**[2]** The :code:`cudaq::qvector` is a dynamically constructed owning container for :code:`cuda::qudit` (:code:`std::vector`-like), 
-and since it owns the quantum memory, it cannot be copied or moved. 
-
-**[3]** The :code:`cudaq::qvector` is templated on the dimensionality of the contained 
-quantum information unit, and defaults to :math:`2` for qubit systems.
-
-**[4]** The :code:`cudaq::qvector` can only be instantiated within CUDA-Q kernels
-
-**[5]** All qudits in the :code:`cudaq::qvector` start in the :code:`|0>` computational basis state. 
-
-**[6]** The :code:`cudaq::qvector` provides an API for individual qubit extraction and sub-register slicing. 
-Programmers can extract the front :math:`N` :code:`qudits`, the back :math:`N` :code:`qudits`, and the 
-inner slice starting at a given index and including user-specified :code:`count` :code:`qudits`.
-
-The :code:`cudaq::qview` should take on the following structure:
-
-.. code-block:: cpp
-    
-    namespace cudaq { 
-      template <std::size_t Levels = 2>
-      class qvector {
-        private:
-          std::vector<qudit<Levels>> qudits;
-
-        public:
-          // Construct a qreg with `size` qudits in the |0> state.
-          qvector(std::size_t size);
-          qvector(const qvector&) = delete;
-
-          // Iterator interface.
-          auto begin();
-          auto end();
-
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
-
-          // Returns the `[0, count)` qudits.
-          qview<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qview<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
- 
-          // Returns the `[start, start+count)` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the `{start, start + stride, ...}` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t stride, std::size_t end);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-
-          // Destroys all contained qudits. Postcondition: `size() == 0`.
-          void clear();
-      };
-    }
-
-.. tab:: C++ 
-
-  .. code-block:: cpp 
-
-    // Allocate 20 qubits, std::vector-like semantics
-    cudaq::qvector q(20);
-    // Get first qubit
-    auto first = q.front();
-    // Get first 5 qubits
-    auto first_5 = q.front(5);
-    // Get last qubit 
-    auto last = q.back();
-    // Can loop over qubits with size() method
-    for (int i = 0; i < q.size(); i++) {
-      ... do something with q[i] ...
-    }
-    // Range based for loop supported 
-    for (auto & qb : q) {
-      ... do something with qb ...
-    }
-
-.. tab:: Python 
-
-  .. code-block:: python 
-
-    # Allocate 20 qubits, vector-like semantics
-    q = cudaq.qvector(20)
-    # Get the first qubit 
-    first = q.front()
-    # Get the first 5 qubits 
-    first_5 = q.front(5)
-    # Get the last qubit 
-    last = q.back()
-    # Can loop over qubits with size or len function 
-    for i in range(len(q)):
-      .. do something with q[i] ..
-    # Range based for loop 
-    for qb in q:
-      .. do something with qb .. 
-
-
-:code:`cudaq::qarray<N, Levels = 2>`
-++++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qarray<N, Levels>` (where :code:`N` is an integral constant) is a statically 
-allocated container (:code:`std::array`-like). The utility of this type is in the compile-time 
-knowledge of allocated containers of qudits that may directly enable ahead-of-time quantum 
-optimization and synthesis. 
-
-**[2]** The second template parameter defaults to :math:`2`-level :code:`cudaq::qudit`.
-
-**[3]** The :code:`cudaq::qarray` owns the quantum memory it contains, and therefore cannot be copied or moved.
-
-**[4]** The :code:`cudaq::qarray` can only be instantiated within CUDA-Q kernels
-
-**[5]** All qudits in the :code:`cudaq::qarray` start in the :code:`|0>` computational basis state. 
-
-**[6]** The :code:`cudaq::qarray` provides an API for individual qubit extraction and sub-register slicing. 
-Programmers can extract the front :math:`N` :code:`qudits`, the back :math:`N` :code:`qudits`, and the 
-inner slice starting at a given index and including user-specified :code:`count` :code:`qudits`.
-
-The :code:`cudaq::qarray` should take on the following structure:
-
-.. code-block:: cpp 
-
-    namespace cudaq {
-      template <std::size_t N, std::size_t Levels = 2>
-      class qarray {
-        private:
-          std::array<qudit<Levels>, N> qudits;
-
-        public:
-          // Construct a qreg with `size` qudits in the |0> state.
-          qarray();
-          qarray(const qvector&) = delete;
-          qarray(qarray &&) = delete;
-
-          qarray& operator=(const qarray &) = delete;
-
-          // Iterator interface.
-          auto begin();
-          auto end();
-
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
-
-          // Returns the `[0, count)` qudits.
-          qview<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qview<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
-
-          // Returns the `[start, start+count)` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the `{start, start + stride, ...}` qudits.
-          qview<Levels>
-          slice(std::size_t start, std::size_t stride, std::size_t end);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-
-          // Destroys all contained qudits. Postcondition: `size() == 0`.
-          void clear();
-      };
-    }
-
-:code:`cudaq::qspan<N, Levels>` (Deprecated. Use :code:`cudaq::qview<Levels>` instead.)
-++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qspan` is a non-owning reference to a part of the discrete quantum
-memory space, a :code:`std::span`-like C++ range of :code:`cudaq::qudit` 
-(see C++ `span <https://en.cppreference.com/w/cpp/container/span>`_). It does not
-own its elements. It takes a single template parameter indicating the levels for 
-the underlying qudits that it stores. This parameter defaults to 2 for qubits. 
-It takes on the following structure:
-
-.. code-block:: cpp
-    
-    namespace cudaq {
-      template <std::size_t Levels = 2>
-      class qspan {
-        private:
-          std::span<qudit<Levels>> qubits;
-        public:
-          // Construct a span that refers to the qudits in `other`.
-          qspan(std::ranges::range<qudit<Levels>> auto& other);
-          qspan(qspan const& other);
- 
-          // Iterator interface.
-          auto begin();
-          auto end();
- 
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
- 
-          // Returns the `[0, count)` qudits.
-          qspan<Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qspan<Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
- 
-          // Returns the `[start, start+count)` qudits.
-          qspan<Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
-      };
-    }
-
-:code:`cudaq::qreg<N, Levels>` (Deprecated. Use :code:`cudaq::qvector<Levels>` instead.)
-++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-**[1]** :code:`cudaq::qreg<N, Levels>` models a register of the discrete quantum memory space - a
-C++ container of :code:`cudaq::qudit`.  As a container, it owns its elements and
-their storage. :code:`qreg<dyn, Levels>` is a dynamically allocated container
-(:code:`std::vector`-like, see C++ `vector <https://en.cppreference.com/w/cpp/container/vector>`_).
-:code:`cudaq::qreg<N, Levels>` (where N is an integral
-constant) is a statically allocated container (:code:`std::array`-like, 
-see `array <https://en.cppreference.com/w/cpp/container/array>`_). 
-Its template parameters default to dynamic allocation and :code:`cudaq::qudit<2>`.
-
-.. code-block:: cpp
-
-    namespace cudaq {
-      template <std::size_t N = dyn, std::size_t Levels = 2>
-      class qreg {
-        private:
-          std::conditional_t<
-            N == dyn,
-            std::vector<qudit<Levels>>,
-            std::array<qudit<Levels>, N>
-          > qudits;
-        public:
-          // Construct a qreg with `size` qudits in the |0> state.
-          qreg(std::size_t size) requires (N == dyn);
-          qreg(qreg const&) = delete;
- 
-          // Iterator interface.
-          auto begin();
-          auto end();
- 
-          // Returns the qudit at `idx`.
-          qudit<Levels>& operator[](const std::size_t idx);
- 
-          // Returns the `[0, count)` qudits.
-          qspan<dyn, Levels> front(std::size_t count);
-          // Returns the first qudit.
-          qudit<Levels>& front();
-          // Returns the `[count, size())` qudits.
-          qspan<dyn, Levels> back(std::size_t count);
-          // Returns the last qudit.
-          qudit<Levels>& back();
- 
-          // Returns the `[start, start+count)` qudits.
-          qspan<dyn, Levels>
-          slice(std::size_t start, std::size_t count);
-
-          // Returns the number of contained qudits.
-          std::size_t size() const;
- 
-          // Destroys all contained qudits. Postcondition: `size() == 0`.
-          void clear();
-      };
-    } 
-
-:code:`qreg` instances can only be instantiated from within quantum kernels,
-they cannot be instantiated in host code. All qubits in the :code:`qreg` 
-start in the :code:`|0>` computational basis state. 
-
-.. code-block:: cpp
-
-    // Allocate 20 qubits, std::vector-like semantics
-    cudaq::qreg q(20);
-    auto first = q.front();
-    auto first_5 = q.front(5);
-    auto last = q.back();
-    for (int i = 0; i < q.size(); i++) {
-      ... do something with q[i] ...
-    }
-    for (auto & qb : q) {
-      ... do something with qb ...
-    }
- 
-    // std::array-like semantics
-    cudaq::qreg<5> fiveCompileTimeQubits;
\ No newline at end of file
diff --git a/pr-2308/sphinx/specification/index.rst b/pr-2308/sphinx/specification/index.rst
deleted file mode 100644
index 6895a6de94..0000000000
--- a/pr-2308/sphinx/specification/index.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-*****************
-Specifications
-*****************
-
-The following specifications for the CUDA-Q programming model and toolchain are available:
-
-.. toctree::
-   :maxdepth: 2
-
-      Language Specification <cudaq.rst>
-      Quake Specification <quake-dialect>
diff --git a/pr-2308/sphinx/specification/quake-dialect.md b/pr-2308/sphinx/specification/quake-dialect.md
deleted file mode 100644
index 0650f71206..0000000000
--- a/pr-2308/sphinx/specification/quake-dialect.md
+++ /dev/null
@@ -1,150 +0,0 @@
-# Quake Dialect
-
-## General Introduction
-
-The quantum circuit model is the most widely used model of quantum
-computation.  It provides a convenient tool for formulating quantum
-algorithms and an architecture for the physical construction of quantum
-computers.
-
-A _quantum circuit_ represents a computation as a sequence of quantum
-operators applied to quantum data.  In our case, the quantum data is a set
-of quantum bits, or qubits for short.  Physically, a qubit is an object with
-only two distinguishable states, i.e., it is a two-state quantum mechanical
-system such as a spin-1/2 particle.
-
-Conceptually, a _quantum operator_ is an effect that might modify the state
-of a subset of qubits. Most often, this effect is unitary evolution---in
-this case, we say that the operator is a _unitary_.  The number of target
-qubits an operator acts upon is an intrinsic property.
-
-A _quantum instruction_ is the embodiment of a quantum operator when applied
-to a specific subset of qubits.  The number of qubits must be equal to (or
-greater than) the number of target qubits intrinsic to the operator.  If
-greater, the extra qubits are considered controls.
-
-## Motivation
-
-The main motivation behind Quake's value model is to directly expose
-quantum and classical data dependencies for optimization purposes,
-i.e., to represent the dataflow in quantum computations.  In contrast
-to Quake's memory model, which uses memory semantics (quantum
-operators act as side-effects on qubit references), the value model
-uses value semantics, that is quantum operators consume and produce
-values. These values are not truly SSA values, however, as operations
-still have side-effects on the value itself and the value cannot be
-copied.
-
-Let's see an example to clarify the distinction between the models.  Take the
-following Quake implementation of some toy quantum computation:
-
-```cpp
-func.func foo(%veq : !quake.veq<2>) {
-    // Boilerplate to extract each qubit from the vector
-    %c0 = arith.constant 0 : index
-    %c1 = arith.constant 1 : index
-    %q0 = quake.extract_ref %veq[%c0] : (!quake.veq<2>, index) -> !quake.ref
-    %q1 = quake.extract_ref %veq[%c1] : (!quake.veq<2>, index) -> !quake.ref
-
-    // We apply some operators to those extracted qubits
-    // ... bunch of operators using %q0 and %q1 ...
-    quake.h %q0 : (!quake.ref) -> ()
-
-    // We decide to measure the vector
-    %result = quake.mz %veq : (!quake.veq<2>) -> cc.stdvec<i1>
-
-    // And then apply another Hadamard to %q0
-    quake.h %q0 : (!quake.ref) -> ()
-    // ...
-}
-```
-
-Now imagine we want to optimize this code by removing pair of adjacent
-adjoint operators, e.g., if we have a pair Hadamard operations next to each
-other on the same qubit---visually:
-
-```text
-    ┌───┐ ┌───┐         ┌───┐
-   ─┤ H ├─┤ H ├─  =  ───┤ I ├───  =  ─────────────
-    └───┘ └───┘         └───┘
-```
-
-Where `I` is the identity operator. Now note that a naive implementation of
-this optimization for Quake would optimize away both `quake.h` operators
-being applied to `%q0`.  Such an implementation would have missed the fact
-that a measurement is being applied to the vector, `%veq`, which contains
-`%q0`.
-
-Of course it is possible to correctly implement this optimization for Quake.
-However such an implementation would be quite error-prone and require
-complex analyses.  For this reason, Quake has overloaded gates.
-
-In the value model operators consume values and return new values:
-
-```text
-  %q0_1 = quake.op %q0_0 : (!quake.wire) -> !quake.wire
-```
-
-We can visualize the difference between memory and value representation as:
-
-```text
-            Memory                                   Value
-
-        ┌──┐ ┌──┐     ┌──┐                  ┌──┐ %q0_1 ┌──┐     ┌──┐
-   %q0 ─┤  ├─┤  ├─···─┤  ├─ %q0  vs  %q0_0 ─┤  ├───────┤  ├─···─┤  ├─ %q0_Z
-        └──┘ └──┘     └──┘                  └──┘       └──┘     └──┘
-```
-
-If we look at the implementation again, we notice that the problem
-with the naive optimization happens because the Hadamard operators are
-implicitly connected by the same value `%q0`. In value form, all the gates
-are explicitly connected by distinct values, which eliminates the need
-to do further analysis via implicit side-effects.
-The following is the implementation in value form.
-
-```text
-func.func @foo(%array : !quake.qvec<2>) {
-    // Boilerplate to extract each qubit
-    %c0 = arith.constant 0 : index
-    %c1 = arith.constant 1 : index
-    %r0 = quake.extract_ref %array[%c0] : (!quake.qvec<2>, index) -> !quake.qref
-    %r1 = quake.extract_ref %array[%c1] : (!quake.qvec<2>, index) -> !quake.qref
-
-    // Unwrap the quantum references to expose the wires.
-    %q0 = quake.unwrap %r0 : (!quake.qref) -> !quake.wire
-    %q1 = quake.unwrap %r1 : (!quake.qref) -> !quake.wire
-
-    // Misc. operators applied
-    %q0_M = quake.h %q0_L : (!quake.wire) -> !quake.wire
-
-    // Re-wrap the wire to its original source
-    quake.wrap %q0_M to %r0 : !quake.wire, !quake.qref
-    quake.wrap %q1_X to %r1 : !quake.wire, !quake.qref
-
-    // Measure the entire vector of quantum references
-    %result = quake.mz %array : (!quake.qvec<2>) -> !cc.stdvec<i1>
-
-    // Unwrap the wire for qubit 0 again
-    %q0_P = quake.unwrap %r0 : (!quake.qref) -> !quake.wire
-    ...
-    %q0_Z = quake.h %q0_Y : (!quake.wire) -> !quake.wire
-    // Re-wrap the wire back to the original reference
-    quake.wrap %q0_Z to %r0 : !quake.wire, !quake.qref
-    return
-}
-```
-
-In this code we can more straightforwardly see that the Hadamard
-operators cannot cancel each other.  One way of reasoning about this
-is as follows: In value form we need to follow a chain of values to
-know the qubit operators are being applied to, in this example:
-
-```text
-Mmeory                          Value
-    %q0         [%q0_0, %q0_1 ... %q0_L, %q0_M; %q0_P ... %q0_Y, %q0_Z]
-
-```
-
-We know that one Hadamard is applied to `%q0_L` and generates `%q0_M`, and
-the other is applied `%q0_Y` and generates `%q0_Z`.  Hence, there is no
-connection between them---which means they cannot cancel each other out.
diff --git a/pr-2308/sphinx/targets/cpp/braket.cpp b/pr-2308/sphinx/targets/cpp/braket.cpp
deleted file mode 100644
index fb032daa9d..0000000000
--- a/pr-2308/sphinx/targets/cpp/braket.cpp
+++ /dev/null
@@ -1,45 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target braket braket.cpp -o out.x && ./out.x
-// ```
-// This will submit the job to the Amazon Braket state vector simulator
-// (default). Alternatively, users can choose any of the available devices by
-// specifying its `ARN` with the `--braket-machine`, e.g.,
-// ```
-// nvq++ --target braket --braket-machine \
-// "arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet" braket.cpp -o out.x
-// ./out.x
-// ```
-// Assumes a valid set of credentials have been set prior to execution.
-
-#include <cudaq.h>
-#include <fstream>
-
-// Define a simple quantum kernel to execute on Amazon Braket.
-struct ghz {
-  // Maximally entangled state between 5 qubits.
-  auto operator()() __qpu__ {
-    cudaq::qvector q(5);
-    h(q[0]);
-    for (int i = 0; i < 4; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  // Submit asynchronously (e.g., continue executing
-  // code in the file until the job has been returned).
-  auto future = cudaq::sample_async(ghz{});
-  // ... classical code to execute in the meantime ...
-
-  // Get the results of the read in future.
-  auto async_counts = future.get();
-  async_counts.dump();
-
-  // OR: Submit synchronously (e.g., wait for the job
-  // result to be returned before proceeding).
-  auto counts = cudaq::sample(ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/fermioniq.cpp b/pr-2308/sphinx/targets/cpp/fermioniq.cpp
deleted file mode 100644
index 7ad8da3e99..0000000000
--- a/pr-2308/sphinx/targets/cpp/fermioniq.cpp
+++ /dev/null
@@ -1,30 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target fermioniq fermioniq.cpp -o out.x && ./out.x
-// ```
-// This will submit the job to the fermioniq emulator.
-// ```
-// nvq++ --target fermioniq
-// fermioniq.cpp -o out.x && ./out.x
-// ```
-
-#include <cudaq.h>
-#include <fstream>
-
-// Define a simple quantum kernel to execute on fermioniq.
-struct ghz {
-  // Maximally entangled state between 5 qubits.
-  auto operator()() __qpu__ {
-    cudaq::qvector q(3);
-    h(q[0]);
-    for (int i = 0; i < 2; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  auto counts = cudaq::sample(1001, ghz());
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/infleqtion.cpp b/pr-2308/sphinx/targets/cpp/infleqtion.cpp
deleted file mode 100644
index 4a61b71571..0000000000
--- a/pr-2308/sphinx/targets/cpp/infleqtion.cpp
+++ /dev/null
@@ -1,65 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target infleqtion infleqtion.cpp -o out.x && ./out.x
-// ```
-// This will submit the job to the Infleqtion's ideal simulator,
-// cq_sqale_simulator (default). Alternatively, we can enable hardware noise
-// model simulation by specifying `noise-sim` to the flag `--infleqtion-method`,
-// e.g.,
-// ```
-// nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu
-// --infleqtion-method noise-sim infleqtion.cpp -o out.x && ./out.x
-// ```
-// where "noise-sim" instructs Superstaq to perform a noisy emulation of the
-// QPU. An ideal dry-run execution on the QPU may be performed by passing
-// `dry-run` to the `--infleqtion-method` flag, e.g.,
-// ```
-// nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu
-// --infleqtion-method dry-run infleqtion.cpp -o out.x && ./out.x
-// ```
-// Note: If targeting ideal cloud simulation, `--infleqtion-machine
-// cq_sqale_simulator` is optional since it is the default configuration if not
-// provided.
-
-#include <cudaq.h>
-#include <fstream>
-
-// Define a simple quantum kernel to execute on Infleqtion backends.
-struct ghz {
-  // Maximally entangled state between 5 qubits.
-  auto operator()() __qpu__ {
-    cudaq::qvector q(5);
-    h(q[0]);
-    for (int i = 0; i < 4; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  // Submit to infleqtion asynchronously (e.g., continue executing
-  // code in the file until the job has been returned).
-  auto future = cudaq::sample_async(ghz{});
-  // ... classical code to execute in the meantime ...
-
-  // Can write the future to file:
-  {
-    std::ofstream out("saveMe.json");
-    out << future;
-  }
-
-  // Then come back and read it in later.
-  cudaq::async_result<cudaq::sample_result> readIn;
-  std::ifstream in("saveMe.json");
-  in >> readIn;
-
-  // Get the results of the read in future.
-  auto async_counts = readIn.get();
-  async_counts.dump();
-
-  // OR: Submit to infleqtion synchronously (e.g., wait for the job
-  // result to be returned before proceeding).
-  auto counts = cudaq::sample(ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/ionq.cpp b/pr-2308/sphinx/targets/cpp/ionq.cpp
deleted file mode 100644
index 193daba796..0000000000
--- a/pr-2308/sphinx/targets/cpp/ionq.cpp
+++ /dev/null
@@ -1,60 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target ionq ionq.cpp -o out.x && ./out.x
-// ```
-// This will submit the job to the IonQ ideal simulator target (default).
-// Alternatively, we can enable hardware noise model simulation by specifying
-// the `--ionq-noise-model`, e.g.,
-// ```
-// nvq++ --target ionq --ionq-machine simulator --ionq-noise-model aria-1
-// ionq.cpp -o out.x && ./out.x
-// ```
-// where we set the noise model to mimic the 'aria-1' hardware device.
-// Please refer to your IonQ Cloud dashboard for the list of simulator noise
-// models.
-// Note: `--ionq-machine simulator` is  optional since 'simulator' is the
-// default configuration if not provided. Assumes a valid set of credentials
-// have been stored.
-
-#include <cudaq.h>
-#include <fstream>
-
-// Define a simple quantum kernel to execute on IonQ.
-struct ghz {
-  // Maximally entangled state between 5 qubits.
-  auto operator()() __qpu__ {
-    cudaq::qvector q(5);
-    h(q[0]);
-    for (int i = 0; i < 4; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  // Submit to IonQ asynchronously (e.g., continue executing
-  // code in the file until the job has been returned).
-  auto future = cudaq::sample_async(ghz{});
-  // ... classical code to execute in the meantime ...
-
-  // Can write the future to file:
-  {
-    std::ofstream out("saveMe.json");
-    out << future;
-  }
-
-  // Then come back and read it in later.
-  cudaq::async_result<cudaq::sample_result> readIn;
-  std::ifstream in("saveMe.json");
-  in >> readIn;
-
-  // Get the results of the read in future.
-  auto async_counts = readIn.get();
-  async_counts.dump();
-
-  // OR: Submit to IonQ synchronously (e.g., wait for the job
-  // result to be returned before proceeding).
-  auto counts = cudaq::sample(ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/iqm.cpp b/pr-2308/sphinx/targets/cpp/iqm.cpp
deleted file mode 100644
index 3414bc9260..0000000000
--- a/pr-2308/sphinx/targets/cpp/iqm.cpp
+++ /dev/null
@@ -1,63 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target iqm iqm.cpp --iqm-machine Adonis -o out.x && ./out.x
-// ```
-// Assumes a valid set of credentials have been stored.
-
-#include <cudaq.h>
-#include <fstream>
-
-// Define a simple quantum kernel to execute on IQM Server.
-struct adonis_ghz {
-  // Maximally entangled state between 5 qubits on Adonis QPU.
-  //       QB1
-  //        |
-  // QB2 - QB3 - QB4
-  //        |
-  //       QB5
-
-  void operator()() __qpu__ {
-    cudaq::qvector q(5);
-    h(q[0]);
-
-    // Note that the CUDA-Q compiler will automatically generate the
-    // necessary instructions to swap qubits to satisfy the required
-    // connectivity constraints for the Adonis QPU. In this program, that means
-    // that despite QB1 not being physically connected to QB2, the user can
-    // still perform joint operations q[0] and q[1] because the compiler will
-    // automatically (and transparently) inject the necessary swap instructions
-    // to execute the user's program without the user having to worry about the
-    // physical constraints.
-    for (int i = 0; i < 4; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  // Submit to IQM Server asynchronously. E.g, continue executing
-  // code in the file until the job has been returned.
-  auto future = cudaq::sample_async(adonis_ghz{});
-  // ... classical code to execute in the meantime ...
-
-  // Can write the future to file:
-  {
-    std::ofstream out("saveMe.json");
-    out << future;
-  }
-
-  // Then come back and read it in later.
-  cudaq::async_result<cudaq::sample_result> readIn;
-  std::ifstream in("saveMe.json");
-  in >> readIn;
-
-  // Get the results of the read in future.
-  auto async_counts = readIn.get();
-  async_counts.dump();
-
-  // OR: Submit to IQM Server synchronously. E.g, wait for the job
-  // result to be returned before proceeding.
-  auto counts = cudaq::sample(adonis_ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/nvqc_qml.cpp b/pr-2308/sphinx/targets/cpp/nvqc_qml.cpp
deleted file mode 100644
index c6a12d958b..0000000000
--- a/pr-2308/sphinx/targets/cpp/nvqc_qml.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target nvqc nvqc_qml.cpp -o out.x
-// ./out.x
-// ```
-// Assumes a valid NVQC API key has been set in the `NVQC_API_KEY` environment
-// variable. Please refer to the documentations for information about how to
-// attain NVQC API key.
-
-// This example demonstrates the simulation of large parameterized quantum
-// circuits with NVQC. These parameterized circuits can be applied to quantum
-// machine learning to classify data points, e.g. see
-// https://arxiv.org/pdf/1906.07682.pdf.
-
-#include <ctime>
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/optimizers.h>
-#include <random>
-
-struct kernel {
-
-  auto operator()(const int n_qubits,
-                  const std::vector<double> parameters) __qpu__ {
-
-    cudaq::qvector qubits(n_qubits);
-    h(qubits);
-
-    for (size_t i = 0; i < n_qubits; i++) {
-      rx(parameters[i], qubits[i]);
-    }
-
-    for (size_t i = 0; i < n_qubits; i++) {
-      ry(parameters[i + n_qubits], qubits[i]);
-    }
-
-    h(qubits);
-
-    for (size_t i = 0; i < n_qubits; i++) {
-      rz(parameters[i + n_qubits * 2], qubits[i]);
-    }
-
-    for (size_t i = 0; i < n_qubits; i += 2) {
-      cx(qubits[i], qubits[i + 1]);
-    }
-
-    for (size_t i = 0; i < n_qubits; i++) {
-      rz(parameters[i + n_qubits * 2], qubits[i]);
-    }
-
-    for (size_t i = 0; i < n_qubits; i += 2) {
-      cy(qubits[i], qubits[i + 1]);
-    }
-
-    for (size_t i = 0; i < n_qubits; i++) {
-      ry(parameters[i + n_qubits], qubits[i]);
-    }
-
-    for (size_t i = 0; i < n_qubits; i += 2) {
-      cz(qubits[i], qubits[i + 1]);
-    }
-
-    x(qubits);
-    y(qubits);
-    h(qubits);
-  }
-};
-
-std::vector<double> initial_parameters(int n_parameters, int seed) {
-
-  std::default_random_engine generator(seed);
-  std::uniform_real_distribution<float> distribution(0.0, 1.0);
-  std::vector<double> parameters(n_parameters);
-
-  for (size_t i = 0; i < n_parameters; i++) {
-    parameters[i] = distribution(generator);
-  }
-  return parameters;
-}
-
-int main() {
-
-  const int n_qubits = 26;
-  const int n_parameters = 3 * n_qubits;
-  std::vector<double> parameters = initial_parameters(n_parameters, 13);
-  auto h = cudaq::spin::z(0);
-
-  auto start = clock();
-  auto exp_val = cudaq::observe(kernel{}, h, n_qubits, parameters);
-  auto end = clock();
-
-  printf("Expectation Value: %f \n", exp_val.expectation());
-  printf("Runtime: %fs \n", float(end - start) / float(CLOCKS_PER_SEC));
-
-  return 0;
-}
diff --git a/pr-2308/sphinx/targets/cpp/nvqc_sample.cpp b/pr-2308/sphinx/targets/cpp/nvqc_sample.cpp
deleted file mode 100644
index 4e2aa69b36..0000000000
--- a/pr-2308/sphinx/targets/cpp/nvqc_sample.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target nvqc nvqc_sample.cpp -o out.x
-// ./out.x
-// ```
-// Assumes a valid NVQC API key has been set in the `NVQC_API_KEY` environment
-// variable. Please refer to the documentations for information about how to
-// attain NVQC API key.
-
-#include <cudaq.h>
-#include <iostream>
-
-// Define a simple quantum kernel to execute on NVQC.
-struct ghz {
-  // Maximally entangled state between 25 qubits.
-  auto operator()() __qpu__ {
-    constexpr int NUM_QUBITS = 25;
-    cudaq::qvector q(NUM_QUBITS);
-    h(q[0]);
-    for (int i = 0; i < NUM_QUBITS - 1; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    auto result = mz(q);
-  }
-};
-
-int main() {
-  // Submit to NVQC asynchronously (e.g., continue executing
-  // code in the file until the job has been returned).
-  auto async_counts_handle = cudaq::sample_async(ghz{});
-  // ... classical code to execute in the meantime ...
-  std::cout << "Waiting for NVQC result...\n";
-
-  // Calling .get() on the handle to synchronize the result.
-  auto async_counts = async_counts_handle.get();
-  async_counts.dump();
-
-  // OR: Submit to NVQC synchronously (e.g., wait for the job
-  // result to be returned before proceeding).
-  auto counts = cudaq::sample(ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/nvqc_state.cpp b/pr-2308/sphinx/targets/cpp/nvqc_state.cpp
deleted file mode 100644
index d7cb6317f8..0000000000
--- a/pr-2308/sphinx/targets/cpp/nvqc_state.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target nvqc --nvqc-backend tensornet nvqc_state.cpp -o out.x
-// ./out.x
-// ```
-// Assumes a valid NVQC API key has been set in the `NVQC_API_KEY` environment
-// variable. Please refer to the documentations for information about how to
-// attain NVQC API key.
-
-#include "cudaq/algorithms/get_state.h"
-#include <cudaq.h>
-#include <iostream>
-
-int main() {
-  auto kernel = cudaq::make_kernel();
-  const std::size_t NUM_QUBITS = 20;
-  auto q = kernel.qalloc(NUM_QUBITS);
-  kernel.h(q[0]);
-  for (std::size_t qId = 0; qId < NUM_QUBITS - 1; ++qId)
-    kernel.x<cudaq::ctrl>(q[qId], q[qId + 1]);
-  auto state = cudaq::get_state(kernel);
-  std::cout << "Amplitude(00..00) = " << state[0] << "\n";
-  std::cout << "Amplitude(11..11) = " << state[(1ULL << NUM_QUBITS) - 1]
-            << "\n";
-}
diff --git a/pr-2308/sphinx/targets/cpp/nvqc_vqe.cpp b/pr-2308/sphinx/targets/cpp/nvqc_vqe.cpp
deleted file mode 100644
index 88c32c5051..0000000000
--- a/pr-2308/sphinx/targets/cpp/nvqc_vqe.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target nvqc --nvqc-nqpus 3 nvqc_vqe.cpp -o out.x
-// ./out.x
-// ```
-// Note: we set `nqpus` to 3 to establish 3 concurrent NVQC job submission
-// pipes.
-// Assumes a valid NVQC API key has been set in the `NVQC_API_KEY` environment
-// variable. Please refer to the documentations for information about how to
-// attain NVQC API key.
-
-#include <cudaq.h>
-#include <cudaq/algorithm.h>
-#include <cudaq/gradients.h>
-#include <cudaq/optimizers.h>
-#include <iostream>
-
-int main() {
-  using namespace cudaq::spin;
-  cudaq::spin_op h = 5.907 - 2.1433 * x(0) * x(1) - 2.1433 * y(0) * y(1) +
-                     .21829 * z(0) - 6.125 * z(1);
-
-  auto [ansatz, theta] = cudaq::make_kernel<double>();
-  auto q = ansatz.qalloc();
-  auto r = ansatz.qalloc();
-  ansatz.x(q);
-  ansatz.ry(theta, r);
-  ansatz.x<cudaq::ctrl>(r, q);
-
-  // Run VQE with a gradient-based optimizer.
-  // Delegate cost function and gradient computation across different NVQC-based
-  // QPUs. Note: depending on the user's account, there might be different
-  // number of NVQC worker instances available. Hence, although we're making
-  // concurrent job submissions across multiple QPUs, the speedup would be
-  // determined by the number of NVQC worker instances.
-  cudaq::optimizers::lbfgs optimizer;
-  auto [opt_val, opt_params] = optimizer.optimize(
-      /*dim=*/1, /*opt_function*/ [&](const std::vector<double> &params,
-                                      std::vector<double> &grads) {
-        // Queue asynchronous jobs to do energy evaluations across multiple QPUs
-        auto energy_future =
-            cudaq::observe_async(/*qpu_id=*/0, ansatz, h, params[0]);
-        const double paramShift = M_PI_2;
-        auto plus_future = cudaq::observe_async(/*qpu_id=*/1, ansatz, h,
-                                                params[0] + paramShift);
-        auto minus_future = cudaq::observe_async(/*qpu_id=*/2, ansatz, h,
-                                                 params[0] - paramShift);
-        grads[0] = (plus_future.get().expectation() -
-                    minus_future.get().expectation()) /
-                   2.0;
-        return energy_future.get().expectation();
-      });
-  std::cout << "Minimum energy = " << opt_val << " (expected -1.74886).\n";
-}
diff --git a/pr-2308/sphinx/targets/cpp/orca.cpp b/pr-2308/sphinx/targets/cpp/orca.cpp
deleted file mode 100644
index 78f8a00a57..0000000000
--- a/pr-2308/sphinx/targets/cpp/orca.cpp
+++ /dev/null
@@ -1,98 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target orca --orca-url $ORCA_ACCESS_URL orca.cpp -o out.x && ./out.x
-// ```
-// To use the ORCA Computing target you will need to set the ORCA_ACCESS_URL
-// environment variable or pass the URL to the `--orca-url` flag.
-
-#include <chrono>
-#include <cudaq.h>
-#include <cudaq/orca.h>
-#include <fstream>
-#include <iostream>
-#include <thread>
-
-int main() {
-  using namespace std::this_thread;     // sleep_for, sleep_until
-  using namespace std::chrono_literals; // `ns`, `us`, `ms`, `s`, `h`, etc.
-
-  // A time-bin boson sampling experiment: An input state of 4 indistinguishable
-  // photons mixed with 4 vacuum states across 8 time bins (modes) enter the
-  // time bin interferometer (TBI). The interferometer is composed of two loops
-  // each with a beam splitter (and optionally with a corresponding phase
-  // shifter). Each photon can either be stored in a loop to interfere with the
-  // next photon or exit the loop to be measured. Since there are 8 time bins
-  // and 2 loops, there is a total of 14 beam splitters (and optionally 14 phase
-  // shifters) in the interferometer, which is the number of controllable
-  // parameters.
-
-  // half of 8 time bins is filled with a single photon and the other half is
-  // filled with the vacuum state (empty)
-  std::vector<std::size_t> input_state = {1, 0, 1, 0, 1, 0, 1, 0};
-
-  // The time bin interferometer in this example has two loops, each of length 1
-  std::vector<std::size_t> loop_lengths = {1, 1};
-
-  // helper variables to calculate the number of beam splitters and phase
-  // shifters needed in the TBI
-  std::size_t sum_loop_lengths{std::accumulate(
-      loop_lengths.begin(), loop_lengths.end(), static_cast<std::size_t>(0))};
-  const std::size_t n_loops = loop_lengths.size();
-  const std::size_t n_modes = input_state.size();
-  const std::size_t n_beam_splitters = n_loops * n_modes - sum_loop_lengths;
-
-  // beam splitter angles (created as a linear spaced vector of angles)
-  std::vector<double> bs_angles =
-      cudaq::linspace(M_PI / 3, M_PI / 6, n_beam_splitters);
-
-  // Optionally, we can also specify the phase shifter angles (created as a
-  // linear spaced vector of angles), if the system includes phase shifters
-  // ```
-  // std::vector<double> ps_angles = cudaq::linspace(M_PI / 3, M_PI / 5,
-  // n_beam_splitters);
-  // ```
-
-  // we can also set number of requested samples
-  int n_samples = 10000;
-
-  // Submit to ORCA synchronously (e.g., wait for the job result to be
-  // returned before proceeding with the rest of the execution).
-  std::cout << "Submitting to ORCA Server synchronously" << std::endl;
-  auto counts =
-      cudaq::orca::sample(input_state, loop_lengths, bs_angles, n_samples);
-
-  // Print the results
-  counts.dump();
-
-  // If the system includes phase shifters, the phase shifter angles can be
-  // included in the call
-
-  // ```
-  // auto counts = cudaq::orca::sample(input_state, loop_lengths, bs_angles,
-  //                                   ps_angles, n_samples);
-  // ```
-
-  // Alternatively we can submit to ORCA asynchronously (e.g., continue
-  // executing code in the file until the job has been returned).
-  std::cout << "Submitting to ORCA Server asynchronously" << std::endl;
-  auto async_results = cudaq::orca::sample_async(input_state, loop_lengths,
-                                                 bs_angles, n_samples);
-
-  // Can write the future to file:
-  {
-    std::ofstream out("saveMe.json");
-    out << async_results;
-  }
-
-  // Then come back and read it in later.
-  cudaq::async_result<cudaq::sample_result> readIn;
-  std::ifstream in("saveMe.json");
-  in >> readIn;
-
-  sleep_for(200ms); // wait for the job to be processed
-  // Get the results of the read in future.
-  auto async_counts = readIn.get();
-  async_counts.dump();
-
-  return 0;
-}
\ No newline at end of file
diff --git a/pr-2308/sphinx/targets/cpp/orca_mqpu.cpp b/pr-2308/sphinx/targets/cpp/orca_mqpu.cpp
deleted file mode 100644
index 96b3f68b1b..0000000000
--- a/pr-2308/sphinx/targets/cpp/orca_mqpu.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ orca_mqpu.cpp --target orca --orca-url \
-// "http://localhost:3035,http://localhost:3037" -o out.x && ./out.x
-// ```
-// See accompanying example `orca.cpp` for detailed explanation.
-
-#include <cudaq.h>
-#include <cudaq/orca.h>
-#include <fstream>
-#include <iostream>
-
-int main() {
-
-  auto &platform = cudaq::get_platform();
-  auto num_qpus = platform.num_qpus();
-  printf("Number of QPUs: %zu\n", num_qpus);
-
-  // A time-bin boson sampling experiment
-  std::vector<std::size_t> input_state = {1, 0, 1, 0, 1, 0, 1, 0};
-  std::vector<std::size_t> loop_lengths = {1, 1};
-  std::size_t sum_loop_lengths{std::accumulate(
-      loop_lengths.begin(), loop_lengths.end(), static_cast<std::size_t>(0))};
-  const std::size_t n_loops = loop_lengths.size();
-  const std::size_t n_modes = input_state.size();
-  const std::size_t n_beam_splitters = n_loops * n_modes - sum_loop_lengths;
-  std::vector<double> bs_angles =
-      cudaq::linspace(M_PI / 3, M_PI / 6, n_beam_splitters);
-  int n_samples = 10000;
-
-  std::cout << "Submitting to ORCA Server asynchronously" << std::endl;
-  std::vector<cudaq::async_sample_result> countFutures;
-  for (std::size_t i = 0; i < num_qpus; i++) {
-    countFutures.emplace_back(cudaq::orca::sample_async(
-        input_state, loop_lengths, bs_angles, n_samples, i));
-  }
-
-  for (auto &counts : countFutures) {
-    counts.get().dump();
-  }
-  return 0;
-}
diff --git a/pr-2308/sphinx/targets/cpp/photonics.cpp b/pr-2308/sphinx/targets/cpp/photonics.cpp
deleted file mode 100644
index 521ace318b..0000000000
--- a/pr-2308/sphinx/targets/cpp/photonics.cpp
+++ /dev/null
@@ -1,27 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --library-mode --target orca-photonics photonics.cpp
-// ./a.out
-// ```
-
-#include "cudaq/photonics.h"
-#include "cudaq.h"
-
-struct photonicsKernel {
-  void operator()() __qpu__ {
-    cudaq::qvector<3> qumodes(2);
-    create(qumodes[0]);
-    create(qumodes[1]);
-    create(qumodes[1]);
-    mz(qumodes);
-  }
-};
-
-int main() {
-
-  auto counts = cudaq::sample(photonicsKernel{});
-  counts.dump();
-
-  auto state = cudaq::get_state(photonicsKernel{});
-  state.dump();
-}
diff --git a/pr-2308/sphinx/targets/cpp/photonics_tbi_get_state.cpp b/pr-2308/sphinx/targets/cpp/photonics_tbi_get_state.cpp
deleted file mode 100644
index 3b27be44f5..0000000000
--- a/pr-2308/sphinx/targets/cpp/photonics_tbi_get_state.cpp
+++ /dev/null
@@ -1,69 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target orca-photonics photonics_tbi_get_state.cpp && ./a.out
-// ```
-
-#include <cudaq.h>
-#include <cudaq/photonics.h>
-#include <iostream>
-
-// Global variables
-static constexpr std::size_t one = 1;
-
-static constexpr std::size_t n_modes = 8;
-static constexpr std::array<std::size_t, n_modes> input_state = {1, 0, 1, 0,
-                                                                 1, 0, 1, 0};
-
-static constexpr std::size_t d =
-    std::accumulate(input_state.begin(), input_state.end(), one);
-
-struct TBI {
-  auto operator()(std::vector<double> const &bs_angles,
-                  std::vector<double> const &ps_angles,
-                  std::vector<std::size_t> const &input_state,
-                  std::vector<std::size_t> const &loop_lengths) __qpu__ {
-    auto n_modes = ::n_modes;
-    const auto d = ::d;
-
-    cudaq::qvector<d> qumodes(n_modes); // |00...00> d-dimensions
-    for (std::size_t i = 0; i < n_modes; i++) {
-      for (std::size_t j = 0; j < input_state[i]; j++) {
-        create(qumodes[i]); // setting to |input_state>
-      }
-    }
-
-    std::size_t c = 0;
-    for (std::size_t ll : loop_lengths) {
-      for (std::size_t i = 0; i < (n_modes - ll); i++) {
-        beam_splitter(qumodes[i], qumodes[i + ll], bs_angles[c]);
-        phase_shift(qumodes[i], ps_angles[c]);
-        c++;
-      }
-    }
-  }
-};
-
-int main() {
-  std::size_t n_loops = 2;
-  std::vector<std::size_t> loop_lengths = {1, 1};
-  std::vector<std::size_t> input_state(std::begin(::input_state),
-                                       std::end(::input_state));
-
-  const std::size_t zero = 0;
-  std::size_t sum_loop_lenghts{
-      std::accumulate(loop_lengths.begin(), loop_lengths.end(), zero)};
-
-  std::size_t n_beam_splitters = n_loops * ::n_modes - sum_loop_lenghts;
-
-  std::vector<double> bs_angles =
-      cudaq::linspace(M_PI / 3, M_PI / 6, n_beam_splitters);
-  std::vector<double> ps_angles =
-      cudaq::linspace(M_PI / 3, M_PI / 5, n_beam_splitters);
-
-  auto state =
-      cudaq::get_state(TBI{}, bs_angles, ps_angles, input_state, loop_lengths);
-
-  state.dump();
-
-  return 0;
-}
\ No newline at end of file
diff --git a/pr-2308/sphinx/targets/cpp/photonics_tbi_sample.cpp b/pr-2308/sphinx/targets/cpp/photonics_tbi_sample.cpp
deleted file mode 100644
index 90da940ec2..0000000000
--- a/pr-2308/sphinx/targets/cpp/photonics_tbi_sample.cpp
+++ /dev/null
@@ -1,70 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target orca-photonics photonics_tbi_sample.cpp && ./a.out
-// ```
-
-#include <cudaq.h>
-#include <cudaq/photonics.h>
-#include <iostream>
-
-// Global variables
-static constexpr std::size_t one = 1;
-
-static constexpr std::size_t n_modes = 8;
-static constexpr std::array<std::size_t, n_modes> input_state = {1, 0, 1, 0,
-                                                                 1, 0, 1, 0};
-
-static constexpr std::size_t d =
-    std::accumulate(input_state.begin(), input_state.end(), one);
-
-struct TBI {
-  auto operator()(std::vector<double> const &bs_angles,
-                  std::vector<double> const &ps_angles,
-                  std::vector<std::size_t> const &input_state,
-                  std::vector<std::size_t> const &loop_lengths) __qpu__ {
-    auto n_modes = ::n_modes;
-    const auto d = ::d;
-
-    cudaq::qvector<d> qumodes(n_modes); // |00...00> d-dimensions
-    for (std::size_t i = 0; i < n_modes; i++) {
-      for (std::size_t j = 0; j < input_state[i]; j++) {
-        create(qumodes[i]); // setting to |input_state>
-      }
-    }
-
-    std::size_t c = 0;
-    for (std::size_t ll : loop_lengths) {
-      for (std::size_t i = 0; i < (n_modes - ll); i++) {
-        beam_splitter(qumodes[i], qumodes[i + ll], bs_angles[c]);
-        phase_shift(qumodes[i], ps_angles[c]);
-        c++;
-      }
-    }
-    mz(qumodes);
-  }
-};
-
-int main() {
-  std::size_t n_loops = 2;
-  std::vector<std::size_t> loop_lengths = {1, 1};
-  std::vector<std::size_t> input_state(std::begin(::input_state),
-                                       std::end(::input_state));
-
-  const std::size_t zero = 0;
-  std::size_t sum_loop_lenghts{
-      std::accumulate(loop_lengths.begin(), loop_lengths.end(), zero)};
-
-  std::size_t n_beam_splitters = n_loops * ::n_modes - sum_loop_lenghts;
-
-  std::vector<double> bs_angles =
-      cudaq::linspace(M_PI / 3, M_PI / 6, n_beam_splitters);
-  std::vector<double> ps_angles =
-      cudaq::linspace(M_PI / 3, M_PI / 5, n_beam_splitters);
-
-  auto counts = cudaq::sample(1000000, TBI{}, bs_angles, ps_angles, input_state,
-                              loop_lengths);
-
-  counts.dump();
-
-  return 0;
-}
\ No newline at end of file
diff --git a/pr-2308/sphinx/targets/cpp/quantinuum.cpp b/pr-2308/sphinx/targets/cpp/quantinuum.cpp
deleted file mode 100644
index 63b3eaf905..0000000000
--- a/pr-2308/sphinx/targets/cpp/quantinuum.cpp
+++ /dev/null
@@ -1,51 +0,0 @@
-// Compile and run with:
-// ```
-// nvq++ --target quantinuum --quantinuum-machine H1-2E quantinuum.cpp -o out.x
-// ./out.x
-// ```
-// Assumes a valid set of credentials have been stored.
-// To first confirm the correctness of the program locally,
-// Add a --emulate to the `nvq++` command above.
-
-#include <cudaq.h>
-#include <fstream>
-
-// Define a simple quantum kernel to execute on Quantinuum.
-struct ghz {
-  // Maximally entangled state between 5 qubits.
-  auto operator()() __qpu__ {
-    cudaq::qvector q(5);
-    h(q[0]);
-    for (int i = 0; i < 4; i++) {
-      x<cudaq::ctrl>(q[i], q[i + 1]);
-    }
-    mz(q);
-  }
-};
-
-int main() {
-  // Submit to Quantinuum asynchronously (e.g., continue executing
-  // code in the file until the job has been returned).
-  auto future = cudaq::sample_async(ghz{});
-  // ... classical code to execute in the meantime ...
-
-  // Can write the future to file:
-  {
-    std::ofstream out("saveMe.json");
-    out << future;
-  }
-
-  // Then come back and read it in later.
-  cudaq::async_result<cudaq::sample_result> readIn;
-  std::ifstream in("saveMe.json");
-  in >> readIn;
-
-  // Get the results of the read in future.
-  auto async_counts = readIn.get();
-  async_counts.dump();
-
-  // OR: Submit to Quantinuum synchronously (e.g., wait for the job
-  // result to be returned before proceeding).
-  auto counts = cudaq::sample(ghz{});
-  counts.dump();
-}
diff --git a/pr-2308/sphinx/targets/python/braket.py b/pr-2308/sphinx/targets/python/braket.py
deleted file mode 100644
index d9c06f4cad..0000000000
--- a/pr-2308/sphinx/targets/python/braket.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import cudaq
-
-# NOTE: Amazon Braket credentials must be set before running this program.
-# Amazon Braket costs apply.
-cudaq.set_target("braket")
-
-# The default device is SV1, state vector simulator. Users may choose any of
-# the available devices by supplying its `ARN` with the `machine` parameter.
-# For example,
-# ```
-# cudaq.set_target("braket", machine="arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet")
-# ```
-
-
-# Create the kernel we'd like to execute
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(2)
-    h(qvector[0])
-    x.ctrl(qvector[0], qvector[1])
-    mz(qvector)
-
-
-# Execute and print out the results.
-
-# Option A:
-# By using the asynchronous `cudaq.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by Amazon Braket.
-async_results = cudaq.sample_async(kernel)
-# ... more classical code to run ...
-
-async_counts = async_results.get()
-print(async_counts)
-
-# Option B:
-# By using the synchronous `cudaq.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been returned from Amazon Braket.
-counts = cudaq.sample(kernel)
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/fermioniq.py b/pr-2308/sphinx/targets/python/fermioniq.py
deleted file mode 100644
index fbcfb917c9..0000000000
--- a/pr-2308/sphinx/targets/python/fermioniq.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import cudaq
-import os
-
-remote_config = os.environ.get("FERMIONIQ_REMOTE_CONFIG_ID", "")
-project_id = os.environ.get("FERMIONIQ_PROJECT_ID", "")
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-cudaq.set_target("fermioniq", **{
-    "remote_config": remote_config,
-    "project_id": project_id
-})
-
-
-# Create the kernel we'd like to execute on Fermioniq.
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(2)
-    h(qvector[0])
-    x.ctrl(qvector[0], qvector[1])
-    mz(qvector[0])
-    mz(qvector[1])
-
-
-# Submit to Fermioniq's endpoint and confirm the program is valid.
-result = cudaq.sample(kernel)
-
-print(result)
diff --git a/pr-2308/sphinx/targets/python/fermioniq_observables.py b/pr-2308/sphinx/targets/python/fermioniq_observables.py
deleted file mode 100644
index 855d2a6f1c..0000000000
--- a/pr-2308/sphinx/targets/python/fermioniq_observables.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# ============================================================================ #
-# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
-# All rights reserved.                                                         #
-#                                                                              #
-# This source code and the accompanying materials are made available under     #
-# the terms of the Apache License 2.0 which accompanies this distribution.     #
-# ============================================================================ #
-
-import os
-import cudaq
-from cudaq import spin
-
-remote_config = os.environ.get("FERMIONIQ_REMOTE_CONFIG_ID", "")
-project_id = os.environ.get("FERMIONIQ_PROJECT_ID", "")
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-cudaq.set_target("fermioniq", **{
-    "remote_config": remote_config,
-    "project_id": project_id
-})
-
-
-@cudaq.kernel
-def kernel(theta: float):
-    qvector = cudaq.qvector(2)
-    x(qvector[0])
-    ry(theta, qvector[1])
-    x.ctrl(qvector[1], qvector[0])
-
-
-spin_operator = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - 2.1433 * spin.y(
-    0) * spin.y(1) + .21829 * spin.z(0) - 6.125 * spin.z(1)
-
-# Pre-computed angle that minimizes the energy expectation of the `spin_operator`.
-angle = 0.59
-
-energy = cudaq.observe(kernel, spin_operator, angle).expectation()
-print(f"Energy is {energy}")
diff --git a/pr-2308/sphinx/targets/python/infleqtion.py b/pr-2308/sphinx/targets/python/infleqtion.py
deleted file mode 100644
index 20cb330d1d..0000000000
--- a/pr-2308/sphinx/targets/python/infleqtion.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import cudaq
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-# To use different targets in the same file, you must update
-# it via another call to `cudaq.set_target()`
-cudaq.set_target("infleqtion")
-
-
-# Create the kernel we'd like to execute on Infleqtion.
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(2)
-    h(qvector[0])
-    x.ctrl(qvector[0], qvector[1])
-    mz(qvector)
-
-
-# Note: All measurements must be terminal when performing the sampling.
-
-# Execute on Infleqtion and print out the results.
-
-# Option A (recommended):
-# By using the asynchronous `cudaq.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by the Superstaq API. This is ideal when submitting via a queue
-# over the cloud.
-async_results = cudaq.sample_async(kernel)
-# ... more classical code to run ...
-
-# We can either retrieve the results later in the program with
-# ```
-# async_counts = async_results.get()
-# ```
-# or we can also write the job reference (`async_results`) to
-# a file and load it later or from a different process.
-file = open("future.txt", "w")
-file.write(str(async_results))
-file.close()
-
-# We can later read the file content and retrieve the job
-# information and results.
-same_file = open("future.txt", "r")
-retrieved_async_results = cudaq.AsyncSampleResult(str(same_file.read()))
-
-counts = retrieved_async_results.get()
-print(counts)
-
-# Option B:
-# By using the synchronous `cudaq.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been returned from Superstaq.
-counts = cudaq.sample(kernel)
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/ionq.py b/pr-2308/sphinx/targets/python/ionq.py
deleted file mode 100644
index ded91bc060..0000000000
--- a/pr-2308/sphinx/targets/python/ionq.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import cudaq
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-# To use different targets in the same file, you must update
-# it via another call to `cudaq.set_target()`
-cudaq.set_target("ionq")
-
-
-# Create the kernel we'd like to execute on IonQ.
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(2)
-    h(qvector[0])
-    x.ctrl(qvector[0], qvector[1])
-
-
-# Note: All qubits will be measured at the end upon performing
-# the sampling. You may encounter a pre-flight error on IonQ
-# backends if you include explicit measurements.
-
-# Execute on IonQ and print out the results.
-
-# Option A:
-# By using the asynchronous `cudaq.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by IonQ. This is ideal when submitting via a queue over
-# the cloud.
-async_results = cudaq.sample_async(kernel)
-# ... more classical code to run ...
-
-# We can either retrieve the results later in the program with
-# ```
-# async_counts = async_results.get()
-# ```
-# or we can also write the job reference (`async_results`) to
-# a file and load it later or from a different process.
-file = open("future.txt", "w")
-file.write(str(async_results))
-file.close()
-
-# We can later read the file content and retrieve the job
-# information and results.
-same_file = open("future.txt", "r")
-retrieved_async_results = cudaq.AsyncSampleResult(str(same_file.read()))
-
-counts = retrieved_async_results.get()
-print(counts)
-
-# Option B:
-# By using the synchronous `cudaq.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been returned from IonQ.
-counts = cudaq.sample(kernel)
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/iqm.py b/pr-2308/sphinx/targets/python/iqm.py
deleted file mode 100644
index fdf4dadd8f..0000000000
--- a/pr-2308/sphinx/targets/python/iqm.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import cudaq
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-# To use different targets in the same file, you must update
-# it via another call to `cudaq.set_target()`
-cudaq.set_target("iqm",
-                 url="http://localhost/cocos",
-                 **{"qpu-architecture": "Adonis"})
-
-# Adonis QPU architecture:
-#       QB1
-#        |
-# QB2 - QB3 - QB4
-#        |
-#       QB5
-
-
-# Create the kernel we'd like to execute on IQM.
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(5)
-    h(qvector[2])  # QB3
-    x.ctrl(qvector[2], qvector[0])
-    mz(qvector)
-
-
-# Execute on IQM Server and print out the results.
-
-# Option A:
-# By using the asynchronous `cudaq.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by IQM Server. This is ideal when submitting via a queue over
-# the cloud.
-async_results = cudaq.sample_async(kernel)
-# ... more classical code to run ...
-
-# We can either retrieve the results later in the program with
-# ```
-# async_counts = async_results.get()
-# ```
-# or we can also write the job reference (`async_results`) to
-# a file and load it later or from a different process.
-file = open("future.txt", "w")
-file.write(str(async_results))
-file.close()
-
-# We can later read the file content and retrieve the job
-# information and results.
-same_file = open("future.txt", "r")
-retrieved_async_results = cudaq.AsyncSampleResult(str(same_file.read()))
-
-counts = retrieved_async_results.get()
-print(counts)
-
-# Option B:
-# By using the synchronous `cudaq.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been returned from IQM Server.
-counts = cudaq.sample(kernel)
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/nvqc_mgpu.py b/pr-2308/sphinx/targets/python/nvqc_mgpu.py
deleted file mode 100644
index 00b7d806e5..0000000000
--- a/pr-2308/sphinx/targets/python/nvqc_mgpu.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import argparse
-import cudaq
-import random
-
-# This example assumes the NVQC API key has been set in the `NVQC_API_KEY` environment variable.
-# If not, you can set the API Key environment variable in the Python script with:
-# ```
-# os.environ["NVQC_API_KEY"] = "<YOUR NVQC API KEY>"`
-# ```
-
-
-def random_bitstring(length: int):
-    bitstring = ""
-    for bit in range(length):
-        bitstring += str(random.randint(0, 1))
-    return bitstring
-
-
-def oracle(kernel: cudaq.Kernel, register: cudaq.QuakeValue,
-           auxillary_qubit: cudaq.QuakeValue, hidden_bitstring: str):
-    """
-    The inner-product oracle for the Bernstein-Vazirani algorithm.
-    """
-    for index, bit in enumerate(hidden_bitstring):
-        if bit == "0":
-            # Apply identity operation to the qubit if it's
-            # in the 0-state.
-            # In this case, we do nothing.
-            pass
-        else:
-            # Otherwise, apply a `cx` gate with the current qubit as
-            # the control and the auxillary qubit as the target.
-            kernel.cx(control=register[index], target=auxillary_qubit)
-
-
-def bernstein_vazirani(qubit_count: int):
-    """
-    Returns a kernel implementing the Bernstein-Vazirani algorithm
-    for a random, hidden bitstring.
-    """
-    kernel = cudaq.make_kernel()
-    # Allocate the specified number of qubits - this
-    # corresponds to the length of the hidden bitstring.
-    qubits = kernel.qalloc(qubit_count)
-    # Allocate an extra auxillary qubit.
-    auxillary_qubit = kernel.qalloc()
-
-    # Prepare the auxillary qubit.
-    kernel.h(auxillary_qubit)
-    kernel.z(auxillary_qubit)
-
-    # Place the rest of the register in a superposition state.
-    kernel.h(qubits)
-
-    # Generate a random, hidden bitstring for the oracle
-    # to encode. Note: we define the bitstring here so
-    # as to be able to return it for verification.
-    hidden_bitstring = random_bitstring(qubit_count)
-
-    # Query the oracle.
-    oracle(kernel, qubits, auxillary_qubit, hidden_bitstring)
-
-    # Apply another set of Hadamards to the register.
-    kernel.h(qubits)
-
-    # Apply measurement gates to just the `qubits`
-    # (excludes the auxillary qubit).
-    kernel.mz(qubits)
-    return kernel, hidden_bitstring
-
-
-# This example demonstrated GPU-accelerated simulator backends on NVQC can easily handle a large number of qubits.
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        prog='python',
-        description='Run a Bernstein-Vazirani algorithm using NVQC.',
-        epilog=
-        'For more information about CUDA-Q, see https://nvidia.github.io/cuda-quantum'
-    )
-    parser.add_argument('--size',
-                        type=int,
-                        required=False,
-                        default=30,
-                        help='The number of bits in the secret string.')
-    parser.add_argument('--ngpus',
-                        type=int,
-                        required=False,
-                        default=1,
-                        help='The number of NVQC GPUs to run the simulation.')
-    parser.add_argument('--seed',
-                        type=int,
-                        required=False,
-                        default=0,
-                        help='The random seed to generate the secret string.')
-    args = parser.parse_args()
-
-    # Depending on the number of GPUs requested, you can
-    # set the size of the secret string to around 31-34 (total qubit count = string length + 1) when
-    # you pass the `--ngpus` as a command line argument.
-    qubit_count = args.size
-    if args.seed != 0:
-        random.seed(args.seed)
-
-    cudaq.set_target("nvqc", backend="nvidia-mgpu", ngpus=args.ngpus)
-
-    print(
-        f"Running on NVQC using 'nvidia-mgpu' simulator backend with {args.ngpus} GPU(s) ..."
-    )
-    kernel, hidden_bitstring = bernstein_vazirani(qubit_count)
-    result = cudaq.sample(kernel)
-
-    print(f"encoded bitstring = {hidden_bitstring}")
-    print(f"measured state = {result.most_probable()}")
-    print(f"Were we successful? {hidden_bitstring == result.most_probable()}")
diff --git a/pr-2308/sphinx/targets/python/nvqc_sample.py b/pr-2308/sphinx/targets/python/nvqc_sample.py
deleted file mode 100644
index 55c11b4f1f..0000000000
--- a/pr-2308/sphinx/targets/python/nvqc_sample.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import cudaq
-
-# This example assumes the NVQC API key has been set in the `NVQC_API_KEY` environment variable.
-# If not, you can set the API Key environment variable in the Python script with:
-# ```
-# os.environ["NVQC_API_KEY"] = "<YOUR NVQC API KEY>"`
-# ```
-cudaq.set_target("nvqc", backend="tensornet")
-
-# Note: The `tensornet` simulator is capable of distributing tensor contraction operations across multiple GPUs.
-# User can use the `ngpus` option to target a multi-GPU NVQC endpoint.
-# For example, to use the `tensornet` simulator with 8 GPUs, we can do
-# `cudaq.set_target("nvqc", backend="tensornet", ngpus=8)`
-# Please refer to your NVQC dashboard for the list of available multi-GPU configurations.
-num_qubits = 50
-kernel = cudaq.make_kernel()
-qubits = kernel.qalloc(num_qubits)
-# Place qubits in superposition state.
-kernel.h(qubits[0])
-for i in range(num_qubits - 1):
-    kernel.cx(qubits[i], qubits[i + 1])
-# Measure.
-kernel.mz(qubits)
-
-counts = cudaq.sample(kernel, shots_count=100)
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/nvqc_state.py b/pr-2308/sphinx/targets/python/nvqc_state.py
deleted file mode 100644
index eae5c42339..0000000000
--- a/pr-2308/sphinx/targets/python/nvqc_state.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import cudaq
-
-# This example assumes the NVQC API key has been set in the `NVQC_API_KEY` environment variable.
-# If not, you can set the API Key environment variable in the Python script with:
-# ```
-# os.environ["NVQC_API_KEY"] = "<YOUR NVQC API KEY>"`
-# ```
-
-cudaq.set_target("nvqc")
-
-num_qubits = 20
-kernel = cudaq.make_kernel()
-qubits = kernel.qalloc(num_qubits)
-# Place qubits in GHZ state.
-kernel.h(qubits[0])
-for i in range(num_qubits - 1):
-    kernel.cx(qubits[i], qubits[i + 1])
-
-state = cudaq.get_state(kernel)
-print("Amplitude(00..00) =", state[0])
-print("Amplitude(11..11) =", state[2**num_qubits - 1])
diff --git a/pr-2308/sphinx/targets/python/nvqc_vqe.py b/pr-2308/sphinx/targets/python/nvqc_vqe.py
deleted file mode 100644
index da90150a6e..0000000000
--- a/pr-2308/sphinx/targets/python/nvqc_vqe.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import cudaq
-from cudaq import spin
-import math
-
-# This example assumes the NVQC API key has been set in the `NVQC_API_KEY` environment variable.
-# If not, you can set the API Key environment variable in the Python script with:
-# ```
-# os.environ["NVQC_API_KEY"] = "<YOUR NVQC API KEY>"`
-# ```
-
-cudaq.set_target("nvqc", nqpus=3)
-
-print("Number of QPUs:", cudaq.get_target().num_qpus())
-
-
-# Note: depending on the user's account, there might be different
-# number of NVQC worker instances available. Hence, although we're making
-# concurrent job submissions across multiple QPUs, the speedup would be
-# determined by the number of NVQC worker instances.
-# Create the parameterized ansatz
-@cudaq.kernel
-def ansatz(theta: float):
-    qvector = cudaq.qvector(2)
-    x(qvector[0])
-    ry(theta, qvector[1])
-    x.ctrl(qvector[1], qvector[0])
-
-
-# Define its spin Hamiltonian.
-hamiltonian = (5.907 - 2.1433 * spin.x(0) * spin.x(1) -
-               2.1433 * spin.y(0) * spin.y(1) + 0.21829 * spin.z(0) -
-               6.125 * spin.z(1))
-
-
-def opt_gradient(parameter_vector):
-    # Evaluate energy and gradient on different remote QPUs
-    # (i.e., concurrent job submissions to NVQC)
-    energy_future = cudaq.observe_async(ansatz,
-                                        hamiltonian,
-                                        parameter_vector[0],
-                                        qpu_id=0)
-    plus_future = cudaq.observe_async(ansatz,
-                                      hamiltonian,
-                                      parameter_vector[0] + 0.5 * math.pi,
-                                      qpu_id=1)
-    minus_future = cudaq.observe_async(ansatz,
-                                       hamiltonian,
-                                       parameter_vector[0] - 0.5 * math.pi,
-                                       qpu_id=2)
-    return (energy_future.get().expectation(), [
-        (plus_future.get().expectation() - minus_future.get().expectation()) /
-        2.0
-    ])
-
-
-optimizer = cudaq.optimizers.LBFGS()
-optimal_value, optimal_parameters = optimizer.optimize(1, opt_gradient)
-print("Ground state energy =", optimal_value)
-print("Optimal parameters =", optimal_parameters)
diff --git a/pr-2308/sphinx/targets/python/oqc.py b/pr-2308/sphinx/targets/python/oqc.py
deleted file mode 100644
index d333746b05..0000000000
--- a/pr-2308/sphinx/targets/python/oqc.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import cudaq
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-# To use different targets in the same file, you must update
-# it via another call to `cudaq.set_target()`
-
-# To use the OQC target you will need to set the following environment variables
-# OQC_URL
-# OQC_EMAIL
-# OQC_PASSWORD
-# To setup an account, contact oqc_qcaas_support@oxfordquantumcircuits.com
-
-cudaq.set_target("oqc")
-
-
-# Create the kernel we'd like to execute on OQC.
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(2)
-    h(qvector[0])
-    x.ctrl[qvector[1], qvector[1]]
-    mz(qvector)
-
-
-# Option A:
-# By using the asynchronous `cudaq.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by OQC. This is ideal when submitting via a queue over
-# the cloud.
-async_results = cudaq.sample_async(kernel)
-# ... more classical code to run ...
-
-# We can either retrieve the results later in the program with
-# ```
-# async_counts = async_results.get()
-# ```
-# or we can also write the job reference (`async_results`) to
-# a file and load it later or from a different process.
-file = open("future.txt", "w")
-file.write(str(async_results))
-file.close()
-
-# We can later read the file content and retrieve the job
-# information and results.
-same_file = open("future.txt", "r")
-retrieved_async_results = cudaq.AsyncSampleResult(str(same_file.read()))
-
-counts = retrieved_async_results.get()
-print(counts)
-
-# Option B:
-# By using the synchronous `cudaq.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been returned from OQC.
-counts = cudaq.sample(kernel)
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/orca.py b/pr-2308/sphinx/targets/python/orca.py
deleted file mode 100644
index 79f5cfd770..0000000000
--- a/pr-2308/sphinx/targets/python/orca.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import cudaq
-import time
-
-import numpy as np
-import os
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-# To use different targets in the same file, you must update
-# it via another call to `cudaq.set_target()`
-
-# To use the ORCA Computing target you will need to set the ORCA_ACCESS_URL
-# environment variable or pass a URL.
-orca_url = os.getenv("ORCA_ACCESS_URL", "http://localhost/sample")
-
-cudaq.set_target("orca", url=orca_url)
-
-# A time-bin boson sampling experiment: An input state of 4 indistinguishable
-# photons mixed with 4 vacuum states across 8 time bins (modes) enter the
-# time bin interferometer (TBI). The interferometer is composed of two loops
-# each with a beam splitter (and optionally with a corresponding phase
-# shifter). Each photon can either be stored in a loop to interfere with the
-# next photon or exit the loop to be measured. Since there are 8 time bins
-# and 2 loops, there is a total of 14 beam splitters (and optionally 14 phase
-# shifters) in the interferometer, which is the number of controllable
-# parameters.
-
-# half of 8 time bins is filled with a single photon and the other half is
-# filled with the vacuum state (empty)
-input_state = [1, 0, 1, 0, 1, 0, 1, 0]
-
-# The time bin interferometer in this example has two loops, each of length 1
-loop_lengths = [1, 1]
-
-# Calculate the number of beam splitters and phase shifters
-n_beam_splitters = len(loop_lengths) * len(input_state) - sum(loop_lengths)
-
-# beam splitter angles
-bs_angles = np.linspace(np.pi / 3, np.pi / 6, n_beam_splitters)
-
-# Optionally, we can also specify the phase shifter angles, if the system
-# includes phase shifters
-# ```
-# ps_angles = np.linspace(np.pi / 3, np.pi / 5, n_beam_splitters)
-# ```
-
-# we can also set number of requested samples
-n_samples = 10000
-
-# Option A:
-# By using the synchronous `cudaq.orca.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been returned from ORCA Server.
-print("Submitting to ORCA Server synchronously")
-counts = cudaq.orca.sample(input_state, loop_lengths, bs_angles, n_samples)
-
-# If the system includes phase shifters, the phase shifter angles can be
-# included in the call
-# ```
-# counts = cudaq.orca.sample(input_state, loop_lengths, bs_angles, ps_angles,
-#                            n_samples)
-# ```
-
-# Print the results
-print(counts)
-
-# Option B:
-# By using the asynchronous `cudaq.orca.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by Orca. This is ideal when submitting via a queue over
-# the cloud.
-print("Submitting to ORCA Server asynchronously")
-async_results = cudaq.orca.sample_async(input_state, loop_lengths, bs_angles,
-                                        n_samples)
-# ... more classical code to run ...
-
-# We can either retrieve the results later in the program with
-# ```
-# async_counts = async_results.get()
-# ```
-# or we can also write the job reference (`async_results`) to
-# a file and load it later or from a different process.
-file = open("future.txt", "w")
-file.write(str(async_results))
-file.close()
-
-# We can later read the file content and retrieve the job
-# information and results.
-time.sleep(0.2)  # wait for the job to be processed
-same_file = open("future.txt", "r")
-retrieved_async_results = cudaq.AsyncSampleResult(str(same_file.read()))
-
-counts = retrieved_async_results.get()
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/orca_mqpu.py b/pr-2308/sphinx/targets/python/orca_mqpu.py
deleted file mode 100644
index 74fe7c1b11..0000000000
--- a/pr-2308/sphinx/targets/python/orca_mqpu.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import cudaq
-import numpy as np
-import os
-
-# See accompanying example `orca.py` for detailed explanation.
-
-# To use the ORCA Computing target you will need to set the ORCA_ACCESS_URL
-# environment variable or pass URL(s).
-orca_urls = os.getenv("ORCA_ACCESS_URL",
-                      "http://localhost:3035,http://localhost:3037")
-
-cudaq.set_target("orca", url=orca_urls)
-
-qpu_count = cudaq.get_target().num_qpus()
-print("Number of virtual QPUs:", qpu_count)
-
-# A time-bin boson sampling experiment
-input_state = [1, 0, 1, 0, 1, 0, 1, 0]
-loop_lengths = [1, 1]
-n_beam_splitters = len(loop_lengths) * len(input_state) - sum(loop_lengths)
-bs_angles = np.linspace(np.pi / 3, np.pi / 6, n_beam_splitters)
-n_samples = 10000
-
-count_futures = []
-for i in range(qpu_count):
-    result = cudaq.orca.sample_async(input_state,
-                                     loop_lengths,
-                                     bs_angles,
-                                     n_samples,
-                                     qpu_id=i)
-    count_futures.append(result)
-
-print("Sampling jobs launched for asynchronous processing.")
-
-for counts in count_futures:
-    counts.get().dump()
diff --git a/pr-2308/sphinx/targets/python/photonics.py b/pr-2308/sphinx/targets/python/photonics.py
deleted file mode 100644
index ecf1696f3f..0000000000
--- a/pr-2308/sphinx/targets/python/photonics.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import cudaq
-
-cudaq.set_target("orca-photonics")
-
-
-@cudaq.kernel
-def photonicsKernel():
-    qutrits = [qudit(3) for _ in range(2)]
-    create(qutrits[0])
-    create(qutrits[1])
-    create(qutrits[1])
-    mz(qutrits)
-
-
-counts = cudaq.sample(photonicsKernel)
-print(counts)
-
-state = cudaq.get_state(photonicsKernel)
-print(state)
diff --git a/pr-2308/sphinx/targets/python/photonics_tbi_get_state.py b/pr-2308/sphinx/targets/python/photonics_tbi_get_state.py
deleted file mode 100644
index a80fc8f429..0000000000
--- a/pr-2308/sphinx/targets/python/photonics_tbi_get_state.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import cudaq
-import numpy as np
-
-cudaq.set_target("orca-photonics")
-
-
-@cudaq.kernel
-def TBI(
-    bs_angles: list[float],
-    ps_angles: list[float],
-    input_state: list[int],
-    loop_lengths: list[int],
-):
-    n_modes = len(input_state)
-    level = sum(input_state) + 1  # qudit level
-
-    qumodes = [qudit(level) for _ in range(n_modes)]
-
-    for i in range(n_modes):
-        for _ in range(input_state[i]):
-            create(qumodes[i])
-
-    counter = 0
-    for j in loop_lengths:
-        for i in range(n_modes - j):
-            beam_splitter(qumodes[i], qumodes[i + j], bs_angles[counter])
-            phase_shift(qumodes[i], ps_angles[counter])
-            counter += 1
-
-
-input_state = [1, 0, 1, 0, 1, 0, 1, 0]
-loop_lengths = [1, 1]
-n_beam_splitters = len(loop_lengths) * len(input_state) - sum(loop_lengths)
-bs_angles = np.linspace(np.pi / 3, np.pi / 6, n_beam_splitters)
-ps_angles = np.linspace(np.pi / 3, np.pi / 5, n_beam_splitters)
-
-state = cudaq.get_state(TBI, bs_angles, ps_angles, input_state, loop_lengths)
-state.dump()
\ No newline at end of file
diff --git a/pr-2308/sphinx/targets/python/photonics_tbi_sample.py b/pr-2308/sphinx/targets/python/photonics_tbi_sample.py
deleted file mode 100644
index e381c6c46a..0000000000
--- a/pr-2308/sphinx/targets/python/photonics_tbi_sample.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import cudaq
-import numpy as np
-
-cudaq.set_target("orca-photonics")
-
-
-@cudaq.kernel
-def TBI(
-    bs_angles: list[float],
-    ps_angles: list[float],
-    input_state: list[int],
-    loop_lengths: list[int],
-):
-    n_modes = len(input_state)
-    level = sum(input_state) + 1  # qudit level
-
-    qumodes = [qudit(level) for _ in range(n_modes)]
-
-    for i in range(n_modes):
-        for _ in range(input_state[i]):
-            create(qumodes[i])
-
-    counter = 0
-    for j in loop_lengths:
-        for i in range(n_modes - j):
-            beam_splitter(qumodes[i], qumodes[i + j], bs_angles[counter])
-            phase_shift(qumodes[i], ps_angles[counter])
-            counter += 1
-
-    mz(qumodes)
-
-
-input_state = [1, 0, 1, 0, 1, 0, 1, 0]
-loop_lengths = [1, 1]
-
-n_beam_splitters = len(loop_lengths) * len(input_state) - sum(loop_lengths)
-bs_angles = np.linspace(np.pi / 3, np.pi / 6, n_beam_splitters)
-ps_angles = np.linspace(np.pi / 3, np.pi / 5, n_beam_splitters)
-
-counts = cudaq.sample(TBI,
-                      bs_angles,
-                      ps_angles,
-                      input_state,
-                      loop_lengths,
-                      shots_count=1000000)
-counts.dump()
diff --git a/pr-2308/sphinx/targets/python/quantinuum.py b/pr-2308/sphinx/targets/python/quantinuum.py
deleted file mode 100644
index 9db699f301..0000000000
--- a/pr-2308/sphinx/targets/python/quantinuum.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import cudaq
-
-# You only have to set the target once! No need to redefine it
-# for every execution call on your kernel.
-# By default, we will submit to the Quantinuum syntax checker.
-cudaq.set_target("quantinuum")
-
-
-# Create the kernel we'd like to execute on Quantinuum.
-@cudaq.kernel
-def kernel():
-    qvector = cudaq.qvector(2)
-    h(qvector[0])
-    x.ctrl(qvector[0], qvector[1])
-    mz(qvector[0])
-    mz(qvector[1])
-
-
-# Submit to Quantinuum's endpoint and confirm the program is valid.
-
-# Option A:
-# By using the synchronous `cudaq.sample`, the execution of
-# any remaining classical code in the file will occur only
-# after the job has been executed by the Quantinuum service.
-# We will use the synchronous call to submit to the syntax
-# checker to confirm the validity of the program.
-syntax_check = cudaq.sample(kernel)
-if (syntax_check):
-    print("Syntax check passed! Kernel is ready for submission.")
-
-# Now we can update the target to the Quantinuum emulator and
-# execute our program.
-cudaq.set_target("quantinuum", machine="H1-2E")
-
-# Option B:
-# By using the asynchronous `cudaq.sample_async`, the remaining
-# classical code will be executed while the job is being handled
-# by Quantinuum. This is ideal when submitting via a queue over
-# the cloud.
-async_results = cudaq.sample_async(kernel)
-# ... more classical code to run ...
-
-# We can either retrieve the results later in the program with
-# ```
-# async_counts = async_results.get()
-# ```
-# or we can also write the job reference (`async_results`) to
-# a file and load it later or from a different process.
-file = open("future.txt", "w")
-file.write(str(async_results))
-file.close()
-
-# We can later read the file content and retrieve the job
-# information and results.
-same_file = open("future.txt", "r")
-retrieved_async_results = cudaq.AsyncSampleResult(str(same_file.read()))
-
-counts = retrieved_async_results.get()
-print(counts)
diff --git a/pr-2308/sphinx/targets/python/quera_basic.py b/pr-2308/sphinx/targets/python/quera_basic.py
deleted file mode 100644
index 9321f69a09..0000000000
--- a/pr-2308/sphinx/targets/python/quera_basic.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import cudaq
-from cudaq.operator import *
-import numpy as np
-
-## NOTE: QuEra Aquila system is available via Amazon Braket.
-# Credentials must be set before running this program.
-# Amazon Braket costs apply.
-
-# This example illustrates how to use QuEra's Aquila device on Braket with CUDA-Q.
-# It is a CUDA-Q implementation of the getting started materials for Braket available here:
-# https://docs.aws.amazon.com/braket/latest/developerguide/braket-get-started-hello-ahs.html
-
-cudaq.set_target("quera")
-
-# Define the 2-dimensional atom arrangement
-a = 5.7e-6
-register = []
-register.append(tuple(np.array([0.5, 0.5 + 1 / np.sqrt(2)]) * a))
-register.append(tuple(np.array([0.5 + 1 / np.sqrt(2), 0.5]) * a))
-register.append(tuple(np.array([0.5 + 1 / np.sqrt(2), -0.5]) * a))
-register.append(tuple(np.array([0.5, -0.5 - 1 / np.sqrt(2)]) * a))
-register.append(tuple(np.array([-0.5, -0.5 - 1 / np.sqrt(2)]) * a))
-register.append(tuple(np.array([-0.5 - 1 / np.sqrt(2), -0.5]) * a))
-register.append(tuple(np.array([-0.5 - 1 / np.sqrt(2), 0.5]) * a))
-register.append(tuple(np.array([-0.5, 0.5 + 1 / np.sqrt(2)]) * a))
-
-time_max = 4e-6  # seconds
-time_ramp = 1e-7  # seconds
-omega_max = 6300000.0  # rad / sec
-delta_start = -5 * omega_max
-delta_end = 5 * omega_max
-
-# Times for the piece-wise linear waveforms
-steps = [0.0, time_ramp, time_max - time_ramp, time_max]
-schedule = Schedule(steps, ["t"])
-# Rabi frequencies at each step
-omega = ScalarOperator(lambda t: omega_max if time_ramp < t < time_max else 0.0)
-# Global phase at each step
-phi = ScalarOperator.const(0.0)
-# Global detuning at each step
-delta = ScalarOperator(lambda t: delta_end
-                       if time_ramp < t < time_max else delta_start)
-
-async_result = evolve_async(RydbergHamiltonian(atom_sites=register,
-                                               amplitude=omega,
-                                               phase=phi,
-                                               delta_global=delta),
-                            schedule=schedule,
-                            shots_count=10).get()
-async_result.dump()
-
-## Sample result
-# ```
-# {
-#   __global__ : { 12121222:1 21202221:1 21212121:2 21212122:1 21221212:1 21221221:2 22121221:1 22221221:1 }
-#    post_sequence : { 01010111:1 10101010:2 10101011:1 10101110:1 10110101:1 10110110:2 11010110:1 11110110:1 }
-#    pre_sequence : { 11101111:1 11111111:9 }
-# }
-# ```
-
-## Interpreting result
-# `pre_sequence` has the measurement bits, one for each atomic site, before the
-# quantum evolution is run. The count is aggregated across shots. The value is
-# 0 if site is empty, 1 if site is filled.
-# `post_sequence` has the measurement bits, one for each atomic site, at the
-# end of the quantum evolution. The count is aggregated across shots. The value
-# is 0 if atom is in Rydberg state or site is empty, 1 if atom is in ground
-# state.
-# `__global__` has the aggregate of the state counts from all the successful
-# shots. The value is 0 if site is empty, 1 if atom is in Rydberg state (up
-# state spin) and 2 if atom is in ground state (down state spin).
diff --git a/pr-2308/sphinx/targets/python/quera_intro.py b/pr-2308/sphinx/targets/python/quera_intro.py
deleted file mode 100644
index 3a66f45223..0000000000
--- a/pr-2308/sphinx/targets/python/quera_intro.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import cudaq
-from cudaq.operator import *
-import numpy as np
-
-# This example illustrates how to use QuEra's Aquila device on Braket with CUDA-Q.
-# It is a CUDA-Q implementation of the getting started materials for Braket available here:
-# https://github.com/amazon-braket/amazon-braket-examples/blob/main/examples/analog_hamiltonian_simulation/01_Introduction_to_Aquila.ipynb
-
-cudaq.set_target("quera")
-
-separation = 5e-6
-block_separation = 15e-6
-k_max = 5
-m_max = 5
-
-register = []
-for k in range(k_max):
-    for m in range(m_max):
-        register.append((block_separation * m,
-                         block_separation * k + separation / np.sqrt(3)))
-        register.append((block_separation * m - separation / 2,
-                         block_separation * k - separation / (2 * np.sqrt(3))))
-        register.append((block_separation * m + separation / 2,
-                         block_separation * k - separation / (2 * np.sqrt(3))))
-
-omega_const = 1.5e7  # rad / s
-time_ramp = 5e-8  # s
-time_plateau = 7.091995761561453e-08  # s
-time_max = 2 * time_ramp + time_plateau  # s
-
-# Schedule of time steps.
-steps = [0, time_ramp, time_ramp + time_plateau, time_max]
-schedule = Schedule(steps, ["t"])
-
-
-def trapezoidal_signal(t):
-    slope = omega_const / time_ramp
-    y_intercept = slope * time_max
-    if 0 < t < time_ramp + time_plateau:
-        return slope * t
-    if time_ramp < t < time_max:
-        return omega_const
-    if time_ramp + time_plateau < t < time_max:
-        return (-slope * t) + y_intercept
-    return 0.0
-
-
-omega = ScalarOperator(lambda t: trapezoidal_signal(t))
-phi = ScalarOperator.const(0.0)
-delta = ScalarOperator.const(0.0)
-
-evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
-                                             amplitude=omega,
-                                             phase=phi,
-                                             delta_global=delta),
-                          schedule=schedule)
-evolution_result.dump()
diff --git a/pr-2308/sphinx/using/advanced.html b/pr-2308/sphinx/using/advanced.html
deleted file mode 100644
index d8a05d9e12..0000000000
--- a/pr-2308/sphinx/using/advanced.html
+++ /dev/null
@@ -1 +0,0 @@
-<html><head><meta http-equiv="refresh" content="0; url=integration/integration.html"></head></html>
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/applications.rst b/pr-2308/sphinx/using/applications.rst
deleted file mode 100644
index b3119decf4..0000000000
--- a/pr-2308/sphinx/using/applications.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-*************************
-CUDA-Q Applications
-*************************
-
-Applications that give an in depth view of CUDA-Q and its applications in Python.
-
-.. nbgallery::
-
-    /applications/python/bernstein_vazirani.ipynb
-    /applications/python/unitary_compilation_diffusion_models.ipynb
-    /applications/python/trotter.ipynb
-    /applications/python/cost_minimization.ipynb
-    /applications/python/deutschs_algorithm.ipynb
-    /applications/python/divisive_clustering_coresets.ipynb
-    /applications/python/shors.ipynb
-    /applications/python/hybrid_qnns.ipynb
-    /applications/python/qaoa.ipynb
-    /applications/python/digitized_counterdiabatic_qaoa.ipynb
-    /applications/python/krylov.ipynb
-    /applications/python/afqmc.ipynb
-    /applications/python/quantum_fourier_transform.ipynb
-    /applications/python/quantum_teleportation.ipynb
-    /applications/python/quantum_volume.ipynb
-    /applications/python/readout_error_mitigation.ipynb
-    /applications/python/vqe.ipynb
-    /applications/python/vqe_advanced.ipynb
-    /applications/python/hadamard_test.ipynb
-    /applications/python/logical_aim_sqale.ipynb
-
diff --git a/pr-2308/sphinx/using/backends/backends.rst b/pr-2308/sphinx/using/backends/backends.rst
deleted file mode 100644
index 9ee2bbbbbf..0000000000
--- a/pr-2308/sphinx/using/backends/backends.rst
+++ /dev/null
@@ -1,41 +0,0 @@
-CUDA-Q Backends
-**********************
-
-.. toctree::
-   :caption: Backend Targets
-   :maxdepth: 1
-
-      Simulation <simulators.rst>
-      Quantum Hardware <hardware.rst>
-      NVIDIA Quantum Cloud <nvqc.rst>
-      Multi-Processor Platforms <platform.rst>
-
-**The following is a comprehensive list of the available targets in CUDA-Q:**
-
-* :ref:`anyon <anyon-backend>`
-* :ref:`braket <braket-backend>`
-* :ref:`density-matrix-cpu <default-simulator>`
-* :ref:`fermioniq <fermioniq-backend>`
-* :ref:`infleqtion <infleqtion-backend>`
-* :ref:`ionq <ionq-backend>`
-* :ref:`iqm <iqm-backend>`
-* :ref:`nvidia <nvidia-backend>`
-* :ref:`nvidia-fp64 <nvidia-fp64-backend>`
-* :ref:`nvidia-mgpu <nvidia-mgpu-backend>`
-* :ref:`nvidia-mqpu <mqpu-platform>`
-* :ref:`nvidia-mqpu-fp64 <mqpu-platform>`
-* :doc:`nvqc <nvqc>`
-* :ref:`oqc <oqc-backend>`
-* :ref:`orca <orca-backend>`
-* :ref:`qpp-cpu <qpp-cpu-backend>`
-* :ref:`quantinuum <quantinuum-backend>`
-* :ref:`quera <quera-backend>`
-* :ref:`remote-mqpu <mqpu-platform>`
-* :ref:`stim <stim-backend>`
-* :ref:`tensornet <tensor-backends>`
-* :ref:`tensornet-mps <tensor-backends>`
-
-.. deprecated:: 0.8
-   The `nvidia-fp64`, `nvidia-mgpu`, `nvidia-mqpu`, and `nvidia-mqpu-fp64` targets can be
-   enabled as extensions of the unified `nvidia` target (see `nvidia` :ref:`target documentation <nvidia-backend>`).
-   These target names might be removed in a future release.
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/backends/dynamics.rst b/pr-2308/sphinx/using/backends/dynamics.rst
deleted file mode 100644
index a5b8c056b8..0000000000
--- a/pr-2308/sphinx/using/backends/dynamics.rst
+++ /dev/null
@@ -1,275 +0,0 @@
-CUDA-Q Dynamics 
-*********************************
-
-CUDA-Q enables the design, simulation and execution of quantum dynamics via 
-the ``evolve`` API. Specifically, this API allows us to solve the time evolution 
-of quantum systems or models. In the simulation mode, CUDA-Q provides the ``dynamics``
-backend target, which is based on the cuQuantum library, optimized for performance and scale
-on NVIDIA GPU.
-
-Quick Start
-+++++++++++
-
-In the example below, we demonstrate a simple time evolution simulation workflow comprising of the 
-following steps:
-
-1. Define a quantum system model
-
-A quantum system model is defined by a Hamiltonian. 
-For example, a superconducting `transmon <https://en.wikipedia.org/wiki/Transmon>`_ qubit can be modeled by the following Hamiltonian
-
-.. math:: 
-    
-    H = \frac{\omega_z}{2} \sigma_z + \omega_x \cos(\omega_d t)\sigma_x,
-
-where :math:`\sigma_z` and :math:`\sigma_x` are Pauli Z and X operators, respectively.
-
-Using CUDA-Q `operator`, the above time-dependent Hamiltonian can be set up as follows.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Transmon]
-        :end-before: [End Transmon]
-
-In particular, `ScalarOperator` provides an easy way to model arbitrary time-dependent control signals.   
-Details about CUDA-Q `operator`, including builtin operators that it supports can be found :ref:`here <operators>`.
-
-2. Setup the evolution simulation
-
-The below code snippet shows how to simulate the time-evolution of the above system
-with `cudaq.evolve`.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Evolve]
-        :end-before: [End Evolve]
-
-Specifically, we need to set up the simulation by providing:
-
-- The system model in terms of a Hamiltonian as well as any decoherence terms, so-called `collapse_operators`.
-
-- The dimensionality of component systems in the model. CUDA-Q `evolve` allows users to model arbitrary multi-level systems, such as photonic Fock space.
-
-- The initial quantum state.
-
-- The time schedule, aka time steps, of the evolution.
-
-- Any 'observable' operator that we want to measure the expectation value with respect to the evolving state.
-
-
-.. note::
-
-    By default, `evolve` will only return the final state and expectation values.
-    To save intermediate results (at each time step specified in the schedule),
-    the `store_intermediate_results` flag must be set to `True`.
-
-3. Retrieve and plot the results
-
-Once the simulation is complete, we can retrieve the final state and the expectation values
-as well as intermediate values at each time step (with `store_intermediate_results=True`).
-
-For example, we can plot the Pauli expectation value for the above simulation as follows.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Plot]
-        :end-before: [End Plot]
-
-In particular, for each time step, `evolve` captures an array of expectation values, one for each  
-observable. Hence, we convert them into sequences for plotting purposes.
-
-
-Operator
-+++++++++++
-
-.. _operators:
-
-CUDA-Q provides builtin definitions for commonly-used operators, 
-such as the ladder operators (:math:`a` and :math:`a^\dagger`) of a harmonic oscillator, 
-the Pauli spin operators for a two-level system, etc.
-
-Here is a list of those operators.
-
-.. list-table:: Builtin Operators
-        :widths: 20 50 
-        :header-rows: 1
-
-        *   - Name
-            - Description
-        *   - `identity`
-            - Identity operator
-        *   - `zero`
-            - Zero or null operator
-        *   - `annihilate`
-            - Bosonic annihilation operator (:math:`a`)
-        *   - `create`
-            - Bosonic creation operator (:math:`a^\dagger`)
-        *   - `number`
-            - Number operator of a bosonic mode (equivalent to :math:`a^\dagger a`)
-        *   - `parity`
-            - Parity operator of a bosonic mode (defined as :math:`e^{i\pi a^\dagger a}`)
-        *   - `displace`
-            - Displacement operator of complex amplitude :math:`\alpha` (`displacement`). It is defined as :math:`e^{\alpha a^\dagger - \alpha^* a}`.  
-        *   - `squeeze`
-            - Squeezing operator of complex squeezing amplitude :math:`z` (`squeezing`). It is defined as :math:`\exp(\frac{1}{2}(z^*a^2 - z a^{\dagger 2}))`.
-        *   - `position`
-            - Position operator (equivalent to :math:`(a^\dagger + a)/2`)
-        *   - `momentum`
-            - Momentum operator (equivalent to :math:`i(a^\dagger - a)/2`)
-        *   - `spin.x`
-            - Pauli :math:`\sigma_x` operator
-        *   - `spin.y`
-            - Pauli :math:`\sigma_y` operator
-        *   - `spin.z`
-            - Pauli :math:`\sigma_z` operator
-        *   - `spin.plus`
-            - Pauli raising (:math:`\sigma_+`) operator
-        *   - `spin.minus`
-            - Pauli lowering (:math:`\sigma_-`) operator
-
-As an example, let's look at the Jaynes-Cummings model, which describes 
-the interaction between a two-level atom and a light (Boson) field.
-
-Mathematically, the Hamiltonian can be expressed as
-
-.. math:: 
-    
-    H = \omega_c a^\dagger a + \omega_a \frac{\sigma_z}{2} + \frac{\Omega}{2}(a\sigma_+ + a^\dagger \sigma_-).
-
-This Hamiltonian can be converted to CUDA-Q `Operator` representation with
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Jaynes-Cummings]
-        :end-before: [End Jaynes-Cummings]
-
-In the above code snippet, we map the cavity light field to degree index 1 and the two-level atom to degree index 0. 
-The description of composite quantum system dynamics is independent from the Hilbert space of the system components.
-The latter is specified by the dimension map that is provided to the `cudaq.evolve` call. 
-
-
-Time-Dependent Dynamics
-++++++++++++++++++++++++
-
-.. _time_dependent:
-
-In the previous examples of operator construction, we assumed that the systems under consideration were described by time-independent Hamiltonian. 
-However, we may want to simulate systems whose Hamiltonian operators have explicit time dependence.
-
-CUDA-Q provides multiple ways to construct time-dependent operators.
-
-1. Time-dependent coefficient
-
-CUDA-Q `ScalarOperator` can be used to wrap a Python function that returns the coefficient value at a specific time.
-
-As an example, we will look at a time-dependent Hamiltonian of the form :math:`H = H_0 + f(t)H_1`, 
-where :math:`f(t)` is the time-dependent driving strength given as :math:`cos(\omega t)`.
-
-The following code sets up the problem
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Hamiltonian]
-        :end-before: [End Hamiltonian]
-
-2. Time-dependent operator
-
-We can also construct a time-dependent operator from a function that returns a complex matrix representing the time dynamics of 
-that operator.
-
-As an example, let's looks at the `displacement operator <https://en.wikipedia.org/wiki/Displacement_operator>`__. It can be defined as follows:
-
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin DefineOp]
-        :end-before: [End DefineOp]
-
-The defined operator is parameterized by the `displacement` amplitude. To create simulate the evolution of an 
-operator under a time dependent displacement amplitude, we can define how the amplitude changes in time:
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Schedule1]
-        :end-before: [End Schedule1]
-
-Let's say we want to add a squeezing term to the system operator. We can independently vary the squeezing 
-amplitude and the displacement amplitude by instantiating a schedule with a custom function that returns 
-the desired value for each parameter: 
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/backends/dynamics.py
-        :language: python
-        :start-after: [Begin Schedule2]
-        :end-before: [End Schedule2]
-
-Numerical Integrators
-++++++++++++++++++++++
-
-.. _integrators:
-
-CUDA-Q provides a set of numerical integrators, to be used with the ``dynamics``
-backend target.
-
-.. list-table:: Numerical Integrators
-        :widths: 20 50 
-        :header-rows: 1
-
-        *   - Name
-            - Description
-        *   - `RungeKuttaIntegrator`
-            - Explicit 4th-order Runge-Kutta method (default integrator)
-        *   - `ScipyZvodeIntegrator`
-            - Complex-valued variable-coefficient ordinary differential equation solver (provided by SciPy)
-        *   - `CUDATorchDiffEqDopri5Integrator`
-            - Runge-Kutta of order 5 of Dormand-Prince-Shampine (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqAdaptiveHeunIntegrator`
-            - Runge-Kutta of order 2 (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqBosh3Integrator`
-            - Runge-Kutta of order 3 of Bogacki-Shampine (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqDopri8Integrator`
-            - Runge-Kutta of order 8 of Dormand-Prince-Shampine (provided by `torchdiffeq`)  
-        *   - `CUDATorchDiffEqEulerIntegrator`
-            - Euler method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqExplicitAdamsIntegrator`
-            - Explicit Adams-Bashforth method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqImplicitAdamsIntegrator`
-            - Implicit Adams-Bashforth-Moulton method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqMidpointIntegrator`
-            - Midpoint method (provided by `torchdiffeq`) 
-        *   - `CUDATorchDiffEqRK4Integrator`
-            - Fourth-order Runge-Kutta with 3/8 rule (provided by `torchdiffeq`) 
-     
-.. note::
-    To use Torch-based integrators, users need to install `torchdiffeq` (e.g., with `pip install torchdiffeq`).
-    This is an optional dependency of CUDA-Q, thus will not be installed by default.
-
-.. warning:: 
-    Torch-based integrators require a CUDA-enabled Torch installation. Depending on your platform (e.g., `aarch64`),
-    the default Torch pip package may not have CUDA support. 
-
-    The below command can be used to verify your installation:
-
-    .. code:: bash
-
-        python3 -c "import torch; print(torch.version.cuda)"
-
-    If the output is a '`None`' string, it indicates that your Torch installation does not support CUDA.
-    In this case, you need to install a CUDA-enabled Torch package via other mechanisms, e.g., building Torch from source or
-    using their Docker images.
- 
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/backends/hardware.rst b/pr-2308/sphinx/using/backends/hardware.rst
deleted file mode 100644
index 33f4907cec..0000000000
--- a/pr-2308/sphinx/using/backends/hardware.rst
+++ /dev/null
@@ -1,903 +0,0 @@
-CUDA-Q Hardware Backends
-*********************************
-
-CUDA-Q supports submission to a set of hardware providers. 
-To submit to a hardware backend, you need an account with the respective provider.
-
-
-Amazon Braket
-==================================
-
-.. _braket-backend:
-
-`Amazon Braket <https://aws.amazon.com/braket/>`__ is a fully managed AWS 
-service which provides Jupyter notebook environments, high-performance quantum 
-circuit simulators, and secure, on-demand access to various quantum computers.
-To get started users must enable Amazon Braket in their AWS account by following 
-`these instructions <https://docs.aws.amazon.com/braket/latest/developerguide/braket-enable-overview.html>`__.
-To learn more about Amazon Braket, you can view the `Amazon Braket Documentation <https://docs.aws.amazon.com/braket/>`__ 
-and `Amazon Braket Examples <https://github.com/amazon-braket/amazon-braket-examples>`__.
-A list of available devices and regions can be found `here <https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html>`__. 
-
-Users can run CUDA-Q programs on Amazon Braket with `Hybrid Job <https://docs.aws.amazon.com/braket/latest/developerguide/braket-what-is-hybrid-job.html>`__.
-See `this guide <https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-first.html>`__ to get started.
-
-Setting Credentials
-```````````````````
-
-After enabling Amazon Braket in AWS, set credentials using any of the documented `methods <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`__.
-One of the simplest ways is to use `AWS CLI <https://aws.amazon.com/cli/>`__.
-
-.. code:: bash
-
-    aws configure
-
-Alternatively, users can set the following environment variables.
-
-.. code:: bash
-
-  export AWS_DEFAULT_REGION="<region>"
-  export AWS_ACCESS_KEY_ID="<key_id>"
-  export AWS_SECRET_ACCESS_KEY="<access_key>"
-  export AWS_SESSION_TOKEN="<token>"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in Amazon Braket,
-pass the flag ``--target braket`` to the ``nvq++`` compiler.
-By default jobs are submitted to the state vector simulator, `SV1`.
-
-.. code:: bash
-
-    nvq++ --target braket src.cpp
-
-
-To execute your kernels on different device, pass the ``--braket-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target braket --braket-machine "arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet" src.cpp ...
-
-where ``arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet`` refers to IQM Garnet QPU.
-
-To emulate the device locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. 
-
-.. code:: bash
-
-    nvq++ --emulate --target braket src.cpp
-
-To see a complete example for using Amazon Braket backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target("braket")
-
-By default, jobs are submitted to the state vector simulator, `SV1`.
-
-To specify which Amazon Braket device to use, set the :code:`machine` parameter.
-
-.. code:: python
-
-    device_arn = "arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet"
-    cudaq.set_target("braket", machine=device_arn)
-
-where ``arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet`` refers to IQM Garnet QPU.
-
-To emulate the device locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``.
-
-.. code:: python
-
-    cudaq.set_target("braket", emulate=True)
-
-The number of shots for a kernel execution can be set through the ``shots_count``
-argument to ``cudaq.sample``. By default, the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=100)
-
-To see a complete example for using Amazon Braket backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-.. note:: 
-
-    The ``cudaq.observe`` API is not yet supported on the `braket` target.
-
-Infleqtion
-==================================
-
-.. _infleqtion-backend:
-
-Infleqtion is a quantum hardware provider of gate-based neutral atom quantum computers. Their backends may be
-accessed via `Superstaq <https://superstaq.infleqtion.com/>`__, Infleqtion’s cross-platform software API
-that performs low-level compilation and cross-layer optimization. To get started users can create a Superstaq
-account by following `these instructions <https://superstaq.readthedocs.io/en/latest/get_started/credentials.html>`__.
-
-For access to Infleqtion's neutral atom quantum computer, Sqale,
-`pre-registration <https://www.infleqtion.com/sqale-preregistration>`__ is now open.
-
-Setting Credentials
-`````````````````````````
-
-Programmers of CUDA-Q may access Infleqtion backends from either C++ or Python. Generate
-an API key from your `Superstaq account <https://superstaq.infleqtion.com/profile>`__ and export
-it as an environment variable:
-
-.. code:: bash
-
-  export SUPERSTAQ_API_KEY="superstaq_api_key"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution on Infleqtion's backends,
-pass the flag ``--target infleqtion`` to the ``nvq++`` compiler.
-
-.. code:: bash
-
-    nvq++ --target infleqtion src.cpp
-
-This will take the API key and handle all authentication with, and submission to, Infleqtion's QPU 
-(or simulator). By default, quantum kernel code will be submitted to Infleqtion's Sqale
-simulator.
-
-To execute your kernels on a QPU, pass the ``--infleqtion-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu src.cpp ...
-
-where ``cq_sqale_qpu`` is an example of a physical QPU.
-
-To run an ideal dry-run execution on the QPU, additionally pass ``dry-run`` with the ``--infleqtion-method`` 
-flag to the ``nvq++`` compiler:
-
-.. code:: bash
-
-    nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu --infleqtion-method dry-run src.cpp ...
-
-To noisily simulate the QPU instead, pass ``noise-sim`` to the ``--infleqtion-method`` flag like so:
-
-.. code:: bash
-
-    nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu --infleqtion-method noise-sim src.cpp ...
-
-Alternatively, to emulate the Infleqtion machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target infleqtion src.cpp
-
-To see a complete example for using Infleqtion's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion")
-
-By default, quantum kernel code will be submitted to Infleqtion's Sqale
-simulator.
-
-To specify which Infleqtion QPU to use, set the :code:`machine` parameter.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", machine="cq_sqale_qpu")
-
-where ``cq_sqale_qpu`` is an example of a physical QPU.
-
-To run an ideal dry-run execution of the QPU, additionally set the ``method`` flag to ``"dry-run"``.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", machine="cq_sqale_qpu", method="dry-run")
-
-To noisily simulate the QPU instead, set the ``method`` flag to ``"noise-sim"``.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", machine="cq_sqale_qpu", method="noise-sim")
-
-Alternatively, to emulate the Infleqtion machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target("infleqtion", emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=100)
-
-To see a complete example for using Infleqtion's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-Moreover, for an end-to-end application workflow example executed on the Infleqtion QPU, take a look at the 
-:doc:`Anderson Impurity Model ground state solver <../applications>` notebook.
-
-IonQ
-==================================
-
-.. _ionq-backend:
-
-Setting Credentials
-`````````````````````````
-
-Programmers of CUDA-Q may access the `IonQ Quantum Cloud
-<https://cloud.ionq.com/>`__ from either C++ or Python. Generate
-an API key from your `IonQ account <https://cloud.ionq.com/>`__ and export
-it as an environment variable:
-
-.. code:: bash
-
-  export IONQ_API_KEY="ionq_generated_api_key"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in the IonQ Cloud,
-pass the flag ``--target ionq`` to the ``nvq++`` compiler.
-
-.. code:: bash
-
-    nvq++ --target ionq src.cpp
-
-This will take the API key and handle all authentication with, and submission to,
-the IonQ QPU(s). By default, quantum kernel code will be submitted to the IonQ
-simulator.
-
-.. note:: 
-
-    A "target" in :code:`cudaq` refers to a quantum compute provider, such as :code:`ionq`.
-    However, IonQ's documentation uses the term "target" to refer to specific QPU's themselves.
-
-To execute your kernels on a QPU, pass the ``--ionq-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target ionq --ionq-machine qpu.aria-1 src.cpp ...
-
-where ``qpu.aria-1`` is an example of a physical QPU.
-
-A list of available QPUs can be found `in the API documentation
-<https://docs.ionq.com/#tag/jobs>`__. To see which backends are available 
-with your subscription login to your `IonQ account <https://cloud.ionq.com/jobs>`__.
-
-To emulate the IonQ machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target 
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target ionq src.cpp
-
-To see a complete example for using IonQ's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target('ionq')
-
-By default, quantum kernel code will be submitted to the IonQ
-simulator.
-
-.. note:: 
-
-    A "target" in :code:`cudaq` refers to a quantum compute provider, such as :code:`ionq`.
-    However, IonQ's documentation uses the term "target" to refer to specific QPU's themselves.
-
-To specify which IonQ QPU to use, set the :code:`qpu` parameter.
-
-.. code:: python
-
-    cudaq.set_target("ionq", qpu="qpu.aria-1")
-
-where ``qpu.aria-1`` is an example of a physical QPU.
-
-A list of available QPUs can be found `in the API documentation
-<https://docs.ionq.com/#tag/jobs>`__. To see which backends are available 
-with your subscription login to your `IonQ account <https://cloud.ionq.com/jobs>`__.
-
-To emulate the IonQ machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target 
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('ionq', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using IonQ's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-Anyon Technologies/Anyon Computing
-==================================
-
-.. _anyon-backend:
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access the Anyon API from either
-C++ or Python. Anyon requires a credential configuration file with username and password. 
-The configuration file can be generated as follows, replacing
-the ``<username>`` and ``<password>`` in the first line with your Anyon Technologies
-account details. The credential in the file will be used by CUDA-Q to login to Anyon quantum services 
-and will be updated by CUDA-Q with an obtained API token and refresh token. 
-Note, the credential line will be deleted in the updated configuration file. 
-
-.. code:: bash
-    
-    echo 'credentials: {"username":"<username>","password":"<password>"}' > $HOME/.anyon_config
-
-Users can also login and get the keys manually using the following commands:
-
-.. code:: bash
-
-    # You may need to run: `apt-get update && apt-get install curl jq`
-    curl -X POST --user "<username>:<password>"  -H "Content-Type: application/json" \
-    https://api.anyon.cloud:5000/login > credentials.json
-    id_token=`cat credentials.json | jq -r '."id_token"'`
-    refresh_token=`cat credentials.json | jq -r '."refresh_token"'`
-    echo "key: $id_token" > ~/.anyon_config
-    echo "refresh: $refresh_token" >> ~/.anyon_config
-
-The path to the configuration can be specified as an environment variable:
-
-.. code:: bash
-
-    export CUDAQ_ANYON_CREDENTIALS=$HOME/.anyon_config
-
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in the Anyon Technologies backends,
-pass the flag ``--target anyon`` to the ``nvq++`` compiler. CUDA-Q will 
-authenticate via the Anyon Technologies REST API using the credential in your configuration file.
-
-.. code:: bash
-
-    nvq++ --target anyon --<backend-type> <machine> src.cpp ...
-
-To execute your kernels using Anyon Technologies backends, pass the ``--anyon-machine`` flag to the ``nvq++`` compiler
-as the ``--<backend-type>`` to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target anyon --anyon-machine telegraph-8q src.cpp ...
-
-where ``telegraph-8q`` is an example of a physical QPU (Architecture: Telegraph, Qubit Count: 8).
-
-Currently, ``telegraph-8q`` and ``berkeley-25q`` are available for access over CUDA-Q.
-
-To emulate the Anyon Technologies machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag as the ``--<backend-type>`` to ``nvq++``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --target anyon --emulate src.cpp
-
-To see a complete example for using Anyon's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq.set_target()`` function.
-
-To execute your kernels using Anyon Technologies backends, specify which machine to submit quantum kernels to
-by setting the :code:`machine` parameter of the target. 
-If :code:`machine` is not specified, the default machine will be ``telegraph-8q``.
-
-.. code:: python
-
-    cudaq.set_target('anyon', machine='telegraph-8q')
-
-As shown above, ``telegraph-8q`` is an example of a physical QPU.
-
-To emulate the Anyon Technologies machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('anyon', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python 
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using Anyon's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-IQM
-==================================
-
-.. _iqm-backend:
-
-Support for submissions to IQM is currently under development. 
-In particular, two-qubit gates can only be performed on adjacent qubits. For more information, we refer to the respective hardware documentation.
-Support for automatically injecting the necessary operations during compilation to execute arbitrary multi-qubit gates will be added in future versions.
-
-Setting Credentials
-`````````````````````````
-
-Programmers of CUDA-Q may access the IQM Server from either C++ or Python. Following the `quick start guide <https://iqm-finland.github.io/cortex-cli/readme.html#using-cortex-cli>`__, install `iqm-cortex-cli` and login to initialize the tokens file.
-The path to the tokens file can either be passed explicitly via an environment variable or it will be loaded automatically if located in
-the default location :code:`~/.cache/iqm-cortex-cli/tokens.json`.
-
-.. code:: bash
-
-    export IQM_TOKENS_FILE="path/to/tokens.json"
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution on an IQM Server,
-pass the ``--target iqm`` flag to the ``nvq++`` compiler, along with a specified ``--iqm-machine``.
-
-.. note::
-    The ``--iqm-machine`` is  a mandatory argument. This provided architecture must match
-    the device architecture that the program has been compiled against. The hardware architecture for a
-    specific IQM Server may be checked  via `https://<IQM server>/cocos/quantum-architecture`.
-
-.. code:: bash
-
-    nvq++ --target iqm --iqm-machine Adonis src.cpp
-
-Once the binary for a specific IQM QPU architecture is compiled, it can be executed against any IQM Server with the same QPU architecture:
-
-.. code:: bash
-
-    nvq++ --target iqm --iqm-machine Adonis src.cpp -o program
-    IQM_SERVER_URL="https://demo.qc.iqm.fi/cocos" ./program
-
-    # Executing the same program against an IQM Server with a different underlying QPU
-    # architecture will result in an error.
-    IQM_SERVER_URL="https://<Apollo IQM Server>/cocos" ./program
-
-To emulate the IQM machine locally, without submitting to the IQM Server,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target iqm --iqm-machine Adonis src.cpp
-
-To see a complete example for using IQM server backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target("iqm", url="https://<IQM Server>/cocos", **{"qpu-architecture": "Adonis"})
-
-To emulate the IQM Server locally, without submitting to the IQM Server,
-you can also set the ``emulate`` flag to ``True``. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('iqm', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using IQM server backends, take a look at our :doc:`Python examples<../examples/examples>`.
-
-OQC
-==================================
-
-.. _oqc-backend:
-
-`Oxford Quantum Circuits <https://oxfordquantumcircuits.com/>`__ (OQC) is currently providing CUDA-Q integration for multiple Quantum Processing Unit types.
-The 8 qubit ring topology Lucy device and the 32 qubit Kagome lattice topology Toshiko device are both supported via machine options described below.
-
-Setting Credentials
-`````````````````````````
-
-In order to use the OQC devices you will need to register.
-Registration is achieved by contacting `oqc_qcaas_support@oxfordquantumcircuits.com`.
-
-Once registered you will be able to authenticate with your ``email`` and ``password``
-
-There are three environment variables that the OQC target will look for during configuration:
-
-1. ``OQC_URL``
-2. ``OQC_EMAIL``
-3. ``OQC_PASSWORD`` - is mandatory
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution on the OQC platform, provide the flag ``--target oqc`` to the ``nvq++`` compiler.
-
-Users may provide their :code:`email` and :code:`url` as extra arguments
-
-.. code:: bash
-
-    nvq++ --target oqc --oqc-email <email> --oqc-url <url> src.cpp -o executable
-
-Where both environment variables and extra arguments are supplied, precedent is given to the extra arguments.
-To run the output, provide the runtime loaded variables and invoke the pre-built executable
-
-.. code:: bash
-
-   OQC_PASSWORD=<password> ./executable
-
-To emulate the OQC device locally, without submitting through the OQC QCaaS services, you can pass the ``--emulate`` flag to ``nvq++``.
-This will emit any target specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target oqc src.cpp -o executable
-
-
-.. note::
-
-    The oqc target supports a ``--oqc-machine`` option.
-    The default is the 8 qubit Lucy device.
-    You can set this to be either ``toshiko`` or ``lucy`` via this flag.
-
-.. note::
-
-    The OQC quantum assembly toolchain (qat) which is used to compile and execute instructions can be found on github as `oqc-community/qat <https://github.com/oqc-community/qat>`__
-
-Submission from Python
-`````````````````````````
-
-To set which OQC URL, set the :code:`url` parameter.
-To set which OQC email, set the :code:`email` parameter.
-To set which OQC machine, set the :code:`machine` parameter.
-
-.. code:: python
-
-    import os
-    import cudaq
-    # ...
-    os.environ['OQC_PASSWORD'] = password
-    cudaq.set_target("oqc", url=url, machine="lucy")
-
-You can then execute a kernel against the platform using the OQC Lucy device
-
-.. code:: python
-
-    kernel = cudaq.make_kernel()
-    qvec = kernel.qalloc(2)
-    kernel.h(qvec[0])
-    kernel.x(qvec[1])
-    kernel.cx(qvec[0], qvec[1])
-    kernel.mz(qvec)
-    str(cudaq.sample(kernel=kernel, shots_count=1000))
-
-
-ORCA Computing
-==================================
-
-.. _orca-backend:
-
-ORCA Computing's PT Series implement the boson sampling model of quantum computation, in which 
-multiple single photons are interfered with each other within a network of beam splitters, and 
-photon detectors measure where the photons leave this network. This process is implemented within 
-a time-bin interferometer (TBI) architecture where photons are created in different time-bins 
-and interfered within a network of delay lines. This can be represented by a circuit diagram, 
-like the one below, where this illustration example corresponds to 4 photons in 8 modes sent into 
-alternating time-bins in a circuit composed of two delay lines in series.
-
-.. image:: ../examples/images/orca_tbi.png
-   :width: 400px
-   :align: center
-
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access the ORCA API from either C++ or Python. There is an environment 
-variable ``ORCA_ACCESS_URL`` that can be set so that the ORCA target can look for it during 
-configuration.
-
-.. code:: bash
-
-  export ORCA_ACCESS_URL="https://<ORCA API Server>"
-
-
-Sometimes the requests to the PT-1 require an authentication token. This token can be set as an
-environment variable named ``ORCA_AUTH_TOKEN``. For example, if the token is :code:`AbCdEf123456`,
-you can set the environment variable as follows:
-
-.. code:: bash
-
-  export ORCA_AUTH_TOKEN="AbCdEf123456"
-
-
-Submission from C++
-`````````````````````````
-
-To execute a boson sampling experiment on the ORCA platform, provide the flag 
-``--target orca`` to the ``nvq++`` compiler. You should then pass the ``--orca-url`` flag set with 
-the previously set environment variable ``$ORCA_ACCESS_URL`` or an :code:`url`.
-
-.. code:: bash
-
-    nvq++ --target orca --orca-url $ORCA_ACCESS_URL src.cpp -o executable
-
-or
-
-.. code:: bash
-
-    nvq++ --target orca --orca-url <url> src.cpp -o executable
-
-To run the output, invoke the executable
-
-.. code:: bash
-
-   ./executable
-
-
-To see a complete example for using ORCA server backends, take a look at our :doc:`C++ examples <../examples/hardware_providers>`.
-
-Submission from Python
-`````````````````````````
-
-To set which ORCA URL to be used, set the :code:`url` parameter.
-
-.. code:: python
-
-    import os
-    import cudaq
-    # ...
-    orca_url = os.getenv("ORCA_ACCESS_URL", "http://localhost/sample")
-
-    cudaq.set_target("orca", url=orca_url)
-
-
-You can then execute a time-bin boson sampling experiment against the platform using an ORCA device.
-
-.. code:: python
-
-    bs_angles = [np.pi / 3, np.pi / 6]
-    input_state = [1, 1, 1]
-    loop_lengths = [1]
-    counts = cudaq.orca.sample(input_state, loop_lengths, bs_angles)
-
-To see a complete example for using ORCA's backends, take a look at our :doc:`Python examples <../examples/hardware_providers>`.
-
-Quantinuum
-==================================
-
-.. _quantinuum-backend:
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access the Quantinuum API from either
-C++ or Python. Quantinuum requires a credential configuration file. 
-The configuration file can be generated as follows, replacing
-the ``email`` and ``credentials`` in the first line with your Quantinuum
-account details.
-
-.. code:: bash
-
-    # You may need to run: `apt-get update && apt-get install curl jq`
-    curl -X POST -H "Content Type: application/json" \
-        -d '{ "email":"<your_alias>@email.com","password":"<your_password>" }' \
-        https://qapi.quantinuum.com/v1/login > $HOME/credentials.json
-    id_token=`cat $HOME/credentials.json | jq -r '."id-token"'`
-    refresh_token=`cat $HOME/credentials.json | jq -r '."refresh-token"'`
-    echo "key: $id_token" >> $HOME/.quantinuum_config
-    echo "refresh: $refresh_token" >> $HOME/.quantinuum_config
-
-The path to the configuration can be specified as an environment variable:
-
-.. code:: bash
-
-    export CUDAQ_QUANTINUUM_CREDENTIALS=$HOME/.quantinuum_config
-
-
-Submission from C++
-`````````````````````````
-
-To target quantum kernel code for execution in the Quantinuum backends,
-pass the flag ``--target quantinuum`` to the ``nvq++`` compiler. CUDA-Q will 
-authenticate via the Quantinuum REST API using the credential in your configuration file.
-By default, quantum kernel code will be submitted to the Quantinuum syntax checker.
-Submission to the syntax checker merely validates the program; the kernels are not executed.
-
-.. code:: bash
-
-    nvq++ --target quantinuum src.cpp ...
-
-To execute your kernels, pass the ``--quantinuum-machine`` flag to the ``nvq++`` compiler
-to specify which machine to submit quantum kernels to:
-
-.. code:: bash
-
-    nvq++ --target quantinuum --quantinuum-machine H1-2 src.cpp ...
-
-where ``H1-2`` is an example of a physical QPU. Hardware specific
-emulators may be accessed by appending an ``E`` to the end (e.g, ``H1-2E``). For 
-access to the syntax checker for the provided machine, you may append an ``SC`` 
-to the end (e.g, ``H1-1SC``).
-
-For a comprehensive list of available machines, login to your `Quantinuum user account <https://um.qapi.quantinuum.com/user>`__ 
-and navigate to the "Account" tab, where you should find a table titled "Machines".
-
-To emulate the Quantinuum machine locally, without submitting through the cloud,
-you can also pass the ``--emulate`` flag to ``nvq++``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: bash
-
-    nvq++ --emulate --target quantinuum src.cpp
-
-To see a complete example for using Quantinuum's backends, take a look at our :doc:`C++ examples <../examples/examples>`.
-
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target('quantinuum')
-
-By default, quantum kernel code will be submitted to the Quantinuum syntax checker.
-Submission to the syntax checker merely validates the program; the kernels are not executed.
-
-To execute your kernels, specify which machine to submit quantum kernels to
-by setting the :code:`machine` parameter of the target.
-
-.. code:: python
-
-    cudaq.set_target('quantinuum', machine='H1-2')
-
-where ``H1-2`` is an example of a physical QPU. Hardware specific
-emulators may be accessed by appending an ``E`` to the end (e.g, ``H1-2E``). For 
-access to the syntax checker for the provided machine, you may append an ``SC`` 
-to the end (e.g, ``H1-1SC``).
-
-For a comprehensive list of available machines, login to your `Quantinuum user account <https://um.qapi.quantinuum.com/user>`__ 
-and navigate to the "Account" tab, where you should find a table titled "Machines".
-
-To emulate the Quantinuum machine locally, without submitting through the cloud,
-you can also set the ``emulate`` flag to ``True``. This will emit any target 
-specific compiler warnings and diagnostics, before running a noise free emulation.
-
-.. code:: python
-
-    cudaq.set_target('quantinuum', emulate=True)
-
-The number of shots for a kernel execution can be set through
-the ``shots_count`` argument to ``cudaq.sample`` or ``cudaq.observe``. By default,
-the ``shots_count`` is set to 1000.
-
-.. code:: python 
-
-    cudaq.sample(kernel, shots_count=10000)
-
-To see a complete example for using Quantinuum's backends, take a look at our :doc:`Python examples <../examples/examples>`.
-
-QuEra Computing
-==================================
-
-.. _quera-backend:
-
-Setting Credentials
-```````````````````
-
-Programmers of CUDA-Q may access Aquila, QuEra's first generation of quantum
-processing unit (QPU) via Amazon Braket. Hence, users must first enable Braket by 
-following `these instructions <https://docs.aws.amazon.com/braket/latest/developerguide/braket-enable-overview.html>`__. 
-Then set credentials using any of the documented `methods <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`__.
-One of the simplest ways is to use `AWS CLI <https://aws.amazon.com/cli/>`__.
-
-.. code:: bash
-
-    aws configure
-
-Alternatively, users can set the following environment variables.
-
-.. code:: bash
-
-  export AWS_DEFAULT_REGION="us-east-1"
-  export AWS_ACCESS_KEY_ID="<key_id>"
-  export AWS_SECRET_ACCESS_KEY="<access_key>"
-  export AWS_SESSION_TOKEN="<token>"
-
-
-Submission from C++
-`````````````````````````
-
-Not yet supported.
-
-
-Submission from Python
-`````````````````````````
-
-The target to which quantum kernels are submitted 
-can be controlled with the ``cudaq::set_target()`` function.
-
-.. code:: python
-
-    cudaq.set_target('quera')
-
-By default, analog Hamiltonian will be submitted to the Aquila system.
-
-Aquila is a "field programmable qubit array" operated as an analog 
-Hamiltonian simulator on a user-configurable architecture, executing 
-programmable coherent quantum dynamics on up to 256 neutral-atom qubits.
-Refer to QuEra's `whitepaper <https://cdn.prod.website-files.com/643b94c382e84463a9e52264/648f5bf4d19795aaf36204f7_Whitepaper%20June%2023.pdf>`__ for details.
-
-Due to the nature of the underlying hardware, this target only supports the 
-``evolve`` and ``evolve_async`` APIs.
-The `hamiltonian` must be an `Operator` of the type `RydbergHamiltonian`. Only 
-other parameters supported are `schedule` (mandatory) and `shots_count` (optional).
-
-For example,
-
-.. code:: python
-
-    evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
-                                                 amplitude=omega,
-                                                 phase=phi,
-                                                 delta_global=delta),
-                               schedule=schedule)
-
-The number of shots for a kernel execution can be set through the ``shots_count``
-argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is 
-set to 100.
-
-.. code:: python 
-
-    cudaq.evolve(RydbergHamiltonian(...), schedule=s, shots_count=1000)
-
-To see a complete example for using QuEra's backend, take a look at our :doc:`Python examples <../examples/hardware_providers>`.
diff --git a/pr-2308/sphinx/using/backends/nvqc.rst b/pr-2308/sphinx/using/backends/nvqc.rst
deleted file mode 100644
index 7586d08719..0000000000
--- a/pr-2308/sphinx/using/backends/nvqc.rst
+++ /dev/null
@@ -1,256 +0,0 @@
-NVIDIA Quantum Cloud
----------------------
-NVIDIA Quantum Cloud (NVQC) offers universal access to the world’s most powerful computing platform, 
-for every quantum researcher to do their life’s work.
-To learn more about NVQC visit this `link <https://www.nvidia.com/en-us/solutions/quantum-computing/cloud>`__. 
-
-Apply for early access `here <https://developer.nvidia.com/quantum-cloud-early-access-join>`__. 
-Access to the Quantum Cloud early access program requires an NVIDIA Developer account.
-
-Quick Start
-+++++++++++
-Once you have been approved for an early access to NVQC, you will be able to follow these instructions to use it.
-
-1. Follow the instructions in your NVQC Early Access welcome email to obtain an API Key for NVQC. 
-You can also find the instructions `here <https://developer.nvidia.com/quantum-cloud-early-access-members>`__ (link available only for approved users)
-
-2. Set the environment variable `NVQC_API_KEY` to the API Key obtained above.
-
- .. code-block:: console
-
-    export NVQC_API_KEY=<your NVQC API key>
-
-You may wish to persist that environment variable between bash sessions, e.g., by adding it to your `$HOME/.bashrc` file.
-
-3. Run your first NVQC example
-
-The following is a typical CUDA-Q kernel example. 
-By selecting the `nvqc` target, the quantum circuit simulation will run on NVQC in the cloud, rather than running locally.
-
-
-.. tab:: Python
-    
-    .. literalinclude:: ../../snippets/python/using/cudaq/nvqc/nvqc_intro.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-    .. code-block:: console
-        
-        [2024-03-14 19:26:31.438] Submitting jobs to NVQC service with 1 GPU(s). Max execution time: 3600 seconds (excluding queue wait time).
-
-        ================ NVQC Device Info ================
-        GPU Device Name: "NVIDIA H100 80GB HBM3"
-        CUDA Driver Version / Runtime Version: 12.2 / 12.0
-        Total global memory (GB): 79.1
-        Memory Clock Rate (MHz): 2619.000
-        GPU Clock Rate (MHz): 1980.000
-        ==================================================
-        { 1111111111111111111111111:486 0000000000000000000000000:514 }
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/nvqc/nvqc_intro.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-
-    The code above is saved in `nvqc_intro.cpp` and compiled with the following command, targeting the :code:`nvqc` platform
-
-    .. code-block:: console
-
-        nvq++ nvqc_intro.cpp -o nvqc_intro.x --target nvqc 
-        ./nvqc_intro.x
-
-        [2024-03-14 19:25:05.545] Submitting jobs to NVQC service with 1 GPU(s). Max execution time: 3600 seconds (excluding queue wait time).
-
-        ================ NVQC Device Info ================
-        GPU Device Name: "NVIDIA H100 80GB HBM3"
-        CUDA Driver Version / Runtime Version: 12.2 / 12.0
-        Total global memory (GB): 79.1
-        Memory Clock Rate (MHz): 2619.000
-        GPU Clock Rate (MHz): 1980.000
-        ==================================================
-        { 
-        __global__ : { 1111111111111111111111111:487 0000000000000000000000000:513 }
-        result : { 1111111111111111111111111:487 0000000000000000000000000:513 }
-        }
-
-
-Simulator Backend Selection
-++++++++++++++++++++++++++++
-
-NVQC hosts all CUDA-Q simulator backends (see :doc:`simulators`). 
-You may use the NVQC `backend` (Python) or `--nvqc-backend` (C++) option to select the simulator to be used by the service.
-
-For example, to request the `tensornet` simulator backend, the user can do the following for C++ or Python.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        cudaq.set_target("nvqc", backend="tensornet")
-
-.. tab:: C++
-    
-    .. code-block:: console
-
-        nvq++ nvqc_sample.cpp -o nvqc_sample.x --target nvqc --nvqc-backend tensornet
-
-
-.. note::
-
-  By default, the single-GPU single-precision `custatevec-fp32` simulator backend will be selected if backend information is not specified.
-
-Multiple GPUs
-+++++++++++++
-
-Some CUDA-Q simulator backends are capable of multi-GPU distribution as detailed in :doc:`simulators`.
-For example, the `nvidia-mgpu` backend can partition and distribute state vector simulation to multiple GPUs to simulate 
-a larger number of qubits, whose state vector size grows beyond the memory size of a single GPU.
-
-To select a specific number of GPUs on the NVQC managed service, the following `ngpus` (Python) or `--nvqc-ngpus` (C++) option can be used.
-
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        cudaq.set_target("nvqc", backend="nvidia-mgpu", ngpus=4)
-
-.. tab:: C++
-
-    .. code-block:: console
-
-        nvq++ nvqc_sample.cpp -o nvqc_sample.x --target nvqc --nvqc-backend nvidia-mgpu --nvqc-ngpus 4
-
-
-.. note::
-
-    If your NVQC subscription does not contain service instances that have the specified number of GPUs, 
-    you may encounter the following error.
-
-    .. code-block:: console
-        
-        Unable to find NVQC deployment with 16 GPUs.
-        Available deployments have {1, 2, 4, 8} GPUs.
-        Please check your `ngpus` value (Python) or `--nvqc-ngpus` value (C++).
-
-.. note::
-
-    Not all simulator backends are capable of utilizing multiple GPUs. 
-    When requesting a multiple-GPU service with a single-GPU simulator backend, 
-    you might encounter the following log message:
-
-    .. code-block:: console
-        
-        The requested backend simulator (custatevec-fp32) is not capable of using all 4 GPUs requested.
-        Only one GPU will be used for simulation.
-        Please refer to CUDA-Q documentation for a list of multi-GPU capable simulator backends.
-
-    Consider removing the `ngpus` value (Python) or `--nvqc-ngpus` value (C++) to use the default of 1 GPU 
-    if you don't need to use a multi-GPU backend to better utilize NVQC resources.
-
-    Please refer to the table below for a list of backend simulator names along with its multi-GPU capability.
-
-    .. list-table:: Simulator Backends
-        :widths: 20 50 10 10
-        :header-rows: 1
-
-        *   - Name
-            - Description
-            - GPU Accelerated 
-            - Multi-GPU 
-        *   - `qpp`
-            - CPU-only state vector simulator
-            - no
-            - no
-        *   - `dm`
-            - CPU-only density matrix simulator
-            - no
-            - no
-        *   - `custatevec-fp32`
-            - Single-precision `cuStateVec` simulator
-            - yes
-            - no
-        *   - `custatevec-fp64`
-            - Double-precision `cuStateVec` simulator
-            - yes
-            - no
-        *   - `tensornet`
-            - Double-precision `cuTensorNet` full tensor network contraction simulator
-            - yes
-            - yes
-        *   - `tensornet-mps`
-            - Double-precision `cuTensorNet` matrix-product state simulator
-            - yes
-            - no
-        *   - `nvidia-mgpu`
-            - Double-precision `cuStateVec` multi-GPU simulator
-            - yes
-            - yes
-    
-
-Multiple QPUs Asynchronous Execution
-+++++++++++++++++++++++++++++++++++++
-
-NVQC provides scalable QPU virtualization services, whereby clients
-can submit asynchronous jobs simultaneously to NVQC. These jobs are
-handled by a pool of service worker instances.
-
-For example, in the following code snippet, using the `nqpus` (Python) or `--nvqc-nqpus` (C++) configuration option,
-the user instantiates 3 virtual QPU instances to submit simulation jobs to NVQC
-calculating the expectation value along with parameter-shift gradients simultaneously.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/nvqc/nvqc_mqpu.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/nvqc/nvqc_mqpu.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-
-    The code above is saved in `nvqc_vqe.cpp` and compiled with the following command, targeting the :code:`nvqc` platform with 3 virtual QPUs.
-
-    .. code-block:: console
-
-        nvq++ nvqc_vqe.cpp -o nvqc_vqe.x --target nvqc --nvqc-nqpus 3 
-        ./nvqc_vqe.x
-
-
-.. note::
-
-    The NVQC managed-service has a pool of worker instances processing incoming requests on a 
-    first-come-first-serve basis. Thus, the attainable speedup using multiple virtual QPUs vs. 
-    sequential execution on a single QPU depends on the NVQC service load. For example, 
-    if the number of free workers is greater than the number of requested virtual QPUs, a linear
-    (ideal) speedup could be achieved. On the other hand, if all the service workers are busy, 
-    multi-QPU distribution may not deliver any substantial speedup.  
-
-FAQ
-++++
-
-1. How do I get more information about my NVQC API submission?
-
-The environment variable `NVQC_LOG_LEVEL` can be used to turn on and off
-certain logs. There are three levels:
-
-- Info (`info`): basic information about NVQC is logged to the console. This is the default.
-
-- Off (`off` or `0`): disable all NVQC logging.
-
-- Trace: (`trace`): log additional information for each NVQC job execution (including timing)
-
-2. I want to persist my API key to a configuration file.
-
-You may persist your NVQC API Key to a credential configuration file in lieu of 
-using the `NVQC_API_KEY` environment variable. 
-The configuration file can be generated as follows, replacing
-the `api_key` value with your NVQC API Key.
-
-.. code:: bash
-
-    echo "key: <api_key>" >> $HOME/.nvqc_config
-
diff --git a/pr-2308/sphinx/using/backends/platform.rst b/pr-2308/sphinx/using/backends/platform.rst
deleted file mode 100644
index da019f3f65..0000000000
--- a/pr-2308/sphinx/using/backends/platform.rst
+++ /dev/null
@@ -1,341 +0,0 @@
-Multi-Processor Platforms
----------------------------------------------------
-The CUDA-Q machine model elucidates the various devices considered in the 
-broader quantum-classical compute node context. Programmers will have one or many 
-host CPUs, zero or many NVIDIA GPUs, a classical QPU control space, and the
-quantum register itself. Moreover, the :doc:`specification </specification/cudaq/platform>`
-notes that the underlying platform may expose multiple QPUs. In the near-term,
-this will be unlikely with physical QPU instantiations, but the availability of
-GPU-based circuit simulators on NVIDIA multi-GPU architectures does give one an
-opportunity to think about programming such a multi-QPU architecture in the near-term.
-CUDA-Q starts by enabling one to query information about the underlying quantum
-platform via the :code:`quantum_platform` abstraction. This type exposes a
-:code:`num_qpus()` method that can be used to query the number of available
-QPUs for asynchronous CUDA-Q kernel and :code:`cudaq::` function invocations.
-Each available QPU is assigned a logical index, and programmers can launch
-specific asynchronous function invocations targeting a desired QPU.
-
-.. _mqpu-platform:
-
-NVIDIA `MQPU` Platform
-++++++++++++++++++++++
-
-In the multi-QPU mode (:code:`mqpu` option), the NVIDIA target provides a simulated QPU for every available NVIDIA GPU on the underlying system. 
-Each QPU is simulated via a `cuStateVec` simulator backend as defined by the NVIDIA target. For more information about using multiple GPUs 
-to simulate each virtual QPU, or using a different backend for virtual QPUs, please see :ref:`remote MQPU platform <remote-mqpu-platform>`.
-This target enables asynchronous parallel execution of quantum kernel tasks.
-
-Here is a simple example demonstrating its usage.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/sample_async.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/sample_async.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-
-    One can specify the target multi-QPU architecture with the :code:`--target` flag:
-    
-    .. code-block:: console
-
-        nvq++ sample_async.cpp --target nvidia --target-option mqpu
-        ./a.out
-
-CUDA-Q exposes asynchronous versions of the default :code:`cudaq` algorithmic
-primitive functions like :code:`sample`, :code:`observe`, and :code:`get_state` 
-(e.g., :code:`sample_async` function in the above code snippets).
-
-Depending on the number of GPUs available on the system, the :code:`nvidia` multi-QPU platform will create the same number of virtual QPU instances.
-For example, on a system with 4 GPUs, the above code will distribute the four sampling tasks among those :code:`GPUEmulatedQPU` instances.
-
-The results might look like the following 4 different random samplings:
-
-.. code-block:: console
-  
-    Number of QPUs: 4
-    { 10011:28 01100:28 ... }
-    { 10011:37 01100:25 ... }
-    { 10011:29 01100:25 ... }
-    { 10011:33 01100:30 ... }
-
-.. note::
-
-  By default, the :code:`nvidia` multi-QPU platform will utilize all available GPUs (number of QPUs instances is equal to the number of GPUs).
-  To specify the number QPUs to be instantiated, one can set the :code:`CUDAQ_MQPU_NGPUS` environment variable.
-  For example, use :code:`export CUDAQ_MQPU_NGPUS=2` to specify that only 2 QPUs (GPUs) are needed.
-
-Since the underlying :code:`GPUEmulatedQPU` is a simulator backend, we can also retrieve the state vector from each
-QPU via the :code:`cudaq::get_state_async` (C++) or :code:`cudaq.get_state_async` (Python) as shown in the bellow code snippets.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/get_state_async.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/get_state_async.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-
-    One can specify the target multi-QPU architecture with the :code:`--target` flag:
-    
-    .. code-block:: console
-
-        nvq++ get_state_async.cpp --target nvidia --target-option mqpu
-        ./a.out
-
-.. deprecated:: 0.8
-    The :code:`nvidia-mqpu` and :code:`nvidia-mqpu-fp64` targets, which are equivalent to the multi-QPU options `mqpu,fp32` and `mqpu,fp64`, respectively, of the :code:`nvidia` target, are deprecated and will be removed in a future release.
-
-Parallel distribution mode
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The CUDA-Q :code:`nvidia` multi-QPU platform supports two modes of parallel distribution of expectation value computation:
-
-* MPI: distribute the expectation value computations across available MPI ranks and GPUs for each Hamiltonian term.
-* Thread: distribute the expectation value computations among available GPUs via standard C++ threads (each thread handles one GPU).
-
-For instance, if all GPUs are available on a single node, thread-based parallel distribution 
-(:code:`cudaq::parallel::thread` in C++ or :code:`cudaq.parallel.thread` in Python, as shown in the above example) is sufficient.
-On the other hand, if one wants to distribute the tasks across GPUs on multiple nodes, e.g., on a compute cluster, MPI distribution mode
-should be used.
-
-An example of MPI distribution mode usage in both C++ and Python is given below:
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/observe_mqpu_mpi.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-    .. code-block:: console
-
-        mpiexec -np <N> python3 file.py
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/observe_mqpu_mpi.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-    .. code-block:: console
-
-        nvq++ file.cpp --target nvidia --target-option mqpu
-        mpiexec -np <N> a.out
-
-In the above example, the parallel distribution mode was set to :code:`mpi` using :code:`cudaq::parallel::mpi` in C++ or :code:`cudaq.parallel.mpi` in Python.
-CUDA-Q provides MPI utility functions to initialize, finalize, or query (rank, size, etc.) the MPI runtime. 
-Last but not least, the compiled executable (C++) or Python script needs to be launched with an appropriate MPI command, 
-e.g., :code:`mpiexec`, :code:`mpirun`, :code:`srun`, etc.
-
-Remote `MQPU` Platform
-+++++++++++++++++++++++++++
-
-.. _remote-mqpu-platform:
-
-As shown in the above examples, the multi-QPU NVIDIA platform enables
-multi-QPU distribution whereby each QPU is simulated by a :ref:`single NVIDIA GPU <cuQuantum single-GPU>`.
-To run multi-QPU workloads on different simulator backends, one can use the :code:`remote-mqpu` platform,
-which encapsulates simulated QPUs as independent HTTP REST server instances. 
-The following code illustrates how to launch asynchronous sampling tasks on multiple virtual QPUs, 
-each simulated by a `tensornet` simulator backend.
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/sample_async_remote.py
-        :language: python
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/sample_async_remote.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-    The code above is saved in `sample_async.cpp` and compiled with the following command, targeting the :code:`remote-mqpu` platform:
-
-    .. code-block:: console
-
-        nvq++ sample_async.cpp -o sample_async.x --target remote-mqpu --remote-mqpu-backend tensornet --remote-mqpu-auto-launch 2
-        ./sample_async.x
-
-In the above code snippets, the :code:`remote-mqpu` platform was used in the auto-launch mode,
-whereby a specific number of server instances, i.e., virtual QPUs, are launched on the local machine
-in the background. The remote QPU daemon service, :code:`cudaq-qpud`, will also be shut down automatically
-at the end of the session.
-
-.. note:: 
-    By default, auto launching daemon services do not support MPI parallelism.
-    Hence, using the `nvidia-mgpu` backend to simulate each virtual QPU requires 
-    manually launching each server instance. How to do that is explained in the rest of this section.
-
-.. _custom_remote_qpud_launch:
-
-To customize how many and which GPUs are used for simulating each virtual QPU, one can launch each server manually.
-For instance, on a machine with 8 NVIDIA GPUs, one may wish to partition those GPUs into
-4 virtual QPU instances, each manages 2 GPUs. To do so, first launch a :code:`cudaq-qpud` server for each virtual QPU:
-
-.. tab:: Python
-
-     .. See scripts/validate_wheel.sh for examples of how similar commands are run automatically during release validation.
-
-     .. code-block:: bash
-         
-         # Use cudaq-qpud.py wrapper script to automatically find dependencies for the Python wheel configuration.
-         cudaq_location=`python3 -m pip show cudaq | grep -e 'Location: .*$'`
-         qpud_py="${cudaq_location#Location: }/bin/cudaq-qpud.py"
-         CUDA_VISIBLE_DEVICES=0,1 mpiexec -np 2 python3 "$qpud_py" --port <QPU 1 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=2,3 mpiexec -np 2 python3 "$qpud_py" --port <QPU 2 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=4,5 mpiexec -np 2 python3 "$qpud_py" --port <QPU 3 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=6,7 mpiexec -np 2 python3 "$qpud_py" --port <QPU 4 TCP/IP port number>
-
-.. tab:: C++
-     
-     .. code-block:: bash
-         
-         # It is assumed that your $LD_LIBRARY_PATH is able to find all the necessary dependencies.
-         CUDA_VISIBLE_DEVICES=0,1 mpiexec -np 2 cudaq-qpud --port <QPU 1 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=2,3 mpiexec -np 2 cudaq-qpud --port <QPU 2 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=4,5 mpiexec -np 2 cudaq-qpud --port <QPU 3 TCP/IP port number>
-         CUDA_VISIBLE_DEVICES=6,7 mpiexec -np 2 cudaq-qpud --port <QPU 4 TCP/IP port number>
-
-
-In the above code snippet, four :code:`nvidia-mgpu` daemons are started in MPI context via the :code:`mpiexec` launcher.
-This activates MPI runtime environment required by the :code:`nvidia-mgpu` backend. Each QPU daemon is assigned a unique 
-TCP/IP port number via the :code:`--port` command-line option. The :code:`CUDA_VISIBLE_DEVICES` environment variable restricts the GPU devices 
-that each QPU daemon sees so that it targets specific GPUs. 
-
-With these invocations, each virtual QPU is locally addressable at the URL `localhost:<port>`. 
-
-.. warning:: 
-
-    There is no authentication required to communicate with this server app. 
-    Hence, please make sure to either (1) use a non-public TCP/IP port for internal use or 
-    (2) use firewalls or other security mechanisms to manage user access. 
-
-User code can then target these QPUs for multi-QPU workloads, such as asynchronous sample or observe shown above for the multi-QPU NVIDIA platform platform.
-
-.. tab:: Python
-
-     .. code:: python 
-
-        cudaq.set_target("remote-mqpu", url="localhost:<port1>,localhost:<port2>,localhost:<port3>,localhost:<port4>", backend="nvidia-mgpu")
-        
-.. tab:: C++
-
-    .. code-block:: console
-
-        nvq++ distributed.cpp --target remote-mqpu --remote-mqpu-url localhost:<port1>,localhost:<port2>,localhost:<port3>,localhost:<port4> --remote-mqpu-backend nvidia-mgpu
-    
-
-Each URL is treated as an independent QPU, hence the number of QPUs (:code:`num_qpus()`) is equal to the number of URLs provided. 
-The multi-node multi-GPU simulator backend (:code:`nvidia-mgpu`) is requested via the :code:`--remote-mqpu-backend` command-line option.
-
-.. note:: 
-
-    The requested backend (:code:`nvidia-mgpu`) will be executed inside the context of the QPU daemon service, thus 
-    inherits its GPU resource allocation (two GPUs per backend simulator instance). 
-
-Supported Kernel Arguments
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The platform serializes kernel invocation to QPU daemons via REST APIs. 
-Please refer to the `Open API Docs <../../openapi.html>`_  for the latest API information.
-Runtime arguments are serialized into a flat memory buffer (`args` field of the request JSON). 
-For more information about argument type serialization, please see :ref:`the table below <type_serialization_table>`.
-
-When using a remote backend to simulate each virtual QPU, 
-by default, we currently do not support passing complex data structures, 
-such as nested vectors or class objects, or other kernels as arguments to the entry point kernels.
-These type limitations only apply to the **entry-point** kernel and not when passing arguments
-to other quantum kernels.
-
-Support for the full range of argument types within CUDA-Q can be enabled by compiling the 
-code with the :code:`--enable-mlir` option. This flag forces quantum kernels to be compiled with 
-the CUDA-Q MLIR-based compiler. As a result, runtime arguments can be resolved by the CUDA 
-Quantum compiler infrastructure to support wider range of argument types. However, certain
-language constructs within quantum kernels may not yet be fully supported.
-
-.. _type_serialization_table:
-
-.. list-table:: Kernel argument serialization
-   :widths: 50 50 50
-   :header-rows: 1
-
-   * - Data type
-     - Example
-     - Serialization
-   * -  Trivial type (occupies a contiguous memory area)
-     -  `int`, `std::size_t`, `double`, etc.
-     - Byte data (via `memcpy`)
-   * - `std::vector` of trivial type
-     - `std::vector<int>`, `std::vector<double>`, etc. 
-     - Total vector size in bytes as a 64-bit integer followed by serialized data of all vector elements.
-   * - `cudaq::pauli_word`
-     - `cudaq::pauli_word("IXIZ")`
-     - Same as `std::vector<char>`: total vector size in bytes as a 64-bit integer followed by serialized data of all characters.
-   * - Single-level nested `std::vector` of supported `std::vector` types
-     - `std::vector<std::vector<int>>`, `std::vector<cudaq::pauli_word>`, etc. 
-     - Number of top-level elements (as a 64-bit integer) followed sizes in bytes of element vectors (as a contiguous array of 64-bit integers) then serialized data of the inner vectors.
-     
-For CUDA-Q kernels that return a value, the remote platform supports returning simple data types of 
-`bool`, integral (e.g., `int` or `std::size_t`), and floating-point types (`float` or `double`) 
-when MLIR-based compilation is enabled (:code:`--enable-mlir`).
-
-Accessing Simulated Quantum State
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The remote `MQPU` platform supports accessing simulator backend's state vector via the 
-`cudaq::get_state` (C++) or `cudaq.get_state` (Python) APIs, similar to local simulator backends.
-
-State data can be retrieved as a full state vector or as individual basis states' amplitudes.
-The later is designed for large quantum states, which incurred data transfer overheads.
-
-.. tab:: Python
-
-    .. code:: python 
-        
-        state = cudaq.get_state(kernel)
-        amplitudes = state.amplitudes(['0000', '1111'])
-        
-.. tab:: C++
-
-    .. code-block:: cpp
-        
-        auto state = cudaq::get_state(kernel)
-        auto amplitudes = state.amplitudes({{0, 0, 0, 0}, {1, 1, 1, 1}});
-
-In the above example, the amplitudes of the two requested states are returned.
-
-For C++ quantum kernels [*]_ compiled with the CUDA-Q MLIR-based compiler and Python kernels,
-state accessor is evaluated in a just-in-time/on-demand manner, and hence can be customize to
-users' need.
-
-For instance, in the above amplitude access example, if the state vector is very large, e.g.,
-multi-GPU distributed state vectors or tensor-network encoded quantum states, the full state vector
-will not be retrieved when `get_state` is called. Instead, when the `amplitudes` accessor is called,
-a specific amplitude calculation request will be sent to the server. 
-Thus, only the amplitudes of those basis states will be computed and returned. 
-
-Similarly, for state overlap calculation, if deferred state evaluation is available (Python/MLIR-based compiler)
-for both of the operand quantum states, a custom overlap calculation request will be constructed and sent to the server.
-Only the final overlap result will be returned, thereby eliminating back-and-forth state data transfers. 
-
-.. [*] Only C++ quantum kernels whose names are available via run-time type information (RTTI) are supported.
-    For example, quantum kernels expressed as named `struct` are supported but not standalone functions.
-    Kernels that do not have deferred state evaluation support will perform synchronous `get_state`, whereby the full state
-    vector is returned from the server immediately.   
diff --git a/pr-2308/sphinx/using/backends/simulators.rst b/pr-2308/sphinx/using/backends/simulators.rst
deleted file mode 100644
index a3dba35d92..0000000000
--- a/pr-2308/sphinx/using/backends/simulators.rst
+++ /dev/null
@@ -1,615 +0,0 @@
-CUDA-Q Simulation Backends
-*********************************
-
-.. _nvidia-backend:
-
-The simulation backends that are currently available in CUDA-Q are as follows.
-
-State Vector Simulators
-==================================
-
-The :code:`nvidia` target provides a state vector simulator accelerated with 
-the :code:`cuStateVec` library. 
-
-The :code:`nvidia` target supports multiple configurable options.
-
-Features 
-+++++++++
-
-* Floating-point precision configuration 
-
-The floating point precision of the state vector data can be configured to either 
-double (`fp64`) or single (`fp32`) precision. This option can be chosen for the optimal performance and accuracy.
-
-
-* Distributed simulation
-
-The :code:`nvidia` target supports distributing state vector simulations to multiple GPUs and multiple nodes (`mgpu` :ref:`distribution <nvidia-mgpu-backend>`)
-and multi-QPU (`mqpu` :ref:`platform <mqpu-platform>`) distribution whereby each QPU is simulated via a single-GPU simulator instance.
-
-
-* Host CPU memory utilization 
-
-Host CPU memory can be leveraged in addition to GPU memory to accommodate the state vector 
-(i.e., maximizing the number of qubits to be simulated).
-
-.. _cuQuantum single-GPU:
-
-
-Single-GPU 
-++++++++++++++++++++++++++++++++++
-
-To execute a program on the :code:`nvidia` target, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target nvidia
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('nvidia')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target nvidia program.cpp [...] -o program.x
-        ./program.x
-
-.. _nvidia-fp64-backend:
-
-By default, this will leverage :code:`FP32` floating point types for the simulation. To 
-switch to :code:`FP64`, specify the :code:`--target-option fp64` `nvq++` command line option for `C++` and `Python` or 
-use `cudaq.set_target('nvidia', option='fp64')` for Python in-source target modification instead. 
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target nvidia --target-option fp64
-
-    The precision of the :code:`nvidia` target can also be modified in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('nvidia', option='fp64')
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target nvidia --target-option fp64 program.cpp [...] -o program.x
-        ./program.x
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU and CUDA runtime libraries. If you do not have these dependencies installed, you may encounter an error stating `Invalid simulator requested`. See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-
-In the single-GPU mode, the :code:`nvidia` target provides the following
-environment variable options. Any environment variables must be set prior to
-setting the target.
-
-.. list-table:: **Environment variable options supported in single-GPU mode**
-  :widths: 20 30 50
-
-  * - Option
-    - Value
-    - Description
-  * - ``CUDAQ_FUSION_MAX_QUBITS``
-    - positive integer
-    - The max number of qubits used for gate fusion. The default value is `4`.
-  * - ``CUDAQ_FUSION_DIAGONAL_GATE_MAX_QUBITS``
-    - integer greater than or equal to -1
-    - The max number of qubits used for diagonal gate fusion. The default value is set to `-1` and the fusion size will be automatically adjusted for the better performance. If 0, the gate fusion for diagonal gates is disabled.
-  * - ``CUDAQ_FUSION_NUM_HOST_THREADS``
-    - positive integer
-    - Number of CPU threads used for circuit processing. The default value is `8`.
-  * - ``CUDAQ_MAX_CPU_MEMORY_GB``
-    - non-negative integer, or `NONE`
-    - CPU memory size (in GB) allowed for state-vector migration. `NONE` means unlimited (up to physical memory constraints). Default is 0GB (disabled, variable is not set to any value).
-  * - ``CUDAQ_MAX_GPU_MEMORY_GB``
-    - positive integer, or `NONE`
-    - GPU memory (in GB) allowed for on-device state-vector allocation. As the state-vector size exceeds this limit, host memory will be utilized for migration. `NONE` means unlimited (up to physical memory constraints). This is the default.
-
-.. deprecated:: 0.8
-    The :code:`nvidia-fp64` targets, which is equivalent setting the `fp64` option on the :code:`nvidia` target, 
-    is deprecated and will be removed in a future release.
-
-.. _nvidia-mgpu-backend:
-
-Multi-node multi-GPU
-++++++++++++++++++++++++++++++++++
-
-The NVIDIA target also provides a state vector simulator accelerated with 
-the :code:`cuStateVec` library with support for Multi-Node, Multi-GPU distribution of the 
-state vector, in addition to a single GPU.
-
-The multi-node multi-GPU simulator expects to run within an MPI context.
-To execute a program on the multi-node multi-GPU NVIDIA target, use the following commands 
-(adjust the value of the :code:`-np` flag as needed to reflect available GPU resources on your system):
-
-.. tab:: Python
-
-    Double precision simulation:
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 program.py [...] --target nvidia --target-option fp64,mgpu
-
-    Single precision simulation:
-    
-    .. code:: bash 
-
-        mpiexec -np 2 python3 program.py [...] --target nvidia --target-option fp32,mgpu
-
-    .. note::
-
-      If you installed CUDA-Q via :code:`pip`, you will need to install the necessary MPI dependencies separately;
-      please follow the instructions for installing dependencies in the `Project Description <https://pypi.org/project/cuda-quantum/#description>`__.
-
-    In addition to using MPI in the simulator, you can use it in your application code by installing `mpi4py <https://mpi4py.readthedocs.io/>`__, and 
-    invoking the program with the command
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 -m mpi4py program.py [...] --target nvidia --target-option fp64,mgpu
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('nvidia', option='mgpu,fp64')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-    .. note::
-        
-        * The order of the option settings are interchangeable.
-          For example, `cudaq.set_target('nvidia', option='mgpu,fp64')` is equivalent to `cudaq.set_target('nvidia', option='fp64,mgpu')`.
-
-        * The `nvidia` target has single-precision as the default setting. Thus, using `option='mgpu'` implies that `option='mgpu,fp32'`.  
-
-.. tab:: C++
-
-    Double precision simulation:
-
-    .. code:: bash 
-
-        nvq++ --target nvidia  --target-option mgpu,fp64 program.cpp [...] -o program.x
-        mpiexec -np 2 ./program.x
-
-    Single precision simulation:
-
-    .. code:: bash 
-
-        nvq++ --target nvidia  --target-option mgpu,fp32 program.cpp [...] -o program.x
-        mpiexec -np 2 ./program.x
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU, CUDA runtime libraries, as well as an MPI installation. If you do not have these dependencies installed, you may encounter either an error stating `invalid simulator requested` (missing CUDA libraries), or an error along the lines of `failed to launch kernel` (missing MPI installation). See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-  
-  The number of processes and nodes should be always power-of-2. 
-
-  Host-device state vector migration is also supported in the multi-node multi-GPU configuration. 
-
-
-In addition to those environment variable options supported in the single-GPU mode,
-the :code:`nvidia` target provides the following environment variable options particularly for 
-the multi-node multi-GPU configuration. Any environment variables must be set
-prior to setting the target.
-
-.. list-table:: **Additional environment variable options for multi-node multi-GPU mode**
-  :widths: 20 30 50
-
-  * - Option
-    - Value
-    - Description
-  * - ``CUDAQ_MGPU_LIB_MPI``
-    - string
-    - The shared library name for inter-process communication. The default value is `libmpi.so`.
-  * - ``CUDAQ_MGPU_COMM_PLUGIN_TYPE``
-    - `AUTO`, `EXTERNAL`, `OpenMPI`, or `MPICH` 
-    - Selecting :code:`cuStateVec` `CommPlugin` for inter-process communication. The default is `AUTO`. If `EXTERNAL` is selected, `CUDAQ_MGPU_LIB_MPI` should point to an implementation of :code:`cuStateVec` `CommPlugin` interface.
-  * - ``CUDAQ_MGPU_NQUBITS_THRESH``
-    - positive integer
-    - The qubit count threshold where state vector distribution is activated. Below this threshold, simulation is performed as independent (non-distributed) tasks across all MPI processes for optimal performance. Default is 25. 
-  * - ``CUDAQ_MGPU_FUSE``
-    - positive integer
-    - The max number of qubits used for gate fusion. The default value is `6` if there are more than one MPI processes or `4` otherwise.
-  * - ``CUDAQ_MGPU_P2P_DEVICE_BITS``
-    - positive integer
-    - Specify the number of GPUs that can communicate by using GPUDirect P2P. Default value is 0 (P2P communication is disabled).
-  * - ``CUDAQ_GPU_FABRIC``
-    - `MNNVL`, `NVL`, or `NONE`
-    - Automatically set the number of P2P device bits based on the total number of processes when multi-node NVLink (`MNNVL`) is selected; or the number of processes per node when NVLink (`NVL`) is selected; or disable P2P (with `NONE`). 
-  * - ``CUDAQ_GLOBAL_INDEX_BITS``
-    - comma-separated list of positive integers
-    - Specify the inter-node network structure (faster to slower). For example, assuming a 8 nodes, 4 GPUs/node simulation whereby network communication is faster, this `CUDAQ_GLOBAL_INDEX_BITS` environment variable can be set to `3,2`. The first `3` represents **8** nodes with fast communication and the second `2` represents **4** 8-node groups in those total 32 nodes. Default is an empty list (no customization based on network structure of the cluster).
-  * - ``CUDAQ_HOST_DEVICE_MIGRATION_LEVEL``
-    - positive integer
-    - Specify host-device memory migration w.r.t. the network structure. If provided, this setting determines the position to insert the number of migration index bits to the `CUDAQ_GLOBAL_INDEX_BITS` list. By default, if not set, the number of migration index bits (CPU-GPU data transfers) is appended to the end of the array of index bits (aka, state vector distribution scheme). This default behavior is optimized for systems with fast GPU-GPU interconnects (NVLink, InfiniBand, etc.) 
-
-.. deprecated:: 0.8
-    The :code:`nvidia-mgpu` target, which is equivalent to the multi-node multi-GPU double-precision option (`mgpu,fp64`) of the :code:`nvidia`
-    is deprecated and will be removed in a future release.
-
-The above configuration options of the :code:`nvidia` backend 
-can be tuned to reduce your simulation runtimes. One of the
-performance improvements is to fuse multiple gates together during runtime. For
-example, :code:`x(qubit0)` and :code:`x(qubit1)` can be fused together into a
-single 4x4 matrix operation on the state vector rather than 2 separate 2x2
-matrix operations on the state vector. This fusion reduces memory bandwidth on
-the GPU because the state vector is transferred into and out of memory fewer
-times. By default, up to 4 gates are fused together for single-GPU simulations,
-and up to 6 gates are fused together for multi-GPU simulations. The number of
-gates fused can **significantly** affect performance of some circuits, so users
-can override the default fusion level by setting the setting `CUDAQ_MGPU_FUSE`
-environment variable to another integer value as shown below.
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        CUDAQ_MGPU_FUSE=5 mpiexec -np 2 python3 program.py [...] --target nvidia --target-option mgpu,fp64
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target nvidia --target-option mgpu,fp64 program.cpp [...] -o program.x
-        CUDAQ_MGPU_FUSE=5 mpiexec -np 2 ./program.x
-
-.. _OpenMP CPU-only:
-
-OpenMP CPU-only
-++++++++++++++++++++++++++++++++++
-
-.. _qpp-cpu-backend:
-
-This target provides a state vector simulator based on the CPU-only, OpenMP threaded `Q++ <https://github.com/softwareqinc/qpp>`_ library.
-This is the default target when running on CPU-only systems.
-
-To execute a program on the :code:`qpp-cpu` target even if a GPU-accelerated backend is available, 
-use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target qpp-cpu
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('qpp-cpu')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target qpp-cpu program.cpp [...] -o program.x
-        ./program.x
-
-Tensor Network Simulators
-==================================
-
-.. _tensor-backends:
-
-CUDA-Q provides a couple of tensor-network simulator targets accelerated with 
-the :code:`cuTensorNet` library. 
-These backends are available for use from both C++ and Python.
-
-Tensor network simulators are suitable for large-scale simulation of certain classes of quantum circuits involving many qubits beyond the memory limit of state vector based simulators. For example, computing the expectation value of a Hamiltonian via :code:`cudaq::observe` can be performed efficiently, thanks to :code:`cuTensorNet` contraction optimization capability. On the other hand, conditional circuits, i.e., those with mid-circuit measurements or reset, despite being supported by both backends, may result in poor performance. 
-
-Multi-node multi-GPU
-+++++++++++++++++++++++++++++++++++
-
-The :code:`tensornet` backend represents quantum states and circuits as tensor networks in an exact form (no approximation). 
-Measurement samples and expectation values are computed via tensor network contractions. 
-This backend supports multi-node, multi-GPU distribution of tensor operations required to evaluate and simulate the circuit.
-
-To execute a program on the :code:`tensornet` target using a *single GPU*, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target tensornet
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('tensornet')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target tensornet program.cpp [...] -o program.x
-        ./program.x
-
-If you have *multiple GPUs* available on your system, you can use MPI to automatically distribute parallelization across the visible GPUs. 
-
-.. note::
-
-  If you installed the CUDA-Q Python wheels, distribution across multiple GPUs is currently not supported for this backend.
-  We will add support for it in future releases. For more information, see this `GitHub issue <https://github.com/NVIDIA/cuda-quantum/issues/920>`__.
-
-Use the following commands to enable distribution across multiple GPUs (adjust the value of the :code:`-np` flag as needed to reflect available GPU resources on your system):
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 program.py [...] --target tensornet
-
-    In addition to using MPI in the simulator, you can use it in your application code by installing `mpi4py <https://mpi4py.readthedocs.io/>`__, and 
-    invoking the program with the command
-
-    .. code:: bash 
-
-        mpiexec -np 2 python3 -m mpi4py program.py [...] --target tensornet
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target tensornet program.cpp [...] -o program.x
-        mpiexec -np 2 ./program.x
-
-.. note::
-
-  If the `CUTENSORNET_COMM_LIB` environment variable is not set, MPI parallelization on the :code:`tensornet` backend may fail.
-  If you are using a CUDA-Q container, this variable is pre-configured and no additional setup is needed. If you are customizing your installation or have built CUDA-Q from source, please follow the instructions for `activating the distributed interface <https://docs.nvidia.com/cuda/cuquantum/latest/getting-started/index.html#from-nvidia-devzone>`__ for the `cuTensorNet` library. This requires 
-  :ref:`installing CUDA development dependencies <additional-cuda-tools>`, and setting the `CUTENSORNET_COMM_LIB`
-  environment variable to the newly built `libcutensornet_distributed_interface_mpi.so` library.
-
-Specific aspects of the simulation can be configured by setting the following of environment variables:
-
-* **`CUDA_VISIBLE_DEVICES=X`**: Makes the process only see GPU X on multi-GPU nodes. Each MPI process must only see its own dedicated GPU. For example, if you run 8 MPI processes on a DGX system with 8 GPUs, each MPI process should be assigned its own dedicated GPU via `CUDA_VISIBLE_DEVICES` when invoking `mpiexec` (or `mpirun`) commands. 
-* **`OMP_PLACES=cores`**: Set this environment variable to improve CPU parallelization.
-* **`OMP_NUM_THREADS=X`**: To enable CPU parallelization, set X to `NUMBER_OF_CORES_PER_NODE/NUMBER_OF_GPUS_PER_NODE`.
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU and CUDA runtime libraries. 
-  If you do not have these dependencies installed, you may encounter an error stating `Invalid simulator requested`. 
-  See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-
-.. note::
-
-  Setting random seed, via :code:`cudaq::set_random_seed`, is not supported for this backend due to a limitation of the :code:`cuTensorNet` library. This will be fixed in future release once this feature becomes available.
-
-
-Matrix product state 
-+++++++++++++++++++++++++++++++++++
-
-The :code:`tensornet-mps` backend is based on the matrix product state (MPS) representation of the state vector/wave function, exploiting the sparsity in the tensor network via tensor decomposition techniques such as QR and SVD. As such, this backend is an approximate simulator, whereby the number of singular values may be truncated to keep the MPS size tractable. 
-The :code:`tensornet-mps` backend only supports single-GPU simulation. Its approximate nature allows the :code:`tensornet-mps` backend to handle a large number of qubits for certain classes of quantum circuits on a relatively small memory footprint.
-
-To execute a program on the :code:`tensornet-mps` target, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target tensornet-mps
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('tensornet-mps')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target tensornet-mps program.cpp [...] -o program.x
-        ./program.x
-
-Specific aspects of the simulation can be configured by defining the following environment variables:
-
-* **`CUDAQ_MPS_MAX_BOND=X`**: The maximum number of singular values to keep (fixed extent truncation). Default: 64.
-* **`CUDAQ_MPS_ABS_CUTOFF=X`**: The cutoff for the largest singular value during truncation. Eigenvalues that are smaller will be trimmed out. Default: 1e-5.
-* **`CUDAQ_MPS_RELATIVE_CUTOFF=X`**: The cutoff for the maximal singular value relative to the largest eigenvalue. Eigenvalues that are smaller than this fraction of the largest singular value will be trimmed out. Default: 1e-5
-* **`CUDAQ_MPS_SVD_ALGO=X`**: The SVD algorithm to use. Valid values are: `GESVD` (QR algorithm), `GESVDJ` (Jacobi method), `GESVDP` (`polar decomposition <https://epubs.siam.org/doi/10.1137/090774999>`__), `GESVDR` (`randomized methods <https://epubs.siam.org/doi/10.1137/090771806>`__). Default: `GESVDJ`.
-
-.. note:: 
-
-  This backend requires an NVIDIA GPU and CUDA runtime libraries. 
-  If you do not have these dependencies installed, you may encounter an error stating `Invalid simulator requested`. 
-  See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
-
-.. note::
-
-  Setting random seed, via :code:`cudaq::set_random_seed`, is not supported for this backend due to a limitation of the :code:`cuTensorNet` library. This will be fixed in future release once this feature becomes available.
-
-.. note::
-    The parallelism of Jacobi method (the default `CUDAQ_MPS_SVD_ALGO` setting) gives GPU better performance on small and medium size matrices.
-    If you expect a large number of singular values (e.g., increasing the `CUDAQ_MPS_MAX_BOND` setting), please adjust the `CUDAQ_MPS_SVD_ALGO` setting accordingly.  
-
-Clifford-Only Simulator
-==================================
-
-Stim (CPU)
-++++++++++++++++++++++++++++++++++
-
-.. _stim-backend:
-
-This target provides a fast simulator for circuits containing *only* Clifford
-gates. Any non-Clifford gates (such as T gates and Toffoli gates) are not
-supported. This simulator is based on the `Stim <https://github.com/quantumlib/Stim>`_
-library.
-
-To execute a program on the :code:`stim` target, use the following commands:
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        python3 program.py [...] --target stim
-
-    The target can also be defined in the application code by calling
-
-    .. code:: python 
-
-        cudaq.set_target('stim')
-
-    If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
-
-.. tab:: C++
-
-    .. code:: bash 
-
-        nvq++ --target stim program.cpp [...] -o program.x
-        ./program.x
-
-.. note::
-    CUDA-Q currently executes kernels using a "shot-by-shot" execution approach.
-    This allows for conditional gate execution (i.e. full control flow), but it
-    can be slower than executing Stim a single time and generating all the shots
-    from that single execution.
-
-Fermioniq
-==================================
-
-.. _fermioniq-backend:
-
-`Fermioniq <https://fermioniq.com/>`__ offers a cloud-based tensor-network emulation platform, `Ava <https://www.fermioniq.com/ava/>`__, 
-for the approximate simulation of large-scale quantum circuits beyond the memory limit of state vector and exact tensor network based methods. 
-
-The level of approximation can be controlled by setting the bond dimension: larger values yield more accurate simulations at the expense 
-of slower computation time. For a detailed description of Ava users are referred to the `online documentation <https://docs.fermioniq.com/>`__.
-
-Users of CUDA-Q can access a simplified version of the full Fermioniq emulator (`Ava <https://www.fermioniq.com/ava/>`__) from either
-C++ or Python. This version currently supports emulation of quantum circuits without noise, and can return measurement samples and/or 
-compute expectation values of observables.
-
-.. note::
-    In order to use the Fermioniq emulator, users must provide access credentials. These can be requested by contacting info@fermioniq.com 
-
-    The credentials must be set via two environment variables:
-    `FERMIONIQ_ACCESS_TOKEN_ID` and `FERMIONIQ_ACCESS_TOKEN_SECRET`.
-
-.. tab:: Python
-
-    The target to which quantum kernels are submitted 
-    can be controlled with the ``cudaq::set_target()`` function.
-
-    .. code:: python
-
-        cudaq.set_target('fermioniq')
-
-    You will have to specify a remote configuration id for the Fermioniq backend
-    during compilation.
-
-    .. code:: python
-
-        cudaq.set_target("fermioniq", **{
-            "remote_config": remote_config_id
-        })
-
-    For a comprehensive list of all remote configurations, please contact Fermioniq directly.
-
-    When your organization requires you to define a project id, you have to specify
-    the project id during compilation.
-
-    .. code:: python
-
-        cudaq.set_target("fermioniq", **{
-            "project_id": project_id
-        })
-
-    To specify the bond dimension, you can pass the ``bond_dim`` parameter.
-
-    .. code:: python 
-
-        cudaq.set_target("fermioniq", **{
-            "bond_dim": 5
-        })
-
-.. tab:: C++
-
-    To target quantum kernel code for execution in the Fermioniq backends,
-    pass the flag ``--target fermioniq`` to the ``nvq++`` compiler. CUDA-Q will 
-    authenticate via the Fermioniq REST API using the environment variables 
-    set earlier.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq src.cpp ...
-
-    You will have to specify a remote configuration id for the Fermioniq backend
-    during compilation.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq --fermioniq-remote-config <remote_config_id> src.cpp ...
-
-    For a comprehensive list of all remote configurations, please contact Fermioniq directly.
-
-    When your organization requires you to define a project id, you have to specify
-    the project id during compilation.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq --fermioniq-project-id <project_id> src.cpp ...
-
-    To specify the bond dimension, you can pass the ``fermioniq-bond-dim`` parameter.
-
-    .. code:: bash
-
-        nvq++ --target fermioniq --fermioniq-bond-dim 10 src.cpp ...
-
-Default Simulator
-==================================
-
-.. _default-simulator:
-
-If no explicit target is set, i.e., if the code is compiled without any :code:`--target` flags, then CUDA-Q makes a default choice for the simulator.
-
-If an NVIDIA GPU and CUDA runtime libraries are available, the default target is set to `nvidia`. This will utilize the :ref:`cuQuantum single-GPU state vector simulator <cuQuantum single-GPU>`.  
-On CPU-only systems, the default target is set to `qpp-cpu` which uses the :ref:`OpenMP CPU-only simulator <OpenMP CPU-only>`.
-
-The default simulator can be overridden by the environment variable `CUDAQ_DEFAULT_SIMULATOR`. If no target is explicitly specified and the environment variable has a valid value, then it will take effect.
-This environment variable can be set to any non-hardware backend. Any invalid value is ignored.
-
-For CUDA-Q Python API, the environment variable at the time when `cudaq` module is imported is relevant, not the value of the environment variable at the time when the simulator is invoked.
-
-For example,
-
-.. tab:: Python
-
-    .. code:: bash 
-
-        CUDAQ_DEFAULT_SIMULATOR=density-matrix-cpu python3 program.py [...]
-        
-.. tab:: C++
-
-    .. code:: bash 
-
-        CUDAQ_DEFAULT_SIMULATOR=density-matrix-cpu nvq++ program.cpp [...] -o program.x
-        ./program.x
-
-This will use the density matrix simulator target.
-
-
-.. note:: 
-
-    To use targets that require an NVIDIA GPU and CUDA runtime libraries, the dependencies must be installed, else you may encounter an error stating `Invalid simulator requested`. See the section :ref:`dependencies-and-compatibility` for more information about how to install dependencies.
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/basics/basics.rst b/pr-2308/sphinx/using/basics/basics.rst
deleted file mode 100644
index 308ba7492c..0000000000
--- a/pr-2308/sphinx/using/basics/basics.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-CUDA-Q Basics
-*******************
-
-.. toctree::
-   :caption: Contents
-   :maxdepth: 2
-
-      What is a CUDA-Q Kernel? <kernel_intro.rst>
-      Building your first CUDA-Q Program <build_kernel.rst>
-      Running your first CUDA-Q Program <run_kernel.rst>
-      Troubleshooting <troubleshooting.rst>
diff --git a/pr-2308/sphinx/using/basics/build_kernel.rst b/pr-2308/sphinx/using/basics/build_kernel.rst
deleted file mode 100644
index 08a8d9f9cc..0000000000
--- a/pr-2308/sphinx/using/basics/build_kernel.rst
+++ /dev/null
@@ -1,30 +0,0 @@
-Building your first CUDA-Q Program
------------------------------------------
-
-.. tab:: Python
-
-  We can define our quantum kernel as a typical Python function, with the additional use of the
-  `@cudaq.kernel` decorator. Let's begin with a simple GHZ-state example, producing a state of
-  maximal entanglement amongst an allocated set of qubits. 
-  
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-      :language: python
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-.. tab:: C++
-
-  We can define our quantum kernel as we do any other typed callable in C++, through the use of the
-  `__qpu__` annotation. For the following example, we will define a kernel for a simple GHZ-state as
-  a standard free function.
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-      :language: cpp
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-This kernel function can accept any number of arguments, allowing for flexibility in the construction
-of the quantum program. In this case, the `qubit_count` argument allows us to dynamically control the
-number of qubits allocated to the kernel. As we will see in further :doc:`examples <../examples/examples>`,
-we could also use these arguments to control various parameters of the gates themselves, such as rotation
-angles.
diff --git a/pr-2308/sphinx/using/basics/kernel_intro.rst b/pr-2308/sphinx/using/basics/kernel_intro.rst
deleted file mode 100644
index aaafcea8a5..0000000000
--- a/pr-2308/sphinx/using/basics/kernel_intro.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-What is a CUDA-Q kernel?
--------------------------------
-
-Quantum kernels are defined as functions that are executed on a quantum processing unit (QPU) or
-a simulated QPU. They generalize quantum circuits and provide a new abstraction for quantum programming.
-Quantum kernels can be combined with classical functions to create quantum-classical applications
-that can be executed on a heterogeneous system of QPUs, GPUs, and CPUs to solve real-world problems.
-
-**What’s the difference between a quantum kernel and a quantum circuit?**
-
-Every quantum circuit is a kernel, but not every quantum kernel is a circuit. For instance, a quantum
-kernel can be built up from other kernels, allowing us to interpret a large quantum program as a sequence
-of subroutines or subcircuits.  
-
-Moreover, since quantum kernels are functions, there is more expressibility available compared to a
-standard quantum circuit. We can not only parameterize the kernel, but can also contain classical control
-flow statements (`if`, `for`, `while`, etc.), and classical computations such as additions, multiplication, etc.
-Conditional statements on quantum memory and qubit measurements can be included in quantum kernels to enable 
-dynamic circuits and fast feedback, particularly useful for quantum error correction. To learn more about what
-language constructs are supported within quantum kernels, take a look at the CUDA-Q 
-:doc:`specification <../../specification/cudaq/kernels>`.
-
-**How do I build and run a quantum kernel?**
-
-Once a quantum kernel has been defined in a program, it may be called as a typical function, or can be executed
-using the `sample` or `observe` primitives.
-
-Let’s take a closer look at how to build and execute a quantum kernel with CUDA-Q.
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/basics/run_kernel.rst b/pr-2308/sphinx/using/basics/run_kernel.rst
deleted file mode 100644
index 37a82cf3b4..0000000000
--- a/pr-2308/sphinx/using/basics/run_kernel.rst
+++ /dev/null
@@ -1,258 +0,0 @@
-Running your first CUDA-Q Program
-----------------------------------------
-
-Now that you have defined your first quantum kernel, let's look at different options for how to execute it.
-In CUDA-Q, quantum circuits are stored as quantum kernels. For estimating the probability distribution of 
-a measured quantum state in a circuit, we use the ``sample`` function call, and for computing the
-expectation value of a quantum state with a given observable, we use the ``observe`` function call.
-
-Sample
-++++++++
-
-Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q `sample` call enables this.
-
-.. tab:: Python
-
-  The :func:`cudaq.sample` method takes a kernel and its arguments as inputs, and returns a :class:`cudaq.SampleResult`.
-
-.. tab:: C++
-
-  The `cudaq::sample` method takes a kernel and its arguments as inputs, and returns a `cudaq::SampleResult`.
-
-This result dictionary contains the distribution of measured states for the system.
-
-Continuing with the GHZ kernel defined in :doc:`Building Your First CUDA-Q Program <build_kernel>`,
-we will set the concrete value of our `qubit_count` to be two.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin Sample1]
-        :end-before: [End Sample1]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-        :language: cpp
-        :start-after: [Begin Sample1]
-        :end-before: [End Sample1]
-
-The code above can be run like any other program:
-
-.. tab:: Python
-
-  Assuming the program is saved in the file `sample.py`, we can execute is with the command
-
-  .. code-block:: console
-
-      python3 sample.py
-
-.. tab:: C++
-
-  Assuming the program is saved in the file `sample.cpp`, we can now compile this file with the `nvq++` toolchain, and then run the compiled executable.
-
-  .. code-block:: console
-
-      nvq++ sample.cpp
-      ./a.out
-
-By default, `sample` produces an ensemble of 1000 shots. This can be changed by specifying an integer argument
-for the `shots_count`.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin Sample2]
-        :end-before: [End Sample2]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-        :language: cpp
-        :start-after: [Begin Sample2]
-        :end-before: [End Sample2]
-
-Note that there is a subtle difference between how sample is executed with the target device set to a simulator or with the target device set to a QPU. When run on a simulator, the quantum state is built once and then sampled repeatedly, where the number of samples is defined by `shots_count`. When executed on quantum hardware, the quantum state collapses upon measurement and hence needs to be rebuilt every time to collect a sample.
-
-A variety of methods can be used to extract useful information from a `SampleResult`. For example,
-to return the most probable measurement and its respective probability:
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin Sample3]
-        :end-before: [End Sample3]
-
-  See the :doc:`API specification <../../../api/languages/python_api>` for further information.
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_sample.cpp
-        :language: cpp
-        :start-after: [Begin Sample3]
-        :end-before: [End Sample3]
-
-  See the :doc:`API specification <../../../api/languages/cpp_api>` for further information.
-
-Sampling a distribution can be a time intensive task. An asynchronous version of sample exists and can be useful to parallelize your application. Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result.
-
-Asynchronous execution allows to easily parallelize execution of multiple kernels on a multi-processor platform. Such a platform
-is available, for example, by choosing the target `nvidia-mqpu`:
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_sample.py
-        :language: python
-        :start-after: [Begin SampleAsync]
-        :end-before: [End SampleAsync]
-
-.. note::
-
-  This kind of parallelization is most effective
-  if you actually have multiple QPU or CPU available. Otherwise, the 
-  sampling will still have to execute sequentially due to resource constraints. 
-
-More information about parallelizing execution can be found at :doc:`../backends/platform` page.
-
-Observe
-+++++++++
-
-The observe function allows us to calculate expectation values for a defined quantum operator, that is the value of :math:`\bra{\psi}H\ket{\psi}`, where :math:`H` is the desired operator and :math:`\ket{\psi}` is the quantum state after executing a given kernel. 
-
-.. tab:: Python
-
-  The :func:`cudaq.observe` method takes a kernel and its arguments as inputs, along with a :class:`cudaq.SpinOperator`.
-
-  Using the `cudaq.spin` module, operators may be defined as a linear combination of Pauli strings. Functions, such
-  as :func:`cudaq.spin.i`, :func:`cudaq.spin.x`, :func:`cudaq.spin.y`, :func:`cudaq.spin.z` may be used to construct more
-  complex spin Hamiltonians on multiple qubits.
-
-.. tab:: C++
-
-  The `cudaq::observe` method takes a kernel and its arguments as inputs, along with a `cudaq::spin_op`.
-
-  Within the `cudaq::spin` namespace, operators may be defined as a linear combination of Pauli strings. Functions, such
-  as `cudaq::spin::i`, `cudaq::spin::x`, `cudaq::spin::y`, `cudaq::spin::z` may be used to construct more
-  complex spin Hamiltonians on multiple qubits.
-
-Below is an example of a spin operator object consisting of a `Z(0)` operator, or a Pauli Z-operator on the qubit zero. 
-This is followed by the construction of a kernel with a single qubit in an equal superposition. 
-The Hamiltonian is printed to confirm it has been constructed properly.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_observe.py
-        :language: python
-        :start-after: [Begin Observe1]
-        :end-before: [End Observe1]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_observe.cpp
-        :language: cpp
-        :start-after: [Begin Observe1]
-        :end-before: [End Observe1]
-
-The `observe` function takes a kernel, any kernel arguments, and a spin operator as inputs and produces an `ObserveResult` object.
-The expectation value can be printed using the `expectation` method. 
-
-.. note:: 
-  
-  It is important to exclude a measurement in the kernel, otherwise the expectation value will be determined from a collapsed 
-  classical state. For this example, the expected result of 0.0 is produced.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_observe.py
-        :language: python
-        :start-after: [Begin Observe2]
-        :end-before: [End Observe2]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_observe.cpp
-        :language: cpp
-        :start-after: [Begin Observe2]
-        :end-before: [End Observe2]
-
-Unlike `sample`, the default `shots_count` for `observe` is 1. This result is deterministic and equivalent to the
-expectation value in the limit of infinite shots.  To produce an approximate expectation value from sampling, `shots_count` can
-be specified to any integer.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/first_observe.py
-        :language: python
-        :start-after: [Begin Observe3]
-        :end-before: [End Observe3]
-
-.. tab:: C++
-
-  .. literalinclude:: ../../snippets/cpp/using/first_observe.cpp
-        :language: cpp
-        :start-after: [Begin Observe3]
-        :end-before: [End Observe3]
-
-Similar to `sample_async` above, observe also supports asynchronous execution. 
-More information about parallelizing execution can be found at 
-:doc:`../backends/platform` page.
-
-Running on a GPU
-++++++++++++++++++
-
-.. tab:: Python
-
-  Using :func:`cudaq.set_target`, different targets can be specified for kernel execution.
-
-.. tab:: C++
-
-  Using the `--target` argument to `nvq++`, different targets can be specified for kernel execution.
-
-If a local GPU is detected, the target will default to `nvidia`. Otherwise, the CPU-based simulation
-target, `qpp-cpu`,  will be selected.
-  
-We will demonstrate the benefits of using a GPU by sampling our GHZ kernel with 25 qubits and a
-`shots_count` of 1 million. Using a GPU accelerates this task by more than 35x. To learn about
-all of the available targets and ways to accelerate kernel execution, visit the
-:doc:`Backends <../backends/backends>` page.
-
-.. tab:: Python
-
-  .. literalinclude:: ../../snippets/python/using/time.py
-        :language: python
-        :start-after: [Begin Time]
-        :end-before: [End Time]
-
-.. tab:: C++
-
-  To compare the performance, we can create a simple timing script that isolates just the call
-  to `cudaq::sample`. We are still using the same GHZ kernel as earlier, but the following
-  modification made to the main function:
-
-  .. literalinclude:: ../../snippets/cpp/using/time.cpp
-    :language: cpp
-    :start-after: [Begin Time]
-    :end-before: [End Time]
-
-  First we execute on the CPU backend:
-
-  .. code:: console
-
-    nvq++ --target=qpp-cpu sample.cpp
-    ./a.out
-  
-  seeing an output of the order:
-  ``It took 22.8337 seconds.``
-
-  Now we can execute on the GPU enabled backend:
-
-  .. code:: console
-
-    nvq++ --target=nvidia sample.cpp
-    ./a.out
-
-  seeing an output of the order:
-  ``It took 3.18988 seconds.``
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/basics/troubleshooting.rst b/pr-2308/sphinx/using/basics/troubleshooting.rst
deleted file mode 100644
index 608e7086f6..0000000000
--- a/pr-2308/sphinx/using/basics/troubleshooting.rst
+++ /dev/null
@@ -1,41 +0,0 @@
-Troubleshooting
------------------
-
-Debugging and Verbose Simulation Output
-+++++++++++++++++++++++++++++++++++++++++
-
-One helpful mechanism of debugging CUDA-Q simulation execution is
-the :code:`CUDAQ_LOG_LEVEL` environment variable. For any CUDA-Q
-executable, just prepend this and turn it on:
-
-.. tab:: Python
-
-  .. code-block:: bash
-
-      CUDAQ_LOG_LEVEL=info python3 file.py
-
-.. tab:: C++
-
-    .. code-block:: bash
-
-      CUDAQ_LOG_LEVEL=info ./a.out
-
-Similarly, one may write the IR to their console or to a file before remote
-submission. This may be done through the :code:`CUDAQ_DUMP_JIT_IR` environment
-variable. For any CUDA-Q executable, just prepend as follows:
-
-.. tab:: Python
-
-  .. code-block:: bash
-
-      CUDAQ_DUMP_JIT_IR=1 python3 file.py
-      # or
-      CUDAQ_DUMP_JIT_IR=<output_filename> python3 file.py
-
-.. tab:: C++
-
-  .. code-block:: bash
-
-      CUDAQ_DUMP_JIT_IR=1 ./a.out
-      # or
-      CUDAQ_DUMP_JIT_IR=<output_filename> ./a.out
\ No newline at end of file
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diff --git a/pr-2308/sphinx/using/examples/building_kernels.rst b/pr-2308/sphinx/using/examples/building_kernels.rst
deleted file mode 100644
index 3cd3a4e324..0000000000
--- a/pr-2308/sphinx/using/examples/building_kernels.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-Building Kernels
--------------------------
-This section will cover the most basic CUDA-Q construct, a quantum kernel.
-Topics include, building kernels, initializing states, and applying gate operations.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../examples/python/building_kernels.py
-      :language: python
diff --git a/pr-2308/sphinx/using/examples/examples.rst b/pr-2308/sphinx/using/examples/examples.rst
deleted file mode 100644
index f62c32afd9..0000000000
--- a/pr-2308/sphinx/using/examples/examples.rst
+++ /dev/null
@@ -1,23 +0,0 @@
-*************************
-CUDA-Q by Example
-*************************
-
-Examples that illustrate how to use CUDA-Q for application development are available in C++ and Python.
-
-.. toctree::
-   :maxdepth: 1
-
-      Introduction <introduction.rst>
-      Building Kernels <building_kernels.rst>
-      Quantum Operations <quantum_operations.rst>
-      Measuring Kernels <../../examples/python/measuring_kernels.ipynb>
-      Visualizing Kernels <../../examples/python/visualization.ipynb>
-      Executing Kernels <../../examples/python/executing_kernels.ipynb>
-      Computing Expectation Values <expectation_values.rst>
-      Multi-Control Synthesis <multi_control.rst>
-      Multi-GPU Workflows <multi_gpu_workflows.rst>
-      Optimizers & Gradients  <../../examples/python/optimizers_gradients.ipynb>
-      Noisy Simulations <../../examples/python/noisy_simulations.ipynb>
-      Constructing Operators <../../examples/python/operators.ipynb>
-      Performance Optimizations <../../examples/python/performance_optimizations.ipynb>
-      Using Quantum Hardware Providers <hardware_providers.rst>
diff --git a/pr-2308/sphinx/using/examples/expectation_values.rst b/pr-2308/sphinx/using/examples/expectation_values.rst
deleted file mode 100644
index 254302e5ed..0000000000
--- a/pr-2308/sphinx/using/examples/expectation_values.rst
+++ /dev/null
@@ -1,66 +0,0 @@
-Computing Expectation Values
------------------------------
-
-CUDA-Q provides generic library functions enabling one to compute expectation values 
-of quantum spin operators with respect to a parameterized CUDA-Q kernel. Let's take a look 
-at an example of this:
-
-.. tab:: C++
-
-   .. literalinclude:: ../../examples/cpp/basics/expectation_values.cpp
-      :language: cpp
-
-   Here we define a parameterized CUDA-Q kernel, a callable type named :code:`ansatz` that takes as 
-   input a single angle :code:`theta`. This angle becomes the argument of a single :code:`ry` rotation. 
-
-   In host code, we define a Hamiltonian operator via the CUDA-Q :code:`spin_op` type. 
-   CUDA-Q provides a generic function :code:`cudaq::observe`. This function takes as input three terms. 
-   The first two terms are a parameterized kernel and the :code:`spin_op` whose expectation value we wish to compute.
-   The last term contains the runtime parameters at which we evaluate the parameterized kernel. 
-
-   The return type of this function is an :code:`cudaq::observe_result` which contains all the data 
-   from the execution, but is trivially convertible to a double, resulting in the expectation value we are interested in. 
-
-   To compile and execute this code, we run the following:
-
-   .. code:: bash 
-
-      nvq++ expectation_values.cpp -o exp_vals.x 
-      ./exp_vals.x 
-
-
-Parallelizing across Multiple Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-:doc:`multi-processor platforms <../backends/platform>` page.
-
-
-One typical use case of :doc:`multi-processor platforms <../backends/platform>` is to distribute the
-expectation value computations of a multi-term Hamiltonian across multiple virtual QPUs.
-
-The following shows an example using the :code:`nvidia-mqpu` platform:
-
-.. tab:: Python
-
-    .. literalinclude:: ../../snippets/python/using/cudaq/platform/observe_mqpu.py
-        :language: python
-        :start-after: [Begin Documentation]
-
-.. tab:: C++
-
-    .. literalinclude:: ../../snippets/cpp/using/cudaq/platform/observe_mqpu.cpp
-        :language: cpp
-        :start-after: [Begin Documentation]
-        :end-before: [End Documentation]
-
-
-    One can then target the :code:`nvidia-mqpu` platform by executing the following commands:
-
-    .. code-block:: console
-
-        nvq++ observe_mqpu.cpp -target nvidia-mqpu
-        ./a.out
-
-In the above code snippets, since the Hamiltonian contains four non-identity terms, there are four quantum circuits that need to be executed
-in order to compute the expectation value of that Hamiltonian and given the quantum state prepared by the ansatz kernel. When the :code:`nvidia-mqpu` platform
-is selected, these circuits will be distributed across all available QPUs. The final expectation value result is computed from all QPU execution results.
diff --git a/pr-2308/sphinx/using/examples/hardware_providers.rst b/pr-2308/sphinx/using/examples/hardware_providers.rst
deleted file mode 100644
index 6abbb8dea7..0000000000
--- a/pr-2308/sphinx/using/examples/hardware_providers.rst
+++ /dev/null
@@ -1,142 +0,0 @@
-Using Quantum Hardware Providers
------------------------------------
-
-CUDA-Q contains support for using a set of hardware providers (Amazon Braket, 
-Infleqtion, IonQ, IQM, OQC, ORCA Computing, Quantinuum, and QuEra Computing). 
-For more information about executing quantum kernels on different hardware 
-backends, please take a look at :doc:`hardware <../backends/hardware>`.
-
-Amazon Braket
-==================================
-
-The following code illustrates how to run kernels on Amazon Braket's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/braket.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/braket.cpp
-      :language: cpp
-
-Infleqtion
-==================================
-
-The following code illustrates how to run kernels on Infleqtion's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/infleqtion.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/infleqtion.cpp
-      :language: cpp
-
-IonQ
-==================================
-
-The following code illustrates how to run kernels on IonQ's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/ionq.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/ionq.cpp
-      :language: cpp
-
-IQM
-==================================
-
-The following code illustrates how to run kernels on IQM's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/iqm.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/iqm.cpp
-      :language: cpp
-
-OQC
-==================================
-
-The following code illustrates how to run kernels on OQC's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/oqc.py
-      :language: python
-
-ORCA Computing
-==================================
-
-The following code illustrates how to run kernels on ORCA Computing's backends.
-
-ORCA Computing's PT Series implement the boson sampling model of quantum computation, in which 
-multiple photons are interfered with each other within a network of beam splitters, and photon 
-detectors measure where the photons leave this network.
-
-The following image shows the schematic of a Time Bin Interferometer (TBI) boson sampling experiment 
-that runs on ORCA Computing's backends. A TBI uses optical delay lines with reconfigurable coupling 
-parameters. A TBI can be represented by a circuit diagram, like the one below, where this 
-illustration example corresponds to 4 photons in 8 modes sent into alternating time-bins in a circuit 
-composed of two delay lines in series. 
-
-.. image:: ./images/orca_tbi.png
-   :width: 400px
-   :align: center
-
-The parameters needed to define the time bin interferometer are the the input state, the loop 
-lengths, beam splitter angles, and optionally the phase shifter angles, and the number of samples.
-The *input state* is the initial state of the photons in the time bin interferometer, 
-the left-most entry corresponds to the first mode entering the loop.
-The *loop lengths* are the lengths of the different loops in the time bin interferometer.
-The *beam splitter angles* and the phase shifter angles are controllable
-parameters of the time bin interferometer.
-
-This experiment is performed on ORCA's backends by the code below.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/orca.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/orca.cpp
-      :language: cpp
-      
-Quantinuum
-==================================
-
-The following code illustrates how to run kernels on Quantinuum's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/quantinuum.py
-      :language: python
-
-.. tab:: C++
-
-   .. literalinclude:: ../../targets/cpp/quantinuum.cpp
-      :language: cpp
-
-QuEra Computing
-==================================
-
-The following code illustrates how to run kernels on QuEra's backends.
-
-.. tab:: Python
-
-   .. literalinclude:: ../../targets/python/quera_basic.py
-      :language: python
-
diff --git a/pr-2308/sphinx/using/examples/images/orca_tbi.png b/pr-2308/sphinx/using/examples/images/orca_tbi.png
deleted file mode 100644
index 10907b1aa0..0000000000
Binary files a/pr-2308/sphinx/using/examples/images/orca_tbi.png and /dev/null differ
diff --git a/pr-2308/sphinx/using/examples/introduction.rst b/pr-2308/sphinx/using/examples/introduction.rst
deleted file mode 100644
index a12fb6cdad..0000000000
--- a/pr-2308/sphinx/using/examples/introduction.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-Introduction
---------------------------------
-
-Welcome to CUDA-Q! On this page we will illustrate CUDA-Q with several examples. 
-
-.. tab:: Python
-
-   We're going to take a look at how to construct quantum programs through CUDA-Q's `Kernel` API.
-
-   When you create a `Kernel` and invoke its methods, a quantum program is constructed that can then be executed by calling, for example, `cudaq::sample`. Let's take a closer look!
-
-   .. literalinclude:: ../../examples/python/intro.py
-      :language: python
-
-.. tab:: C++
-
-   We're going to take a look at how to construct quantum programs using CUDA-Q kernels.
-
-   CUDA-Q kernels are any typed callable in the language that is annotated with the :code:`__qpu__` attribute. Let's take a look at a very 
-   simple "Hello World" example, specifically a CUDA-Q kernel that prepares a GHZ state on a programmer-specified number of qubits. 
-
-   .. literalinclude:: ../../examples/cpp/basics/static_kernel.cpp
-      :language: cpp
-
-   Here we see that we can define a custom :code:`struct` that is templated on a :code:`size_t` parameter. 
-   Our kernel expression is free to use this template parameter in the allocation of a 
-   compile-time-known register of qubits. Within the kernel, we are free to apply various quantum operations, 
-   like a Hadamard on qubit 0 :code:`h(q[0])`. Controlled operations are **modifications** of single-qubit 
-   operations, like the :code:`x<cudaq::ctrl>(q[0],q[1])` operation which implements a controlled-X gate. We 
-   can measure single qubits or entire registers. 
-
-   In this example we are interested in sampling the final state produced by this CUDA-Q kernel. 
-   To do so, we leverage the generic :code:`cudaq::sample` function, which returns a data type 
-   encoding the qubit measurement strings and the corresponding number of times that string 
-   was observed (here the default number of shots is used, :code:`1000`).
-
-   The following example illustrates how to compile and execute this code.
-
-   .. code:: bash 
-
-      nvq++ static_kernel.cpp -o ghz.x
-      ./ghz.x
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/examples/multi_control.rst b/pr-2308/sphinx/using/examples/multi_control.rst
deleted file mode 100644
index 045e367488..0000000000
--- a/pr-2308/sphinx/using/examples/multi_control.rst
+++ /dev/null
@@ -1,30 +0,0 @@
-Multi-control Synthesis
--------------------------
-Now let's take a look at how CUDA-Q allows one to control a general unitary 
-on an arbitrary number of control qubits. 
-
-.. tab:: Python
-
-   Our first option is to describe our general unitary by another pre-defined
-   CUDA-Q kernel.
-
-   .. literalinclude:: ../../examples/python/multi_controlled_operations.py
-      :language: python
-      :start-after: [Begin OptionA]
-      :end-before: [End OptionA]
-
-   Alternatively, one may pass multiple arguments for control qubits or vectors
-   to any controlled operation.
-
-   .. literalinclude:: ../../examples/python/multi_controlled_operations.py
-      :language: python
-      :start-after: [Begin OptionB]
-      :end-before: [End OptionB]
-
-.. tab:: C++ 
-
-   For this scenario, our general unitary can be described by another pre-defined 
-   CUDA-Q kernel expression. 
-
-   .. literalinclude:: ../../examples/cpp/basics/multi_controlled_operations.cpp
-      :language: cpp
diff --git a/pr-2308/sphinx/using/examples/multi_gpu_workflows.rst b/pr-2308/sphinx/using/examples/multi_gpu_workflows.rst
deleted file mode 100644
index 4cdf2504aa..0000000000
--- a/pr-2308/sphinx/using/examples/multi_gpu_workflows.rst
+++ /dev/null
@@ -1,155 +0,0 @@
-Multi-GPU Workflows
-===================
-
-There are many backends available with CUDA-Q which enable seamless
-switching between GPUs, QPUs and CPUs and also allow for workflows
-involving multiple architectures working in tandem.
-
-Available Targets
--------------------
-
--  **`qpp-cpu`**: The QPP based CPU backend which is multithreaded to
-   maximize the usage of available cores on your system.
-
--  **`nvidia`**: GPU-accelerated state-vector based backend which accelerates quantum circuit
-   simulation on NVIDIA GPUs powered by cuQuantum.
-
--  **`nvidia-mgpu`**: Allows for scaling circuit simulation on multiple GPUs.
-
--  **`nvidia-mqpu`**: Enables users to program workflows utilizing
-   multiple virtual quantum processors in parallel, where each QPU is simulated by the `nvidia` backend.
-
--  **`remote-mqpu`**: Enables users to program workflows utilizing
-   multiple virtual quantum processors in parallel, where the backend used to simulate each QPU is configurable.
-
-Please see :doc:`../backends/backends` for a full list of all available backends. 
-Below we explore how to effectively utilize multiple CUDA-Q targets with the same GHZ state preparation code
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
-    :language: python
-    :start-after: [Begin state]
-    :end-before: [End state]
-
-You can execute the code by running a statevector simulator on your CPU:
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
-    :language: python
-    :start-after: [Begin CPU]
-    :end-before: [End CPU]
-
-.. parsed-literal::
-
-    { 00:475 11:525 }
-
-You will notice a speedup of up to **2500x** in executing the circuit below on
-NVIDIA GPUs vs CPUs:
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
-    :language: python
-    :start-after: [Begin GPU]
-    :end-before: [End GPU]
-
-.. parsed-literal::
-
-    { 0000000000000000000000000:510 1111111111111111111111111:490 }
-
-If one incrementally increases the qubit count, we
-reach a limit where the memory required is beyond the capabilities of a
-single GPU: A :math:`n` qubit quantum state has :math:`2^n` complex amplitudes, each
-of which require 8 bytes of memory to store. Hence the total memory
-required to store a :math:`n` qubit quantum state is :math:`8` bytes
-:math:`\times 2^n`. For :math:`n = 30` qubits, this is roughly :math:`8`
-GB but for :math:`n = 40`, this exponentially increases to 8700 GB.
-
-
-Parallelization across Multiple Processors
----------------------------------------------
-
-The ``nvidia-mgpu`` target allows for memory from additional
-GPUs to be pooled enabling qubit counts to be scaled.
-Execution on the ``nvidia-mgpu`` backend is enabled via ``mpirun``. Users
-need to create a ``.py`` file with their code and run the command below
-in terminal:
-
-``mpirun -np 4 python3 test.py``
-
-where 4 is the number of GPUs one has access to and ``test`` is the file
-name chosen.
-
-The ``nvidia-mqpu`` target uses a statevector simulator to simulate execution 
-on each virtual QPU.
-The ``remote-mqpu`` platform allows to freely configure what backend is used 
-for each platform QPU. 
-For more information about the different platform targets, please take a look at
-:doc:`../backends/platform`.
-
-Batching Hamiltonian Terms
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Expectation value computations of multi-term Hamiltonians can be
-asynchronously processed via the ``mqpu`` platform.
-
-.. image:: ../../applications/python/images/hsplit.png
-
-For workflows involving multiple GPUs, save the code below in a
-``filename.py`` file and execute via:
-``mpirun -np n python3 filename.py`` where ``n`` is an integer
-specifying the number of GPUs you have access to.
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/hamiltonian_batching.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    mpi is initialized?  True
-    rank 0 num_ranks 1
-
-
-Circuit Batching
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Execution of parameterized circuits with different parameters can be
-executed asynchronously via the ``mqpu`` platform.
-
-.. image:: ../../applications/python/images/circsplit.png
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin prepare]
-    :end-before: [End prepare]
-
-Let's time the execution on single GPU.
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin single]
-    :end-before: [End single]
-
-.. parsed-literal::
-
-    31.7 s ± 990 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
-
-Now let's try to time multi GPU run.
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin split]
-    :end-before: [End split]
-
-.. parsed-literal::
-
-    We have 10000 parameters which we would like to execute
-    We split this into 4 batches of 2500 , 2500 , 2500 , 2500
-
-
-.. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/circuit_batching.py
-    :language: python
-    :start-after: [Begin multiple]
-    :end-before: [End multiple]
-
-.. parsed-literal::
-
-    85.3 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
-
diff --git a/pr-2308/sphinx/using/examples/quantum_operations.rst b/pr-2308/sphinx/using/examples/quantum_operations.rst
deleted file mode 100644
index 3ce35eb098..0000000000
--- a/pr-2308/sphinx/using/examples/quantum_operations.rst
+++ /dev/null
@@ -1,175 +0,0 @@
-Quantum Computing 101
-======================
-
-The fundamental unit of classical information storage, processing and
-transmission is the bit. Analogously, we define its quantum counterpart,
-a quantum bit or simply the qubit.
-
-Classical bits are transistor elements whose states can be altered to
-perform computations. Similarly qubits too have physical realizations
-within superconducting materials, ion-traps and photonic systems. We
-shall not concern ourselves with specific qubit architectures but rather
-think of them as systems which obey the laws of quantum mechanics and
-the mathematical language physicists have developed to describe the
-theory: linear algebra.
-
-Quantum States
------------------------------
-
-Information storage scales linearly if bits have a single state. Access
-to multiple states, namely a 0 and a 1 allows for information encoding
-to scale logarithmically. Similarly we define a qubit to have the states
-:math:`\ket{0}` and :math:`\ket{1}` in Dirac notation where:
-
-.. math:: \ket{0} = \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix}
-
-.. math:: \ket{1} = \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}
-
-Rather than just the two states each classical bit can be in,
-quantum theory allows one to explore linear combinations of states,
-also called superpositions:
-
-.. math::   \ket{\psi} = \alpha\ket{0} + \beta\ket{1} 
-
-where :math:`\alpha` and :math:`\beta` :math:`\in \mathbb{C}`. It is
-important to note that this is still the state of one qubit; 
-although we have two kets, they both represent a
-superposition state of one qubit.
-
-If we have two classical bits, the possible states we could encode
-information in would be 00, 01, 10 and 11. Correspondingly, multiple
-qubits can be combined and the possible combinations of their states
-used to process information.
-
-A two qubit system has four computational basis states:
-:math:`\ket{00}, \ket{01}, \ket{10}, \ket{11}`.
-
-More generally, the quantum state of a :math:`n` qubit system is written
-as a sum of :math:`2^n` possible basis states where the coefficients
-track the probability of the system collapsing into that state if a
-measurement is applied.
-
-For :math:`n = 500`, :math:`2^n \approx 10^{150}` which is greater than
-the number of atoms in the universe. Storing the complex numbers
-associated with :math:`2^{500}` amplitudes would not be feasible using
-bits and classical computations but nature seems to only require 500
-qubits to do so. The art of quantum computation is thus to build quantum
-systems that we can manipulate with fine precision such that evolving a
-large statevector can be offloaded onto a quantum computer.
-
-
-Quantum Gates
------------------------------
-
-We can manipulate the state of a qubit via quantum gates. 
-For example, the Pauli X gate allows us to flip the state of the qubit:
-
-.. math::  X \ket{0} = \ket{1} 
-
-.. math::  \begin{bmatrix} 0 & 1 \\ 1 & 0 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix} 
-
-.. literalinclude:: ../../snippets/python/using/examples/pauli_x_gate.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    { 1:1000 }
-
-The Hadamard gate allows us to put the qubit in an equal superposition
-state:
-
-.. math::  H \ket{0} =  \tfrac{1}{\sqrt{2}} \ket{0} + \tfrac{1}{\sqrt{2}} \ket{1}  \equiv \ket{+}
-
-.. math:: \tfrac{1}{\sqrt{2}}\begin{bmatrix} 1 & 1 \\ 1 & -1 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} = \tfrac{1}{\sqrt{2}} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} + \tfrac{1}{\sqrt{2}} \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}. 
-
-The probability of finding the qubit in the :math:`\ket{0}` or :math:`\ket{1}` state is hence
-:math:`\lvert \tfrac{1}{\sqrt{2}} \rvert ^2 = \tfrac{1}{2}`. Lets verify
-this with some code:
-
-.. literalinclude:: ../../snippets/python/using/examples/hadamard_gate.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    { 0:502 1:498 }
-
-For a qubit in a superposition state, quantum gates
-act linearly:
-
-.. math::    X (\alpha\ket{0} + \beta\ket{1}) = \alpha\ket{1} + \beta\ket{0} 
-
-As we evolve quantum states via quantum gates, the normalization
-condition requires that the sum of modulus squared of amplitudes must
-equal 1 at all times:
-
-.. math::   \ket{\psi} = \alpha\ket{0} + \beta\ket{1},          |\alpha|^2 + |\beta|^2 = 1. 
-
-This is to adhere to the conservation of probabilities which translates
-to a constraint on types of quantum gates we can define.
-For a general quantum state :math:`\ket{\psi}`, upholding the
-normalization condition requires quantum gates to be unitary, that is
-:math:`U^{\dagger}U = U^{*^{T}}U = \mathbb{I}`.
-
-Just like the single-qubit gates above, we can define 
-multi-qubit gates to act on multiple-qubits.
-The controlled-NOT or CNOT gate, for example, acts on 2 qubits: the control qubit and
-the target qubit. Its effect is to flip the target if the control is in
-the excited :math:`\ket{1}` state.
-
-.. literalinclude:: ../../snippets/python/using/examples/cnot_gate.py
-    :language: python
-    :start-after: [Begin Docs]
-    :end-before: [End Docs]
-
-.. parsed-literal::
-
-    { 11:1000 }
-    
-
-The CNOT gate in matrix notation is represented as
-
-.. math::  CNOT \equiv \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 1 & 0 \end{bmatrix} 
-
-and one can check that :math:`CNOT^\dagger CNOT = \mathbb{I}`.
-Its effect on the computational basis states is:
-
-.. math::  CNOT\ket{00} = \ket{00} 
-
-.. math::  CNOT\ket{01} = \ket{01} 
-
-.. math::  CNOT\ket{10} = \ket{11} 
-
-.. math::  CNOT\ket{11} = \ket{10} 
-
-For a full list of gates supported in CUDA-Q see :doc:`../../api/default_ops`. 
-
-Measurements
------------------------------
-
-Quantum theory is probabilistic and hence requires statistical inference
-to derive observations. Prior to measurement, the state of a qubit is
-all possible combinations of :math:`\alpha` and :math:`\beta` and upon
-measurement, wavefunction collapse yields either a classical 0 or 1.
-
-The mathematical theory devised to explain quantum phenomena tells us
-that the probability of observing the qubit in the state
-:math:`\ket{0}` or :math:`\ket{1}`, yielding a classical 0 or 1, is
-:math:`\lvert \alpha \rvert ^2` or :math:`\lvert \beta \rvert ^2`, respectively. 
-
-As we see in the example of the Hadamard gate above,
-the result 0 or 1 each is yielded roughly 50% of the times as predicted 
-by the postulate stated above thus proving the theory.
-
-Classically, we cannot encode information within states such as 00 + 11
-but quantum mechanics allows us to write linear superpositions
-
-.. math::   \ket{\psi} = \alpha_{00}\ket{00} + \alpha_{01}\ket{01} + \alpha_{10}\ket{10} + \alpha_{11}\ket{11}
-
-where the probability of measuring :math:`x = 00, 01, 10, 11` occurs
-with probability :math:`\lvert \alpha_{x} \rvert ^2` with the
-normalization condition that
-:math:`\sum_{x \in \{ 0,1 \}^2} \lvert \alpha_{x} \rvert ^2 = 1`.
diff --git a/pr-2308/sphinx/using/extending/_noise.rst b/pr-2308/sphinx/using/extending/_noise.rst
deleted file mode 100644
index 0ff2132811..0000000000
--- a/pr-2308/sphinx/using/extending/_noise.rst
+++ /dev/null
@@ -1,114 +0,0 @@
-Noise Modeling with CUDA-Q
-********************************
-
-CUDA-Q models incoherent noise via quantum channels, linear 
-completely-positive and trace-preserving (CPTP) maps. Specifically 
-CUDA-Q requires the definition of a quantum channel via Kraus operators
-that satisfy the CPTP condition (:math:`\sum_i K_i^\dagger K_i = \mathbf{I}`). 
-
-Noise channels are composed of a list of Kraus operators and can be 
-assigned for application after the application of a quantum operation. These 
-channels can be stacked - multiple noise processes can be appended 
-for a given quantum operation. These channels are specified to be applied to 
-one or many qubits for a given operation. A Kraus channel should be able to 
-be applied to a single qubit operation, or a multi-qubit, controlled operation. 
-
-These channels transform the underlying density matrix representation of the quantum 
-state in the following way 
-
-.. math::
-
-    \rho_{new} = \sum_i K_i \rho K_i^\dagger. 
-
-Design 
-------
-
-To model this in CUDA-Q, we put forward :code:`cudaq::kraus_op`, :code:`cudaq::kraus_channel`, 
-and :code:`cudaq::noise_model`. 
-
-A :code:`cudaq::kraus_op` encapsulates a :code:`vector` of :code:`complex` 
-data modeling a 1-D flatten representation of a matrix with row-major ordering. It 
-can be constructed from a :code:`vector` of data or an :code:`initializer_list` of data.
-
-A :code:`cudaq::kraus_channel` encapsulates a :code:`vector` of :code:`kraus_op` instances that 
-make up the quantum channel. The size of a :code:`kraus_channel` 
-is the number of :code:`kraus_ops` it contains. :code:`kraus_channel` instances can be constructed 
-from a variadic list of :code:`initializer_list` that enables :code:`kraus_op` construction in place. 
-It can also be constructed directly from a vector of :code:`kraus_ops`. The 
-constructor should validate the completeness (CPTP) relation. 
-
-A :code:`cudaq::noise_model` encapsulates a mapping of quantum operation names to a 
-vector of :code:`kraus_channel` that is to be applied after invocation of that 
-quantum operation. A :code:`noise_model` can be constructed with a nullary constructor, and 
-:code:`kraus_channels` can be added via :code:`add_channel` and :code:`add_all_qubit_channel` methods with 
-the operation given as a string or as a template argument. 
-The operation name or the template type specifies the quantum operation the channel applies to 
-(e.g. :code:`model.add_channel\<cudaq::types::h\>(channel)` or :code:`model.add_channel("h", channel)`). 
-Clients (e.g. simulator backends) can retrieve the :code:`kraus_channel` to 
-apply to the simulated state via a :code:`noise_model::get_channel(...)` call. 
-
-When adding an error channel to a noise model for a quantum operation
-we can assign the noise channel to instances of that operation on specific qubit operands or 
-to any occurrence of the operation, regardless of which qubits it acts on. 
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        # Add a noise channel to z gate on qubit 0
-        noise.add_channel('z', [0], noise_channel)
-        # Add a noise channel to x gate, regardless of qubit operands. 
-        noise.add_all_qubit_channel('x', noise_channel)
-
-
-.. tab:: C++
-
-    .. code-block:: cpp
-        
-        // Add a noise channel to z gate on qubit 0
-        noise.add_channel("z", {0}, noise_channel);
-        // Add a noise channel to x gate, regardless of qubit operands. 
-        noise.add_all_qubit_channel("x", noise_channel)
-
-In addition to static noise channels, users can also define a noise channel as a 
-callback function, which returns a concrete channel definition in terms of Kraus matrices 
-depending on the gate operands and gate parameters if any.
-
-.. tab:: Python
-
-    .. code-block:: python
-
-        # Noise channel callback function
-        def noise_cb(qubits, params):
-           # Construct a channel based on specific operands and parameters
-           ...
-           return noise_channel 
-        
-        # Add a dynamic noise channel to the 'rx' gate.
-        noise.add_channel('rx', noise_cb)
-
-
-.. tab:: C++
-
-    .. code-block:: cpp
-        
-        // Add a dynamic noise channel to the 'rx' gate.
-        noise.add_channel("rx",
-            [](const auto &qubits, const auto &params) -> cudaq::kraus_channel {
-                // Construct a channel based on specific operands and parameters
-                ...
-                return noiseChannel;
-            });
-
-
-Noise models can be constructed via the :code:`cudaq::noise_model` and specified for 
-execution via a public :code:`cudaq::set_noise(cudaq::noise_model&)` function. This function 
-should forward the :code:`noise_model` to the current :code:`quantum_platform` which can attach it 
-to the current :code:`ExecutionContext` and retrieved by backend simulators. The
-:code:`noise_model` must stay in scope in order to be successfully used by the
-backend simulators, and you must call :code:`cudaq::unset_noise()` when you are
-done with the noise model.
-
-The :code:`kraus_op` matrix data assumes a LSB qubit ordering. 
-
-To get started with noise modeling in CUDA-Q, take a look at the :doc:`/using/examples`.
diff --git a/pr-2308/sphinx/using/extending/cudaq_ir.rst b/pr-2308/sphinx/using/extending/cudaq_ir.rst
deleted file mode 100644
index da32cb9233..0000000000
--- a/pr-2308/sphinx/using/extending/cudaq_ir.rst
+++ /dev/null
@@ -1,260 +0,0 @@
-Working with the CUDA-Q IR
-********************************
-Let's see the output of :code:`nvq++` in verbose mode. Consider a simple code like the one below, saved to file :code:`simple.cpp`.
-
-.. code-block:: console 
-
-    #include <cudaq.h>
-
-    struct ghz {
-      void operator()(int N) __qpu__ {
-        cudaq::qvector q(N);
-        h(q[0]);
-        for (int i = 0; i < N - 1; i++) {
-          x<cudaq::ctrl>(q[i], q[i + 1]);
-        }
-        mz(q);
-      }
-    };
-
-    int main() { ... }
-
- We see the following output from :code:`nvq++` verbose mode (up to some absolute paths).
-
-.. code-block:: console 
-
-    $ nvq++ simple.cpp -v -save-temps
-    
-    cudaq-quake --emit-llvm-file simple.cpp -o simple.qke
-    cudaq-opt --pass-pipeline=builtin.module(canonicalize,lambda-lifting,canonicalize,apply-op-specialization,kernel-execution,indirect-to-direct-calls,inline,func.func(quake-add-metadata),device-code-loader{use-quake=1},expand-measurements,func.func(lower-to-cfg),canonicalize,cse) simple.qke -o simple.qke.LpsXpu
-    cudaq-translate --convert-to=qir simple.qke.LpsXpu -o simple.ll.p3De4L
-    fixup-linkage.pl simple.qke simple.ll
-    llc --relocation-model=pic --filetype=obj -O2 simple.ll.p3De4L -o simple.qke.o
-    llc --relocation-model=pic --filetype=obj -O2 simple.ll -o simple.classic.o
-    clang++ -L/usr/lib/gcc/x86_64-linux-gnu/12 -L/usr/lib64 -L/lib/x86_64-linux-gnu -L/lib64 -L/usr/lib/x86_64-linux-gnu -L/lib -L/usr/lib -L/usr/local/cuda/lib64/stubs -r simple.qke.o simple.classic.o -o simple.o
-    clang++ -Wl,-rpath,lib -Llib -L/usr/lib/gcc/x86_64-linux-gnu/12 -L/usr/lib64 -L/lib/x86_64-linux-gnu -L/lib64 -L/usr/lib/x86_64-linux-gnu -L/lib -L/usr/lib -L/usr/local/cuda/lib64/stubs simple.o -lcudaq -lcudaq-common -lcudaq-mlir-runtime -lcudaq-builder -lcudaq-ensmallen -lcudaq-nlopt -lcudaq-spin -lcudaq-em-default -lcudaq-platform-default -lnvqir -lnvqir-qpp
-
-This workflow orchestration is represented in the figure below: 
-
-.. image:: ../../_static/nvqpp_workflow.png
-
-We start by mapping CUDA-Q C++ kernel representations (structs, lambdas, and free functions) 
-to the Quake dialect. Since we added :code:`-save-temps`, 
-we can look at the IR code that was produced. The base Quake file, :code:`simple.qke`, contains the following: 
-
-.. code-block:: console 
-
-    module attributes {qtx.mangled_name_map = {__nvqpp__mlirgen__ghz = "_ZN3ghzclEi"}} {
-        func.func @__nvqpp__mlirgen__ghz(%arg0: i32) attributes {"cudaq-entrypoint", "cudaq-kernel"} {
-           %alloca = memref.alloca() : memref<i32>
-           memref.store %arg0, %alloca[] : memref<i32>
-           %0 = memref.load %alloca[] : memref<i32>
-           %1 = arith.extsi %0 : i32 to i64
-           %2 = quake.alloca(%1 : i64) !quake.veq<?>
-           %c0_i32 = arith.constant 0 : i32
-           %3 = arith.extsi %c0_i32 : i32 to i64
-           %4 = quake.extract_ref %2[%3] : (!quake.veq<?>, i64) -> !quake.ref
-           quake.h %4 : (!quake.ref) -> ()
-           cc.scope {
-            %c0_i32_0 = arith.constant 0 : i32
-            %alloca_1 = memref.alloca() : memref<i32>
-            memref.store %c0_i32_0, %alloca_1[] : memref<i32>
-            cc.loop while {
-                %6 = memref.load %alloca_1[] : memref<i32>
-                %7 = memref.load %alloca[] : memref<i32>
-                %c1_i32 = arith.constant 1 : i32
-                %8 = arith.subi %7, %c1_i32 : i32
-                %9 = arith.cmpi slt, %6, %8 : i32
-                cc.condition %9
-            } do {
-              cc.scope {
-                %6 = memref.load %alloca_1[] : memref<i32>
-                %7 = arith.extsi %6 : i32 to i64
-                %8 = quake.extract_ref %2[%7] : (!quake.veq<?>, i64) -> !quake.ref
-                %9 = memref.load %alloca_1[] : memref<i32>
-                %c1_i32 = arith.constant 1 : i32
-                %10 = arith.addi %9, %c1_i32 : i32
-                %11 = arith.extsi %10 : i32 to i64
-                %12 = quake.extract_ref %2[%11] : (!quake.veq<?>, i64) -> !quake.ref
-                quake.x [%8] %12 : (!quake.ref, !quake.ref) -> ()
-              }
-              cc.continue
-            } step {
-                %6 = memref.load %alloca_1[] : memref<i32>
-                %c1_i32 = arith.constant 1 : i32
-                %7 = arith.addi %6, %c1_i32 : i32
-                memref.store %7, %alloca_1[] : memref<i32>
-            }
-            }
-            %5 = quake.mz %2 : (!quake.veq<?>) -> !cc.stdvec<i1>
-            return
-        }
-    }
-
-This base Quake file is unoptimized and unchanged. It is produced by the 
-:code:`cudaq-quake` tool, which also allows us to output the full LLVM IR representation 
-for the code. This LLVM IR is classical-only, and is directly produced by :code:`clang++` 
-code-generation. The LLVM IR file :code:`simple.ll` contains the CUDA-Q kernel 
-:code:`operator()(Args...)` LLVM function, with a mangled name. Ultimately, we 
-want to replace this function with our own MLIR-generated function. 
-
-Next, the :code:`cudaq-opt` tool is invoked on the :code:`simple.qke` file. This runs an
-MLIR pass pipeline that canonicalizes and optimizes the code. It will also process quantum 
-lambdas, lift those lambdas to functions, and synthesis adjoint and controlled versions of 
-CUDA-Q kernel functions if necessary. The most important pass that this step applies is the 
-:code:`kernel-execution` pass, which synthesizes a new entry point LLVM function with the 
-same name and signature as the original :code:`operator()(Args...)` call function in the 
-classical :code:`simple.ll` file. We also extract all Quake code representations as strings
-and register them with the CUDA-Q runtime for runtime IR introspection. 
-
-After :code:`cudaq-opt`, the :code:`cudaq-translate` tool is used to lower the transformed 
-Quake representation to an LLVM IR representation, specifically the QIR. We finish by lowering 
-this representation to object code via standard LLVM tools (e.g. :code:`llc`), and merge all 
-object files into a single object file, ensuring that our new mangled :code:`operator()(Args...)` 
-call is injected first, thereby overwriting the original. Finally, based on user compile flags, 
-we configure the link line with specific libraries that implement the :code:`quantum_platform` 
-(here the :code:`libcudaq-platform-default.so`) and NVQIR circuit simulator backend (the 
-:code:`libnvqir-qpp.so` Q++ CPU-only simulation backend). These latter libraries are controlled 
-via the :code:`--platform` and :code:`--target` compiler flags. 
-
-.. image:: ../../_static/dialects.png
-
-The above figure demonstrate the MLIR dialects involved and the overall workflow mapping 
-high-level language constructs to lower-level MLIR dialect code, and ultimately LLVM IR. 
-
-CUDA-Q also provides value-semantics form of Quake for static circuit
-representation. This dialect directly enables robust circuit 
-optimizations via data-flow analysis of the representative circuit. This dialect 
-is typically produced just-in-time when the structure of the circuit is fully known. 
-
-You will notice that there are a number of CUDA-Q executable tools installed as part 
-of this open beta release. These tools are directly related to the generation, 
-processing, optimization, and lowering of the core :code:`nvq++` compiler representations.
-The tools available are 
-
-1. :code:`cudaq-quake` - Lower C++ to Quake, can also output classical LLVM IR file
-2. :code:`cudaq-opt` - Process Quake with various MLIR Passes
-3. :code:`cudaq-translate` - Lower Quake to external representations like QIR
-
-CUDA-Q and :code:`nvq++` rely on Quake for the core quantum intermediate representation.
-Quake represents an IR closer to the CUDA-Q source language and models qubits and
-quantum instructions via memory semantics. Quake can be fully dynamic and in
-that sense represents a quantum circuit template or generator. With runtime 
-arguments fully specified, Quake code can be used to generate or synthesize
-a fully known quantum circuit. The value semantics form of Quake can thus be
-used as a representation for fully known
-or synthesized quantum circuits. Its utility, therefore, lies in its ability to 
-optimize quantum code. It departs from the memory semantics model of Quake and 
-expresses the flow of quantum information explicitly as MLIR values.
-This approach makes it easier for finding circuit patterns and leveraging it for common 
-optimization tasks. 
-
-To demonstrate how these tools work together, let's take the simple GHZ CUDA-Q 
-program and lower the kernel from C++ to Quake, synthesize that Quake code, 
-and produce QIR. Recall the code snippet for the kernel
-
-.. code-block:: cpp 
-
-    // Define a quantum kernel
-    struct ghz {
-      auto operator()() __qpu__ {
-        cudaq::qarray<5> q;
-        h(q[0]);
-        for (int i = 0; i < 4; i++) 
-          x<cudaq::ctrl>(q[i], q[i + 1]);
-        mz(q);
-      }
-    };
-
-Using the toolchain, we can lower this directly to QIR,
-
-.. code-block:: console
-
-    cudaq-quake simple.cpp | cudaq-opt --canonicalize | cudaq-translate --convert-to=qir 
-
-which prints: 
-
-.. code-block:: console 
-
-    ; ModuleID = 'LLVMDialectModule'
-    source_filename = "LLVMDialectModule"
-    target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128"
-    target triple = "x86_64-unknown-linux-gnu"
-
-    %Array = type opaque
-    %Qubit = type opaque
-    %Result = type opaque
-
-    declare void @invokeWithControlQubits(i64, void (%Array*, %Qubit*)*, ...) local_unnamed_addr
-
-    declare void @__quantum__qis__x__ctl(%Array*, %Qubit*)
-
-    declare %Result* @__quantum__qis__mz(%Qubit*) local_unnamed_addr
-
-    declare void @__quantum__rt__qubit_release_array(%Array*) local_unnamed_addr
-
-    declare i64 @__quantum__rt__array_get_size_1d(%Array*) local_unnamed_addr
-
-    declare void @__quantum__qis__h(%Qubit*) local_unnamed_addr
-
-    declare i8* @__quantum__rt__array_get_element_ptr_1d(%Array*, i64) local_unnamed_addr
-
-    declare %Array* @__quantum__rt__qubit_allocate_array(i64) local_unnamed_addr
-
-    define void @__nvqpp__mlirgen__ghz(i32 %0) local_unnamed_addr {
-      %2 = sext i32 %0 to i64
-      %3 = tail call %Array* @__quantum__rt__qubit_allocate_array(i64 %2)
-      %4 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 0)
-      %5 = bitcast i8* %4 to %Qubit**
-      %6 = load %Qubit*, %Qubit** %5, align 8
-      tail call void @__quantum__qis__h(%Qubit* %6)
-      %7 = add i32 %0, -1
-      %8 = icmp sgt i32 %7, 0
-      br i1 %8, label %.lr.ph.preheader, label %._crit_edge
-
-    .lr.ph.preheader:                                 ; preds = %1
-      %wide.trip.count = zext i32 %7 to i64
-      br label %.lr.ph
-
-    .lr.ph:                                           ; preds = %.lr.ph.preheader, %.lr.ph
-      %indvars.iv = phi i64 [ 0, %.lr.ph.preheader ], [ %indvars.iv.next, %.lr.ph ]
-      %9 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 %indvars.iv)
-      %10 = bitcast i8* %9 to %Qubit**
-      %11 = load %Qubit*, %Qubit** %10, align 8
-      %indvars.iv.next = add nuw nsw i64 %indvars.iv, 1
-      %12 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 %indvars.iv.next)
-      %13 = bitcast i8* %12 to %Qubit**
-      %14 = load %Qubit*, %Qubit** %13, align 8
-      tail call void (i64, void (%Array*, %Qubit*)*, ...) @invokeWithControlQubits(i64 1, void (%Array*, %Qubit*)* nonnull @__quantum__qis__x__ctl, %Qubit* %11, %Qubit* %14)
-      %exitcond.not = icmp eq i64 %indvars.iv.next, %wide.trip.count
-      br i1 %exitcond.not, label %._crit_edge, label %.lr.ph
-
-    ._crit_edge:                                      ; preds = %.lr.ph, %1
-      %15 = tail call i64 @__quantum__rt__array_get_size_1d(%Array* %3)
-      %16 = icmp sgt i64 %15, 0
-      br i1 %16, label %.lr.ph3, label %._crit_edge4
-
-    .lr.ph3:                                          ; preds = %._crit_edge, %.lr.ph3
-      %17 = phi i64 [ %22, %.lr.ph3 ], [ 0, %._crit_edge ]
-      %18 = tail call i8* @__quantum__rt__array_get_element_ptr_1d(%Array* %3, i64 %17)
-      %19 = bitcast i8* %18 to %Qubit**
-      %20 = load %Qubit*, %Qubit** %19, align 8
-      %21 = tail call %Result* @__quantum__qis__mz(%Qubit* %20)
-      %22 = add nuw nsw i64 %17, 1
-      %exitcond5.not = icmp eq i64 %22, %15
-      br i1 %exitcond5.not, label %._crit_edge4, label %.lr.ph3
-
-    ._crit_edge4:                                     ; preds = %.lr.ph3, %._crit_edge
-      tail call void @__quantum__rt__qubit_release_array(%Array* %3)
-      ret void
-    }
-
-    !llvm.module.flags = !{!0}
-
-    !0 = !{i32 2, !"Debug Info Version", i32 3}
-
-
-Note that the results of each tool can be piped to further tools, creating a
-composable pipeline of compiler lowering tools. 
-
-
diff --git a/pr-2308/sphinx/using/extending/extending.rst b/pr-2308/sphinx/using/extending/extending.rst
deleted file mode 100644
index fd755f7969..0000000000
--- a/pr-2308/sphinx/using/extending/extending.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-Extending CUDA-Q
-****************************
-
-.. toctree::
-  :maxdepth: 1
-
-  Create a new NVQIR Simulator <nvqir_simulator.rst>
-  Working with CUDA-Q IR <cudaq_ir.rst>
-  Create an MLIR Pass for CUDA-Q <mlir_pass.rst>
diff --git a/pr-2308/sphinx/using/extending/mlir_pass.rst b/pr-2308/sphinx/using/extending/mlir_pass.rst
deleted file mode 100644
index 8d61325ea1..0000000000
--- a/pr-2308/sphinx/using/extending/mlir_pass.rst
+++ /dev/null
@@ -1,97 +0,0 @@
-Create your own CUDA-Q Compiler Pass 
-******************************************
-
-The CUDA-Q IR can be transformed, analyzed, or optimized 
-using standard MLIR patterns and tools. CUDA-Q provides a registration 
-mechanism for the :code:`cudaq-opt` tool that allows one to create, load, and 
-use custom MLIR passes on Quake code. 
-
-CUDA-Q MLIR Passes can only be created within an existing CUDA-Q 
-development environment. Therefore, you must clone the repository and add your 
-Pass code as part of the existing CUDA-Q CMake system. 
-
-As an example, clone the repository and add the following directory structure 
-under :code:`lib`, :code:`lib/Plugins/MyCustomPlugin/`. Within this directory create a 
-:code:`CMakeLists.txt` file and a :code:`MyCustomPlugin.cpp` file. In the CMake file, 
-add the following: 
-
-.. code:: cmake 
-
-    add_llvm_pass_plugin(MyCustomPlugin MyCustomPlugin.cpp)
-
-Creating a CUDA-Q IR pass starts with the implementation of an 
-:code:`mlir::OperationPass`. A full discussion of the MLIR Pass infrastructure 
-is beyond the scope of this document; please see 
-`MLIR Passes <https://mlir.llvm.org/docs/PassManagement>`_. To create such 
-a pass, start with the following template in the :code:`MyCustomPlugin.cpp` file:
-
-.. code:: cpp 
-    
-    #include "cudaq/Optimizer/Dialect/Quake/QuakeDialect.h"
-    #include "cudaq/Optimizer/Dialect/Quake/QuakeOps.h"
-    #include "cudaq/Support/Plugin.h"
-    #include "mlir/Rewrite/FrozenRewritePatternSet.h"
-    #include "mlir/Transforms/DialectConversion.h"
-
-    // Here is an example MLIR Pass that one can write externally and 
-    // use via the cudaq-opt tool, with the --load-cudaq-plugin flag. 
-    // The pass here is simple, replace Hadamard operations with S operations. 
-
-    using namespace mlir;
-
-    namespace {
-
-    struct ReplaceH : public OpRewritePattern<quake::HOp> {
-      using OpRewritePattern::OpRewritePattern;
-      LogicalResult matchAndRewrite(quake::HOp hOp,
-                                    PatternRewriter &rewriter) const override {
-        rewriter.replaceOpWithNewOp<quake::SOp>(
-            hOp, hOp.isAdj(), hOp.getParameters(), hOp.getControls(),
-            hOp.getTargets());
-        return success();
-      }
-    };
-
-    class CustomPassPlugin
-        : public PassWrapper<CustomPassPlugin, OperationPass<func::FuncOp>> {
-    public:
-      MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(CustomPassPlugin)
-  
-      llvm::StringRef getArgument() const override { return "cudaq-custom-pass"; }
-
-      void runOnOperation() override {
-        auto circuit = getOperation();
-        auto ctx = circuit.getContext();
-
-        RewritePatternSet patterns(ctx);
-        patterns.insert<ReplaceH>(ctx);
-        ConversionTarget target(*ctx);
-        target.addLegalDialect<quake::QuakeDialect>();
-        target.addIllegalOp<quake::HOp>();
-        if (failed(applyPartialConversion(circuit, target, std::move(patterns)))) {
-          circuit.emitOpError("simple pass failed");
-          signalPassFailure();
-        }
-      }
-    };
-
-    } // namespace
-
-    CUDAQ_REGISTER_MLIR_PASS(CustomPassPlugin)
-
-This example serves as a very simple template for creating custom MLIR 
-Passes that analyze the CUDA-Q Quake representation and perform 
-some general transformation. In this example, we create a rewrite pattern 
-that replaces :code:`Hadamard` operations with :code:`S` operations. 
-
-Ensure that :code:`add_subdirectory(Plugins)` is in the :code:`lib/CMakeLists.txt` file, 
-and also that there is a :code:`lib/Plugins/CMakeLists.txt` file that adds your 
-plugin directory with :code:`add_subdirectory`.
-
-Then build CUDA-Q and you will have a :code:`MyCustomPlugin.so` plugin library 
-in the install. You can load the plugin and use it with :code:`cudaq-opt` as follows: 
-
-.. code:: bash 
-
-    cudaq-opt --load-cudaq-plugin MyCustomPlugin.so file.qke -cudaq-custom-pass
-
diff --git a/pr-2308/sphinx/using/extending/nvqir_simulator.rst b/pr-2308/sphinx/using/extending/nvqir_simulator.rst
deleted file mode 100644
index 899d2989c6..0000000000
--- a/pr-2308/sphinx/using/extending/nvqir_simulator.rst
+++ /dev/null
@@ -1,172 +0,0 @@
-Extending CUDA-Q with a new Simulator
-*******************************************
-
-Backend circuit simulation in CUDA-Q is enabled via the 
-NVQIR library (:code:`libnvqir`). CUDA-Q code is ultimately lowered 
-to the LLVM IR in a manner that is adherent to the `QIR specification <https://qir-alliance.org>`_.
-NVQIR provides function implementations for the various declared functions 
-in the specification, which in turn delegate to an extensible simulation 
-architecture. 
-
-The primary extension point for NVQIR is the :code:`CircuitSimulator` class. This class
-exposes an API that enables qubit allocation and deallocation, quantum operation 
-invocation, and measurement and sampling. Subtypes of this class are free to 
-override these methods to affect simulation of the quantum code in any 
-simulation-strategy-specific manner (e.g., state vector, tensor network, etc.). Moreover, 
-subtypes are free to implement simulators that leverage classical accelerated computing. 
-
-In this document, we'll detail this simulator interface and walk through how to extend it for new types of simulation. 
-
-:code:`CircuitSimulator`
-------------------------
-
-The :code:`CircuitSimulator` type is defined in :code:`runtime/nvqir/CircuitSimulator.h`. It
-exposes a public API to `libnvqir` that is immediately subclassed in the :code:`CircuitSimulatorBase` 
-type. This type is templated on the floating point type used in the simulator's computations (e.g. :code:`double,float`).
-This templated type handles a lot of the base functionality required for allocating and deallocated qubits, 
-as well as measurement, sampling, and observation under a number of execution contexts. 
-This is the type that downstream simulation developers should extend. 
-
-The actual definition of the quantum state data structure, and its overall evolution are 
-left as tasks for :code:`CircuitSimulatorBase` subclasses. Examples of simulation subtypes can be found 
-in :code:`runtime/nvqir/qpp/QppCircuitSimulator.cpp` or :code:`runtime/nvqir/custatevec/CuStateVecCircuitSimulator.cpp`.
-The :code:`QppCircuitSimulator` models the state vector using the `Q++ <https://github.com/softwareqinc/qpp>`_ library, which 
-leverages the :code:`Eigen::Matrix` type and OpenMP threading for matrix-vector operations. 
-The :code:`CuStateVecCircuitSimulator` type models the state vector on an NVIDIA GPU device 
-by leveraging the cuQuantum library. 
-
-The key methods that need to be overridden by subtypes of :code:`CircuitSimulatorBase` are as follows:
-
-.. list-table:: Required Circuit Simulator Subtype Method Overrides
-
-    * - Method Name 
-      - Method Arguments
-      - Method Description
-    * - :code:`addQubitToState`
-      - :code:`void` 
-      - Add a qubit to the underlying state representation.
-    * - :code:`addQubitsToState`
-      - :code:`nQubits : std::size_t` 
-      - Add the specified number of qubits to the underlying state representation.
-    * - :code:`resetQubit`
-      - :code:`qubitIdx : std::size_t`
-      - Reset the state of the qubit at the given index to :code:`|0>`
-    * - :code:`resetQubitStateImpl`
-      - :code:`void` 
-      - Clear the entire state representation (reset to 0 qubits).
-    * - :code:`applyGate`
-      - :code:`task : GateApplicationTask`
-      - Apply the specified gate described by the :code:`GateApplicationTask`. This type encodes the control and target qubit indices, optional rotational parameters, and the gate matrix data. 
-    * - :code:`measureQubit`
-      - :code:`qubitIdx : std::size_t -> bool` (returns bit result as bool)
-      - Measure the qubit, produce a bit result, collapse the state.
-    * - :code:`sample`
-      - :code`qubitIdxs : std::vector<std::size_t>, shots : int`
-      - Sample the current multi-qubit state on the provided qubit indices over a certain number of shots
-    * - :code:`name`
-      - :code:`void`
-      - Return the name of this CircuitSimulator, must be the same as the name used in :code:`nvq++ -qpu NAME ...`
-
-To extend a subtype class, you will need to create a new :code:`cpp` implementation file with the same name as your 
-subtype class name. In this file, you will subclass the :code:`CircuitSimulatorBase<FloatType>` and implement the methods in 
-the above table. Finally, the subclass must be registered with the NVQIR library so that it 
-can be picked up and used when a user specifies :code:`nvq++ --target mySimulator ...` from the command line (or :code:`cudaq.set_target('mySimulator')` in Python.)
-Type registration can be performed with a provided NVQIR macro, 
-
-.. code:: cpp 
-
-    NVQIR_REGISTER_SIMULATOR(MySimulatorClassName, mySimulator)
-
-where :code:`MySimulatorClassName` is the name of your subtype, and :code:`mySimulator` is the 
-same name as what :code:`MySimulatorClassName::name()` returns, and what you desire the 
-:code:`-qpu NAME` name to be. 
-
-A further requirement is that the code be compiled into its own standalone shared library 
-with name :code:`libnvqir-NAME.{so,dylib}`, where NAME is the 
-same name as what :code:`MySimulatorClassName::name()` returns, and what you desire the 
-:code:`-qpu NAME` name to be. You will also need to create a :code:`NAME.config` file that 
-contains the following contents 
-
-.. code:: bash 
-
-    NVQIR_SIMULATION_BACKEND="NAME"
-
-The library must be installed in :code:`$CUDA_QUANTUM_PATH/lib` and the configuration file 
-must be installed to :code:`$CUDA_QUANTUM_PATH/platforms`.
-
-Let's see this in action 
-------------------------
-
-CUDA-Q provides some CMake utilities to make the creation of your new simulation library 
-easier. Specifically, by using :code:`find_package(NVQIR)`, you'll get access to a :code:`nvqir_add_backend` function
-that will automate much of the boilerplate for creating your library and configuration file.
-
-Let's assume you want a simulation subtype named :code:`MySimulator`. You can create a folder or 
-repository for this code called :code:`my-simulator` and add :code:`MySimulator.cpp` and 
-:code:`CMakeLists.txt` files. Fill the CMake file with the following: 
-
-.. code:: cmake 
-
-    cmake_minimum_required(VERSION 3.24 FATAL_ERROR)
-    project(DemoCreateNVQIRBackend VERSION 1.0.0 LANGUAGES CXX)
-    find_package(NVQIR REQUIRED)
-    nvqir_add_backend(MySimulator MySimulator.cpp "")
-
-and then fill out your :code:`MySimulator.cpp` file with your subtype implementation. For example, 
-
-.. code:: cpp
-
-    #include "CircuitSimulator.h"
-
-    namespace {
-
-      class MySimulator : public nvqir::CircuitSimulatorBase<double> {
-
-      protected:
-        /// @brief Grow the state vector by one qubit.
-        void addQubitToState() override { ... }
-
-        /// @brief Grow the state vector by `count` qubit.
-        void addQubitsToState(std::size_t count) override { ... }
-
-        /// @brief Reset the qubit state.
-        void resetQubitStateImpl() override { ... }
-
-        /// @brief Apply the given gate
-        void applyGate(const GateApplicationTask &task) override { ... }
-
-      public:
-        MySimulator() = default;
-        virtual ~MySimulator() = default;
-        
-        bool measureQubit(std::size_t qubitIdx) override { ... }
-
-        void resetQubit(std::size_t &qubitIdx) override { ... }
-
-        cudaq::SampleResult sample(std::vector<std::size_t> &measuredBits,
-                              int shots) override { ... }
-
-        const std::string_view name() const override { return "MySimulator"; }
-      
-      };
-
-    } // namespace
-
-    /// Register this Simulator with NVQIR.
-    NVQIR_REGISTER_SIMULATOR(MySimulator)
-
-To build, install, and use this simulation backend, run the following from the top-level of :code:`my-simulator`:
-
-.. code:: bash 
-
-    export CUDA_QUANTUM_PATH=/path/to/cuda_quantum/install
-    mkdir build && cd build 
-    cmake .. -G Ninja -DNVQIR_DIR="$CUDA_QUANTUM_PATH/lib/cmake/nvqir"
-    ninja install 
-
-Then given any CUDA-Q source file, you can compile and target your backend simulator with the following: 
-
-.. code:: bash 
-
-    nvq++ file.cpp --target MySimulator 
-    ./a.out 
diff --git a/pr-2308/sphinx/using/install/data_center_install.rst b/pr-2308/sphinx/using/install/data_center_install.rst
deleted file mode 100644
index 8b0fb1a0d0..0000000000
--- a/pr-2308/sphinx/using/install/data_center_install.rst
+++ /dev/null
@@ -1,476 +0,0 @@
-Installation from Source
-=========================
-
-In most cases, you should not need to build CUDA-Q from source. For the
-best experience, we recommend using a container runtime to avoid conflicts with
-other software tools installed on the system. Note that `Singularity
-<https://docs.sylabs.io/guides/2.6/user-guide/faq.html#what-is-so-special-about-singularity>`__
-or `Docker rootless mode <https://docs.docker.com/engine/security/rootless/>`__
-address common issue or concerns that are often the motivation for
-avoiding the use of containers. Singularity, for example, can be installed 
-in a user folder and its installation does not require admin permissions; see
-:ref:`this section <install-singularity-image>` for more detailed instructions 
-on how to do that. Our installation guide also contains instructions for how to 
-:ref:`connect an IDE <local-development-with-vscode>` to a running container.
-
-If you do not want use a container runtime, we also provide pre-built binaries
-for using CUDA-Q with C++, and Python wheels for using CUDA-Q with Python. 
-These binaries and wheels are built following the instructions 
-in this guide and should work for you as long as your system meets the compatibility 
-requirements listed under :ref:`Prerequisites <compatibility-prebuilt-binaries>`. 
-To install the pre-built binaries, please follow the instructions 
-:ref:`here <install-prebuilt-binaries>`. To install the Python wheels, please 
-follow the instructions :ref:`here <install-python-wheels>`.
-
-If your system is not listed as supported by our official packages, e.g. because you would 
-like to use CUDA-Q on an operating system that uses an older C standard library, 
-please follow this guide carefully without skipping any steps to build and install
-CUDA-Q from source. The rest of this guide details system requirements 
-during the build and after installation, and walks through the installation steps.
-
-.. note::
-
-  CUDA-Q contains some components that are only included as
-  pre-built binaries and not part of our open source repository. We are working on
-  either open-sourcing these components or making them available as separate downloads
-  in the future. Even without these components, almost all features of CUDA-Q
-  will be enabled in a source build, though some pieces may be less performant. 
-  At this time, the :ref:`multi-GPU state vector simulator <nvidia-mgpu-backend>` 
-  backend will not be included if you build CUDA-Q from source.
-
-
-.. _compatibility-prebuilt-binaries:
-
-Prerequisites
-++++++++++++++++++++++++++++++++++++
-
-The following pre-requisites need to be satisfied both on the build system and
-on the host system, that is the system where the built CUDA-Q binaries
-will be installed and used.
-
-- Linux operating system. The instructions in this guide have been validated
-  with the `AlmaLinux 8 image <https://hub.docker.com/u/almalinux>`__ that
-  serves as the base image for the `manylinux_2_28 image
-  <https://github.com/pypa/manylinux>`__, and should work for the operating
-  systems CentOS 8, Debian 11 and 12, Fedora 38 and 39, OpenSUSE/SLED/SLES 15.5
-  and 15.6, RHEL 8 and 9, Rocky 8 and 9, and Ubuntu 24.04 and 22.04. Other 
-  operating systems may work, but have not been tested.
-- `Bash <https://www.gnu.org/software/bash/>`__ shell. The CUDA-Q 
-  build, install and run scripts expect to use `/bin/bash`.
-- `GNU C library <https://www.gnu.org/software/libc/>`__. 
-  Make sure that the version on the host system is the same one
-  or newer than the version on the build system. Our own builds
-  use version 2.28.
-- CPU with either x86-64 (x86-64-v3 architecture and newer) or ARM64
-  (ARM v8-A architecture and newer). Other architectures may work but are not
-  tested and may require adjustments to the build instructions.
-- Needed **only on the host** system: NVIDIA GPU with Volta, Turing, Ampere, Ada, or
-  Hopper architecture and `Compute Capability
-  <https://developer.nvidia.com/cuda-gpus>`__ 7+. Make sure you have the latest
-  `drivers <https://www.nvidia.com/download/index.aspx>`__ installed for your
-  GPU, and double check that the driver version listed by the `nvidia-smi`
-  command is 470.57.02 or newer. You do *not* need to have a GPU available on the
-  build system; the CUDA compiler needed for the build can be installed and used
-  without a GPU.
-
-We strongly recommend using a virtual environment for the build that includes
-*only* the tools and dependencies listed in this guide. If you have additional
-software installed, you will need to make sure that the build is linking against
-the correct libraries and versions.
-
-
-Build Dependencies
-++++++++++++++++++++++++++++++++++++
-
-In addition to the prerequisites listed above, you will need to install the
-following prerequisites in your build environment prior to proceeding with 
-the build as described in the subsequent sections:
-
-- Python version 3.10 or newer: If you intend to build CUDA-Q with Python
-  support, make sure the Python version on the build system matches the version
-  on the host system. If you intend to only build the C++ support for
-  CUDA-Q, the Python interpreter is required only for some of the 
-  LLVM build scripts and the Python version used for the build does not have 
-  to match the version on the host system.
-- Common tools: `wget`, `git`, `unzip`. The commands in the rest of this guide assume
-  that these tools are present on the build system, but they can be replaced by
-  other alternatives (such as, for example, manually going to a web page and
-  downloading a file/folder).
-
-The above prerequisites are no longer needed once CUDA-Q is built and 
-do not need to be present on the host system.
-
-.. note::
-
-  The CUDA-Q build scripts and the commands listed in the rest of this 
-  document assume you are using `bash` as the shell for your build.
-
-In addition to installing the needed build dependencies listed above, make sure
-to set the following environment variables prior to proceeding:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>InstallLocations]
-    :end-before: [<InstallLocations]
-
-These environment variables *must* be set during the build. We strongly
-recommend that their value is set to a path that does *not* already exist; 
-this will ensure that these components are built/installed as needed when
-building CUDA-Q.
-The configured paths can be chosen freely, but the paths specified during the
-build are also where the corresponding libraries will be installed on the 
-host system. We are working on making this more flexible in the future.
-
-.. note::
-
-  Please do **not** set `LLVM_INSTALL_PREFIX` to an existing directory;
-  To avoid compatibility issues, it is important to use the same compiler
-  to build the LLVM/MLIR dependencies from source as is later used to 
-  build CUDA-Q itself.
-
-.. note::
-
-  If you are setting the `CURL_INSTALL_PREFIX` variable to an existing 
-  CURL installation (not recommended), please make sure the command 
-  `curl --version` lists HTTP and HTTPS as supported protocols. If these 
-  protocols are not listed, please instead set the `CURL_INSTALL_PREFIX` 
-  variable to a path that does *not* exist. In that case, a suitable 
-  library will be automatically built from source as part of
-  building CUDA-Q.
-
-If you deviate from the instructions below for installing one of the
-dependencies and instead install it, for example, via package manager, you will
-need to make sure that the installation path matches the path you set for the
-corresponding environment variable(s).
-
-CUDA
-~~~~~~~~~~
-
-Building CUDA-Q requires a full installation of the CUDA toolkit.
-**You can install the CUDA toolkit and use the CUDA compiler without having a GPU.**
-The instructions are tested using version 11.8 and 12.0, but other CUDA 11 or 12 versions
-should work, as long as the CUDA runtime version on the host system matches the 
-CUDA version used for the build, and the installed driver on the host 
-system supports that CUDA version. We recommend using the latest CUDA version
-that is supported by the driver on the host system.
-
-Download a suitable `CUDA version <https://developer.nvidia.com/cuda-toolkit-archive>`__
-following the installation guide for your platform in the online documentation
-linked on that page.
-
-Within the tested AlmaLinux 8 environment, for example, the following commands
-install CUDA 12.0:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAInstall]
-    :end-before: [<CUDAInstall]
-
-Toolchain
-~~~~~~~~~~
-
-The compiler toolchain used for the build must be a supported 
-`CUDA host compiler <https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#supported-host-compilers>`__
-for the installed CUDA version.
-The following instructions have been tested with `GCC-11 <https://gcc.gnu.org/index.html>`__.
-Other toolchains may be supported but have not been tested.
-
-Within the tested AlmaLinux 8 environment, for example, the following commands
-install GCC 11:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>gccInstall]
-    :end-before: [<gccInstall]
-
-Independent on which compiler toolchain you installed, set the following
-environment variables to point to the respective compilers on your build system:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>ToolchainConfiguration]
-    :end-before: [<ToolchainConfiguration]
-
-- The variables `CC` and `CXX` *must* be set for the CUDA-Q build.
-- To use GPU-acceleration in CUDA-Q, make sure to set `CUDACXX` to 
-  your CUDA compiler, and `CUDAHOSTCXX` to the CUDA compatible host 
-  compiler you are using. If the CUDA compiler is not found when building
-  CUDA-Q, some components and backends will be omitted automatically 
-  during the build.
-
-
-Building CUDA-Q
-++++++++++++++++++++++++++++++++++++
-
-This installation guide has been written for a specific version/commit of CUDA-Q. 
-Make sure to obtain the source code for that version. 
-Clone the CUDA-Q `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__ and
-checkout the appropriate branch, tag, or commit. 
-Note that the build scripts assume that they are run from within a git repository, 
-and merely downloading the source code as ZIP archive hence will not work.
-
-Please follow the instructions in the respective subsection(s) to build the necessary 
-components for using CUDA-Q from C++ and/or Python.
-After the build, check that the GPU-accelerated components have been built by confirming
-that the file `nvidia.config` exists in the `$CUDAQ_INSTALL_PREFIX/targets` folder.
-We also recommend checking the build log printed to the console to confirm that all desired 
-components have been built. 
-
-.. note::
-
-  The CUDA-Q build will compile or omit optional components automatically depending
-  on whether the necessary pre-requisites are found in the build environment.
-  If you see a message that a component has been skipped, and/or the CUDA compiler
-  is not properly detected, make sure you followed the 
-  instructions for installing the necessary prerequisites and build dependencies, 
-  and have set the necessary environment variables as described in this document.
-
-.. _cudaq-python-from-source:
-
-Python Support
-+++++++++++++++++++++++++++++++
-
-The most convenient way to enable Python support within CUDA-Q is to build 
-a `wheel <https://pythonwheels.com/>`__ that can then easily be installed
-using `pip`. To ensure the wheel can be installed on the host system, make sure to
-use the same Python version for the build as the one that is installed on the host system.
-To build a CUDA-Q Python wheel, you will need to install the following additional 
-Python-specific tools:
-
-- Python development headers: The development headers for your Python version are installed
-  in the way as you installed Python itself. If you installed Python via the package manager
-  for your system, you may need to install an additional package to get the development headers.
-  The package name is usually your python version followed by either a `-dev` or `-devel` suffix.
-  If you are using a `Conda environment <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-python.html>`__,
-  the necessary headers should already be installed.
-- Pip package manager: Make sure the `pip` module is enable for your Python version, and that your `pip` version is 24 or newer.
-  We refer to the Python `documentation <https://pip.pypa.io/en/stable/installation/>`__ for
-  more information about installing/enabling `pip`.
-- Python modules: Install the additional modules `numpy`, `build`, `auditwheel`, and `patchelf` for your
-  Python version, e.g. `python3 -m pip install numpy build auditwheel patchelf`.
-
-.. note::
-
-  The wheel build by default is configured to depend on CUDA 12. To build a wheel for CUDA 11, 
-  you need to adjust the dependencies and project name in the `pyproject.toml` file. 
-
-From within the folder where you cloned the CUDA-Q repository, run the following
-command to build the CUDA-Q Python wheel:
-
-.. literalinclude:: ../../../../docker/build/assets.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumPythonBuild]
-    :end-before: [<CUDAQuantumPythonBuild]
-
-.. note::
-
-  A version identifier will be automatically assigned to the wheel based on the commit
-  history. You can manually override this detection to give a more descriptive identifier
-  by setting the environment variable `SETUPTOOLS_SCM_PRETEND_VERSION` to the desired
-  value before building the wheel.
-
-After the initial build, `auditwheel <https://github.com/pypa/auditwheel>`__ is used to 
-include dependencies in the wheel, if necessary, and correctly label the wheel.
-We recommend not including the CUDA runtime libraries and instead install them separately
-on the host system following the instructions in the next section. The following 
-command builds the final wheel, not including CUDA dependencies: 
-
-.. literalinclude:: ../../../../docker/build/assets.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumWheel]
-    :end-before: [<CUDAQuantumWheel]
-
-The command above will create a new wheel in the `wheelhouse` folder. This wheel can be
-installed on any `compatible platform <https://packaging.python.org/en/latest/specifications/platform-compatibility-tags/>`__.
-
-.. note::
-  You can confirm that the wheel is indeed compatible with your host platform by 
-  checking that the wheel tag (i.e. the file name ending of the `.whl` file) is listed under
-  "Compatible Tags" when running the command `python3 -m pip debug --verbose` on the host.
-
-.. _cudaq-cpp-from-source:
-
-C++ Support
-+++++++++++++++++++++++++++++++
-
-From within the folder where you cloned the CUDA-Q repository, run the following
-command to build CUDA-Q:
-
-.. literalinclude:: ../../../../docker/build/assets.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumCppBuild]
-    :end-before: [<CUDAQuantumCppBuild]
-
-Note that `lld` is primarily needed when the build or host system does not already
-have an existing default linker on its path; CUDA-Q supports the same linkers as
-`clang` does.
-
-To easily migrate the built binaries to the host system, we recommend creating a
-`self-extracting archive <https://makeself.io/>`__. To do so, download the 
-`makeself script(s) <https://github.com/megastep/makeself>`__ and move the necessary 
-files to install into a separate folder using the command
-
-.. literalinclude:: ../../../../docker/release/installer.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumAssets]
-    :end-before: [<CUDAQuantumAssets]
-
-You can then create a self-extracting archive with the command
-
-.. code-block:: bash
-
-    ./makeself.sh --gzip --sha256 --license cuda_quantum_assets/cudaq/LICENSE \
-        cuda_quantum_assets install_cuda_quantum.$(uname -m) \
-        "CUDA-Q toolkit for heterogeneous quantum-classical workflows" \
-        bash cudaq/migrate_assets.sh -t /opt/nvidia/cudaq
-
-Installation on the Host
-++++++++++++++++++++++++++++++++++++
-
-Make sure your host system satisfies the `Prerequisites`_ listed above.
-
-- To use CUDA-Q with Python, you should have a working
-  Python installation on the host system, including the `pip` package manager.
-- To use CUDA-Q with C++, you should make sure that you have the necessary development
-  headers of the C standard library installed. You can check this by searching for
-  `features.h`, commonly found in `/usr/include/`. You can install the necessary headers
-  via package manager (usually the package name is called something like `glibc-devel`
-  or `libc6-devel`). These headers are also included with any installation of GCC.
-
-If you followed the instructions for building the 
-:ref:`CUDA-Q Python wheel <cudaq-python-from-source>`,
-copy the built `.whl` file to the host system, and install it using `pip`; e.g.
-
-.. code-block:: bash
-
-    pip install cuda_quantum*.whl
-
-.. TODO: update pypi links throughout the docs
-
-To install the necessary CUDA and MPI dependencies for some of the components, 
-you can either follow the instructions on `PyPI.org <https://pypi.org/project/cuda-quantum/>`__, 
-replacing `pip install cudaq` with the command above, or you can follow the
-instructions in the remaining sections of this document to customize and better
-optimize them for your host system.
-
-If you followed the instructions for building the 
-:ref:`CUDA-Q C++ tools <cudaq-cpp-from-source>`,
-copy the `install_cuda_quantum` file that you created to the host system, 
-and install it by running the commands
-
-.. code-block:: bash
-
-    sudo bash install_cuda_quantum.$(uname -m) --accept
-    . /opt/nvidia/cudaq/set_env.sh
-
-This will extract the built assets and move them to the correct locations.
-The `set_env.sh` script in `/opt/nvidia/cudaq` defines the necessary environment
-variables to use CUDA-Q. To avoid having to set them manually every time a 
-new shell is opened, we highly recommend adding the following lines to
-the `/etc/profile` file:
-
-.. code-block:: bash
-
-    if [ -f /opt/nvidia/cudaq/set_env.sh ];
-      . /opt/nvidia/cudaq/set_env.sh
-    fi
-
-.. note::
-  CUDA-Q as built following the instructions above includes and uses the LLVM
-  C++ standard library. This will not interfere with any other C++ standard library
-  you may have on your system. Pre-built external libraries, you may want to use with 
-  CUDA-Q, such as specific optimizers for example, have a C API to ensure compatibility
-  across different versions of the C++ standard library and will work with CUDA-Q without 
-  issues. The same is true for all distributed CUDA libraries. To build you own CUDA 
-  libraries that can be used with CUDA-Q, please take a look at :doc:`../integration/cuda_gpu`.
-
-The remaining sections in this document list additional runtime dependencies 
-that are not included in the migrated assets and are needed to use some of the 
-CUDA-Q features and components.
-
-CUDA Runtime Libraries
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-To use GPU-acceleration in CUDA-Q you will need to install the necessary 
-CUDA runtime libraries. Their version (at least the version major) needs to match the version
-used for the build. While not necessary, we recommend installing 
-the complete CUDA toolkit like you did for the CUDA-Q build.
-If you prefer to only install the minimal set of runtime libraries, the following 
-commands, for example, install the necessary packages for the AlmaLinux 8 environment:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>CUDARTInstall]
-    :end-before: [<CUDARTInstall]
-
-MPI
-~~~~~~
-
-To work with all CUDA-Q backends, a CUDA-aware MPI installation is required. 
-If you do not have an existing CUDA-aware MPI installation, you can build one from 
-source. To do so, in addition to the CUDA runtime libraries listed above 
-you will need to install the CUDA runtime development package 
-(`cuda-cudart-devel-${version_suffix}` or `cuda-cudart-dev-${version_suffix}`, 
-depending on your distribution).
-
-The following commands build a sufficient CUDA-aware OpenMPI installation.
-To make best use of MPI, we recommend a more fully featured installation including
-additional configurations that fit your host system.
-The commands below assume you have the necessary prerequisites for the OpenMPI build
-installed on the build system. Within the tested AlmaLinux 8 environment, for example,
-the packages `autoconf`, `libtool`, `flex`, and `make` need to be installed.
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>OpenMPIBuild]
-    :end-before: [<OpenMPIBuild]
-
-Confirm that you have a suitable MPI implementation installed. For OpenMPI and MPICH,
-for example, this can be done by compiling and running the following program:
-
-.. literalinclude:: ../../../../docker/test/installer/mpi_cuda_check.cpp
-    :language: cpp
-
-.. note::
-
-  If you are encountering an error similar to "The value of the MCA parameter `plm_rsh_agent` 
-  was set to a path that could not be found", please make sure you have an SSH Client installed
-  or update the MCA parameter to another suitable agent.
-  MPI uses `SSH <https://en.wikipedia.org/wiki/Secure_Shell>`__ or
-  `RSH <https://en.wikipedia.org/wiki/Remote_Shell>`__ to communicate with each node
-  unless another resource manager, such as
-  `SLURM <https://slurm.schedmd.com/overview.html>`__, is used. 
-
-Different MPI implementations are supported via a plugin infrastructure in CUDA-Q.
-Once you have a CUDA-aware MPI installation on your host system, you can 
-configure CUDA-Q to use it by activating the necessary plugin.
-Plugins for OpenMPI and MPICH are included in CUDA-Q and can be activated by
-setting the environment variable `MPI_PATH` to the MPI installation folder 
-and then running the command
-
-.. code-block:: bash
-
-    bash "${CUDA_QUANTUM_PATH}/distributed_interfaces/activate_custom_mpi.sh"
-
-.. note::
-
-  To activate the MPI plugin for the Python support, replace `${CUDA_QUANTUM_PATH}`
-  with the path that is listed under "Location" when you run the command
-  `pip show cuda-quantum`.
-
-If you use a different MPI implementation than OpenMPI or MPICH, you will need to 
-implement the necessary plugin interface yourself prior to activating the plugin 
-with the command above.
-
-.. TODO:
-  For more information about building and activating a custom MPI plugin, see ...
diff --git a/pr-2308/sphinx/using/install/install.rst b/pr-2308/sphinx/using/install/install.rst
deleted file mode 100644
index ad3e59decb..0000000000
--- a/pr-2308/sphinx/using/install/install.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-Installation Guide
-*******************************************
-
-.. _install-cuda-quantum:
-
-.. toctree::
-  :caption: Contents
-  :maxdepth: 1
-
-  Local Installation <local_installation.rst>
-  Data Center Installation <data_center_install.rst>
diff --git a/pr-2308/sphinx/using/install/local_installation.rst b/pr-2308/sphinx/using/install/local_installation.rst
deleted file mode 100644
index 737ca2a336..0000000000
--- a/pr-2308/sphinx/using/install/local_installation.rst
+++ /dev/null
@@ -1,933 +0,0 @@
-Local Installation
-===================
-
-Introduction
--------------------
-
-This guide walks through how to :ref:`install CUDA-Q <install-cuda-quantum>` on your system, and how to set up :ref:`VS Code for local development <local-development-with-vscode>`.
-The section on :ref:`connecting to a remote host <connect-to-remote>` contains some
-guidance for application development on a remote host where CUDA-Q is installed.
-
-The following sections contain instructions for how to install CUDA-Q on your machine using
-
-- :ref:`Docker <install-docker-image>`: A fully featured CUDA-Q installation including all C++ and Python tools is available as a `Docker <https://docs.docker.com/get-started/overview/>`__ image.
-- :ref:`Singularity <install-singularity-image>`: A `Singularity <https://docs.sylabs.io/guides/latest/user-guide/introduction.html>`__ container can easily be created based on our Docker images. 
-- :ref:`PyPI <install-python-wheels>`: Additionally, we distribute pre-built Python wheels via `PyPI <https://pypi.org>`__.
-- :ref:`Pre-built binaries <install-prebuilt-binaries>`: We also provide pre-built C++ binaries, bundled as `self-extracting archive <https://makeself.io/>`__, that work across a range of Linux operating systems.
-
-If you would like to build CUDA-Q from source to deploy on an HPC system without relying on a container runtime, please follow the instructions for :doc:`data_center_install`. 
-If, on the other hand, you want to contribute to the development of CUDA-Q itself and hence want to 
-build a custom version of CUDA-Q from source, follow the instructions on the 
-`CUDA-Q GitHub repository <https://github.com/NVIDIA/cuda-quantum/blob/main/Building.md>`__ instead.
-
-If you are unsure which option suits you best, we recommend using our :ref:`Docker image <install-docker-image>` to develop your applications in a controlled environment that does not depend on, or interfere with, other software
-that is installed on your system.
-
-.. _install-docker-image:
-
-Docker
-++++++++++++++++++++++++++++++++++++
-
-To download and use our Docker images, you will need to install and launch the Docker engine. 
-If you do not already have Docker installed on your system, you can get it by downloading and installing `Docker Desktop <https://docs.docker.com/get-docker/>`_. 
-If you do not have the necessary administrator permissions to install software on your machine,
-take a look at the section below on how to use `Singularity`_ instead.
-
-Docker images for all CUDA-Q releases are available on the `NGC Container Registry`_.
-In addition to publishing `stable releases <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__, 
-we also publish Docker images whenever we update certain branches on our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`_.
-These images are published in our `nightly channel on NGC <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum/tags>`__.
-To download the latest version on the main branch of our GitHub repository, built to work with CUDA 12, for example, use the command
-
-.. code-block:: console
-
-    docker pull nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-.. _NGC Container Registry: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum
-
-Early prototypes for features we are considering can be tried out by using the image tags starting 
-with `experimental`. The `README` in the `/home/cudaq` folder in the container gives more details 
-about the feature. We welcome and appreciate your feedback about these early prototypes; 
-how popular they are will help inform whether we should include them in future releases.
-
-Once you have downloaded an image, the container can be run using the command
-
-.. code-block:: console
-
-    docker run -it --name cuda-quantum nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-Replace the image name and/or tag in the command above, if necessary, with the one you want to use.
-This will give you terminal access to the created container. To enable support 
-for GPU-accelerated backends, you will need to pass the :code:`--gpus` flag when launching
-the container, for example:
-
-.. code-block:: console
-
-    docker run -it --gpus all --name cuda-quantum nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-.. note:: 
-
-  This command will fail if you do not have a suitable NVIDIA GPU available, or if your driver 
-  version is insufficient. To improve compatibility with older drivers, you may need to install the 
-  `NVIDIA container toolkit`_.
-
-.. _NVIDIA container toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
-
-You can stop and exit the container by typing the command :code:`exit`. If you did not specify
-:code:`--rm` flag when launching the container, the container still exists after exiting, as well as any 
-changes you made in it. You can get back to it using
-the command :code:`docker start -i cuda-quantum`. 
-You can delete an existing container and any changes you made using :code:`docker rm -v cuda-quantum`.
-
-When working with Docker images, the files inside the container are not visible outside the container environment. 
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-please take a look at the section on :ref:`Development with VS Code <docker-in-vscode>`.
-
-Alternatively, it is possible, but not recommended, to launch an SSH server inside the container environment and connect an IDE using SSH. To do so, make sure you have generated a suitable RSA key pair; if your `~/.ssh/` folder does not already contain the files `id_rsa.pub` and `id.rsa`,
-follow the instructions for generating a new SSH key on `this page <https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent>`__.
-You can then launch the container and connect to it via SSH by executing the following commands:
-
-.. code-block:: console
-
-    docker run -itd --gpus all --name cuda-quantum -p 2222:22 nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-    docker exec cuda-quantum bash -c "sudo apt-get install -y --no-install-recommends openssh-server"
-    docker exec cuda-quantum bash -c "sudo sed -i -E "s/#?\s*UsePAM\s+.+/UsePAM yes/g" /etc/ssh/sshd_config"
-    docker cp ~/.ssh/id_rsa.pub cuda-quantum:/home/cudaq/.ssh/authorized_keys
-    docker exec -d cuda-quantum bash -c "sudo -E /usr/sbin/sshd -D"
-    ssh cudaq@localhost -p 2222 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o GlobalKnownHostsFile=/dev/null
-
-
-.. _install-singularity-image:
-
-Singularity
-++++++++++++++++++++++++++++++++++++
-
-You can use `Singularity <https://github.com/sylabs/singularity>`__ to run a CUDA-Q container in a folder without needing administrator permissions.
-If you do not already have Singularity installed, you can build a relocatable installation from source. 
-To do so on Linux or WSL, make sure you have the `necessary prerequisites <https://docs.sylabs.io/guides/4.0/user-guide/quick_start.html#prerequisites>`__ installed, download a suitable version of the `go toolchain <https://docs.sylabs.io/guides/4.0/user-guide/quick_start.html#install-go>`__, and make sure the `go` binaries are on your :code:`PATH`. You can then build Singularity with the commands
-
-.. code-block:: console
-
-    wget https://github.com/sylabs/singularity/releases/download/v4.0.1/singularity-ce-4.0.1.tar.gz
-    tar -xzf singularity-ce-4.0.1.tar.gz singularity-ce-4.0.1/ && rm singularity-ce-4.0.1.tar.gz && cd singularity-ce-4.0.1/
-    ./mconfig --without-suid --prefix="$HOME/.local/singularity"
-    make -C ./builddir && make -C ./builddir install && cd .. && rm -rf singularity-ce-4.0.1/
-    echo 'PATH="$PATH:$HOME/.local/singularity/bin/"' >> ~/.profile && source ~/.profile
-
-For more information about using Singularity on other systems, take a look at the `admin guide <https://docs.sylabs.io/guides/4.0/admin-guide/installation.html#installation-on-windows-or-mac>`__.
-
-Once you have singularity installed, create a file `cuda-quantum.def` with the following content:
-
-.. code-block:: console
-
-    Bootstrap: docker
-    From: nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
-
-    %runscript
-        mount devpts /dev/pts -t devpts
-        cp -r /home/cudaq/* .
-        bash
-
-Replace the image name and/or tag in the `From` line, if necessary, with the one you want to use;
-In addition to publishing `stable releases <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags>`__, 
-we also publish Docker images whenever we update certain branches on our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`_.
-These images are published in our `nightly channel on NGC <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum/tags>`__.
-Early prototypes for features we are considering can be tried out by using the image tags starting 
-with `experimental`. We welcome and appreciate your feedback about these early prototypes; 
-how popular they are will help inform whether we should include them in future releases.
-
-You can then create a CUDA-Q container by running the following commands:
-
-.. code-block:: console
-
-    singularity build --fakeroot cuda-quantum.sif cuda-quantum.def
-    singularity run --writable --fakeroot cuda-quantum.sif
-
-In addition to the files in your current folder, you should now
-see a `README` file, as well as examples and tutorials.
-To enable support for GPU-accelerated backends, you will need to pass the
-the :code:`--nv` flag when running the container:
-
-.. code-block:: console
-
-    singularity run --writable --fakeroot --nv cuda-quantum.sif
-    nvidia-smi
-
-The output of the command above lists the GPUs that are visible and accessible in the container environment.
-
-.. note:: 
-
-  If you do not see any GPUs listed in the output of `nvidia-smi`, 
-  it means the container environment is unable to access a suitable NVIDIA GPU. 
-  This can happen if your driver version is insufficient, or if you are 
-  working on WSL. To improve compatibility with older drivers, or to enable GPU support
-  on WSL, please install the `NVIDIA container toolkit`_, and update the singularity configuration 
-  to set `use nvidia-container-cli` to `yes` and configure the correct `nvidia-container-cli path`. 
-  The two commands below use `sed` to do that:
-
-  .. code-block:: console
-
-    sed -i 's/use nvidia-container-cli = no/use nvidia-container-cli = yes/' "$HOME/.local/singularity/etc/singularity/singularity.conf"
-    sed -i 's/# nvidia-container-cli path =/nvidia-container-cli path = \/usr\/bin\/nvidia-container-cli/' "$HOME/.local/singularity/etc/singularity/singularity.conf"
-
-You can exit the container environment by typing the command :code:`exit`.
-Any changes you made will still be visible after you exit the container, and you can re-enable the 
-container environment at any time using the `run` command above.
-
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-please take a look at the section on :ref:`Development with VS Code <singularity-in-vscode>`.
-
-
-.. _install-python-wheels:
-
-Python wheels
-++++++++++++++++++++++++++++++++++++
-
-CUDA-Q Python wheels are available on `PyPI.org <https://pypi.org/project/cuda-quantum>`__. 
-Installation instructions can be found in the `project description <https://pypi.org/project/cuda-quantum/#description>`__.
-For more information about available versions and documentation,
-see :doc:`../../releases`.
-
-There are currently no source distributions available on PyPI, but you can download the 
-source code for the latest version of the CUDA-Q Python wheels from our 
-`GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__. 
-The source code for previous versions can be downloaded from the respective 
-`GitHub Release <https://github.com/NVIDIA/cuda-quantum/releases>`__.
-
-At this time, wheels are distributed for Linux operating systems only. 
-If your platform is not :ref:`officially supported <dependencies-and-compatibility>` and
-`pip` does not find a compatible wheel to install, you can build your own 
-wheel from source following the instructions here: :doc:`data_center_install`.
-
-To build the CUDA-Q Python API for the purpose of contributing to
-our `GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__,
-follow the instructions for 
-`Setting up your Environment <https://github.com/NVIDIA/cuda-quantum/blob/main/Dev_Setup.md>`__, 
-and then run the following commands in the repository root:
-
-.. code-block:: console
-
-    bash scripts/install_prerequisites.sh
-    pip install . --user
-
-.. _install-prebuilt-binaries:
-
-Pre-built binaries
-++++++++++++++++++++++++++++++++++++
-
-Starting with the 0.6.0 release, we provide pre-built binaries for using 
-CUDA-Q with C++. Support for using CUDA-Q with Python can be installed 
-side-by-side with the pre-built binaries for C++ by following the instructions on 
-`PyPI.org <https://pypi.org/project/cuda-quantum>`__.
-The pre-built binaries work across a range of Linux operating systems listed 
-under :ref:`dependencies-and-compatibility`. 
-
-Before installing our pre-built binaries, please make sure that your 
-operating system is using the `GNU C library <https://www.gnu.org/software/libc/>`__ 
-version 2.28 or newer. You can confirm this by checking the output of the command 
-`ldd --version`. If this command does not exist, or shows an older version than 2.28, 
-please double check that your operating system is listed as 
-:ref:`supported <dependencies-and-compatibility>`. If you use an operating system
-with an older GNU C library version, you will need to build the installer from 
-source following the instructions in :doc:`data_center_install`.
-
-You can download the `install_cuda_quantum` file for your processor architecture from
-the assets of the respective `GitHub release <https://github.com/NVIDIA/cuda-quantum/releases>`__. 
-The installer is a `self-extracting archive <https://makeself.io/>`__ that contains the 
-pre-built binaries as well as a script to move them to the correct locations. You will need
-`bash`, `tar`, and `gzip` (usually already installed on most Linux distributions) to run 
-the installer.
-The installation location of CUDA-Q is not currently configurable and using the installer
-hence requires admin privileges on the system. We may revise that in the future; please see and
-upvote the corresponding `GitHub issue <https://github.com/NVIDIA/cuda-quantum/issues/1075>`__.
-
-To install CUDA-Q, execute the command
-
-.. literalinclude:: ../../../../docker/test/installer/linux.Dockerfile
-    :language: bash
-    :dedent:
-    :start-after: [>CUDAQuantumInstall]
-    :end-before: [<CUDAQuantumInstall]
-
-.. note:: 
-
-  To use GPU-accelerated backends, you will need to install the necessary CUDA runtime libraries. 
-  For more information see the corresponding section on 
-  :ref:`Additional CUDA Tools <cuda-dependencies-prebuilt-binaries>`.
-
-The installation ensures that the necessary environment variables for
-using the CUDA-Q toolchain are set upon login for all POSIX shells.
-Confirm that the `nvq++` command is found. If it is not, please make sure 
-to set the environment variables defined by the `set_env.sh` script in the 
-CUDA-Q installation folder (usually `/usr/local/cudaq` or `/opt/nvidia/cudaq`).
-
-If an MPI installation is available in the directory defined by `MPI_PATH`, 
-the installer automatically enables MPI support in CUDA-Q.
-If you do not have MPI installed on your system, you can simply
-leave that path empty, and CUDA-Q will be installed without MPI support.
-If you install MPI at a later point in time, you can activate the MPI support in CUDA 
-Quantum by setting the `MPI_PATH` variable to its installation location and 
-executing the commands
-
-.. code-block:: console
-
-    MPI_PATH=/usr/local/openmpi # update this path as needed
-    bash "${CUDA_QUANTUM_PATH}/distributed_interfaces/activate_custom_mpi.sh"
-
-.. note::
-
-  Please make sure that you have the necessary development headers of the C standard 
-  library installed. You can check this by searching for `features.h`, commonly found
-  in `/usr/include/`. You can install the necessary headers via package manager 
-  (usually the package name is called something like `glibc-devel` or `libc6-dev`). 
-  These headers are also included with any installation of GCC.
-
-.. _local-development-with-vscode:
-
-Development with VS Code
-------------------------------------
-
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-we recommend using `VS Code <https://code.visualstudio.com/>`_. VS Code provides a seamless
-development experience on all platforms, and is also available without installation via web browser. 
-This sections describes how to connect VS Code to a running container on your machine.
-The section on :ref:`connecting to a remote host <connect-to-remote>` contains information on
-how to set up your development environment when accessing CUDA-Q on a remote host instead.
-
-.. _docker-in-vscode:
-
-Using a Docker container
-++++++++++++++++++++++++++++++++++++++++
-
-Before connecting VS Code, open a terminal/shell, 
-and start the CUDA-Q Docker container following the 
-instructions in the :ref:`section above <install-docker-image>`. 
-
-If you have a local installation of `VS Code <https://code.visualstudio.com/>`_ 
-you can connect to the running container using the  
-`Dev Containers <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers>`__ extension. If you want to use VS Code in the web browser, please follow the instructions
-in the section `Developing with Remote Tunnels`_ instead.
-
-.. |:spellcheck-disable:| replace:: \
-.. |:spellcheck-enable:| replace:: \
-
-After installing the
-`Dev Containers <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers>`__ extension, launch VS Code, open the Command Palette with `Ctrl+Shift+P`, and enter 
-|:spellcheck-disable:|"Dev Containers: Attach to Running Container"|:spellcheck-enable:|.
-You should see and select the running `cuda-quantum` container in the list. 
-After the window reloaded, enter "File: Open Folder" in the Command Palette to open the `/home/cudaq/` folder.
-
-To run the examples, open the Command Palette and enter "View: Show Terminal"
-to launch an integrated terminal. You are now all set to 
-:ref:`get started <post-installation>` with CUDA-Q development.
-
-.. _singularity-in-vscode:
-
-Using a Singularity container
-++++++++++++++++++++++++++++++++++++++++
-
-If you have a GitHub or Microsoft account, we recommend that you connect 
-to a CUDA-Q container using tunnels. To do so, launch a CUDA-Q Singularity 
-container following the instructions in the :ref:`section above <install-singularity-image>`,
-and then follow the instructions in the section `Developing with Remote Tunnels`_.
-
-If you cannot use tunnels, you need a local installation of 
-`VS Code <https://code.visualstudio.com/>`_ and you need to install 
-the `Remote - SSH <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh>`__ extension. 
-Make sure you also have a suitable SSH key pair; if your `~/.ssh/` folder does not already contain
-the files `id_rsa.pub` and `id.rsa`, follow the instructions for generating a new SSH key on 
-`this page <https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent>`__.
-
-To connect VS Code to a running CUDA-Q container, 
-the most convenient setup is to install and run an SSH server 
-in the Singularity container. Open a terminal/shell in a separate window,
-and enter the following commands to create a suitable sandbox:
-
-.. code-block:: console
-
-    singularity build --sandbox cuda-quantum-sandbox cuda-quantum.sif
-    singularity exec --writable --fakeroot cuda-quantum-sandbox \
-      apt-get install -y --no-install-recommends openssh-server
-    cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
-
-You can launch this sandbox by entering the commands below. Please see the `Singularity`_ section above
-for more information about how to get the `cuda-quantum.sif` image, and how to enable GPU-acceleration
-with the `--nv` flag.
-
-.. code-block:: console
-
-    singularity run --writable --fakeroot --nv --network-args="portmap=22:2222/tcp" cuda-quantum-sandbox
-    /usr/sbin/sshd -D -p 2222 -E sshd_output.txt
-
-.. note::
-
-  Make sure to use a free port. You can check if the SSH server is ready and listening
-  by looking at the log in `sshd_output.txt`. If the port is already in use, you can 
-  replace the number `2222` by any free TCP port in the range `1025-65535` in all
-  commands.
-
-Entering `Ctrl+C` followed by `exit` will stop the running container. You can re-start
-it at any time by entering the two commands above. While the container is running,
-open the Command Palette in VS Code with `Ctrl+Shift+P`, enter "Remote-SSH: Add new
-SSH Host", and enter the following SSH command:
-
-.. code-block:: console
-
-    ssh root@localhost -p 2222 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o GlobalKnownHostsFile=/dev/null
-
-.. note::
-
-  If you are working on Windows and are building and running the Singularity container in WSL,
-  make sure to copy the used SSH keys to the Windows partition, such that VS Code can connect with 
-  the expected key. Alternatively, add the used public key to the `/root/.ssh/authorized_keys` file in 
-  the Singularity container.
-
-You can then connect to the host by opening the Command Palette, entering
-"Remote SSH: Connect Current Window to Host", and choosing the newly created host.
-After the window reloaded, enter "File: Open Folder" in the 
-Command Palette to open the desired folder.
-
-To run the examples, open the Command Palette and enter "View: Show Terminal"
-to launch an integrated terminal. You are now all set to 
-:ref:`get started <post-installation>` with CUDA-Q development.
-
-
-.. _connect-to-remote:
-
-Connecting to a Remote Host
-------------------------------------
-
-Depending on the setup on the remote host, there are a couple of different options 
-for developing CUDA-Q applications.
-
-- If a CUDA-Q container is running on the remote host,
-  and you have a GitHub or Microsoft account, take a look at
-  `Developing with Remote Tunnels`_. This works for both Docker
-  and Singularity containers on the remote host, and should also
-  work for other containers.
-- If you cannot use tunnels, or if you want to work with an
-  existing CUDA-Q installation without using a container, 
-  take a look at `Remote Access via SSH`_ instead.
-
-.. _connect-vscode-via-tunnel:
-
-Developing with Remote Tunnels
-++++++++++++++++++++++++++++++++++++
-
-`Remote access via tunnel <https://code.visualstudio.com/blogs/2022/12/07/remote-even-better>`__
-can easily be enabled with the `VS Code CLI <https://code.visualstudio.com/docs/editor/command-line>`__.
-This allows to connect either a local installation of `VS Code <https://code.visualstudio.com/>`_, 
-or the `VS Code Web UI <https://vscode.dev/>`__, to a running CUDA-Q container on the same or a different machine. 
-
-Creating a secure connection requires authenticating with the same GitHub or Microsoft account on each end.
-Once authenticated, an SSH connection over the tunnel provides end-to-end encryption. To download the VS Code CLI, if necessary, and create a tunnel, execute the 
-following command in the running CUDA-Q container,
-and follow the instructions to authenticate:
-
-.. code-block:: console
-
-    vscode-setup tunnel --name cuda-quantum-remote --accept-server-license-terms
-
-You can then either `open VS Code in a web browser <https://vscode.dev/tunnel/cuda-quantum-remote/home/cudaq/>`__, or connect a local installation of VS Code.
-To connect a local installation of VS Code, make sure you have the 
-`Remote - Tunnels <https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-server>`__ extension installed,
-then open the Command Palette with `Ctrl+Shift+P`, enter "Remote Tunnels: Connect to Tunnel", 
-and enter `cuda-quantum-remote`. After the window reloaded, enter "File: Open Folder" in the Command Palette 
-to open the `/home/cudaq/` folder.
-
-You should see a pop up asking if you want to install the recommended extensions. Selecting to install them will
-configure VS Code with extensions for working with C++, Python, and Jupyter.
-You can always see the list of recommended extensions that aren't installed yet by clicking on the "Extensions" icon in the sidebar and navigating to the "Recommended" tab.
-
-Remote Access via SSH
-++++++++++++++++++++++++++++++++++++
-
-To facilitate application development with, for example, debugging, code completion, hover information, and so on, 
-you can connect a local installation of `VS Code <https://code.visualstudio.com/>`_ to a remote host via SSH. 
-
-.. note:: 
-
-  For the best user experience, we recommend to launch a CUDA-Q container on the remote host, 
-  and then connect :ref:`VS Code using tunnels <connect-vscode-via-tunnel>`.
-  If a connection via tunnel is not possible, this section describes using SSH instead.
-
-To do so, make sure you have `Remote - SSH <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh>`__ extension installed.
-Open the Command Palette with `Ctrl+Shift+P`, enter "Remote-SSH: Add new
-SSH Host", and enter the SSH command to connect to your account on the remote host.
-You can then connect to the host by opening the Command Palette, entering
-"Remote SSH: Connect Current Window to Host", and choosing the newly created host.
-
-When prompted, choose Linux as the operating system, and enter your
-password. After the window reloaded, enter "File: Open Folder" in the 
-Command Palette to open the desired folder. Our GitHub repository contains
-a folder with VS Code configurations including a list of recommended extensions for
-working with CUDA-Q; you can copy `these configurations <https://github.com/NVIDIA/cuda-quantum/tree/main/docker/release/config/.vscode>`__ into the a folder named `.vscode` in your workspace to use them.
-
-If you want to work with an existing CUDA-Q installation on the remote host, you are all set.
-Alternatively, you can use Singularity to build and run a container following the instructions in 
-:ref:`this section <install-singularity-image>`. Once the `cuda-quantum.sif` image is built and 
-available in your home directory on the remote host, you can update your VS Code configuration 
-to enable/improve completion, hover information, and other development tools within the container.
-
-To do so, open the Command Palette and enter "Remote-SSH: Open SSH Configuration File". 
-Add a new entry to that file with the command to launch the container, and edit the configuration 
-of the remote host, titled `remote-host` in the snippets below, to add a new identifier:
-
-.. code-block:: console
-
-    Host cuda-quantum~*
-      RemoteCommand singularity run --writable --fakeroot --nv ~/cuda-quantum.sif
-      RequestTTY yes
-
-    Host remote-host cuda-quantum~remote-host
-      HostName ...
-      ...
-
-You will need to edit a couple of VS Code setting to make use of the newly defined remote command;
-open the Command Palette, enter "Preferences: Open User Settings (JSON)", and add or update the 
-following configurations:
-
-.. code-block:: console
-
-    "remote.SSH.enableRemoteCommand": true,
-    "remote.SSH.useLocalServer": true,
-    "remote.SSH.remoteServerListenOnSocket": false,
-    "remote.SSH.connectTimeout": 120,
-    "remote.SSH.serverInstallPath": {
-        "cuda-quantum~remote-host": "~/.vscode-container/cuda-quantum",
-    },
-
-After saving the changes, you should now be able to select `cuda-quantum~remote-host` as the host
-when connecting via SSH, which will launch the CUDA-Q container and connect VS Code to it.
-
-.. note::
-
-  If the connection to `cuda-quantum~remote-host` fails, you may need to specify the full
-  path to the `singularity` executable on the remote host, since environment variables, 
-  and specifically the configured `PATH` may be different during launch than in your user account.
-
-DGX Cloud
---------------------------------
-
-If you are using `DGX Cloud <https://www.nvidia.com/en-us/data-center/dgx-cloud/>`__, 
-you can easily use it to run CUDA-Q applications.
-While submitting jobs to DGX Cloud directly from within CUDA-Q is not (yet) supported,
-you can use the NGC CLI to launch and interact with workloads in DGX Cloud.
-The following sections detail how to do that, and how to connect JupyterLab and/or VS Code
-to a running CUDA-Q job in DGX Cloud.
-
-.. _dgx-cloud-setup:
-
-Get Started
-+++++++++++++++++++++++++++++++
-
-To get started with DGX Cloud, you can 
-`request access here <https://www.nvidia.com/en-us/data-center/dgx-cloud/trial/>`__.
-Once you have access, `sign in <https://ngc.nvidia.com/signin>`__ to your account,
-and `generate an API key <https://ngc.nvidia.com/setup/api-key>`__. 
-`Install the NGC CLI <https://ngc.nvidia.com/setup/installers/cli>`__
-and configure it with 
-
-.. code-block:: console
-
-    ngc config set
-
-entering the API key you just generated when prompted, and configure other settings as appropriate.
-
-.. note::
-
-  The rest of this section assumes you have CLI version 3.33.0. If you 
-  have an older version installed, you can upgrade to the latest version using the command
-
-  .. code-block:: console
-
-      ngc version upgrade 3.33.0
-  
-  See also the `NGC CLI documentation <https://docs.ngc.nvidia.com/cli/index.html>`__
-  for more information about available commands.
-
-You can see all information about available compute resources and ace instances
-with the command 
-
-.. code-block:: console
-
-    ngc base-command ace list
-
-Confirm that you can submit a job with the command
-
-.. code-block:: console
-
-    ngc base-command job run \
-      --name Job-001 --total-runtime 60s \
-      --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \
-      --ace <ace_name> --instance <instance_name> \
-      --commandline 'echo "Hello from DGX Cloud!"'
-
-replacing `<ace_name>` and `<instance_name>` with the name of the ace and instance you want 
-to execute the job on.
-You should now see that job listed when you run the command
-
-.. code-block:: console
-
-    ngc base-command job list
-
-Once it has completed you can download the job results using the command
-
-.. code-block:: console
-
-    ngc base-command result download <job_id>
-
-replacing `<job_id>` with the id of the job you just submitted.
-You should see a new folder named `<job_id>` with the job log that contains 
-the output "Hello from DGX Cloud!".
-
-For more information about how to use the NGC CLI to interact with DGX Cloud, 
-we refer to the `NGC CLI documentation <https://docs.ngc.nvidia.com/cli/index.html>`__.
-
-Use JupyterLab
-+++++++++++++++++++++++++++++++
-
-Once you can :ref:`run jobs on DGX Cloud <dgx-cloud-setup>`, you can launch an interactive job 
-to use CUDA-Q with `JupyterLab <https://jupyterlab.readthedocs.io/en/latest/>`__ 
-running on DGX Cloud:
-
-.. code-block:: console
-
-    ngc base-command job run \
-      --name Job-interactive-001 --total-runtime 600s \
-      --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \
-      --ace <ace_name> --instance <instance_name> \
-      --port 8888 --commandline 'jupyter-lab-setup <my-custom-token> --port=8888'
-
-Replace `<my-custom-token>` in the command above with a custom token that you can freely choose.
-You will use this token to authenticate with JupyterLab;
-Go to the `job portal <https://bc.ngc.nvidia.com/jobs>`__, click on the job you just launched, and click on the link
-under |:spellcheck-disable:|"URL/Hostname"|:spellcheck-enable:| in Service Mapped Ports. 
-
-.. note::
-
-  It may take a couple of minutes for DGX Cloud to launch and for the URL to become active, even after it appears in the Service Mapped Ports section;
-  if you encounter a "404: Not Found" error, be patient and try again in a couple of minutes.
-
-Once this URL opens, you should see the JupyterLab authentication page; enter the 
-token you selected above to get access to the running CUDA-Q container.
-On the left you should see a folder with tutorials. Happy coding!
-
-Use VS Code
-+++++++++++++++++++++++++++++++
-
-Once you can :ref:`run jobs on DGX Cloud <dgx-cloud-setup>`, you can launch an interactive job 
-to use CUDA-Q with a local installation of `VS Code <https://code.visualstudio.com/>`_, 
-or the `VS Code Web UI <https://vscode.dev/>`__, running on DGX Cloud:
-
-.. code-block:: console
-
-    ngc base-command job run \
-      --name Job-interactive-001 --total-runtime 600s \
-      --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \
-      --ace <ace_name> --instance <instance_name> \
-      --commandline 'vscode-setup tunnel --name cuda-quantum-dgx --accept-server-license-terms'
-
-Go to the `job portal <https://bc.ngc.nvidia.com/jobs>`__, click on the job you just launched, and select the "Log"
-tab. Once the job is running, you should see instructions there for how to connect to the device the job is running on.
-These instructions include a link to open and the code to enter on that page; follow the instructions to authenticate. 
-Once you have authenticated, you can either 
-`open VS Code in a web browser <https://vscode.dev/tunnel/cuda-quantum-dgx/home/cudaq/>`__, 
-or connect a local installation of VS Code.
-To connect a local installation of VS Code, make sure you have the 
-`Remote - Tunnels <https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-server>`__ extension installed,
-then open the Command Palette with `Ctrl+Shift+P`, enter "Remote Tunnels: Connect to Tunnel", 
-and enter `cuda-quantum-remote`. After the window reloaded, enter "File: Open Folder" in the Command Palette 
-to open the `/home/cudaq/` folder.
-
-You should see a pop up asking if you want to install the recommended extensions. Selecting to install them will
-configure VS Code with extensions for working with C++, Python, and Jupyter.
-You can always see the list of recommended extensions that aren't installed yet by clicking on the "Extensions" icon in the sidebar and navigating to the "Recommended" tab.
-
-If you enter "View: Show Explorer" in the Command Palette, you should see a folder with tutorials and examples
-to help you get started. Take a look at `Next Steps`_ to dive into CUDA-Q development.
-
-.. _additional-cuda-tools:
-
-Additional CUDA Tools
-------------------------------------
-
-CUDA-Q makes use of GPU-acceleration in certain backends and components. 
-Depending on how you installed CUDA-Q, you may need to install 
-certain CUDA libraries separately to take advantage of these.
-
-Installation via PyPI
-++++++++++++++++++++++++++++++++++++
-
-If you installed CUDA-Q via `PyPI <https://pypi.org/project/cuda-quantum>`__, please follow the installation instructions there to install the necessary CUDA dependencies.
-
-Installation In Container Images
-++++++++++++++++++++++++++++++++++++
-
-If you are using the CUDA-Q container image, the image already contains all necessary runtime libraries to use all CUDA-Q components. To take advantage of GPU-acceleration, make sure
-to enable GPU support when you launch the container, that is pass the `--gpus all` flag when launching 
-the container with Docker and the `--nv` flag when launching the container with Singularity.
-
-Note that the image does not contain all development dependencies for CUDA, such as, for example the `nvcc` compiler. You can install all CUDA development dependencies by running the command 
-
-.. code-block:: console
-
-    sudo apt-get install cuda-toolkit-12.0
-
-inside the container. Make sure the toolkit version you install matches the CUDA runtime installation in the container.
-Most Python packages that use GPU-acceleration, such as for example `CuPy <https://cupy.dev>`__, require an existing CUDA installation. 
-After installing the `cuda-toolkit-12.0` you can install CuPy for CUDA 12 with the command
-
-.. code-block:: console
-
-    python3 -m pip install cupy-cuda12x
-
-.. _cuda-dependencies-prebuilt-binaries:
-
-Installing Pre-built Binaries
-++++++++++++++++++++++++++++++++++++
-
-If you installed pre-built binaries for CUDA-Q, you will need to install 
-the necessary CUDA runtime libraries to use GPU-acceleration in CUDA-Q. 
-If you prefer to only install the minimal set of runtime libraries, the following 
-commands, for example, install the necessary packages for RHEL 8:
-
-.. literalinclude:: ../../../../scripts/configure_build.sh
-    :language: bash
-    :dedent:
-    :start-after: [>CUDARTInstall]
-    :end-before: [<CUDARTInstall]
-
-More detailed instructions for your platform can be found in the online documentation
-linked for that `CUDA version <https://developer.nvidia.com/cuda-toolkit-archive>`__. 
-Please make sure to install CUDA version 11.8 or newer, and confirm that your 
-`GPU driver <https://www.nvidia.com/download/index.aspx>`__ supports that version.
-While the above packages are sufficient to use GPU-acceleration within CUDA-Q, 
-we recommend installing the complete CUDA toolkit (`cuda-toolkit-12-0`) that also 
-includes the `nvcc` compiler. A separate CUDA-Q installer is available for CUDA 11, 
-built against version 11.8, and for CUDA 12, built against version 12.0.
-
-.. _distributed-computing-with-mpi:
-
-Distributed Computing with MPI
-------------------------------------
-
-CUDA-Q supports the Message Passing Interface (MPI) parallelism via a plugin interface.
-It is possible to activate or replace such an MPI plugin without re-installing or re-compiling CUDA-Q.
-MPI calls via CUDA-Q API for C++ and Python will be delegated to the currently activated plugin at runtime.
-
-.. tab:: Built-in MPI Support
-
-  The :ref:`CUDA-Q Docker image <install-docker-image>` is shipped with a pre-built MPI plugin based on an 
-  optimized OpenMPI installation included in the image. No action is required to use this plugin. 
-  We recommend using this plugin unless the container host has an existing MPI implementation other than OpenMPI.
-
-  If you are not using the Docker image, or are using the image on a system that has a
-  vendor-optimized MPI library pre-installed, please follow the instructions in the "Custom MPI Support" tab 
-  to enable MPI support.
-
-.. tab:: Custom MPI Support
-
-  If you are not using the Docker image, or are using the image on a system that has a
-  vendor-optimized MPI library pre-installed, CUDA-Q can be configured to use the local MPI installation by
-  manually activating a suitable plugin post-installation.
-  To do so, 
-
-  - Make sure the environment variable `CUDA_QUANTUM_PATH` points to the CUDA-Q installation directory. 
-    If you installed CUDA-Q using the `installer <install-prebuilt-binaries>`, or if you are using the CUDA-Q 
-    container image, this variable should already be defined. If you installed the CUDA-Q 
-    `Python wheels <install-python-wheels>`, set this variable to the directory listed under "Location" when you run the 
-    command `pip show cudaq`.
-
-  - Set the environment variable `MPI_PATH` to the location of your MPI installation. In particular, `${MPI_PATH}/include` 
-    is expected to contain the `mpi.h` header and `${MPI_PATH}/lib64` or `${MPI_PATH}/lib` is expected to contain `libmpi.so`.
-
-  - Execute the following command to complete the activation:
-
-    .. code-block:: console
-
-        bash $CUDA_QUANTUM_PATH/distributed_interfaces/activate_custom_mpi.sh
-
-  .. note::
-
-    HPC data centers often have a vendor-optimized MPI library pre-installed on their system. 
-    If you are using our container images, installing that MPI implementation in the container 
-    and manually activating the plugin following the steps above ensure the best performance,
-    and guarantee compatibility when MPI injection into a container occurs. 
-
-  Manually activating an MPI plugin replaces any existing plugin; After the initial activation, the newly built 
-  `libcudaq_distributed_interface_mpi.so` in the installation directory will subsequently always be used to 
-  handle CUDA-Q MPI calls.
-
-  .. note::
-
-    Executing the activation script from the CUDA-Q installation directory requires *write* permissions to that directory.
-    If you do not have the necessary permissions, copy the `distributed_interfaces` sub-directory to a local location and execute the 
-    activation script from there.
-
-    In this scenario, since the activated plugin (`libcudaq_distributed_interface_mpi.so`) is outside the CUDA-Q installation,
-    you must set the environment variable `$CUDAQ_MPI_COMM_LIB` to the path of that shared library.
-    This is done automatically when executing that activation script, but you may wish to persist that environment variable 
-    between bash sessions, e.g., by adding it to the `.bashrc` file.
-
-.. _updating-cuda-quantum:
-
-Updating CUDA-Q
---------------------------------
-
-If you installed the CUDA-Q Python wheels, you can update to the latest release
-by running the command 
-
-.. code-block:: console
-
-    python3 -m pip install --upgrade cudaq
-
-.. note::
-
-  Please check if you have an existing installation of the `cuda-quantum`, 
-  `cudaq-quantum-cu11`, or `cuda-quantum-cu12` package, 
-  and uninstall it prior to installing `cudaq`. The `cudaq` package supersedes the
-  `cuda-quantum` package and will install a suitable binary distribution (either 
-  `cuda-quantum-cu11` or `cuda-quantum-cu12`) for your system. Multiple versions 
-  of a CUDA-Q binary distribution will conflict with each other and not work properly.
-
-If you previously installed the CUDA-Q pre-built binaries, you should first uninstall your 
-current CUDA-Q installation before installing the new version using the installer. 
-To uninstall your current CUDA-Q version, run the command
-
-.. code-block:: console
-
-    sudo bash "${CUDA_QUANTUM_PATH}/uninstall.sh" -y
-
-The `uninstall.sh` script is generated during installation, and will remove all files 
-and folders that were created as part of the installation, whether they were modified 
-in the meantime or not. It does not remove any additional files that existed prior 
-to the installation or that you have added to the installation location since then. 
-You can then download and install the new version of CUDA-Q following the
-instructions :ref:`above <install-prebuilt-binaries>`.
-
-.. _dependencies-and-compatibility:
-
-Dependencies and Compatibility
---------------------------------
-
-CUDA-Q can be used to compile and run quantum programs on a CPU-only system, but a GPU is highly recommended and necessary to use the GPU-based simulators, see also :doc:`../backends/simulators`.
-
-The supported CPUs include x86_64 (x86-64-v3 architecture and newer) and ARM64 (ARM v8-A architecture and newer).
-
-.. note:: 
-
-  Some of the components included in the CUDA-Q Python wheels depend on an existing CUDA installation on your system. For more information about installing the CUDA-Q Python wheels, take a look at :ref:`this section <install-python-wheels>`.
-
-The following table summarizes the required components.
-
-.. list-table:: Supported Systems
-    :widths: 30 50
-    :header-rows: 0
-
-    * - CPU architectures
-      - x86_64, ARM64
-    * - Operating System
-      - Linux
-    * - Tested Distributions
-      - CentOS 8; Debian 11, 12; Fedora 38, 39; OpenSUSE/SLED/SLES 15.5, 15.6; RHEL 8, 9; Rocky 8, 9; Ubuntu 22.04, 24.04
-    * - Python versions
-      - 3.10+
-
-.. list-table:: Requirements for GPU Simulation
-    :widths: 30 50
-    :header-rows: 0
-
-    * - GPU Architectures
-      - Volta, Turing, Ampere, Ada, Hopper
-    * - NVIDIA GPU with Compute Capability
-      - 7.0+
-    * - CUDA
-      - 11.x (Driver 470.57.02+), 12.x (Driver 525.60.13+)
-
-Detailed information about supported drivers for different CUDA versions and be found `here <https://docs.nvidia.com/deploy/cuda-compatibility/>`__.
-
-
-.. _post-installation:
-
-Next Steps
-----------
-
-You can now compile and/or run the C++ and Python examples using the terminal.
-To open a terminal in VS Code, open the Command Palette with `Ctrl+Shift+P` and 
-enter "View: Show Terminal".
-
-.. image:: ../../_static/getToWork.png 
-
-The CUDA-Q image contains a folder with examples and tutorials in the :code:`/home/cudaq` directory. 
-These examples are provided to get you started with CUDA-Q and understanding 
-the programming and execution model. 
-If you are not using a container image, you can find these examples on our 
-`GitHub repository <https://github.com/NVIDIA/cuda-quantum>`__.
-
-Let's start by running a simple program to validate your installation.
-The samples contain an implementation of a 
-`Bernstein-Vazirani algorithm <https://en.wikipedia.org/wiki/Bernstein%E2%80%93Vazirani_algorithm>`__. 
-To run the example, execute the command:
-
-.. tab:: Python
-
-  .. code-block:: console
-
-      python examples/python/bernstein_vazirani.py --size 5
-
-.. tab:: C++
-
-  .. code-block:: console
-
-      nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp && ./a.out
-
-This will execute the program on the :ref:`default simulator <default-simulator>`, which will use GPU-acceleration if 
-a suitable GPU has been detected. To confirm that the GPU acceleration works, you can 
-increase the size of the secret string, and pass the target as a command line argument:
-
-.. tab:: Python
-
-  .. code-block:: console
-
-      python examples/python/bernstein_vazirani.py --size 25 --target nvidia
-
-.. tab:: C++
-
-  .. code-block:: console
-
-      nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp -DSIZE=25 --target nvidia && ./a.out
-
-This program should complete fairly quickly. Depending on the available memory on your GPU,
-you can set the size of the secret string to up to 28-32 when running on the `nvidia` target. 
-
-.. note::
-
-  If you get an error that the CUDA driver version is insufficient or no GPU has been detected,
-  check that you have enabled GPU support when launching the container by passing the `--gpus all` flag
-  (for :ref:`Docker <install-docker-image>`) or the `--nv` flag (for :ref:`Singularity <install-singularity-image>`).
-  If you are not running a container, you can execute the command `nvidia-smi` to confirm your setup;
-  if the command is unknown or fails, you do not have a GPU or do not have a driver installed. If the command
-  succeeds, please confirm that your CUDA and driver version matches the 
-  :ref:`supported versions <dependencies-and-compatibility>`.
-
-Let's compare that to using only your CPU:
-
-.. tab:: Python
-
-  .. code-block:: console
-
-      python examples/python/bernstein_vazirani.py --size 25 --target qpp-cpu
-
-.. tab:: C++
-
-  .. code-block:: console
-
-      nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp -DSIZE=25 --target qpp-cpu && ./a.out
-
-When you execute this command, the program simply seems to hang; that is because it takes
-a long time for the CPU-only backend to simulate 28+ qubits! Cancel the execution with `Ctrl+C`.
-
-You are now all set to start developing quantum applications using CUDA-Q!
-Please proceed to :doc:`Basics <../basics/basics>` for an introduction
-to the fundamental features of CUDA-Q.
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/integration/cmake_app.rst b/pr-2308/sphinx/using/integration/cmake_app.rst
deleted file mode 100644
index d54a761284..0000000000
--- a/pr-2308/sphinx/using/integration/cmake_app.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-CUDA-Q and CMake
-**********************
-
-CUDA-Q provides a full compiler toolchain for building 
-out quantum-classical applications. Since most C++ projects 
-provide some sort of integration with CMake, it becomes imperative 
-for our toolchain to integrate well with the CMake build system. 
-
-CUDA-Q provides CMake configuration files that allow downstream 
-application developers to create hybrid quantum-classical source code 
-and build it with the typical CMake workflow, which will natively 
-leverage :code:`nvq++` for compilation of CUDA-Q source files. 
-
-To see how this works, let's create a trivial example. Suppose we have 
-a folder named :code:`myCudaQApp` and within that folder we have 
-a simple CUDA-Q source file, like `ghz.cpp`. With a local 
-CUDA-Q installation present on the system, we can create the 
-following top-level :code:`CMakeLists.txt` file to build this file with 
-:code:`nvq++`:
-
-.. code:: bash 
-
-    cmake_minimum_required(VERSION 3.24 FATAL_ERROR)
-    project(myCudaQApp LANGUAGES CXX)
-    find_package(CUDAQ REQUIRED)
-    add_executable(ghz_exe ghz.cpp)
-
-To configure and build this file, all we need to do is execute the following from the top level of the :code:`myCudaQApp` folder:
-
-.. code:: bash 
-
-    mkdir build && cd build 
-    cmake .. -G Ninja -DCUDAQ_DIR="$CUDA_QUANTUM_PATH/lib/cmake/cudaq"
-    ninja
-    ./ghz_exe 
-
-Notice that by running the :code:`ninja` command with :code:`--verbose`, you'll see that 
-:code:`nvq++` was used to compile and link the executable. 
-
diff --git a/pr-2308/sphinx/using/integration/cuda_gpu.rst b/pr-2308/sphinx/using/integration/cuda_gpu.rst
deleted file mode 100644
index 4ad7c09f89..0000000000
--- a/pr-2308/sphinx/using/integration/cuda_gpu.rst
+++ /dev/null
@@ -1,114 +0,0 @@
-Using CUDA and CUDA-Q in a Project
-****************************************
-
-It may be the case that a project that uses CUDA-Q kernels may also
-want to use CUDA code to do computation on a GPU.  This is possible by using
-both the CUDA Toolkit and CUDA-Q tools. More about programming GPUs
-in CUDA can be found in the `Quick Start Guide
-<https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html>`__.
-
-Once the `nvcc` compiler is installed, it is possible to write
-CUDA kernels and have them execute on the system GPU. See NVIDIA's `An
-Easy Introduction to CUDA C and C++
-<https://developer.nvidia.com/blog/easy-introduction-cuda-c-and-c/>`__
-for more information on getting started with CUDA.
-
-CUDA code uses a unique syntax and is, typically, saved in a file with
-the extension `.cu`. For our example, assume we have written our
-CUDA code in the file `my_proj.cu`.
-
-CUDA-Q code is a library-based extension of C++ and uses
-standard conforming C++ syntax. Typically, a quantum kernel would be
-saved in a file with the `.cpp` extension. Again for our
-example, let's assume that we've written quantum kernels and saved
-them in the file `my_proj_quantum.cpp`.
-
-By default, CUDA-Q uses C++ 20 and builds source code against the 
-LLVM C++ standard library (`libc++`). To create a CUDA library that 
-can link against CUDA-Q code, make sure to define an API that does 
-not rely on C++ data structures that rely on a specific C++ toolchain
-for all functions intended to be called from CUDA-Q (see also 
-:ref:`clike-shim`). For example, if you define a CUDA kernel in `my_proj.cu`
-
-.. code-block:: cpp
-
-    template <typename CudaDataType>
-    __global__ void cudaSetFirstNElements(CudaDataType *sv, const CudaDataType *__restrict__ sv2, int64_t N) {
-    int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
-        if (i < N) {
-            sv[i].x = sv2[i].x;
-            sv[i].y = sv2[i].y;
-        } else {
-            sv[i].x = 0.0;
-            sv[i].y = 0.0;
-        }
-    }
-
-define the following template and all desired template specializations
-
-.. code-block:: cpp
-
-    template <typename CudaDataType>
-    void setFirstNElements(uint32_t n_blocks, 
-                           int32_t threads_per_block, 
-                           void *newDeviceStateVector, 
-                           void *deviceStateVector,
-                           std::size_t previousStateDimension) {
-        cudaSetFirstNElements<<<n_blocks, threads_per_block>>>(
-            reinterpret_cast<CudaDataType *>(newDeviceStateVector),
-            reinterpret_cast<CudaDataType *>(deviceStateVector),
-            previousStateDimension);
-    }
-
-    template void
-    setFirstNElements<cuFloatComplex>(uint32_t n_blocks, 
-                                      int32_t threads_per_block, 
-                                      void *newDeviceStateVector, 
-                                      void *deviceStateVector,
-                                      std::size_t previousStateDimension);
-
-    template void
-    setFirstNElements<cuDoubleComplex>(uint32_t n_blocks, 
-                                       int32_t threads_per_block, 
-                                       void *newDeviceStateVector, 
-                                       void *deviceStateVector,
-                                       std::size_t previousStateDimension);
-
-Create the corresponding header file `my_proj.h` declaring the `setFirstNElements`
-template. You can then call `setFirstNElements` from within `my_proj_quantum.cpp`
-by including `my_proj.h`, for example
-
-.. code-block:: cpp
-
-    #include "cuComplex.h"
-    #include "cuda_runtime.h"
-    #include "device_launch_parameters.h"
-    #include "my_proj.h"
-
-    using namespace my_kernels; // the namespace where you defined setFirstNElements
-
-    int main () {
-        const uint32_t n_blocks = 10;
-        const uint32_t threads_per_block = 5;
-
-        void *deviceStateVector;
-        cudaMalloc((void **)&deviceStateVector, 2 * sizeof(cuDoubleComplex));
-        // ...
-        void *newDeviceStateVector;
-        cudaMalloc((void **)&newDeviceStateVector, 2 * sizeof(cuDoubleComplex));
-        setFirstNElements<cuDoubleComplex>(n_blocks, threads_per_block,
-                                           newDeviceStateVector, deviceStateVector,
-                                           2);
-        return 0;
-    }
-
-To get an executable, compile the code with
-
-.. code-block:: bash
-
-    nvcc -c -std=c++17 -Xcompiler -fPIC my_proj.cu -o my_proj.o
-    nvq++ my_proj_quantum.cpp my_proj.o -I"${CUDA_HOME}/include/" -L"${CUDA_HOME}/lib64" -lcudart -o my_executable
-
-Above, :code:`nvq++` is used for the link step and will make sure the CUDA-Q
-runtime libraries are linked correctly to the executable program.
-The CUDA runtime is explicitly added to this command.
diff --git a/pr-2308/sphinx/using/integration/integration.rst b/pr-2308/sphinx/using/integration/integration.rst
deleted file mode 100644
index ce6d6ab094..0000000000
--- a/pr-2308/sphinx/using/integration/integration.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-Integration with other Software Tools
-**************************************
-
-.. toctree::
-  :maxdepth: 1
-
-  Downstream CMake Integration <cmake_app.rst>
-  Combining CUDA with CUDA-Q <cuda_gpu.rst>
-  Integrating with Third-Party Libraries <libraries.rst>
-
-Information about customizing or extending the CUDA-Q toolchain can be found in :doc:`../extending/extending`.
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/integration/libraries.rst b/pr-2308/sphinx/using/integration/libraries.rst
deleted file mode 100644
index 0e7c7c6fcd..0000000000
--- a/pr-2308/sphinx/using/integration/libraries.rst
+++ /dev/null
@@ -1,226 +0,0 @@
-Integrating with Third-Party Libraries
-****************************************
-
-CUDA-Q enables easily integrating quantum kernels with existing state-of-the-art tools
-for classical computing and high performance computing. While quantum kernels are expressed 
-in Python or C++, quantum hardware backends only support a very limited set of classical 
-instructions. Correspondingly, only a select set of libraries and functions that are 
-directly supported by the CUDA-Q compiler can be called from within quantum kernels.
-Code that is not marked as kernel code, on the other hand, can be used to invoke the 
-defined quantum kernels as well as any classical tools and libraries. CUDA-Q provides 
-a complete toolset to build complex applications running on multi-processor systems involving
-CPUs, GPUs and QPUs. 
-
-In Python, integration with any third-party Python package is handled
-automatically without any need for additional configuration. For C++, the rest of this
-document outlines how to integration with libraries that are compile with a different 
-compiler than `nvq++`.
-
-
-Calling a CUDA-Q library from C++
---------------------------------------
-
-The CUDA-Q compiler is a fully-featured C++ compiler able to process arbitrary C++ code outside
-of quantum kernels. In fact, the CUDA-Q installation includes and invokes a custom-built Clang 
-compiler under the hood. In general, it hence is not necessary to use a different compiler to 
-compile C++ host code that calls into quantum kernels; the host code is compiled along with 
-the quantum kernels into a single library or executable by the CUDA-Q toolchain.
-
-If you would still like to create a library that can be included in libraries and executables 
-built with a different toolchain, you can configure `nvq++` to include and encapsulate all its
-C++ dependencies, such that the CUDA-Q library only depends on C libraries. For example, 
-if you have a file `quantum_kernel.cpp` containing 
-
-.. code:: C++
-
-    #include "cudaq.h"
-    #include <iostream>
-    #include <sstream>
-
-    namespace quantum {
-
-    __qpu__ void bell() {
-      cudaq::qvector qs(2);
-      h(qs[0]);
-      cnot(qs[0], qs[1]);
-      mz(qs);
-    }
-
-    // Usually, one would probably return something like
-    // the most probably value in the distribution rather
-    // than the string dump. However, the bell kernel here
-    // has a 50/50 chance of returning 00 or 11.
-    const char* bell_distribution() {
-      auto dist = cudaq::sample(bell);
-
-      std::stringbuf buffer;
-      std::ostream os(&buffer);
-      dist.dump(os);
-      auto sample_results = buffer.str();
-
-      char* ptr = new char[sample_results.size() + 1];
-      strcpy(ptr, sample_results.c_str());
-      return ptr;
-    }
-
-    } // namespace quantum
-
-you can create such a library with the command
-
-.. code:: bash
-
-    nvq++ quantum_kernel.cpp -shared -static-libstdc++ -fPIC -o libquantum_kernel.so
-
-To use this library in a project built with an external toolchain, define a suitable 
-header file `quantum_kernel.h` containing
-
-.. code:: C++
-
-    namespace quantum {
-      const char* bell_distribution();
-    }
-
-and include it in your project. For example, if `external.cpp` contains
-
-.. code:: C++ 
-
-    #include <iostream>
-    #include "quantum_kernel.h"
-
-    int main () {
-      // some code
-      auto bell_dist = quantum::bell_distribution();
-      std::cout << bell_dist << std::endl;
-      delete[] bell_dist;
-      // more code
-      return 0;
-    }
-
-you can compile it with `g++` and link it with the built `libquantum_kernel.so` library
-using the command
-
-.. code:: bash
-
-    g++ external.cpp -lquantum_kernel -L"$(pwd)" -Wl,-rpath="$(pwd)" -I"$(pwd)"
-
-Note that the API defined in `quantum_kernel.h` does not depend on any data types that 
-rely on the specific implementation of the C++ standard library. For example, we return a 
-`const char*` instead of a `std::string` value. This is important to ensure that the data 
-is handled correctly regardless of which C++ standard library is used. If you include
-data types that depend on a specific C++ ABI, the linker should detect this incompatibility
-and should generate an error. See the section :ref:`clike-shim` below to learn more about 
-what to watch out for when defining an API to interface between libraries that are built 
-with different compilers. 
-
-
-Calling an C++ library from CUDA-Q
---------------------------------------
-
-CUDA-Q comes with a complete C++ toolchain, and is configured to use its own C++ standard
-library by default. When calling a third-party library from within a CUDA-Q application,
-the quantum kernels and the surrounding application code are compiled with the `nvq++` compiler, 
-whereas the third-party library may be compiled with a different toolchain. 
-For most widely distributed libraries, this will work out of the box. The exception are 
-third-party libraries that are pre-compiled (opposed to being header-only libraries) 
-and dynamically depend on a different C++ standard library than CUDA-Q uses. 
-This is the case for a library `third_party_library.so` if `libstdc++.so` is listed 
-in the output of the command
-
-.. code:: bash
-
-    ldd third_party_library.so
-
-To use such a library with CUDA-Q, you have to install the static version of the 
-library. The `nvq++` compiler can be configured to link a static library 
-`libexternal_library.a` instead of the shared one with the same flags as GCC/Clang:
-
-.. code:: bash
-
-    nvq++ my_code.cpp -Wl,-Bstatic -lexternal_library -Wl,-Bdynamic ...
-
-If the compilation fails due to undefined symbols during linking, then `libexternal_library.a`
-does not include the C++ standard library components it depends on. In this case, you can create a 
-suitable static library that contains the external library as well as its `libstdc++` dependencies
-by combining `libexternal_library.a` and `libstdc++.a` into a single flattened archive.
-For example, if the external library is built with GCC and located in `/usr/lib`,
-the following commands create a suitable archive using the GNU archiver:
-
-.. code:: bash
-
-    ar cruT libexternal_library_complete.a /usr/lib/libexternal_library.a "$(g++ -print-file-name=libstdc++.a)"
-    echo -e 'create libexternal_library_complete.a\naddlib libexternal_library_complete.a\nsave\nend' | ar -M
-
-The above commands create a new static library `libexternal_library_complete.a` in the current directory. 
-To use it, adjust the `nvq++` command accordingly:
-
-.. code:: bash
-
-    nvq++ my_code.cpp -L. -Wl,-Bstatic -lexternal_library_complete -Wl,-Bdynamic ...
-
-.. note::
-
-  If the third-party library functions you are calling from your CUDA-Q code 
-  consume or produce data types that explicitly depend on the C++ standard library implementation,
-  a small shim is needed to properly encapsulate these dependencies, as described in
-  :ref:`clike-shim`.
-
-
-.. _clike-shim:
-
-Interfacing between binaries compiled with a different toolchains
----------------------------------------------------------------------
-
-To avoid incompatibilities between different C++ implementations, 
-two binaries built using a different C++ compiler should be careful when passing data types between them
-that rely on a specific C++ implementation. While primitive data types can be passed without
-issues, attempting to pass a string, for example, will result in a linking failure; a `char*` must 
-be passed instead of a string.
-
-Most other data types, including templated types, can be passed as long as their alignment matches.
-While different compilers generally align data differently, it is possible to force a particular 
-alignment when defining the data type or value. For example, if we define a struct
-
-.. code:: C++
-
-    #pragma pack(push, 4)
-    template <typename ScalarType>
-    struct Entry {
-      bool is_integral;
-      ScalarType value;
-    };
-    #pragma pack(pop)
-
-the `pragma` directives ensure that each item will be 4-byte aligned, regardless of the 
-default setting that can differ between compilers.
-
-Even function pointers can usually be passed, as long as their argument and return types can be passed.
-When using templated functions and data types, the header file should only include their declaration as well 
-as explicit instantiation for all valid template arguments. For example,
-
-.. code:: C++
-
-    template <typename CudaDataType>
-    void initializeDeviceStateVector(uint32_t n_blocks, 
-                                    int32_t threads_per_block, 
-                                    void *deviceStateVector,
-                                    std::size_t stateDimension) {
-      cudaInitializeDeviceStateVector<<<n_blocks, threads_per_block>>>(
-        reinterpret_cast<CudaDataType *>(deviceStateVector), stateDimension);
-    }
-
-    template void
-    initializeDeviceStateVector<cuFloatComplex>(uint32_t n_blocks, 
-                                    int32_t threads_per_block, 
-                                    void *deviceStateVector,
-                                    std::size_t stateDimension);
-
-    template void
-    initializeDeviceStateVector<cuDoubleComplex>(uint32_t n_blocks, 
-                                    int32_t threads_per_block, 
-                                    void *deviceStateVector,
-                                    std::size_t stateDimension);
-
-The most cumbersome aspect when libraries built with different toolchains are combined into a single executable
-is exception handling. Since the handling relies on a specific ABI, an thrown exception is not necessarily 
-recognized as such in a library that uses a different ABI implementation. While matching the exception type 
-may not work as expected, a catch-all will reliably catch any thrown exception. 
diff --git a/pr-2308/sphinx/using/python.html b/pr-2308/sphinx/using/python.html
deleted file mode 100644
index e469576d37..0000000000
--- a/pr-2308/sphinx/using/python.html
+++ /dev/null
@@ -1 +0,0 @@
-<html><head><meta http-equiv="refresh" content="0; url=examples/examples.html"></head></html>
\ No newline at end of file
diff --git a/pr-2308/sphinx/using/quick_start.rst b/pr-2308/sphinx/using/quick_start.rst
deleted file mode 100644
index d020dbbb19..0000000000
--- a/pr-2308/sphinx/using/quick_start.rst
+++ /dev/null
@@ -1,131 +0,0 @@
-Quick Start
-===================
-
-**NVIDIA CUDA-Q**
-
-CUDA-Q streamlines hybrid application development and promotes productivity and scalability
-in quantum computing. It offers a unified programming model designed for a hybrid
-setting |---| that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for 
-programming in Python and in C++. Learn more about the `key benefits of CUDA-Q <https://developer.nvidia.com/cuda-q>`_.
-
-This Quick Start page guides you through installing CUDA-Q and running your first program.
-If you have already installed and configured CUDA-Q, or if you are using our 
-`Docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum>`_, you can move directly to our
-:doc:`Basics Section <basics/basics>`. More information about working with containers and Docker alternatives can be 
-found in our complete :doc:`Installation Guide <install/install>`.
-
-Install CUDA-Q
-----------------------------
-
-.. tab:: Python
-
-   To develop CUDA-Q applications using Python, 
-   please follow the instructions for `installing CUDA-Q <https://pypi.org/project/cuda-quantum/>`_ from PyPI. 
-   If you have an NVIDIA GPU, make sure to also follow the instructions for enabling GPU-acceleration.
-
-   .. include:: ../../../python/README.md
-      :parser: myst_parser.sphinx_
-      :start-after: (Begin complete install)
-      :end-before: (End complete install)
-
-   Once you completed the installation, please follow the instructions
-   :ref:`below <validate-installation>` to run your first CUDA-Q program!
-
-.. tab:: C++
-
-   To develop CUDA-Q applications using C++, please make sure you have a C++ toolchain installed
-   that supports C++20, for example `g++` version 11 or newer.
-   Download the `install_cuda_quantum` file for your processor architecture and CUDA version (`_cu11` suffix for CUDA 11 and `_cu12` suffix for CUDA 12) 
-   from the assets of the respective `GitHub release <https://github.com/NVIDIA/cuda-quantum/releases>`__; 
-   hat is the file with the `aarch64` extension for ARM processors, and the one with `x86_64` for, e.g., Intel and AMD processors.
-
-   To install CUDA-Q, execute the commands
-
-   .. code-block:: bash
-
-      sudo -E bash install_cuda_quantum*.$(uname -m) --accept 
-      . /etc/profile
-
-   If you have an NVIDIA GPU, please also install the `CUDA Toolkit <https://developer.nvidia.com/cuda-downloads>`__ to enable GPU-acceleration within CUDA-Q.
-
-   Please see the complete :ref:`installation guide <install-prebuilt-binaries>` for more details, including
-
-   - a list of :ref:`supported operating systems <dependencies-and-compatibility>`, 
-   - instructions on how to :ref:`enable MPI parallelization <distributed-computing-with-mpi>` within CUDA-Q, and
-   - information about :ref:`updating CUDA-Q <updating-cuda-quantum>`.
-
-   Once you completed the installation, please follow the instructions
-   :ref:`below <validate-installation>` to run your first CUDA-Q program!   
-
-.. |---|   unicode:: U+2014 .. EM DASH
-   :trim:
-
-.. _validate-installation:
-
-Validate your Installation
-----------------------------
-
-Let's run a simple program to validate your installation.
-The quantum kernel in the following program creates and measures the state 
-:math:`(|00\rangle + |11\rangle) / \sqrt{2}`. That means each kernel execution should 
-either yield `00` or `11`. The program samples, meaning it executes, the kernel 1000 times
-and prints how many times each output was measured. On average, the values `00` and `11`
-should be observed around 500 times each.
-
-.. tab:: Python
-
-   Create a file titled `program.py`, containing the following code:
-
-   .. literalinclude:: /snippets/python/quick_start.py
-      :language: python
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-   Run this program as you do any other Python program, for example:
-
-   .. code-block:: console
-
-      python3 program.py
-
-.. tab:: C++
-
-   Create a file titled `program.cpp`, containing the following code:
-
-   .. literalinclude:: /snippets/cpp/quick_start.cpp
-      :language: cpp
-      :start-after: [Begin Documentation]
-      :end-before: [End Documentation]
-
-   Compile the program using the `nvq++` compiler and run the built application with the following command:
-
-   .. code-block:: console
-
-      nvq++ program.cpp -o program.x && ./program.x
-
-If you have an NVIDIA GPU the program uses GPU acceleration by default.
-To confirm that this works as expected and to see the effects of GPU acceleration, you can 
-increase the numbers of qubits the program uses to 28 and
-compare the time to execute the program on the 
-`nvidia` target (:ref:`GPU-accelerated statevector simulator <cuQuantum single-GPU>`) to the time when setting the target to `qpp-cpu` (:ref:`OpenMP parallelized CPU-only statevector simulator <OpenMP CPU-only>`):
-
-.. tab:: Python
-
-   .. code-block:: console
-
-      python3 program.py 28 --target nvidia
-
-.. tab:: C++
-
-   .. code-block:: console
-
-      nvq++ program.cpp -o program.x --target nvidia && ./program.x 28
-
-When you change the target to `qpp-cpu`, the program simply seems to hang; that is because it takes a long time for the CPU-only backend to simulate 28+ qubits! Cancel the execution with `Ctrl+C`.
-
-For more information about enabling GPU-acceleration, please see
-our complete :ref:`Installation Guide <additional-cuda-tools>`.
-For further information on available targets, see :doc:`Backends <backends/backends>`.
-
-You are now all set to start developing quantum applications using CUDA-Q!
-Please proceed to :doc:`Basics <basics/basics>` for an introduction
-to the fundamental features of CUDA-Q.
diff --git a/pr-2308/sphinx/versions.rst b/pr-2308/sphinx/versions.rst
deleted file mode 100644
index 9394f5a52e..0000000000
--- a/pr-2308/sphinx/versions.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-************************
-CUDA-Q Versions
-************************
-
-.. include:: releases.rst
-    :start-line: 4
diff --git a/pr-2308/using/applications.html b/pr-2308/using/applications.html
deleted file mode 100644
index 7087335c04..0000000000
--- a/pr-2308/using/applications.html
+++ /dev/null
@@ -1,863 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>CUDA-Q Applications &mdash; NVIDIA CUDA-Q  documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/tabs.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/cudaq_override.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/sg_gallery.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-gallery.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/_static/cudaq_override.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/tabs.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
-    <script src="../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../genindex.html" />
-    <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Bernstein-Vazirani Algorithm" href="../applications/python/bernstein_vazirani.html" />
-    <link rel="prev" title="Using Quantum Hardware Providers" href="examples/hardware_providers.html" /> 
-</head>
-
-<body class="wy-body-for-nav"> 
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-side-nav-search"  style="background: #76b900" > 
-
-          
-          
-          <a href="../index.html" class="icon icon-home">
-            NVIDIA CUDA-Q
-          </a>
-              <div class="version">
-                amd64-pr-2308
-              </div>
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
-    <input type="hidden" name="check_keywords" value="yes" />
-    <input type="hidden" name="area" value="default" />
-  </form>
-</div>
-
-  <style>
-    /* Sidebar header (and topbar for mobile) */
-    .wy-side-nav-search, .wy-nav-top {
-      background: #76b900;
-    }
-
-    .wy-side-nav-search a:link, .wy-nav-top a:link {
-      color: #fff;
-    }
-    .wy-side-nav-search a:visited, .wy-nav-top a:visited {
-      color: #fff;
-    }
-    .wy-side-nav-search a:hover, .wy-nav-top a:hover {
-      color: #fff;
-    }
-
-    .wy-menu-vertical a:link, .wy-menu-vertical a:visited {
-      color: #d9d9d9
-    }
-
-    .wy-menu-vertical a:active {
-      background-color: #76b900
-    }
-
-    .wy-side-nav-search>div.version {
-      color: rgba(0, 0, 0, 0.3)
-    }
-
-    /* override table width restrictions */
-    .wy-table-responsive table td, .wy-table-responsive table th {
-        white-space: normal;
-    }
-
-    .wy-table-responsive {
-        margin-bottom: 24px;
-        max-width: 100%;
-        overflow: visible;
-    }
-  </style>
-  
-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/quantum_operations.html#measurements">Measurements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#id2">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#utilities">Utilities</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.translate"><code class="docutils literal notranslate"><span class="pre">translate()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.evolve_async"><code class="docutils literal notranslate"><span class="pre">evolve_async()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#backend-configuration">Backend Configuration</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.has_target"><code class="docutils literal notranslate"><span class="pre">has_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_target"><code class="docutils literal notranslate"><span class="pre">get_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_targets"><code class="docutils literal notranslate"><span class="pre">get_targets()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.set_noise"><code class="docutils literal notranslate"><span class="pre">set_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.unset_noise"><code class="docutils literal notranslate"><span class="pre">unset_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.initialize_cudaq"><code class="docutils literal notranslate"><span class="pre">initialize_cudaq()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.num_available_gpus"><code class="docutils literal notranslate"><span class="pre">num_available_gpus()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.set_random_seed"><code class="docutils literal notranslate"><span class="pre">set_random_seed()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#data-types">Data Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.SimulationPrecision"><code class="docutils literal notranslate"><span class="pre">SimulationPrecision</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Target"><code class="docutils literal notranslate"><span class="pre">Target</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.State"><code class="docutils literal notranslate"><span class="pre">State</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Tensor"><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.ComplexMatrix"><code class="docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Schedule"><code class="docutils literal notranslate"><span class="pre">Schedule</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.integrator.BaseIntegrator"><code class="docutils literal notranslate"><span class="pre">BaseIntegrator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.AsyncEvolveResult"><code class="docutils literal notranslate"><span class="pre">AsyncEvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.OperatorSum"><code class="docutils literal notranslate"><span class="pre">OperatorSum</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ElementaryOperator"><code class="docutils literal notranslate"><span class="pre">ElementaryOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ProductOperator"><code class="docutils literal notranslate"><span class="pre">ProductOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.RydbergHamiltonian"><code class="docutils literal notranslate"><span class="pre">RydbergHamiltonian</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ScalarOperator"><code class="docutils literal notranslate"><span class="pre">ScalarOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.definitions.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.y"><code class="docutils literal notranslate"><span class="pre">spin.y()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.z"><code class="docutils literal notranslate"><span class="pre">spin.z()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.cudm_state.CuDensityMatState"><code class="docutils literal notranslate"><span class="pre">CuDensityMatState</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.cudm_state.to_cupy_array"><code class="docutils literal notranslate"><span class="pre">to_cupy_array()</span></code></a></li>
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-<a class="reference internal" href="../applications/python/logical_aim_sqale.html"None>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/expectation_values.html">   Computing Expectation Values</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
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-  <section id="cuda-q-dynamics">
-<h1>CUDA-Q Dynamics<a class="headerlink" href="#cuda-q-dynamics" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q enables the design, simulation and execution of quantum dynamics via
-the <code class="docutils literal notranslate"><span class="pre">evolve</span></code> API. Specifically, this API allows us to solve the time evolution
-of quantum systems or models. In the simulation mode, CUDA-Q provides the <code class="docutils literal notranslate"><span class="pre">dynamics</span></code>
-backend target, which is based on the cuQuantum library, optimized for performance and scale
-on NVIDIA GPU.</p>
-<section id="quick-start">
-<h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading">¶</a></h2>
-<p>In the example below, we demonstrate a simple time evolution simulation workflow comprising of the
-following steps:</p>
-<ol class="arabic simple">
-<li><p>Define a quantum system model</p></li>
-</ol>
-<p>A quantum system model is defined by a Hamiltonian.
-For example, a superconducting <a class="reference external" href="https://en.wikipedia.org/wiki/Transmon">transmon</a> qubit can be modeled by the following Hamiltonian</p>
-<div class="math notranslate nohighlight">
-\[H = \frac{\omega_z}{2} \sigma_z + \omega_x \cos(\omega_d t)\sigma_x,\]</div>
-<p>where <span class="math notranslate nohighlight">\(\sigma_z\)</span> and <span class="math notranslate nohighlight">\(\sigma_x\)</span> are Pauli Z and X operators, respectively.</p>
-<p>Using CUDA-Q <code class="code docutils literal notranslate"><span class="pre">operator</span></code>, the above time-dependent Hamiltonian can be set up as follows.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">cudaq.operator</span> <span class="kn">import</span> <span class="o">*</span>
-
-<span class="c1"># Qubit Hamiltonian</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">omega_z</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="c1"># Add modulated driving term to the Hamiltonian</span>
-<span class="n">hamiltonian</span> <span class="o">+=</span> <span class="n">omega_x</span> <span class="o">*</span> <span class="n">ScalarOperator</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">omega_d</span> <span class="o">*</span> <span class="n">t</span><span class="p">))</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span>
-    <span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In particular, <code class="code docutils literal notranslate"><span class="pre">ScalarOperator</span></code> provides an easy way to model arbitrary time-dependent control signals.
-Details about CUDA-Q <code class="code docutils literal notranslate"><span class="pre">operator</span></code>, including builtin operators that it supports can be found <a class="reference internal" href="#operators"><span class="std std-ref">here</span></a>.</p>
-<ol class="arabic simple" start="2">
-<li><p>Setup the evolution simulation</p></li>
-</ol>
-<p>The below code snippet shows how to simulate the time-evolution of the above system
-with <code class="code docutils literal notranslate"><span class="pre">cudaq.evolve</span></code>.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">cupy</span> <span class="k">as</span> <span class="nn">cp</span>
-
-<span class="c1"># Set the target to our dynamics simulator</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;dynamics&quot;</span><span class="p">)</span>
-
-<span class="c1"># Dimensions of sub-systems: a single two-level system.</span>
-<span class="n">dimensions</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
-
-<span class="c1"># Initial state of the system (ground state).</span>
-<span class="n">rho0</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">from_data</span><span class="p">(</span>
-    <span class="n">cp</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">cp</span><span class="o">.</span><span class="n">complex128</span><span class="p">))</span>
-
-<span class="c1"># Schedule of time steps.</span>
-<span class="n">steps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">t_final</span><span class="p">,</span> <span class="n">n_steps</span><span class="p">)</span>
-<span class="n">schedule</span> <span class="o">=</span> <span class="n">Schedule</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;t&quot;</span><span class="p">])</span>
-
-<span class="c1"># Run the simulation.</span>
-<span class="n">evolution_result</span> <span class="o">=</span> <span class="n">evolve</span><span class="p">(</span><span class="n">hamiltonian</span><span class="p">,</span>
-                          <span class="n">dimensions</span><span class="p">,</span>
-                          <span class="n">schedule</span><span class="p">,</span>
-                          <span class="n">rho0</span><span class="p">,</span>
-                          <span class="n">observables</span><span class="o">=</span><span class="p">[</span><span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
-                                       <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
-                                       <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)],</span>
-                          <span class="n">collapse_operators</span><span class="o">=</span><span class="p">[],</span>
-                          <span class="n">store_intermediate_results</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Specifically, we need to set up the simulation by providing:</p>
-<ul class="simple">
-<li><p>The system model in terms of a Hamiltonian as well as any decoherence terms, so-called <code class="code docutils literal notranslate"><span class="pre">collapse_operators</span></code>.</p></li>
-<li><p>The dimensionality of component systems in the model. CUDA-Q <code class="code docutils literal notranslate"><span class="pre">evolve</span></code> allows users to model arbitrary multi-level systems, such as photonic Fock space.</p></li>
-<li><p>The initial quantum state.</p></li>
-<li><p>The time schedule, aka time steps, of the evolution.</p></li>
-<li><p>Any ‘observable’ operator that we want to measure the expectation value with respect to the evolving state.</p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>By default, <code class="code docutils literal notranslate"><span class="pre">evolve</span></code> will only return the final state and expectation values.
-To save intermediate results (at each time step specified in the schedule),
-the <code class="code docutils literal notranslate"><span class="pre">store_intermediate_results</span></code> flag must be set to <code class="code docutils literal notranslate"><span class="pre">True</span></code>.</p>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Retrieve and plot the results</p></li>
-</ol>
-<p>Once the simulation is complete, we can retrieve the final state and the expectation values
-as well as intermediate values at each time step (with <code class="code docutils literal notranslate"><span class="pre">store_intermediate_results=True</span></code>).</p>
-<p>For example, we can plot the Pauli expectation value for the above simulation as follows.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_result</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">idx</span><span class="p">,</span> <span class="n">res</span><span class="p">:</span> <span class="p">[</span>
-    <span class="n">exp_vals</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="k">for</span> <span class="n">exp_vals</span> <span class="ow">in</span> <span class="n">res</span><span class="o">.</span><span class="n">expectation_values</span><span class="p">()</span>
-<span class="p">]</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">get_result</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">evolution_result</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">get_result</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">evolution_result</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">get_result</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">evolution_result</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Expectation value&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Time&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s2">&quot;Sigma-X&quot;</span><span class="p">,</span> <span class="s2">&quot;Sigma-Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Sigma-Z&quot;</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In particular, for each time step, <code class="code docutils literal notranslate"><span class="pre">evolve</span></code> captures an array of expectation values, one for each
-observable. Hence, we convert them into sequences for plotting purposes.</p>
-</section>
-<section id="operator">
-<h2>Operator<a class="headerlink" href="#operator" title="Permalink to this heading">¶</a></h2>
-<p id="operators">CUDA-Q provides builtin definitions for commonly-used operators,
-such as the ladder operators (<span class="math notranslate nohighlight">\(a\)</span> and <span class="math notranslate nohighlight">\(a^\dagger\)</span>) of a harmonic oscillator,
-the Pauli spin operators for a two-level system, etc.</p>
-<p>Here is a list of those operators.</p>
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Builtin Operators</span><a class="headerlink" href="#id1" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 29%" />
-<col style="width: 71%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Description</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">identity</span></code></p></td>
-<td><p>Identity operator</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">zero</span></code></p></td>
-<td><p>Zero or null operator</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">annihilate</span></code></p></td>
-<td><p>Bosonic annihilation operator (<span class="math notranslate nohighlight">\(a\)</span>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">create</span></code></p></td>
-<td><p>Bosonic creation operator (<span class="math notranslate nohighlight">\(a^\dagger\)</span>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">number</span></code></p></td>
-<td><p>Number operator of a bosonic mode (equivalent to <span class="math notranslate nohighlight">\(a^\dagger a\)</span>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">parity</span></code></p></td>
-<td><p>Parity operator of a bosonic mode (defined as <span class="math notranslate nohighlight">\(e^{i\pi a^\dagger a}\)</span>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">displace</span></code></p></td>
-<td><p>Displacement operator of complex amplitude <span class="math notranslate nohighlight">\(\alpha\)</span> (<code class="code docutils literal notranslate"><span class="pre">displacement</span></code>). It is defined as <span class="math notranslate nohighlight">\(e^{\alpha a^\dagger - \alpha^* a}\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">squeeze</span></code></p></td>
-<td><p>Squeezing operator of complex squeezing amplitude <span class="math notranslate nohighlight">\(z\)</span> (<code class="code docutils literal notranslate"><span class="pre">squeezing</span></code>). It is defined as <span class="math notranslate nohighlight">\(\exp(\frac{1}{2}(z^*a^2 - z a^{\dagger 2}))\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">position</span></code></p></td>
-<td><p>Position operator (equivalent to <span class="math notranslate nohighlight">\((a^\dagger + a)/2\)</span>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">momentum</span></code></p></td>
-<td><p>Momentum operator (equivalent to <span class="math notranslate nohighlight">\(i(a^\dagger - a)/2\)</span>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">spin.x</span></code></p></td>
-<td><p>Pauli <span class="math notranslate nohighlight">\(\sigma_x\)</span> operator</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">spin.y</span></code></p></td>
-<td><p>Pauli <span class="math notranslate nohighlight">\(\sigma_y\)</span> operator</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">spin.z</span></code></p></td>
-<td><p>Pauli <span class="math notranslate nohighlight">\(\sigma_z\)</span> operator</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">spin.plus</span></code></p></td>
-<td><p>Pauli raising (<span class="math notranslate nohighlight">\(\sigma_+\)</span>) operator</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">spin.minus</span></code></p></td>
-<td><p>Pauli lowering (<span class="math notranslate nohighlight">\(\sigma_-\)</span>) operator</p></td>
-</tr>
-</tbody>
-</table>
-<p>As an example, let’s look at the Jaynes-Cummings model, which describes
-the interaction between a two-level atom and a light (Boson) field.</p>
-<p>Mathematically, the Hamiltonian can be expressed as</p>
-<div class="math notranslate nohighlight">
-\[H = \omega_c a^\dagger a + \omega_a \frac{\sigma_z}{2} + \frac{\Omega}{2}(a\sigma_+ + a^\dagger \sigma_-).\]</div>
-<p>This Hamiltonian can be converted to CUDA-Q <code class="code docutils literal notranslate"><span class="pre">Operator</span></code> representation with</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">omega_c</span> <span class="o">*</span> <span class="n">operators</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">operators</span><span class="o">.</span><span class="n">annihilate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> \
-                <span class="o">+</span> <span class="p">(</span><span class="n">omega_a</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> \
-                <span class="o">+</span> <span class="p">(</span><span class="n">Omega</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">operators</span><span class="o">.</span><span class="n">annihilate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">plus</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">operators</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">minus</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In the above code snippet, we map the cavity light field to degree index 1 and the two-level atom to degree index 0.
-The description of composite quantum system dynamics is independent from the Hilbert space of the system components.
-The latter is specified by the dimension map that is provided to the <code class="code docutils literal notranslate"><span class="pre">cudaq.evolve</span></code> call.</p>
-</section>
-<section id="time-dependent-dynamics">
-<h2>Time-Dependent Dynamics<a class="headerlink" href="#time-dependent-dynamics" title="Permalink to this heading">¶</a></h2>
-<p id="time-dependent">In the previous examples of operator construction, we assumed that the systems under consideration were described by time-independent Hamiltonian.
-However, we may want to simulate systems whose Hamiltonian operators have explicit time dependence.</p>
-<p>CUDA-Q provides multiple ways to construct time-dependent operators.</p>
-<ol class="arabic simple">
-<li><p>Time-dependent coefficient</p></li>
-</ol>
-<p>CUDA-Q <code class="code docutils literal notranslate"><span class="pre">ScalarOperator</span></code> can be used to wrap a Python function that returns the coefficient value at a specific time.</p>
-<p>As an example, we will look at a time-dependent Hamiltonian of the form <span class="math notranslate nohighlight">\(H = H_0 + f(t)H_1\)</span>,
-where <span class="math notranslate nohighlight">\(f(t)\)</span> is the time-dependent driving strength given as <span class="math notranslate nohighlight">\(cos(\omega t)\)</span>.</p>
-<p>The following code sets up the problem</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the static (drift) and control terms</span>
-<span class="n">H0</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">H1</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">H</span> <span class="o">=</span> <span class="n">H0</span> <span class="o">+</span> <span class="n">ScalarOperator</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">omega</span> <span class="o">*</span> <span class="n">t</span><span class="p">))</span> <span class="o">*</span> <span class="n">H1</span>
-</pre></div>
-</div>
-</div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Time-dependent operator</p></li>
-</ol>
-<p>We can also construct a time-dependent operator from a function that returns a complex matrix representing the time dynamics of
-that operator.</p>
-<p>As an example, let’s looks at the <a class="reference external" href="https://en.wikipedia.org/wiki/Displacement_operator">displacement operator</a>. It can be defined as follows:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">scipy</span>
-<span class="kn">from</span> <span class="nn">cudaq.operator</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">numpy.typing</span> <span class="kn">import</span> <span class="n">NDArray</span>
-
-
-<span class="k">def</span> <span class="nf">displacement_matrix</span><span class="p">(</span>
-        <span class="n">dimension</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">displacement</span><span class="p">:</span> <span class="n">NumericType</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">NDArray</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">complexfloating</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns the displacement operator matrix.</span>
-<span class="sd">    Args:</span>
-<span class="sd">        displacement: Amplitude of the displacement operator.</span>
-<span class="sd">            See also https://en.wikipedia.org/wiki/Displacement_operator.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">displacement</span> <span class="o">=</span> <span class="nb">complex</span><span class="p">(</span><span class="n">displacement</span><span class="p">)</span>
-    <span class="n">term1</span> <span class="o">=</span> <span class="n">displacement</span> <span class="o">*</span> <span class="n">operators</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to_matrix</span><span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="n">dimension</span><span class="p">})</span>
-    <span class="n">term2</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(</span><span class="n">displacement</span><span class="p">)</span> <span class="o">*</span> <span class="n">operators</span><span class="o">.</span><span class="n">annihilate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to_matrix</span><span class="p">(</span>
-        <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">dimension</span><span class="p">})</span>
-    <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">expm</span><span class="p">(</span><span class="n">term1</span> <span class="o">-</span> <span class="n">term2</span><span class="p">)</span>
-
-
-<span class="c1"># The second argument here indicates the the defined operator</span>
-<span class="c1"># acts on a single degree of freedom, which can have any dimension.</span>
-<span class="c1"># An argument [2], for example, would indicate that it can only</span>
-<span class="c1"># act on a single degree of freedom with dimension two.</span>
-<span class="n">ElementaryOperator</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="s2">&quot;displace&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">displacement_matrix</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">displacement</span><span class="p">(</span><span class="n">degree</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ElementaryOperator</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Instantiates a displacement operator acting on the given degree of freedom.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">ElementaryOperator</span><span class="p">(</span><span class="s2">&quot;displace&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">degree</span><span class="p">])</span>
-
-
-</pre></div>
-</div>
-</div>
-</div>
-<p>The defined operator is parameterized by the <code class="code docutils literal notranslate"><span class="pre">displacement</span></code> amplitude. To create simulate the evolution of an
-operator under a time dependent displacement amplitude, we can define how the amplitude changes in time:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--6-input--1" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># Define a system consisting of a single degree of freedom (0) with dimension 3.</span>
-<span class="n">system_dimensions</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
-<span class="n">system_operator</span> <span class="o">=</span> <span class="n">displacement</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="c1"># Define the time dependency of the system operator as a schedule that linearly</span>
-<span class="c1"># increases the displacement parameter from 0 to 1.</span>
-<span class="n">time_dependence</span> <span class="o">=</span> <span class="n">Schedule</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="p">[</span><span class="s1">&#39;displacement&#39;</span><span class="p">])</span>
-<span class="n">initial_state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">from_data</span><span class="p">(</span>
-    <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
-
-<span class="c1"># Simulate the evolution of the system under this time dependent operator.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="n">system_operator</span><span class="p">,</span> <span class="n">system_dimensions</span><span class="p">,</span> <span class="n">time_dependence</span><span class="p">,</span> <span class="n">initial_state</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Let’s say we want to add a squeezing term to the system operator. We can independently vary the squeezing
-amplitude and the displacement amplitude by instantiating a schedule with a custom function that returns
-the desired value for each parameter:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--7-input--1" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">system_operator</span> <span class="o">=</span> <span class="n">displacement</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">operators</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-
-<span class="c1"># Define a schedule such that displacement amplitude increases linearly in time</span>
-<span class="c1"># but the squeezing amplitude decreases, that is follows the inverse schedule.</span>
-<span class="k">def</span> <span class="nf">parameter_values</span><span class="p">(</span><span class="n">time_steps</span><span class="p">):</span>
-
-    <span class="k">def</span> <span class="nf">compute_value</span><span class="p">(</span><span class="n">param_name</span><span class="p">,</span> <span class="n">step_idx</span><span class="p">):</span>
-        <span class="k">match</span> <span class="n">param_name</span><span class="p">:</span>
-            <span class="k">case</span> <span class="s1">&#39;displacement&#39;</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">time_steps</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">step_idx</span><span class="p">)]</span>
-            <span class="k">case</span> <span class="s1">&#39;squeezing&#39;</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">time_steps</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">step_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-            <span class="k">case</span><span class="w"> </span><span class="k">_</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;value for parameter </span><span class="si">{</span><span class="n">param_name</span><span class="si">}</span><span class="s2"> undefined&quot;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">Schedule</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">time_steps</span><span class="p">)),</span> <span class="n">system_operator</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span>
-                    <span class="n">compute_value</span><span class="p">)</span>
-
-
-<span class="n">time_dependence</span> <span class="o">=</span> <span class="n">parameter_values</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="n">system_operator</span><span class="p">,</span> <span class="n">system_dimensions</span><span class="p">,</span> <span class="n">time_dependence</span><span class="p">,</span> <span class="n">initial_state</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="numerical-integrators">
-<h2>Numerical Integrators<a class="headerlink" href="#numerical-integrators" title="Permalink to this heading">¶</a></h2>
-<p id="integrators">CUDA-Q provides a set of numerical integrators, to be used with the <code class="docutils literal notranslate"><span class="pre">dynamics</span></code>
-backend target.</p>
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Numerical Integrators</span><a class="headerlink" href="#id2" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 29%" />
-<col style="width: 71%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Description</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">RungeKuttaIntegrator</span></code></p></td>
-<td><p>Explicit 4th-order Runge-Kutta method (default integrator)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">ScipyZvodeIntegrator</span></code></p></td>
-<td><p>Complex-valued variable-coefficient ordinary differential equation solver (provided by SciPy)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqDopri5Integrator</span></code></p></td>
-<td><p>Runge-Kutta of order 5 of Dormand-Prince-Shampine (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqAdaptiveHeunIntegrator</span></code></p></td>
-<td><p>Runge-Kutta of order 2 (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqBosh3Integrator</span></code></p></td>
-<td><p>Runge-Kutta of order 3 of Bogacki-Shampine (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqDopri8Integrator</span></code></p></td>
-<td><p>Runge-Kutta of order 8 of Dormand-Prince-Shampine (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqEulerIntegrator</span></code></p></td>
-<td><p>Euler method (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqExplicitAdamsIntegrator</span></code></p></td>
-<td><p>Explicit Adams-Bashforth method (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqImplicitAdamsIntegrator</span></code></p></td>
-<td><p>Implicit Adams-Bashforth-Moulton method (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqMidpointIntegrator</span></code></p></td>
-<td><p>Midpoint method (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">CUDATorchDiffEqRK4Integrator</span></code></p></td>
-<td><p>Fourth-order Runge-Kutta with 3/8 rule (provided by <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code>)</p></td>
-</tr>
-</tbody>
-</table>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To use Torch-based integrators, users need to install <code class="code docutils literal notranslate"><span class="pre">torchdiffeq</span></code> (e.g., with <code class="code docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torchdiffeq</span></code>).
-This is an optional dependency of CUDA-Q, thus will not be installed by default.</p>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Torch-based integrators require a CUDA-enabled Torch installation. Depending on your platform (e.g., <code class="code docutils literal notranslate"><span class="pre">aarch64</span></code>),
-the default Torch pip package may not have CUDA support.</p>
-<p>The below command can be used to verify your installation:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import torch; print(torch.version.cuda)&quot;</span>
-</pre></div>
-</div>
-<p>If the output is a ‘<code class="code docutils literal notranslate"><span class="pre">None</span></code>’ string, it indicates that your Torch installation does not support CUDA.
-In this case, you need to install a CUDA-enabled Torch package via other mechanisms, e.g., building Torch from source or
-using their Docker images.</p>
-</div>
-</section>
-</section>
-
-
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-states">Quantum States</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-</ul>
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-  <section id="cuda-q-hardware-backends">
-<h1>CUDA-Q Hardware Backends<a class="headerlink" href="#cuda-q-hardware-backends" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q supports submission to a set of hardware providers.
-To submit to a hardware backend, you need an account with the respective provider.</p>
-<section id="amazon-braket">
-<h2>Amazon Braket<a class="headerlink" href="#amazon-braket" title="Permalink to this heading">¶</a></h2>
-<p id="braket-backend"><a class="reference external" href="https://aws.amazon.com/braket/">Amazon Braket</a> is a fully managed AWS
-service which provides Jupyter notebook environments, high-performance quantum
-circuit simulators, and secure, on-demand access to various quantum computers.
-To get started users must enable Amazon Braket in their AWS account by following
-<a class="reference external" href="https://docs.aws.amazon.com/braket/latest/developerguide/braket-enable-overview.html">these instructions</a>.
-To learn more about Amazon Braket, you can view the <a class="reference external" href="https://docs.aws.amazon.com/braket/">Amazon Braket Documentation</a>
-and <a class="reference external" href="https://github.com/amazon-braket/amazon-braket-examples">Amazon Braket Examples</a>.
-A list of available devices and regions can be found <a class="reference external" href="https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html">here</a>.</p>
-<p>Users can run CUDA-Q programs on Amazon Braket with <a class="reference external" href="https://docs.aws.amazon.com/braket/latest/developerguide/braket-what-is-hybrid-job.html">Hybrid Job</a>.
-See <a class="reference external" href="https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-first.html">this guide</a> to get started.</p>
-<section id="setting-credentials">
-<h3>Setting Credentials<a class="headerlink" href="#setting-credentials" title="Permalink to this heading">¶</a></h3>
-<p>After enabling Amazon Braket in AWS, set credentials using any of the documented <a class="reference external" href="https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html">methods</a>.
-One of the simplest ways is to use <a class="reference external" href="https://aws.amazon.com/cli/">AWS CLI</a>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>aws<span class="w"> </span>configure
-</pre></div>
-</div>
-<p>Alternatively, users can set the following environment variables.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">AWS_DEFAULT_REGION</span><span class="o">=</span><span class="s2">&quot;&lt;region&gt;&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_ACCESS_KEY_ID</span><span class="o">=</span><span class="s2">&quot;&lt;key_id&gt;&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_SECRET_ACCESS_KEY</span><span class="o">=</span><span class="s2">&quot;&lt;access_key&gt;&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_SESSION_TOKEN</span><span class="o">=</span><span class="s2">&quot;&lt;token&gt;&quot;</span>
-</pre></div>
-</div>
-</section>
-<section id="submission-from-c">
-<h3>Submission from C++<a class="headerlink" href="#submission-from-c" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution in Amazon Braket,
-pass the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">braket</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler.
-By default jobs are submitted to the state vector simulator, <code class="code docutils literal notranslate"><span class="pre">SV1</span></code>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>braket<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To execute your kernels on different device, pass the <code class="docutils literal notranslate"><span class="pre">--braket-machine</span></code> flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler
-to specify which machine to submit quantum kernels to:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>braket<span class="w"> </span>--braket-machine<span class="w"> </span><span class="s2">&quot;arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet&quot;</span><span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet</span></code> refers to IQM Garnet QPU.</p>
-<p>To emulate the device locally, without submitting through the cloud,
-you can also pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--emulate<span class="w"> </span>--target<span class="w"> </span>braket<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To see a complete example for using Amazon Braket backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="submission-from-python">
-<h3>Submission from Python<a class="headerlink" href="#submission-from-python" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;braket&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>By default, jobs are submitted to the state vector simulator, <code class="code docutils literal notranslate"><span class="pre">SV1</span></code>.</p>
-<p>To specify which Amazon Braket device to use, set the <code class="code docutils literal notranslate"><span class="pre">machine</span></code> parameter.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">device_arn</span> <span class="o">=</span> <span class="s2">&quot;arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet&quot;</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;braket&quot;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="n">device_arn</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet</span></code> refers to IQM Garnet QPU.</p>
-<p>To emulate the device locally, without submitting through the cloud,
-you can also set the <code class="docutils literal notranslate"><span class="pre">emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;braket&quot;</span><span class="p">,</span> <span class="n">emulate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code>
-argument to <code class="docutils literal notranslate"><span class="pre">cudaq.sample</span></code>. By default, the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is set to 1000.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using Amazon Braket backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">Python examples</span></a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The <code class="docutils literal notranslate"><span class="pre">cudaq.observe</span></code> API is not yet supported on the <code class="code docutils literal notranslate"><span class="pre">braket</span></code> target.</p>
-</div>
-</section>
-</section>
-<section id="infleqtion">
-<h2>Infleqtion<a class="headerlink" href="#infleqtion" title="Permalink to this heading">¶</a></h2>
-<p id="infleqtion-backend">Infleqtion is a quantum hardware provider of gate-based neutral atom quantum computers. Their backends may be
-accessed via <a class="reference external" href="https://superstaq.infleqtion.com/">Superstaq</a>, Infleqtion’s cross-platform software API
-that performs low-level compilation and cross-layer optimization. To get started users can create a Superstaq
-account by following <a class="reference external" href="https://superstaq.readthedocs.io/en/latest/get_started/credentials.html">these instructions</a>.</p>
-<p>For access to Infleqtion’s neutral atom quantum computer, Sqale,
-<a class="reference external" href="https://www.infleqtion.com/sqale-preregistration">pre-registration</a> is now open.</p>
-<section id="id1">
-<h3>Setting Credentials<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access Infleqtion backends from either C++ or Python. Generate
-an API key from your <a class="reference external" href="https://superstaq.infleqtion.com/profile">Superstaq account</a> and export
-it as an environment variable:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">SUPERSTAQ_API_KEY</span><span class="o">=</span><span class="s2">&quot;superstaq_api_key&quot;</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h3>Submission from C++<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution on Infleqtion’s backends,
-pass the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">infleqtion</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>infleqtion<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>This will take the API key and handle all authentication with, and submission to, Infleqtion’s QPU
-(or simulator). By default, quantum kernel code will be submitted to Infleqtion’s Sqale
-simulator.</p>
-<p>To execute your kernels on a QPU, pass the <code class="docutils literal notranslate"><span class="pre">--infleqtion-machine</span></code> flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler
-to specify which machine to submit quantum kernels to:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>infleqtion<span class="w"> </span>--infleqtion-machine<span class="w"> </span>cq_sqale_qpu<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">cq_sqale_qpu</span></code> is an example of a physical QPU.</p>
-<p>To run an ideal dry-run execution on the QPU, additionally pass <code class="docutils literal notranslate"><span class="pre">dry-run</span></code> with the <code class="docutils literal notranslate"><span class="pre">--infleqtion-method</span></code>
-flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>infleqtion<span class="w"> </span>--infleqtion-machine<span class="w"> </span>cq_sqale_qpu<span class="w"> </span>--infleqtion-method<span class="w"> </span>dry-run<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>To noisily simulate the QPU instead, pass <code class="docutils literal notranslate"><span class="pre">noise-sim</span></code> to the <code class="docutils literal notranslate"><span class="pre">--infleqtion-method</span></code> flag like so:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>infleqtion<span class="w"> </span>--infleqtion-machine<span class="w"> </span>cq_sqale_qpu<span class="w"> </span>--infleqtion-method<span class="w"> </span>noise-sim<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>Alternatively, to emulate the Infleqtion machine locally, without submitting through the cloud,
-you can also pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--emulate<span class="w"> </span>--target<span class="w"> </span>infleqtion<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To see a complete example for using Infleqtion’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="id3">
-<h3>Submission from Python<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>By default, quantum kernel code will be submitted to Infleqtion’s Sqale
-simulator.</p>
-<p>To specify which Infleqtion QPU to use, set the <code class="code docutils literal notranslate"><span class="pre">machine</span></code> parameter.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s2">&quot;cq_sqale_qpu&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">cq_sqale_qpu</span></code> is an example of a physical QPU.</p>
-<p>To run an ideal dry-run execution of the QPU, additionally set the <code class="docutils literal notranslate"><span class="pre">method</span></code> flag to <code class="docutils literal notranslate"><span class="pre">&quot;dry-run&quot;</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s2">&quot;cq_sqale_qpu&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;dry-run&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To noisily simulate the QPU instead, set the <code class="docutils literal notranslate"><span class="pre">method</span></code> flag to <code class="docutils literal notranslate"><span class="pre">&quot;noise-sim&quot;</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s2">&quot;cq_sqale_qpu&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;noise-sim&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Alternatively, to emulate the Infleqtion machine locally, without submitting through the cloud,
-you can also set the <code class="docutils literal notranslate"><span class="pre">emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">,</span> <span class="n">emulate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> argument to <code class="docutils literal notranslate"><span class="pre">cudaq.sample</span></code> or <code class="docutils literal notranslate"><span class="pre">cudaq.observe</span></code>. By default,
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is set to 1000.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using Infleqtion’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">Python examples</span></a>.
-Moreover, for an end-to-end application workflow example executed on the Infleqtion QPU, take a look at the
-<a class="reference internal" href="../applications.html"><span class="doc">Anderson Impurity Model ground state solver</span></a> notebook.</p>
-</section>
-</section>
-<section id="ionq">
-<h2>IonQ<a class="headerlink" href="#ionq" title="Permalink to this heading">¶</a></h2>
-<section id="ionq-backend">
-<span id="id4"></span><h3>Setting Credentials<a class="headerlink" href="#ionq-backend" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access the <a class="reference external" href="https://cloud.ionq.com/">IonQ Quantum Cloud</a> from either C++ or Python. Generate
-an API key from your <a class="reference external" href="https://cloud.ionq.com/">IonQ account</a> and export
-it as an environment variable:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">IONQ_API_KEY</span><span class="o">=</span><span class="s2">&quot;ionq_generated_api_key&quot;</span>
-</pre></div>
-</div>
-</section>
-<section id="id5">
-<h3>Submission from C++<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution in the IonQ Cloud,
-pass the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">ionq</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>ionq<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>This will take the API key and handle all authentication with, and submission to,
-the IonQ QPU(s). By default, quantum kernel code will be submitted to the IonQ
-simulator.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>A “target” in <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> refers to a quantum compute provider, such as <code class="code docutils literal notranslate"><span class="pre">ionq</span></code>.
-However, IonQ’s documentation uses the term “target” to refer to specific QPU’s themselves.</p>
-</div>
-<p>To execute your kernels on a QPU, pass the <code class="docutils literal notranslate"><span class="pre">--ionq-machine</span></code> flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler
-to specify which machine to submit quantum kernels to:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>ionq<span class="w"> </span>--ionq-machine<span class="w"> </span>qpu.aria-1<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">qpu.aria-1</span></code> is an example of a physical QPU.</p>
-<p>A list of available QPUs can be found <a class="reference external" href="https://docs.ionq.com/#tag/jobs">in the API documentation</a>. To see which backends are available
-with your subscription login to your <a class="reference external" href="https://cloud.ionq.com/jobs">IonQ account</a>.</p>
-<p>To emulate the IonQ machine locally, without submitting through the cloud,
-you can also pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--emulate<span class="w"> </span>--target<span class="w"> </span>ionq<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To see a complete example for using IonQ’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="id6">
-<h3>Submission from Python<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;ionq&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>By default, quantum kernel code will be submitted to the IonQ
-simulator.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>A “target” in <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> refers to a quantum compute provider, such as <code class="code docutils literal notranslate"><span class="pre">ionq</span></code>.
-However, IonQ’s documentation uses the term “target” to refer to specific QPU’s themselves.</p>
-</div>
-<p>To specify which IonQ QPU to use, set the <code class="code docutils literal notranslate"><span class="pre">qpu</span></code> parameter.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;ionq&quot;</span><span class="p">,</span> <span class="n">qpu</span><span class="o">=</span><span class="s2">&quot;qpu.aria-1&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">qpu.aria-1</span></code> is an example of a physical QPU.</p>
-<p>A list of available QPUs can be found <a class="reference external" href="https://docs.ionq.com/#tag/jobs">in the API documentation</a>. To see which backends are available
-with your subscription login to your <a class="reference external" href="https://cloud.ionq.com/jobs">IonQ account</a>.</p>
-<p>To emulate the IonQ machine locally, without submitting through the cloud,
-you can also set the <code class="docutils literal notranslate"><span class="pre">emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;ionq&#39;</span><span class="p">,</span> <span class="n">emulate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> argument to <code class="docutils literal notranslate"><span class="pre">cudaq.sample</span></code> or <code class="docutils literal notranslate"><span class="pre">cudaq.observe</span></code>. By default,
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is set to 1000.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using IonQ’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">Python examples</span></a>.</p>
-</section>
-</section>
-<section id="anyon-technologies-anyon-computing">
-<h2>Anyon Technologies/Anyon Computing<a class="headerlink" href="#anyon-technologies-anyon-computing" title="Permalink to this heading">¶</a></h2>
-<section id="anyon-backend">
-<span id="id7"></span><h3>Setting Credentials<a class="headerlink" href="#anyon-backend" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access the Anyon API from either
-C++ or Python. Anyon requires a credential configuration file with username and password.
-The configuration file can be generated as follows, replacing
-the <code class="docutils literal notranslate"><span class="pre">&lt;username&gt;</span></code> and <code class="docutils literal notranslate"><span class="pre">&lt;password&gt;</span></code> in the first line with your Anyon Technologies
-account details. The credential in the file will be used by CUDA-Q to login to Anyon quantum services
-and will be updated by CUDA-Q with an obtained API token and refresh token.
-Note, the credential line will be deleted in the updated configuration file.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">echo</span><span class="w"> </span><span class="s1">&#39;credentials: {&quot;username&quot;:&quot;&lt;username&gt;&quot;,&quot;password&quot;:&quot;&lt;password&gt;&quot;}&#39;</span><span class="w"> </span>&gt;<span class="w"> </span><span class="nv">$HOME</span>/.anyon_config
-</pre></div>
-</div>
-<p>Users can also login and get the keys manually using the following commands:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># You may need to run: `apt-get update &amp;&amp; apt-get install curl jq`</span>
-curl<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span>--user<span class="w"> </span><span class="s2">&quot;&lt;username&gt;:&lt;password&gt;&quot;</span><span class="w">  </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
-https://api.anyon.cloud:5000/login<span class="w"> </span>&gt;<span class="w"> </span>credentials.json
-<span class="nv">id_token</span><span class="o">=</span><span class="sb">`</span>cat<span class="w"> </span>credentials.json<span class="w"> </span><span class="p">|</span><span class="w"> </span>jq<span class="w"> </span>-r<span class="w"> </span><span class="s1">&#39;.&quot;id_token&quot;&#39;</span><span class="sb">`</span>
-<span class="nv">refresh_token</span><span class="o">=</span><span class="sb">`</span>cat<span class="w"> </span>credentials.json<span class="w"> </span><span class="p">|</span><span class="w"> </span>jq<span class="w"> </span>-r<span class="w"> </span><span class="s1">&#39;.&quot;refresh_token&quot;&#39;</span><span class="sb">`</span>
-<span class="nb">echo</span><span class="w"> </span><span class="s2">&quot;key: </span><span class="nv">$id_token</span><span class="s2">&quot;</span><span class="w"> </span>&gt;<span class="w"> </span>~/.anyon_config
-<span class="nb">echo</span><span class="w"> </span><span class="s2">&quot;refresh: </span><span class="nv">$refresh_token</span><span class="s2">&quot;</span><span class="w"> </span>&gt;&gt;<span class="w"> </span>~/.anyon_config
-</pre></div>
-</div>
-<p>The path to the configuration can be specified as an environment variable:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDAQ_ANYON_CREDENTIALS</span><span class="o">=</span><span class="nv">$HOME</span>/.anyon_config
-</pre></div>
-</div>
-</section>
-<section id="id8">
-<h3>Submission from C++<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution in the Anyon Technologies backends,
-pass the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">anyon</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler. CUDA-Q will
-authenticate via the Anyon Technologies REST API using the credential in your configuration file.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>anyon<span class="w"> </span>--&lt;backend-type&gt;<span class="w"> </span>&lt;machine&gt;<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>To execute your kernels using Anyon Technologies backends, pass the <code class="docutils literal notranslate"><span class="pre">--anyon-machine</span></code> flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler
-as the <code class="docutils literal notranslate"><span class="pre">--&lt;backend-type&gt;</span></code> to specify which machine to submit quantum kernels to:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>anyon<span class="w"> </span>--anyon-machine<span class="w"> </span>telegraph-8q<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">telegraph-8q</span></code> is an example of a physical QPU (Architecture: Telegraph, Qubit Count: 8).</p>
-<p>Currently, <code class="docutils literal notranslate"><span class="pre">telegraph-8q</span></code> and <code class="docutils literal notranslate"><span class="pre">berkeley-25q</span></code> are available for access over CUDA-Q.</p>
-<p>To emulate the Anyon Technologies machine locally, without submitting through the cloud,
-you can also pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag as the <code class="docutils literal notranslate"><span class="pre">--&lt;backend-type&gt;</span></code> to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>. This will emit any target
-specific compiler warnings and diagnostics, before running a noise free emulation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>anyon<span class="w"> </span>--emulate<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To see a complete example for using Anyon’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="id9">
-<h3>Submission from Python<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq.set_target()</span></code> function.</p>
-<p>To execute your kernels using Anyon Technologies backends, specify which machine to submit quantum kernels to
-by setting the <code class="code docutils literal notranslate"><span class="pre">machine</span></code> parameter of the target.
-If <code class="code docutils literal notranslate"><span class="pre">machine</span></code> is not specified, the default machine will be <code class="docutils literal notranslate"><span class="pre">telegraph-8q</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;anyon&#39;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s1">&#39;telegraph-8q&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>As shown above, <code class="docutils literal notranslate"><span class="pre">telegraph-8q</span></code> is an example of a physical QPU.</p>
-<p>To emulate the Anyon Technologies machine locally, without submitting through the cloud,
-you can also set the <code class="docutils literal notranslate"><span class="pre">emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This will emit any target
-specific compiler warnings and diagnostics, before running a noise free emulation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;anyon&#39;</span><span class="p">,</span> <span class="n">emulate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> argument to <code class="docutils literal notranslate"><span class="pre">cudaq.sample</span></code> or <code class="docutils literal notranslate"><span class="pre">cudaq.observe</span></code>. By default,
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is set to 1000.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using Anyon’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">Python examples</span></a>.</p>
-</section>
-</section>
-<section id="iqm">
-<h2>IQM<a class="headerlink" href="#iqm" title="Permalink to this heading">¶</a></h2>
-<p id="iqm-backend">Support for submissions to IQM is currently under development.
-In particular, two-qubit gates can only be performed on adjacent qubits. For more information, we refer to the respective hardware documentation.
-Support for automatically injecting the necessary operations during compilation to execute arbitrary multi-qubit gates will be added in future versions.</p>
-<section id="id10">
-<h3>Setting Credentials<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access the IQM Server from either C++ or Python. Following the <a class="reference external" href="https://iqm-finland.github.io/cortex-cli/readme.html#using-cortex-cli">quick start guide</a>, install <code class="code docutils literal notranslate"><span class="pre">iqm-cortex-cli</span></code> and login to initialize the tokens file.
-The path to the tokens file can either be passed explicitly via an environment variable or it will be loaded automatically if located in
-the default location <code class="code docutils literal notranslate"><span class="pre">~/.cache/iqm-cortex-cli/tokens.json</span></code>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">IQM_TOKENS_FILE</span><span class="o">=</span><span class="s2">&quot;path/to/tokens.json&quot;</span>
-</pre></div>
-</div>
-</section>
-<section id="id11">
-<h3>Submission from C++<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution on an IQM Server,
-pass the <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">iqm</span></code> flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler, along with a specified <code class="docutils literal notranslate"><span class="pre">--iqm-machine</span></code>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The <code class="docutils literal notranslate"><span class="pre">--iqm-machine</span></code> is  a mandatory argument. This provided architecture must match
-the device architecture that the program has been compiled against. The hardware architecture for a
-specific IQM Server may be checked  via <code class="code docutils literal notranslate"><span class="pre">https://&lt;IQM</span> <span class="pre">server&gt;/cocos/quantum-architecture</span></code>.</p>
-</div>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>iqm<span class="w"> </span>--iqm-machine<span class="w"> </span>Adonis<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>Once the binary for a specific IQM QPU architecture is compiled, it can be executed against any IQM Server with the same QPU architecture:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>iqm<span class="w"> </span>--iqm-machine<span class="w"> </span>Adonis<span class="w"> </span>src.cpp<span class="w"> </span>-o<span class="w"> </span>program
-<span class="nv">IQM_SERVER_URL</span><span class="o">=</span><span class="s2">&quot;https://demo.qc.iqm.fi/cocos&quot;</span><span class="w"> </span>./program
-
-<span class="c1"># Executing the same program against an IQM Server with a different underlying QPU</span>
-<span class="c1"># architecture will result in an error.</span>
-<span class="nv">IQM_SERVER_URL</span><span class="o">=</span><span class="s2">&quot;https://&lt;Apollo IQM Server&gt;/cocos&quot;</span><span class="w"> </span>./program
-</pre></div>
-</div>
-<p>To emulate the IQM machine locally, without submitting to the IQM Server,
-you can also pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--emulate<span class="w"> </span>--target<span class="w"> </span>iqm<span class="w"> </span>--iqm-machine<span class="w"> </span>Adonis<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To see a complete example for using IQM server backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="id12">
-<h3>Submission from Python<a class="headerlink" href="#id12" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;iqm&quot;</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="s2">&quot;https://&lt;IQM Server&gt;/cocos&quot;</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span><span class="s2">&quot;qpu-architecture&quot;</span><span class="p">:</span> <span class="s2">&quot;Adonis&quot;</span><span class="p">})</span>
-</pre></div>
-</div>
-<p>To emulate the IQM Server locally, without submitting to the IQM Server,
-you can also set the <code class="docutils literal notranslate"><span class="pre">emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This will emit any target
-specific compiler diagnostics, before running a noise free emulation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;iqm&#39;</span><span class="p">,</span> <span class="n">emulate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> argument to <code class="docutils literal notranslate"><span class="pre">cudaq.sample</span></code> or <code class="docutils literal notranslate"><span class="pre">cudaq.observe</span></code>. By default,
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is set to 1000.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using IQM server backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">Python examples</span></a>.</p>
-</section>
-</section>
-<section id="oqc">
-<h2>OQC<a class="headerlink" href="#oqc" title="Permalink to this heading">¶</a></h2>
-<p id="oqc-backend"><a class="reference external" href="https://oxfordquantumcircuits.com/">Oxford Quantum Circuits</a> (OQC) is currently providing CUDA-Q integration for multiple Quantum Processing Unit types.
-The 8 qubit ring topology Lucy device and the 32 qubit Kagome lattice topology Toshiko device are both supported via machine options described below.</p>
-<section id="id13">
-<h3>Setting Credentials<a class="headerlink" href="#id13" title="Permalink to this heading">¶</a></h3>
-<p>In order to use the OQC devices you will need to register.
-Registration is achieved by contacting <code class="code docutils literal notranslate"><span class="pre">oqc_qcaas_support&#64;oxfordquantumcircuits.com</span></code>.</p>
-<p>Once registered you will be able to authenticate with your <code class="docutils literal notranslate"><span class="pre">email</span></code> and <code class="docutils literal notranslate"><span class="pre">password</span></code></p>
-<p>There are three environment variables that the OQC target will look for during configuration:</p>
-<ol class="arabic simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">OQC_URL</span></code></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">OQC_EMAIL</span></code></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">OQC_PASSWORD</span></code> - is mandatory</p></li>
-</ol>
-</section>
-<section id="id14">
-<h3>Submission from C++<a class="headerlink" href="#id14" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution on the OQC platform, provide the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">oqc</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler.</p>
-<p>Users may provide their <code class="code docutils literal notranslate"><span class="pre">email</span></code> and <code class="code docutils literal notranslate"><span class="pre">url</span></code> as extra arguments</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>oqc<span class="w"> </span>--oqc-email<span class="w"> </span>&lt;email&gt;<span class="w"> </span>--oqc-url<span class="w"> </span>&lt;url&gt;<span class="w"> </span>src.cpp<span class="w"> </span>-o<span class="w"> </span>executable
-</pre></div>
-</div>
-<p>Where both environment variables and extra arguments are supplied, precedent is given to the extra arguments.
-To run the output, provide the runtime loaded variables and invoke the pre-built executable</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">OQC_PASSWORD</span><span class="o">=</span>&lt;password&gt;<span class="w"> </span>./executable
-</pre></div>
-</div>
-<p>To emulate the OQC device locally, without submitting through the OQC QCaaS services, you can pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>.
-This will emit any target specific compiler warnings and diagnostics, before running a noise free emulation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--emulate<span class="w"> </span>--target<span class="w"> </span>oqc<span class="w"> </span>src.cpp<span class="w"> </span>-o<span class="w"> </span>executable
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The oqc target supports a <code class="docutils literal notranslate"><span class="pre">--oqc-machine</span></code> option.
-The default is the 8 qubit Lucy device.
-You can set this to be either <code class="docutils literal notranslate"><span class="pre">toshiko</span></code> or <code class="docutils literal notranslate"><span class="pre">lucy</span></code> via this flag.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The OQC quantum assembly toolchain (qat) which is used to compile and execute instructions can be found on github as <a class="reference external" href="https://github.com/oqc-community/qat">oqc-community/qat</a></p>
-</div>
-</section>
-<section id="id15">
-<h3>Submission from Python<a class="headerlink" href="#id15" title="Permalink to this heading">¶</a></h3>
-<p>To set which OQC URL, set the <code class="code docutils literal notranslate"><span class="pre">url</span></code> parameter.
-To set which OQC email, set the <code class="code docutils literal notranslate"><span class="pre">email</span></code> parameter.
-To set which OQC machine, set the <code class="code docutils literal notranslate"><span class="pre">machine</span></code> parameter.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="c1"># ...</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;OQC_PASSWORD&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">password</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;oqc&quot;</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s2">&quot;lucy&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You can then execute a kernel against the platform using the OQC Lucy device</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-<span class="n">qvec</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qvec</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="n">qvec</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qvec</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvec</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qvec</span><span class="p">)</span>
-<span class="nb">str</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">))</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="orca-computing">
-<h2>ORCA Computing<a class="headerlink" href="#orca-computing" title="Permalink to this heading">¶</a></h2>
-<p id="orca-backend">ORCA Computing’s PT Series implement the boson sampling model of quantum computation, in which
-multiple single photons are interfered with each other within a network of beam splitters, and
-photon detectors measure where the photons leave this network. This process is implemented within
-a time-bin interferometer (TBI) architecture where photons are created in different time-bins
-and interfered within a network of delay lines. This can be represented by a circuit diagram,
-like the one below, where this illustration example corresponds to 4 photons in 8 modes sent into
-alternating time-bins in a circuit composed of two delay lines in series.</p>
-<a class="reference internal image-reference" href="../../_images/orca_tbi.png"><img alt="../../_images/orca_tbi.png" class="align-center" src="../../_images/orca_tbi.png" style="width: 400px;" /></a>
-<section id="id16">
-<h3>Setting Credentials<a class="headerlink" href="#id16" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access the ORCA API from either C++ or Python. There is an environment
-variable <code class="docutils literal notranslate"><span class="pre">ORCA_ACCESS_URL</span></code> that can be set so that the ORCA target can look for it during
-configuration.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">ORCA_ACCESS_URL</span><span class="o">=</span><span class="s2">&quot;https://&lt;ORCA API Server&gt;&quot;</span>
-</pre></div>
-</div>
-<p>Sometimes the requests to the PT-1 require an authentication token. This token can be set as an
-environment variable named <code class="docutils literal notranslate"><span class="pre">ORCA_AUTH_TOKEN</span></code>. For example, if the token is <code class="code docutils literal notranslate"><span class="pre">AbCdEf123456</span></code>,
-you can set the environment variable as follows:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">ORCA_AUTH_TOKEN</span><span class="o">=</span><span class="s2">&quot;AbCdEf123456&quot;</span>
-</pre></div>
-</div>
-</section>
-<section id="id17">
-<h3>Submission from C++<a class="headerlink" href="#id17" title="Permalink to this heading">¶</a></h3>
-<p>To execute a boson sampling experiment on the ORCA platform, provide the flag
-<code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">orca</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler. You should then pass the <code class="docutils literal notranslate"><span class="pre">--orca-url</span></code> flag set with
-the previously set environment variable <code class="docutils literal notranslate"><span class="pre">$ORCA_ACCESS_URL</span></code> or an <code class="code docutils literal notranslate"><span class="pre">url</span></code>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>orca<span class="w"> </span>--orca-url<span class="w"> </span><span class="nv">$ORCA_ACCESS_URL</span><span class="w"> </span>src.cpp<span class="w"> </span>-o<span class="w"> </span>executable
-</pre></div>
-</div>
-<p>or</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>orca<span class="w"> </span>--orca-url<span class="w"> </span>&lt;url&gt;<span class="w"> </span>src.cpp<span class="w"> </span>-o<span class="w"> </span>executable
-</pre></div>
-</div>
-<p>To run the output, invoke the executable</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./executable
-</pre></div>
-</div>
-<p>To see a complete example for using ORCA server backends, take a look at our <a class="reference internal" href="../examples/hardware_providers.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="id18">
-<h3>Submission from Python<a class="headerlink" href="#id18" title="Permalink to this heading">¶</a></h3>
-<p>To set which ORCA URL to be used, set the <code class="code docutils literal notranslate"><span class="pre">url</span></code> parameter.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="c1"># ...</span>
-<span class="n">orca_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;ORCA_ACCESS_URL&quot;</span><span class="p">,</span> <span class="s2">&quot;http://localhost/sample&quot;</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;orca&quot;</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">orca_url</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You can then execute a time-bin boson sampling experiment against the platform using an ORCA device.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">bs_angles</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">6</span><span class="p">]</span>
-<span class="n">input_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">loop_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">orca</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">input_state</span><span class="p">,</span> <span class="n">loop_lengths</span><span class="p">,</span> <span class="n">bs_angles</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using ORCA’s backends, take a look at our <a class="reference internal" href="../examples/hardware_providers.html"><span class="doc">Python examples</span></a>.</p>
-</section>
-</section>
-<section id="quantinuum">
-<h2>Quantinuum<a class="headerlink" href="#quantinuum" title="Permalink to this heading">¶</a></h2>
-<section id="quantinuum-backend">
-<span id="id19"></span><h3>Setting Credentials<a class="headerlink" href="#quantinuum-backend" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access the Quantinuum API from either
-C++ or Python. Quantinuum requires a credential configuration file.
-The configuration file can be generated as follows, replacing
-the <code class="docutils literal notranslate"><span class="pre">email</span></code> and <code class="docutils literal notranslate"><span class="pre">credentials</span></code> in the first line with your Quantinuum
-account details.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># You may need to run: `apt-get update &amp;&amp; apt-get install curl jq`</span>
-curl<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>-d<span class="w"> </span><span class="s1">&#39;{ &quot;email&quot;:&quot;&lt;your_alias&gt;@email.com&quot;,&quot;password&quot;:&quot;&lt;your_password&gt;&quot; }&#39;</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>https://qapi.quantinuum.com/v1/login<span class="w"> </span>&gt;<span class="w"> </span><span class="nv">$HOME</span>/credentials.json
-<span class="nv">id_token</span><span class="o">=</span><span class="sb">`</span>cat<span class="w"> </span><span class="nv">$HOME</span>/credentials.json<span class="w"> </span><span class="p">|</span><span class="w"> </span>jq<span class="w"> </span>-r<span class="w"> </span><span class="s1">&#39;.&quot;id-token&quot;&#39;</span><span class="sb">`</span>
-<span class="nv">refresh_token</span><span class="o">=</span><span class="sb">`</span>cat<span class="w"> </span><span class="nv">$HOME</span>/credentials.json<span class="w"> </span><span class="p">|</span><span class="w"> </span>jq<span class="w"> </span>-r<span class="w"> </span><span class="s1">&#39;.&quot;refresh-token&quot;&#39;</span><span class="sb">`</span>
-<span class="nb">echo</span><span class="w"> </span><span class="s2">&quot;key: </span><span class="nv">$id_token</span><span class="s2">&quot;</span><span class="w"> </span>&gt;&gt;<span class="w"> </span><span class="nv">$HOME</span>/.quantinuum_config
-<span class="nb">echo</span><span class="w"> </span><span class="s2">&quot;refresh: </span><span class="nv">$refresh_token</span><span class="s2">&quot;</span><span class="w"> </span>&gt;&gt;<span class="w"> </span><span class="nv">$HOME</span>/.quantinuum_config
-</pre></div>
-</div>
-<p>The path to the configuration can be specified as an environment variable:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDAQ_QUANTINUUM_CREDENTIALS</span><span class="o">=</span><span class="nv">$HOME</span>/.quantinuum_config
-</pre></div>
-</div>
-</section>
-<section id="id20">
-<h3>Submission from C++<a class="headerlink" href="#id20" title="Permalink to this heading">¶</a></h3>
-<p>To target quantum kernel code for execution in the Quantinuum backends,
-pass the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">quantinuum</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler. CUDA-Q will
-authenticate via the Quantinuum REST API using the credential in your configuration file.
-By default, quantum kernel code will be submitted to the Quantinuum syntax checker.
-Submission to the syntax checker merely validates the program; the kernels are not executed.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>quantinuum<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>To execute your kernels, pass the <code class="docutils literal notranslate"><span class="pre">--quantinuum-machine</span></code> flag to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler
-to specify which machine to submit quantum kernels to:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>quantinuum<span class="w"> </span>--quantinuum-machine<span class="w"> </span>H1-2<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">H1-2</span></code> is an example of a physical QPU. Hardware specific
-emulators may be accessed by appending an <code class="docutils literal notranslate"><span class="pre">E</span></code> to the end (e.g, <code class="docutils literal notranslate"><span class="pre">H1-2E</span></code>). For
-access to the syntax checker for the provided machine, you may append an <code class="docutils literal notranslate"><span class="pre">SC</span></code>
-to the end (e.g, <code class="docutils literal notranslate"><span class="pre">H1-1SC</span></code>).</p>
-<p>For a comprehensive list of available machines, login to your <a class="reference external" href="https://um.qapi.quantinuum.com/user">Quantinuum user account</a>
-and navigate to the “Account” tab, where you should find a table titled “Machines”.</p>
-<p>To emulate the Quantinuum machine locally, without submitting through the cloud,
-you can also pass the <code class="docutils literal notranslate"><span class="pre">--emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">nvq++</span></code>. This will emit any target
-specific compiler warnings and diagnostics, before running a noise free emulation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--emulate<span class="w"> </span>--target<span class="w"> </span>quantinuum<span class="w"> </span>src.cpp
-</pre></div>
-</div>
-<p>To see a complete example for using Quantinuum’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">C++ examples</span></a>.</p>
-</section>
-<section id="id21">
-<h3>Submission from Python<a class="headerlink" href="#id21" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;quantinuum&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>By default, quantum kernel code will be submitted to the Quantinuum syntax checker.
-Submission to the syntax checker merely validates the program; the kernels are not executed.</p>
-<p>To execute your kernels, specify which machine to submit quantum kernels to
-by setting the <code class="code docutils literal notranslate"><span class="pre">machine</span></code> parameter of the target.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;quantinuum&#39;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s1">&#39;H1-2&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>where <code class="docutils literal notranslate"><span class="pre">H1-2</span></code> is an example of a physical QPU. Hardware specific
-emulators may be accessed by appending an <code class="docutils literal notranslate"><span class="pre">E</span></code> to the end (e.g, <code class="docutils literal notranslate"><span class="pre">H1-2E</span></code>). For
-access to the syntax checker for the provided machine, you may append an <code class="docutils literal notranslate"><span class="pre">SC</span></code>
-to the end (e.g, <code class="docutils literal notranslate"><span class="pre">H1-1SC</span></code>).</p>
-<p>For a comprehensive list of available machines, login to your <a class="reference external" href="https://um.qapi.quantinuum.com/user">Quantinuum user account</a>
-and navigate to the “Account” tab, where you should find a table titled “Machines”.</p>
-<p>To emulate the Quantinuum machine locally, without submitting through the cloud,
-you can also set the <code class="docutils literal notranslate"><span class="pre">emulate</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This will emit any target
-specific compiler warnings and diagnostics, before running a noise free emulation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;quantinuum&#39;</span><span class="p">,</span> <span class="n">emulate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> argument to <code class="docutils literal notranslate"><span class="pre">cudaq.sample</span></code> or <code class="docutils literal notranslate"><span class="pre">cudaq.observe</span></code>. By default,
-the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is set to 1000.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using Quantinuum’s backends, take a look at our <a class="reference internal" href="../examples/examples.html"><span class="doc">Python examples</span></a>.</p>
-</section>
-</section>
-<section id="quera-computing">
-<h2>QuEra Computing<a class="headerlink" href="#quera-computing" title="Permalink to this heading">¶</a></h2>
-<section id="quera-backend">
-<span id="id22"></span><h3>Setting Credentials<a class="headerlink" href="#quera-backend" title="Permalink to this heading">¶</a></h3>
-<p>Programmers of CUDA-Q may access Aquila, QuEra’s first generation of quantum
-processing unit (QPU) via Amazon Braket. Hence, users must first enable Braket by
-following <a class="reference external" href="https://docs.aws.amazon.com/braket/latest/developerguide/braket-enable-overview.html">these instructions</a>.
-Then set credentials using any of the documented <a class="reference external" href="https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html">methods</a>.
-One of the simplest ways is to use <a class="reference external" href="https://aws.amazon.com/cli/">AWS CLI</a>.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>aws<span class="w"> </span>configure
-</pre></div>
-</div>
-<p>Alternatively, users can set the following environment variables.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">AWS_DEFAULT_REGION</span><span class="o">=</span><span class="s2">&quot;us-east-1&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_ACCESS_KEY_ID</span><span class="o">=</span><span class="s2">&quot;&lt;key_id&gt;&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_SECRET_ACCESS_KEY</span><span class="o">=</span><span class="s2">&quot;&lt;access_key&gt;&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_SESSION_TOKEN</span><span class="o">=</span><span class="s2">&quot;&lt;token&gt;&quot;</span>
-</pre></div>
-</div>
-</section>
-<section id="id23">
-<h3>Submission from C++<a class="headerlink" href="#id23" title="Permalink to this heading">¶</a></h3>
-<p>Not yet supported.</p>
-</section>
-<section id="id24">
-<h3>Submission from Python<a class="headerlink" href="#id24" title="Permalink to this heading">¶</a></h3>
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;quera&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>By default, analog Hamiltonian will be submitted to the Aquila system.</p>
-<p>Aquila is a “field programmable qubit array” operated as an analog
-Hamiltonian simulator on a user-configurable architecture, executing
-programmable coherent quantum dynamics on up to 256 neutral-atom qubits.
-Refer to QuEra’s <a class="reference external" href="https://cdn.prod.website-files.com/643b94c382e84463a9e52264/648f5bf4d19795aaf36204f7_Whitepaper%20June%2023.pdf">whitepaper</a> for details.</p>
-<p>Due to the nature of the underlying hardware, this target only supports the
-<code class="docutils literal notranslate"><span class="pre">evolve</span></code> and <code class="docutils literal notranslate"><span class="pre">evolve_async</span></code> APIs.
-The <code class="code docutils literal notranslate"><span class="pre">hamiltonian</span></code> must be an <code class="code docutils literal notranslate"><span class="pre">Operator</span></code> of the type <code class="code docutils literal notranslate"><span class="pre">RydbergHamiltonian</span></code>. Only
-other parameters supported are <code class="code docutils literal notranslate"><span class="pre">schedule</span></code> (mandatory) and <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code> (optional).</p>
-<p>For example,</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">evolution_result</span> <span class="o">=</span> <span class="n">evolve</span><span class="p">(</span><span class="n">RydbergHamiltonian</span><span class="p">(</span><span class="n">atom_sites</span><span class="o">=</span><span class="n">register</span><span class="p">,</span>
-                                             <span class="n">amplitude</span><span class="o">=</span><span class="n">omega</span><span class="p">,</span>
-                                             <span class="n">phase</span><span class="o">=</span><span class="n">phi</span><span class="p">,</span>
-                                             <span class="n">delta_global</span><span class="o">=</span><span class="n">delta</span><span class="p">),</span>
-                           <span class="n">schedule</span><span class="o">=</span><span class="n">schedule</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The number of shots for a kernel execution can be set through the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code>
-argument to <code class="docutils literal notranslate"><span class="pre">evolve</span></code> or <code class="docutils literal notranslate"><span class="pre">evolve_async</span></code>. By default, the <code class="docutils literal notranslate"><span class="pre">shots_count</span></code> is
-set to 100.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="n">RydbergHamiltonian</span><span class="p">(</span><span class="o">...</span><span class="p">),</span> <span class="n">schedule</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To see a complete example for using QuEra’s backend, take a look at our <a class="reference internal" href="../examples/hardware_providers.html"><span class="doc">Python examples</span></a>.</p>
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#observe">Observe</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
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-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#adjoint-and-controlled-operations">Adjoint and Controlled Operations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#measurements-on-qubits">Measurements on Qubits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#mz"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#id1"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
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-  <section id="nvidia-quantum-cloud">
-<h1>NVIDIA Quantum Cloud<a class="headerlink" href="#nvidia-quantum-cloud" title="Permalink to this heading">¶</a></h1>
-<p>NVIDIA Quantum Cloud (NVQC) offers universal access to the world’s most powerful computing platform,
-for every quantum researcher to do their life’s work.
-To learn more about NVQC visit this <a class="reference external" href="https://www.nvidia.com/en-us/solutions/quantum-computing/cloud">link</a>.</p>
-<p>Apply for early access <a class="reference external" href="https://developer.nvidia.com/quantum-cloud-early-access-join">here</a>.
-Access to the Quantum Cloud early access program requires an NVIDIA Developer account.</p>
-<section id="quick-start">
-<h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading">¶</a></h2>
-<p>Once you have been approved for an early access to NVQC, you will be able to follow these instructions to use it.</p>
-<p>1. Follow the instructions in your NVQC Early Access welcome email to obtain an API Key for NVQC.
-You can also find the instructions <a class="reference external" href="https://developer.nvidia.com/quantum-cloud-early-access-members">here</a> (link available only for approved users)</p>
-<ol class="arabic simple" start="2">
-<li><p>Set the environment variable <code class="code docutils literal notranslate"><span class="pre">NVQC_API_KEY</span></code> to the API Key obtained above.</p></li>
-</ol>
-<blockquote>
-<div><div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">export NVQC_API_KEY=&lt;your NVQC API key&gt;</span>
-</pre></div>
-</div>
-</div></blockquote>
-<p>You may wish to persist that environment variable between bash sessions, e.g., by adding it to your <code class="code docutils literal notranslate"><span class="pre">$HOME/.bashrc</span></code> file.</p>
-<ol class="arabic simple" start="3">
-<li><p>Run your first NVQC example</p></li>
-</ol>
-<p>The following is a typical CUDA-Q kernel example.
-By selecting the <code class="code docutils literal notranslate"><span class="pre">nvqc</span></code> target, the quantum circuit simulation will run on NVQC in the cloud, rather than running locally.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvqc&quot;</span><span class="p">)</span>
-<span class="n">num_qubits</span> <span class="o">=</span> <span class="mi">25</span>
-<span class="c1"># Define a simple quantum kernel to execute on NVQC.</span>
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-<span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="n">num_qubits</span><span class="p">)</span>
-<span class="c1"># Maximally entangled state between 25 qubits.</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_qubits</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">[2024-03-14 19:26:31.438] Submitting jobs to NVQC service with 1 GPU(s). Max execution time: 3600 seconds (excluding queue wait time).</span>
-
-<span class="go">================ NVQC Device Info ================</span>
-<span class="go">GPU Device Name: &quot;NVIDIA H100 80GB HBM3&quot;</span>
-<span class="go">CUDA Driver Version / Runtime Version: 12.2 / 12.0</span>
-<span class="go">Total global memory (GB): 79.1</span>
-<span class="go">Memory Clock Rate (MHz): 2619.000</span>
-<span class="go">GPU Clock Rate (MHz): 1980.000</span>
-<span class="go">==================================================</span>
-<span class="go">{ 1111111111111111111111111:486 0000000000000000000000000:514 }</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="c1">// Define a simple quantum kernel to execute on NVQC.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Maximally entangled state between 25 qubits.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">NUM_QUBITS</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">25</span><span class="p">;</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="n">NUM_QUBITS</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">NUM_QUBITS</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>The code above is saved in <code class="code docutils literal notranslate"><span class="pre">nvqc_intro.cpp</span></code> and compiled with the following command, targeting the <code class="code docutils literal notranslate"><span class="pre">nvqc</span></code> platform</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ nvqc_intro.cpp -o nvqc_intro.x --target nvqc</span>
-<span class="go">./nvqc_intro.x</span>
-
-<span class="go">[2024-03-14 19:25:05.545] Submitting jobs to NVQC service with 1 GPU(s). Max execution time: 3600 seconds (excluding queue wait time).</span>
-
-<span class="go">================ NVQC Device Info ================</span>
-<span class="go">GPU Device Name: &quot;NVIDIA H100 80GB HBM3&quot;</span>
-<span class="go">CUDA Driver Version / Runtime Version: 12.2 / 12.0</span>
-<span class="go">Total global memory (GB): 79.1</span>
-<span class="go">Memory Clock Rate (MHz): 2619.000</span>
-<span class="go">GPU Clock Rate (MHz): 1980.000</span>
-<span class="go">==================================================</span>
-<span class="go">{</span>
-<span class="go">__global__ : { 1111111111111111111111111:487 0000000000000000000000000:513 }</span>
-<span class="go">result : { 1111111111111111111111111:487 0000000000000000000000000:513 }</span>
-<span class="go">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="simulator-backend-selection">
-<h2>Simulator Backend Selection<a class="headerlink" href="#simulator-backend-selection" title="Permalink to this heading">¶</a></h2>
-<p>NVQC hosts all CUDA-Q simulator backends (see <a class="reference internal" href="simulators.html"><span class="doc">CUDA-Q Simulation Backends</span></a>).
-You may use the NVQC <code class="code docutils literal notranslate"><span class="pre">backend</span></code> (Python) or <code class="code docutils literal notranslate"><span class="pre">--nvqc-backend</span></code> (C++) option to select the simulator to be used by the service.</p>
-<p>For example, to request the <code class="code docutils literal notranslate"><span class="pre">tensornet</span></code> simulator backend, the user can do the following for C++ or Python.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvqc&quot;</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;tensornet&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ nvqc_sample.cpp -o nvqc_sample.x --target nvqc --nvqc-backend tensornet</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>By default, the single-GPU single-precision <code class="code docutils literal notranslate"><span class="pre">custatevec-fp32</span></code> simulator backend will be selected if backend information is not specified.</p>
-</div>
-</section>
-<section id="multiple-gpus">
-<h2>Multiple GPUs<a class="headerlink" href="#multiple-gpus" title="Permalink to this heading">¶</a></h2>
-<p>Some CUDA-Q simulator backends are capable of multi-GPU distribution as detailed in <a class="reference internal" href="simulators.html"><span class="doc">CUDA-Q Simulation Backends</span></a>.
-For example, the <code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend can partition and distribute state vector simulation to multiple GPUs to simulate
-a larger number of qubits, whose state vector size grows beyond the memory size of a single GPU.</p>
-<p>To select a specific number of GPUs on the NVQC managed service, the following <code class="code docutils literal notranslate"><span class="pre">ngpus</span></code> (Python) or <code class="code docutils literal notranslate"><span class="pre">--nvqc-ngpus</span></code> (C++) option can be used.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvqc&quot;</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;nvidia-mgpu&quot;</span><span class="p">,</span> <span class="n">ngpus</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ nvqc_sample.cpp -o nvqc_sample.x --target nvqc --nvqc-backend nvidia-mgpu --nvqc-ngpus 4</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If your NVQC subscription does not contain service instances that have the specified number of GPUs,
-you may encounter the following error.</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">Unable to find NVQC deployment with 16 GPUs.</span>
-<span class="go">Available deployments have {1, 2, 4, 8} GPUs.</span>
-<span class="go">Please check your `ngpus` value (Python) or `--nvqc-ngpus` value (C++).</span>
-</pre></div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Not all simulator backends are capable of utilizing multiple GPUs.
-When requesting a multiple-GPU service with a single-GPU simulator backend,
-you might encounter the following log message:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">The requested backend simulator (custatevec-fp32) is not capable of using all 4 GPUs requested.</span>
-<span class="go">Only one GPU will be used for simulation.</span>
-<span class="go">Please refer to CUDA-Q documentation for a list of multi-GPU capable simulator backends.</span>
-</pre></div>
-</div>
-<p>Consider removing the <code class="code docutils literal notranslate"><span class="pre">ngpus</span></code> value (Python) or <code class="code docutils literal notranslate"><span class="pre">--nvqc-ngpus</span></code> value (C++) to use the default of 1 GPU
-if you don’t need to use a multi-GPU backend to better utilize NVQC resources.</p>
-<p>Please refer to the table below for a list of backend simulator names along with its multi-GPU capability.</p>
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Simulator Backends</span><a class="headerlink" href="#id1" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 22%" />
-<col style="width: 56%" />
-<col style="width: 11%" />
-<col style="width: 11%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Description</p></th>
-<th class="head"><p>GPU Accelerated</p></th>
-<th class="head"><p>Multi-GPU</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">qpp</span></code></p></td>
-<td><p>CPU-only state vector simulator</p></td>
-<td><p>no</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">dm</span></code></p></td>
-<td><p>CPU-only density matrix simulator</p></td>
-<td><p>no</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">custatevec-fp32</span></code></p></td>
-<td><p>Single-precision <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> simulator</p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">custatevec-fp64</span></code></p></td>
-<td><p>Double-precision <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> simulator</p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">tensornet</span></code></p></td>
-<td><p>Double-precision <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> full tensor network contraction simulator</p></td>
-<td><p>yes</p></td>
-<td><p>yes</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">tensornet-mps</span></code></p></td>
-<td><p>Double-precision <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> matrix-product state simulator</p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code></p></td>
-<td><p>Double-precision <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> multi-GPU simulator</p></td>
-<td><p>yes</p></td>
-<td><p>yes</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="multiple-qpus-asynchronous-execution">
-<h2>Multiple QPUs Asynchronous Execution<a class="headerlink" href="#multiple-qpus-asynchronous-execution" title="Permalink to this heading">¶</a></h2>
-<p>NVQC provides scalable QPU virtualization services, whereby clients
-can submit asynchronous jobs simultaneously to NVQC. These jobs are
-handled by a pool of service worker instances.</p>
-<p>For example, in the following code snippet, using the <code class="code docutils literal notranslate"><span class="pre">nqpus</span></code> (Python) or <code class="code docutils literal notranslate"><span class="pre">--nvqc-nqpus</span></code> (C++) configuration option,
-the user instantiates 3 virtual QPU instances to submit simulation jobs to NVQC
-calculating the expectation value along with parameter-shift gradients simultaneously.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-<span class="kn">import</span> <span class="nn">math</span>
-
-<span class="c1"># Use NVQC with 3 virtual QPUs</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvqc&quot;</span><span class="p">,</span> <span class="n">nqpus</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of QPUs:&quot;</span><span class="p">,</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">())</span>
-<span class="c1"># Create the parameterized ansatz</span>
-<span class="n">kernel</span><span class="p">,</span> <span class="n">theta</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-<span class="n">qreg</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="n">qreg</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">qreg</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qreg</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qreg</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="c1"># Define its spin Hamiltonian.</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="p">(</span><span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span>
-               <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.21829</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span>
-               <span class="mf">6.125</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-
-
-<span class="k">def</span> <span class="nf">opt_gradient</span><span class="p">(</span><span class="n">parameter_vector</span><span class="p">):</span>
-    <span class="c1"># Evaluate energy and gradient on different remote QPUs</span>
-    <span class="c1"># (i.e., concurrent job submissions to NVQC)</span>
-    <span class="n">energy_future</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                        <span class="n">hamiltonian</span><span class="p">,</span>
-                                        <span class="n">parameter_vector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                                        <span class="n">qpu_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">plus_future</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                      <span class="n">hamiltonian</span><span class="p">,</span>
-                                      <span class="n">parameter_vector</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span>
-                                      <span class="n">qpu_id</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">minus_future</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                       <span class="n">hamiltonian</span><span class="p">,</span>
-                                       <span class="n">parameter_vector</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span>
-                                       <span class="n">qpu_id</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">energy_future</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">(),</span> <span class="p">[</span>
-        <span class="p">(</span><span class="n">plus_future</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span> <span class="o">-</span> <span class="n">minus_future</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">expectation</span><span class="p">())</span> <span class="o">/</span>
-        <span class="mf">2.0</span>
-    <span class="p">])</span>
-
-
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">LBFGS</span><span class="p">()</span>
-<span class="n">optimal_value</span><span class="p">,</span> <span class="n">optimal_parameters</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt_gradient</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground state energy =&quot;</span><span class="p">,</span> <span class="n">optimal_value</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Optimal parameters =&quot;</span><span class="p">,</span> <span class="n">optimal_parameters</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/algorithm.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/gradients.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/optimizers.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">                     </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">theta</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">make_kernel</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="p">();</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">q</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ansatz</span><span class="p">.</span><span class="n">qalloc</span><span class="p">();</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">r</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ansatz</span><span class="p">.</span><span class="n">qalloc</span><span class="p">();</span>
-<span class="w">  </span><span class="n">ansatz</span><span class="p">.</span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="n">ansatz</span><span class="p">.</span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span>
-<span class="w">  </span><span class="n">ansatz</span><span class="p">.</span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Run VQE with a gradient-based optimizer.</span>
-<span class="w">  </span><span class="c1">// Delegate cost function and gradient computation across different NVQC-based</span>
-<span class="w">  </span><span class="c1">// QPUs.</span>
-<span class="w">  </span><span class="c1">// Note: this needs to be compiled with `--nvqc-nqpus 3` create 3 virtual</span>
-<span class="w">  </span><span class="c1">// QPUs.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">optimizers</span><span class="o">::</span><span class="n">lbfgs</span><span class="w"> </span><span class="n">optimizer</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">opt_val</span><span class="p">,</span><span class="w"> </span><span class="n">opt_params</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">optimize</span><span class="p">(</span>
-<span class="w">      </span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="cm">/*opt_function*/</span><span class="w"> </span><span class="p">[</span><span class="o">&amp;</span><span class="p">](</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">params</span><span class="p">,</span>
-<span class="w">                                      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">grads</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="c1">// Queue asynchronous jobs to do energy evaluations across multiple QPUs</span>
-<span class="w">        </span><span class="k">auto</span><span class="w"> </span><span class="n">energy_future</span><span class="w"> </span><span class="o">=</span>
-<span class="w">            </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe_async</span><span class="p">(</span><span class="cm">/*qpu_id=*/</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">        </span><span class="k">const</span><span class="w"> </span><span class="kt">double</span><span class="w"> </span><span class="n">paramShift</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">M_PI_2</span><span class="p">;</span>
-<span class="w">        </span><span class="k">auto</span><span class="w"> </span><span class="n">plus_future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe_async</span><span class="p">(</span><span class="cm">/*qpu_id=*/</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">h</span><span class="p">,</span>
-<span class="w">                                                </span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">paramShift</span><span class="p">);</span>
-<span class="w">        </span><span class="k">auto</span><span class="w"> </span><span class="n">minus_future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe_async</span><span class="p">(</span><span class="cm">/*qpu_id=*/</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">h</span><span class="p">,</span>
-<span class="w">                                                 </span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">paramShift</span><span class="p">);</span>
-<span class="w">        </span><span class="n">grads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">plus_future</span><span class="p">.</span><span class="n">get</span><span class="p">().</span><span class="n">expectation</span><span class="p">()</span><span class="w"> </span><span class="o">-</span>
-<span class="w">                    </span><span class="n">minus_future</span><span class="p">.</span><span class="n">get</span><span class="p">().</span><span class="n">expectation</span><span class="p">())</span><span class="w"> </span><span class="o">/</span>
-<span class="w">                   </span><span class="mf">2.0</span><span class="p">;</span>
-<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="n">energy_future</span><span class="p">.</span><span class="n">get</span><span class="p">().</span><span class="n">expectation</span><span class="p">();</span>
-<span class="w">      </span><span class="p">});</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Minimum energy = &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">opt_val</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot; (expected -1.74886).</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>The code above is saved in <code class="code docutils literal notranslate"><span class="pre">nvqc_vqe.cpp</span></code> and compiled with the following command, targeting the <code class="code docutils literal notranslate"><span class="pre">nvqc</span></code> platform with 3 virtual QPUs.</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ nvqc_vqe.cpp -o nvqc_vqe.x --target nvqc --nvqc-nqpus 3</span>
-<span class="go">./nvqc_vqe.x</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The NVQC managed-service has a pool of worker instances processing incoming requests on a
-first-come-first-serve basis. Thus, the attainable speedup using multiple virtual QPUs vs.
-sequential execution on a single QPU depends on the NVQC service load. For example,
-if the number of free workers is greater than the number of requested virtual QPUs, a linear
-(ideal) speedup could be achieved. On the other hand, if all the service workers are busy,
-multi-QPU distribution may not deliver any substantial speedup.</p>
-</div>
-</section>
-<section id="faq">
-<h2>FAQ<a class="headerlink" href="#faq" title="Permalink to this heading">¶</a></h2>
-<ol class="arabic simple">
-<li><p>How do I get more information about my NVQC API submission?</p></li>
-</ol>
-<p>The environment variable <code class="code docutils literal notranslate"><span class="pre">NVQC_LOG_LEVEL</span></code> can be used to turn on and off
-certain logs. There are three levels:</p>
-<ul class="simple">
-<li><p>Info (<code class="code docutils literal notranslate"><span class="pre">info</span></code>): basic information about NVQC is logged to the console. This is the default.</p></li>
-<li><p>Off (<code class="code docutils literal notranslate"><span class="pre">off</span></code> or <code class="code docutils literal notranslate"><span class="pre">0</span></code>): disable all NVQC logging.</p></li>
-<li><p>Trace: (<code class="code docutils literal notranslate"><span class="pre">trace</span></code>): log additional information for each NVQC job execution (including timing)</p></li>
-</ul>
-<ol class="arabic simple" start="2">
-<li><p>I want to persist my API key to a configuration file.</p></li>
-</ol>
-<p>You may persist your NVQC API Key to a credential configuration file in lieu of
-using the <code class="code docutils literal notranslate"><span class="pre">NVQC_API_KEY</span></code> environment variable.
-The configuration file can be generated as follows, replacing
-the <code class="code docutils literal notranslate"><span class="pre">api_key</span></code> value with your NVQC API Key.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">echo</span><span class="w"> </span><span class="s2">&quot;key: &lt;api_key&gt;&quot;</span><span class="w"> </span>&gt;&gt;<span class="w"> </span><span class="nv">$HOME</span>/.nvqc_config
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_control.html">   Multi-Control Synthesis</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-  <section id="multi-processor-platforms">
-<h1>Multi-Processor Platforms<a class="headerlink" href="#multi-processor-platforms" title="Permalink to this heading">¶</a></h1>
-<p>The CUDA-Q machine model elucidates the various devices considered in the
-broader quantum-classical compute node context. Programmers will have one or many
-host CPUs, zero or many NVIDIA GPUs, a classical QPU control space, and the
-quantum register itself. Moreover, the <a class="reference internal" href="../../specification/cudaq/platform.html"><span class="doc">specification</span></a>
-notes that the underlying platform may expose multiple QPUs. In the near-term,
-this will be unlikely with physical QPU instantiations, but the availability of
-GPU-based circuit simulators on NVIDIA multi-GPU architectures does give one an
-opportunity to think about programming such a multi-QPU architecture in the near-term.
-CUDA-Q starts by enabling one to query information about the underlying quantum
-platform via the <code class="code docutils literal notranslate"><span class="pre">quantum_platform</span></code> abstraction. This type exposes a
-<code class="code docutils literal notranslate"><span class="pre">num_qpus()</span></code> method that can be used to query the number of available
-QPUs for asynchronous CUDA-Q kernel and <code class="code docutils literal notranslate"><span class="pre">cudaq::</span></code> function invocations.
-Each available QPU is assigned a logical index, and programmers can launch
-specific asynchronous function invocations targeting a desired QPU.</p>
-<section id="nvidia-mqpu-platform">
-<span id="mqpu-platform"></span><h2>NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform<a class="headerlink" href="#nvidia-mqpu-platform" title="Permalink to this heading">¶</a></h2>
-<p>In the multi-QPU mode (<code class="code docutils literal notranslate"><span class="pre">mqpu</span></code> option), the NVIDIA target provides a simulated QPU for every available NVIDIA GPU on the underlying system.
-Each QPU is simulated via a <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> simulator backend as defined by the NVIDIA target. For more information about using multiple GPUs
-to simulate each virtual QPU, or using a different backend for virtual QPUs, please see <a class="reference internal" href="#id1"><span class="std std-ref">remote MQPU platform</span></a>.
-This target enables asynchronous parallel execution of quantum kernel tasks.</p>
-<p>Here is a simple example demonstrating its usage.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-<span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span>
-<span class="n">qpu_count</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of QPUs:&quot;</span><span class="p">,</span> <span class="n">qpu_count</span><span class="p">)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="c1"># Place qubits in superposition state.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-    <span class="c1"># Measure.</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="n">count_futures</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">qpu</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qpu_count</span><span class="p">):</span>
-    <span class="n">count_futures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">qpu_id</span><span class="o">=</span><span class="n">qpu</span><span class="p">))</span>
-
-<span class="k">for</span> <span class="n">counts</span> <span class="ow">in</span> <span class="n">count_futures</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">get</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">kernelToBeSampled</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">int</span><span class="w"> </span><span class="n">runtimeParam</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="n">runtimeParam</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="c1">// Get the quantum_platform singleton</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">platform</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">get_platform</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// Query the number of QPUs in the system</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">num_qpus</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">platform</span><span class="p">.</span><span class="n">num_qpus</span><span class="p">();</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Number of QPUs: %zu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// We will launch asynchronous sampling tasks</span>
-<span class="w">  </span><span class="c1">// and will store the results immediately as a future</span>
-<span class="w">  </span><span class="c1">// we can query at some later point</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">countFutures</span><span class="p">;</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">countFutures</span><span class="p">.</span><span class="n">emplace_back</span><span class="p">(</span>
-<span class="w">        </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">kernelToBeSampled</span><span class="p">,</span><span class="w"> </span><span class="mi">5</span><span class="w"> </span><span class="cm">/*runtimeParam*/</span><span class="p">));</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">//</span>
-<span class="w">  </span><span class="c1">// Go do other work, asynchronous execution of sample tasks on-going</span>
-<span class="w">  </span><span class="c1">//</span>
-
-<span class="w">  </span><span class="c1">// Get the results, note future::get() will kick off a wait</span>
-<span class="w">  </span><span class="c1">// if the results are not yet available.</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">counts</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">countFutures</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">counts</span><span class="p">.</span><span class="n">get</span><span class="p">().</span><span class="n">dump</span><span class="p">();</span>
-<span class="w">  </span><span class="p">}</span>
-</pre></div>
-</div>
-<p>One can specify the target multi-QPU architecture with the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> flag:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ sample_async.cpp --target nvidia --target-option mqpu</span>
-<span class="go">./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>CUDA-Q exposes asynchronous versions of the default <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> algorithmic
-primitive functions like <code class="code docutils literal notranslate"><span class="pre">sample</span></code>, <code class="code docutils literal notranslate"><span class="pre">observe</span></code>, and <code class="code docutils literal notranslate"><span class="pre">get_state</span></code>
-(e.g., <code class="code docutils literal notranslate"><span class="pre">sample_async</span></code> function in the above code snippets).</p>
-<p>Depending on the number of GPUs available on the system, the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> multi-QPU platform will create the same number of virtual QPU instances.
-For example, on a system with 4 GPUs, the above code will distribute the four sampling tasks among those <code class="code docutils literal notranslate"><span class="pre">GPUEmulatedQPU</span></code> instances.</p>
-<p>The results might look like the following 4 different random samplings:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">Number of QPUs: 4</span>
-<span class="go">{ 10011:28 01100:28 ... }</span>
-<span class="go">{ 10011:37 01100:25 ... }</span>
-<span class="go">{ 10011:29 01100:25 ... }</span>
-<span class="go">{ 10011:33 01100:30 ... }</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>By default, the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> multi-QPU platform will utilize all available GPUs (number of QPUs instances is equal to the number of GPUs).
-To specify the number QPUs to be instantiated, one can set the <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MQPU_NGPUS</span></code> environment variable.
-For example, use <code class="code docutils literal notranslate"><span class="pre">export</span> <span class="pre">CUDAQ_MQPU_NGPUS=2</span></code> to specify that only 2 QPUs (GPUs) are needed.</p>
-</div>
-<p>Since the underlying <code class="code docutils literal notranslate"><span class="pre">GPUEmulatedQPU</span></code> is a simulator backend, we can also retrieve the state vector from each
-QPU via the <code class="code docutils literal notranslate"><span class="pre">cudaq::get_state_async</span></code> (C++) or <code class="code docutils literal notranslate"><span class="pre">cudaq.get_state_async</span></code> (Python) as shown in the bellow code snippets.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-<span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span>
-<span class="n">qpu_count</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of QPUs:&quot;</span><span class="p">,</span> <span class="n">qpu_count</span><span class="p">)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-    <span class="c1"># Place qubits in GHZ State</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">qubit</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="n">qubit</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="n">qubit</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-
-
-<span class="n">state_futures</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">qpu</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qpu_count</span><span class="p">):</span>
-    <span class="n">state_futures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_state_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qpu_id</span><span class="o">=</span><span class="n">qpu</span><span class="p">))</span>
-
-<span class="k">for</span> <span class="n">state</span> <span class="ow">in</span> <span class="n">state_futures</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">state</span><span class="o">.</span><span class="n">get</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">kernelToRun</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">int</span><span class="w"> </span><span class="n">runtimeParam</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="n">runtimeParam</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">runtimeParam</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="c1">// Get the quantum_platform singleton</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">platform</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">get_platform</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// Query the number of QPUs in the system</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">num_qpus</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">platform</span><span class="p">.</span><span class="n">num_qpus</span><span class="p">();</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Number of QPUs: %zu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// We will launch asynchronous tasks</span>
-<span class="w">  </span><span class="c1">// and will store the results immediately as a future</span>
-<span class="w">  </span><span class="c1">// we can query at some later point</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_state_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">stateFutures</span><span class="p">;</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">stateFutures</span><span class="p">.</span><span class="n">emplace_back</span><span class="p">(</span>
-<span class="w">        </span><span class="n">cudaq</span><span class="o">::</span><span class="n">get_state_async</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">kernelToRun</span><span class="p">,</span><span class="w"> </span><span class="mi">5</span><span class="w"> </span><span class="cm">/*runtimeParam*/</span><span class="p">));</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">//</span>
-<span class="w">  </span><span class="c1">// Go do other work, asynchronous execution of tasks on-going</span>
-<span class="w">  </span><span class="c1">//</span>
-
-<span class="w">  </span><span class="c1">// Get the results, note future::get() will kick off a wait</span>
-<span class="w">  </span><span class="c1">// if the results are not yet available.</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">state</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">stateFutures</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">state</span><span class="p">.</span><span class="n">get</span><span class="p">().</span><span class="n">dump</span><span class="p">();</span>
-<span class="w">  </span><span class="p">}</span>
-</pre></div>
-</div>
-<p>One can specify the target multi-QPU architecture with the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> flag:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ get_state_async.cpp --target nvidia --target-option mqpu</span>
-<span class="go">./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 0.8: </span>The <code class="code docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> and <code class="code docutils literal notranslate"><span class="pre">nvidia-mqpu-fp64</span></code> targets, which are equivalent to the multi-QPU options <code class="code docutils literal notranslate"><span class="pre">mqpu,fp32</span></code> and <code class="code docutils literal notranslate"><span class="pre">mqpu,fp64</span></code>, respectively, of the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target, are deprecated and will be removed in a future release.</p>
-</div>
-<section id="parallel-distribution-mode">
-<h3>Parallel distribution mode<a class="headerlink" href="#parallel-distribution-mode" title="Permalink to this heading">¶</a></h3>
-<p>The CUDA-Q <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> multi-QPU platform supports two modes of parallel distribution of expectation value computation:</p>
-<ul class="simple">
-<li><p>MPI: distribute the expectation value computations across available MPI ranks and GPUs for each Hamiltonian term.</p></li>
-<li><p>Thread: distribute the expectation value computations among available GPUs via standard C++ threads (each thread handles one GPU).</p></li>
-</ul>
-<p>For instance, if all GPUs are available on a single node, thread-based parallel distribution
-(<code class="code docutils literal notranslate"><span class="pre">cudaq::parallel::thread</span></code> in C++ or <code class="code docutils literal notranslate"><span class="pre">cudaq.parallel.thread</span></code> in Python, as shown in the above example) is sufficient.
-On the other hand, if one wants to distribute the tasks across GPUs on multiple nodes, e.g., on a compute cluster, MPI distribution mode
-should be used.</p>
-<p>An example of MPI distribution mode usage in both C++ and Python is given below:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">mpi</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-
-
-<span class="c1"># Define spin ansatz.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">angle</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-
-<span class="c1"># Define spin Hamiltonian.</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span>
-    <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">.21829</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">6.125</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="n">exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="mf">0.59</span><span class="p">,</span>
-                        <span class="n">execution</span><span class="o">=</span><span class="n">cudaq</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">mpi</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-<span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">mpi</span><span class="o">.</span><span class="n">rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Expectation value: &quot;</span><span class="p">,</span> <span class="n">exp_val</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">mpi</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">mpiexec -np &lt;N&gt; python3 file.py</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">mpi</span><span class="o">::</span><span class="n">initialize</span><span class="p">();</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">                     </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">ansatz</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">;</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="kt">double</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">parallel</span><span class="o">::</span><span class="n">mpi</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="mf">0.59</span><span class="p">);</span>
-<span class="w">  </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">mpi</span><span class="o">::</span><span class="n">rank</span><span class="p">()</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Expectation value: %lf</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="p">);</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">mpi</span><span class="o">::</span><span class="n">finalize</span><span class="p">();</span>
-</pre></div>
-</div>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ file.cpp --target nvidia --target-option mqpu</span>
-<span class="go">mpiexec -np &lt;N&gt; a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In the above example, the parallel distribution mode was set to <code class="code docutils literal notranslate"><span class="pre">mpi</span></code> using <code class="code docutils literal notranslate"><span class="pre">cudaq::parallel::mpi</span></code> in C++ or <code class="code docutils literal notranslate"><span class="pre">cudaq.parallel.mpi</span></code> in Python.
-CUDA-Q provides MPI utility functions to initialize, finalize, or query (rank, size, etc.) the MPI runtime.
-Last but not least, the compiled executable (C++) or Python script needs to be launched with an appropriate MPI command,
-e.g., <code class="code docutils literal notranslate"><span class="pre">mpiexec</span></code>, <code class="code docutils literal notranslate"><span class="pre">mpirun</span></code>, <code class="code docutils literal notranslate"><span class="pre">srun</span></code>, etc.</p>
-</section>
-</section>
-<section id="remote-mqpu-platform">
-<h2>Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform<a class="headerlink" href="#remote-mqpu-platform" title="Permalink to this heading">¶</a></h2>
-<p id="id1">As shown in the above examples, the multi-QPU NVIDIA platform enables
-multi-QPU distribution whereby each QPU is simulated by a <a class="reference internal" href="simulators.html#cuquantum-single-gpu"><span class="std std-ref">single NVIDIA GPU</span></a>.
-To run multi-QPU workloads on different simulator backends, one can use the <code class="code docutils literal notranslate"><span class="pre">remote-mqpu</span></code> platform,
-which encapsulates simulated QPUs as independent HTTP REST server instances.
-The following code illustrates how to launch asynchronous sampling tasks on multiple virtual QPUs,
-each simulated by a <code class="code docutils literal notranslate"><span class="pre">tensornet</span></code> simulator backend.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>    <span class="c1"># Specified as program input, e.g.</span>
-    <span class="c1"># ```</span>
-    <span class="c1"># backend = &quot;tensornet&quot;; servers = &quot;2&quot;</span>
-    <span class="c1"># ```</span>
-    <span class="n">backend</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">backend</span>
-    <span class="n">servers</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">servers</span>
-
-    <span class="c1"># Define a kernel to be sampled.</span>
-    <span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-    <span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">controls_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="n">controls</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">controls_count</span><span class="p">)</span>
-        <span class="n">targets</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-        <span class="c1"># Place controls in superposition state.</span>
-        <span class="n">h</span><span class="p">(</span><span class="n">controls</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-            <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">controls</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">target</span><span class="p">])</span>
-        <span class="c1"># Measure.</span>
-        <span class="n">mz</span><span class="p">(</span><span class="n">controls</span><span class="p">)</span>
-        <span class="n">mz</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-
-    <span class="c1"># Set the target to execute on and query the number of QPUs in the system;</span>
-    <span class="c1"># The number of QPUs is equal to the number of (auto-)launched server instances.</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;remote-mqpu&quot;</span><span class="p">,</span>
-                     <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">,</span>
-                     <span class="n">auto_launch</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">servers</span><span class="p">)</span> <span class="k">if</span> <span class="n">servers</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
-                     <span class="n">url</span><span class="o">=</span><span class="s2">&quot;&quot;</span> <span class="k">if</span> <span class="n">servers</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span> <span class="k">else</span> <span class="n">servers</span><span class="p">)</span>
-    <span class="n">qpu_count</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of virtual QPUs:&quot;</span><span class="p">,</span> <span class="n">qpu_count</span><span class="p">)</span>
-
-    <span class="c1"># We will launch asynchronous sampling tasks,</span>
-    <span class="c1"># and will store the results as a future we can query at some later point.</span>
-    <span class="c1"># Each QPU (indexed by an unique Id) is associated with a remote REST server.</span>
-    <span class="n">count_futures</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qpu_count</span><span class="p">):</span>
-
-        <span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">qpu_id</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
-        <span class="n">count_futures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sampling jobs launched for asynchronous processing.&quot;</span><span class="p">)</span>
-
-    <span class="c1"># Go do other work, asynchronous execution of sample tasks on-going.</span>
-    <span class="c1"># Get the results, note future::get() will kick off a wait</span>
-    <span class="c1"># if the results are not yet available.</span>
-    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">count_futures</span><span class="p">)):</span>
-        <span class="n">counts</span> <span class="o">=</span> <span class="n">count_futures</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="c1">// Define a kernel to be sampled.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="p">[</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">nrControls</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">make_kernel</span><span class="o">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="p">();</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">controls</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">kernel</span><span class="p">.</span><span class="n">qalloc</span><span class="p">(</span><span class="n">nrControls</span><span class="p">);</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">targets</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">kernel</span><span class="p">.</span><span class="n">qalloc</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="w">  </span><span class="n">kernel</span><span class="p">.</span><span class="n">h</span><span class="p">(</span><span class="n">controls</span><span class="p">);</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">tidx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">tidx</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">tidx</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">kernel</span><span class="p">.</span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">controls</span><span class="p">,</span><span class="w"> </span><span class="n">targets</span><span class="p">[</span><span class="n">tidx</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="w">  </span><span class="n">kernel</span><span class="p">.</span><span class="n">mz</span><span class="p">(</span><span class="n">controls</span><span class="p">);</span>
-<span class="w">  </span><span class="n">kernel</span><span class="p">.</span><span class="n">mz</span><span class="p">(</span><span class="n">targets</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Query the number of QPUs in the system;</span>
-<span class="w">  </span><span class="c1">// The number of QPUs is equal to the number of (auto-)launched server</span>
-<span class="w">  </span><span class="c1">// instances.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">platform</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">get_platform</span><span class="p">();</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">num_qpus</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">platform</span><span class="p">.</span><span class="n">num_qpus</span><span class="p">();</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Number of QPUs: %zu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// We will launch asynchronous sampling tasks,</span>
-<span class="w">  </span><span class="c1">// and will store the results as a future we can query at some later point.</span>
-<span class="w">  </span><span class="c1">// Each QPU (indexed by an unique Id) is associated with a remote REST server.</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">countFutures</span><span class="p">;</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">countFutures</span><span class="p">.</span><span class="n">emplace_back</span><span class="p">(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span>
-<span class="w">        </span><span class="cm">/*qpuId=*/</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="cm">/*nrControls=*/</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">));</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">// Go do other work, asynchronous execution of sample tasks on-going</span>
-<span class="w">  </span><span class="c1">// Get the results, note future::get() will kick off a wait</span>
-<span class="w">  </span><span class="c1">// if the results are not yet available.</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">counts</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">countFutures</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">counts</span><span class="p">.</span><span class="n">get</span><span class="p">().</span><span class="n">dump</span><span class="p">();</span>
-<span class="w">  </span><span class="p">}</span>
-</pre></div>
-</div>
-<p>The code above is saved in <code class="code docutils literal notranslate"><span class="pre">sample_async.cpp</span></code> and compiled with the following command, targeting the <code class="code docutils literal notranslate"><span class="pre">remote-mqpu</span></code> platform:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ sample_async.cpp -o sample_async.x --target remote-mqpu --remote-mqpu-backend tensornet --remote-mqpu-auto-launch 2</span>
-<span class="go">./sample_async.x</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In the above code snippets, the <code class="code docutils literal notranslate"><span class="pre">remote-mqpu</span></code> platform was used in the auto-launch mode,
-whereby a specific number of server instances, i.e., virtual QPUs, are launched on the local machine
-in the background. The remote QPU daemon service, <code class="code docutils literal notranslate"><span class="pre">cudaq-qpud</span></code>, will also be shut down automatically
-at the end of the session.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>By default, auto launching daemon services do not support MPI parallelism.
-Hence, using the <code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend to simulate each virtual QPU requires
-manually launching each server instance. How to do that is explained in the rest of this section.</p>
-</div>
-<p id="custom-remote-qpud-launch">To customize how many and which GPUs are used for simulating each virtual QPU, one can launch each server manually.
-For instance, on a machine with 8 NVIDIA GPUs, one may wish to partition those GPUs into
-4 virtual QPU instances, each manages 2 GPUs. To do so, first launch a <code class="code docutils literal notranslate"><span class="pre">cudaq-qpud</span></code> server for each virtual QPU:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use cudaq-qpud.py wrapper script to automatically find dependencies for the Python wheel configuration.</span>
-<span class="nv">cudaq_location</span><span class="o">=</span><span class="sb">`</span>python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>show<span class="w"> </span>cudaq<span class="w"> </span><span class="p">|</span><span class="w"> </span>grep<span class="w"> </span>-e<span class="w"> </span><span class="s1">&#39;Location: .*$&#39;</span><span class="sb">`</span>
-<span class="nv">qpud_py</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">cudaq_location</span><span class="p">#Location: </span><span class="si">}</span><span class="s2">/bin/cudaq-qpud.py&quot;</span>
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">0</span>,1<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$qpud_py</span><span class="s2">&quot;</span><span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">1</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">2</span>,3<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$qpud_py</span><span class="s2">&quot;</span><span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">2</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">4</span>,5<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$qpud_py</span><span class="s2">&quot;</span><span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">3</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">6</span>,7<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$qpud_py</span><span class="s2">&quot;</span><span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">4</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># It is assumed that your $LD_LIBRARY_PATH is able to find all the necessary dependencies.</span>
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">0</span>,1<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>cudaq-qpud<span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">1</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">2</span>,3<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>cudaq-qpud<span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">2</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">4</span>,5<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>cudaq-qpud<span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">3</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-<span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">6</span>,7<span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>cudaq-qpud<span class="w"> </span>--port<span class="w"> </span>&lt;QPU<span class="w"> </span><span class="m">4</span><span class="w"> </span>TCP/IP<span class="w"> </span>port<span class="w"> </span>number&gt;
-</pre></div>
-</div>
-</div>
-</div>
-<p>In the above code snippet, four <code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> daemons are started in MPI context via the <code class="code docutils literal notranslate"><span class="pre">mpiexec</span></code> launcher.
-This activates MPI runtime environment required by the <code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend. Each QPU daemon is assigned a unique
-TCP/IP port number via the <code class="code docutils literal notranslate"><span class="pre">--port</span></code> command-line option. The <code class="code docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environment variable restricts the GPU devices
-that each QPU daemon sees so that it targets specific GPUs.</p>
-<p>With these invocations, each virtual QPU is locally addressable at the URL <code class="code docutils literal notranslate"><span class="pre">localhost:&lt;port&gt;</span></code>.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>There is no authentication required to communicate with this server app.
-Hence, please make sure to either (1) use a non-public TCP/IP port for internal use or
-(2) use firewalls or other security mechanisms to manage user access.</p>
-</div>
-<p>User code can then target these QPUs for multi-QPU workloads, such as asynchronous sample or observe shown above for the multi-QPU NVIDIA platform platform.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;remote-mqpu&quot;</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="s2">&quot;localhost:&lt;port1&gt;,localhost:&lt;port2&gt;,localhost:&lt;port3&gt;,localhost:&lt;port4&gt;&quot;</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;nvidia-mgpu&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--5-input--2" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ distributed.cpp --target remote-mqpu --remote-mqpu-url localhost:&lt;port1&gt;,localhost:&lt;port2&gt;,localhost:&lt;port3&gt;,localhost:&lt;port4&gt; --remote-mqpu-backend nvidia-mgpu</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Each URL is treated as an independent QPU, hence the number of QPUs (<code class="code docutils literal notranslate"><span class="pre">num_qpus()</span></code>) is equal to the number of URLs provided.
-The multi-node multi-GPU simulator backend (<code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code>) is requested via the <code class="code docutils literal notranslate"><span class="pre">--remote-mqpu-backend</span></code> command-line option.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The requested backend (<code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code>) will be executed inside the context of the QPU daemon service, thus
-inherits its GPU resource allocation (two GPUs per backend simulator instance).</p>
-</div>
-<section id="supported-kernel-arguments">
-<h3>Supported Kernel Arguments<a class="headerlink" href="#supported-kernel-arguments" title="Permalink to this heading">¶</a></h3>
-<p>The platform serializes kernel invocation to QPU daemons via REST APIs.
-Please refer to the <a class="reference external" href="../../openapi.html">Open API Docs</a>  for the latest API information.
-Runtime arguments are serialized into a flat memory buffer (<code class="code docutils literal notranslate"><span class="pre">args</span></code> field of the request JSON).
-For more information about argument type serialization, please see <a class="reference internal" href="#type-serialization-table"><span class="std std-ref">the table below</span></a>.</p>
-<p>When using a remote backend to simulate each virtual QPU,
-by default, we currently do not support passing complex data structures,
-such as nested vectors or class objects, or other kernels as arguments to the entry point kernels.
-These type limitations only apply to the <strong>entry-point</strong> kernel and not when passing arguments
-to other quantum kernels.</p>
-<p>Support for the full range of argument types within CUDA-Q can be enabled by compiling the
-code with the <code class="code docutils literal notranslate"><span class="pre">--enable-mlir</span></code> option. This flag forces quantum kernels to be compiled with
-the CUDA-Q MLIR-based compiler. As a result, runtime arguments can be resolved by the CUDA
-Quantum compiler infrastructure to support wider range of argument types. However, certain
-language constructs within quantum kernels may not yet be fully supported.</p>
-<span id="type-serialization-table"></span><table class="docutils align-default" id="id4">
-<caption><span class="caption-text">Kernel argument serialization</span><a class="headerlink" href="#id4" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 33%" />
-<col style="width: 33%" />
-<col style="width: 33%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Data type</p></th>
-<th class="head"><p>Example</p></th>
-<th class="head"><p>Serialization</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Trivial type (occupies a contiguous memory area)</p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">int</span></code>, <code class="code docutils literal notranslate"><span class="pre">std::size_t</span></code>, <code class="code docutils literal notranslate"><span class="pre">double</span></code>, etc.</p></td>
-<td><p>Byte data (via <code class="code docutils literal notranslate"><span class="pre">memcpy</span></code>)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">std::vector</span></code> of trivial type</p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">std::vector&lt;int&gt;</span></code>, <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;double&gt;</span></code>, etc.</p></td>
-<td><p>Total vector size in bytes as a 64-bit integer followed by serialized data of all vector elements.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">cudaq::pauli_word</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">cudaq::pauli_word(&quot;IXIZ&quot;)</span></code></p></td>
-<td><p>Same as <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;char&gt;</span></code>: total vector size in bytes as a 64-bit integer followed by serialized data of all characters.</p></td>
-</tr>
-<tr class="row-odd"><td><p>Single-level nested <code class="code docutils literal notranslate"><span class="pre">std::vector</span></code> of supported <code class="code docutils literal notranslate"><span class="pre">std::vector</span></code> types</p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">std::vector&lt;std::vector&lt;int&gt;&gt;</span></code>, <code class="code docutils literal notranslate"><span class="pre">std::vector&lt;cudaq::pauli_word&gt;</span></code>, etc.</p></td>
-<td><p>Number of top-level elements (as a 64-bit integer) followed sizes in bytes of element vectors (as a contiguous array of 64-bit integers) then serialized data of the inner vectors.</p></td>
-</tr>
-</tbody>
-</table>
-<p>For CUDA-Q kernels that return a value, the remote platform supports returning simple data types of
-<code class="code docutils literal notranslate"><span class="pre">bool</span></code>, integral (e.g., <code class="code docutils literal notranslate"><span class="pre">int</span></code> or <code class="code docutils literal notranslate"><span class="pre">std::size_t</span></code>), and floating-point types (<code class="code docutils literal notranslate"><span class="pre">float</span></code> or <code class="code docutils literal notranslate"><span class="pre">double</span></code>)
-when MLIR-based compilation is enabled (<code class="code docutils literal notranslate"><span class="pre">--enable-mlir</span></code>).</p>
-</section>
-<section id="accessing-simulated-quantum-state">
-<h3>Accessing Simulated Quantum State<a class="headerlink" href="#accessing-simulated-quantum-state" title="Permalink to this heading">¶</a></h3>
-<p>The remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> platform supports accessing simulator backend’s state vector via the
-<code class="code docutils literal notranslate"><span class="pre">cudaq::get_state</span></code> (C++) or <code class="code docutils literal notranslate"><span class="pre">cudaq.get_state</span></code> (Python) APIs, similar to local simulator backends.</p>
-<p>State data can be retrieved as a full state vector or as individual basis states’ amplitudes.
-The later is designed for large quantum states, which incurred data transfer overheads.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--6-input--1" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="n">amplitudes</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">amplitudes</span><span class="p">([</span><span class="s1">&#39;0000&#39;</span><span class="p">,</span> <span class="s1">&#39;1111&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--6-input--2" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">state</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="k">auto</span><span class="w"> </span><span class="n">amplitudes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">state</span><span class="p">.</span><span class="n">amplitudes</span><span class="p">({{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">}});</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In the above example, the amplitudes of the two requested states are returned.</p>
-<p>For C++ quantum kernels <a class="footnote-reference brackets" href="#id3" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>*<span class="fn-bracket">]</span></a> compiled with the CUDA-Q MLIR-based compiler and Python kernels,
-state accessor is evaluated in a just-in-time/on-demand manner, and hence can be customize to
-users’ need.</p>
-<p>For instance, in the above amplitude access example, if the state vector is very large, e.g.,
-multi-GPU distributed state vectors or tensor-network encoded quantum states, the full state vector
-will not be retrieved when <code class="code docutils literal notranslate"><span class="pre">get_state</span></code> is called. Instead, when the <code class="code docutils literal notranslate"><span class="pre">amplitudes</span></code> accessor is called,
-a specific amplitude calculation request will be sent to the server.
-Thus, only the amplitudes of those basis states will be computed and returned.</p>
-<p>Similarly, for state overlap calculation, if deferred state evaluation is available (Python/MLIR-based compiler)
-for both of the operand quantum states, a custom overlap calculation request will be constructed and sent to the server.
-Only the final overlap result will be returned, thereby eliminating back-and-forth state data transfers.</p>
-<aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id3" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">*</a><span class="fn-bracket">]</span></span>
-<p>Only C++ quantum kernels whose names are available via run-time type information (RTTI) are supported.
-For example, quantum kernels expressed as named <code class="code docutils literal notranslate"><span class="pre">struct</span></code> are supported but not standalone functions.
-Kernels that do not have deferred state evaluation support will perform synchronous <code class="code docutils literal notranslate"><span class="pre">get_state</span></code>, whereby the full state
-vector is returned from the server immediately.</p>
-</aside>
-</aside>
-</section>
-</section>
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-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
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-</ul>
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-</ul>
-</li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/python_api.html">Python API</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.initialize_cudaq"><code class="docutils literal notranslate"><span class="pre">initialize_cudaq()</span></code></a></li>
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-  <section id="cuda-q-simulation-backends">
-<h1>CUDA-Q Simulation Backends<a class="headerlink" href="#cuda-q-simulation-backends" title="Permalink to this heading">¶</a></h1>
-<p id="nvidia-backend">The simulation backends that are currently available in CUDA-Q are as follows.</p>
-<section id="state-vector-simulators">
-<h2>State Vector Simulators<a class="headerlink" href="#state-vector-simulators" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target provides a state vector simulator accelerated with
-the <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> library.</p>
-<p>The <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target supports multiple configurable options.</p>
-<section id="features">
-<h3>Features<a class="headerlink" href="#features" title="Permalink to this heading">¶</a></h3>
-<ul class="simple">
-<li><p>Floating-point precision configuration</p></li>
-</ul>
-<p>The floating point precision of the state vector data can be configured to either
-double (<code class="code docutils literal notranslate"><span class="pre">fp64</span></code>) or single (<code class="code docutils literal notranslate"><span class="pre">fp32</span></code>) precision. This option can be chosen for the optimal performance and accuracy.</p>
-<ul class="simple">
-<li><p>Distributed simulation</p></li>
-</ul>
-<p>The <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target supports distributing state vector simulations to multiple GPUs and multiple nodes (<code class="code docutils literal notranslate"><span class="pre">mgpu</span></code> <a class="reference internal" href="#nvidia-mgpu-backend"><span class="std std-ref">distribution</span></a>)
-and multi-QPU (<code class="code docutils literal notranslate"><span class="pre">mqpu</span></code> <a class="reference internal" href="platform.html#mqpu-platform"><span class="std std-ref">platform</span></a>) distribution whereby each QPU is simulated via a single-GPU simulator instance.</p>
-<ul class="simple">
-<li><p>Host CPU memory utilization</p></li>
-</ul>
-<p>Host CPU memory can be leveraged in addition to GPU memory to accommodate the state vector
-(i.e., maximizing the number of qubits to be simulated).</p>
-</section>
-<section id="single-gpu">
-<span id="cuquantum-single-gpu"></span><h3>Single-GPU<a class="headerlink" href="#single-gpu" title="Permalink to this heading">¶</a></h3>
-<p>To execute a program on the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target, use the following commands:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>nvidia
-</pre></div>
-</div>
-<p>The target can also be defined in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If a target is set in the application code, this target will override the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> command line flag given during program invocation.</p>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<p id="nvidia-fp64-backend">By default, this will leverage <code class="code docutils literal notranslate"><span class="pre">FP32</span></code> floating point types for the simulation. To
-switch to <code class="code docutils literal notranslate"><span class="pre">FP64</span></code>, specify the <code class="code docutils literal notranslate"><span class="pre">--target-option</span> <span class="pre">fp64</span></code> <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> command line option for <code class="code docutils literal notranslate"><span class="pre">C++</span></code> and <code class="code docutils literal notranslate"><span class="pre">Python</span></code> or
-use <code class="code docutils literal notranslate"><span class="pre">cudaq.set_target('nvidia',</span> <span class="pre">option='fp64')</span></code> for Python in-source target modification instead.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>fp64
-</pre></div>
-</div>
-<p>The precision of the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target can also be modified in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s1">&#39;fp64&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>fp64<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This backend requires an NVIDIA GPU and CUDA runtime libraries. If you do not have these dependencies installed, you may encounter an error stating <code class="code docutils literal notranslate"><span class="pre">Invalid</span> <span class="pre">simulator</span> <span class="pre">requested</span></code>. See the section <a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility"><span class="std std-ref">Dependencies and Compatibility</span></a> for more information about how to install dependencies.</p>
-</div>
-<p>In the single-GPU mode, the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target provides the following
-environment variable options. Any environment variables must be set prior to
-setting the target.</p>
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text"><strong>Environment variable options supported in single-GPU mode</strong></span><a class="headerlink" href="#id4" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 20%" />
-<col style="width: 30%" />
-<col style="width: 50%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p>Option</p></td>
-<td><p>Value</p></td>
-<td><p>Description</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_FUSION_MAX_QUBITS</span></code></p></td>
-<td><p>positive integer</p></td>
-<td><p>The max number of qubits used for gate fusion. The default value is <code class="code docutils literal notranslate"><span class="pre">4</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_FUSION_DIAGONAL_GATE_MAX_QUBITS</span></code></p></td>
-<td><p>integer greater than or equal to -1</p></td>
-<td><p>The max number of qubits used for diagonal gate fusion. The default value is set to <code class="code docutils literal notranslate"><span class="pre">-1</span></code> and the fusion size will be automatically adjusted for the better performance. If 0, the gate fusion for diagonal gates is disabled.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_FUSION_NUM_HOST_THREADS</span></code></p></td>
-<td><p>positive integer</p></td>
-<td><p>Number of CPU threads used for circuit processing. The default value is <code class="code docutils literal notranslate"><span class="pre">8</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MAX_CPU_MEMORY_GB</span></code></p></td>
-<td><p>non-negative integer, or <code class="code docutils literal notranslate"><span class="pre">NONE</span></code></p></td>
-<td><p>CPU memory size (in GB) allowed for state-vector migration. <code class="code docutils literal notranslate"><span class="pre">NONE</span></code> means unlimited (up to physical memory constraints). Default is 0GB (disabled, variable is not set to any value).</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MAX_GPU_MEMORY_GB</span></code></p></td>
-<td><p>positive integer, or <code class="code docutils literal notranslate"><span class="pre">NONE</span></code></p></td>
-<td><p>GPU memory (in GB) allowed for on-device state-vector allocation. As the state-vector size exceeds this limit, host memory will be utilized for migration. <code class="code docutils literal notranslate"><span class="pre">NONE</span></code> means unlimited (up to physical memory constraints). This is the default.</p></td>
-</tr>
-</tbody>
-</table>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 0.8: </span>The <code class="code docutils literal notranslate"><span class="pre">nvidia-fp64</span></code> targets, which is equivalent setting the <code class="code docutils literal notranslate"><span class="pre">fp64</span></code> option on the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target,
-is deprecated and will be removed in a future release.</p>
-</div>
-</section>
-<section id="multi-node-multi-gpu">
-<span id="nvidia-mgpu-backend"></span><h3>Multi-node multi-GPU<a class="headerlink" href="#multi-node-multi-gpu" title="Permalink to this heading">¶</a></h3>
-<p>The NVIDIA target also provides a state vector simulator accelerated with
-the <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> library with support for Multi-Node, Multi-GPU distribution of the
-state vector, in addition to a single GPU.</p>
-<p>The multi-node multi-GPU simulator expects to run within an MPI context.
-To execute a program on the multi-node multi-GPU NVIDIA target, use the following commands
-(adjust the value of the <code class="code docutils literal notranslate"><span class="pre">-np</span></code> flag as needed to reflect available GPU resources on your system):</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<p>Double precision simulation:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>fp64,mgpu
-</pre></div>
-</div>
-<p>Single precision simulation:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>fp32,mgpu
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you installed CUDA-Q via <code class="code docutils literal notranslate"><span class="pre">pip</span></code>, you will need to install the necessary MPI dependencies separately;
-please follow the instructions for installing dependencies in the <a class="reference external" href="https://pypi.org/project/cuda-quantum/#description">Project Description</a>.</p>
-</div>
-<p>In addition to using MPI in the simulator, you can use it in your application code by installing <a class="reference external" href="https://mpi4py.readthedocs.io/">mpi4py</a>, and
-invoking the program with the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span>-m<span class="w"> </span>mpi4py<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>fp64,mgpu
-</pre></div>
-</div>
-<p>The target can also be defined in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s1">&#39;mgpu,fp64&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If a target is set in the application code, this target will override the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> command line flag given during program invocation.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<ul class="simple">
-<li><p>The order of the option settings are interchangeable.
-For example, <code class="code docutils literal notranslate"><span class="pre">cudaq.set_target('nvidia',</span> <span class="pre">option='mgpu,fp64')</span></code> is equivalent to <code class="code docutils literal notranslate"><span class="pre">cudaq.set_target('nvidia',</span> <span class="pre">option='fp64,mgpu')</span></code>.</p></li>
-<li><p>The <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target has single-precision as the default setting. Thus, using <code class="code docutils literal notranslate"><span class="pre">option='mgpu'</span></code> implies that <code class="code docutils literal notranslate"><span class="pre">option='mgpu,fp32'</span></code>.</p></li>
-</ul>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<p>Double precision simulation:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w">  </span>--target-option<span class="w"> </span>mgpu,fp64<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>./program.x
-</pre></div>
-</div>
-<p>Single precision simulation:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w">  </span>--target-option<span class="w"> </span>mgpu,fp32<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This backend requires an NVIDIA GPU, CUDA runtime libraries, as well as an MPI installation. If you do not have these dependencies installed, you may encounter either an error stating <code class="code docutils literal notranslate"><span class="pre">invalid</span> <span class="pre">simulator</span> <span class="pre">requested</span></code> (missing CUDA libraries), or an error along the lines of <code class="code docutils literal notranslate"><span class="pre">failed</span> <span class="pre">to</span> <span class="pre">launch</span> <span class="pre">kernel</span></code> (missing MPI installation). See the section <a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility"><span class="std std-ref">Dependencies and Compatibility</span></a> for more information about how to install dependencies.</p>
-<p>The number of processes and nodes should be always power-of-2.</p>
-<p>Host-device state vector migration is also supported in the multi-node multi-GPU configuration.</p>
-</div>
-<p>In addition to those environment variable options supported in the single-GPU mode,
-the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target provides the following environment variable options particularly for
-the multi-node multi-GPU configuration. Any environment variables must be set
-prior to setting the target.</p>
-<table class="docutils align-default" id="id5">
-<caption><span class="caption-text"><strong>Additional environment variable options for multi-node multi-GPU mode</strong></span><a class="headerlink" href="#id5" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 20%" />
-<col style="width: 30%" />
-<col style="width: 50%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p>Option</p></td>
-<td><p>Value</p></td>
-<td><p>Description</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_LIB_MPI</span></code></p></td>
-<td><p>string</p></td>
-<td><p>The shared library name for inter-process communication. The default value is <code class="code docutils literal notranslate"><span class="pre">libmpi.so</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_COMM_PLUGIN_TYPE</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">AUTO</span></code>, <code class="code docutils literal notranslate"><span class="pre">EXTERNAL</span></code>, <code class="code docutils literal notranslate"><span class="pre">OpenMPI</span></code>, or <code class="code docutils literal notranslate"><span class="pre">MPICH</span></code></p></td>
-<td><p>Selecting <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> <code class="code docutils literal notranslate"><span class="pre">CommPlugin</span></code> for inter-process communication. The default is <code class="code docutils literal notranslate"><span class="pre">AUTO</span></code>. If <code class="code docutils literal notranslate"><span class="pre">EXTERNAL</span></code> is selected, <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MGPU_LIB_MPI</span></code> should point to an implementation of <code class="code docutils literal notranslate"><span class="pre">cuStateVec</span></code> <code class="code docutils literal notranslate"><span class="pre">CommPlugin</span></code> interface.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_NQUBITS_THRESH</span></code></p></td>
-<td><p>positive integer</p></td>
-<td><p>The qubit count threshold where state vector distribution is activated. Below this threshold, simulation is performed as independent (non-distributed) tasks across all MPI processes for optimal performance. Default is 25.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code></p></td>
-<td><p>positive integer</p></td>
-<td><p>The max number of qubits used for gate fusion. The default value is <code class="code docutils literal notranslate"><span class="pre">6</span></code> if there are more than one MPI processes or <code class="code docutils literal notranslate"><span class="pre">4</span></code> otherwise.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_P2P_DEVICE_BITS</span></code></p></td>
-<td><p>positive integer</p></td>
-<td><p>Specify the number of GPUs that can communicate by using GPUDirect P2P. Default value is 0 (P2P communication is disabled).</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_GPU_FABRIC</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">MNNVL</span></code>, <code class="code docutils literal notranslate"><span class="pre">NVL</span></code>, or <code class="code docutils literal notranslate"><span class="pre">NONE</span></code></p></td>
-<td><p>Automatically set the number of P2P device bits based on the total number of processes when multi-node NVLink (<code class="code docutils literal notranslate"><span class="pre">MNNVL</span></code>) is selected; or the number of processes per node when NVLink (<code class="code docutils literal notranslate"><span class="pre">NVL</span></code>) is selected; or disable P2P (with <code class="code docutils literal notranslate"><span class="pre">NONE</span></code>).</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_GLOBAL_INDEX_BITS</span></code></p></td>
-<td><p>comma-separated list of positive integers</p></td>
-<td><p>Specify the inter-node network structure (faster to slower). For example, assuming a 8 nodes, 4 GPUs/node simulation whereby network communication is faster, this <code class="code docutils literal notranslate"><span class="pre">CUDAQ_GLOBAL_INDEX_BITS</span></code> environment variable can be set to <code class="code docutils literal notranslate"><span class="pre">3,2</span></code>. The first <code class="code docutils literal notranslate"><span class="pre">3</span></code> represents <strong>8</strong> nodes with fast communication and the second <code class="code docutils literal notranslate"><span class="pre">2</span></code> represents <strong>4</strong> 8-node groups in those total 32 nodes. Default is an empty list (no customization based on network structure of the cluster).</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDAQ_HOST_DEVICE_MIGRATION_LEVEL</span></code></p></td>
-<td><p>positive integer</p></td>
-<td><p>Specify host-device memory migration w.r.t. the network structure. If provided, this setting determines the position to insert the number of migration index bits to the <code class="code docutils literal notranslate"><span class="pre">CUDAQ_GLOBAL_INDEX_BITS</span></code> list. By default, if not set, the number of migration index bits (CPU-GPU data transfers) is appended to the end of the array of index bits (aka, state vector distribution scheme). This default behavior is optimized for systems with fast GPU-GPU interconnects (NVLink, InfiniBand, etc.)</p></td>
-</tr>
-</tbody>
-</table>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 0.8: </span>The <code class="code docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> target, which is equivalent to the multi-node multi-GPU double-precision option (<code class="code docutils literal notranslate"><span class="pre">mgpu,fp64</span></code>) of the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code>
-is deprecated and will be removed in a future release.</p>
-</div>
-<p>The above configuration options of the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> backend
-can be tuned to reduce your simulation runtimes. One of the
-performance improvements is to fuse multiple gates together during runtime. For
-example, <code class="code docutils literal notranslate"><span class="pre">x(qubit0)</span></code> and <code class="code docutils literal notranslate"><span class="pre">x(qubit1)</span></code> can be fused together into a
-single 4x4 matrix operation on the state vector rather than 2 separate 2x2
-matrix operations on the state vector. This fusion reduces memory bandwidth on
-the GPU because the state vector is transferred into and out of memory fewer
-times. By default, up to 4 gates are fused together for single-GPU simulations,
-and up to 6 gates are fused together for multi-GPU simulations. The number of
-gates fused can <strong>significantly</strong> affect performance of some circuits, so users
-can override the default fusion level by setting the setting <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code>
-environment variable to another integer value as shown below.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDAQ_MGPU_FUSE</span><span class="o">=</span><span class="m">5</span><span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>mgpu,fp64
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>nvidia<span class="w"> </span>--target-option<span class="w"> </span>mgpu,fp64<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-<span class="nv">CUDAQ_MGPU_FUSE</span><span class="o">=</span><span class="m">5</span><span class="w"> </span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>./program.x
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="openmp-cpu-only">
-<span id="id1"></span><h3>OpenMP CPU-only<a class="headerlink" href="#openmp-cpu-only" title="Permalink to this heading">¶</a></h3>
-<p id="qpp-cpu-backend">This target provides a state vector simulator based on the CPU-only, OpenMP threaded <a class="reference external" href="https://github.com/softwareqinc/qpp">Q++</a> library.
-This is the default target when running on CPU-only systems.</p>
-<p>To execute a program on the <code class="code docutils literal notranslate"><span class="pre">qpp-cpu</span></code> target even if a GPU-accelerated backend is available,
-use the following commands:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>qpp-cpu
-</pre></div>
-</div>
-<p>The target can also be defined in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;qpp-cpu&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If a target is set in the application code, this target will override the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> command line flag given during program invocation.</p>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>qpp-cpu<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="tensor-network-simulators">
-<h2>Tensor Network Simulators<a class="headerlink" href="#tensor-network-simulators" title="Permalink to this heading">¶</a></h2>
-<p id="tensor-backends">CUDA-Q provides a couple of tensor-network simulator targets accelerated with
-the <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> library.
-These backends are available for use from both C++ and Python.</p>
-<p>Tensor network simulators are suitable for large-scale simulation of certain classes of quantum circuits involving many qubits beyond the memory limit of state vector based simulators. For example, computing the expectation value of a Hamiltonian via <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> can be performed efficiently, thanks to <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> contraction optimization capability. On the other hand, conditional circuits, i.e., those with mid-circuit measurements or reset, despite being supported by both backends, may result in poor performance.</p>
-<section id="id2">
-<h3>Multi-node multi-GPU<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h3>
-<p>The <code class="code docutils literal notranslate"><span class="pre">tensornet</span></code> backend represents quantum states and circuits as tensor networks in an exact form (no approximation).
-Measurement samples and expectation values are computed via tensor network contractions.
-This backend supports multi-node, multi-GPU distribution of tensor operations required to evaluate and simulate the circuit.</p>
-<p>To execute a program on the <code class="code docutils literal notranslate"><span class="pre">tensornet</span></code> target using a <em>single GPU</em>, use the following commands:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>tensornet
-</pre></div>
-</div>
-<p>The target can also be defined in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;tensornet&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If a target is set in the application code, this target will override the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> command line flag given during program invocation.</p>
-</div>
-<input class="tab-input" id="tab-set--5-input--2" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>tensornet<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<p>If you have <em>multiple GPUs</em> available on your system, you can use MPI to automatically distribute parallelization across the visible GPUs.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you installed the CUDA-Q Python wheels, distribution across multiple GPUs is currently not supported for this backend.
-We will add support for it in future releases. For more information, see this <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/issues/920">GitHub issue</a>.</p>
-</div>
-<p>Use the following commands to enable distribution across multiple GPUs (adjust the value of the <code class="code docutils literal notranslate"><span class="pre">-np</span></code> flag as needed to reflect available GPU resources on your system):</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--6-input--1" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>tensornet
-</pre></div>
-</div>
-<p>In addition to using MPI in the simulator, you can use it in your application code by installing <a class="reference external" href="https://mpi4py.readthedocs.io/">mpi4py</a>, and
-invoking the program with the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>python3<span class="w"> </span>-m<span class="w"> </span>mpi4py<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>tensornet
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--6-input--2" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>tensornet<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">2</span><span class="w"> </span>./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If the <code class="code docutils literal notranslate"><span class="pre">CUTENSORNET_COMM_LIB</span></code> environment variable is not set, MPI parallelization on the <code class="code docutils literal notranslate"><span class="pre">tensornet</span></code> backend may fail.
-If you are using a CUDA-Q container, this variable is pre-configured and no additional setup is needed. If you are customizing your installation or have built CUDA-Q from source, please follow the instructions for <a class="reference external" href="https://docs.nvidia.com/cuda/cuquantum/latest/getting-started/index.html#from-nvidia-devzone">activating the distributed interface</a> for the <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> library. This requires
-<a class="reference internal" href="../install/local_installation.html#additional-cuda-tools"><span class="std std-ref">installing CUDA development dependencies</span></a>, and setting the <code class="code docutils literal notranslate"><span class="pre">CUTENSORNET_COMM_LIB</span></code>
-environment variable to the newly built <code class="code docutils literal notranslate"><span class="pre">libcutensornet_distributed_interface_mpi.so</span></code> library.</p>
-</div>
-<p>Specific aspects of the simulation can be configured by setting the following of environment variables:</p>
-<ul class="simple">
-<li><p><strong>`CUDA_VISIBLE_DEVICES=X`</strong>: Makes the process only see GPU X on multi-GPU nodes. Each MPI process must only see its own dedicated GPU. For example, if you run 8 MPI processes on a DGX system with 8 GPUs, each MPI process should be assigned its own dedicated GPU via <code class="code docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> when invoking <code class="code docutils literal notranslate"><span class="pre">mpiexec</span></code> (or <code class="code docutils literal notranslate"><span class="pre">mpirun</span></code>) commands.</p></li>
-<li><p><strong>`OMP_PLACES=cores`</strong>: Set this environment variable to improve CPU parallelization.</p></li>
-<li><p><strong>`OMP_NUM_THREADS=X`</strong>: To enable CPU parallelization, set X to <code class="code docutils literal notranslate"><span class="pre">NUMBER_OF_CORES_PER_NODE/NUMBER_OF_GPUS_PER_NODE</span></code>.</p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This backend requires an NVIDIA GPU and CUDA runtime libraries.
-If you do not have these dependencies installed, you may encounter an error stating <code class="code docutils literal notranslate"><span class="pre">Invalid</span> <span class="pre">simulator</span> <span class="pre">requested</span></code>.
-See the section <a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility"><span class="std std-ref">Dependencies and Compatibility</span></a> for more information about how to install dependencies.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Setting random seed, via <code class="code docutils literal notranslate"><span class="pre">cudaq::set_random_seed</span></code>, is not supported for this backend due to a limitation of the <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> library. This will be fixed in future release once this feature becomes available.</p>
-</div>
-</section>
-<section id="matrix-product-state">
-<h3>Matrix product state<a class="headerlink" href="#matrix-product-state" title="Permalink to this heading">¶</a></h3>
-<p>The <code class="code docutils literal notranslate"><span class="pre">tensornet-mps</span></code> backend is based on the matrix product state (MPS) representation of the state vector/wave function, exploiting the sparsity in the tensor network via tensor decomposition techniques such as QR and SVD. As such, this backend is an approximate simulator, whereby the number of singular values may be truncated to keep the MPS size tractable.
-The <code class="code docutils literal notranslate"><span class="pre">tensornet-mps</span></code> backend only supports single-GPU simulation. Its approximate nature allows the <code class="code docutils literal notranslate"><span class="pre">tensornet-mps</span></code> backend to handle a large number of qubits for certain classes of quantum circuits on a relatively small memory footprint.</p>
-<p>To execute a program on the <code class="code docutils literal notranslate"><span class="pre">tensornet-mps</span></code> target, use the following commands:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--7-input--1" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>tensornet-mps
-</pre></div>
-</div>
-<p>The target can also be defined in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;tensornet-mps&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If a target is set in the application code, this target will override the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> command line flag given during program invocation.</p>
-</div>
-<input class="tab-input" id="tab-set--7-input--2" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>tensornet-mps<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<p>Specific aspects of the simulation can be configured by defining the following environment variables:</p>
-<ul class="simple">
-<li><p><strong>`CUDAQ_MPS_MAX_BOND=X`</strong>: The maximum number of singular values to keep (fixed extent truncation). Default: 64.</p></li>
-<li><p><strong>`CUDAQ_MPS_ABS_CUTOFF=X`</strong>: The cutoff for the largest singular value during truncation. Eigenvalues that are smaller will be trimmed out. Default: 1e-5.</p></li>
-<li><p><strong>`CUDAQ_MPS_RELATIVE_CUTOFF=X`</strong>: The cutoff for the maximal singular value relative to the largest eigenvalue. Eigenvalues that are smaller than this fraction of the largest singular value will be trimmed out. Default: 1e-5</p></li>
-<li><p><strong>`CUDAQ_MPS_SVD_ALGO=X`</strong>: The SVD algorithm to use. Valid values are: <code class="code docutils literal notranslate"><span class="pre">GESVD</span></code> (QR algorithm), <code class="code docutils literal notranslate"><span class="pre">GESVDJ</span></code> (Jacobi method), <code class="code docutils literal notranslate"><span class="pre">GESVDP</span></code> (<a class="reference external" href="https://epubs.siam.org/doi/10.1137/090774999">polar decomposition</a>), <code class="code docutils literal notranslate"><span class="pre">GESVDR</span></code> (<a class="reference external" href="https://epubs.siam.org/doi/10.1137/090771806">randomized methods</a>). Default: <code class="code docutils literal notranslate"><span class="pre">GESVDJ</span></code>.</p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This backend requires an NVIDIA GPU and CUDA runtime libraries.
-If you do not have these dependencies installed, you may encounter an error stating <code class="code docutils literal notranslate"><span class="pre">Invalid</span> <span class="pre">simulator</span> <span class="pre">requested</span></code>.
-See the section <a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility"><span class="std std-ref">Dependencies and Compatibility</span></a> for more information about how to install dependencies.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Setting random seed, via <code class="code docutils literal notranslate"><span class="pre">cudaq::set_random_seed</span></code>, is not supported for this backend due to a limitation of the <code class="code docutils literal notranslate"><span class="pre">cuTensorNet</span></code> library. This will be fixed in future release once this feature becomes available.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The parallelism of Jacobi method (the default <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MPS_SVD_ALGO</span></code> setting) gives GPU better performance on small and medium size matrices.
-If you expect a large number of singular values (e.g., increasing the <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MPS_MAX_BOND</span></code> setting), please adjust the <code class="code docutils literal notranslate"><span class="pre">CUDAQ_MPS_SVD_ALGO</span></code> setting accordingly.</p>
-</div>
-</section>
-</section>
-<section id="clifford-only-simulator">
-<h2>Clifford-Only Simulator<a class="headerlink" href="#clifford-only-simulator" title="Permalink to this heading">¶</a></h2>
-<section id="stim-cpu">
-<h3>Stim (CPU)<a class="headerlink" href="#stim-cpu" title="Permalink to this heading">¶</a></h3>
-<p id="stim-backend">This target provides a fast simulator for circuits containing <em>only</em> Clifford
-gates. Any non-Clifford gates (such as T gates and Toffoli gates) are not
-supported. This simulator is based on the <a class="reference external" href="https://github.com/quantumlib/Stim">Stim</a>
-library.</p>
-<p>To execute a program on the <code class="code docutils literal notranslate"><span class="pre">stim</span></code> target, use the following commands:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--8-input--1" name="tab-set--8" type="radio"><label class="tab-label" for="tab-set--8-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>--target<span class="w"> </span>stim
-</pre></div>
-</div>
-<p>The target can also be defined in the application code by calling</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;stim&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If a target is set in the application code, this target will override the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> command line flag given during program invocation.</p>
-</div>
-<input class="tab-input" id="tab-set--8-input--2" name="tab-set--8" type="radio"><label class="tab-label" for="tab-set--8-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>stim<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>CUDA-Q currently executes kernels using a “shot-by-shot” execution approach.
-This allows for conditional gate execution (i.e. full control flow), but it
-can be slower than executing Stim a single time and generating all the shots
-from that single execution.</p>
-</div>
-</section>
-</section>
-<section id="fermioniq">
-<h2>Fermioniq<a class="headerlink" href="#fermioniq" title="Permalink to this heading">¶</a></h2>
-<p id="fermioniq-backend"><a class="reference external" href="https://fermioniq.com/">Fermioniq</a> offers a cloud-based tensor-network emulation platform, <a class="reference external" href="https://www.fermioniq.com/ava/">Ava</a>,
-for the approximate simulation of large-scale quantum circuits beyond the memory limit of state vector and exact tensor network based methods.</p>
-<p>The level of approximation can be controlled by setting the bond dimension: larger values yield more accurate simulations at the expense
-of slower computation time. For a detailed description of Ava users are referred to the <a class="reference external" href="https://docs.fermioniq.com/">online documentation</a>.</p>
-<p>Users of CUDA-Q can access a simplified version of the full Fermioniq emulator (<a class="reference external" href="https://www.fermioniq.com/ava/">Ava</a>) from either
-C++ or Python. This version currently supports emulation of quantum circuits without noise, and can return measurement samples and/or
-compute expectation values of observables.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>In order to use the Fermioniq emulator, users must provide access credentials. These can be requested by contacting <a class="reference external" href="mailto:info&#37;&#52;&#48;fermioniq&#46;com">info<span>&#64;</span>fermioniq<span>&#46;</span>com</a></p>
-<p>The credentials must be set via two environment variables:
-<code class="code docutils literal notranslate"><span class="pre">FERMIONIQ_ACCESS_TOKEN_ID</span></code> and <code class="code docutils literal notranslate"><span class="pre">FERMIONIQ_ACCESS_TOKEN_SECRET</span></code>.</p>
-</div>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--9-input--1" name="tab-set--9" type="radio"><label class="tab-label" for="tab-set--9-input--1">Python</label><div class="tab-content docutils">
-<p>The target to which quantum kernels are submitted
-can be controlled with the <code class="docutils literal notranslate"><span class="pre">cudaq::set_target()</span></code> function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;fermioniq&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You will have to specify a remote configuration id for the Fermioniq backend
-during compilation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;fermioniq&quot;</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span>
-    <span class="s2">&quot;remote_config&quot;</span><span class="p">:</span> <span class="n">remote_config_id</span>
-<span class="p">})</span>
-</pre></div>
-</div>
-<p>For a comprehensive list of all remote configurations, please contact Fermioniq directly.</p>
-<p>When your organization requires you to define a project id, you have to specify
-the project id during compilation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;fermioniq&quot;</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span>
-    <span class="s2">&quot;project_id&quot;</span><span class="p">:</span> <span class="n">project_id</span>
-<span class="p">})</span>
-</pre></div>
-</div>
-<p>To specify the bond dimension, you can pass the <code class="docutils literal notranslate"><span class="pre">bond_dim</span></code> parameter.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;fermioniq&quot;</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span>
-    <span class="s2">&quot;bond_dim&quot;</span><span class="p">:</span> <span class="mi">5</span>
-<span class="p">})</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--9-input--2" name="tab-set--9" type="radio"><label class="tab-label" for="tab-set--9-input--2">C++</label><div class="tab-content docutils">
-<p>To target quantum kernel code for execution in the Fermioniq backends,
-pass the flag <code class="docutils literal notranslate"><span class="pre">--target</span> <span class="pre">fermioniq</span></code> to the <code class="docutils literal notranslate"><span class="pre">nvq++</span></code> compiler. CUDA-Q will
-authenticate via the Fermioniq REST API using the environment variables
-set earlier.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>fermioniq<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>You will have to specify a remote configuration id for the Fermioniq backend
-during compilation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>fermioniq<span class="w"> </span>--fermioniq-remote-config<span class="w"> </span>&lt;remote_config_id&gt;<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>For a comprehensive list of all remote configurations, please contact Fermioniq directly.</p>
-<p>When your organization requires you to define a project id, you have to specify
-the project id during compilation.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>fermioniq<span class="w"> </span>--fermioniq-project-id<span class="w"> </span>&lt;project_id&gt;<span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-<p>To specify the bond dimension, you can pass the <code class="docutils literal notranslate"><span class="pre">fermioniq-bond-dim</span></code> parameter.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>--target<span class="w"> </span>fermioniq<span class="w"> </span>--fermioniq-bond-dim<span class="w"> </span><span class="m">10</span><span class="w"> </span>src.cpp<span class="w"> </span>...
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="default-simulator">
-<h2>Default Simulator<a class="headerlink" href="#default-simulator" title="Permalink to this heading">¶</a></h2>
-<p id="id3">If no explicit target is set, i.e., if the code is compiled without any <code class="code docutils literal notranslate"><span class="pre">--target</span></code> flags, then CUDA-Q makes a default choice for the simulator.</p>
-<p>If an NVIDIA GPU and CUDA runtime libraries are available, the default target is set to <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code>. This will utilize the <a class="reference internal" href="#cuquantum-single-gpu"><span class="std std-ref">cuQuantum single-GPU state vector simulator</span></a>.
-On CPU-only systems, the default target is set to <code class="code docutils literal notranslate"><span class="pre">qpp-cpu</span></code> which uses the <a class="reference internal" href="#openmp-cpu-only"><span class="std std-ref">OpenMP CPU-only simulator</span></a>.</p>
-<p>The default simulator can be overridden by the environment variable <code class="code docutils literal notranslate"><span class="pre">CUDAQ_DEFAULT_SIMULATOR</span></code>. If no target is explicitly specified and the environment variable has a valid value, then it will take effect.
-This environment variable can be set to any non-hardware backend. Any invalid value is ignored.</p>
-<p>For CUDA-Q Python API, the environment variable at the time when <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> module is imported is relevant, not the value of the environment variable at the time when the simulator is invoked.</p>
-<p>For example,</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--10-input--1" name="tab-set--10" type="radio"><label class="tab-label" for="tab-set--10-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDAQ_DEFAULT_SIMULATOR</span><span class="o">=</span>density-matrix-cpu<span class="w"> </span>python3<span class="w"> </span>program.py<span class="w"> </span><span class="o">[</span>...<span class="o">]</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--10-input--2" name="tab-set--10" type="radio"><label class="tab-label" for="tab-set--10-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDAQ_DEFAULT_SIMULATOR</span><span class="o">=</span>density-matrix-cpu<span class="w"> </span>nvq++<span class="w"> </span>program.cpp<span class="w"> </span><span class="o">[</span>...<span class="o">]</span><span class="w"> </span>-o<span class="w"> </span>program.x
-./program.x
-</pre></div>
-</div>
-</div>
-</div>
-<p>This will use the density matrix simulator target.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To use targets that require an NVIDIA GPU and CUDA runtime libraries, the dependencies must be installed, else you may encounter an error stating <code class="code docutils literal notranslate"><span class="pre">Invalid</span> <span class="pre">simulator</span> <span class="pre">requested</span></code>. See the section <a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility"><span class="std std-ref">Dependencies and Compatibility</span></a> for more information about how to install dependencies.</p>
-</div>
-</section>
-</section>
-
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-states">Quantum States</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-  <section id="building-your-first-cuda-q-program">
-<h1>Building your first CUDA-Q Program<a class="headerlink" href="#building-your-first-cuda-q-program" title="Permalink to this heading">¶</a></h1>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<p>We can define our quantum kernel as a typical Python function, with the additional use of the
-<code class="code docutils literal notranslate"><span class="pre">&#64;cudaq.kernel</span></code> decorator. Let’s begin with a simple GHZ-state example, producing a state of
-maximal entanglement amongst an allocated set of qubits.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="c1"># Define our kernel.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="c1"># Allocate our qubits.</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="c1"># Place the first qubit in the superposition state.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="c1"># Loop through the allocated qubits and apply controlled-X,</span>
-    <span class="c1"># or CNOT, operations between them.</span>
-    <span class="k">for</span> <span class="n">qubit</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="n">qubit</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="n">qubit</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-    <span class="c1"># Measure the qubits.</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<p>We can define our quantum kernel as we do any other typed callable in C++, through the use of the
-<code class="code docutils literal notranslate"><span class="pre">__qpu__</span></code> annotation. For the following example, we will define a kernel for a simple GHZ-state as
-a standard free function.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">kernel</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Allocate our qubits.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// Place the first qubit in the superposition state.</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="c1">// Loop through the allocated qubits and apply controlled-X,</span>
-<span class="w">  </span><span class="c1">// or CNOT, operations between them.</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">qubit</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">))</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="n">qubit</span><span class="p">],</span><span class="w"> </span><span class="n">qvector</span><span class="p">[</span><span class="n">qubit</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="w">  </span><span class="c1">// Measure the qubits.</span>
-<span class="w">  </span><span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">);</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This kernel function can accept any number of arguments, allowing for flexibility in the construction
-of the quantum program. In this case, the <code class="code docutils literal notranslate"><span class="pre">qubit_count</span></code> argument allows us to dynamically control the
-number of qubits allocated to the kernel. As we will see in further <a class="reference internal" href="../examples/examples.html"><span class="doc">examples</span></a>,
-we could also use these arguments to control various parameters of the gates themselves, such as rotation
-angles.</p>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<h1>What is a CUDA-Q kernel?<a class="headerlink" href="#what-is-a-cuda-q-kernel" title="Permalink to this heading">¶</a></h1>
-<p>Quantum kernels are defined as functions that are executed on a quantum processing unit (QPU) or
-a simulated QPU. They generalize quantum circuits and provide a new abstraction for quantum programming.
-Quantum kernels can be combined with classical functions to create quantum-classical applications
-that can be executed on a heterogeneous system of QPUs, GPUs, and CPUs to solve real-world problems.</p>
-<p><strong>What’s the difference between a quantum kernel and a quantum circuit?</strong></p>
-<p>Every quantum circuit is a kernel, but not every quantum kernel is a circuit. For instance, a quantum
-kernel can be built up from other kernels, allowing us to interpret a large quantum program as a sequence
-of subroutines or subcircuits.</p>
-<p>Moreover, since quantum kernels are functions, there is more expressibility available compared to a
-standard quantum circuit. We can not only parameterize the kernel, but can also contain classical control
-flow statements (<code class="code docutils literal notranslate"><span class="pre">if</span></code>, <code class="code docutils literal notranslate"><span class="pre">for</span></code>, <code class="code docutils literal notranslate"><span class="pre">while</span></code>, etc.), and classical computations such as additions, multiplication, etc.
-Conditional statements on quantum memory and qubit measurements can be included in quantum kernels to enable
-dynamic circuits and fast feedback, particularly useful for quantum error correction. To learn more about what
-language constructs are supported within quantum kernels, take a look at the CUDA-Q
-<a class="reference internal" href="../../specification/cudaq/kernels.html"><span class="doc">specification</span></a>.</p>
-<p><strong>How do I build and run a quantum kernel?</strong></p>
-<p>Once a quantum kernel has been defined in a program, it may be called as a typical function, or can be executed
-using the <code class="code docutils literal notranslate"><span class="pre">sample</span></code> or <code class="code docutils literal notranslate"><span class="pre">observe</span></code> primitives.</p>
-<p>Let’s take a closer look at how to build and execute a quantum kernel with CUDA-Q.</p>
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id20">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id24">Submission from Python</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#faq">FAQ</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
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-  <section id="running-your-first-cuda-q-program">
-<h1>Running your first CUDA-Q Program<a class="headerlink" href="#running-your-first-cuda-q-program" title="Permalink to this heading">¶</a></h1>
-<p>Now that you have defined your first quantum kernel, let’s look at different options for how to execute it.
-In CUDA-Q, quantum circuits are stored as quantum kernels. For estimating the probability distribution of
-a measured quantum state in a circuit, we use the <code class="docutils literal notranslate"><span class="pre">sample</span></code> function call, and for computing the
-expectation value of a quantum state with a given observable, we use the <code class="docutils literal notranslate"><span class="pre">observe</span></code> function call.</p>
-<section id="sample">
-<h2>Sample<a class="headerlink" href="#sample" title="Permalink to this heading">¶</a></h2>
-<p>Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q <code class="code docutils literal notranslate"><span class="pre">sample</span></code> call enables this.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<p>The <a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample" title="cudaq.sample"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.sample()</span></code></a> method takes a kernel and its arguments as inputs, and returns a <a class="reference internal" href="../../api/languages/python_api.html#cudaq.SampleResult" title="cudaq.SampleResult"><code class="xref py py-class docutils literal notranslate"><span class="pre">cudaq.SampleResult</span></code></a>.</p>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code> method takes a kernel and its arguments as inputs, and returns a <code class="code docutils literal notranslate"><span class="pre">cudaq::SampleResult</span></code>.</p>
-</div>
-</div>
-<p>This result dictionary contains the distribution of measured states for the system.</p>
-<p>Continuing with the GHZ kernel defined in <a class="reference internal" href="build_kernel.html"><span class="doc">Building Your First CUDA-Q Program</span></a>,
-we will set the concrete value of our <code class="code docutils literal notranslate"><span class="pre">qubit_count</span></code> to be two.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">cudaq</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">))</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">)</span>
-<span class="c1"># Should see a roughly 50/50 distribution between the |00&gt; and</span>
-<span class="c1"># |11&gt; states. Example: {00: 505  11: 495}</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measurement distribution:&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">qubit_count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">result_0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="cm">/* kernel args */</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// Should see a roughly 50/50 distribution between the |00&gt; and</span>
-<span class="w">  </span><span class="c1">// |11&gt; states. Example: {00: 505  11: 495}</span>
-<span class="w">  </span><span class="n">result_0</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The code above can be run like any other program:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<p>Assuming the program is saved in the file <code class="code docutils literal notranslate"><span class="pre">sample.py</span></code>, we can execute is with the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python3 sample.py</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<p>Assuming the program is saved in the file <code class="code docutils literal notranslate"><span class="pre">sample.cpp</span></code>, we can now compile this file with the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> toolchain, and then run the compiled executable.</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ sample.cpp</span>
-<span class="go">./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>By default, <code class="code docutils literal notranslate"><span class="pre">sample</span></code> produces an ensemble of 1000 shots. This can be changed by specifying an integer argument
-for the <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># With an increased shots count, we will still see the same 50/50 distribution,</span>
-<span class="c1"># but now with 10,000 total measurements instead of the default 1000.</span>
-<span class="c1"># Example: {00: 5005  11: 4995}</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measurement distribution:&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="c1">// With an increased shots count, we will still see the same 50/50</span>
-<span class="w">  </span><span class="c1">// distribution, but now with 10,000 total measurements instead of the default</span>
-<span class="w">  </span><span class="c1">// 1000. Example: {00: 5005  11: 4995}</span>
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">shots_count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">10000</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">result_1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">shots_count</span><span class="p">,</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">);</span>
-<span class="w">  </span><span class="n">result_1</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Note that there is a subtle difference between how sample is executed with the target device set to a simulator or with the target device set to a QPU. When run on a simulator, the quantum state is built once and then sampled repeatedly, where the number of samples is defined by <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code>. When executed on quantum hardware, the quantum state collapses upon measurement and hence needs to be rebuilt every time to collect a sample.</p>
-<p>A variety of methods can be used to extract useful information from a <code class="code docutils literal notranslate"><span class="pre">SampleResult</span></code>. For example,
-to return the most probable measurement and its respective probability:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">most_probable_result</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">most_probable</span><span class="p">()</span>
-<span class="n">probability</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">most_probable_result</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Most probable result: &quot;</span> <span class="o">+</span> <span class="n">most_probable_result</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measured with probability &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">probability</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>See the <a class="reference internal" href="../../api/languages/python_api.html"><span class="doc">API specification</span></a> for further information.</p>
-</div>
-<input class="tab-input" id="tab-set--4-input--2" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">result_1</span><span class="p">.</span><span class="n">most_probable</span><span class="p">()</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"> </span><span class="c1">// prints: `00`</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">result_1</span><span class="p">.</span><span class="n">probability</span><span class="p">(</span><span class="n">result_1</span><span class="p">.</span><span class="n">most_probable</span><span class="p">())</span>
-<span class="w">            </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"> </span><span class="c1">// prints: `0.5005`</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>See the <a class="reference internal" href="../../api/languages/cpp_api.html"><span class="doc">API specification</span></a> for further information.</p>
-</div>
-</div>
-<p>Sampling a distribution can be a time intensive task. An asynchronous version of sample exists and can be useful to parallelize your application. Asynchronous programming is a technique that enables your program to start a potentially long-running task and still be able to be responsive to other events while that task runs, rather than having to wait until that task has finished. Once that task has finished, your program is presented with the result.</p>
-<p>Asynchronous execution allows to easily parallelize execution of multiple kernels on a multi-processor platform. Such a platform
-is available, for example, by choosing the target <code class="code docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code>:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel2</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="c1"># Allocate our qubits.</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="c1"># Place all qubits in a uniform superposition.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-    <span class="c1"># Measure the qubits.</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="n">num_gpus</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span>
-<span class="k">if</span> <span class="n">num_gpus</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-    <span class="c1"># Set the target to include multiple virtual QPUs.</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-    <span class="c1"># Asynchronous execution on multiple virtual QPUs, each simulated by an NVIDIA GPU.</span>
-    <span class="n">result_1</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                  <span class="n">qubit_count</span><span class="p">,</span>
-                                  <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                                  <span class="n">qpu_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">result_2</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel2</span><span class="p">,</span>
-                                  <span class="n">qubit_count</span><span class="p">,</span>
-                                  <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                                  <span class="n">qpu_id</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="c1"># Schedule for execution on the same virtual QPU.</span>
-    <span class="n">result_1</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                                  <span class="n">qubit_count</span><span class="p">,</span>
-                                  <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                                  <span class="n">qpu_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">result_2</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel2</span><span class="p">,</span>
-                                  <span class="n">qubit_count</span><span class="p">,</span>
-                                  <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                                  <span class="n">qpu_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measurement distribution for kernel:&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">result_1</span><span class="o">.</span><span class="n">get</span><span class="p">()))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measurement distribution for kernel2:&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">result_2</span><span class="o">.</span><span class="n">get</span><span class="p">()))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This kind of parallelization is most effective
-if you actually have multiple QPU or CPU available. Otherwise, the
-sampling will still have to execute sequentially due to resource constraints.</p>
-</div>
-<p>More information about parallelizing execution can be found at <a class="reference internal" href="../backends/platform.html"><span class="doc">Multi-Processor Platforms</span></a> page.</p>
-</section>
-<section id="observe">
-<h2>Observe<a class="headerlink" href="#observe" title="Permalink to this heading">¶</a></h2>
-<p>The observe function allows us to calculate expectation values for a defined quantum operator, that is the value of <span class="math notranslate nohighlight">\(\bra{\psi}H\ket{\psi}\)</span>, where <span class="math notranslate nohighlight">\(H\)</span> is the desired operator and <span class="math notranslate nohighlight">\(\ket{\psi}\)</span> is the quantum state after executing a given kernel.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--6-input--1" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--1">Python</label><div class="tab-content docutils">
-<p>The <a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe" title="cudaq.observe"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.observe()</span></code></a> method takes a kernel and its arguments as inputs, along with a <a class="reference internal" href="../../api/languages/python_api.html#cudaq.SpinOperator" title="cudaq.SpinOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">cudaq.SpinOperator</span></code></a>.</p>
-<p>Using the <code class="code docutils literal notranslate"><span class="pre">cudaq.spin</span></code> module, operators may be defined as a linear combination of Pauli strings. Functions, such
-as <a class="reference internal" href="../../api/languages/python_api.html#cudaq.spin.i" title="cudaq.spin.i"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.spin.i()</span></code></a>, <a class="reference internal" href="../../api/languages/python_api.html#cudaq.spin.x" title="cudaq.spin.x"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.spin.x()</span></code></a>, <a class="reference internal" href="../../api/languages/python_api.html#cudaq.spin.y" title="cudaq.spin.y"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.spin.y()</span></code></a>, <a class="reference internal" href="../../api/languages/python_api.html#cudaq.spin.z" title="cudaq.spin.z"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.spin.z()</span></code></a> may be used to construct more
-complex spin Hamiltonians on multiple qubits.</p>
-</div>
-<input class="tab-input" id="tab-set--6-input--2" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--2">C++</label><div class="tab-content docutils">
-<p>The <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code> method takes a kernel and its arguments as inputs, along with a <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code>.</p>
-<p>Within the <code class="code docutils literal notranslate"><span class="pre">cudaq::spin</span></code> namespace, operators may be defined as a linear combination of Pauli strings. Functions, such
-as <code class="code docutils literal notranslate"><span class="pre">cudaq::spin::i</span></code>, <code class="code docutils literal notranslate"><span class="pre">cudaq::spin::x</span></code>, <code class="code docutils literal notranslate"><span class="pre">cudaq::spin::y</span></code>, <code class="code docutils literal notranslate"><span class="pre">cudaq::spin::z</span></code> may be used to construct more
-complex spin Hamiltonians on multiple qubits.</p>
-</div>
-</div>
-<p>Below is an example of a spin operator object consisting of a <code class="code docutils literal notranslate"><span class="pre">Z(0)</span></code> operator, or a Pauli Z-operator on the qubit zero.
-This is followed by the construction of a kernel with a single qubit in an equal superposition.
-The Hamiltonian is printed to confirm it has been constructed properly.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--7-input--1" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="n">operator</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">operator</span><span class="p">)</span>  <span class="c1"># prints: [1+0j] Z</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--7-input--2" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/algorithm.h&gt;</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-
-<span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">kernel</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">qubit</span><span class="p">;</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">qubit</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="n">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">spin_operator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// Prints: [1+0j] Z</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">spin_operator</span><span class="p">.</span><span class="n">to_string</span><span class="p">()</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The <code class="code docutils literal notranslate"><span class="pre">observe</span></code> function takes a kernel, any kernel arguments, and a spin operator as inputs and produces an <code class="code docutils literal notranslate"><span class="pre">ObserveResult</span></code> object.
-The expectation value can be printed using the <code class="code docutils literal notranslate"><span class="pre">expectation</span></code> method.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>It is important to exclude a measurement in the kernel, otherwise the expectation value will be determined from a collapsed
-classical state. For this example, the expected result of 0.0 is produced.</p>
-</div>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--8-input--1" name="tab-set--8" type="radio"><label class="tab-label" for="tab-set--8-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">operator</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">())</span>  <span class="c1"># prints: 0.0</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--8-input--2" name="tab-set--8" type="radio"><label class="tab-label" for="tab-set--8-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">result_0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">spin_operator</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// Expectation value of kernel with respect to single `Z` term</span>
-<span class="w">  </span><span class="c1">// should print: 0.0</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;&lt;kernel | spin_operator | kernel&gt; = &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">result_0</span><span class="p">.</span><span class="n">expectation</span><span class="p">()</span>
-<span class="w">            </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Unlike <code class="code docutils literal notranslate"><span class="pre">sample</span></code>, the default <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code> for <code class="code docutils literal notranslate"><span class="pre">observe</span></code> is 1. This result is deterministic and equivalent to the
-expectation value in the limit of infinite shots.  To produce an approximate expectation value from sampling, <code class="code docutils literal notranslate"><span class="pre">shots_count</span></code> can
-be specified to any integer.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--9-input--1" name="tab-set--9" type="radio"><label class="tab-label" for="tab-set--9-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">())</span>  <span class="c1"># prints non-zero value</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--9-input--2" name="tab-set--9" type="radio"><label class="tab-label" for="tab-set--9-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">result_1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">spin_operator</span><span class="p">);</span>
-<span class="w">  </span><span class="c1">// Expectation value of kernel with respect to single `Z` term,</span>
-<span class="w">  </span><span class="c1">// but instead of a single deterministic execution of the kernel,</span>
-<span class="w">  </span><span class="c1">// we sample over 1000 shots. We should now print an expectation</span>
-<span class="w">  </span><span class="c1">// value that is close to, but not quite, zero.</span>
-<span class="w">  </span><span class="c1">// Example: 0.025</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;&lt;kernel | spin_operator | kernel&gt; = &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">result_1</span><span class="p">.</span><span class="n">expectation</span><span class="p">()</span>
-<span class="w">            </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Similar to <code class="code docutils literal notranslate"><span class="pre">sample_async</span></code> above, observe also supports asynchronous execution.
-More information about parallelizing execution can be found at
-<a class="reference internal" href="../backends/platform.html"><span class="doc">Multi-Processor Platforms</span></a> page.</p>
-</section>
-<section id="running-on-a-gpu">
-<h2>Running on a GPU<a class="headerlink" href="#running-on-a-gpu" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--10-input--1" name="tab-set--10" type="radio"><label class="tab-label" for="tab-set--10-input--1">Python</label><div class="tab-content docutils">
-<p>Using <a class="reference internal" href="../../api/languages/python_api.html#cudaq.set_target" title="cudaq.set_target"><code class="xref py py-func docutils literal notranslate"><span class="pre">cudaq.set_target()</span></code></a>, different targets can be specified for kernel execution.</p>
-</div>
-<input class="tab-input" id="tab-set--10-input--2" name="tab-set--10" type="radio"><label class="tab-label" for="tab-set--10-input--2">C++</label><div class="tab-content docutils">
-<p>Using the <code class="code docutils literal notranslate"><span class="pre">--target</span></code> argument to <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code>, different targets can be specified for kernel execution.</p>
-</div>
-</div>
-<p>If a local GPU is detected, the target will default to <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code>. Otherwise, the CPU-based simulation
-target, <code class="code docutils literal notranslate"><span class="pre">qpp-cpu</span></code>,  will be selected.</p>
-<p>We will demonstrate the benefits of using a GPU by sampling our GHZ kernel with 25 qubits and a
-<code class="code docutils literal notranslate"><span class="pre">shots_count</span></code> of 1 million. Using a GPU accelerates this task by more than 35x. To learn about
-all of the available targets and ways to accelerate kernel execution, visit the
-<a class="reference internal" href="../backends/backends.html"><span class="doc">Backends</span></a> page.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--11-input--1" name="tab-set--11" type="radio"><label class="tab-label" for="tab-set--11-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="c1"># Will time the execution of our sample call.</span>
-<span class="n">code_to_time</span> <span class="o">=</span> <span class="s1">&#39;cudaq.sample(kernel, qubit_count, shots_count=1000000)&#39;</span>
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="mi">1</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="k">else</span> <span class="mi">25</span>
-
-<span class="c1"># Execute on CPU backend.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;qpp-cpu&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;CPU time&#39;</span><span class="p">)</span>  <span class="c1"># Example: 27.57462 s.</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="n">stmt</span><span class="o">=</span><span class="n">code_to_time</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">(),</span> <span class="n">number</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-
-<span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="c1"># Execute on GPU backend.</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s1">&#39;nvidia&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;GPU time&#39;</span><span class="p">)</span>  <span class="c1"># Example: 0.773286 s.</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="n">stmt</span><span class="o">=</span><span class="n">code_to_time</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">(),</span> <span class="n">number</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--11-input--2" name="tab-set--11" type="radio"><label class="tab-label" for="tab-set--11-input--2">C++</label><div class="tab-content docutils">
-<p>To compare the performance, we can create a simple timing script that isolates just the call
-to <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code>. We are still using the same GHZ kernel as earlier, but the following
-modification made to the main function:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="w"> </span><span class="o">*</span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">qubit_count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">argc</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">atoi</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="mi">25</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">shots_count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1000000</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">start</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">chrono</span><span class="o">::</span><span class="n">high_resolution_clock</span><span class="o">::</span><span class="n">now</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// Timing just the sample execution.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">shots_count</span><span class="p">,</span><span class="w"> </span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">);</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">stop</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">chrono</span><span class="o">::</span><span class="n">high_resolution_clock</span><span class="o">::</span><span class="n">now</span><span class="p">();</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">duration</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">chrono</span><span class="o">::</span><span class="n">duration</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="p">(</span><span class="n">stop</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start</span><span class="p">);</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;It took &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">duration</span><span class="p">.</span><span class="n">count</span><span class="p">()</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot; seconds.</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>First we execute on the CPU backend:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ --target=qpp-cpu sample.cpp</span>
-<span class="go">./a.out</span>
-</pre></div>
-</div>
-<p>seeing an output of the order:
-<code class="docutils literal notranslate"><span class="pre">It</span> <span class="pre">took</span> <span class="pre">22.8337</span> <span class="pre">seconds.</span></code></p>
-<p>Now we can execute on the GPU enabled backend:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ --target=nvidia sample.cpp</span>
-<span class="go">./a.out</span>
-</pre></div>
-</div>
-<p>seeing an output of the order:
-<code class="docutils literal notranslate"><span class="pre">It</span> <span class="pre">took</span> <span class="pre">3.18988</span> <span class="pre">seconds.</span></code></p>
-</div>
-</div>
-</section>
-</section>
-
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-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-<section id="debugging-and-verbose-simulation-output">
-<h2>Debugging and Verbose Simulation Output<a class="headerlink" href="#debugging-and-verbose-simulation-output" title="Permalink to this heading">¶</a></h2>
-<p>One helpful mechanism of debugging CUDA-Q simulation execution is
-the <code class="code docutils literal notranslate"><span class="pre">CUDAQ_LOG_LEVEL</span></code> environment variable. For any CUDA-Q
-executable, just prepend this and turn it on:</p>
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-<p>Similarly, one may write the IR to their console or to a file before remote
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-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDAQ_DUMP_JIT_IR</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>python3<span class="w"> </span>file.py
-<span class="c1"># or</span>
-<span class="nv">CUDAQ_DUMP_JIT_IR</span><span class="o">=</span>&lt;output_filename&gt;<span class="w"> </span>python3<span class="w"> </span>file.py
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-<span class="c1"># or</span>
-<span class="nv">CUDAQ_DUMP_JIT_IR</span><span class="o">=</span>&lt;output_filename&gt;<span class="w"> </span>./a.out
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="building-kernels">
-<h1>Building Kernels<a class="headerlink" href="#building-kernels" title="Permalink to this heading">¶</a></h1>
-<p>This section will cover the most basic CUDA-Q construct, a quantum kernel.
-Topics include, building kernels, initializing states, and applying gate operations.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ### Defining Kernels</span>
-<span class="c1">#</span>
-<span class="c1"># Kernels are the building blocks of quantum algorithms in CUDA-Q. A kernel is specified by using the following syntax. `cudaq.qubit` builds a register consisting of a single qubit, while `cudaq.qvector` builds a register of $N$ qubits.</span>
-
-<span class="c1"># In[14]:</span>
-
-<span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># In[15]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">A</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">B</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-    <span class="n">C</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-
-
-<span class="c1"># Inputs to kernels are defined by specifying a parameter in the kernel definition along with the appropriate type. The kernel below takes an integer to define a register of N qubits.</span>
-
-<span class="c1"># In[16]:</span>
-
-<span class="n">N</span> <span class="o">=</span> <span class="mi">2</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">N</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">register</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
-
-
-<span class="c1"># ### Initializing states</span>
-<span class="c1">#</span>
-<span class="c1"># It is often helpful to define an initial state for a kernel. There are a few ways to do this in CUDA-Q. Note, method 5 is particularly useful for cases where the state of one kernel is passed into a second kernel to prepare its initial state.</span>
-<span class="c1">#</span>
-<span class="c1"># 1. Passing complex vectors as parameters</span>
-<span class="c1"># 2. Capturing complex vectors</span>
-<span class="c1"># 3. Precision-agnostic API</span>
-<span class="c1"># 4. Define as CUDA-Q amplitudes</span>
-<span class="c1"># 5. Pass in a state from another kernel</span>
-
-<span class="c1"># In[17]:</span>
-
-<span class="c1"># Passing complex vectors as parameters</span>
-<span class="n">c</span> <span class="o">=</span> <span class="p">[</span><span class="mf">.707</span> <span class="o">+</span> <span class="mi">0</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span> <span class="o">-</span> <span class="mf">.707</span><span class="n">j</span><span class="p">]</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">vec</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">complex</span><span class="p">]):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
-
-
-<span class="c1"># Capturing complex vectors</span>
-<span class="n">c</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.70710678</span> <span class="o">+</span> <span class="mi">0</span><span class="n">j</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">]</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-
-
-<span class="c1"># Precision-Agnostic API</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.70710678</span> <span class="o">+</span> <span class="mi">0</span><span class="n">j</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">cudaq</span><span class="o">.</span><span class="n">complex</span><span class="p">())</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-
-
-<span class="c1"># Define as CUDA-Q amplitudes</span>
-<span class="n">c</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">amplitudes</span><span class="p">([</span><span class="mf">0.70710678</span> <span class="o">+</span> <span class="mi">0</span><span class="n">j</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">])</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-
-
-<span class="c1"># Pass in a state from another kernel</span>
-<span class="n">c</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.70710678</span> <span class="o">+</span> <span class="mi">0</span><span class="n">j</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.70710678</span><span class="p">]</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel_initial</span><span class="p">():</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-
-
-<span class="n">state_to_pass</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_state</span><span class="p">(</span><span class="n">kernel_initial</span><span class="p">)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">state</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">State</span><span class="p">):</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
-
-
-<span class="n">kernel</span><span class="p">(</span><span class="n">state_to_pass</span><span class="p">)</span>
-
-<span class="c1"># ### Applying Gates</span>
-<span class="c1">#</span>
-<span class="c1">#</span>
-<span class="c1"># After a kernel is constructed, gates can be applied to start building out a quantum circuit.</span>
-<span class="c1"># All the predefined gates in CUDA-Q can be found here:</span>
-<span class="c1"># https://nvidia.github.io/cuda-quantum/api/default_ops.</span>
-<span class="c1">#</span>
-<span class="c1">#</span>
-<span class="c1"># Gates can be applied to all qubits in a register:</span>
-
-<span class="c1"># In[18]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">register</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">register</span><span class="p">)</span>
-
-
-<span class="c1"># Or, to individual qubits in a register:</span>
-
-<span class="c1"># In[19]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">register</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">register</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>  <span class="c1"># first qubit</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">register</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>  <span class="c1"># last qubit</span>
-
-
-<span class="c1"># ### Controlled Operations</span>
-<span class="c1">#</span>
-<span class="c1"># Controlled operations are available for any gate and can be used by adding `.ctrl` to the end of any gate, followed by specification of the control qubit and the target qubit.</span>
-
-<span class="c1"># In[20]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">register</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">register</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-           <span class="n">register</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>  <span class="c1"># CNOT gate applied with qubit 0 as control</span>
-
-
-<span class="c1"># ### Multi-Controlled Operations</span>
-<span class="c1">#</span>
-<span class="c1"># It is valid for more than one qubit to be used for multi-controlled gates. The control qubits are specified as a list.</span>
-
-<span class="c1"># In[21]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">register</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">register</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">register</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
-           <span class="n">register</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>  <span class="c1"># X applied to qubit two controlled by qubit 0 and 1</span>
-
-
-<span class="c1"># You can also call a controlled kernel within a kernel:</span>
-
-<span class="c1"># In[22]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">x_kernel</span><span class="p">(</span><span class="n">qubit</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="c1"># A kernel that will call `x_kernel` as a controlled operation.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-
-    <span class="n">control_vector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-    <span class="n">x</span><span class="p">(</span><span class="n">control_vector</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">control_vector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">x_kernel</span><span class="p">,</span> <span class="n">control_vector</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
-
-
-<span class="c1"># The above is equivalent to:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-
-<span class="c1"># ### Adjoint Operations</span>
-<span class="c1">#</span>
-<span class="c1"># The adjoint of a gate can be applied by appending the gate with the `adj` designation.</span>
-
-<span class="c1"># In[23]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">register</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">t</span><span class="o">.</span><span class="n">adj</span><span class="p">(</span><span class="n">register</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-
-<span class="c1"># ### Custom Operations</span>
-<span class="c1">#</span>
-<span class="c1"># Custom gate operations can be specified using `cudaq.register_operation`. A one-dimensional Numpy array specifies the unitary matrix to be applied. The entries of the array read from top to bottom through the rows.</span>
-
-<span class="c1"># In[24]:</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">register_operation</span><span class="p">(</span><span class="s2">&quot;custom_x&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">custom_x</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">custom_x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="c1"># ### Building Kernels with Kernels</span>
-<span class="c1">#</span>
-<span class="c1"># For many complex applications, it is helpful for a kernel to call another kernel to perform a specific subroutine. The example blow shows how `kernel_A` can be called within `kernel_B` to perform CNOT operations.</span>
-
-<span class="c1"># In[25]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel_A</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubit_0</span><span class="p">,</span> <span class="n">qubit_1</span><span class="p">)</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel_B</span><span class="p">():</span>
-    <span class="n">reg</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
-        <span class="n">kernel_A</span><span class="p">(</span><span class="n">reg</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">reg</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-
-
-<span class="c1"># ### Parameterized Kernels</span>
-<span class="c1">#</span>
-<span class="c1"># It is often useful to define parameterized circuit kernels which can be used for applications like VQE.</span>
-
-<span class="c1"># In[26]:</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">rx</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="n">thetas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">.024</span><span class="p">,</span> <span class="mf">.543</span><span class="p">]</span>
-
-<span class="n">kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-
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-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="introduction.html">   Introduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_operations.html#quantum-states">Quantum States</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
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-</ul>
-</li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-</li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-c">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id3">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#iqm">IQM</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#orca-computing">ORCA Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quantinuum">Quantinuum</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quera-computing">QuEra Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id24">Submission from Python</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#operator">Operator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda">CUDA</a></li>
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-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/python_api.html">Python API</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Computing Expectation Values</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
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-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda">CUDA</a></li>
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-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#utilities">Utilities</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
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-  <section id="computing-expectation-values">
-<h1>Computing Expectation Values<a class="headerlink" href="#computing-expectation-values" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q provides generic library functions enabling one to compute expectation values
-of quantum spin operators with respect to a parameterized CUDA-Q kernel. Let’s take a look
-at an example of this:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ expectation_values.cpp -o d2.x &amp;&amp; ./d2.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/algorithm.h&gt;</span>
-
-<span class="c1">// The example here shows a simple use case for the `cudaq::observe`</span>
-<span class="c1">// function in computing expected values of provided spin_ops.</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">ansatz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()(</span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="c1">// Build up your spin op algebraically</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">                     </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Observe takes the kernel, the spin_op, and the concrete</span>
-<span class="w">  </span><span class="c1">// parameters for the kernel</span>
-<span class="w">  </span><span class="kt">double</span><span class="w"> </span><span class="n">energy</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="p">(</span><span class="n">ansatz</span><span class="p">{},</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="mf">.59</span><span class="p">);</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Energy is %lf</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">energy</span><span class="p">);</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>Here we define a parameterized CUDA-Q kernel, a callable type named <code class="code docutils literal notranslate"><span class="pre">ansatz</span></code> that takes as
-input a single angle <code class="code docutils literal notranslate"><span class="pre">theta</span></code>. This angle becomes the argument of a single <code class="code docutils literal notranslate"><span class="pre">ry</span></code> rotation.</p>
-<p>In host code, we define a Hamiltonian operator via the CUDA-Q <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> type.
-CUDA-Q provides a generic function <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code>. This function takes as input three terms.
-The first two terms are a parameterized kernel and the <code class="code docutils literal notranslate"><span class="pre">spin_op</span></code> whose expectation value we wish to compute.
-The last term contains the runtime parameters at which we evaluate the parameterized kernel.</p>
-<p>The return type of this function is an <code class="code docutils literal notranslate"><span class="pre">cudaq::observe_result</span></code> which contains all the data
-from the execution, but is trivially convertible to a double, resulting in the expectation value we are interested in.</p>
-<p>To compile and execute this code, we run the following:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>expectation_values.cpp<span class="w"> </span>-o<span class="w"> </span>exp_vals.x
-./exp_vals.x
-</pre></div>
-</div>
-</div>
-</div>
-<section id="parallelizing-across-multiple-processors">
-<h2>Parallelizing across Multiple Processors<a class="headerlink" href="#parallelizing-across-multiple-processors" title="Permalink to this heading">¶</a></h2>
-<p><a class="reference internal" href="../backends/platform.html"><span class="doc">multi-processor platforms</span></a> page.</p>
-<p>One typical use case of <a class="reference internal" href="../backends/platform.html"><span class="doc">multi-processor platforms</span></a> is to distribute the
-expectation value computations of a multi-term Hamiltonian across multiple virtual QPUs.</p>
-<p>The following shows an example using the <code class="code docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> platform:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-<span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span>
-<span class="n">num_qpus</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">num_qpus</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of QPUs:&quot;</span><span class="p">,</span> <span class="n">num_qpus</span><span class="p">)</span>
-
-
-<span class="c1"># Define spin ansatz.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">angle</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">ry</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-
-<span class="c1"># Define spin Hamiltonian.</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="mf">5.907</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.1433</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span>
-    <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">.21829</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">6.125</span> <span class="o">*</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="n">exp_val</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span>
-                        <span class="n">hamiltonian</span><span class="p">,</span>
-                        <span class="mf">0.59</span><span class="p">,</span>
-                        <span class="n">execution</span><span class="o">=</span><span class="n">cudaq</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">thread</span><span class="p">)</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Expectation value: &quot;</span><span class="p">,</span> <span class="n">exp_val</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">cudaq</span><span class="o">::</span><span class="nn">spin</span><span class="p">;</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">spin_op</span><span class="w"> </span><span class="n">h</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">5.907</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">2.1433</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">+</span>
-<span class="w">                     </span><span class="mf">.21829</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mf">6.125</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">z</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Get the quantum_platform singleton</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">platform</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">get_platform</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// Query the number of QPUs in the system</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">num_qpus</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">platform</span><span class="p">.</span><span class="n">num_qpus</span><span class="p">();</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Number of QPUs: %zu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">num_qpus</span><span class="p">);</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">ansatz</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[](</span><span class="kt">double</span><span class="w"> </span><span class="n">theta</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="n">q</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">;</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="n">ry</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">);</span>
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="w"> </span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="kt">double</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">observe</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">parallel</span><span class="o">::</span><span class="kr">thread</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ansatz</span><span class="p">,</span><span class="w"> </span><span class="n">h</span><span class="p">,</span><span class="w"> </span><span class="mf">0.59</span><span class="p">);</span>
-<span class="w">  </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Expectation value: %lf</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>One can then target the <code class="code docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> platform by executing the following commands:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ observe_mqpu.cpp -target nvidia-mqpu</span>
-<span class="go">./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>In the above code snippets, since the Hamiltonian contains four non-identity terms, there are four quantum circuits that need to be executed
-in order to compute the expectation value of that Hamiltonian and given the quantum state prepared by the ansatz kernel. When the <code class="code docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> platform
-is selected, these circuits will be distributed across all available QPUs. The final expectation value result is computed from all QPU execution results.</p>
-</section>
-</section>
-
-
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deleted file mode 100644
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-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quantum_operations.html#quantum-gates">Quantum Gates</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-  <section id="using-quantum-hardware-providers">
-<h1>Using Quantum Hardware Providers<a class="headerlink" href="#using-quantum-hardware-providers" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q contains support for using a set of hardware providers (Amazon Braket,
-Infleqtion, IonQ, IQM, OQC, ORCA Computing, Quantinuum, and QuEra Computing).
-For more information about executing quantum kernels on different hardware
-backends, please take a look at <a class="reference internal" href="../backends/hardware.html"><span class="doc">hardware</span></a>.</p>
-<section id="amazon-braket">
-<h2>Amazon Braket<a class="headerlink" href="#amazon-braket" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on Amazon Braket’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># NOTE: Amazon Braket credentials must be set before running this program.</span>
-<span class="c1"># Amazon Braket costs apply.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;braket&quot;</span><span class="p">)</span>
-
-<span class="c1"># The default device is SV1, state vector simulator. Users may choose any of</span>
-<span class="c1"># the available devices by supplying its `ARN` with the `machine` parameter.</span>
-<span class="c1"># For example,</span>
-<span class="c1"># ```</span>
-<span class="c1"># cudaq.set_target(&quot;braket&quot;, machine=&quot;arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet&quot;)</span>
-<span class="c1"># ```</span>
-
-
-<span class="c1"># Create the kernel we&#39;d like to execute</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="c1"># Execute and print out the results.</span>
-
-<span class="c1"># Option A:</span>
-<span class="c1"># By using the asynchronous `cudaq.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by Amazon Braket.</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="n">async_counts</span> <span class="o">=</span> <span class="n">async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">async_counts</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the synchronous `cudaq.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been returned from Amazon Braket.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target braket braket.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// This will submit the job to the Amazon Braket state vector simulator</span>
-<span class="c1">// (default). Alternatively, users can choose any of the available devices by</span>
-<span class="c1">// specifying its `ARN` with the `--braket-machine`, e.g.,</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target braket --braket-machine \</span>
-<span class="c1">// &quot;arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet&quot; braket.cpp -o out.x</span>
-<span class="c1">// ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// Assumes a valid set of credentials have been set prior to execution.</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
-
-<span class="c1">// Define a simple quantum kernel to execute on Amazon Braket.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Maximally entangled state between 5 qubits.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Submit asynchronously (e.g., continue executing</span>
-<span class="w">  </span><span class="c1">// code in the file until the job has been returned).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="c1">// ... classical code to execute in the meantime ...</span>
-
-<span class="w">  </span><span class="c1">// Get the results of the read in future.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">future</span><span class="p">.</span><span class="n">get</span><span class="p">();</span>
-<span class="w">  </span><span class="n">async_counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// OR: Submit synchronously (e.g., wait for the job</span>
-<span class="w">  </span><span class="c1">// result to be returned before proceeding).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="infleqtion">
-<h2>Infleqtion<a class="headerlink" href="#infleqtion" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on Infleqtion’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># You only have to set the target once! No need to redefine it</span>
-<span class="c1"># for every execution call on your kernel.</span>
-<span class="c1"># To use different targets in the same file, you must update</span>
-<span class="c1"># it via another call to `cudaq.set_target()`</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;infleqtion&quot;</span><span class="p">)</span>
-
-
-<span class="c1"># Create the kernel we&#39;d like to execute on Infleqtion.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="c1"># Note: All measurements must be terminal when performing the sampling.</span>
-
-<span class="c1"># Execute on Infleqtion and print out the results.</span>
-
-<span class="c1"># Option A (recommended):</span>
-<span class="c1"># By using the asynchronous `cudaq.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by the Superstaq API. This is ideal when submitting via a queue</span>
-<span class="c1"># over the cloud.</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="c1"># We can either retrieve the results later in the program with</span>
-<span class="c1"># ```</span>
-<span class="c1"># async_counts = async_results.get()</span>
-<span class="c1"># ```</span>
-<span class="c1"># or we can also write the job reference (`async_results`) to</span>
-<span class="c1"># a file and load it later or from a different process.</span>
-<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">async_results</span><span class="p">))</span>
-<span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-<span class="c1"># We can later read the file content and retrieve the job</span>
-<span class="c1"># information and results.</span>
-<span class="n">same_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-<span class="n">retrieved_async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">same_file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">retrieved_async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the synchronous `cudaq.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been returned from Superstaq.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target infleqtion infleqtion.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// This will submit the job to the Infleqtion&#39;s ideal simulator,</span>
-<span class="c1">// cq_sqale_simulator (default). Alternatively, we can enable hardware noise</span>
-<span class="c1">// model simulation by specifying `noise-sim` to the flag `--infleqtion-method`,</span>
-<span class="c1">// e.g.,</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu</span>
-<span class="c1">// --infleqtion-method noise-sim infleqtion.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// where &quot;noise-sim&quot; instructs Superstaq to perform a noisy emulation of the</span>
-<span class="c1">// QPU. An ideal dry-run execution on the QPU may be performed by passing</span>
-<span class="c1">// `dry-run` to the `--infleqtion-method` flag, e.g.,</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target infleqtion --infleqtion-machine cq_sqale_qpu</span>
-<span class="c1">// --infleqtion-method dry-run infleqtion.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// Note: If targeting ideal cloud simulation, `--infleqtion-machine</span>
-<span class="c1">// cq_sqale_simulator` is optional since it is the default configuration if not</span>
-<span class="c1">// provided.</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
-
-<span class="c1">// Define a simple quantum kernel to execute on Infleqtion backends.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Maximally entangled state between 5 qubits.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Submit to infleqtion asynchronously (e.g., continue executing</span>
-<span class="w">  </span><span class="c1">// code in the file until the job has been returned).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="c1">// ... classical code to execute in the meantime ...</span>
-
-<span class="w">  </span><span class="c1">// Can write the future to file:</span>
-<span class="w">  </span><span class="p">{</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">future</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">// Then come back and read it in later.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_result</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="n">in</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">  </span><span class="n">in</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// Get the results of the read in future.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">readIn</span><span class="p">.</span><span class="n">get</span><span class="p">();</span>
-<span class="w">  </span><span class="n">async_counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// OR: Submit to infleqtion synchronously (e.g., wait for the job</span>
-<span class="w">  </span><span class="c1">// result to be returned before proceeding).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="ionq">
-<h2>IonQ<a class="headerlink" href="#ionq" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on IonQ’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># You only have to set the target once! No need to redefine it</span>
-<span class="c1"># for every execution call on your kernel.</span>
-<span class="c1"># To use different targets in the same file, you must update</span>
-<span class="c1"># it via another call to `cudaq.set_target()`</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;ionq&quot;</span><span class="p">)</span>
-
-
-<span class="c1"># Create the kernel we&#39;d like to execute on IonQ.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="c1"># Note: All qubits will be measured at the end upon performing</span>
-<span class="c1"># the sampling. You may encounter a pre-flight error on IonQ</span>
-<span class="c1"># backends if you include explicit measurements.</span>
-
-<span class="c1"># Execute on IonQ and print out the results.</span>
-
-<span class="c1"># Option A:</span>
-<span class="c1"># By using the asynchronous `cudaq.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by IonQ. This is ideal when submitting via a queue over</span>
-<span class="c1"># the cloud.</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="c1"># We can either retrieve the results later in the program with</span>
-<span class="c1"># ```</span>
-<span class="c1"># async_counts = async_results.get()</span>
-<span class="c1"># ```</span>
-<span class="c1"># or we can also write the job reference (`async_results`) to</span>
-<span class="c1"># a file and load it later or from a different process.</span>
-<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">async_results</span><span class="p">))</span>
-<span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-<span class="c1"># We can later read the file content and retrieve the job</span>
-<span class="c1"># information and results.</span>
-<span class="n">same_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-<span class="n">retrieved_async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">same_file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">retrieved_async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the synchronous `cudaq.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been returned from IonQ.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target ionq ionq.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// This will submit the job to the IonQ ideal simulator target (default).</span>
-<span class="c1">// Alternatively, we can enable hardware noise model simulation by specifying</span>
-<span class="c1">// the `--ionq-noise-model`, e.g.,</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target ionq --ionq-machine simulator --ionq-noise-model aria-1</span>
-<span class="c1">// ionq.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// where we set the noise model to mimic the &#39;aria-1&#39; hardware device.</span>
-<span class="c1">// Please refer to your IonQ Cloud dashboard for the list of simulator noise</span>
-<span class="c1">// models.</span>
-<span class="c1">// Note: `--ionq-machine simulator` is  optional since &#39;simulator&#39; is the</span>
-<span class="c1">// default configuration if not provided. Assumes a valid set of credentials</span>
-<span class="c1">// have been stored.</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
-
-<span class="c1">// Define a simple quantum kernel to execute on IonQ.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Maximally entangled state between 5 qubits.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Submit to IonQ asynchronously (e.g., continue executing</span>
-<span class="w">  </span><span class="c1">// code in the file until the job has been returned).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="c1">// ... classical code to execute in the meantime ...</span>
-
-<span class="w">  </span><span class="c1">// Can write the future to file:</span>
-<span class="w">  </span><span class="p">{</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">future</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">// Then come back and read it in later.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_result</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="n">in</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">  </span><span class="n">in</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// Get the results of the read in future.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">readIn</span><span class="p">.</span><span class="n">get</span><span class="p">();</span>
-<span class="w">  </span><span class="n">async_counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// OR: Submit to IonQ synchronously (e.g., wait for the job</span>
-<span class="w">  </span><span class="c1">// result to be returned before proceeding).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="iqm">
-<h2>IQM<a class="headerlink" href="#iqm" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on IQM’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># You only have to set the target once! No need to redefine it</span>
-<span class="c1"># for every execution call on your kernel.</span>
-<span class="c1"># To use different targets in the same file, you must update</span>
-<span class="c1"># it via another call to `cudaq.set_target()`</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;iqm&quot;</span><span class="p">,</span>
-                 <span class="n">url</span><span class="o">=</span><span class="s2">&quot;http://localhost/cocos&quot;</span><span class="p">,</span>
-                 <span class="o">**</span><span class="p">{</span><span class="s2">&quot;qpu-architecture&quot;</span><span class="p">:</span> <span class="s2">&quot;Adonis&quot;</span><span class="p">})</span>
-
-<span class="c1"># Adonis QPU architecture:</span>
-<span class="c1">#       QB1</span>
-<span class="c1">#        |</span>
-<span class="c1"># QB2 - QB3 - QB4</span>
-<span class="c1">#        |</span>
-<span class="c1">#       QB5</span>
-
-
-<span class="c1"># Create the kernel we&#39;d like to execute on IQM.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>  <span class="c1"># QB3</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="c1"># Execute on IQM Server and print out the results.</span>
-
-<span class="c1"># Option A:</span>
-<span class="c1"># By using the asynchronous `cudaq.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by IQM Server. This is ideal when submitting via a queue over</span>
-<span class="c1"># the cloud.</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="c1"># We can either retrieve the results later in the program with</span>
-<span class="c1"># ```</span>
-<span class="c1"># async_counts = async_results.get()</span>
-<span class="c1"># ```</span>
-<span class="c1"># or we can also write the job reference (`async_results`) to</span>
-<span class="c1"># a file and load it later or from a different process.</span>
-<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">async_results</span><span class="p">))</span>
-<span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-<span class="c1"># We can later read the file content and retrieve the job</span>
-<span class="c1"># information and results.</span>
-<span class="n">same_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-<span class="n">retrieved_async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">same_file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">retrieved_async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the synchronous `cudaq.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been returned from IQM Server.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target iqm iqm.cpp --iqm-machine Adonis -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// Assumes a valid set of credentials have been stored.</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
-
-<span class="c1">// Define a simple quantum kernel to execute on IQM Server.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">adonis_ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Maximally entangled state between 5 qubits on Adonis QPU.</span>
-<span class="w">  </span><span class="c1">//       QB1</span>
-<span class="w">  </span><span class="c1">//        |</span>
-<span class="w">  </span><span class="c1">// QB2 - QB3 - QB4</span>
-<span class="w">  </span><span class="c1">//        |</span>
-<span class="w">  </span><span class="c1">//       QB5</span>
-
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="c1">// Note that the CUDA-Q compiler will automatically generate the</span>
-<span class="w">    </span><span class="c1">// necessary instructions to swap qubits to satisfy the required</span>
-<span class="w">    </span><span class="c1">// connectivity constraints for the Adonis QPU. In this program, that means</span>
-<span class="w">    </span><span class="c1">// that despite QB1 not being physically connected to QB2, the user can</span>
-<span class="w">    </span><span class="c1">// still perform joint operations q[0] and q[1] because the compiler will</span>
-<span class="w">    </span><span class="c1">// automatically (and transparently) inject the necessary swap instructions</span>
-<span class="w">    </span><span class="c1">// to execute the user&#39;s program without the user having to worry about the</span>
-<span class="w">    </span><span class="c1">// physical constraints.</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Submit to IQM Server asynchronously. E.g, continue executing</span>
-<span class="w">  </span><span class="c1">// code in the file until the job has been returned.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">adonis_ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="c1">// ... classical code to execute in the meantime ...</span>
-
-<span class="w">  </span><span class="c1">// Can write the future to file:</span>
-<span class="w">  </span><span class="p">{</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">future</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">// Then come back and read it in later.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_result</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="n">in</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">  </span><span class="n">in</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// Get the results of the read in future.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">readIn</span><span class="p">.</span><span class="n">get</span><span class="p">();</span>
-<span class="w">  </span><span class="n">async_counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// OR: Submit to IQM Server synchronously. E.g, wait for the job</span>
-<span class="w">  </span><span class="c1">// result to be returned before proceeding.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">adonis_ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="oqc">
-<h2>OQC<a class="headerlink" href="#oqc" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on OQC’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--4-input--1" name="tab-set--4" type="radio"><label class="tab-label" for="tab-set--4-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># You only have to set the target once! No need to redefine it</span>
-<span class="c1"># for every execution call on your kernel.</span>
-<span class="c1"># To use different targets in the same file, you must update</span>
-<span class="c1"># it via another call to `cudaq.set_target()`</span>
-
-<span class="c1"># To use the OQC target you will need to set the following environment variables</span>
-<span class="c1"># OQC_URL</span>
-<span class="c1"># OQC_EMAIL</span>
-<span class="c1"># OQC_PASSWORD</span>
-<span class="c1"># To setup an account, contact oqc_qcaas_support@oxfordquantumcircuits.com</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;oqc&quot;</span><span class="p">)</span>
-
-
-<span class="c1"># Create the kernel we&#39;d like to execute on OQC.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">[</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="c1"># Option A:</span>
-<span class="c1"># By using the asynchronous `cudaq.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by OQC. This is ideal when submitting via a queue over</span>
-<span class="c1"># the cloud.</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="c1"># We can either retrieve the results later in the program with</span>
-<span class="c1"># ```</span>
-<span class="c1"># async_counts = async_results.get()</span>
-<span class="c1"># ```</span>
-<span class="c1"># or we can also write the job reference (`async_results`) to</span>
-<span class="c1"># a file and load it later or from a different process.</span>
-<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">async_results</span><span class="p">))</span>
-<span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-<span class="c1"># We can later read the file content and retrieve the job</span>
-<span class="c1"># information and results.</span>
-<span class="n">same_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-<span class="n">retrieved_async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">same_file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">retrieved_async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the synchronous `cudaq.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been returned from OQC.</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="orca-computing">
-<h2>ORCA Computing<a class="headerlink" href="#orca-computing" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on ORCA Computing’s backends.</p>
-<p>ORCA Computing’s PT Series implement the boson sampling model of quantum computation, in which
-multiple photons are interfered with each other within a network of beam splitters, and photon
-detectors measure where the photons leave this network.</p>
-<p>The following image shows the schematic of a Time Bin Interferometer (TBI) boson sampling experiment
-that runs on ORCA Computing’s backends. A TBI uses optical delay lines with reconfigurable coupling
-parameters. A TBI can be represented by a circuit diagram, like the one below, where this
-illustration example corresponds to 4 photons in 8 modes sent into alternating time-bins in a circuit
-composed of two delay lines in series.</p>
-<a class="reference internal image-reference" href="../../_images/orca_tbi.png"><img alt="../../_images/orca_tbi.png" class="align-center" src="../../_images/orca_tbi.png" style="width: 400px;" /></a>
-<p>The parameters needed to define the time bin interferometer are the the input state, the loop
-lengths, beam splitter angles, and optionally the phase shifter angles, and the number of samples.
-The <em>input state</em> is the initial state of the photons in the time bin interferometer,
-the left-most entry corresponds to the first mode entering the loop.
-The <em>loop lengths</em> are the lengths of the different loops in the time bin interferometer.
-The <em>beam splitter angles</em> and the phase shifter angles are controllable
-parameters of the time bin interferometer.</p>
-<p>This experiment is performed on ORCA’s backends by the code below.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--5-input--1" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">import</span> <span class="nn">time</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="c1"># You only have to set the target once! No need to redefine it</span>
-<span class="c1"># for every execution call on your kernel.</span>
-<span class="c1"># To use different targets in the same file, you must update</span>
-<span class="c1"># it via another call to `cudaq.set_target()`</span>
-
-<span class="c1"># To use the ORCA Computing target you will need to set the ORCA_ACCESS_URL</span>
-<span class="c1"># environment variable or pass a URL.</span>
-<span class="n">orca_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;ORCA_ACCESS_URL&quot;</span><span class="p">,</span> <span class="s2">&quot;http://localhost/sample&quot;</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;orca&quot;</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">orca_url</span><span class="p">)</span>
-
-<span class="c1"># A time-bin boson sampling experiment: An input state of 4 indistinguishable</span>
-<span class="c1"># photons mixed with 4 vacuum states across 8 time bins (modes) enter the</span>
-<span class="c1"># time bin interferometer (TBI). The interferometer is composed of two loops</span>
-<span class="c1"># each with a beam splitter (and optionally with a corresponding phase</span>
-<span class="c1"># shifter). Each photon can either be stored in a loop to interfere with the</span>
-<span class="c1"># next photon or exit the loop to be measured. Since there are 8 time bins</span>
-<span class="c1"># and 2 loops, there is a total of 14 beam splitters (and optionally 14 phase</span>
-<span class="c1"># shifters) in the interferometer, which is the number of controllable</span>
-<span class="c1"># parameters.</span>
-
-<span class="c1"># half of 8 time bins is filled with a single photon and the other half is</span>
-<span class="c1"># filled with the vacuum state (empty)</span>
-<span class="n">input_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-
-<span class="c1"># The time bin interferometer in this example has two loops, each of length 1</span>
-<span class="n">loop_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-
-<span class="c1"># Calculate the number of beam splitters and phase shifters</span>
-<span class="n">n_beam_splitters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">loop_lengths</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_state</span><span class="p">)</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">loop_lengths</span><span class="p">)</span>
-
-<span class="c1"># beam splitter angles</span>
-<span class="n">bs_angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">6</span><span class="p">,</span> <span class="n">n_beam_splitters</span><span class="p">)</span>
-
-<span class="c1"># Optionally, we can also specify the phase shifter angles, if the system</span>
-<span class="c1"># includes phase shifters</span>
-<span class="c1"># ```</span>
-<span class="c1"># ps_angles = np.linspace(np.pi / 3, np.pi / 5, n_beam_splitters)</span>
-<span class="c1"># ```</span>
-
-<span class="c1"># we can also set number of requested samples</span>
-<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">10000</span>
-
-<span class="c1"># Option A:</span>
-<span class="c1"># By using the synchronous `cudaq.orca.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been returned from ORCA Server.</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Submitting to ORCA Server synchronously&quot;</span><span class="p">)</span>
-<span class="n">counts</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">orca</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">input_state</span><span class="p">,</span> <span class="n">loop_lengths</span><span class="p">,</span> <span class="n">bs_angles</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)</span>
-
-<span class="c1"># If the system includes phase shifters, the phase shifter angles can be</span>
-<span class="c1"># included in the call</span>
-<span class="c1"># ```</span>
-<span class="c1"># counts = cudaq.orca.sample(input_state, loop_lengths, bs_angles, ps_angles,</span>
-<span class="c1">#                            n_samples)</span>
-<span class="c1"># ```</span>
-
-<span class="c1"># Print the results</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the asynchronous `cudaq.orca.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by Orca. This is ideal when submitting via a queue over</span>
-<span class="c1"># the cloud.</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Submitting to ORCA Server asynchronously&quot;</span><span class="p">)</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">orca</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">input_state</span><span class="p">,</span> <span class="n">loop_lengths</span><span class="p">,</span> <span class="n">bs_angles</span><span class="p">,</span>
-                                        <span class="n">n_samples</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="c1"># We can either retrieve the results later in the program with</span>
-<span class="c1"># ```</span>
-<span class="c1"># async_counts = async_results.get()</span>
-<span class="c1"># ```</span>
-<span class="c1"># or we can also write the job reference (`async_results`) to</span>
-<span class="c1"># a file and load it later or from a different process.</span>
-<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">async_results</span><span class="p">))</span>
-<span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-<span class="c1"># We can later read the file content and retrieve the job</span>
-<span class="c1"># information and results.</span>
-<span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>  <span class="c1"># wait for the job to be processed</span>
-<span class="n">same_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-<span class="n">retrieved_async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">same_file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">retrieved_async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--5-input--2" name="tab-set--5" type="radio"><label class="tab-label" for="tab-set--5-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target orca --orca-url $ORCA_ACCESS_URL orca.cpp -o out.x &amp;&amp; ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// To use the ORCA Computing target you will need to set the ORCA_ACCESS_URL</span>
-<span class="c1">// environment variable or pass the URL to the `--orca-url` flag.</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;chrono&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/orca.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;thread&gt;</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">std</span><span class="o">::</span><span class="nn">this_thread</span><span class="p">;</span><span class="w">     </span><span class="c1">// sleep_for, sleep_until</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">std</span><span class="o">::</span><span class="nn">chrono_literals</span><span class="p">;</span><span class="w"> </span><span class="c1">// `ns`, `us`, `ms`, `s`, `h`, etc.</span>
-
-<span class="w">  </span><span class="c1">// A time-bin boson sampling experiment: An input state of 4 indistinguishable</span>
-<span class="w">  </span><span class="c1">// photons mixed with 4 vacuum states across 8 time bins (modes) enter the</span>
-<span class="w">  </span><span class="c1">// time bin interferometer (TBI). The interferometer is composed of two loops</span>
-<span class="w">  </span><span class="c1">// each with a beam splitter (and optionally with a corresponding phase</span>
-<span class="w">  </span><span class="c1">// shifter). Each photon can either be stored in a loop to interfere with the</span>
-<span class="w">  </span><span class="c1">// next photon or exit the loop to be measured. Since there are 8 time bins</span>
-<span class="w">  </span><span class="c1">// and 2 loops, there is a total of 14 beam splitters (and optionally 14 phase</span>
-<span class="w">  </span><span class="c1">// shifters) in the interferometer, which is the number of controllable</span>
-<span class="w">  </span><span class="c1">// parameters.</span>
-
-<span class="w">  </span><span class="c1">// half of 8 time bins is filled with a single photon and the other half is</span>
-<span class="w">  </span><span class="c1">// filled with the vacuum state (empty)</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">input_state</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">};</span>
-
-<span class="w">  </span><span class="c1">// The time bin interferometer in this example has two loops, each of length 1</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">loop_lengths</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">};</span>
-
-<span class="w">  </span><span class="c1">// helper variables to calculate the number of beam splitters and phase</span>
-<span class="w">  </span><span class="c1">// shifters needed in the TBI</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">sum_loop_lengths</span><span class="p">{</span><span class="n">std</span><span class="o">::</span><span class="n">accumulate</span><span class="p">(</span>
-<span class="w">      </span><span class="n">loop_lengths</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span><span class="w"> </span><span class="n">loop_lengths</span><span class="p">.</span><span class="n">end</span><span class="p">(),</span><span class="w"> </span><span class="k">static_cast</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">0</span><span class="p">))};</span>
-<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">n_loops</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">loop_lengths</span><span class="p">.</span><span class="n">size</span><span class="p">();</span>
-<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">n_modes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_state</span><span class="p">.</span><span class="n">size</span><span class="p">();</span>
-<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">n_beam_splitters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">n_loops</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">n_modes</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">sum_loop_lengths</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// beam splitter angles (created as a linear spaced vector of angles)</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">bs_angles</span><span class="w"> </span><span class="o">=</span>
-<span class="w">      </span><span class="n">cudaq</span><span class="o">::</span><span class="n">linspace</span><span class="p">(</span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="n">M_PI</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">6</span><span class="p">,</span><span class="w"> </span><span class="n">n_beam_splitters</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Optionally, we can also specify the phase shifter angles (created as a</span>
-<span class="w">  </span><span class="c1">// linear spaced vector of angles), if the system includes phase shifters</span>
-<span class="w">  </span><span class="c1">// ```</span>
-<span class="w">  </span><span class="c1">// std::vector&lt;double&gt; ps_angles = cudaq::linspace(M_PI / 3, M_PI / 5,</span>
-<span class="w">  </span><span class="c1">// n_beam_splitters);</span>
-<span class="w">  </span><span class="c1">// ```</span>
-
-<span class="w">  </span><span class="c1">// we can also set number of requested samples</span>
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">n_samples</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">10000</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// Submit to ORCA synchronously (e.g., wait for the job result to be</span>
-<span class="w">  </span><span class="c1">// returned before proceeding with the rest of the execution).</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Submitting to ORCA Server synchronously&quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span>
-<span class="w">      </span><span class="n">cudaq</span><span class="o">::</span><span class="n">orca</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">input_state</span><span class="p">,</span><span class="w"> </span><span class="n">loop_lengths</span><span class="p">,</span><span class="w"> </span><span class="n">bs_angles</span><span class="p">,</span><span class="w"> </span><span class="n">n_samples</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Print the results</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// If the system includes phase shifters, the phase shifter angles can be</span>
-<span class="w">  </span><span class="c1">// included in the call</span>
-
-<span class="w">  </span><span class="c1">// ```</span>
-<span class="w">  </span><span class="c1">// auto counts = cudaq::orca::sample(input_state, loop_lengths, bs_angles,</span>
-<span class="w">  </span><span class="c1">//                                   ps_angles, n_samples);</span>
-<span class="w">  </span><span class="c1">// ```</span>
-
-<span class="w">  </span><span class="c1">// Alternatively we can submit to ORCA asynchronously (e.g., continue</span>
-<span class="w">  </span><span class="c1">// executing code in the file until the job has been returned).</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Submitting to ORCA Server asynchronously&quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_results</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">orca</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">input_state</span><span class="p">,</span><span class="w"> </span><span class="n">loop_lengths</span><span class="p">,</span>
-<span class="w">                                                 </span><span class="n">bs_angles</span><span class="p">,</span><span class="w"> </span><span class="n">n_samples</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Can write the future to file:</span>
-<span class="w">  </span><span class="p">{</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">async_results</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">// Then come back and read it in later.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_result</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="n">in</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">  </span><span class="n">in</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-
-<span class="w">  </span><span class="n">sleep_for</span><span class="p">(</span><span class="mi">200</span><span class="n">ms</span><span class="p">);</span><span class="w"> </span><span class="c1">// wait for the job to be processed</span>
-<span class="w">  </span><span class="c1">// Get the results of the read in future.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">readIn</span><span class="p">.</span><span class="n">get</span><span class="p">();</span>
-<span class="w">  </span><span class="n">async_counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="quantinuum">
-<h2>Quantinuum<a class="headerlink" href="#quantinuum" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on Quantinuum’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--6-input--1" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="c1"># You only have to set the target once! No need to redefine it</span>
-<span class="c1"># for every execution call on your kernel.</span>
-<span class="c1"># By default, we will submit to the Quantinuum syntax checker.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;quantinuum&quot;</span><span class="p">)</span>
-
-
-<span class="c1"># Create the kernel we&#39;d like to execute on Quantinuum.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="c1"># Submit to Quantinuum&#39;s endpoint and confirm the program is valid.</span>
-
-<span class="c1"># Option A:</span>
-<span class="c1"># By using the synchronous `cudaq.sample`, the execution of</span>
-<span class="c1"># any remaining classical code in the file will occur only</span>
-<span class="c1"># after the job has been executed by the Quantinuum service.</span>
-<span class="c1"># We will use the synchronous call to submit to the syntax</span>
-<span class="c1"># checker to confirm the validity of the program.</span>
-<span class="n">syntax_check</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="k">if</span> <span class="p">(</span><span class="n">syntax_check</span><span class="p">):</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Syntax check passed! Kernel is ready for submission.&quot;</span><span class="p">)</span>
-
-<span class="c1"># Now we can update the target to the Quantinuum emulator and</span>
-<span class="c1"># execute our program.</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;quantinuum&quot;</span><span class="p">,</span> <span class="n">machine</span><span class="o">=</span><span class="s2">&quot;H1-2E&quot;</span><span class="p">)</span>
-
-<span class="c1"># Option B:</span>
-<span class="c1"># By using the asynchronous `cudaq.sample_async`, the remaining</span>
-<span class="c1"># classical code will be executed while the job is being handled</span>
-<span class="c1"># by Quantinuum. This is ideal when submitting via a queue over</span>
-<span class="c1"># the cloud.</span>
-<span class="n">async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="c1"># ... more classical code to run ...</span>
-
-<span class="c1"># We can either retrieve the results later in the program with</span>
-<span class="c1"># ```</span>
-<span class="c1"># async_counts = async_results.get()</span>
-<span class="c1"># ```</span>
-<span class="c1"># or we can also write the job reference (`async_results`) to</span>
-<span class="c1"># a file and load it later or from a different process.</span>
-<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">async_results</span><span class="p">))</span>
-<span class="n">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-<span class="c1"># We can later read the file content and retrieve the job</span>
-<span class="c1"># information and results.</span>
-<span class="n">same_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;future.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-<span class="n">retrieved_async_results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">AsyncSampleResult</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">same_file</span><span class="o">.</span><span class="n">read</span><span class="p">()))</span>
-
-<span class="n">counts</span> <span class="o">=</span> <span class="n">retrieved_async_results</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--6-input--2" name="tab-set--6" type="radio"><label class="tab-label" for="tab-set--6-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ --target quantinuum --quantinuum-machine H1-2E quantinuum.cpp -o out.x</span>
-<span class="c1">// ./out.x</span>
-<span class="c1">// ```</span>
-<span class="c1">// Assumes a valid set of credentials have been stored.</span>
-<span class="c1">// To first confirm the correctness of the program locally,</span>
-<span class="c1">// Add a --emulate to the `nvq++` command above.</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
-
-<span class="c1">// Define a simple quantum kernel to execute on Quantinuum.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Maximally entangled state between 5 qubits.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">q</span><span class="p">(</span><span class="mi">5</span><span class="p">);</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// Submit to Quantinuum asynchronously (e.g., continue executing</span>
-<span class="w">  </span><span class="c1">// code in the file until the job has been returned).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">future</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_async</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="c1">// ... classical code to execute in the meantime ...</span>
-
-<span class="w">  </span><span class="c1">// Can write the future to file:</span>
-<span class="w">  </span><span class="p">{</span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">future</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="c1">// Then come back and read it in later.</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">async_result</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample_result</span><span class="o">&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="n">in</span><span class="p">(</span><span class="s">&quot;saveMe.json&quot;</span><span class="p">);</span>
-<span class="w">  </span><span class="n">in</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">readIn</span><span class="p">;</span>
-
-<span class="w">  </span><span class="c1">// Get the results of the read in future.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">async_counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">readIn</span><span class="p">.</span><span class="n">get</span><span class="p">();</span>
-<span class="w">  </span><span class="n">async_counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">  </span><span class="c1">// OR: Submit to Quantinuum synchronously (e.g., wait for the job</span>
-<span class="w">  </span><span class="c1">// result to be returned before proceeding).</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz</span><span class="p">{});</span>
-<span class="w">  </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="quera-computing">
-<h2>QuEra Computing<a class="headerlink" href="#quera-computing" title="Permalink to this heading">¶</a></h2>
-<p>The following code illustrates how to run kernels on QuEra’s backends.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--7-input--1" name="tab-set--7" type="radio"><label class="tab-label" for="tab-set--7-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq.operator</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1">## NOTE: QuEra Aquila system is available via Amazon Braket.</span>
-<span class="c1"># Credentials must be set before running this program.</span>
-<span class="c1"># Amazon Braket costs apply.</span>
-
-<span class="c1"># This example illustrates how to use QuEra&#39;s Aquila device on Braket with CUDA-Q.</span>
-<span class="c1"># It is a CUDA-Q implementation of the getting started materials for Braket available here:</span>
-<span class="c1"># https://docs.aws.amazon.com/braket/latest/developerguide/braket-get-started-hello-ahs.html</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;quera&quot;</span><span class="p">)</span>
-
-<span class="c1"># Define the 2-dimensional atom arrangement</span>
-<span class="n">a</span> <span class="o">=</span> <span class="mf">5.7e-6</span>
-<span class="n">register</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.5</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.5</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.5</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.5</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mf">0.5</span><span class="p">])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-<span class="n">register</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)])</span> <span class="o">*</span> <span class="n">a</span><span class="p">))</span>
-
-<span class="n">time_max</span> <span class="o">=</span> <span class="mf">4e-6</span>  <span class="c1"># seconds</span>
-<span class="n">time_ramp</span> <span class="o">=</span> <span class="mf">1e-7</span>  <span class="c1"># seconds</span>
-<span class="n">omega_max</span> <span class="o">=</span> <span class="mf">6300000.0</span>  <span class="c1"># rad / sec</span>
-<span class="n">delta_start</span> <span class="o">=</span> <span class="o">-</span><span class="mi">5</span> <span class="o">*</span> <span class="n">omega_max</span>
-<span class="n">delta_end</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">omega_max</span>
-
-<span class="c1"># Times for the piece-wise linear waveforms</span>
-<span class="n">steps</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">time_ramp</span><span class="p">,</span> <span class="n">time_max</span> <span class="o">-</span> <span class="n">time_ramp</span><span class="p">,</span> <span class="n">time_max</span><span class="p">]</span>
-<span class="n">schedule</span> <span class="o">=</span> <span class="n">Schedule</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;t&quot;</span><span class="p">])</span>
-<span class="c1"># Rabi frequencies at each step</span>
-<span class="n">omega</span> <span class="o">=</span> <span class="n">ScalarOperator</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">omega_max</span> <span class="k">if</span> <span class="n">time_ramp</span> <span class="o">&lt;</span> <span class="n">t</span> <span class="o">&lt;</span> <span class="n">time_max</span> <span class="k">else</span> <span class="mf">0.0</span><span class="p">)</span>
-<span class="c1"># Global phase at each step</span>
-<span class="n">phi</span> <span class="o">=</span> <span class="n">ScalarOperator</span><span class="o">.</span><span class="n">const</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
-<span class="c1"># Global detuning at each step</span>
-<span class="n">delta</span> <span class="o">=</span> <span class="n">ScalarOperator</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">delta_end</span>
-                       <span class="k">if</span> <span class="n">time_ramp</span> <span class="o">&lt;</span> <span class="n">t</span> <span class="o">&lt;</span> <span class="n">time_max</span> <span class="k">else</span> <span class="n">delta_start</span><span class="p">)</span>
-
-<span class="n">async_result</span> <span class="o">=</span> <span class="n">evolve_async</span><span class="p">(</span><span class="n">RydbergHamiltonian</span><span class="p">(</span><span class="n">atom_sites</span><span class="o">=</span><span class="n">register</span><span class="p">,</span>
-                                               <span class="n">amplitude</span><span class="o">=</span><span class="n">omega</span><span class="p">,</span>
-                                               <span class="n">phase</span><span class="o">=</span><span class="n">phi</span><span class="p">,</span>
-                                               <span class="n">delta_global</span><span class="o">=</span><span class="n">delta</span><span class="p">),</span>
-                            <span class="n">schedule</span><span class="o">=</span><span class="n">schedule</span><span class="p">,</span>
-                            <span class="n">shots_count</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="n">async_result</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-
-<span class="c1">## Sample result</span>
-<span class="c1"># ```</span>
-<span class="c1"># {</span>
-<span class="c1">#   __global__ : { 12121222:1 21202221:1 21212121:2 21212122:1 21221212:1 21221221:2 22121221:1 22221221:1 }</span>
-<span class="c1">#    post_sequence : { 01010111:1 10101010:2 10101011:1 10101110:1 10110101:1 10110110:2 11010110:1 11110110:1 }</span>
-<span class="c1">#    pre_sequence : { 11101111:1 11111111:9 }</span>
-<span class="c1"># }</span>
-<span class="c1"># ```</span>
-
-<span class="c1">## Interpreting result</span>
-<span class="c1"># `pre_sequence` has the measurement bits, one for each atomic site, before the</span>
-<span class="c1"># quantum evolution is run. The count is aggregated across shots. The value is</span>
-<span class="c1"># 0 if site is empty, 1 if site is filled.</span>
-<span class="c1"># `post_sequence` has the measurement bits, one for each atomic site, at the</span>
-<span class="c1"># end of the quantum evolution. The count is aggregated across shots. The value</span>
-<span class="c1"># is 0 if atom is in Rydberg state or site is empty, 1 if atom is in ground</span>
-<span class="c1"># state.</span>
-<span class="c1"># `__global__` has the aggregate of the state counts from all the successful</span>
-<span class="c1"># shots. The value is 0 if site is empty, 1 if atom is in Rydberg state (up</span>
-<span class="c1"># state spin) and 2 if atom is in ground state (down state spin).</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-
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-            NVIDIA CUDA-Q
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="examples.html">   Examples</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id21">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#faq">FAQ</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-  <section id="introduction">
-<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h1>
-<p>Welcome to CUDA-Q! On this page we will illustrate CUDA-Q with several examples.</p>
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-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<p>We’re going to take a look at how to construct quantum programs through CUDA-Q’s <code class="code docutils literal notranslate"><span class="pre">Kernel</span></code> API.</p>
-<p>When you create a <code class="code docutils literal notranslate"><span class="pre">Kernel</span></code> and invoke its methods, a quantum program is constructed that can then be executed by calling, for example, <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code>. Let’s take a closer look!</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="c1"># We begin by defining the `Kernel` that we will construct our</span>
-<span class="c1"># program with.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">    This is our first CUDA-Q kernel.</span>
-<span class="sd">    &#39;&#39;&#39;</span>
-    <span class="c1"># Next, we can allocate a single qubit to the kernel via `qubit()`.</span>
-    <span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-    <span class="c1"># Now we can begin adding instructions to apply to this qubit!</span>
-    <span class="c1"># Here we&#39;ll just add every non-parameterized</span>
-    <span class="c1"># single qubit gate that is supported by CUDA-Q.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-    <span class="n">y</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-    <span class="n">z</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-    <span class="n">t</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-    <span class="n">s</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-    <span class="c1"># Next, we add a measurement to the kernel so that we can sample</span>
-    <span class="c1"># the measurement results on our simulator!</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="c1"># Finally, we can execute this kernel on the state vector simulator</span>
-<span class="c1"># by calling `cudaq.sample`. This will execute the provided kernel</span>
-<span class="c1"># `shots_count` number of times and return the sampled distribution</span>
-<span class="c1"># as a `cudaq.SampleResult` dictionary.</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-
-<span class="c1"># Now let&#39;s take a look at the `SampleResult` we&#39;ve gotten back!</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<p>We’re going to take a look at how to construct quantum programs using CUDA-Q kernels.</p>
-<p>CUDA-Q kernels are any typed callable in the language that is annotated with the <code class="code docutils literal notranslate"><span class="pre">__qpu__</span></code> attribute. Let’s take a look at a very
-simple “Hello World” example, specifically a CUDA-Q kernel that prepares a GHZ state on a programmer-specified number of qubits.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ static_kernel.cpp -o ghz.x &amp;&amp; ./ghz.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="c1">// Define a CUDA-Q kernel that is fully specified</span>
-<span class="c1">// at compile time via templates.</span>
-<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="o">&gt;</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">    </span><span class="c1">// Compile-time sized array like std::array</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="n">N</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ghz</span><span class="o">&lt;</span><span class="mi">10</span><span class="o">&gt;</span><span class="p">{};</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">);</span>
-
-<span class="w">  </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="n">cudaq</span><span class="o">::</span><span class="n">mpi</span><span class="o">::</span><span class="n">is_initialized</span><span class="p">()</span><span class="w"> </span><span class="o">||</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">mpi</span><span class="o">::</span><span class="n">rank</span><span class="p">()</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">counts</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span>
-
-<span class="w">    </span><span class="c1">// Fine grain access to the bits and counts</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="p">[</span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">counts</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Observed: %s, %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bits</span><span class="p">.</span><span class="n">data</span><span class="p">(),</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>Here we see that we can define a custom <code class="code docutils literal notranslate"><span class="pre">struct</span></code> that is templated on a <code class="code docutils literal notranslate"><span class="pre">size_t</span></code> parameter.
-Our kernel expression is free to use this template parameter in the allocation of a
-compile-time-known register of qubits. Within the kernel, we are free to apply various quantum operations,
-like a Hadamard on qubit 0 <code class="code docutils literal notranslate"><span class="pre">h(q[0])</span></code>. Controlled operations are <strong>modifications</strong> of single-qubit
-operations, like the <code class="code docutils literal notranslate"><span class="pre">x&lt;cudaq::ctrl&gt;(q[0],q[1])</span></code> operation which implements a controlled-X gate. We
-can measure single qubits or entire registers.</p>
-<p>In this example we are interested in sampling the final state produced by this CUDA-Q kernel.
-To do so, we leverage the generic <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code> function, which returns a data type
-encoding the qubit measurement strings and the corresponding number of times that string
-was observed (here the default number of shots is used, <code class="code docutils literal notranslate"><span class="pre">1000</span></code>).</p>
-<p>The following example illustrates how to compile and execute this code.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>static_kernel.cpp<span class="w"> </span>-o<span class="w"> </span>ghz.x
-./ghz.x
-</pre></div>
-</div>
-</div>
-</div>
-</section>
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
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-</ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
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-</ul>
-</li>
-</ul>
-</li>
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-<li class="toctree-l3"><a class="reference internal" href="expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
-</ul>
-</li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Multi-Control Synthesis</a></li>
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-</ul>
-</li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
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-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
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-  <section id="multi-control-synthesis">
-<h1>Multi-control Synthesis<a class="headerlink" href="#multi-control-synthesis" title="Permalink to this heading">¶</a></h1>
-<p>Now let’s take a look at how CUDA-Q allows one to control a general unitary
-on an arbitrary number of control qubits.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<p>Our first option is to describe our general unitary by another pre-defined
-CUDA-Q kernel.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># A kernel that performs an X-gate on a provided qubit.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">x_kernel</span><span class="p">(</span><span class="n">qubit</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">):</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="c1"># A kernel that will call `x_kernel` as a controlled operation.</span>
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">control_vector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">target</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">control_vector</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">control_vector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">control</span><span class="p">(</span><span class="n">x_kernel</span><span class="p">,</span> <span class="n">control_vector</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
-
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Alternatively, one may pass multiple arguments for control qubits or vectors
-to any controlled operation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">([</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">)</span>
-
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<p>For this scenario, our general unitary can be described by another pre-defined
-CUDA-Q kernel expression.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// nvq++ multi_controlled_operations.cpp -o ccnot.x &amp;&amp; ./ccnot.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq/algorithm.h&gt;</span>
-
-<span class="c1">// Here we demonstrate how one might apply multi-controlled</span>
-<span class="c1">// operations on a general CUDA-Q kernel.</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ApplyX</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">qubit</span><span class="w"> </span><span class="o">&amp;</span><span class="n">q</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span><span class="w"> </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">ccnot_test</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// constrain the signature of the incoming kernel</span>
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="nf">operator</span><span class="p">()(</span><span class="n">cudaq</span><span class="o">::</span><span class="n">takes_qubit</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="n">apply_x</span><span class="p">)</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="n">qs</span><span class="p">(</span><span class="mi">3</span><span class="p">);</span>
-
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">qs</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="c1">// Control U (apply_x) on the first two qubits of</span>
-<span class="w">    </span><span class="c1">// the allocated register.</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">control</span><span class="p">(</span><span class="n">apply_x</span><span class="p">,</span><span class="w"> </span><span class="n">qs</span><span class="p">.</span><span class="n">front</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span><span class="w"> </span><span class="n">qs</span><span class="p">[</span><span class="mi">2</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// We can achieve the same thing as above via</span>
-<span class="w">  </span><span class="c1">// a lambda expression.</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">ccnot</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[]()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="n">q</span><span class="p">(</span><span class="mi">3</span><span class="p">);</span>
-
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">    </span><span class="n">x</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="mi">2</span><span class="p">]);</span>
-
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">};</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ccnot</span><span class="p">);</span>
-
-<span class="w">  </span><span class="c1">// Fine grain access to the bits and counts</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="p">[</span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">counts</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Observed: %s, %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bits</span><span class="p">.</span><span class="n">data</span><span class="p">(),</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">counts2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">ccnot_test</span><span class="p">{},</span><span class="w"> </span><span class="n">ApplyX</span><span class="p">{});</span>
-
-<span class="w">  </span><span class="c1">// Fine grain access to the bits and counts</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="p">[</span><span class="n">bits</span><span class="p">,</span><span class="w"> </span><span class="n">count</span><span class="p">]</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">counts2</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Observed: %s, %lu</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bits</span><span class="p">.</span><span class="n">data</span><span class="p">(),</span><span class="w"> </span><span class="n">count</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-  <section id="multi-gpu-workflows">
-<h1>Multi-GPU Workflows<a class="headerlink" href="#multi-gpu-workflows" title="Permalink to this heading">¶</a></h1>
-<p>There are many backends available with CUDA-Q which enable seamless
-switching between GPUs, QPUs and CPUs and also allow for workflows
-involving multiple architectures working in tandem.</p>
-<section id="available-targets">
-<h2>Available Targets<a class="headerlink" href="#available-targets" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
-<li><p><strong>`qpp-cpu`</strong>: The QPP based CPU backend which is multithreaded to
-maximize the usage of available cores on your system.</p></li>
-<li><p><strong>`nvidia`</strong>: GPU-accelerated state-vector based backend which accelerates quantum circuit
-simulation on NVIDIA GPUs powered by cuQuantum.</p></li>
-<li><p><strong>`nvidia-mgpu`</strong>: Allows for scaling circuit simulation on multiple GPUs.</p></li>
-<li><p><strong>`nvidia-mqpu`</strong>: Enables users to program workflows utilizing
-multiple virtual quantum processors in parallel, where each QPU is simulated by the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> backend.</p></li>
-<li><p><strong>`remote-mqpu`</strong>: Enables users to program workflows utilizing
-multiple virtual quantum processors in parallel, where the backend used to simulate each QPU is configurable.</p></li>
-</ul>
-<p>Please see <a class="reference internal" href="../backends/backends.html"><span class="doc">CUDA-Q Backends</span></a> for a full list of all available backends.
-Below we explore how to effectively utilize multiple CUDA-Q targets with the same GHZ state preparation code</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">ghz_state</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-        <span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">sample_ghz_state</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;A function that will sample a variable sized GHZ state.&quot;&quot;&quot;</span>
-    <span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">ghz_state</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">,</span> <span class="n">shots_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">result</span>
-
-
-</pre></div>
-</div>
-<p>You can execute the code by running a statevector simulator on your CPU:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cpu_result</span> <span class="o">=</span> <span class="n">sample_ghz_state</span><span class="p">(</span><span class="n">qubit_count</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">&quot;qpp-cpu&quot;</span><span class="p">)</span>
-<span class="n">cpu_result</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> <span class="mi">00</span><span class="p">:</span><span class="mi">475</span> <span class="mi">11</span><span class="p">:</span><span class="mi">525</span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>You will notice a speedup of up to <strong>2500x</strong> in executing the circuit below on
-NVIDIA GPUs vs CPUs:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="n">gpu_result</span> <span class="o">=</span> <span class="n">sample_ghz_state</span><span class="p">(</span><span class="n">qubit_count</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s2">&quot;nvidia&quot;</span><span class="p">)</span>
-    <span class="n">gpu_result</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> <span class="mi">0000000000000000000000000</span><span class="p">:</span><span class="mi">510</span> <span class="mi">1111111111111111111111111</span><span class="p">:</span><span class="mi">490</span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>If one incrementally increases the qubit count, we
-reach a limit where the memory required is beyond the capabilities of a
-single GPU: A <span class="math notranslate nohighlight">\(n\)</span> qubit quantum state has <span class="math notranslate nohighlight">\(2^n\)</span> complex amplitudes, each
-of which require 8 bytes of memory to store. Hence the total memory
-required to store a <span class="math notranslate nohighlight">\(n\)</span> qubit quantum state is <span class="math notranslate nohighlight">\(8\)</span> bytes
-<span class="math notranslate nohighlight">\(\times 2^n\)</span>. For <span class="math notranslate nohighlight">\(n = 30\)</span> qubits, this is roughly <span class="math notranslate nohighlight">\(8\)</span>
-GB but for <span class="math notranslate nohighlight">\(n = 40\)</span>, this exponentially increases to 8700 GB.</p>
-</section>
-<section id="parallelization-across-multiple-processors">
-<h2>Parallelization across Multiple Processors<a class="headerlink" href="#parallelization-across-multiple-processors" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> target allows for memory from additional
-GPUs to be pooled enabling qubit counts to be scaled.
-Execution on the <code class="docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend is enabled via <code class="docutils literal notranslate"><span class="pre">mpirun</span></code>. Users
-need to create a <code class="docutils literal notranslate"><span class="pre">.py</span></code> file with their code and run the command below
-in terminal:</p>
-<p><code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-np</span> <span class="pre">4</span> <span class="pre">python3</span> <span class="pre">test.py</span></code></p>
-<p>where 4 is the number of GPUs one has access to and <code class="docutils literal notranslate"><span class="pre">test</span></code> is the file
-name chosen.</p>
-<p>The <code class="docutils literal notranslate"><span class="pre">nvidia-mqpu</span></code> target uses a statevector simulator to simulate execution
-on each virtual QPU.
-The <code class="docutils literal notranslate"><span class="pre">remote-mqpu</span></code> platform allows to freely configure what backend is used
-for each platform QPU.
-For more information about the different platform targets, please take a look at
-<a class="reference internal" href="../backends/platform.html"><span class="doc">Multi-Processor Platforms</span></a>.</p>
-<section id="batching-hamiltonian-terms">
-<h3>Batching Hamiltonian Terms<a class="headerlink" href="#batching-hamiltonian-terms" title="Permalink to this heading">¶</a></h3>
-<p>Expectation value computations of multi-term Hamiltonians can be
-asynchronously processed via the <code class="docutils literal notranslate"><span class="pre">mqpu</span></code> platform.</p>
-<img alt="../../_images/hsplit.png" src="../../_images/hsplit.png" />
-<p>For workflows involving multiple GPUs, save the code below in a
-<code class="docutils literal notranslate"><span class="pre">filename.py</span></code> file and execute via:
-<code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-np</span> <span class="pre">n</span> <span class="pre">python3</span> <span class="pre">filename.py</span></code> where <code class="docutils literal notranslate"><span class="pre">n</span></code> is an integer
-specifying the number of GPUs you have access to.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-
-<span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;This example requires a GPU to run. No GPU detected.&quot;</span><span class="p">)</span>
-    <span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">mpi</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
-
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">15</span>
-<span class="n">term_count</span> <span class="o">=</span> <span class="mi">100000</span>
-
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">()</span>
-<span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-<span class="n">kernel</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-<span class="c1"># We create a random Hamiltonian</span>
-<span class="n">hamiltonian</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">SpinOperator</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">,</span> <span class="n">term_count</span><span class="p">)</span>
-
-<span class="c1"># The observe calls allows us to calculate the expectation value of the Hamiltonian with respect to a specified kernel.</span>
-
-<span class="c1"># Single node, single GPU.</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">)</span>
-<span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-<span class="c1"># If we have multiple GPUs/ QPUs available, we can parallelize the workflow with the addition of an argument in the observe call.</span>
-
-<span class="c1"># Single node, multi-GPU.</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">execution</span><span class="o">=</span><span class="n">cudaq</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">thread</span><span class="p">)</span>
-<span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-<span class="c1"># Multi-node, multi-GPU.</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">hamiltonian</span><span class="p">,</span> <span class="n">execution</span><span class="o">=</span><span class="n">cudaq</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">mpi</span><span class="p">)</span>
-<span class="n">result</span><span class="o">.</span><span class="n">expectation</span><span class="p">()</span>
-
-<span class="n">cudaq</span><span class="o">.</span><span class="n">mpi</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>mpi is initialized?  True
-rank 0 num_ranks 1
-</pre></div>
-</div>
-</section>
-<section id="circuit-batching">
-<h3>Circuit Batching<a class="headerlink" href="#circuit-batching" title="Permalink to this heading">¶</a></h3>
-<p>Execution of parameterized circuits with different parameters can be
-executed asynchronously via the <code class="docutils literal notranslate"><span class="pre">mqpu</span></code> platform.</p>
-<img alt="../../_images/circsplit.png" src="../../_images/circsplit.png" />
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-<span class="kn">from</span> <span class="nn">cudaq</span> <span class="kn">import</span> <span class="n">spin</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="k">if</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;This example requires a GPU to run. No GPU detected.&quot;</span><span class="p">)</span>
-    <span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">cudaq</span><span class="o">.</span><span class="n">set_target</span><span class="p">(</span><span class="s2">&quot;nvidia&quot;</span><span class="p">,</span> <span class="n">option</span><span class="o">=</span><span class="s2">&quot;mqpu&quot;</span><span class="p">)</span>
-
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">sample_count</span> <span class="o">=</span> <span class="mi">10000</span>
-<span class="n">h</span> <span class="o">=</span> <span class="n">spin</span><span class="o">.</span><span class="n">z</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">parameter_count</span> <span class="o">=</span> <span class="n">qubit_count</span>
-
-<span class="c1"># Below we run a circuit for 10000 different input parameters.</span>
-<span class="n">parameters</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                                               <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                                               <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">sample_count</span><span class="p">,</span>
-                                                     <span class="n">parameter_count</span><span class="p">))</span>
-
-<span class="n">kernel</span><span class="p">,</span> <span class="n">params</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">make_kernel</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-
-<span class="n">qubits</span> <span class="o">=</span> <span class="n">kernel</span><span class="o">.</span><span class="n">qalloc</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-<span class="n">qubits_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">))</span>
-
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">):</span>
-    <span class="n">kernel</span><span class="o">.</span><span class="n">rx</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>Let’s time the execution on single GPU.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">parameters</span><span class="p">),</span>
-              <span class="n">number</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># Single GPU result.</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>31.7 s ± 990 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
-</pre></div>
-</div>
-<p>Now let’s try to time multi GPU run.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;We have&#39;</span><span class="p">,</span> <span class="n">parameters</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-      <span class="s1">&#39;parameters which we would like to execute&#39;</span><span class="p">)</span>
-
-<span class="n">xi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span>
-    <span class="n">parameters</span><span class="p">,</span>
-    <span class="mi">4</span><span class="p">)</span>  <span class="c1"># We split our parameters into 4 arrays since we have 4 GPUs available.</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;We split this into&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">),</span> <span class="s1">&#39;batches of&#39;</span><span class="p">,</span> <span class="n">xi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;,&#39;</span><span class="p">,</span>
-      <span class="n">xi</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">xi</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">xi</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">We</span> <span class="n">have</span> <span class="mi">10000</span> <span class="n">parameters</span> <span class="n">which</span> <span class="n">we</span> <span class="n">would</span> <span class="n">like</span> <span class="n">to</span> <span class="n">execute</span>
-<span class="n">We</span> <span class="n">split</span> <span class="n">this</span> <span class="n">into</span> <span class="mi">4</span> <span class="n">batches</span> <span class="n">of</span> <span class="mi">2500</span> <span class="p">,</span> <span class="mi">2500</span> <span class="p">,</span> <span class="mi">2500</span> <span class="p">,</span> <span class="mi">2500</span>
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Timing the execution on a single GPU vs 4 GPUs,</span>
-<span class="c1"># one will see a 4x performance improvement if 4 GPUs are available.</span>
-
-<span class="n">asyncresults</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">num_gpus</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">num_available_gpus</span><span class="p">()</span>
-
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">)):</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-        <span class="n">qpu_id</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="n">num_gpus</span> <span class="o">//</span> <span class="nb">len</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-        <span class="n">asyncresults</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="n">cudaq</span><span class="o">.</span><span class="n">observe_async</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">,</span> <span class="p">:],</span> <span class="n">qpu_id</span><span class="o">=</span><span class="n">qpu_id</span><span class="p">))</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="p">[</span><span class="n">res</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> <span class="k">for</span> <span class="n">res</span> <span class="ow">in</span> <span class="n">asyncresults</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>85.3 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
-</pre></div>
-</div>
-</section>
-</section>
-</section>
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#quantum-states">Quantum States</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#iqm">IQM</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id11">Submission from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda">CUDA</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-</li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-  <section id="quantum-computing-101">
-<h1>Quantum Computing 101<a class="headerlink" href="#quantum-computing-101" title="Permalink to this heading">¶</a></h1>
-<p>The fundamental unit of classical information storage, processing and
-transmission is the bit. Analogously, we define its quantum counterpart,
-a quantum bit or simply the qubit.</p>
-<p>Classical bits are transistor elements whose states can be altered to
-perform computations. Similarly qubits too have physical realizations
-within superconducting materials, ion-traps and photonic systems. We
-shall not concern ourselves with specific qubit architectures but rather
-think of them as systems which obey the laws of quantum mechanics and
-the mathematical language physicists have developed to describe the
-theory: linear algebra.</p>
-<section id="quantum-states">
-<h2>Quantum States<a class="headerlink" href="#quantum-states" title="Permalink to this heading">¶</a></h2>
-<p>Information storage scales linearly if bits have a single state. Access
-to multiple states, namely a 0 and a 1 allows for information encoding
-to scale logarithmically. Similarly we define a qubit to have the states
-<span class="math notranslate nohighlight">\(\ket{0}\)</span> and <span class="math notranslate nohighlight">\(\ket{1}\)</span> in Dirac notation where:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\ket{0} = \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix}\end{split}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}\ket{1} = \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}\end{split}\]</div>
-<p>Rather than just the two states each classical bit can be in,
-quantum theory allows one to explore linear combinations of states,
-also called superpositions:</p>
-<div class="math notranslate nohighlight">
-\[\ket{\psi} = \alpha\ket{0} + \beta\ket{1}\]</div>
-<p>where <span class="math notranslate nohighlight">\(\alpha\)</span> and <span class="math notranslate nohighlight">\(\beta\)</span> <span class="math notranslate nohighlight">\(\in \mathbb{C}\)</span>. It is
-important to note that this is still the state of one qubit;
-although we have two kets, they both represent a
-superposition state of one qubit.</p>
-<p>If we have two classical bits, the possible states we could encode
-information in would be 00, 01, 10 and 11. Correspondingly, multiple
-qubits can be combined and the possible combinations of their states
-used to process information.</p>
-<p>A two qubit system has four computational basis states:
-<span class="math notranslate nohighlight">\(\ket{00}, \ket{01}, \ket{10}, \ket{11}\)</span>.</p>
-<p>More generally, the quantum state of a <span class="math notranslate nohighlight">\(n\)</span> qubit system is written
-as a sum of <span class="math notranslate nohighlight">\(2^n\)</span> possible basis states where the coefficients
-track the probability of the system collapsing into that state if a
-measurement is applied.</p>
-<p>For <span class="math notranslate nohighlight">\(n = 500\)</span>, <span class="math notranslate nohighlight">\(2^n \approx 10^{150}\)</span> which is greater than
-the number of atoms in the universe. Storing the complex numbers
-associated with <span class="math notranslate nohighlight">\(2^{500}\)</span> amplitudes would not be feasible using
-bits and classical computations but nature seems to only require 500
-qubits to do so. The art of quantum computation is thus to build quantum
-systems that we can manipulate with fine precision such that evolving a
-large statevector can be offloaded onto a quantum computer.</p>
-</section>
-<section id="quantum-gates">
-<h2>Quantum Gates<a class="headerlink" href="#quantum-gates" title="Permalink to this heading">¶</a></h2>
-<p>We can manipulate the state of a qubit via quantum gates.
-For example, the Pauli X gate allows us to flip the state of the qubit:</p>
-<div class="math notranslate nohighlight">
-\[X \ket{0} = \ket{1}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{bmatrix} 0 &amp; 1 \\ 1 &amp; 0 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}\end{split}\]</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="c1"># A single qubit initialized to the ground / zero state.</span>
-    <span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-    <span class="c1"># Apply the Pauli x gate to the qubit.</span>
-    <span class="n">x</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-    <span class="c1"># Measurement operator.</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="c1"># Sample the qubit for 1000 shots to gather statistics.</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">most_probable</span><span class="p">())</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> <span class="mi">1</span><span class="p">:</span><span class="mi">1000</span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>The Hadamard gate allows us to put the qubit in an equal superposition
-state:</p>
-<div class="math notranslate nohighlight">
-\[H \ket{0} =  \tfrac{1}{\sqrt{2}} \ket{0} + \tfrac{1}{\sqrt{2}} \ket{1}  \equiv \ket{+}\]</div>
-<div class="math notranslate nohighlight">
-\[\begin{split}\tfrac{1}{\sqrt{2}}\begin{bmatrix} 1 &amp; 1 \\ 1 &amp; -1 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} = \tfrac{1}{\sqrt{2}} \begin{bmatrix} 1 \\ 0 \\ \end{bmatrix} + \tfrac{1}{\sqrt{2}} \begin{bmatrix} 0 \\ 1 \\ \end{bmatrix}.\end{split}\]</div>
-<p>The probability of finding the qubit in the <span class="math notranslate nohighlight">\(\ket{0}\)</span> or <span class="math notranslate nohighlight">\(\ket{1}\)</span> state is hence
-<span class="math notranslate nohighlight">\(\lvert \tfrac{1}{\sqrt{2}} \rvert ^2 = \tfrac{1}{2}\)</span>. Lets verify
-this with some code:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="c1"># A single qubit initialized to the ground/ zero state.</span>
-    <span class="n">qubit</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qubit</span><span class="p">()</span>
-
-    <span class="c1"># Apply Hadamard gate to single qubit to put it in equal superposition.</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-    <span class="c1"># Measurement operator.</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubit</span><span class="p">)</span>
-
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measured |0&gt; with probability &quot;</span> <span class="o">+</span>
-      <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Measured |1&gt; with probability &quot;</span> <span class="o">+</span>
-      <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s2">&quot;1&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> <span class="mi">0</span><span class="p">:</span><span class="mi">502</span> <span class="mi">1</span><span class="p">:</span><span class="mi">498</span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>For a qubit in a superposition state, quantum gates
-act linearly:</p>
-<div class="math notranslate nohighlight">
-\[X (\alpha\ket{0} + \beta\ket{1}) = \alpha\ket{1} + \beta\ket{0}\]</div>
-<p>As we evolve quantum states via quantum gates, the normalization
-condition requires that the sum of modulus squared of amplitudes must
-equal 1 at all times:</p>
-<div class="math notranslate nohighlight">
-\[\ket{\psi} = \alpha\ket{0} + \beta\ket{1},          |\alpha|^2 + |\beta|^2 = 1.\]</div>
-<p>This is to adhere to the conservation of probabilities which translates
-to a constraint on types of quantum gates we can define.
-For a general quantum state <span class="math notranslate nohighlight">\(\ket{\psi}\)</span>, upholding the
-normalization condition requires quantum gates to be unitary, that is
-<span class="math notranslate nohighlight">\(U^{\dagger}U = U^{*^{T}}U = \mathbb{I}\)</span>.</p>
-<p>Just like the single-qubit gates above, we can define
-multi-qubit gates to act on multiple-qubits.
-The controlled-NOT or CNOT gate, for example, acts on 2 qubits: the control qubit and
-the target qubit. Its effect is to flip the target if the control is in
-the excited <span class="math notranslate nohighlight">\(\ket{1}\)</span> state.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cudaq</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="c1"># 2 qubits both initialized to the ground/ zero state.</span>
-    <span class="n">qvector</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="n">x</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="c1"># Controlled-not gate operation.</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qvector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> <span class="mi">11</span><span class="p">:</span><span class="mi">1000</span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>The CNOT gate in matrix notation is represented as</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}CNOT \equiv \begin{bmatrix} 1 &amp; 0 &amp; 0 &amp; 0 \\ 0 &amp; 1 &amp; 0 &amp; 0 \\ 0 &amp; 0 &amp; 0 &amp; 1 \\ 0 &amp; 0 &amp; 1 &amp; 0 \end{bmatrix}\end{split}\]</div>
-<p>and one can check that <span class="math notranslate nohighlight">\(CNOT^\dagger CNOT = \mathbb{I}\)</span>.
-Its effect on the computational basis states is:</p>
-<div class="math notranslate nohighlight">
-\[CNOT\ket{00} = \ket{00}\]</div>
-<div class="math notranslate nohighlight">
-\[CNOT\ket{01} = \ket{01}\]</div>
-<div class="math notranslate nohighlight">
-\[CNOT\ket{10} = \ket{11}\]</div>
-<div class="math notranslate nohighlight">
-\[CNOT\ket{11} = \ket{10}\]</div>
-<p>For a full list of gates supported in CUDA-Q see <a class="reference internal" href="../../api/default_ops.html"><span class="doc">Quantum Operations</span></a>.</p>
-</section>
-<section id="measurements">
-<h2>Measurements<a class="headerlink" href="#measurements" title="Permalink to this heading">¶</a></h2>
-<p>Quantum theory is probabilistic and hence requires statistical inference
-to derive observations. Prior to measurement, the state of a qubit is
-all possible combinations of <span class="math notranslate nohighlight">\(\alpha\)</span> and <span class="math notranslate nohighlight">\(\beta\)</span> and upon
-measurement, wavefunction collapse yields either a classical 0 or 1.</p>
-<p>The mathematical theory devised to explain quantum phenomena tells us
-that the probability of observing the qubit in the state
-<span class="math notranslate nohighlight">\(\ket{0}\)</span> or <span class="math notranslate nohighlight">\(\ket{1}\)</span>, yielding a classical 0 or 1, is
-<span class="math notranslate nohighlight">\(\lvert \alpha \rvert ^2\)</span> or <span class="math notranslate nohighlight">\(\lvert \beta \rvert ^2\)</span>, respectively.</p>
-<p>As we see in the example of the Hadamard gate above,
-the result 0 or 1 each is yielded roughly 50% of the times as predicted
-by the postulate stated above thus proving the theory.</p>
-<p>Classically, we cannot encode information within states such as 00 + 11
-but quantum mechanics allows us to write linear superpositions</p>
-<div class="math notranslate nohighlight">
-\[\ket{\psi} = \alpha_{00}\ket{00} + \alpha_{01}\ket{01} + \alpha_{10}\ket{10} + \alpha_{11}\ket{11}\]</div>
-<p>where the probability of measuring <span class="math notranslate nohighlight">\(x = 00, 01, 10, 11\)</span> occurs
-with probability <span class="math notranslate nohighlight">\(\lvert \alpha_{x} \rvert ^2\)</span> with the
-normalization condition that
-<span class="math notranslate nohighlight">\(\sum_{x \in \{ 0,1 \}^2} \lvert \alpha_{x} \rvert ^2 = 1\)</span>.</p>
-</section>
-</section>
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-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
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-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="extending.html">   Extending</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
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-  <section id="working-with-the-cuda-q-ir">
-<h1>Working with the CUDA-Q IR<a class="headerlink" href="#working-with-the-cuda-q-ir" title="Permalink to this heading">¶</a></h1>
-<p>Let’s see the output of <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> in verbose mode. Consider a simple code like the one below, saved to file <code class="code docutils literal notranslate"><span class="pre">simple.cpp</span></code>.</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">   #</span>include<span class="w"> </span>&lt;cudaq.h&gt;
-
-<span class="go">   struct ghz {</span>
-<span class="go">     void operator()(int N) __qpu__ {</span>
-<span class="go">       cudaq::qvector q(N);</span>
-<span class="go">       h(q[0]);</span>
-<span class="go">       for (int i = 0; i &lt; N - 1; i++) {</span>
-<span class="go">         x&lt;cudaq::ctrl&gt;(q[i], q[i + 1]);</span>
-<span class="go">       }</span>
-<span class="go">       mz(q);</span>
-<span class="go">     }</span>
-<span class="go">   };</span>
-
-<span class="go">   int main() { ... }</span>
-
-<span class="go">We see the following output from :code:`nvq++` verbose mode (up to some absolute paths).</span>
-</pre></div>
-</div>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>nvq++<span class="w"> </span>simple.cpp<span class="w"> </span>-v<span class="w"> </span>-save-temps
-
-<span class="go">cudaq-quake --emit-llvm-file simple.cpp -o simple.qke</span>
-<span class="go">cudaq-opt --pass-pipeline=builtin.module(canonicalize,lambda-lifting,canonicalize,apply-op-specialization,kernel-execution,indirect-to-direct-calls,inline,func.func(quake-add-metadata),device-code-loader{use-quake=1},expand-measurements,func.func(lower-to-cfg),canonicalize,cse) simple.qke -o simple.qke.LpsXpu</span>
-<span class="go">cudaq-translate --convert-to=qir simple.qke.LpsXpu -o simple.ll.p3De4L</span>
-<span class="go">fixup-linkage.pl simple.qke simple.ll</span>
-<span class="go">llc --relocation-model=pic --filetype=obj -O2 simple.ll.p3De4L -o simple.qke.o</span>
-<span class="go">llc --relocation-model=pic --filetype=obj -O2 simple.ll -o simple.classic.o</span>
-<span class="go">clang++ -L/usr/lib/gcc/x86_64-linux-gnu/12 -L/usr/lib64 -L/lib/x86_64-linux-gnu -L/lib64 -L/usr/lib/x86_64-linux-gnu -L/lib -L/usr/lib -L/usr/local/cuda/lib64/stubs -r simple.qke.o simple.classic.o -o simple.o</span>
-<span class="go">clang++ -Wl,-rpath,lib -Llib -L/usr/lib/gcc/x86_64-linux-gnu/12 -L/usr/lib64 -L/lib/x86_64-linux-gnu -L/lib64 -L/usr/lib/x86_64-linux-gnu -L/lib -L/usr/lib -L/usr/local/cuda/lib64/stubs simple.o -lcudaq -lcudaq-common -lcudaq-mlir-runtime -lcudaq-builder -lcudaq-ensmallen -lcudaq-nlopt -lcudaq-spin -lcudaq-em-default -lcudaq-platform-default -lnvqir -lnvqir-qpp</span>
-</pre></div>
-</div>
-<p>This workflow orchestration is represented in the figure below:</p>
-<img alt="../../_images/nvqpp_workflow.png" src="../../_images/nvqpp_workflow.png" />
-<p>We start by mapping CUDA-Q C++ kernel representations (structs, lambdas, and free functions)
-to the Quake dialect. Since we added <code class="code docutils literal notranslate"><span class="pre">-save-temps</span></code>,
-we can look at the IR code that was produced. The base Quake file, <code class="code docutils literal notranslate"><span class="pre">simple.qke</span></code>, contains the following:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">module attributes {qtx.mangled_name_map = {__nvqpp__mlirgen__ghz = &quot;_ZN3ghzclEi&quot;}} {</span>
-<span class="go">    func.func @__nvqpp__mlirgen__ghz(%arg0: i32) attributes {&quot;cudaq-entrypoint&quot;, &quot;cudaq-kernel&quot;} {</span>
-<span class="gp">       %</span><span class="nv">alloca</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.alloca<span class="o">()</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="go">       memref.store %arg0, %alloca[] : memref&lt;i32&gt;</span>
-<span class="gp">       %</span><span class="nv">0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.load<span class="w"> </span>%alloca<span class="o">[]</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="gp">       %</span><span class="nv">1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.extsi<span class="w"> </span>%0<span class="w"> </span>:<span class="w"> </span>i32<span class="w"> </span>to<span class="w"> </span>i64
-<span class="gp">       %</span><span class="nv">2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>quake.alloca<span class="o">(</span>%1<span class="w"> </span>:<span class="w"> </span>i64<span class="o">)</span><span class="w"> </span>!quake.veq&lt;?&gt;
-<span class="gp">       %</span><span class="nv">c0_i32</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.constant<span class="w"> </span><span class="m">0</span><span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">       %</span><span class="nv">3</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.extsi<span class="w"> </span>%c0_i32<span class="w"> </span>:<span class="w"> </span>i32<span class="w"> </span>to<span class="w"> </span>i64
-<span class="gp">       %</span><span class="nv">4</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>quake.extract_ref<span class="w"> </span>%2<span class="o">[</span>%3<span class="o">]</span><span class="w"> </span>:<span class="w"> </span><span class="o">(</span>!quake.veq&lt;?&gt;,<span class="w"> </span>i64<span class="o">)</span><span class="w"> </span>-&gt;<span class="w"> </span>!quake.ref
-<span class="go">       quake.h %4 : (!quake.ref) -&gt; ()</span>
-<span class="go">       cc.scope {</span>
-<span class="gp">        %</span><span class="nv">c0_i32_0</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.constant<span class="w"> </span><span class="m">0</span><span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">        %</span><span class="nv">alloca_1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.alloca<span class="o">()</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="go">        memref.store %c0_i32_0, %alloca_1[] : memref&lt;i32&gt;</span>
-<span class="go">        cc.loop while {</span>
-<span class="gp">            %</span><span class="nv">6</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.load<span class="w"> </span>%alloca_1<span class="o">[]</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="gp">            %</span><span class="nv">7</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.load<span class="w"> </span>%alloca<span class="o">[]</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="gp">            %</span><span class="nv">c1_i32</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.constant<span class="w"> </span><span class="m">1</span><span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">            %</span><span class="nv">8</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.subi<span class="w"> </span>%7,<span class="w"> </span>%c1_i32<span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">            %</span><span class="nv">9</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.cmpi<span class="w"> </span>slt,<span class="w"> </span>%6,<span class="w"> </span>%8<span class="w"> </span>:<span class="w"> </span>i32
-<span class="go">            cc.condition %9</span>
-<span class="go">        } do {</span>
-<span class="go">          cc.scope {</span>
-<span class="gp">            %</span><span class="nv">6</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.load<span class="w"> </span>%alloca_1<span class="o">[]</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="gp">            %</span><span class="nv">7</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.extsi<span class="w"> </span>%6<span class="w"> </span>:<span class="w"> </span>i32<span class="w"> </span>to<span class="w"> </span>i64
-<span class="gp">            %</span><span class="nv">8</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>quake.extract_ref<span class="w"> </span>%2<span class="o">[</span>%7<span class="o">]</span><span class="w"> </span>:<span class="w"> </span><span class="o">(</span>!quake.veq&lt;?&gt;,<span class="w"> </span>i64<span class="o">)</span><span class="w"> </span>-&gt;<span class="w"> </span>!quake.ref
-<span class="gp">            %</span><span class="nv">9</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.load<span class="w"> </span>%alloca_1<span class="o">[]</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="gp">            %</span><span class="nv">c1_i32</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.constant<span class="w"> </span><span class="m">1</span><span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">            %</span><span class="nv">10</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.addi<span class="w"> </span>%9,<span class="w"> </span>%c1_i32<span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">            %</span><span class="nv">11</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.extsi<span class="w"> </span>%10<span class="w"> </span>:<span class="w"> </span>i32<span class="w"> </span>to<span class="w"> </span>i64
-<span class="gp">            %</span><span class="nv">12</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>quake.extract_ref<span class="w"> </span>%2<span class="o">[</span>%11<span class="o">]</span><span class="w"> </span>:<span class="w"> </span><span class="o">(</span>!quake.veq&lt;?&gt;,<span class="w"> </span>i64<span class="o">)</span><span class="w"> </span>-&gt;<span class="w"> </span>!quake.ref
-<span class="go">            quake.x [%8] %12 : (!quake.ref, !quake.ref) -&gt; ()</span>
-<span class="go">          }</span>
-<span class="go">          cc.continue</span>
-<span class="go">        } step {</span>
-<span class="gp">            %</span><span class="nv">6</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>memref.load<span class="w"> </span>%alloca_1<span class="o">[]</span><span class="w"> </span>:<span class="w"> </span>memref&lt;i32&gt;
-<span class="gp">            %</span><span class="nv">c1_i32</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.constant<span class="w"> </span><span class="m">1</span><span class="w"> </span>:<span class="w"> </span>i32
-<span class="gp">            %</span><span class="nv">7</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arith.addi<span class="w"> </span>%6,<span class="w"> </span>%c1_i32<span class="w"> </span>:<span class="w"> </span>i32
-<span class="go">            memref.store %7, %alloca_1[] : memref&lt;i32&gt;</span>
-<span class="go">        }</span>
-<span class="go">        }</span>
-<span class="gp">        %</span><span class="nv">5</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>quake.mz<span class="w"> </span>%2<span class="w"> </span>:<span class="w"> </span><span class="o">(</span>!quake.veq&lt;?&gt;<span class="o">)</span><span class="w"> </span>-&gt;<span class="w"> </span>!cc.stdvec&lt;i1&gt;
-<span class="go">        return</span>
-<span class="go">    }</span>
-<span class="go">}</span>
-</pre></div>
-</div>
-<p>This base Quake file is unoptimized and unchanged. It is produced by the
-<code class="code docutils literal notranslate"><span class="pre">cudaq-quake</span></code> tool, which also allows us to output the full LLVM IR representation
-for the code. This LLVM IR is classical-only, and is directly produced by <code class="code docutils literal notranslate"><span class="pre">clang++</span></code>
-code-generation. The LLVM IR file <code class="code docutils literal notranslate"><span class="pre">simple.ll</span></code> contains the CUDA-Q kernel
-<code class="code docutils literal notranslate"><span class="pre">operator()(Args...)</span></code> LLVM function, with a mangled name. Ultimately, we
-want to replace this function with our own MLIR-generated function.</p>
-<p>Next, the <code class="code docutils literal notranslate"><span class="pre">cudaq-opt</span></code> tool is invoked on the <code class="code docutils literal notranslate"><span class="pre">simple.qke</span></code> file. This runs an
-MLIR pass pipeline that canonicalizes and optimizes the code. It will also process quantum
-lambdas, lift those lambdas to functions, and synthesis adjoint and controlled versions of
-CUDA-Q kernel functions if necessary. The most important pass that this step applies is the
-<code class="code docutils literal notranslate"><span class="pre">kernel-execution</span></code> pass, which synthesizes a new entry point LLVM function with the
-same name and signature as the original <code class="code docutils literal notranslate"><span class="pre">operator()(Args...)</span></code> call function in the
-classical <code class="code docutils literal notranslate"><span class="pre">simple.ll</span></code> file. We also extract all Quake code representations as strings
-and register them with the CUDA-Q runtime for runtime IR introspection.</p>
-<p>After <code class="code docutils literal notranslate"><span class="pre">cudaq-opt</span></code>, the <code class="code docutils literal notranslate"><span class="pre">cudaq-translate</span></code> tool is used to lower the transformed
-Quake representation to an LLVM IR representation, specifically the QIR. We finish by lowering
-this representation to object code via standard LLVM tools (e.g. <code class="code docutils literal notranslate"><span class="pre">llc</span></code>), and merge all
-object files into a single object file, ensuring that our new mangled <code class="code docutils literal notranslate"><span class="pre">operator()(Args...)</span></code>
-call is injected first, thereby overwriting the original. Finally, based on user compile flags,
-we configure the link line with specific libraries that implement the <code class="code docutils literal notranslate"><span class="pre">quantum_platform</span></code>
-(here the <code class="code docutils literal notranslate"><span class="pre">libcudaq-platform-default.so</span></code>) and NVQIR circuit simulator backend (the
-<code class="code docutils literal notranslate"><span class="pre">libnvqir-qpp.so</span></code> Q++ CPU-only simulation backend). These latter libraries are controlled
-via the <code class="code docutils literal notranslate"><span class="pre">--platform</span></code> and <code class="code docutils literal notranslate"><span class="pre">--target</span></code> compiler flags.</p>
-<img alt="../../_images/dialects.png" src="../../_images/dialects.png" />
-<p>The above figure demonstrate the MLIR dialects involved and the overall workflow mapping
-high-level language constructs to lower-level MLIR dialect code, and ultimately LLVM IR.</p>
-<p>CUDA-Q also provides value-semantics form of Quake for static circuit
-representation. This dialect directly enables robust circuit
-optimizations via data-flow analysis of the representative circuit. This dialect
-is typically produced just-in-time when the structure of the circuit is fully known.</p>
-<p>You will notice that there are a number of CUDA-Q executable tools installed as part
-of this open beta release. These tools are directly related to the generation,
-processing, optimization, and lowering of the core <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> compiler representations.
-The tools available are</p>
-<ol class="arabic simple">
-<li><p><code class="code docutils literal notranslate"><span class="pre">cudaq-quake</span></code> - Lower C++ to Quake, can also output classical LLVM IR file</p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">cudaq-opt</span></code> - Process Quake with various MLIR Passes</p></li>
-<li><p><code class="code docutils literal notranslate"><span class="pre">cudaq-translate</span></code> - Lower Quake to external representations like QIR</p></li>
-</ol>
-<p>CUDA-Q and <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> rely on Quake for the core quantum intermediate representation.
-Quake represents an IR closer to the CUDA-Q source language and models qubits and
-quantum instructions via memory semantics. Quake can be fully dynamic and in
-that sense represents a quantum circuit template or generator. With runtime
-arguments fully specified, Quake code can be used to generate or synthesize
-a fully known quantum circuit. The value semantics form of Quake can thus be
-used as a representation for fully known
-or synthesized quantum circuits. Its utility, therefore, lies in its ability to
-optimize quantum code. It departs from the memory semantics model of Quake and
-expresses the flow of quantum information explicitly as MLIR values.
-This approach makes it easier for finding circuit patterns and leveraging it for common
-optimization tasks.</p>
-<p>To demonstrate how these tools work together, let’s take the simple GHZ CUDA-Q
-program and lower the kernel from C++ to Quake, synthesize that Quake code,
-and produce QIR. Recall the code snippet for the kernel</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Define a quantum kernel</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">ghz</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="k">operator</span><span class="p">()()</span><span class="w"> </span><span class="n">__qpu__</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qarray</span><span class="o">&lt;</span><span class="mi">5</span><span class="o">&gt;</span><span class="w"> </span><span class="n">q</span><span class="p">;</span>
-<span class="w">    </span><span class="n">h</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span>
-<span class="w">      </span><span class="n">x</span><span class="o">&lt;</span><span class="n">cudaq</span><span class="o">::</span><span class="n">ctrl</span><span class="o">&gt;</span><span class="p">(</span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">    </span><span class="n">mz</span><span class="p">(</span><span class="n">q</span><span class="p">);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-</pre></div>
-</div>
-<p>Using the toolchain, we can lower this directly to QIR,</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">cudaq-quake simple.cpp | cudaq-opt --canonicalize | cudaq-translate --convert-to=qir</span>
-</pre></div>
-</div>
-<p>which prints:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">; ModuleID = &#39;LLVMDialectModule&#39;</span>
-<span class="go">source_filename = &quot;LLVMDialectModule&quot;</span>
-<span class="go">target datalayout = &quot;e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128&quot;</span>
-<span class="go">target triple = &quot;x86_64-unknown-linux-gnu&quot;</span>
-
-<span class="gp">%</span><span class="nv">Array</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">type</span><span class="w"> </span>opaque
-<span class="gp">%</span><span class="nv">Qubit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">type</span><span class="w"> </span>opaque
-<span class="gp">%</span><span class="nv">Result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">type</span><span class="w"> </span>opaque
-
-<span class="go">declare void @invokeWithControlQubits(i64, void (%Array*, %Qubit*)*, ...) local_unnamed_addr</span>
-
-<span class="go">declare void @__quantum__qis__x__ctl(%Array*, %Qubit*)</span>
-
-<span class="go">declare %Result* @__quantum__qis__mz(%Qubit*) local_unnamed_addr</span>
-
-<span class="go">declare void @__quantum__rt__qubit_release_array(%Array*) local_unnamed_addr</span>
-
-<span class="go">declare i64 @__quantum__rt__array_get_size_1d(%Array*) local_unnamed_addr</span>
-
-<span class="go">declare void @__quantum__qis__h(%Qubit*) local_unnamed_addr</span>
-
-<span class="go">declare i8* @__quantum__rt__array_get_element_ptr_1d(%Array*, i64) local_unnamed_addr</span>
-
-<span class="go">declare %Array* @__quantum__rt__qubit_allocate_array(i64) local_unnamed_addr</span>
-
-<span class="go">define void @__nvqpp__mlirgen__ghz(i32 %0) local_unnamed_addr {</span>
-<span class="gp">  %</span><span class="nv">2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>sext<span class="w"> </span>i32<span class="w"> </span>%0<span class="w"> </span>to<span class="w"> </span>i64
-<span class="gp">  %</span><span class="nv">3</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>%Array*<span class="w"> </span>@__quantum__rt__qubit_allocate_array<span class="o">(</span>i64<span class="w"> </span>%2<span class="o">)</span>
-<span class="gp">  %</span><span class="nv">4</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>i8*<span class="w"> </span>@__quantum__rt__array_get_element_ptr_1d<span class="o">(</span>%Array*<span class="w"> </span>%3,<span class="w"> </span>i64<span class="w"> </span><span class="m">0</span><span class="o">)</span>
-<span class="gp">  %</span><span class="nv">5</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>bitcast<span class="w"> </span>i8*<span class="w"> </span>%4<span class="w"> </span>to<span class="w"> </span>%Qubit**
-<span class="gp">  %</span><span class="nv">6</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>load<span class="w"> </span>%Qubit*,<span class="w"> </span>%Qubit**<span class="w"> </span>%5,<span class="w"> </span>align<span class="w"> </span><span class="m">8</span>
-<span class="go">  tail call void @__quantum__qis__h(%Qubit* %6)</span>
-<span class="gp">  %</span><span class="nv">7</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>add<span class="w"> </span>i32<span class="w"> </span>%0,<span class="w"> </span>-1
-<span class="gp">  %</span><span class="nv">8</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>icmp<span class="w"> </span>sgt<span class="w"> </span>i32<span class="w"> </span>%7,<span class="w"> </span><span class="m">0</span>
-<span class="go">  br i1 %8, label %.lr.ph.preheader, label %._crit_edge</span>
-
-<span class="go">.lr.ph.preheader:                                 ; preds = %1</span>
-<span class="gp">  %</span>wide.trip.count<span class="w"> </span><span class="o">=</span><span class="w"> </span>zext<span class="w"> </span>i32<span class="w"> </span>%7<span class="w"> </span>to<span class="w"> </span>i64
-<span class="go">  br label %.lr.ph</span>
-
-<span class="go">.lr.ph:                                           ; preds = %.lr.ph.preheader, %.lr.ph</span>
-<span class="gp">  %</span>indvars.iv<span class="w"> </span><span class="o">=</span><span class="w"> </span>phi<span class="w"> </span>i64<span class="w"> </span><span class="o">[</span><span class="w"> </span><span class="m">0</span>,<span class="w"> </span>%.lr.ph.preheader<span class="w"> </span><span class="o">]</span>,<span class="w"> </span><span class="o">[</span><span class="w"> </span>%indvars.iv.next,<span class="w"> </span>%.lr.ph<span class="w"> </span><span class="o">]</span>
-<span class="gp">  %</span><span class="nv">9</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>i8*<span class="w"> </span>@__quantum__rt__array_get_element_ptr_1d<span class="o">(</span>%Array*<span class="w"> </span>%3,<span class="w"> </span>i64<span class="w"> </span>%indvars.iv<span class="o">)</span>
-<span class="gp">  %</span><span class="nv">10</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>bitcast<span class="w"> </span>i8*<span class="w"> </span>%9<span class="w"> </span>to<span class="w"> </span>%Qubit**
-<span class="gp">  %</span><span class="nv">11</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>load<span class="w"> </span>%Qubit*,<span class="w"> </span>%Qubit**<span class="w"> </span>%10,<span class="w"> </span>align<span class="w"> </span><span class="m">8</span>
-<span class="gp">  %</span>indvars.iv.next<span class="w"> </span><span class="o">=</span><span class="w"> </span>add<span class="w"> </span>nuw<span class="w"> </span>nsw<span class="w"> </span>i64<span class="w"> </span>%indvars.iv,<span class="w"> </span><span class="m">1</span>
-<span class="gp">  %</span><span class="nv">12</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>i8*<span class="w"> </span>@__quantum__rt__array_get_element_ptr_1d<span class="o">(</span>%Array*<span class="w"> </span>%3,<span class="w"> </span>i64<span class="w"> </span>%indvars.iv.next<span class="o">)</span>
-<span class="gp">  %</span><span class="nv">13</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>bitcast<span class="w"> </span>i8*<span class="w"> </span>%12<span class="w"> </span>to<span class="w"> </span>%Qubit**
-<span class="gp">  %</span><span class="nv">14</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>load<span class="w"> </span>%Qubit*,<span class="w"> </span>%Qubit**<span class="w"> </span>%13,<span class="w"> </span>align<span class="w"> </span><span class="m">8</span>
-<span class="go">  tail call void (i64, void (%Array*, %Qubit*)*, ...) @invokeWithControlQubits(i64 1, void (%Array*, %Qubit*)* nonnull @__quantum__qis__x__ctl, %Qubit* %11, %Qubit* %14)</span>
-<span class="gp">  %</span>exitcond.not<span class="w"> </span><span class="o">=</span><span class="w"> </span>icmp<span class="w"> </span>eq<span class="w"> </span>i64<span class="w"> </span>%indvars.iv.next,<span class="w"> </span>%wide.trip.count
-<span class="go">  br i1 %exitcond.not, label %._crit_edge, label %.lr.ph</span>
-
-<span class="go">._crit_edge:                                      ; preds = %.lr.ph, %1</span>
-<span class="gp">  %</span><span class="nv">15</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>i64<span class="w"> </span>@__quantum__rt__array_get_size_1d<span class="o">(</span>%Array*<span class="w"> </span>%3<span class="o">)</span>
-<span class="gp">  %</span><span class="nv">16</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>icmp<span class="w"> </span>sgt<span class="w"> </span>i64<span class="w"> </span>%15,<span class="w"> </span><span class="m">0</span>
-<span class="go">  br i1 %16, label %.lr.ph3, label %._crit_edge4</span>
-
-<span class="go">.lr.ph3:                                          ; preds = %._crit_edge, %.lr.ph3</span>
-<span class="gp">  %</span><span class="nv">17</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>phi<span class="w"> </span>i64<span class="w"> </span><span class="o">[</span><span class="w"> </span>%22,<span class="w"> </span>%.lr.ph3<span class="w"> </span><span class="o">]</span>,<span class="w"> </span><span class="o">[</span><span class="w"> </span><span class="m">0</span>,<span class="w"> </span>%._crit_edge<span class="w"> </span><span class="o">]</span>
-<span class="gp">  %</span><span class="nv">18</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>i8*<span class="w"> </span>@__quantum__rt__array_get_element_ptr_1d<span class="o">(</span>%Array*<span class="w"> </span>%3,<span class="w"> </span>i64<span class="w"> </span>%17<span class="o">)</span>
-<span class="gp">  %</span><span class="nv">19</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>bitcast<span class="w"> </span>i8*<span class="w"> </span>%18<span class="w"> </span>to<span class="w"> </span>%Qubit**
-<span class="gp">  %</span><span class="nv">20</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>load<span class="w"> </span>%Qubit*,<span class="w"> </span>%Qubit**<span class="w"> </span>%19,<span class="w"> </span>align<span class="w"> </span><span class="m">8</span>
-<span class="gp">  %</span><span class="nv">21</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>tail<span class="w"> </span>call<span class="w"> </span>%Result*<span class="w"> </span>@__quantum__qis__mz<span class="o">(</span>%Qubit*<span class="w"> </span>%20<span class="o">)</span>
-<span class="gp">  %</span><span class="nv">22</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>add<span class="w"> </span>nuw<span class="w"> </span>nsw<span class="w"> </span>i64<span class="w"> </span>%17,<span class="w"> </span><span class="m">1</span>
-<span class="gp">  %</span>exitcond5.not<span class="w"> </span><span class="o">=</span><span class="w"> </span>icmp<span class="w"> </span>eq<span class="w"> </span>i64<span class="w"> </span>%22,<span class="w"> </span>%15
-<span class="go">  br i1 %exitcond5.not, label %._crit_edge4, label %.lr.ph3</span>
-
-<span class="go">._crit_edge4:                                     ; preds = %.lr.ph3, %._crit_edge</span>
-<span class="go">  tail call void @__quantum__rt__qubit_release_array(%Array* %3)</span>
-<span class="go">  ret void</span>
-<span class="go">}</span>
-
-<span class="go">!llvm.module.flags = !{!0}</span>
-
-<span class="go">!0 = !{i32 2, !&quot;Debug Info Version&quot;, i32 3}</span>
-</pre></div>
-</div>
-<p>Note that the results of each tool can be piped to further tools, creating a
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-<li class="toctree-l2"><a class="reference internal" href="nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-  <section id="create-your-own-cuda-q-compiler-pass">
-<h1>Create your own CUDA-Q Compiler Pass<a class="headerlink" href="#create-your-own-cuda-q-compiler-pass" title="Permalink to this heading">¶</a></h1>
-<p>The CUDA-Q IR can be transformed, analyzed, or optimized
-using standard MLIR patterns and tools. CUDA-Q provides a registration
-mechanism for the <code class="code docutils literal notranslate"><span class="pre">cudaq-opt</span></code> tool that allows one to create, load, and
-use custom MLIR passes on Quake code.</p>
-<p>CUDA-Q MLIR Passes can only be created within an existing CUDA-Q
-development environment. Therefore, you must clone the repository and add your
-Pass code as part of the existing CUDA-Q CMake system.</p>
-<p>As an example, clone the repository and add the following directory structure
-under <code class="code docutils literal notranslate"><span class="pre">lib</span></code>, <code class="code docutils literal notranslate"><span class="pre">lib/Plugins/MyCustomPlugin/</span></code>. Within this directory create a
-<code class="code docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> file and a <code class="code docutils literal notranslate"><span class="pre">MyCustomPlugin.cpp</span></code> file. In the CMake file,
-add the following:</p>
-<div class="highlight-cmake notranslate"><div class="highlight"><pre><span></span><span class="nb">add_llvm_pass_plugin</span><span class="p">(</span><span class="s">MyCustomPlugin</span><span class="w"> </span><span class="s">MyCustomPlugin.cpp</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Creating a CUDA-Q IR pass starts with the implementation of an
-<code class="code docutils literal notranslate"><span class="pre">mlir::OperationPass</span></code>. A full discussion of the MLIR Pass infrastructure
-is beyond the scope of this document; please see
-<a class="reference external" href="https://mlir.llvm.org/docs/PassManagement">MLIR Passes</a>. To create such
-a pass, start with the following template in the <code class="code docutils literal notranslate"><span class="pre">MyCustomPlugin.cpp</span></code> file:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;cudaq/Optimizer/Dialect/Quake/QuakeDialect.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;cudaq/Optimizer/Dialect/Quake/QuakeOps.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;cudaq/Support/Plugin.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;mlir/Rewrite/FrozenRewritePatternSet.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;mlir/Transforms/DialectConversion.h&quot;</span>
-
-<span class="c1">// Here is an example MLIR Pass that one can write externally and</span>
-<span class="c1">// use via the cudaq-opt tool, with the --load-cudaq-plugin flag.</span>
-<span class="c1">// The pass here is simple, replace Hadamard operations with S operations.</span>
-
-<span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">mlir</span><span class="p">;</span>
-
-<span class="k">namespace</span><span class="w"> </span><span class="p">{</span>
-
-<span class="k">struct</span><span class="w"> </span><span class="nc">ReplaceH</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">OpRewritePattern</span><span class="o">&lt;</span><span class="n">quake</span><span class="o">::</span><span class="n">HOp</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">OpRewritePattern</span><span class="o">::</span><span class="n">OpRewritePattern</span><span class="p">;</span>
-<span class="w">  </span><span class="n">LogicalResult</span><span class="w"> </span><span class="nf">matchAndRewrite</span><span class="p">(</span><span class="n">quake</span><span class="o">::</span><span class="n">HOp</span><span class="w"> </span><span class="n">hOp</span><span class="p">,</span>
-<span class="w">                                </span><span class="n">PatternRewriter</span><span class="w"> </span><span class="o">&amp;</span><span class="n">rewriter</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">rewriter</span><span class="p">.</span><span class="n">replaceOpWithNewOp</span><span class="o">&lt;</span><span class="n">quake</span><span class="o">::</span><span class="n">SOp</span><span class="o">&gt;</span><span class="p">(</span>
-<span class="w">        </span><span class="n">hOp</span><span class="p">,</span><span class="w"> </span><span class="n">hOp</span><span class="p">.</span><span class="n">isAdj</span><span class="p">(),</span><span class="w"> </span><span class="n">hOp</span><span class="p">.</span><span class="n">getParameters</span><span class="p">(),</span><span class="w"> </span><span class="n">hOp</span><span class="p">.</span><span class="n">getControls</span><span class="p">(),</span>
-<span class="w">        </span><span class="n">hOp</span><span class="p">.</span><span class="n">getTargets</span><span class="p">());</span>
-<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">success</span><span class="p">();</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="k">class</span><span class="w"> </span><span class="nc">CustomPassPlugin</span>
-<span class="w">    </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">PassWrapper</span><span class="o">&lt;</span><span class="n">CustomPassPlugin</span><span class="p">,</span><span class="w"> </span><span class="n">OperationPass</span><span class="o">&lt;</span><span class="n">func</span><span class="o">::</span><span class="n">FuncOp</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="p">{</span>
-<span class="k">public</span><span class="o">:</span>
-<span class="w">  </span><span class="n">MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID</span><span class="p">(</span><span class="n">CustomPassPlugin</span><span class="p">)</span>
-
-<span class="w">  </span><span class="n">llvm</span><span class="o">::</span><span class="n">StringRef</span><span class="w"> </span><span class="n">getArgument</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="s">&quot;cudaq-custom-pass&quot;</span><span class="p">;</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="n">runOnOperation</span><span class="p">()</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">circuit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">getOperation</span><span class="p">();</span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">ctx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">circuit</span><span class="p">.</span><span class="n">getContext</span><span class="p">();</span>
-
-<span class="w">    </span><span class="n">RewritePatternSet</span><span class="w"> </span><span class="nf">patterns</span><span class="p">(</span><span class="n">ctx</span><span class="p">);</span>
-<span class="w">    </span><span class="n">patterns</span><span class="p">.</span><span class="n">insert</span><span class="o">&lt;</span><span class="n">ReplaceH</span><span class="o">&gt;</span><span class="p">(</span><span class="n">ctx</span><span class="p">);</span>
-<span class="w">    </span><span class="n">ConversionTarget</span><span class="w"> </span><span class="nf">target</span><span class="p">(</span><span class="o">*</span><span class="n">ctx</span><span class="p">);</span>
-<span class="w">    </span><span class="n">target</span><span class="p">.</span><span class="n">addLegalDialect</span><span class="o">&lt;</span><span class="n">quake</span><span class="o">::</span><span class="n">QuakeDialect</span><span class="o">&gt;</span><span class="p">();</span>
-<span class="w">    </span><span class="n">target</span><span class="p">.</span><span class="n">addIllegalOp</span><span class="o">&lt;</span><span class="n">quake</span><span class="o">::</span><span class="n">HOp</span><span class="o">&gt;</span><span class="p">();</span>
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">failed</span><span class="p">(</span><span class="n">applyPartialConversion</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span><span class="w"> </span><span class="n">target</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">patterns</span><span class="p">))))</span><span class="w"> </span><span class="p">{</span>
-<span class="w">      </span><span class="n">circuit</span><span class="p">.</span><span class="n">emitOpError</span><span class="p">(</span><span class="s">&quot;simple pass failed&quot;</span><span class="p">);</span>
-<span class="w">      </span><span class="n">signalPassFailure</span><span class="p">();</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="p">};</span>
-
-<span class="p">}</span><span class="w"> </span><span class="c1">// namespace</span>
-
-<span class="n">CUDAQ_REGISTER_MLIR_PASS</span><span class="p">(</span><span class="n">CustomPassPlugin</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>This example serves as a very simple template for creating custom MLIR
-Passes that analyze the CUDA-Q Quake representation and perform
-some general transformation. In this example, we create a rewrite pattern
-that replaces <code class="code docutils literal notranslate"><span class="pre">Hadamard</span></code> operations with <code class="code docutils literal notranslate"><span class="pre">S</span></code> operations.</p>
-<p>Ensure that <code class="code docutils literal notranslate"><span class="pre">add_subdirectory(Plugins)</span></code> is in the <code class="code docutils literal notranslate"><span class="pre">lib/CMakeLists.txt</span></code> file,
-and also that there is a <code class="code docutils literal notranslate"><span class="pre">lib/Plugins/CMakeLists.txt</span></code> file that adds your
-plugin directory with <code class="code docutils literal notranslate"><span class="pre">add_subdirectory</span></code>.</p>
-<p>Then build CUDA-Q and you will have a <code class="code docutils literal notranslate"><span class="pre">MyCustomPlugin.so</span></code> plugin library
-in the install. You can load the plugin and use it with <code class="code docutils literal notranslate"><span class="pre">cudaq-opt</span></code> as follows:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cudaq-opt<span class="w"> </span>--load-cudaq-plugin<span class="w"> </span>MyCustomPlugin.so<span class="w"> </span>file.qke<span class="w"> </span>-cudaq-custom-pass
-</pre></div>
-</div>
-</section>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Create a new NVQIR Simulator</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-  <section id="extending-cuda-q-with-a-new-simulator">
-<h1>Extending CUDA-Q with a new Simulator<a class="headerlink" href="#extending-cuda-q-with-a-new-simulator" title="Permalink to this heading">¶</a></h1>
-<p>Backend circuit simulation in CUDA-Q is enabled via the
-NVQIR library (<code class="code docutils literal notranslate"><span class="pre">libnvqir</span></code>). CUDA-Q code is ultimately lowered
-to the LLVM IR in a manner that is adherent to the <a class="reference external" href="https://qir-alliance.org">QIR specification</a>.
-NVQIR provides function implementations for the various declared functions
-in the specification, which in turn delegate to an extensible simulation
-architecture.</p>
-<p>The primary extension point for NVQIR is the <code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code> class. This class
-exposes an API that enables qubit allocation and deallocation, quantum operation
-invocation, and measurement and sampling. Subtypes of this class are free to
-override these methods to affect simulation of the quantum code in any
-simulation-strategy-specific manner (e.g., state vector, tensor network, etc.). Moreover,
-subtypes are free to implement simulators that leverage classical accelerated computing.</p>
-<p>In this document, we’ll detail this simulator interface and walk through how to extend it for new types of simulation.</p>
-<section id="circuitsimulator">
-<h2><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code><a class="headerlink" href="#circuitsimulator" title="Permalink to this heading">¶</a></h2>
-<p>The <code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code> type is defined in <code class="code docutils literal notranslate"><span class="pre">runtime/nvqir/CircuitSimulator.h</span></code>. It
-exposes a public API to <code class="code docutils literal notranslate"><span class="pre">libnvqir</span></code> that is immediately subclassed in the <code class="code docutils literal notranslate"><span class="pre">CircuitSimulatorBase</span></code>
-type. This type is templated on the floating point type used in the simulator’s computations (e.g. <code class="code docutils literal notranslate"><span class="pre">double,float</span></code>).
-This templated type handles a lot of the base functionality required for allocating and deallocated qubits,
-as well as measurement, sampling, and observation under a number of execution contexts.
-This is the type that downstream simulation developers should extend.</p>
-<p>The actual definition of the quantum state data structure, and its overall evolution are
-left as tasks for <code class="code docutils literal notranslate"><span class="pre">CircuitSimulatorBase</span></code> subclasses. Examples of simulation subtypes can be found
-in <code class="code docutils literal notranslate"><span class="pre">runtime/nvqir/qpp/QppCircuitSimulator.cpp</span></code> or <code class="code docutils literal notranslate"><span class="pre">runtime/nvqir/custatevec/CuStateVecCircuitSimulator.cpp</span></code>.
-The <code class="code docutils literal notranslate"><span class="pre">QppCircuitSimulator</span></code> models the state vector using the <a class="reference external" href="https://github.com/softwareqinc/qpp">Q++</a> library, which
-leverages the <code class="code docutils literal notranslate"><span class="pre">Eigen::Matrix</span></code> type and OpenMP threading for matrix-vector operations.
-The <code class="code docutils literal notranslate"><span class="pre">CuStateVecCircuitSimulator</span></code> type models the state vector on an NVIDIA GPU device
-by leveraging the cuQuantum library.</p>
-<p>The key methods that need to be overridden by subtypes of <code class="code docutils literal notranslate"><span class="pre">CircuitSimulatorBase</span></code> are as follows:</p>
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Required Circuit Simulator Subtype Method Overrides</span><a class="headerlink" href="#id1" title="Permalink to this table">¶</a></caption>
-<tbody>
-<tr class="row-odd"><td><p>Method Name</p></td>
-<td><p>Method Arguments</p></td>
-<td><p>Method Description</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">addQubitToState</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">void</span></code></p></td>
-<td><p>Add a qubit to the underlying state representation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">addQubitsToState</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">nQubits</span> <span class="pre">:</span> <span class="pre">std::size_t</span></code></p></td>
-<td><p>Add the specified number of qubits to the underlying state representation.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">resetQubit</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">qubitIdx</span> <span class="pre">:</span> <span class="pre">std::size_t</span></code></p></td>
-<td><p>Reset the state of the qubit at the given index to <code class="code docutils literal notranslate"><span class="pre">|0&gt;</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">resetQubitStateImpl</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">void</span></code></p></td>
-<td><p>Clear the entire state representation (reset to 0 qubits).</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">applyGate</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">task</span> <span class="pre">:</span> <span class="pre">GateApplicationTask</span></code></p></td>
-<td><p>Apply the specified gate described by the <code class="code docutils literal notranslate"><span class="pre">GateApplicationTask</span></code>. This type encodes the control and target qubit indices, optional rotational parameters, and the gate matrix data.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">measureQubit</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">qubitIdx</span> <span class="pre">:</span> <span class="pre">std::size_t</span> <span class="pre">-&gt;</span> <span class="pre">bool</span></code> (returns bit result as bool)</p></td>
-<td><p>Measure the qubit, produce a bit result, collapse the state.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">sample</span></code></p></td>
-<td><p>:code`qubitIdxs : std::vector&lt;std::size_t&gt;, shots : int`</p></td>
-<td><p>Sample the current multi-qubit state on the provided qubit indices over a certain number of shots</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">name</span></code></p></td>
-<td><p><code class="code docutils literal notranslate"><span class="pre">void</span></code></p></td>
-<td><p>Return the name of this CircuitSimulator, must be the same as the name used in <code class="code docutils literal notranslate"><span class="pre">nvq++</span> <span class="pre">-qpu</span> <span class="pre">NAME</span> <span class="pre">...</span></code></p></td>
-</tr>
-</tbody>
-</table>
-<p>To extend a subtype class, you will need to create a new <code class="code docutils literal notranslate"><span class="pre">cpp</span></code> implementation file with the same name as your
-subtype class name. In this file, you will subclass the <code class="code docutils literal notranslate"><span class="pre">CircuitSimulatorBase&lt;FloatType&gt;</span></code> and implement the methods in
-the above table. Finally, the subclass must be registered with the NVQIR library so that it
-can be picked up and used when a user specifies <code class="code docutils literal notranslate"><span class="pre">nvq++</span> <span class="pre">--target</span> <span class="pre">mySimulator</span> <span class="pre">...</span></code> from the command line (or <code class="code docutils literal notranslate"><span class="pre">cudaq.set_target('mySimulator')</span></code> in Python.)
-Type registration can be performed with a provided NVQIR macro,</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">NVQIR_REGISTER_SIMULATOR</span><span class="p">(</span><span class="n">MySimulatorClassName</span><span class="p">,</span><span class="w"> </span><span class="n">mySimulator</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>where <code class="code docutils literal notranslate"><span class="pre">MySimulatorClassName</span></code> is the name of your subtype, and <code class="code docutils literal notranslate"><span class="pre">mySimulator</span></code> is the
-same name as what <code class="code docutils literal notranslate"><span class="pre">MySimulatorClassName::name()</span></code> returns, and what you desire the
-<code class="code docutils literal notranslate"><span class="pre">-qpu</span> <span class="pre">NAME</span></code> name to be.</p>
-<p>A further requirement is that the code be compiled into its own standalone shared library
-with name <code class="code docutils literal notranslate"><span class="pre">libnvqir-NAME.{so,dylib}</span></code>, where NAME is the
-same name as what <code class="code docutils literal notranslate"><span class="pre">MySimulatorClassName::name()</span></code> returns, and what you desire the
-<code class="code docutils literal notranslate"><span class="pre">-qpu</span> <span class="pre">NAME</span></code> name to be. You will also need to create a <code class="code docutils literal notranslate"><span class="pre">NAME.config</span></code> file that
-contains the following contents</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">NVQIR_SIMULATION_BACKEND</span><span class="o">=</span><span class="s2">&quot;NAME&quot;</span>
-</pre></div>
-</div>
-<p>The library must be installed in <code class="code docutils literal notranslate"><span class="pre">$CUDA_QUANTUM_PATH/lib</span></code> and the configuration file
-must be installed to <code class="code docutils literal notranslate"><span class="pre">$CUDA_QUANTUM_PATH/platforms</span></code>.</p>
-</section>
-<section id="let-s-see-this-in-action">
-<h2>Let’s see this in action<a class="headerlink" href="#let-s-see-this-in-action" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q provides some CMake utilities to make the creation of your new simulation library
-easier. Specifically, by using <code class="code docutils literal notranslate"><span class="pre">find_package(NVQIR)</span></code>, you’ll get access to a <code class="code docutils literal notranslate"><span class="pre">nvqir_add_backend</span></code> function
-that will automate much of the boilerplate for creating your library and configuration file.</p>
-<p>Let’s assume you want a simulation subtype named <code class="code docutils literal notranslate"><span class="pre">MySimulator</span></code>. You can create a folder or
-repository for this code called <code class="code docutils literal notranslate"><span class="pre">my-simulator</span></code> and add <code class="code docutils literal notranslate"><span class="pre">MySimulator.cpp</span></code> and
-<code class="code docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> files. Fill the CMake file with the following:</p>
-<div class="highlight-cmake notranslate"><div class="highlight"><pre><span></span><span class="nb">cmake_minimum_required</span><span class="p">(</span><span class="s">VERSION</span><span class="w"> </span><span class="s">3.24</span><span class="w"> </span><span class="s">FATAL_ERROR</span><span class="p">)</span>
-<span class="nb">project</span><span class="p">(</span><span class="s">DemoCreateNVQIRBackend</span><span class="w"> </span><span class="s">VERSION</span><span class="w"> </span><span class="s">1.0.0</span><span class="w"> </span><span class="s">LANGUAGES</span><span class="w"> </span><span class="s">CXX</span><span class="p">)</span>
-<span class="nb">find_package</span><span class="p">(</span><span class="s">NVQIR</span><span class="w"> </span><span class="s">REQUIRED</span><span class="p">)</span>
-<span class="nb">nvqir_add_backend</span><span class="p">(</span><span class="s">MySimulator</span><span class="w"> </span><span class="s">MySimulator.cpp</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>and then fill out your <code class="code docutils literal notranslate"><span class="pre">MySimulator.cpp</span></code> file with your subtype implementation. For example,</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;CircuitSimulator.h&quot;</span>
-
-<span class="k">namespace</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">MySimulator</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">nvqir</span><span class="o">::</span><span class="n">CircuitSimulatorBase</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span>
-
-<span class="w">  </span><span class="k">protected</span><span class="o">:</span>
-<span class="w">    </span><span class="c1">/// @brief Grow the state vector by one qubit.</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">addQubitToState</span><span class="p">()</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="c1">/// @brief Grow the state vector by `count` qubit.</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">addQubitsToState</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">count</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="c1">/// @brief Reset the qubit state.</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">resetQubitStateImpl</span><span class="p">()</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="c1">/// @brief Apply the given gate</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="n">applyGate</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">GateApplicationTask</span><span class="w"> </span><span class="o">&amp;</span><span class="n">task</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">  </span><span class="k">public</span><span class="o">:</span>
-<span class="w">    </span><span class="n">MySimulator</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span>
-<span class="w">    </span><span class="k">virtual</span><span class="w"> </span><span class="o">~</span><span class="n">MySimulator</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span>
-
-<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="nf">measureQubit</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">qubitIdx</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="nf">resetQubit</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="o">&amp;</span><span class="n">qubitIdx</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="n">cudaq</span><span class="o">::</span><span class="n">SampleResult</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">measuredBits</span><span class="p">,</span>
-<span class="w">                          </span><span class="kt">int</span><span class="w"> </span><span class="n">shots</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="p">...</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string_view</span><span class="w"> </span><span class="nf">name</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="s">&quot;MySimulator&quot;</span><span class="p">;</span><span class="w"> </span><span class="p">}</span>
-
-<span class="w">  </span><span class="p">};</span>
-
-<span class="p">}</span><span class="w"> </span><span class="c1">// namespace</span>
-
-<span class="c1">/// Register this Simulator with NVQIR.</span>
-<span class="n">NVQIR_REGISTER_SIMULATOR</span><span class="p">(</span><span class="n">MySimulator</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To build, install, and use this simulation backend, run the following from the top-level of <code class="code docutils literal notranslate"><span class="pre">my-simulator</span></code>:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_QUANTUM_PATH</span><span class="o">=</span>/path/to/cuda_quantum/install
-mkdir<span class="w"> </span>build<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>build
-cmake<span class="w"> </span>..<span class="w"> </span>-G<span class="w"> </span>Ninja<span class="w"> </span>-DNVQIR_DIR<span class="o">=</span><span class="s2">&quot;</span><span class="nv">$CUDA_QUANTUM_PATH</span><span class="s2">/lib/cmake/nvqir&quot;</span>
-ninja<span class="w"> </span>install
-</pre></div>
-</div>
-<p>Then given any CUDA-Q source file, you can compile and target your backend simulator with the following:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>file.cpp<span class="w"> </span>--target<span class="w"> </span>MySimulator
-./a.out
-</pre></div>
-</div>
-</section>
-</section>
-
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
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-  <section id="installation-from-source">
-<h1>Installation from Source<a class="headerlink" href="#installation-from-source" title="Permalink to this heading">¶</a></h1>
-<p>In most cases, you should not need to build CUDA-Q from source. For the
-best experience, we recommend using a container runtime to avoid conflicts with
-other software tools installed on the system. Note that <a class="reference external" href="https://docs.sylabs.io/guides/2.6/user-guide/faq.html#what-is-so-special-about-singularity">Singularity</a>
-or <a class="reference external" href="https://docs.docker.com/engine/security/rootless/">Docker rootless mode</a>
-address common issue or concerns that are often the motivation for
-avoiding the use of containers. Singularity, for example, can be installed
-in a user folder and its installation does not require admin permissions; see
-<a class="reference internal" href="local_installation.html#install-singularity-image"><span class="std std-ref">this section</span></a> for more detailed instructions
-on how to do that. Our installation guide also contains instructions for how to
-<a class="reference internal" href="local_installation.html#local-development-with-vscode"><span class="std std-ref">connect an IDE</span></a> to a running container.</p>
-<p>If you do not want use a container runtime, we also provide pre-built binaries
-for using CUDA-Q with C++, and Python wheels for using CUDA-Q with Python.
-These binaries and wheels are built following the instructions
-in this guide and should work for you as long as your system meets the compatibility
-requirements listed under <a class="reference internal" href="#compatibility-prebuilt-binaries"><span class="std std-ref">Prerequisites</span></a>.
-To install the pre-built binaries, please follow the instructions
-<a class="reference internal" href="local_installation.html#install-prebuilt-binaries"><span class="std std-ref">here</span></a>. To install the Python wheels, please
-follow the instructions <a class="reference internal" href="local_installation.html#install-python-wheels"><span class="std std-ref">here</span></a>.</p>
-<p>If your system is not listed as supported by our official packages, e.g. because you would
-like to use CUDA-Q on an operating system that uses an older C standard library,
-please follow this guide carefully without skipping any steps to build and install
-CUDA-Q from source. The rest of this guide details system requirements
-during the build and after installation, and walks through the installation steps.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>CUDA-Q contains some components that are only included as
-pre-built binaries and not part of our open source repository. We are working on
-either open-sourcing these components or making them available as separate downloads
-in the future. Even without these components, almost all features of CUDA-Q
-will be enabled in a source build, though some pieces may be less performant.
-At this time, the <a class="reference internal" href="../backends/simulators.html#nvidia-mgpu-backend"><span class="std std-ref">multi-GPU state vector simulator</span></a>
-backend will not be included if you build CUDA-Q from source.</p>
-</div>
-<section id="prerequisites">
-<span id="compatibility-prebuilt-binaries"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this heading">¶</a></h2>
-<p>The following pre-requisites need to be satisfied both on the build system and
-on the host system, that is the system where the built CUDA-Q binaries
-will be installed and used.</p>
-<ul class="simple">
-<li><p>Linux operating system. The instructions in this guide have been validated
-with the <a class="reference external" href="https://hub.docker.com/u/almalinux">AlmaLinux 8 image</a> that
-serves as the base image for the <a class="reference external" href="https://github.com/pypa/manylinux">manylinux_2_28 image</a>, and should work for the operating
-systems CentOS 8, Debian 11 and 12, Fedora 38 and 39, OpenSUSE/SLED/SLES 15.5
-and 15.6, RHEL 8 and 9, Rocky 8 and 9, and Ubuntu 24.04 and 22.04. Other
-operating systems may work, but have not been tested.</p></li>
-<li><p><a class="reference external" href="https://www.gnu.org/software/bash/">Bash</a> shell. The CUDA-Q
-build, install and run scripts expect to use <code class="code docutils literal notranslate"><span class="pre">/bin/bash</span></code>.</p></li>
-<li><p><a class="reference external" href="https://www.gnu.org/software/libc/">GNU C library</a>.
-Make sure that the version on the host system is the same one
-or newer than the version on the build system. Our own builds
-use version 2.28.</p></li>
-<li><p>CPU with either x86-64 (x86-64-v3 architecture and newer) or ARM64
-(ARM v8-A architecture and newer). Other architectures may work but are not
-tested and may require adjustments to the build instructions.</p></li>
-<li><p>Needed <strong>only on the host</strong> system: NVIDIA GPU with Volta, Turing, Ampere, Ada, or
-Hopper architecture and <a class="reference external" href="https://developer.nvidia.com/cuda-gpus">Compute Capability</a> 7+. Make sure you have the latest
-<a class="reference external" href="https://www.nvidia.com/download/index.aspx">drivers</a> installed for your
-GPU, and double check that the driver version listed by the <code class="code docutils literal notranslate"><span class="pre">nvidia-smi</span></code>
-command is 470.57.02 or newer. You do <em>not</em> need to have a GPU available on the
-build system; the CUDA compiler needed for the build can be installed and used
-without a GPU.</p></li>
-</ul>
-<p>We strongly recommend using a virtual environment for the build that includes
-<em>only</em> the tools and dependencies listed in this guide. If you have additional
-software installed, you will need to make sure that the build is linking against
-the correct libraries and versions.</p>
-</section>
-<section id="build-dependencies">
-<h2>Build Dependencies<a class="headerlink" href="#build-dependencies" title="Permalink to this heading">¶</a></h2>
-<p>In addition to the prerequisites listed above, you will need to install the
-following prerequisites in your build environment prior to proceeding with
-the build as described in the subsequent sections:</p>
-<ul class="simple">
-<li><p>Python version 3.10 or newer: If you intend to build CUDA-Q with Python
-support, make sure the Python version on the build system matches the version
-on the host system. If you intend to only build the C++ support for
-CUDA-Q, the Python interpreter is required only for some of the
-LLVM build scripts and the Python version used for the build does not have
-to match the version on the host system.</p></li>
-<li><p>Common tools: <code class="code docutils literal notranslate"><span class="pre">wget</span></code>, <code class="code docutils literal notranslate"><span class="pre">git</span></code>, <code class="code docutils literal notranslate"><span class="pre">unzip</span></code>. The commands in the rest of this guide assume
-that these tools are present on the build system, but they can be replaced by
-other alternatives (such as, for example, manually going to a web page and
-downloading a file/folder).</p></li>
-</ul>
-<p>The above prerequisites are no longer needed once CUDA-Q is built and
-do not need to be present on the host system.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The CUDA-Q build scripts and the commands listed in the rest of this
-document assume you are using <code class="code docutils literal notranslate"><span class="pre">bash</span></code> as the shell for your build.</p>
-</div>
-<p>In addition to installing the needed build dependencies listed above, make sure
-to set the following environment variables prior to proceeding:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDAQ_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/cudaq
-<span class="nb">export</span><span class="w"> </span><span class="nv">CUQUANTUM_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/cuquantum
-<span class="nb">export</span><span class="w"> </span><span class="nv">CUTENSOR_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/cutensor
-<span class="nb">export</span><span class="w"> </span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/llvm
-<span class="nb">export</span><span class="w"> </span><span class="nv">BLAS_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/blas
-<span class="nb">export</span><span class="w"> </span><span class="nv">ZLIB_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/zlib
-<span class="nb">export</span><span class="w"> </span><span class="nv">OPENSSL_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/openssl
-<span class="nb">export</span><span class="w"> </span><span class="nv">CURL_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/curl
-<span class="nb">export</span><span class="w"> </span><span class="nv">AWS_INSTALL_PREFIX</span><span class="o">=</span>/usr/local/aws
-</pre></div>
-</div>
-<p>These environment variables <em>must</em> be set during the build. We strongly
-recommend that their value is set to a path that does <em>not</em> already exist;
-this will ensure that these components are built/installed as needed when
-building CUDA-Q.
-The configured paths can be chosen freely, but the paths specified during the
-build are also where the corresponding libraries will be installed on the
-host system. We are working on making this more flexible in the future.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Please do <strong>not</strong> set <code class="code docutils literal notranslate"><span class="pre">LLVM_INSTALL_PREFIX</span></code> to an existing directory;
-To avoid compatibility issues, it is important to use the same compiler
-to build the LLVM/MLIR dependencies from source as is later used to
-build CUDA-Q itself.</p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you are setting the <code class="code docutils literal notranslate"><span class="pre">CURL_INSTALL_PREFIX</span></code> variable to an existing
-CURL installation (not recommended), please make sure the command
-<code class="code docutils literal notranslate"><span class="pre">curl</span> <span class="pre">--version</span></code> lists HTTP and HTTPS as supported protocols. If these
-protocols are not listed, please instead set the <code class="code docutils literal notranslate"><span class="pre">CURL_INSTALL_PREFIX</span></code>
-variable to a path that does <em>not</em> exist. In that case, a suitable
-library will be automatically built from source as part of
-building CUDA-Q.</p>
-</div>
-<p>If you deviate from the instructions below for installing one of the
-dependencies and instead install it, for example, via package manager, you will
-need to make sure that the installation path matches the path you set for the
-corresponding environment variable(s).</p>
-<section id="cuda">
-<h3>CUDA<a class="headerlink" href="#cuda" title="Permalink to this heading">¶</a></h3>
-<p>Building CUDA-Q requires a full installation of the CUDA toolkit.
-<strong>You can install the CUDA toolkit and use the CUDA compiler without having a GPU.</strong>
-The instructions are tested using version 11.8 and 12.0, but other CUDA 11 or 12 versions
-should work, as long as the CUDA runtime version on the host system matches the
-CUDA version used for the build, and the installed driver on the host
-system supports that CUDA version. We recommend using the latest CUDA version
-that is supported by the driver on the host system.</p>
-<p>Download a suitable <a class="reference external" href="https://developer.nvidia.com/cuda-toolkit-archive">CUDA version</a>
-following the installation guide for your platform in the online documentation
-linked on that page.</p>
-<p>Within the tested AlmaLinux 8 environment, for example, the following commands
-install CUDA 12.0:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDA_VERSION</span><span class="o">=</span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="k">:-</span><span class="nv">12</span><span class="p">.0</span><span class="si">}</span>
-<span class="nv">CUDA_DOWNLOAD_URL</span><span class="o">=</span>https://developer.download.nvidia.com/compute/cuda/repos
-<span class="c1"># Go to the url above, set the variables below to a suitable distribution</span>
-<span class="c1"># and subfolder for your platform, and uncomment the line below.</span>
-<span class="c1"># DISTRIBUTION=rhel8 CUDA_ARCH_FOLDER=x86_64</span>
-
-dnf<span class="w"> </span>config-manager<span class="w"> </span>--add-repo<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDA_DOWNLOAD_URL</span><span class="si">}</span><span class="s2">/</span><span class="si">${</span><span class="nv">DISTRIBUTION</span><span class="si">}</span><span class="s2">/</span><span class="si">${</span><span class="nv">CUDA_ARCH_FOLDER</span><span class="si">}</span><span class="s2">/cuda-</span><span class="si">${</span><span class="nv">DISTRIBUTION</span><span class="si">}</span><span class="s2">.repo&quot;</span>
-dnf<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>--nobest<span class="w"> </span>--setopt<span class="o">=</span><span class="nv">install_weak_deps</span><span class="o">=</span>False<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>cuda-toolkit-<span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>tr<span class="w"> </span>.<span class="w"> </span>-<span class="k">)</span>
-</pre></div>
-</div>
-</section>
-<section id="toolchain">
-<h3>Toolchain<a class="headerlink" href="#toolchain" title="Permalink to this heading">¶</a></h3>
-<p>The compiler toolchain used for the build must be a supported
-<a class="reference external" href="https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#supported-host-compilers">CUDA host compiler</a>
-for the installed CUDA version.
-The following instructions have been tested with <a class="reference external" href="https://gcc.gnu.org/index.html">GCC-11</a>.
-Other toolchains may be supported but have not been tested.</p>
-<p>Within the tested AlmaLinux 8 environment, for example, the following commands
-install GCC 11:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">GCC_VERSION</span><span class="o">=</span><span class="si">${</span><span class="nv">GCC_VERSION</span><span class="k">:-</span><span class="nv">11</span><span class="si">}</span>
-dnf<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>--nobest<span class="w"> </span>--setopt<span class="o">=</span><span class="nv">install_weak_deps</span><span class="o">=</span>False<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>gcc-toolset-<span class="si">${</span><span class="nv">GCC_VERSION</span><span class="si">}</span>
-<span class="c1"># Enabling the toolchain globally is only needed for debug builds</span>
-<span class="c1"># to ensure that the correct assembler is picked to process debug symbols.</span>
-<span class="nv">enable_script</span><span class="o">=</span><span class="sb">`</span>find<span class="w"> </span>/<span class="w"> </span>-path<span class="w"> </span><span class="s1">&#39;*gcc*&#39;</span><span class="w"> </span>-path<span class="w"> </span><span class="s1">&#39;*&#39;</span><span class="nv">$GCC_VERSIONS</span><span class="s1">&#39;*&#39;</span><span class="w"> </span>-name<span class="w"> </span><span class="nb">enable</span><span class="sb">`</span>
-<span class="k">if</span><span class="w"> </span><span class="o">[</span><span class="w"> </span>-n<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$enable_script</span><span class="s2">&quot;</span><span class="w"> </span><span class="o">]</span><span class="p">;</span><span class="w"> </span><span class="k">then</span>
-<span class="w">    </span>.<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$enable_script</span><span class="s2">&quot;</span>
-<span class="k">fi</span>
-</pre></div>
-</div>
-<p>Independent on which compiler toolchain you installed, set the following
-environment variables to point to the respective compilers on your build system:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">GCC_TOOLCHAIN</span><span class="o">=</span>/opt/rh/gcc-toolset-11/root/usr/
-<span class="nb">export</span><span class="w"> </span><span class="nv">CXX</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">GCC_TOOLCHAIN</span><span class="si">}</span><span class="s2">/bin/g++&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">CC</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">GCC_TOOLCHAIN</span><span class="si">}</span><span class="s2">/bin/gcc&quot;</span>
-<span class="nb">export</span><span class="w"> </span><span class="nv">CUDACXX</span><span class="o">=</span>/usr/local/cuda/bin/nvcc
-<span class="nb">export</span><span class="w"> </span><span class="nv">CUDAHOSTCXX</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">GCC_TOOLCHAIN</span><span class="si">}</span><span class="s2">/bin/g++&quot;</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li><p>The variables <code class="code docutils literal notranslate"><span class="pre">CC</span></code> and <code class="code docutils literal notranslate"><span class="pre">CXX</span></code> <em>must</em> be set for the CUDA-Q build.</p></li>
-<li><p>To use GPU-acceleration in CUDA-Q, make sure to set <code class="code docutils literal notranslate"><span class="pre">CUDACXX</span></code> to
-your CUDA compiler, and <code class="code docutils literal notranslate"><span class="pre">CUDAHOSTCXX</span></code> to the CUDA compatible host
-compiler you are using. If the CUDA compiler is not found when building
-CUDA-Q, some components and backends will be omitted automatically
-during the build.</p></li>
-</ul>
-</section>
-</section>
-<section id="building-cuda-q">
-<h2>Building CUDA-Q<a class="headerlink" href="#building-cuda-q" title="Permalink to this heading">¶</a></h2>
-<p>This installation guide has been written for a specific version/commit of CUDA-Q.
-Make sure to obtain the source code for that version.
-Clone the CUDA-Q <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a> and
-checkout the appropriate branch, tag, or commit.
-Note that the build scripts assume that they are run from within a git repository,
-and merely downloading the source code as ZIP archive hence will not work.</p>
-<p>Please follow the instructions in the respective subsection(s) to build the necessary
-components for using CUDA-Q from C++ and/or Python.
-After the build, check that the GPU-accelerated components have been built by confirming
-that the file <code class="code docutils literal notranslate"><span class="pre">nvidia.config</span></code> exists in the <code class="code docutils literal notranslate"><span class="pre">$CUDAQ_INSTALL_PREFIX/targets</span></code> folder.
-We also recommend checking the build log printed to the console to confirm that all desired
-components have been built.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The CUDA-Q build will compile or omit optional components automatically depending
-on whether the necessary pre-requisites are found in the build environment.
-If you see a message that a component has been skipped, and/or the CUDA compiler
-is not properly detected, make sure you followed the
-instructions for installing the necessary prerequisites and build dependencies,
-and have set the necessary environment variables as described in this document.</p>
-</div>
-</section>
-<section id="python-support">
-<span id="cudaq-python-from-source"></span><h2>Python Support<a class="headerlink" href="#python-support" title="Permalink to this heading">¶</a></h2>
-<p>The most convenient way to enable Python support within CUDA-Q is to build
-a <a class="reference external" href="https://pythonwheels.com/">wheel</a> that can then easily be installed
-using <code class="code docutils literal notranslate"><span class="pre">pip</span></code>. To ensure the wheel can be installed on the host system, make sure to
-use the same Python version for the build as the one that is installed on the host system.
-To build a CUDA-Q Python wheel, you will need to install the following additional
-Python-specific tools:</p>
-<ul class="simple">
-<li><p>Python development headers: The development headers for your Python version are installed
-in the way as you installed Python itself. If you installed Python via the package manager
-for your system, you may need to install an additional package to get the development headers.
-The package name is usually your python version followed by either a <code class="code docutils literal notranslate"><span class="pre">-dev</span></code> or <code class="code docutils literal notranslate"><span class="pre">-devel</span></code> suffix.
-If you are using a <a class="reference external" href="https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-python.html">Conda environment</a>,
-the necessary headers should already be installed.</p></li>
-<li><p>Pip package manager: Make sure the <code class="code docutils literal notranslate"><span class="pre">pip</span></code> module is enable for your Python version, and that your <code class="code docutils literal notranslate"><span class="pre">pip</span></code> version is 24 or newer.
-We refer to the Python <a class="reference external" href="https://pip.pypa.io/en/stable/installation/">documentation</a> for
-more information about installing/enabling <code class="code docutils literal notranslate"><span class="pre">pip</span></code>.</p></li>
-<li><p>Python modules: Install the additional modules <code class="code docutils literal notranslate"><span class="pre">numpy</span></code>, <code class="code docutils literal notranslate"><span class="pre">build</span></code>, <code class="code docutils literal notranslate"><span class="pre">auditwheel</span></code>, and <code class="code docutils literal notranslate"><span class="pre">patchelf</span></code> for your
-Python version, e.g. <code class="code docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">numpy</span> <span class="pre">build</span> <span class="pre">auditwheel</span> <span class="pre">patchelf</span></code>.</p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The wheel build by default is configured to depend on CUDA 12. To build a wheel for CUDA 11,
-you need to adjust the dependencies and project name in the <code class="code docutils literal notranslate"><span class="pre">pyproject.toml</span></code> file.</p>
-</div>
-<p>From within the folder where you cloned the CUDA-Q repository, run the following
-command to build the CUDA-Q Python wheel:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">LLVM_PROJECTS</span><span class="o">=</span><span class="s1">&#39;clang;flang;lld;mlir;python-bindings;openmp;runtimes&#39;</span><span class="w"> </span><span class="se">\</span>
-bash<span class="w"> </span>scripts/install_prerequisites.sh<span class="w"> </span>-t<span class="w"> </span>llvm<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-<span class="nv">CC</span><span class="o">=</span><span class="s2">&quot;</span><span class="nv">$LLVM_INSTALL_PREFIX</span><span class="s2">/bin/clang&quot;</span><span class="w"> </span><span class="se">\</span>
-<span class="nv">CXX</span><span class="o">=</span><span class="s2">&quot;</span><span class="nv">$LLVM_INSTALL_PREFIX</span><span class="s2">/bin/clang++&quot;</span><span class="w"> </span><span class="se">\</span>
-<span class="nv">FC</span><span class="o">=</span><span class="s2">&quot;</span><span class="nv">$LLVM_INSTALL_PREFIX</span><span class="s2">/bin/flang-new&quot;</span><span class="w"> </span><span class="se">\</span>
-python3<span class="w"> </span>-m<span class="w"> </span>build<span class="w"> </span>--wheel
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>A version identifier will be automatically assigned to the wheel based on the commit
-history. You can manually override this detection to give a more descriptive identifier
-by setting the environment variable <code class="code docutils literal notranslate"><span class="pre">SETUPTOOLS_SCM_PRETEND_VERSION</span></code> to the desired
-value before building the wheel.</p>
-</div>
-<p>After the initial build, <a class="reference external" href="https://github.com/pypa/auditwheel">auditwheel</a> is used to
-include dependencies in the wheel, if necessary, and correctly label the wheel.
-We recommend not including the CUDA runtime libraries and instead install them separately
-on the host system following the instructions in the next section. The following
-command builds the final wheel, not including CUDA dependencies:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDAQ_WHEEL</span><span class="o">=</span><span class="s2">&quot;</span><span class="k">$(</span>find<span class="w"> </span>.<span class="w"> </span>-name<span class="w"> </span><span class="s1">&#39;cuda_quantum*.whl&#39;</span><span class="k">)</span><span class="s2">&quot;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-<span class="nv">MANYLINUX_PLATFORM</span><span class="o">=</span><span class="s2">&quot;</span><span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDAQ_WHEEL</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>grep<span class="w"> </span>-o<span class="w"> </span><span class="s1">&#39;[a-z]*linux_[^\.]*&#39;</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>sed<span class="w"> </span>-re<span class="w"> </span><span class="s1">&#39;s/^linux_/manylinux_2_28_/&#39;</span><span class="k">)</span><span class="s2">&quot;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-<span class="nv">LD_LIBRARY_PATH</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LD_LIBRARY_PATH</span><span class="si">}</span><span class="s2">:</span><span class="k">$(</span><span class="nb">pwd</span><span class="k">)</span><span class="s2">/_skbuild/lib&quot;</span><span class="w"> </span><span class="se">\ </span>
-python3<span class="w"> </span>-m<span class="w"> </span>auditwheel<span class="w"> </span>-v<span class="w"> </span>repair<span class="w"> </span><span class="si">${</span><span class="nv">CUDAQ_WHEEL</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--plat<span class="w"> </span><span class="si">${</span><span class="nv">MANYLINUX_PLATFORM</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcublas.so.11<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcublasLt.so.11<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcusolver.so.11<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcutensor.so.2<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcutensornet.so.2<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcustatevec.so.1<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcudart.so.11.0<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libnvToolsExt.so.1<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libnvidia-ml.so.1<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--exclude<span class="w"> </span>libcuda.so.1
-</pre></div>
-</div>
-<p>The command above will create a new wheel in the <code class="code docutils literal notranslate"><span class="pre">wheelhouse</span></code> folder. This wheel can be
-installed on any <a class="reference external" href="https://packaging.python.org/en/latest/specifications/platform-compatibility-tags/">compatible platform</a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>You can confirm that the wheel is indeed compatible with your host platform by
-checking that the wheel tag (i.e. the file name ending of the <code class="code docutils literal notranslate"><span class="pre">.whl</span></code> file) is listed under
-“Compatible Tags” when running the command <code class="code docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">debug</span> <span class="pre">--verbose</span></code> on the host.</p>
-</div>
-</section>
-<section id="c-support">
-<span id="cudaq-cpp-from-source"></span><h2>C++ Support<a class="headerlink" href="#c-support" title="Permalink to this heading">¶</a></h2>
-<p>From within the folder where you cloned the CUDA-Q repository, run the following
-command to build CUDA-Q:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDAQ_ENABLE_STATIC_LINKING</span><span class="o">=</span>TRUE<span class="w"> </span><span class="se">\</span>
-<span class="nv">CUDAQ_REQUIRE_OPENMP</span><span class="o">=</span>TRUE<span class="w"> </span><span class="se">\</span>
-<span class="nv">CUDAQ_WERROR</span><span class="o">=</span>TRUE<span class="w"> </span><span class="se">\</span>
-<span class="nv">CUDAQ_PYTHON_SUPPORT</span><span class="o">=</span>OFF<span class="w"> </span><span class="se">\</span>
-<span class="nv">LLVM_PROJECTS</span><span class="o">=</span><span class="s1">&#39;clang;flang;lld;mlir;openmp;runtimes&#39;</span><span class="w"> </span><span class="se">\</span>
-bash<span class="w"> </span>scripts/build_cudaq.sh<span class="w"> </span>-t<span class="w"> </span>llvm<span class="w"> </span>-v
-</pre></div>
-</div>
-<p>Note that <code class="code docutils literal notranslate"><span class="pre">lld</span></code> is primarily needed when the build or host system does not already
-have an existing default linker on its path; CUDA-Q supports the same linkers as
-<code class="code docutils literal notranslate"><span class="pre">clang</span></code> does.</p>
-<p>To easily migrate the built binaries to the host system, we recommend creating a
-<a class="reference external" href="https://makeself.io/">self-extracting archive</a>. To do so, download the
-<a class="reference external" href="https://github.com/megastep/makeself">makeself script(s)</a> and move the necessary
-files to install into a separate folder using the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mkdir<span class="w"> </span>-p<span class="w"> </span>cuda_quantum_assets/llvm/bin<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mkdir<span class="w"> </span>-p<span class="w"> </span>cuda_quantum_assets/llvm/lib<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mkdir<span class="w"> </span>-p<span class="w"> </span>cuda_quantum_assets/llvm/include<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/bin/&quot;</span>clang*<span class="w"> </span>cuda_quantum_assets/llvm/bin/<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span>cuda_quantum_assets/llvm/bin/clang-format*<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/bin/&quot;</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/bin/llc&quot;</span><span class="w"> </span>cuda_quantum_assets/llvm/bin/llc<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/bin/lld&quot;</span><span class="w"> </span>cuda_quantum_assets/llvm/bin/lld<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/bin/ld.lld&quot;</span><span class="w"> </span>cuda_quantum_assets/llvm/bin/ld.lld<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/lib/&quot;</span>*<span class="w"> </span>cuda_quantum_assets/llvm/lib/<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">LLVM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/include/&quot;</span>*<span class="w"> </span>cuda_quantum_assets/llvm/include/<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUTENSOR_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">&quot;</span><span class="w"> </span>cuda_quantum_assets<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUQUANTUM_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">&quot;</span><span class="w"> </span>cuda_quantum_assets<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDAQ_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">/build_config.xml&quot;</span><span class="w"> </span>cuda_quantum_assets/build_config.xml<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="se">\</span>
-mv<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDAQ_INSTALL_PREFIX</span><span class="si">}</span><span class="s2">&quot;</span><span class="w"> </span>cuda_quantum_assets
-</pre></div>
-</div>
-<p>You can then create a self-extracting archive with the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./makeself.sh<span class="w"> </span>--gzip<span class="w"> </span>--sha256<span class="w"> </span>--license<span class="w"> </span>cuda_quantum_assets/cudaq/LICENSE<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>cuda_quantum_assets<span class="w"> </span>install_cuda_quantum.<span class="k">$(</span>uname<span class="w"> </span>-m<span class="k">)</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span><span class="s2">&quot;CUDA-Q toolkit for heterogeneous quantum-classical workflows&quot;</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>bash<span class="w"> </span>cudaq/migrate_assets.sh<span class="w"> </span>-t<span class="w"> </span>/opt/nvidia/cudaq
-</pre></div>
-</div>
-</section>
-<section id="installation-on-the-host">
-<h2>Installation on the Host<a class="headerlink" href="#installation-on-the-host" title="Permalink to this heading">¶</a></h2>
-<p>Make sure your host system satisfies the <a class="reference internal" href="#prerequisites">Prerequisites</a> listed above.</p>
-<ul class="simple">
-<li><p>To use CUDA-Q with Python, you should have a working
-Python installation on the host system, including the <code class="code docutils literal notranslate"><span class="pre">pip</span></code> package manager.</p></li>
-<li><p>To use CUDA-Q with C++, you should make sure that you have the necessary development
-headers of the C standard library installed. You can check this by searching for
-<code class="code docutils literal notranslate"><span class="pre">features.h</span></code>, commonly found in <code class="code docutils literal notranslate"><span class="pre">/usr/include/</span></code>. You can install the necessary headers
-via package manager (usually the package name is called something like <code class="code docutils literal notranslate"><span class="pre">glibc-devel</span></code>
-or <code class="code docutils literal notranslate"><span class="pre">libc6-devel</span></code>). These headers are also included with any installation of GCC.</p></li>
-</ul>
-<p>If you followed the instructions for building the
-<a class="reference internal" href="#cudaq-python-from-source"><span class="std std-ref">CUDA-Q Python wheel</span></a>,
-copy the built <code class="code docutils literal notranslate"><span class="pre">.whl</span></code> file to the host system, and install it using <code class="code docutils literal notranslate"><span class="pre">pip</span></code>; e.g.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>cuda_quantum*.whl
-</pre></div>
-</div>
-<p>To install the necessary CUDA and MPI dependencies for some of the components,
-you can either follow the instructions on <a class="reference external" href="https://pypi.org/project/cuda-quantum/">PyPI.org</a>,
-replacing <code class="code docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">cudaq</span></code> with the command above, or you can follow the
-instructions in the remaining sections of this document to customize and better
-optimize them for your host system.</p>
-<p>If you followed the instructions for building the
-<a class="reference internal" href="#cudaq-cpp-from-source"><span class="std std-ref">CUDA-Q C++ tools</span></a>,
-copy the <code class="code docutils literal notranslate"><span class="pre">install_cuda_quantum</span></code> file that you created to the host system,
-and install it by running the commands</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>bash<span class="w"> </span>install_cuda_quantum.<span class="k">$(</span>uname<span class="w"> </span>-m<span class="k">)</span><span class="w"> </span>--accept
-.<span class="w"> </span>/opt/nvidia/cudaq/set_env.sh
-</pre></div>
-</div>
-<p>This will extract the built assets and move them to the correct locations.
-The <code class="code docutils literal notranslate"><span class="pre">set_env.sh</span></code> script in <code class="code docutils literal notranslate"><span class="pre">/opt/nvidia/cudaq</span></code> defines the necessary environment
-variables to use CUDA-Q. To avoid having to set them manually every time a
-new shell is opened, we highly recommend adding the following lines to
-the <code class="code docutils literal notranslate"><span class="pre">/etc/profile</span></code> file:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">if</span><span class="w"> </span><span class="o">[</span><span class="w"> </span>-f<span class="w"> </span>/opt/nvidia/cudaq/set_env.sh<span class="w"> </span><span class="o">]</span><span class="p">;</span>
-<span class="w">  </span>.<span class="w"> </span>/opt/nvidia/cudaq/set_env.sh
-<span class="k">fi</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>CUDA-Q as built following the instructions above includes and uses the LLVM
-C++ standard library. This will not interfere with any other C++ standard library
-you may have on your system. Pre-built external libraries, you may want to use with
-CUDA-Q, such as specific optimizers for example, have a C API to ensure compatibility
-across different versions of the C++ standard library and will work with CUDA-Q without
-issues. The same is true for all distributed CUDA libraries. To build you own CUDA
-libraries that can be used with CUDA-Q, please take a look at <a class="reference internal" href="../integration/cuda_gpu.html"><span class="doc">Using CUDA and CUDA-Q in a Project</span></a>.</p>
-</div>
-<p>The remaining sections in this document list additional runtime dependencies
-that are not included in the migrated assets and are needed to use some of the
-CUDA-Q features and components.</p>
-<section id="cuda-runtime-libraries">
-<h3>CUDA Runtime Libraries<a class="headerlink" href="#cuda-runtime-libraries" title="Permalink to this heading">¶</a></h3>
-<p>To use GPU-acceleration in CUDA-Q you will need to install the necessary
-CUDA runtime libraries. Their version (at least the version major) needs to match the version
-used for the build. While not necessary, we recommend installing
-the complete CUDA toolkit like you did for the CUDA-Q build.
-If you prefer to only install the minimal set of runtime libraries, the following
-commands, for example, install the necessary packages for the AlmaLinux 8 environment:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDA_VERSION</span><span class="o">=</span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="k">:-</span><span class="nv">12</span><span class="p">.0</span><span class="si">}</span>
-<span class="nv">CUDA_DOWNLOAD_URL</span><span class="o">=</span>https://developer.download.nvidia.com/compute/cuda/repos
-<span class="c1"># Go to the url above, set the variables below to a suitable distribution</span>
-<span class="c1"># and subfolder for your platform, and uncomment the line below.</span>
-<span class="c1"># DISTRIBUTION=rhel8 CUDA_ARCH_FOLDER=x86_64</span>
-
-<span class="nv">version_suffix</span><span class="o">=</span><span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>tr<span class="w"> </span>.<span class="w"> </span>-<span class="k">)</span>
-dnf<span class="w"> </span>config-manager<span class="w"> </span>--add-repo<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDA_DOWNLOAD_URL</span><span class="si">}</span><span class="s2">/</span><span class="si">${</span><span class="nv">DISTRIBUTION</span><span class="si">}</span><span class="s2">/</span><span class="si">${</span><span class="nv">CUDA_ARCH_FOLDER</span><span class="si">}</span><span class="s2">/cuda-</span><span class="si">${</span><span class="nv">DISTRIBUTION</span><span class="si">}</span><span class="s2">.repo&quot;</span>
-dnf<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>--nobest<span class="w"> </span>--setopt<span class="o">=</span><span class="nv">install_weak_deps</span><span class="o">=</span>False<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>cuda-cudart-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>cuda-nvrtc-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>libcusolver-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>libcublas-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span>
-<span class="k">if</span><span class="w"> </span><span class="o">[</span><span class="w"> </span><span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>cut<span class="w"> </span>-d<span class="w"> </span>.<span class="w"> </span>-f1<span class="k">)</span><span class="w"> </span>-gt<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="o">]</span><span class="p">;</span><span class="w"> </span><span class="k">then</span><span class="w"> </span>
-<span class="w">    </span>dnf<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>--nobest<span class="w"> </span>--setopt<span class="o">=</span><span class="nv">install_weak_deps</span><span class="o">=</span>False<span class="w"> </span><span class="se">\</span>
-<span class="w">        </span>libnvjitlink-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span>
-<span class="k">fi</span>
-</pre></div>
-</div>
-</section>
-<section id="mpi">
-<h3>MPI<a class="headerlink" href="#mpi" title="Permalink to this heading">¶</a></h3>
-<p>To work with all CUDA-Q backends, a CUDA-aware MPI installation is required.
-If you do not have an existing CUDA-aware MPI installation, you can build one from
-source. To do so, in addition to the CUDA runtime libraries listed above
-you will need to install the CUDA runtime development package
-(<code class="code docutils literal notranslate"><span class="pre">cuda-cudart-devel-${version_suffix}</span></code> or <code class="code docutils literal notranslate"><span class="pre">cuda-cudart-dev-${version_suffix}</span></code>,
-depending on your distribution).</p>
-<p>The following commands build a sufficient CUDA-aware OpenMPI installation.
-To make best use of MPI, we recommend a more fully featured installation including
-additional configurations that fit your host system.
-The commands below assume you have the necessary prerequisites for the OpenMPI build
-installed on the build system. Within the tested AlmaLinux 8 environment, for example,
-the packages <code class="code docutils literal notranslate"><span class="pre">autoconf</span></code>, <code class="code docutils literal notranslate"><span class="pre">libtool</span></code>, <code class="code docutils literal notranslate"><span class="pre">flex</span></code>, and <code class="code docutils literal notranslate"><span class="pre">make</span></code> need to be installed.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">OPENMPI_VERSION</span><span class="o">=</span><span class="m">4</span>.1.4
-<span class="nv">OPENMPI_DOWNLOAD_URL</span><span class="o">=</span>https://github.com/open-mpi/ompi
-
-wget<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">OPENMPI_DOWNLOAD_URL</span><span class="si">}</span><span class="s2">/archive/v</span><span class="si">${</span><span class="nv">OPENMPI_VERSION</span><span class="si">}</span><span class="s2">.tar.gz&quot;</span><span class="w"> </span>-O<span class="w"> </span>/tmp/openmpi.tar.gz
-mkdir<span class="w"> </span>-p<span class="w"> </span>~/.openmpi-src<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>tar<span class="w"> </span>xf<span class="w"> </span>/tmp/openmpi.tar.gz<span class="w"> </span>--strip-components<span class="w"> </span><span class="m">1</span><span class="w"> </span>-C<span class="w"> </span>~/.openmpi-src
-rm<span class="w"> </span>-rf<span class="w"> </span>/tmp/openmpi.tar.gz<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>~/.openmpi-src
-./autogen.pl<span class="w"> </span>
-<span class="nv">LDFLAGS</span><span class="o">=</span>-Wl,--as-needed<span class="w"> </span>./configure<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--prefix<span class="o">=</span>/usr/local/openmpi<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--disable-getpwuid<span class="w"> </span>--disable-static<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--disable-debug<span class="w"> </span>--disable-mem-debug<span class="w"> </span>--disable-event-debug<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--disable-mem-profile<span class="w"> </span>--disable-memchecker<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--without-verbs<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--with-cuda<span class="o">=</span>/usr/local/cuda
-make<span class="w"> </span>-j<span class="k">$(</span>nproc<span class="k">)</span><span class="w"> </span>
-make<span class="w"> </span>-j<span class="k">$(</span>nproc<span class="k">)</span><span class="w"> </span>install
-<span class="nb">cd</span><span class="w"> </span>-<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>rm<span class="w"> </span>-rf<span class="w"> </span>~/.openmpi-src
-</pre></div>
-</div>
-<p>Confirm that you have a suitable MPI implementation installed. For OpenMPI and MPICH,
-for example, this can be done by compiling and running the following program:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Compile and run with:</span>
-<span class="c1">// ```</span>
-<span class="c1">// mpic++ mpi_cuda_check.cpp -o check.x &amp;&amp; mpiexec -np 1 ./check.x</span>
-<span class="c1">// ```</span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;mpi.h&quot;</span>
-<span class="cp">#if __has_include(&quot;mpi-ext.h&quot;)</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;mpi-ext.h&quot;</span>
-<span class="cp">#endif</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;stdio.h&gt;</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="w"> </span><span class="o">*</span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">MPI_Init</span><span class="p">(</span><span class="o">&amp;</span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="o">&amp;</span><span class="n">argv</span><span class="p">);</span>
-<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">exit_code</span><span class="p">;</span>
-<span class="w">  </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">MPIX_Query_cuda_support</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;CUDA-aware MPI installation.</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">exit_code</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;Missing CUDA support.</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">);</span>
-<span class="w">    </span><span class="n">exit_code</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="w">  </span><span class="n">MPI_Finalize</span><span class="p">();</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">exit_code</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you are encountering an error similar to “The value of the MCA parameter <code class="code docutils literal notranslate"><span class="pre">plm_rsh_agent</span></code>
-was set to a path that could not be found”, please make sure you have an SSH Client installed
-or update the MCA parameter to another suitable agent.
-MPI uses <a class="reference external" href="https://en.wikipedia.org/wiki/Secure_Shell">SSH</a> or
-<a class="reference external" href="https://en.wikipedia.org/wiki/Remote_Shell">RSH</a> to communicate with each node
-unless another resource manager, such as
-<a class="reference external" href="https://slurm.schedmd.com/overview.html">SLURM</a>, is used.</p>
-</div>
-<p>Different MPI implementations are supported via a plugin infrastructure in CUDA-Q.
-Once you have a CUDA-aware MPI installation on your host system, you can
-configure CUDA-Q to use it by activating the necessary plugin.
-Plugins for OpenMPI and MPICH are included in CUDA-Q and can be activated by
-setting the environment variable <code class="code docutils literal notranslate"><span class="pre">MPI_PATH</span></code> to the MPI installation folder
-and then running the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>bash<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDA_QUANTUM_PATH</span><span class="si">}</span><span class="s2">/distributed_interfaces/activate_custom_mpi.sh&quot;</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To activate the MPI plugin for the Python support, replace <code class="code docutils literal notranslate"><span class="pre">${CUDA_QUANTUM_PATH}</span></code>
-with the path that is listed under “Location” when you run the command
-<code class="code docutils literal notranslate"><span class="pre">pip</span> <span class="pre">show</span> <span class="pre">cuda-quantum</span></code>.</p>
-</div>
-<p>If you use a different MPI implementation than OpenMPI or MPICH, you will need to
-implement the necessary plugin interface yourself prior to activating the plugin
-with the command above.</p>
-</section>
-</section>
-</section>
-
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#matrix-product-state">Matrix product state</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id9">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id12">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id15">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id18">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id21">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#faq">FAQ</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#default-simulator">Default Simulator</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/hardware.html">   Quantum Hardware</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#amazon-braket">Amazon Braket</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#submission-from-python">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id6">Submission from Python</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#anyon-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="#get-started">Get Started</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#installation-via-pypi">Installation via PyPI</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="#updating-cuda-q">Updating CUDA-Q</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="#next-steps">Next Steps</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="data_center_install.html#c-support">C++ Support</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/default_ops.html#unitary-operations-on-qubits">Unitary Operations on Qubits</a><ul>
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-  <section id="local-installation">
-<h1>Local Installation<a class="headerlink" href="#local-installation" title="Permalink to this heading">¶</a></h1>
-<section id="introduction">
-<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h2>
-<p>This guide walks through how to <a class="reference internal" href="install.html#install-cuda-quantum"><span class="std std-ref">install CUDA-Q</span></a> on your system, and how to set up <a class="reference internal" href="#local-development-with-vscode"><span class="std std-ref">VS Code for local development</span></a>.
-The section on <a class="reference internal" href="#connect-to-remote"><span class="std std-ref">connecting to a remote host</span></a> contains some
-guidance for application development on a remote host where CUDA-Q is installed.</p>
-<p>The following sections contain instructions for how to install CUDA-Q on your machine using</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="#install-docker-image"><span class="std std-ref">Docker</span></a>: A fully featured CUDA-Q installation including all C++ and Python tools is available as a <a class="reference external" href="https://docs.docker.com/get-started/overview/">Docker</a> image.</p></li>
-<li><p><a class="reference internal" href="#install-singularity-image"><span class="std std-ref">Singularity</span></a>: A <a class="reference external" href="https://docs.sylabs.io/guides/latest/user-guide/introduction.html">Singularity</a> container can easily be created based on our Docker images.</p></li>
-<li><p><a class="reference internal" href="#install-python-wheels"><span class="std std-ref">PyPI</span></a>: Additionally, we distribute pre-built Python wheels via <a class="reference external" href="https://pypi.org">PyPI</a>.</p></li>
-<li><p><a class="reference internal" href="#install-prebuilt-binaries"><span class="std std-ref">Pre-built binaries</span></a>: We also provide pre-built C++ binaries, bundled as <a class="reference external" href="https://makeself.io/">self-extracting archive</a>, that work across a range of Linux operating systems.</p></li>
-</ul>
-<p>If you would like to build CUDA-Q from source to deploy on an HPC system without relying on a container runtime, please follow the instructions for <a class="reference internal" href="data_center_install.html"><span class="doc">Installation from Source</span></a>.
-If, on the other hand, you want to contribute to the development of CUDA-Q itself and hence want to
-build a custom version of CUDA-Q from source, follow the instructions on the
-<a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/blob/main/Building.md">CUDA-Q GitHub repository</a> instead.</p>
-<p>If you are unsure which option suits you best, we recommend using our <a class="reference internal" href="#install-docker-image"><span class="std std-ref">Docker image</span></a> to develop your applications in a controlled environment that does not depend on, or interfere with, other software
-that is installed on your system.</p>
-<section id="docker">
-<span id="install-docker-image"></span><h3>Docker<a class="headerlink" href="#docker" title="Permalink to this heading">¶</a></h3>
-<p>To download and use our Docker images, you will need to install and launch the Docker engine.
-If you do not already have Docker installed on your system, you can get it by downloading and installing <a class="reference external" href="https://docs.docker.com/get-docker/">Docker Desktop</a>.
-If you do not have the necessary administrator permissions to install software on your machine,
-take a look at the section below on how to use <a class="reference internal" href="#singularity">Singularity</a> instead.</p>
-<p>Docker images for all CUDA-Q releases are available on the <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum">NGC Container Registry</a>.
-In addition to publishing <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">stable releases</a>,
-we also publish Docker images whenever we update certain branches on our <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a>.
-These images are published in our <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum/tags">nightly channel on NGC</a>.
-To download the latest version on the main branch of our GitHub repository, built to work with CUDA 12, for example, use the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">docker pull nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest</span>
-</pre></div>
-</div>
-<p>Early prototypes for features we are considering can be tried out by using the image tags starting
-with <code class="code docutils literal notranslate"><span class="pre">experimental</span></code>. The <code class="code docutils literal notranslate"><span class="pre">README</span></code> in the <code class="code docutils literal notranslate"><span class="pre">/home/cudaq</span></code> folder in the container gives more details
-about the feature. We welcome and appreciate your feedback about these early prototypes;
-how popular they are will help inform whether we should include them in future releases.</p>
-<p>Once you have downloaded an image, the container can be run using the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">docker run -it --name cuda-quantum nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest</span>
-</pre></div>
-</div>
-<p>Replace the image name and/or tag in the command above, if necessary, with the one you want to use.
-This will give you terminal access to the created container. To enable support
-for GPU-accelerated backends, you will need to pass the <code class="code docutils literal notranslate"><span class="pre">--gpus</span></code> flag when launching
-the container, for example:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">docker run -it --gpus all --name cuda-quantum nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This command will fail if you do not have a suitable NVIDIA GPU available, or if your driver
-version is insufficient. To improve compatibility with older drivers, you may need to install the
-<a class="reference external" href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">NVIDIA container toolkit</a>.</p>
-</div>
-<p>You can stop and exit the container by typing the command <code class="code docutils literal notranslate"><span class="pre">exit</span></code>. If you did not specify
-<code class="code docutils literal notranslate"><span class="pre">--rm</span></code> flag when launching the container, the container still exists after exiting, as well as any
-changes you made in it. You can get back to it using
-the command <code class="code docutils literal notranslate"><span class="pre">docker</span> <span class="pre">start</span> <span class="pre">-i</span> <span class="pre">cuda-quantum</span></code>.
-You can delete an existing container and any changes you made using <code class="code docutils literal notranslate"><span class="pre">docker</span> <span class="pre">rm</span> <span class="pre">-v</span> <span class="pre">cuda-quantum</span></code>.</p>
-<p>When working with Docker images, the files inside the container are not visible outside the container environment.
-To facilitate application development with, for example, debugging, code completion, hover information, and so on,
-please take a look at the section on <a class="reference internal" href="#docker-in-vscode"><span class="std std-ref">Development with VS Code</span></a>.</p>
-<p>Alternatively, it is possible, but not recommended, to launch an SSH server inside the container environment and connect an IDE using SSH. To do so, make sure you have generated a suitable RSA key pair; if your <code class="code docutils literal notranslate"><span class="pre">~/.ssh/</span></code> folder does not already contain the files <code class="code docutils literal notranslate"><span class="pre">id_rsa.pub</span></code> and <code class="code docutils literal notranslate"><span class="pre">id.rsa</span></code>,
-follow the instructions for generating a new SSH key on <a class="reference external" href="https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent">this page</a>.
-You can then launch the container and connect to it via SSH by executing the following commands:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">docker run -itd --gpus all --name cuda-quantum -p 2222:22 nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest</span>
-<span class="go">docker exec cuda-quantum bash -c &quot;sudo apt-get install -y --no-install-recommends openssh-server&quot;</span>
-<span class="go">docker exec cuda-quantum bash -c &quot;sudo sed -i -E &quot;s/#?\s*UsePAM\s+.+/UsePAM yes/g&quot; /etc/ssh/sshd_config&quot;</span>
-<span class="go">docker cp ~/.ssh/id_rsa.pub cuda-quantum:/home/cudaq/.ssh/authorized_keys</span>
-<span class="go">docker exec -d cuda-quantum bash -c &quot;sudo -E /usr/sbin/sshd -D&quot;</span>
-<span class="go">ssh cudaq@localhost -p 2222 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o GlobalKnownHostsFile=/dev/null</span>
-</pre></div>
-</div>
-</section>
-<section id="singularity">
-<span id="install-singularity-image"></span><h3>Singularity<a class="headerlink" href="#singularity" title="Permalink to this heading">¶</a></h3>
-<p>You can use <a class="reference external" href="https://github.com/sylabs/singularity">Singularity</a> to run a CUDA-Q container in a folder without needing administrator permissions.
-If you do not already have Singularity installed, you can build a relocatable installation from source.
-To do so on Linux or WSL, make sure you have the <a class="reference external" href="https://docs.sylabs.io/guides/4.0/user-guide/quick_start.html#prerequisites">necessary prerequisites</a> installed, download a suitable version of the <a class="reference external" href="https://docs.sylabs.io/guides/4.0/user-guide/quick_start.html#install-go">go toolchain</a>, and make sure the <code class="code docutils literal notranslate"><span class="pre">go</span></code> binaries are on your <code class="code docutils literal notranslate"><span class="pre">PATH</span></code>. You can then build Singularity with the commands</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">wget https://github.com/sylabs/singularity/releases/download/v4.0.1/singularity-ce-4.0.1.tar.gz</span>
-<span class="go">tar -xzf singularity-ce-4.0.1.tar.gz singularity-ce-4.0.1/ &amp;&amp; rm singularity-ce-4.0.1.tar.gz &amp;&amp; cd singularity-ce-4.0.1/</span>
-<span class="go">./mconfig --without-suid --prefix=&quot;$HOME/.local/singularity&quot;</span>
-<span class="go">make -C ./builddir &amp;&amp; make -C ./builddir install &amp;&amp; cd .. &amp;&amp; rm -rf singularity-ce-4.0.1/</span>
-<span class="go">echo &#39;PATH=&quot;$PATH:$HOME/.local/singularity/bin/&quot;&#39; &gt;&gt; ~/.profile &amp;&amp; source ~/.profile</span>
-</pre></div>
-</div>
-<p>For more information about using Singularity on other systems, take a look at the <a class="reference external" href="https://docs.sylabs.io/guides/4.0/admin-guide/installation.html#installation-on-windows-or-mac">admin guide</a>.</p>
-<p>Once you have singularity installed, create a file <code class="code docutils literal notranslate"><span class="pre">cuda-quantum.def</span></code> with the following content:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">Bootstrap: docker</span>
-<span class="go">From: nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest</span>
-
-<span class="gp">%</span>runscript
-<span class="go">    mount devpts /dev/pts -t devpts</span>
-<span class="go">    cp -r /home/cudaq/* .</span>
-<span class="go">    bash</span>
-</pre></div>
-</div>
-<p>Replace the image name and/or tag in the <code class="code docutils literal notranslate"><span class="pre">From</span></code> line, if necessary, with the one you want to use;
-In addition to publishing <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum/tags">stable releases</a>,
-we also publish Docker images whenever we update certain branches on our <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a>.
-These images are published in our <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nightly/containers/cuda-quantum/tags">nightly channel on NGC</a>.
-Early prototypes for features we are considering can be tried out by using the image tags starting
-with <code class="code docutils literal notranslate"><span class="pre">experimental</span></code>. We welcome and appreciate your feedback about these early prototypes;
-how popular they are will help inform whether we should include them in future releases.</p>
-<p>You can then create a CUDA-Q container by running the following commands:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">singularity build --fakeroot cuda-quantum.sif cuda-quantum.def</span>
-<span class="go">singularity run --writable --fakeroot cuda-quantum.sif</span>
-</pre></div>
-</div>
-<p>In addition to the files in your current folder, you should now
-see a <code class="code docutils literal notranslate"><span class="pre">README</span></code> file, as well as examples and tutorials.
-To enable support for GPU-accelerated backends, you will need to pass the
-the <code class="code docutils literal notranslate"><span class="pre">--nv</span></code> flag when running the container:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">singularity run --writable --fakeroot --nv cuda-quantum.sif</span>
-<span class="go">nvidia-smi</span>
-</pre></div>
-</div>
-<p>The output of the command above lists the GPUs that are visible and accessible in the container environment.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you do not see any GPUs listed in the output of <code class="code docutils literal notranslate"><span class="pre">nvidia-smi</span></code>,
-it means the container environment is unable to access a suitable NVIDIA GPU.
-This can happen if your driver version is insufficient, or if you are
-working on WSL. To improve compatibility with older drivers, or to enable GPU support
-on WSL, please install the <a class="reference external" href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">NVIDIA container toolkit</a>, and update the singularity configuration
-to set <code class="code docutils literal notranslate"><span class="pre">use</span> <span class="pre">nvidia-container-cli</span></code> to <code class="code docutils literal notranslate"><span class="pre">yes</span></code> and configure the correct <code class="code docutils literal notranslate"><span class="pre">nvidia-container-cli</span> <span class="pre">path</span></code>.
-The two commands below use <code class="code docutils literal notranslate"><span class="pre">sed</span></code> to do that:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">sed -i &#39;s/use nvidia-container-cli = no/use nvidia-container-cli = yes/&#39; &quot;$HOME/.local/singularity/etc/singularity/singularity.conf&quot;</span>
-<span class="go">sed -i &#39;s/# nvidia-container-cli path =/nvidia-container-cli path = \/usr\/bin\/nvidia-container-cli/&#39; &quot;$HOME/.local/singularity/etc/singularity/singularity.conf&quot;</span>
-</pre></div>
-</div>
-</div>
-<p>You can exit the container environment by typing the command <code class="code docutils literal notranslate"><span class="pre">exit</span></code>.
-Any changes you made will still be visible after you exit the container, and you can re-enable the
-container environment at any time using the <code class="code docutils literal notranslate"><span class="pre">run</span></code> command above.</p>
-<p>To facilitate application development with, for example, debugging, code completion, hover information, and so on,
-please take a look at the section on <a class="reference internal" href="#singularity-in-vscode"><span class="std std-ref">Development with VS Code</span></a>.</p>
-</section>
-<section id="python-wheels">
-<span id="install-python-wheels"></span><h3>Python wheels<a class="headerlink" href="#python-wheels" title="Permalink to this heading">¶</a></h3>
-<p>CUDA-Q Python wheels are available on <a class="reference external" href="https://pypi.org/project/cuda-quantum">PyPI.org</a>.
-Installation instructions can be found in the <a class="reference external" href="https://pypi.org/project/cuda-quantum/#description">project description</a>.
-For more information about available versions and documentation,
-see <a class="reference internal" href="../../releases.html"><span class="doc">CUDA-Q Releases</span></a>.</p>
-<p>There are currently no source distributions available on PyPI, but you can download the
-source code for the latest version of the CUDA-Q Python wheels from our
-<a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a>.
-The source code for previous versions can be downloaded from the respective
-<a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases">GitHub Release</a>.</p>
-<p>At this time, wheels are distributed for Linux operating systems only.
-If your platform is not <a class="reference internal" href="#dependencies-and-compatibility"><span class="std std-ref">officially supported</span></a> and
-<code class="code docutils literal notranslate"><span class="pre">pip</span></code> does not find a compatible wheel to install, you can build your own
-wheel from source following the instructions here: <a class="reference internal" href="data_center_install.html"><span class="doc">Installation from Source</span></a>.</p>
-<p>To build the CUDA-Q Python API for the purpose of contributing to
-our <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a>,
-follow the instructions for
-<a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/blob/main/Dev_Setup.md">Setting up your Environment</a>,
-and then run the following commands in the repository root:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">bash scripts/install_prerequisites.sh</span>
-<span class="go">pip install . --user</span>
-</pre></div>
-</div>
-</section>
-<section id="pre-built-binaries">
-<span id="install-prebuilt-binaries"></span><h3>Pre-built binaries<a class="headerlink" href="#pre-built-binaries" title="Permalink to this heading">¶</a></h3>
-<p>Starting with the 0.6.0 release, we provide pre-built binaries for using
-CUDA-Q with C++. Support for using CUDA-Q with Python can be installed
-side-by-side with the pre-built binaries for C++ by following the instructions on
-<a class="reference external" href="https://pypi.org/project/cuda-quantum">PyPI.org</a>.
-The pre-built binaries work across a range of Linux operating systems listed
-under <a class="reference internal" href="#dependencies-and-compatibility"><span class="std std-ref">Dependencies and Compatibility</span></a>.</p>
-<p>Before installing our pre-built binaries, please make sure that your
-operating system is using the <a class="reference external" href="https://www.gnu.org/software/libc/">GNU C library</a>
-version 2.28 or newer. You can confirm this by checking the output of the command
-<code class="code docutils literal notranslate"><span class="pre">ldd</span> <span class="pre">--version</span></code>. If this command does not exist, or shows an older version than 2.28,
-please double check that your operating system is listed as
-<a class="reference internal" href="#dependencies-and-compatibility"><span class="std std-ref">supported</span></a>. If you use an operating system
-with an older GNU C library version, you will need to build the installer from
-source following the instructions in <a class="reference internal" href="data_center_install.html"><span class="doc">Installation from Source</span></a>.</p>
-<p>You can download the <code class="code docutils literal notranslate"><span class="pre">install_cuda_quantum</span></code> file for your processor architecture from
-the assets of the respective <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases">GitHub release</a>.
-The installer is a <a class="reference external" href="https://makeself.io/">self-extracting archive</a> that contains the
-pre-built binaries as well as a script to move them to the correct locations. You will need
-<code class="code docutils literal notranslate"><span class="pre">bash</span></code>, <code class="code docutils literal notranslate"><span class="pre">tar</span></code>, and <code class="code docutils literal notranslate"><span class="pre">gzip</span></code> (usually already installed on most Linux distributions) to run
-the installer.
-The installation location of CUDA-Q is not currently configurable and using the installer
-hence requires admin privileges on the system. We may revise that in the future; please see and
-upvote the corresponding <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/issues/1075">GitHub issue</a>.</p>
-<p>To install CUDA-Q, execute the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">MPI_PATH</span><span class="o">=</span>/usr/local/openmpi<span class="w"> </span><span class="se">\</span>
-sudo<span class="w"> </span>-E<span class="w"> </span>bash<span class="w"> </span>install_cuda_quantum*.<span class="k">$(</span>uname<span class="w"> </span>-m<span class="k">)</span><span class="w"> </span>--accept<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>.<span class="w"> </span>/etc/profile
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To use GPU-accelerated backends, you will need to install the necessary CUDA runtime libraries.
-For more information see the corresponding section on
-<a class="reference internal" href="#cuda-dependencies-prebuilt-binaries"><span class="std std-ref">Additional CUDA Tools</span></a>.</p>
-</div>
-<p>The installation ensures that the necessary environment variables for
-using the CUDA-Q toolchain are set upon login for all POSIX shells.
-Confirm that the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> command is found. If it is not, please make sure
-to set the environment variables defined by the <code class="code docutils literal notranslate"><span class="pre">set_env.sh</span></code> script in the
-CUDA-Q installation folder (usually <code class="code docutils literal notranslate"><span class="pre">/usr/local/cudaq</span></code> or <code class="code docutils literal notranslate"><span class="pre">/opt/nvidia/cudaq</span></code>).</p>
-<p>If an MPI installation is available in the directory defined by <code class="code docutils literal notranslate"><span class="pre">MPI_PATH</span></code>,
-the installer automatically enables MPI support in CUDA-Q.
-If you do not have MPI installed on your system, you can simply
-leave that path empty, and CUDA-Q will be installed without MPI support.
-If you install MPI at a later point in time, you can activate the MPI support in CUDA
-Quantum by setting the <code class="code docutils literal notranslate"><span class="pre">MPI_PATH</span></code> variable to its installation location and
-executing the commands</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">MPI_PATH=/usr/local/openmpi # update this path as needed</span>
-<span class="go">bash &quot;${CUDA_QUANTUM_PATH}/distributed_interfaces/activate_custom_mpi.sh&quot;</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Please make sure that you have the necessary development headers of the C standard
-library installed. You can check this by searching for <code class="code docutils literal notranslate"><span class="pre">features.h</span></code>, commonly found
-in <code class="code docutils literal notranslate"><span class="pre">/usr/include/</span></code>. You can install the necessary headers via package manager
-(usually the package name is called something like <code class="code docutils literal notranslate"><span class="pre">glibc-devel</span></code> or <code class="code docutils literal notranslate"><span class="pre">libc6-dev</span></code>).
-These headers are also included with any installation of GCC.</p>
-</div>
-</section>
-</section>
-<section id="development-with-vs-code">
-<span id="local-development-with-vscode"></span><h2>Development with VS Code<a class="headerlink" href="#development-with-vs-code" title="Permalink to this heading">¶</a></h2>
-<p>To facilitate application development with, for example, debugging, code completion, hover information, and so on,
-we recommend using <a class="reference external" href="https://code.visualstudio.com/">VS Code</a>. VS Code provides a seamless
-development experience on all platforms, and is also available without installation via web browser.
-This sections describes how to connect VS Code to a running container on your machine.
-The section on <a class="reference internal" href="#connect-to-remote"><span class="std std-ref">connecting to a remote host</span></a> contains information on
-how to set up your development environment when accessing CUDA-Q on a remote host instead.</p>
-<section id="using-a-docker-container">
-<span id="docker-in-vscode"></span><h3>Using a Docker container<a class="headerlink" href="#using-a-docker-container" title="Permalink to this heading">¶</a></h3>
-<p>Before connecting VS Code, open a terminal/shell,
-and start the CUDA-Q Docker container following the
-instructions in the <a class="reference internal" href="#install-docker-image"><span class="std std-ref">section above</span></a>.</p>
-<p>If you have a local installation of <a class="reference external" href="https://code.visualstudio.com/">VS Code</a>
-you can connect to the running container using the
-<a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers">Dev Containers</a> extension. If you want to use VS Code in the web browser, please follow the instructions
-in the section <a class="reference internal" href="#developing-with-remote-tunnels">Developing with Remote Tunnels</a> instead.</p>
-<p>After installing the
-<a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers">Dev Containers</a> extension, launch VS Code, open the Command Palette with <code class="code docutils literal notranslate"><span class="pre">Ctrl+Shift+P</span></code>, and enter
-”Dev Containers: Attach to Running Container”.
-You should see and select the running <code class="code docutils literal notranslate"><span class="pre">cuda-quantum</span></code> container in the list.
-After the window reloaded, enter “File: Open Folder” in the Command Palette to open the <code class="code docutils literal notranslate"><span class="pre">/home/cudaq/</span></code> folder.</p>
-<p>To run the examples, open the Command Palette and enter “View: Show Terminal”
-to launch an integrated terminal. You are now all set to
-<a class="reference internal" href="#post-installation"><span class="std std-ref">get started</span></a> with CUDA-Q development.</p>
-</section>
-<section id="using-a-singularity-container">
-<span id="singularity-in-vscode"></span><h3>Using a Singularity container<a class="headerlink" href="#using-a-singularity-container" title="Permalink to this heading">¶</a></h3>
-<p>If you have a GitHub or Microsoft account, we recommend that you connect
-to a CUDA-Q container using tunnels. To do so, launch a CUDA-Q Singularity
-container following the instructions in the <a class="reference internal" href="#install-singularity-image"><span class="std std-ref">section above</span></a>,
-and then follow the instructions in the section <a class="reference internal" href="#developing-with-remote-tunnels">Developing with Remote Tunnels</a>.</p>
-<p>If you cannot use tunnels, you need a local installation of
-<a class="reference external" href="https://code.visualstudio.com/">VS Code</a> and you need to install
-the <a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh">Remote - SSH</a> extension.
-Make sure you also have a suitable SSH key pair; if your <code class="code docutils literal notranslate"><span class="pre">~/.ssh/</span></code> folder does not already contain
-the files <code class="code docutils literal notranslate"><span class="pre">id_rsa.pub</span></code> and <code class="code docutils literal notranslate"><span class="pre">id.rsa</span></code>, follow the instructions for generating a new SSH key on
-<a class="reference external" href="https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent">this page</a>.</p>
-<p>To connect VS Code to a running CUDA-Q container,
-the most convenient setup is to install and run an SSH server
-in the Singularity container. Open a terminal/shell in a separate window,
-and enter the following commands to create a suitable sandbox:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">singularity build --sandbox cuda-quantum-sandbox cuda-quantum.sif</span>
-<span class="go">singularity exec --writable --fakeroot cuda-quantum-sandbox \</span>
-<span class="go">  apt-get install -y --no-install-recommends openssh-server</span>
-<span class="go">cat ~/.ssh/id_rsa.pub &gt;&gt; ~/.ssh/authorized_keys</span>
-</pre></div>
-</div>
-<p>You can launch this sandbox by entering the commands below. Please see the <a class="reference internal" href="#singularity">Singularity</a> section above
-for more information about how to get the <code class="code docutils literal notranslate"><span class="pre">cuda-quantum.sif</span></code> image, and how to enable GPU-acceleration
-with the <code class="code docutils literal notranslate"><span class="pre">--nv</span></code> flag.</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">singularity run --writable --fakeroot --nv --network-args=&quot;portmap=22:2222/tcp&quot; cuda-quantum-sandbox</span>
-<span class="go">/usr/sbin/sshd -D -p 2222 -E sshd_output.txt</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Make sure to use a free port. You can check if the SSH server is ready and listening
-by looking at the log in <code class="code docutils literal notranslate"><span class="pre">sshd_output.txt</span></code>. If the port is already in use, you can
-replace the number <code class="code docutils literal notranslate"><span class="pre">2222</span></code> by any free TCP port in the range <code class="code docutils literal notranslate"><span class="pre">1025-65535</span></code> in all
-commands.</p>
-</div>
-<p>Entering <code class="code docutils literal notranslate"><span class="pre">Ctrl+C</span></code> followed by <code class="code docutils literal notranslate"><span class="pre">exit</span></code> will stop the running container. You can re-start
-it at any time by entering the two commands above. While the container is running,
-open the Command Palette in VS Code with <code class="code docutils literal notranslate"><span class="pre">Ctrl+Shift+P</span></code>, enter “Remote-SSH: Add new
-SSH Host”, and enter the following SSH command:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ssh root@localhost -p 2222 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o GlobalKnownHostsFile=/dev/null</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you are working on Windows and are building and running the Singularity container in WSL,
-make sure to copy the used SSH keys to the Windows partition, such that VS Code can connect with
-the expected key. Alternatively, add the used public key to the <code class="code docutils literal notranslate"><span class="pre">/root/.ssh/authorized_keys</span></code> file in
-the Singularity container.</p>
-</div>
-<p>You can then connect to the host by opening the Command Palette, entering
-“Remote SSH: Connect Current Window to Host”, and choosing the newly created host.
-After the window reloaded, enter “File: Open Folder” in the
-Command Palette to open the desired folder.</p>
-<p>To run the examples, open the Command Palette and enter “View: Show Terminal”
-to launch an integrated terminal. You are now all set to
-<a class="reference internal" href="#post-installation"><span class="std std-ref">get started</span></a> with CUDA-Q development.</p>
-</section>
-</section>
-<section id="connecting-to-a-remote-host">
-<span id="connect-to-remote"></span><h2>Connecting to a Remote Host<a class="headerlink" href="#connecting-to-a-remote-host" title="Permalink to this heading">¶</a></h2>
-<p>Depending on the setup on the remote host, there are a couple of different options
-for developing CUDA-Q applications.</p>
-<ul class="simple">
-<li><p>If a CUDA-Q container is running on the remote host,
-and you have a GitHub or Microsoft account, take a look at
-<a class="reference internal" href="#developing-with-remote-tunnels">Developing with Remote Tunnels</a>. This works for both Docker
-and Singularity containers on the remote host, and should also
-work for other containers.</p></li>
-<li><p>If you cannot use tunnels, or if you want to work with an
-existing CUDA-Q installation without using a container,
-take a look at <a class="reference internal" href="#remote-access-via-ssh">Remote Access via SSH</a> instead.</p></li>
-</ul>
-<section id="developing-with-remote-tunnels">
-<span id="connect-vscode-via-tunnel"></span><h3>Developing with Remote Tunnels<a class="headerlink" href="#developing-with-remote-tunnels" title="Permalink to this heading">¶</a></h3>
-<p><a class="reference external" href="https://code.visualstudio.com/blogs/2022/12/07/remote-even-better">Remote access via tunnel</a>
-can easily be enabled with the <a class="reference external" href="https://code.visualstudio.com/docs/editor/command-line">VS Code CLI</a>.
-This allows to connect either a local installation of <a class="reference external" href="https://code.visualstudio.com/">VS Code</a>,
-or the <a class="reference external" href="https://vscode.dev/">VS Code Web UI</a>, to a running CUDA-Q container on the same or a different machine.</p>
-<p>Creating a secure connection requires authenticating with the same GitHub or Microsoft account on each end.
-Once authenticated, an SSH connection over the tunnel provides end-to-end encryption. To download the VS Code CLI, if necessary, and create a tunnel, execute the
-following command in the running CUDA-Q container,
-and follow the instructions to authenticate:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">vscode-setup tunnel --name cuda-quantum-remote --accept-server-license-terms</span>
-</pre></div>
-</div>
-<p>You can then either <a class="reference external" href="https://vscode.dev/tunnel/cuda-quantum-remote/home/cudaq/">open VS Code in a web browser</a>, or connect a local installation of VS Code.
-To connect a local installation of VS Code, make sure you have the
-<a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-server">Remote - Tunnels</a> extension installed,
-then open the Command Palette with <code class="code docutils literal notranslate"><span class="pre">Ctrl+Shift+P</span></code>, enter “Remote Tunnels: Connect to Tunnel”,
-and enter <code class="code docutils literal notranslate"><span class="pre">cuda-quantum-remote</span></code>. After the window reloaded, enter “File: Open Folder” in the Command Palette
-to open the <code class="code docutils literal notranslate"><span class="pre">/home/cudaq/</span></code> folder.</p>
-<p>You should see a pop up asking if you want to install the recommended extensions. Selecting to install them will
-configure VS Code with extensions for working with C++, Python, and Jupyter.
-You can always see the list of recommended extensions that aren’t installed yet by clicking on the “Extensions” icon in the sidebar and navigating to the “Recommended” tab.</p>
-</section>
-<section id="remote-access-via-ssh">
-<h3>Remote Access via SSH<a class="headerlink" href="#remote-access-via-ssh" title="Permalink to this heading">¶</a></h3>
-<p>To facilitate application development with, for example, debugging, code completion, hover information, and so on,
-you can connect a local installation of <a class="reference external" href="https://code.visualstudio.com/">VS Code</a> to a remote host via SSH.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>For the best user experience, we recommend to launch a CUDA-Q container on the remote host,
-and then connect <a class="reference internal" href="#connect-vscode-via-tunnel"><span class="std std-ref">VS Code using tunnels</span></a>.
-If a connection via tunnel is not possible, this section describes using SSH instead.</p>
-</div>
-<p>To do so, make sure you have <a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh">Remote - SSH</a> extension installed.
-Open the Command Palette with <code class="code docutils literal notranslate"><span class="pre">Ctrl+Shift+P</span></code>, enter “Remote-SSH: Add new
-SSH Host”, and enter the SSH command to connect to your account on the remote host.
-You can then connect to the host by opening the Command Palette, entering
-“Remote SSH: Connect Current Window to Host”, and choosing the newly created host.</p>
-<p>When prompted, choose Linux as the operating system, and enter your
-password. After the window reloaded, enter “File: Open Folder” in the
-Command Palette to open the desired folder. Our GitHub repository contains
-a folder with VS Code configurations including a list of recommended extensions for
-working with CUDA-Q; you can copy <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/tree/main/docker/release/config/.vscode">these configurations</a> into the a folder named <code class="code docutils literal notranslate"><span class="pre">.vscode</span></code> in your workspace to use them.</p>
-<p>If you want to work with an existing CUDA-Q installation on the remote host, you are all set.
-Alternatively, you can use Singularity to build and run a container following the instructions in
-<a class="reference internal" href="#install-singularity-image"><span class="std std-ref">this section</span></a>. Once the <code class="code docutils literal notranslate"><span class="pre">cuda-quantum.sif</span></code> image is built and
-available in your home directory on the remote host, you can update your VS Code configuration
-to enable/improve completion, hover information, and other development tools within the container.</p>
-<p>To do so, open the Command Palette and enter “Remote-SSH: Open SSH Configuration File”.
-Add a new entry to that file with the command to launch the container, and edit the configuration
-of the remote host, titled <code class="code docutils literal notranslate"><span class="pre">remote-host</span></code> in the snippets below, to add a new identifier:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">Host cuda-quantum~*</span>
-<span class="go">  RemoteCommand singularity run --writable --fakeroot --nv ~/cuda-quantum.sif</span>
-<span class="go">  RequestTTY yes</span>
-
-<span class="go">Host remote-host cuda-quantum~remote-host</span>
-<span class="go">  HostName ...</span>
-<span class="go">  ...</span>
-</pre></div>
-</div>
-<p>You will need to edit a couple of VS Code setting to make use of the newly defined remote command;
-open the Command Palette, enter “Preferences: Open User Settings (JSON)”, and add or update the
-following configurations:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">&quot;remote.SSH.enableRemoteCommand&quot;: true,</span>
-<span class="go">&quot;remote.SSH.useLocalServer&quot;: true,</span>
-<span class="go">&quot;remote.SSH.remoteServerListenOnSocket&quot;: false,</span>
-<span class="go">&quot;remote.SSH.connectTimeout&quot;: 120,</span>
-<span class="go">&quot;remote.SSH.serverInstallPath&quot;: {</span>
-<span class="go">    &quot;cuda-quantum~remote-host&quot;: &quot;~/.vscode-container/cuda-quantum&quot;,</span>
-<span class="go">},</span>
-</pre></div>
-</div>
-<p>After saving the changes, you should now be able to select <code class="code docutils literal notranslate"><span class="pre">cuda-quantum~remote-host</span></code> as the host
-when connecting via SSH, which will launch the CUDA-Q container and connect VS Code to it.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If the connection to <code class="code docutils literal notranslate"><span class="pre">cuda-quantum~remote-host</span></code> fails, you may need to specify the full
-path to the <code class="code docutils literal notranslate"><span class="pre">singularity</span></code> executable on the remote host, since environment variables,
-and specifically the configured <code class="code docutils literal notranslate"><span class="pre">PATH</span></code> may be different during launch than in your user account.</p>
-</div>
-</section>
-</section>
-<section id="dgx-cloud">
-<h2>DGX Cloud<a class="headerlink" href="#dgx-cloud" title="Permalink to this heading">¶</a></h2>
-<p>If you are using <a class="reference external" href="https://www.nvidia.com/en-us/data-center/dgx-cloud/">DGX Cloud</a>,
-you can easily use it to run CUDA-Q applications.
-While submitting jobs to DGX Cloud directly from within CUDA-Q is not (yet) supported,
-you can use the NGC CLI to launch and interact with workloads in DGX Cloud.
-The following sections detail how to do that, and how to connect JupyterLab and/or VS Code
-to a running CUDA-Q job in DGX Cloud.</p>
-<section id="get-started">
-<span id="dgx-cloud-setup"></span><h3>Get Started<a class="headerlink" href="#get-started" title="Permalink to this heading">¶</a></h3>
-<p>To get started with DGX Cloud, you can
-<a class="reference external" href="https://www.nvidia.com/en-us/data-center/dgx-cloud/trial/">request access here</a>.
-Once you have access, <a class="reference external" href="https://ngc.nvidia.com/signin">sign in</a> to your account,
-and <a class="reference external" href="https://ngc.nvidia.com/setup/api-key">generate an API key</a>.
-<a class="reference external" href="https://ngc.nvidia.com/setup/installers/cli">Install the NGC CLI</a>
-and configure it with</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc config set</span>
-</pre></div>
-</div>
-<p>entering the API key you just generated when prompted, and configure other settings as appropriate.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The rest of this section assumes you have CLI version 3.33.0. If you
-have an older version installed, you can upgrade to the latest version using the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc version upgrade 3.33.0</span>
-</pre></div>
-</div>
-<p>See also the <a class="reference external" href="https://docs.ngc.nvidia.com/cli/index.html">NGC CLI documentation</a>
-for more information about available commands.</p>
-</div>
-<p>You can see all information about available compute resources and ace instances
-with the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc base-command ace list</span>
-</pre></div>
-</div>
-<p>Confirm that you can submit a job with the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc base-command job run \</span>
-<span class="go">  --name Job-001 --total-runtime 60s \</span>
-<span class="go">  --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \</span>
-<span class="go">  --ace &lt;ace_name&gt; --instance &lt;instance_name&gt; \</span>
-<span class="go">  --commandline &#39;echo &quot;Hello from DGX Cloud!&quot;&#39;</span>
-</pre></div>
-</div>
-<p>replacing <code class="code docutils literal notranslate"><span class="pre">&lt;ace_name&gt;</span></code> and <code class="code docutils literal notranslate"><span class="pre">&lt;instance_name&gt;</span></code> with the name of the ace and instance you want
-to execute the job on.
-You should now see that job listed when you run the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc base-command job list</span>
-</pre></div>
-</div>
-<p>Once it has completed you can download the job results using the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc base-command result download &lt;job_id&gt;</span>
-</pre></div>
-</div>
-<p>replacing <code class="code docutils literal notranslate"><span class="pre">&lt;job_id&gt;</span></code> with the id of the job you just submitted.
-You should see a new folder named <code class="code docutils literal notranslate"><span class="pre">&lt;job_id&gt;</span></code> with the job log that contains
-the output “Hello from DGX Cloud!”.</p>
-<p>For more information about how to use the NGC CLI to interact with DGX Cloud,
-we refer to the <a class="reference external" href="https://docs.ngc.nvidia.com/cli/index.html">NGC CLI documentation</a>.</p>
-</section>
-<section id="use-jupyterlab">
-<h3>Use JupyterLab<a class="headerlink" href="#use-jupyterlab" title="Permalink to this heading">¶</a></h3>
-<p>Once you can <a class="reference internal" href="#dgx-cloud-setup"><span class="std std-ref">run jobs on DGX Cloud</span></a>, you can launch an interactive job
-to use CUDA-Q with <a class="reference external" href="https://jupyterlab.readthedocs.io/en/latest/">JupyterLab</a>
-running on DGX Cloud:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc base-command job run \</span>
-<span class="go">  --name Job-interactive-001 --total-runtime 600s \</span>
-<span class="go">  --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \</span>
-<span class="go">  --ace &lt;ace_name&gt; --instance &lt;instance_name&gt; \</span>
-<span class="go">  --port 8888 --commandline &#39;jupyter-lab-setup &lt;my-custom-token&gt; --port=8888&#39;</span>
-</pre></div>
-</div>
-<p>Replace <code class="code docutils literal notranslate"><span class="pre">&lt;my-custom-token&gt;</span></code> in the command above with a custom token that you can freely choose.
-You will use this token to authenticate with JupyterLab;
-Go to the <a class="reference external" href="https://bc.ngc.nvidia.com/jobs">job portal</a>, click on the job you just launched, and click on the link
-under ”URL/Hostname” in Service Mapped Ports.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>It may take a couple of minutes for DGX Cloud to launch and for the URL to become active, even after it appears in the Service Mapped Ports section;
-if you encounter a “404: Not Found” error, be patient and try again in a couple of minutes.</p>
-</div>
-<p>Once this URL opens, you should see the JupyterLab authentication page; enter the
-token you selected above to get access to the running CUDA-Q container.
-On the left you should see a folder with tutorials. Happy coding!</p>
-</section>
-<section id="use-vs-code">
-<h3>Use VS Code<a class="headerlink" href="#use-vs-code" title="Permalink to this heading">¶</a></h3>
-<p>Once you can <a class="reference internal" href="#dgx-cloud-setup"><span class="std std-ref">run jobs on DGX Cloud</span></a>, you can launch an interactive job
-to use CUDA-Q with a local installation of <a class="reference external" href="https://code.visualstudio.com/">VS Code</a>,
-or the <a class="reference external" href="https://vscode.dev/">VS Code Web UI</a>, running on DGX Cloud:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">ngc base-command job run \</span>
-<span class="go">  --name Job-interactive-001 --total-runtime 600s \</span>
-<span class="go">  --image nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest --result /results \</span>
-<span class="go">  --ace &lt;ace_name&gt; --instance &lt;instance_name&gt; \</span>
-<span class="go">  --commandline &#39;vscode-setup tunnel --name cuda-quantum-dgx --accept-server-license-terms&#39;</span>
-</pre></div>
-</div>
-<p>Go to the <a class="reference external" href="https://bc.ngc.nvidia.com/jobs">job portal</a>, click on the job you just launched, and select the “Log”
-tab. Once the job is running, you should see instructions there for how to connect to the device the job is running on.
-These instructions include a link to open and the code to enter on that page; follow the instructions to authenticate.
-Once you have authenticated, you can either
-<a class="reference external" href="https://vscode.dev/tunnel/cuda-quantum-dgx/home/cudaq/">open VS Code in a web browser</a>,
-or connect a local installation of VS Code.
-To connect a local installation of VS Code, make sure you have the
-<a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-server">Remote - Tunnels</a> extension installed,
-then open the Command Palette with <code class="code docutils literal notranslate"><span class="pre">Ctrl+Shift+P</span></code>, enter “Remote Tunnels: Connect to Tunnel”,
-and enter <code class="code docutils literal notranslate"><span class="pre">cuda-quantum-remote</span></code>. After the window reloaded, enter “File: Open Folder” in the Command Palette
-to open the <code class="code docutils literal notranslate"><span class="pre">/home/cudaq/</span></code> folder.</p>
-<p>You should see a pop up asking if you want to install the recommended extensions. Selecting to install them will
-configure VS Code with extensions for working with C++, Python, and Jupyter.
-You can always see the list of recommended extensions that aren’t installed yet by clicking on the “Extensions” icon in the sidebar and navigating to the “Recommended” tab.</p>
-<p>If you enter “View: Show Explorer” in the Command Palette, you should see a folder with tutorials and examples
-to help you get started. Take a look at <a class="reference internal" href="#next-steps">Next Steps</a> to dive into CUDA-Q development.</p>
-</section>
-</section>
-<section id="additional-cuda-tools">
-<span id="id7"></span><h2>Additional CUDA Tools<a class="headerlink" href="#additional-cuda-tools" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q makes use of GPU-acceleration in certain backends and components.
-Depending on how you installed CUDA-Q, you may need to install
-certain CUDA libraries separately to take advantage of these.</p>
-<section id="installation-via-pypi">
-<h3>Installation via PyPI<a class="headerlink" href="#installation-via-pypi" title="Permalink to this heading">¶</a></h3>
-<p>If you installed CUDA-Q via <a class="reference external" href="https://pypi.org/project/cuda-quantum">PyPI</a>, please follow the installation instructions there to install the necessary CUDA dependencies.</p>
-</section>
-<section id="installation-in-container-images">
-<h3>Installation In Container Images<a class="headerlink" href="#installation-in-container-images" title="Permalink to this heading">¶</a></h3>
-<p>If you are using the CUDA-Q container image, the image already contains all necessary runtime libraries to use all CUDA-Q components. To take advantage of GPU-acceleration, make sure
-to enable GPU support when you launch the container, that is pass the <code class="code docutils literal notranslate"><span class="pre">--gpus</span> <span class="pre">all</span></code> flag when launching
-the container with Docker and the <code class="code docutils literal notranslate"><span class="pre">--nv</span></code> flag when launching the container with Singularity.</p>
-<p>Note that the image does not contain all development dependencies for CUDA, such as, for example the <code class="code docutils literal notranslate"><span class="pre">nvcc</span></code> compiler. You can install all CUDA development dependencies by running the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">sudo apt-get install cuda-toolkit-12.0</span>
-</pre></div>
-</div>
-<p>inside the container. Make sure the toolkit version you install matches the CUDA runtime installation in the container.
-Most Python packages that use GPU-acceleration, such as for example <a class="reference external" href="https://cupy.dev">CuPy</a>, require an existing CUDA installation.
-After installing the <code class="code docutils literal notranslate"><span class="pre">cuda-toolkit-12.0</span></code> you can install CuPy for CUDA 12 with the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python3 -m pip install cupy-cuda12x</span>
-</pre></div>
-</div>
-</section>
-<section id="installing-pre-built-binaries">
-<span id="cuda-dependencies-prebuilt-binaries"></span><h3>Installing Pre-built Binaries<a class="headerlink" href="#installing-pre-built-binaries" title="Permalink to this heading">¶</a></h3>
-<p>If you installed pre-built binaries for CUDA-Q, you will need to install
-the necessary CUDA runtime libraries to use GPU-acceleration in CUDA-Q.
-If you prefer to only install the minimal set of runtime libraries, the following
-commands, for example, install the necessary packages for RHEL 8:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDA_VERSION</span><span class="o">=</span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="k">:-</span><span class="nv">12</span><span class="p">.0</span><span class="si">}</span>
-<span class="nv">CUDA_DOWNLOAD_URL</span><span class="o">=</span>https://developer.download.nvidia.com/compute/cuda/repos
-<span class="c1"># Go to the url above, set the variables below to a suitable distribution</span>
-<span class="c1"># and subfolder for your platform, and uncomment the line below.</span>
-<span class="c1"># DISTRIBUTION=rhel8 CUDA_ARCH_FOLDER=x86_64</span>
-
-<span class="nv">version_suffix</span><span class="o">=</span><span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>tr<span class="w"> </span>.<span class="w"> </span>-<span class="k">)</span>
-dnf<span class="w"> </span>config-manager<span class="w"> </span>--add-repo<span class="w"> </span><span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDA_DOWNLOAD_URL</span><span class="si">}</span><span class="s2">/</span><span class="si">${</span><span class="nv">DISTRIBUTION</span><span class="si">}</span><span class="s2">/</span><span class="si">${</span><span class="nv">CUDA_ARCH_FOLDER</span><span class="si">}</span><span class="s2">/cuda-</span><span class="si">${</span><span class="nv">DISTRIBUTION</span><span class="si">}</span><span class="s2">.repo&quot;</span>
-dnf<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>--nobest<span class="w"> </span>--setopt<span class="o">=</span><span class="nv">install_weak_deps</span><span class="o">=</span>False<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>cuda-cudart-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>cuda-nvrtc-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>libcusolver-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>libcublas-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span>
-<span class="k">if</span><span class="w"> </span><span class="o">[</span><span class="w"> </span><span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDA_VERSION</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>cut<span class="w"> </span>-d<span class="w"> </span>.<span class="w"> </span>-f1<span class="k">)</span><span class="w"> </span>-gt<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="o">]</span><span class="p">;</span><span class="w"> </span><span class="k">then</span><span class="w"> </span>
-<span class="w">    </span>dnf<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span>--nobest<span class="w"> </span>--setopt<span class="o">=</span><span class="nv">install_weak_deps</span><span class="o">=</span>False<span class="w"> </span><span class="se">\</span>
-<span class="w">        </span>libnvjitlink-<span class="si">${</span><span class="nv">version_suffix</span><span class="si">}</span>
-<span class="k">fi</span>
-</pre></div>
-</div>
-<p>More detailed instructions for your platform can be found in the online documentation
-linked for that <a class="reference external" href="https://developer.nvidia.com/cuda-toolkit-archive">CUDA version</a>.
-Please make sure to install CUDA version 11.8 or newer, and confirm that your
-<a class="reference external" href="https://www.nvidia.com/download/index.aspx">GPU driver</a> supports that version.
-While the above packages are sufficient to use GPU-acceleration within CUDA-Q,
-we recommend installing the complete CUDA toolkit (<code class="code docutils literal notranslate"><span class="pre">cuda-toolkit-12-0</span></code>) that also
-includes the <code class="code docutils literal notranslate"><span class="pre">nvcc</span></code> compiler. A separate CUDA-Q installer is available for CUDA 11,
-built against version 11.8, and for CUDA 12, built against version 12.0.</p>
-</section>
-</section>
-<section id="distributed-computing-with-mpi">
-<span id="id8"></span><h2>Distributed Computing with MPI<a class="headerlink" href="#distributed-computing-with-mpi" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q supports the Message Passing Interface (MPI) parallelism via a plugin interface.
-It is possible to activate or replace such an MPI plugin without re-installing or re-compiling CUDA-Q.
-MPI calls via CUDA-Q API for C++ and Python will be delegated to the currently activated plugin at runtime.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Built-in MPI Support</label><div class="tab-content docutils">
-<p>The <a class="reference internal" href="#install-docker-image"><span class="std std-ref">CUDA-Q Docker image</span></a> is shipped with a pre-built MPI plugin based on an
-optimized OpenMPI installation included in the image. No action is required to use this plugin.
-We recommend using this plugin unless the container host has an existing MPI implementation other than OpenMPI.</p>
-<p>If you are not using the Docker image, or are using the image on a system that has a
-vendor-optimized MPI library pre-installed, please follow the instructions in the “Custom MPI Support” tab
-to enable MPI support.</p>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">Custom MPI Support</label><div class="tab-content docutils">
-<p>If you are not using the Docker image, or are using the image on a system that has a
-vendor-optimized MPI library pre-installed, CUDA-Q can be configured to use the local MPI installation by
-manually activating a suitable plugin post-installation.
-To do so,</p>
-<ul>
-<li><p>Make sure the environment variable <code class="code docutils literal notranslate"><span class="pre">CUDA_QUANTUM_PATH</span></code> points to the CUDA-Q installation directory.
-If you installed CUDA-Q using the <code class="code docutils literal notranslate"><span class="pre">installer</span> <span class="pre">&lt;install-prebuilt-binaries&gt;</span></code>, or if you are using the CUDA-Q
-container image, this variable should already be defined. If you installed the CUDA-Q
-<code class="code docutils literal notranslate"><span class="pre">Python</span> <span class="pre">wheels</span> <span class="pre">&lt;install-python-wheels&gt;</span></code>, set this variable to the directory listed under “Location” when you run the
-command <code class="code docutils literal notranslate"><span class="pre">pip</span> <span class="pre">show</span> <span class="pre">cudaq</span></code>.</p></li>
-<li><p>Set the environment variable <code class="code docutils literal notranslate"><span class="pre">MPI_PATH</span></code> to the location of your MPI installation. In particular, <code class="code docutils literal notranslate"><span class="pre">${MPI_PATH}/include</span></code>
-is expected to contain the <code class="code docutils literal notranslate"><span class="pre">mpi.h</span></code> header and <code class="code docutils literal notranslate"><span class="pre">${MPI_PATH}/lib64</span></code> or <code class="code docutils literal notranslate"><span class="pre">${MPI_PATH}/lib</span></code> is expected to contain <code class="code docutils literal notranslate"><span class="pre">libmpi.so</span></code>.</p></li>
-<li><p>Execute the following command to complete the activation:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">bash $CUDA_QUANTUM_PATH/distributed_interfaces/activate_custom_mpi.sh</span>
-</pre></div>
-</div>
-</li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>HPC data centers often have a vendor-optimized MPI library pre-installed on their system.
-If you are using our container images, installing that MPI implementation in the container
-and manually activating the plugin following the steps above ensure the best performance,
-and guarantee compatibility when MPI injection into a container occurs.</p>
-</div>
-<p>Manually activating an MPI plugin replaces any existing plugin; After the initial activation, the newly built
-<code class="code docutils literal notranslate"><span class="pre">libcudaq_distributed_interface_mpi.so</span></code> in the installation directory will subsequently always be used to
-handle CUDA-Q MPI calls.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Executing the activation script from the CUDA-Q installation directory requires <em>write</em> permissions to that directory.
-If you do not have the necessary permissions, copy the <code class="code docutils literal notranslate"><span class="pre">distributed_interfaces</span></code> sub-directory to a local location and execute the
-activation script from there.</p>
-<p>In this scenario, since the activated plugin (<code class="code docutils literal notranslate"><span class="pre">libcudaq_distributed_interface_mpi.so</span></code>) is outside the CUDA-Q installation,
-you must set the environment variable <code class="code docutils literal notranslate"><span class="pre">$CUDAQ_MPI_COMM_LIB</span></code> to the path of that shared library.
-This is done automatically when executing that activation script, but you may wish to persist that environment variable
-between bash sessions, e.g., by adding it to the <code class="code docutils literal notranslate"><span class="pre">.bashrc</span></code> file.</p>
-</div>
-</div>
-</div>
-</section>
-<section id="updating-cuda-q">
-<span id="updating-cuda-quantum"></span><h2>Updating CUDA-Q<a class="headerlink" href="#updating-cuda-q" title="Permalink to this heading">¶</a></h2>
-<p>If you installed the CUDA-Q Python wheels, you can update to the latest release
-by running the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python3 -m pip install --upgrade cudaq</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Please check if you have an existing installation of the <code class="code docutils literal notranslate"><span class="pre">cuda-quantum</span></code>,
-<code class="code docutils literal notranslate"><span class="pre">cudaq-quantum-cu11</span></code>, or <code class="code docutils literal notranslate"><span class="pre">cuda-quantum-cu12</span></code> package,
-and uninstall it prior to installing <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code>. The <code class="code docutils literal notranslate"><span class="pre">cudaq</span></code> package supersedes the
-<code class="code docutils literal notranslate"><span class="pre">cuda-quantum</span></code> package and will install a suitable binary distribution (either
-<code class="code docutils literal notranslate"><span class="pre">cuda-quantum-cu11</span></code> or <code class="code docutils literal notranslate"><span class="pre">cuda-quantum-cu12</span></code>) for your system. Multiple versions
-of a CUDA-Q binary distribution will conflict with each other and not work properly.</p>
-</div>
-<p>If you previously installed the CUDA-Q pre-built binaries, you should first uninstall your
-current CUDA-Q installation before installing the new version using the installer.
-To uninstall your current CUDA-Q version, run the command</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">sudo bash &quot;${CUDA_QUANTUM_PATH}/uninstall.sh&quot; -y</span>
-</pre></div>
-</div>
-<p>The <code class="code docutils literal notranslate"><span class="pre">uninstall.sh</span></code> script is generated during installation, and will remove all files
-and folders that were created as part of the installation, whether they were modified
-in the meantime or not. It does not remove any additional files that existed prior
-to the installation or that you have added to the installation location since then.
-You can then download and install the new version of CUDA-Q following the
-instructions <a class="reference internal" href="#install-prebuilt-binaries"><span class="std std-ref">above</span></a>.</p>
-</section>
-<section id="dependencies-and-compatibility">
-<span id="id9"></span><h2>Dependencies and Compatibility<a class="headerlink" href="#dependencies-and-compatibility" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q can be used to compile and run quantum programs on a CPU-only system, but a GPU is highly recommended and necessary to use the GPU-based simulators, see also <a class="reference internal" href="../backends/simulators.html"><span class="doc">CUDA-Q Simulation Backends</span></a>.</p>
-<p>The supported CPUs include x86_64 (x86-64-v3 architecture and newer) and ARM64 (ARM v8-A architecture and newer).</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Some of the components included in the CUDA-Q Python wheels depend on an existing CUDA installation on your system. For more information about installing the CUDA-Q Python wheels, take a look at <a class="reference internal" href="#install-python-wheels"><span class="std std-ref">this section</span></a>.</p>
-</div>
-<p>The following table summarizes the required components.</p>
-<table class="docutils align-default" id="id10">
-<caption><span class="caption-text">Supported Systems</span><a class="headerlink" href="#id10" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 38%" />
-<col style="width: 63%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p>CPU architectures</p></td>
-<td><p>x86_64, ARM64</p></td>
-</tr>
-<tr class="row-even"><td><p>Operating System</p></td>
-<td><p>Linux</p></td>
-</tr>
-<tr class="row-odd"><td><p>Tested Distributions</p></td>
-<td><p>CentOS 8; Debian 11, 12; Fedora 38, 39; OpenSUSE/SLED/SLES 15.5, 15.6; RHEL 8, 9; Rocky 8, 9; Ubuntu 22.04, 24.04</p></td>
-</tr>
-<tr class="row-even"><td><p>Python versions</p></td>
-<td><p>3.10+</p></td>
-</tr>
-</tbody>
-</table>
-<table class="docutils align-default" id="id11">
-<caption><span class="caption-text">Requirements for GPU Simulation</span><a class="headerlink" href="#id11" title="Permalink to this table">¶</a></caption>
-<colgroup>
-<col style="width: 38%" />
-<col style="width: 63%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p>GPU Architectures</p></td>
-<td><p>Volta, Turing, Ampere, Ada, Hopper</p></td>
-</tr>
-<tr class="row-even"><td><p>NVIDIA GPU with Compute Capability</p></td>
-<td><p>7.0+</p></td>
-</tr>
-<tr class="row-odd"><td><p>CUDA</p></td>
-<td><p>11.x (Driver 470.57.02+), 12.x (Driver 525.60.13+)</p></td>
-</tr>
-</tbody>
-</table>
-<p>Detailed information about supported drivers for different CUDA versions and be found <a class="reference external" href="https://docs.nvidia.com/deploy/cuda-compatibility/">here</a>.</p>
-</section>
-<section id="next-steps">
-<span id="post-installation"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this heading">¶</a></h2>
-<p>You can now compile and/or run the C++ and Python examples using the terminal.
-To open a terminal in VS Code, open the Command Palette with <code class="code docutils literal notranslate"><span class="pre">Ctrl+Shift+P</span></code> and
-enter “View: Show Terminal”.</p>
-<img alt="../../_images/getToWork.png" src="../../_images/getToWork.png" />
-<p>The CUDA-Q image contains a folder with examples and tutorials in the <code class="code docutils literal notranslate"><span class="pre">/home/cudaq</span></code> directory.
-These examples are provided to get you started with CUDA-Q and understanding
-the programming and execution model.
-If you are not using a container image, you can find these examples on our
-<a class="reference external" href="https://github.com/NVIDIA/cuda-quantum">GitHub repository</a>.</p>
-<p>Let’s start by running a simple program to validate your installation.
-The samples contain an implementation of a
-<a class="reference external" href="https://en.wikipedia.org/wiki/Bernstein%E2%80%93Vazirani_algorithm">Bernstein-Vazirani algorithm</a>.
-To run the example, execute the command:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python examples/python/bernstein_vazirani.py --size 5</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp &amp;&amp; ./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This will execute the program on the <a class="reference internal" href="../backends/simulators.html#id3"><span class="std std-ref">default simulator</span></a>, which will use GPU-acceleration if
-a suitable GPU has been detected. To confirm that the GPU acceleration works, you can
-increase the size of the secret string, and pass the target as a command line argument:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python examples/python/bernstein_vazirani.py --size 25 --target nvidia</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp -DSIZE=25 --target nvidia &amp;&amp; ./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This program should complete fairly quickly. Depending on the available memory on your GPU,
-you can set the size of the secret string to up to 28-32 when running on the <code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you get an error that the CUDA driver version is insufficient or no GPU has been detected,
-check that you have enabled GPU support when launching the container by passing the <code class="code docutils literal notranslate"><span class="pre">--gpus</span> <span class="pre">all</span></code> flag
-(for <a class="reference internal" href="#install-docker-image"><span class="std std-ref">Docker</span></a>) or the <code class="code docutils literal notranslate"><span class="pre">--nv</span></code> flag (for <a class="reference internal" href="#install-singularity-image"><span class="std std-ref">Singularity</span></a>).
-If you are not running a container, you can execute the command <code class="code docutils literal notranslate"><span class="pre">nvidia-smi</span></code> to confirm your setup;
-if the command is unknown or fails, you do not have a GPU or do not have a driver installed. If the command
-succeeds, please confirm that your CUDA and driver version matches the
-<a class="reference internal" href="#dependencies-and-compatibility"><span class="std std-ref">supported versions</span></a>.</p>
-</div>
-<p>Let’s compare that to using only your CPU:</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--3-input--1" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python examples/python/bernstein_vazirani.py --size 25 --target qpp-cpu</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--3-input--2" name="tab-set--3" type="radio"><label class="tab-label" for="tab-set--3-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ examples/cpp/algorithms/bernstein_vazirani.cpp -DSIZE=25 --target qpp-cpu &amp;&amp; ./a.out</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>When you execute this command, the program simply seems to hang; that is because it takes
-a long time for the CPU-only backend to simulate 28+ qubits! Cancel the execution with <code class="code docutils literal notranslate"><span class="pre">Ctrl+C</span></code>.</p>
-<p>You are now all set to start developing quantum applications using CUDA-Q!
-Please proceed to <a class="reference internal" href="../basics/basics.html"><span class="doc">Basics</span></a> for an introduction
-to the fundamental features of CUDA-Q.</p>
-</section>
-</section>
-
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-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_control.html">   Multi-Control Synthesis</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#single-gpu">Single-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#multi-node-multi-gpu">Multi-node multi-GPU</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#openmp-cpu-only">OpenMP CPU-only</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#tensor-network-simulators">Tensor Network Simulators</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#matrix-product-state">Matrix product state</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#clifford-only-simulator">Clifford-Only Simulator</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/simulators.html#stim-cpu">Stim (CPU)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#fermioniq">Fermioniq</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/simulators.html#default-simulator">Default Simulator</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
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-<li class="toctree-l1 current"><a class="reference internal" href="integration.html">   Integration</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../api/languages/cpp_api.html">C++ API</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/cpp_api.html#platform">Platform</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
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-  <section id="cuda-q-and-cmake">
-<h1>CUDA-Q and CMake<a class="headerlink" href="#cuda-q-and-cmake" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q provides a full compiler toolchain for building
-out quantum-classical applications. Since most C++ projects
-provide some sort of integration with CMake, it becomes imperative
-for our toolchain to integrate well with the CMake build system.</p>
-<p>CUDA-Q provides CMake configuration files that allow downstream
-application developers to create hybrid quantum-classical source code
-and build it with the typical CMake workflow, which will natively
-leverage <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> for compilation of CUDA-Q source files.</p>
-<p>To see how this works, let’s create a trivial example. Suppose we have
-a folder named <code class="code docutils literal notranslate"><span class="pre">myCudaQApp</span></code> and within that folder we have
-a simple CUDA-Q source file, like <code class="code docutils literal notranslate"><span class="pre">ghz.cpp</span></code>. With a local
-CUDA-Q installation present on the system, we can create the
-following top-level <code class="code docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> file to build this file with
-<code class="code docutils literal notranslate"><span class="pre">nvq++</span></code>:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cmake_minimum_required<span class="o">(</span>VERSION<span class="w"> </span><span class="m">3</span>.24<span class="w"> </span>FATAL_ERROR<span class="o">)</span>
-project<span class="o">(</span>myCudaQApp<span class="w"> </span>LANGUAGES<span class="w"> </span>CXX<span class="o">)</span>
-find_package<span class="o">(</span>CUDAQ<span class="w"> </span>REQUIRED<span class="o">)</span>
-add_executable<span class="o">(</span>ghz_exe<span class="w"> </span>ghz.cpp<span class="o">)</span>
-</pre></div>
-</div>
-<p>To configure and build this file, all we need to do is execute the following from the top level of the <code class="code docutils literal notranslate"><span class="pre">myCudaQApp</span></code> folder:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mkdir<span class="w"> </span>build<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>build
-cmake<span class="w"> </span>..<span class="w"> </span>-G<span class="w"> </span>Ninja<span class="w"> </span>-DCUDAQ_DIR<span class="o">=</span><span class="s2">&quot;</span><span class="nv">$CUDA_QUANTUM_PATH</span><span class="s2">/lib/cmake/cudaq&quot;</span>
-ninja
-./ghz_exe
-</pre></div>
-</div>
-<p>Notice that by running the <code class="code docutils literal notranslate"><span class="pre">ninja</span></code> command with <code class="code docutils literal notranslate"><span class="pre">--verbose</span></code>, you’ll see that
-<code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> was used to compile and link the executable.</p>
-</section>
-
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-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-  <section id="using-cuda-and-cuda-q-in-a-project">
-<h1>Using CUDA and CUDA-Q in a Project<a class="headerlink" href="#using-cuda-and-cuda-q-in-a-project" title="Permalink to this heading">¶</a></h1>
-<p>It may be the case that a project that uses CUDA-Q kernels may also
-want to use CUDA code to do computation on a GPU.  This is possible by using
-both the CUDA Toolkit and CUDA-Q tools. More about programming GPUs
-in CUDA can be found in the <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html">Quick Start Guide</a>.</p>
-<p>Once the <code class="code docutils literal notranslate"><span class="pre">nvcc</span></code> compiler is installed, it is possible to write
-CUDA kernels and have them execute on the system GPU. See NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/blog/easy-introduction-cuda-c-and-c/">An
-Easy Introduction to CUDA C and C++</a>
-for more information on getting started with CUDA.</p>
-<p>CUDA code uses a unique syntax and is, typically, saved in a file with
-the extension <code class="code docutils literal notranslate"><span class="pre">.cu</span></code>. For our example, assume we have written our
-CUDA code in the file <code class="code docutils literal notranslate"><span class="pre">my_proj.cu</span></code>.</p>
-<p>CUDA-Q code is a library-based extension of C++ and uses
-standard conforming C++ syntax. Typically, a quantum kernel would be
-saved in a file with the <code class="code docutils literal notranslate"><span class="pre">.cpp</span></code> extension. Again for our
-example, let’s assume that we’ve written quantum kernels and saved
-them in the file <code class="code docutils literal notranslate"><span class="pre">my_proj_quantum.cpp</span></code>.</p>
-<p>By default, CUDA-Q uses C++ 20 and builds source code against the
-LLVM C++ standard library (<code class="code docutils literal notranslate"><span class="pre">libc++</span></code>). To create a CUDA library that
-can link against CUDA-Q code, make sure to define an API that does
-not rely on C++ data structures that rely on a specific C++ toolchain
-for all functions intended to be called from CUDA-Q (see also
-<a class="reference internal" href="libraries.html#clike-shim"><span class="std std-ref">Interfacing between binaries compiled with a different toolchains</span></a>). For example, if you define a CUDA kernel in <code class="code docutils literal notranslate"><span class="pre">my_proj.cu</span></code></p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">CudaDataType</span><span class="o">&gt;</span>
-<span class="n">__global__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">cudaSetFirstNElements</span><span class="p">(</span><span class="n">CudaDataType</span><span class="w"> </span><span class="o">*</span><span class="n">sv</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">CudaDataType</span><span class="w"> </span><span class="o">*</span><span class="n">__restrict__</span><span class="w"> </span><span class="n">sv2</span><span class="p">,</span><span class="w"> </span><span class="kt">int64_t</span><span class="w"> </span><span class="n">N</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="kt">int64_t</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">static_cast</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="p">(</span><span class="n">blockIdx</span><span class="p">.</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">blockDim</span><span class="p">.</span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">threadIdx</span><span class="p">.</span><span class="n">x</span><span class="p">;</span>
-<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">N</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="n">sv</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sv2</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">x</span><span class="p">;</span>
-<span class="w">        </span><span class="n">sv</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sv2</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">y</span><span class="p">;</span>
-<span class="w">    </span><span class="p">}</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="p">{</span>
-<span class="w">        </span><span class="n">sv</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">0.0</span><span class="p">;</span>
-<span class="w">        </span><span class="n">sv</span><span class="p">[</span><span class="n">i</span><span class="p">].</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">0.0</span><span class="p">;</span>
-<span class="w">    </span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>define the following template and all desired template specializations</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">CudaDataType</span><span class="o">&gt;</span>
-<span class="kt">void</span><span class="w"> </span><span class="n">setFirstNElements</span><span class="p">(</span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="p">,</span>
-<span class="w">                       </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                       </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">newDeviceStateVector</span><span class="p">,</span>
-<span class="w">                       </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                       </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">previousStateDimension</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cudaSetFirstNElements</span><span class="o">&lt;&lt;&lt;</span><span class="n">n_blocks</span><span class="p">,</span><span class="w"> </span><span class="n">threads_per_block</span><span class="o">&gt;&gt;&gt;</span><span class="p">(</span>
-<span class="w">        </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">CudaDataType</span><span class="w"> </span><span class="o">*&gt;</span><span class="p">(</span><span class="n">newDeviceStateVector</span><span class="p">),</span>
-<span class="w">        </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">CudaDataType</span><span class="w"> </span><span class="o">*&gt;</span><span class="p">(</span><span class="n">deviceStateVector</span><span class="p">),</span>
-<span class="w">        </span><span class="n">previousStateDimension</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="k">template</span><span class="w"> </span><span class="kt">void</span>
-<span class="n">setFirstNElements</span><span class="o">&lt;</span><span class="n">cuFloatComplex</span><span class="o">&gt;</span><span class="p">(</span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="p">,</span>
-<span class="w">                                  </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                                  </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">newDeviceStateVector</span><span class="p">,</span>
-<span class="w">                                  </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                                  </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">previousStateDimension</span><span class="p">);</span>
-
-<span class="k">template</span><span class="w"> </span><span class="kt">void</span>
-<span class="n">setFirstNElements</span><span class="o">&lt;</span><span class="n">cuDoubleComplex</span><span class="o">&gt;</span><span class="p">(</span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="p">,</span>
-<span class="w">                                   </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                                   </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">newDeviceStateVector</span><span class="p">,</span>
-<span class="w">                                   </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                                   </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">previousStateDimension</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>Create the corresponding header file <code class="code docutils literal notranslate"><span class="pre">my_proj.h</span></code> declaring the <code class="code docutils literal notranslate"><span class="pre">setFirstNElements</span></code>
-template. You can then call <code class="code docutils literal notranslate"><span class="pre">setFirstNElements</span></code> from within <code class="code docutils literal notranslate"><span class="pre">my_proj_quantum.cpp</span></code>
-by including <code class="code docutils literal notranslate"><span class="pre">my_proj.h</span></code>, for example</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;cuComplex.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;cuda_runtime.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;device_launch_parameters.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;my_proj.h&quot;</span>
-
-<span class="k">using</span><span class="w"> </span><span class="k">namespace</span><span class="w"> </span><span class="nn">my_kernels</span><span class="p">;</span><span class="w"> </span><span class="c1">// the namespace where you defined setFirstNElements</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="w"> </span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">10</span><span class="p">;</span>
-<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</span><span class="p">;</span>
-
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">;</span>
-<span class="w">    </span><span class="n">cudaMalloc</span><span class="p">((</span><span class="kt">void</span><span class="w"> </span><span class="o">**</span><span class="p">)</span><span class="o">&amp;</span><span class="n">deviceStateVector</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="n">cuDoubleComplex</span><span class="p">));</span>
-<span class="w">    </span><span class="c1">// ...</span>
-<span class="w">    </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">newDeviceStateVector</span><span class="p">;</span>
-<span class="w">    </span><span class="n">cudaMalloc</span><span class="p">((</span><span class="kt">void</span><span class="w"> </span><span class="o">**</span><span class="p">)</span><span class="o">&amp;</span><span class="n">newDeviceStateVector</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="n">cuDoubleComplex</span><span class="p">));</span>
-<span class="w">    </span><span class="n">setFirstNElements</span><span class="o">&lt;</span><span class="n">cuDoubleComplex</span><span class="o">&gt;</span><span class="p">(</span><span class="n">n_blocks</span><span class="p">,</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                                       </span><span class="n">newDeviceStateVector</span><span class="p">,</span><span class="w"> </span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                                       </span><span class="mi">2</span><span class="p">);</span>
-<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>To get an executable, compile the code with</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvcc<span class="w"> </span>-c<span class="w"> </span>-std<span class="o">=</span>c++17<span class="w"> </span>-Xcompiler<span class="w"> </span>-fPIC<span class="w"> </span>my_proj.cu<span class="w"> </span>-o<span class="w"> </span>my_proj.o
-nvq++<span class="w"> </span>my_proj_quantum.cpp<span class="w"> </span>my_proj.o<span class="w"> </span>-I<span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDA_HOME</span><span class="si">}</span><span class="s2">/include/&quot;</span><span class="w"> </span>-L<span class="s2">&quot;</span><span class="si">${</span><span class="nv">CUDA_HOME</span><span class="si">}</span><span class="s2">/lib64&quot;</span><span class="w"> </span>-lcudart<span class="w"> </span>-o<span class="w"> </span>my_executable
-</pre></div>
-</div>
-<p>Above, <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> is used for the link step and will make sure the CUDA-Q
-runtime libraries are linked correctly to the executable program.
-The CUDA runtime is explicitly added to this command.</p>
-</section>
-
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-            NVIDIA CUDA-Q
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-              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../quick_start.html">   Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#install-cuda-q">Install CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick_start.html#validate-your-installation">Validate your Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../basics/basics.html">   Basics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../basics/kernel_intro.html">   What is a CUDA-Q Kernel?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/build_kernel.html">   Building your first CUDA-Q Program</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../basics/run_kernel.html">   Running your first CUDA-Q Program</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../basics/run_kernel.html#running-on-a-gpu">Running on a GPU</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../basics/troubleshooting.html">   Troubleshooting</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../basics/troubleshooting.html#debugging-and-verbose-simulation-output">Debugging and Verbose Simulation Output</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">   Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/introduction.html">   Introduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/building_kernels.html">   Building Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/quantum_operations.html">   Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-states">Quantum States</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#quantum-gates">Quantum Gates</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/quantum_operations.html#measurements">Measurements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/measuring_kernels.html">   Measuring Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/measuring_kernels.html#Midcircuit-Measurement-and-Conditional-Logic">Midcircuit Measurement and Conditional Logic</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/visualization.html">   Visualizing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Qubit-Visualization">Qubit Visualization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/visualization.html#Kernel-Visualization">Kernel Visualization</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/executing_kernels.html">   Executing Kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample">Sample</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe">Observe</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-state">Get state</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/executing_kernels.html#Parallelization-Techniques">Parallelization Techniques</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Observe-Async">Observe Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Sample-Async">Sample Async</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../examples/python/executing_kernels.html#Get-State-Async">Get State Async</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/expectation_values.html">   Computing Expectation Values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/expectation_values.html#parallelizing-across-multiple-processors">Parallelizing across Multiple Processors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_control.html">   Multi-Control Synthesis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/multi_gpu_workflows.html">   Multi-GPU Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#available-targets">Available Targets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/multi_gpu_workflows.html#parallelization-across-multiple-processors">Parallelization across Multiple Processors</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#batching-hamiltonian-terms">Batching Hamiltonian Terms</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../examples/multi_gpu_workflows.html#circuit-batching">Circuit Batching</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/optimizers_gradients.html">   Optimizers &amp; Gradients</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Built-in-CUDA-Q-Optimizers-and-Gradients">Built in CUDA-Q Optimizers and Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Third-Party-Optimizers">Third-Party Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/optimizers_gradients.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/noisy_simulations.html">   Noisy Simulations</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/operators.html">   Constructing Operators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Constructing-Spin-Operators">Constructing Spin Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/operators.html#Pauli-Words-and-Exponentiating-Pauli-Words">Pauli Words and Exponentiating Pauli Words</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../examples/python/performance_optimizations.html">   Performance Optimizations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/python/performance_optimizations.html#Gate-Fusion">Gate Fusion</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hardware_providers.html">   Using Quantum Hardware Providers</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#amazon-braket">Amazon Braket</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#infleqtion">Infleqtion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#ionq">IonQ</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#iqm">IQM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#oqc">OQC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#orca-computing">ORCA Computing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quantinuum">Quantinuum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hardware_providers.html#quera-computing">QuEra Computing</a></li>
-</ul>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../applications.html">   Applications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Setup-and-compilation">Setup and compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Load-model">Load model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Convert-tensors-to-CUDA-Q">Convert tensors to CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Evaluate-generated-circuits">Evaluate generated circuits</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Simulate-kernels">Simulate kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Compare-unitaries">Compare unitaries</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/unitary_compilation_diffusion_models.html#Choosing-the-circuit-you-need">Choosing the circuit you need</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/trotter.html">Computing Magnetization With The Suzuki-Trotter Approximation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Problem-Setup">Problem Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/trotter.html#Running-the-Simulation">Running the Simulation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/cost_minimization.html">Cost Minimization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html">Deutsch’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-oracles">Quantum oracles</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Phase-oracle">Phase oracle</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Quantum-parallelism">Quantum parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html">Divisive Clustering With Coresets Using CUDA-Q</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Data-preprocessing">Data preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/divisive_clustering_coresets.html#Scaling-simulations-with-CUDA-Q">Scaling simulations with CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Run-VQE-with-CUDA-Q">Run VQE with CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Preparation-of-the-trial-wave-function">Preparation of the trial wave function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/quantum_volume.html">Quantum Volume</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Installing/Loading-Relevant-Packages">Installing/Loading Relevant Packages</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Using-an-Active-Space">Using an Active Space</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#infleqtion">Infleqtion</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id5">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#iqm">IQM</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id11">Submission from C++</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#oqc">OQC</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id17">Submission from C++</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id21">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#quick-start">Quick Start</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#simulator-backend-selection">Simulator Backend Selection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-gpus">Multiple GPUs</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/nvqc.html#faq">FAQ</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/platform.html">   Multi-Processor Platforms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#nvidia-mqpu-platform">NVIDIA <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#parallel-distribution-mode">Parallel distribution mode</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#quick-start">Quick Start</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#operator">Operator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#time-dependent-dynamics">Time-Dependent Dynamics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../install/install.html">   Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../install/local_installation.html">Local Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#introduction">Introduction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#docker">Docker</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#singularity">Singularity</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#python-wheels">Python wheels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#pre-built-binaries">Pre-built binaries</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#development-with-vs-code">Development with VS Code</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-docker-container">Using a Docker container</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#developing-with-remote-tunnels">Developing with Remote Tunnels</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#remote-access-via-ssh">Remote Access via SSH</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installation-via-pypi">Installation via PyPI</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installation-in-container-images">Installation In Container Images</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#updating-cuda-q">Updating CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/local_installation.html#next-steps">Next Steps</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../install/data_center_install.html">Data Center Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#build-dependencies">Build Dependencies</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda">CUDA</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#toolchain">Toolchain</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#building-cuda-q">Building CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#python-support">Python Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#c-support">C++ Support</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../install/data_center_install.html#mpi">MPI</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../specification/quake-dialect.html">   Quake Specification</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../../specification/quake-dialect.html#motivation">Motivation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../api/default_ops.html">Quantum Operations</a><ul>
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-  <section id="integrating-with-third-party-libraries">
-<h1>Integrating with Third-Party Libraries<a class="headerlink" href="#integrating-with-third-party-libraries" title="Permalink to this heading">¶</a></h1>
-<p>CUDA-Q enables easily integrating quantum kernels with existing state-of-the-art tools
-for classical computing and high performance computing. While quantum kernels are expressed
-in Python or C++, quantum hardware backends only support a very limited set of classical
-instructions. Correspondingly, only a select set of libraries and functions that are
-directly supported by the CUDA-Q compiler can be called from within quantum kernels.
-Code that is not marked as kernel code, on the other hand, can be used to invoke the
-defined quantum kernels as well as any classical tools and libraries. CUDA-Q provides
-a complete toolset to build complex applications running on multi-processor systems involving
-CPUs, GPUs and QPUs.</p>
-<p>In Python, integration with any third-party Python package is handled
-automatically without any need for additional configuration. For C++, the rest of this
-document outlines how to integration with libraries that are compile with a different
-compiler than <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code>.</p>
-<section id="calling-a-cuda-q-library-from-c">
-<h2>Calling a CUDA-Q library from C++<a class="headerlink" href="#calling-a-cuda-q-library-from-c" title="Permalink to this heading">¶</a></h2>
-<p>The CUDA-Q compiler is a fully-featured C++ compiler able to process arbitrary C++ code outside
-of quantum kernels. In fact, the CUDA-Q installation includes and invokes a custom-built Clang
-compiler under the hood. In general, it hence is not necessary to use a different compiler to
-compile C++ host code that calls into quantum kernels; the host code is compiled along with
-the quantum kernels into a single library or executable by the CUDA-Q toolchain.</p>
-<p>If you would still like to create a library that can be included in libraries and executables
-built with a different toolchain, you can configure <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> to include and encapsulate all its
-C++ dependencies, such that the CUDA-Q library only depends on C libraries. For example,
-if you have a file <code class="code docutils literal notranslate"><span class="pre">quantum_kernel.cpp</span></code> containing</p>
-<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;cudaq.h&quot;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;sstream&gt;</span>
-
-<span class="k">namespace</span><span class="w"> </span><span class="nn">quantum</span><span class="w"> </span><span class="p">{</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">bell</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">qs</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">qs</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">cnot</span><span class="p">(</span><span class="n">qs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">qs</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
-<span class="w">  </span><span class="n">mz</span><span class="p">(</span><span class="n">qs</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="c1">// Usually, one would probably return something like</span>
-<span class="c1">// the most probably value in the distribution rather</span>
-<span class="c1">// than the string dump. However, the bell kernel here</span>
-<span class="c1">// has a 50/50 chance of returning 00 or 11.</span>
-<span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">bell_distribution</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">dist</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">bell</span><span class="p">);</span>
-
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">stringbuf</span><span class="w"> </span><span class="n">buffer</span><span class="p">;</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="w"> </span><span class="nf">os</span><span class="p">(</span><span class="o">&amp;</span><span class="n">buffer</span><span class="p">);</span>
-<span class="w">  </span><span class="n">dist</span><span class="p">.</span><span class="n">dump</span><span class="p">(</span><span class="n">os</span><span class="p">);</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">sample_results</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">buffer</span><span class="p">.</span><span class="n">str</span><span class="p">();</span>
-
-<span class="w">  </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">ptr</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="kt">char</span><span class="p">[</span><span class="n">sample_results</span><span class="p">.</span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">];</span>
-<span class="w">  </span><span class="n">strcpy</span><span class="p">(</span><span class="n">ptr</span><span class="p">,</span><span class="w"> </span><span class="n">sample_results</span><span class="p">.</span><span class="n">c_str</span><span class="p">());</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">ptr</span><span class="p">;</span>
-<span class="p">}</span>
-
-<span class="p">}</span><span class="w"> </span><span class="c1">// namespace quantum</span>
-</pre></div>
-</div>
-<p>you can create such a library with the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>quantum_kernel.cpp<span class="w"> </span>-shared<span class="w"> </span>-static-libstdc++<span class="w"> </span>-fPIC<span class="w"> </span>-o<span class="w"> </span>libquantum_kernel.so
-</pre></div>
-</div>
-<p>To use this library in a project built with an external toolchain, define a suitable
-header file <code class="code docutils literal notranslate"><span class="pre">quantum_kernel.h</span></code> containing</p>
-<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span><span class="w"> </span><span class="nn">quantum</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="nf">bell_distribution</span><span class="p">();</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>and include it in your project. For example, if <code class="code docutils literal notranslate"><span class="pre">external.cpp</span></code> contains</p>
-<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;quantum_kernel.h&quot;</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="w"> </span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="c1">// some code</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">bell_dist</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">quantum</span><span class="o">::</span><span class="n">bell_distribution</span><span class="p">();</span>
-<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">bell_dist</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
-<span class="w">  </span><span class="k">delete</span><span class="p">[]</span><span class="w"> </span><span class="n">bell_dist</span><span class="p">;</span>
-<span class="w">  </span><span class="c1">// more code</span>
-<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>you can compile it with <code class="code docutils literal notranslate"><span class="pre">g++</span></code> and link it with the built <code class="code docutils literal notranslate"><span class="pre">libquantum_kernel.so</span></code> library
-using the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>g++<span class="w"> </span>external.cpp<span class="w"> </span>-lquantum_kernel<span class="w"> </span>-L<span class="s2">&quot;</span><span class="k">$(</span><span class="nb">pwd</span><span class="k">)</span><span class="s2">&quot;</span><span class="w"> </span>-Wl,-rpath<span class="o">=</span><span class="s2">&quot;</span><span class="k">$(</span><span class="nb">pwd</span><span class="k">)</span><span class="s2">&quot;</span><span class="w"> </span>-I<span class="s2">&quot;</span><span class="k">$(</span><span class="nb">pwd</span><span class="k">)</span><span class="s2">&quot;</span>
-</pre></div>
-</div>
-<p>Note that the API defined in <code class="code docutils literal notranslate"><span class="pre">quantum_kernel.h</span></code> does not depend on any data types that
-rely on the specific implementation of the C++ standard library. For example, we return a
-<code class="code docutils literal notranslate"><span class="pre">const</span> <span class="pre">char*</span></code> instead of a <code class="code docutils literal notranslate"><span class="pre">std::string</span></code> value. This is important to ensure that the data
-is handled correctly regardless of which C++ standard library is used. If you include
-data types that depend on a specific C++ ABI, the linker should detect this incompatibility
-and should generate an error. See the section <a class="reference internal" href="#clike-shim"><span class="std std-ref">Interfacing between binaries compiled with a different toolchains</span></a> below to learn more about
-what to watch out for when defining an API to interface between libraries that are built
-with different compilers.</p>
-</section>
-<section id="calling-an-c-library-from-cuda-q">
-<h2>Calling an C++ library from CUDA-Q<a class="headerlink" href="#calling-an-c-library-from-cuda-q" title="Permalink to this heading">¶</a></h2>
-<p>CUDA-Q comes with a complete C++ toolchain, and is configured to use its own C++ standard
-library by default. When calling a third-party library from within a CUDA-Q application,
-the quantum kernels and the surrounding application code are compiled with the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> compiler,
-whereas the third-party library may be compiled with a different toolchain.
-For most widely distributed libraries, this will work out of the box. The exception are
-third-party libraries that are pre-compiled (opposed to being header-only libraries)
-and dynamically depend on a different C++ standard library than CUDA-Q uses.
-This is the case for a library <code class="code docutils literal notranslate"><span class="pre">third_party_library.so</span></code> if <code class="code docutils literal notranslate"><span class="pre">libstdc++.so</span></code> is listed
-in the output of the command</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ldd<span class="w"> </span>third_party_library.so
-</pre></div>
-</div>
-<p>To use such a library with CUDA-Q, you have to install the static version of the
-library. The <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> compiler can be configured to link a static library
-<code class="code docutils literal notranslate"><span class="pre">libexternal_library.a</span></code> instead of the shared one with the same flags as GCC/Clang:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>my_code.cpp<span class="w"> </span>-Wl,-Bstatic<span class="w"> </span>-lexternal_library<span class="w"> </span>-Wl,-Bdynamic<span class="w"> </span>...
-</pre></div>
-</div>
-<p>If the compilation fails due to undefined symbols during linking, then <code class="code docutils literal notranslate"><span class="pre">libexternal_library.a</span></code>
-does not include the C++ standard library components it depends on. In this case, you can create a
-suitable static library that contains the external library as well as its <code class="code docutils literal notranslate"><span class="pre">libstdc++</span></code> dependencies
-by combining <code class="code docutils literal notranslate"><span class="pre">libexternal_library.a</span></code> and <code class="code docutils literal notranslate"><span class="pre">libstdc++.a</span></code> into a single flattened archive.
-For example, if the external library is built with GCC and located in <code class="code docutils literal notranslate"><span class="pre">/usr/lib</span></code>,
-the following commands create a suitable archive using the GNU archiver:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ar<span class="w"> </span>cruT<span class="w"> </span>libexternal_library_complete.a<span class="w"> </span>/usr/lib/libexternal_library.a<span class="w"> </span><span class="s2">&quot;</span><span class="k">$(</span>g++<span class="w"> </span>-print-file-name<span class="o">=</span>libstdc++.a<span class="k">)</span><span class="s2">&quot;</span>
-<span class="nb">echo</span><span class="w"> </span>-e<span class="w"> </span><span class="s1">&#39;create libexternal_library_complete.a\naddlib libexternal_library_complete.a\nsave\nend&#39;</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>ar<span class="w"> </span>-M
-</pre></div>
-</div>
-<p>The above commands create a new static library <code class="code docutils literal notranslate"><span class="pre">libexternal_library_complete.a</span></code> in the current directory.
-To use it, adjust the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> command accordingly:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>nvq++<span class="w"> </span>my_code.cpp<span class="w"> </span>-L.<span class="w"> </span>-Wl,-Bstatic<span class="w"> </span>-lexternal_library_complete<span class="w"> </span>-Wl,-Bdynamic<span class="w"> </span>...
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If the third-party library functions you are calling from your CUDA-Q code
-consume or produce data types that explicitly depend on the C++ standard library implementation,
-a small shim is needed to properly encapsulate these dependencies, as described in
-<a class="reference internal" href="#clike-shim"><span class="std std-ref">Interfacing between binaries compiled with a different toolchains</span></a>.</p>
-</div>
-</section>
-<section id="interfacing-between-binaries-compiled-with-a-different-toolchains">
-<span id="clike-shim"></span><h2>Interfacing between binaries compiled with a different toolchains<a class="headerlink" href="#interfacing-between-binaries-compiled-with-a-different-toolchains" title="Permalink to this heading">¶</a></h2>
-<p>To avoid incompatibilities between different C++ implementations,
-two binaries built using a different C++ compiler should be careful when passing data types between them
-that rely on a specific C++ implementation. While primitive data types can be passed without
-issues, attempting to pass a string, for example, will result in a linking failure; a <code class="code docutils literal notranslate"><span class="pre">char*</span></code> must
-be passed instead of a string.</p>
-<p>Most other data types, including templated types, can be passed as long as their alignment matches.
-While different compilers generally align data differently, it is possible to force a particular
-alignment when defining the data type or value. For example, if we define a struct</p>
-<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#pragma pack(push, 4)</span>
-<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">ScalarType</span><span class="o">&gt;</span>
-<span class="k">struct</span><span class="w"> </span><span class="nc">Entry</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">is_integral</span><span class="p">;</span>
-<span class="w">  </span><span class="n">ScalarType</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
-<span class="p">};</span>
-<span class="cp">#pragma pack(pop)</span>
-</pre></div>
-</div>
-<p>the <code class="code docutils literal notranslate"><span class="pre">pragma</span></code> directives ensure that each item will be 4-byte aligned, regardless of the
-default setting that can differ between compilers.</p>
-<p>Even function pointers can usually be passed, as long as their argument and return types can be passed.
-When using templated functions and data types, the header file should only include their declaration as well
-as explicit instantiation for all valid template arguments. For example,</p>
-<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">CudaDataType</span><span class="o">&gt;</span>
-<span class="kt">void</span><span class="w"> </span><span class="n">initializeDeviceStateVector</span><span class="p">(</span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="p">,</span>
-<span class="w">                                </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                                </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                                </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">stateDimension</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaInitializeDeviceStateVector</span><span class="o">&lt;&lt;&lt;</span><span class="n">n_blocks</span><span class="p">,</span><span class="w"> </span><span class="n">threads_per_block</span><span class="o">&gt;&gt;&gt;</span><span class="p">(</span>
-<span class="w">    </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">CudaDataType</span><span class="w"> </span><span class="o">*&gt;</span><span class="p">(</span><span class="n">deviceStateVector</span><span class="p">),</span><span class="w"> </span><span class="n">stateDimension</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="k">template</span><span class="w"> </span><span class="kt">void</span>
-<span class="n">initializeDeviceStateVector</span><span class="o">&lt;</span><span class="n">cuFloatComplex</span><span class="o">&gt;</span><span class="p">(</span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="p">,</span>
-<span class="w">                                </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                                </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                                </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">stateDimension</span><span class="p">);</span>
-
-<span class="k">template</span><span class="w"> </span><span class="kt">void</span>
-<span class="n">initializeDeviceStateVector</span><span class="o">&lt;</span><span class="n">cuDoubleComplex</span><span class="o">&gt;</span><span class="p">(</span><span class="kt">uint32_t</span><span class="w"> </span><span class="n">n_blocks</span><span class="p">,</span>
-<span class="w">                                </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">threads_per_block</span><span class="p">,</span>
-<span class="w">                                </span><span class="kt">void</span><span class="w"> </span><span class="o">*</span><span class="n">deviceStateVector</span><span class="p">,</span>
-<span class="w">                                </span><span class="n">std</span><span class="o">::</span><span class="kt">size_t</span><span class="w"> </span><span class="n">stateDimension</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>The most cumbersome aspect when libraries built with different toolchains are combined into a single executable
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-<li class="toctree-l1 current"><a class="current reference internal" href="#">   Quick Start</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/bernstein_vazirani.html">Bernstein-Vazirani Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Classical-case">Classical case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Quantum-case">Quantum case</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/bernstein_vazirani.html#Implementing-in-CUDA-Q">Implementing in CUDA-Q</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html">Compiling Unitaries Using Diffusion Models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Diffusion-model-pipeline">Diffusion model pipeline</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/unitary_compilation_diffusion_models.html#Unitary-compilation">Unitary compilation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#XOR-\oplus">XOR <span class="math notranslate nohighlight">\(\oplus\)</span></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/deutschs_algorithm.html#Deutschs'-Algorithm:">Deutschs’ Algorithm:</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Quantum-functions">Quantum functions</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#Divisive-Clustering-Function">Divisive Clustering Function</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/divisive_clustering_coresets.html#QAOA-Implementation">QAOA Implementation</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/shors.html">Factoring Integers With Shor’s Algorithm</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/shors.html#Shor's-algorithm">Shor’s algorithm</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-classically">Solving the order-finding problem classically</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Solving-the-order-finding-problem-with-a-quantum-algorithm">Solving the order-finding problem with a quantum algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Determining-the-order-from-the-measurement-results-of-the-phase-kernel">Determining the order from the measurement results of the phase kernel</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/shors.html#Postscript">Postscript</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hybrid_qnns.html">Hybrid Quantum Neural Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/qaoa.html">Max-Cut with QAOA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html">Molecular docking via DC-QAOA</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#Setting-up-the-Molecular-Docking-Problem">Setting up the Molecular Docking Problem</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/digitized_counterdiabatic_qaoa.html#CUDA-Q-Implementation">CUDA-Q Implementation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/krylov.html">Multi-reference Quantum Krylov Algorithm - <span class="math notranslate nohighlight">\(H_2\)</span> Molecule</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Setup">Setup</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Computing-the-matrix-elements">Computing the matrix elements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/krylov.html#Determining-the-ground-state-energy-of-the-subspace">Determining the ground state energy of the subspace</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/afqmc.html">Quantum Enhanced Auxiliary Field Quantum Monte Carlo</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Hamiltonian-preparation-for-VQE">Hamiltonian preparation for VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Auxiliary-Field-Quantum-Monte-Carlo-(AFQMC)">Auxiliary Field Quantum Monte Carlo (AFQMC)</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Preparation-of-the-molecular-Hamiltonian">Preparation of the molecular Hamiltonian</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/afqmc.html#Setup-of-the-AFQMC-parameters">Setup of the AFQMC parameters</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html">Quantum Fourier Transform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_fourier_transform.html#Quantum-Fourier-Transform-revisited">Quantum Fourier Transform revisited</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/quantum_teleportation.html">Quantum Teleporation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/quantum_teleportation.html#Teleportation-explained">Teleportation explained</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/readout_error_mitigation.html">Readout Error Mitigation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-single-qubit-noise-model">Inverse confusion matrix from single-qubit noise model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-confusion-matrix-from-k-local-confusion-matrices">Inverse confusion matrix from k local confusion matrices</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/readout_error_mitigation.html#Inverse-of-full-confusion-matrix">Inverse of full confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe.html">Variational Quantum Eigensolver</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Using-CUDA-Q-Optimizers">Using CUDA-Q Optimizers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe.html#Integration-with-Third-Party-Optimizers">Integration with Third-Party Optimizers</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../applications/python/vqe_advanced.html">VQE with gradients, active spaces, and gate fusion</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#The-Basics-of-VQE">The Basics of VQE</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Implementing-VQE-in-CUDA-Q">Implementing VQE in CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Parallel-Parameter-Shift-Gradients">Parallel Parameter Shift Gradients</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../applications/python/vqe_advanced.html#Gate-Fusion-for-Larger-Circuits">Gate Fusion for Larger Circuits</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/hadamard_test.html">Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Numerical-result-as-a-reference:">Numerical result as a reference:</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Using-Sample-to-perform-the-Hadamard-test">Using <code class="docutils literal notranslate"><span class="pre">Sample</span></code> to perform the Hadamard test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/hadamard_test.html#Multi-GPU-evaluation-of-QKSD-matrix-elements-using-the-Hadamard-Test">Multi-GPU evaluation of QKSD matrix elements using the Hadamard Test</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../applications/python/hadamard_test.html#Classically-Diagonalize-the-Subspace-Matrix">Classically Diagonalize the Subspace Matrix</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../applications/python/logical_aim_sqale.html">Anderson Impurity Model ground state solver on Infleqtion’s Sqale</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Performing-logical-Variational-Quantum-Eigensolver-(VQE)-with-CUDA-QX">Performing logical Variational Quantum Eigensolver (VQE) with CUDA-QX</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Constructing-circuits-in-the-[[4,2,2]]-encoding">Constructing circuits in the <code class="docutils literal notranslate"><span class="pre">[[4,2,2]]</span></code> encoding</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Setting-up-submission-and-decoding-workflow">Setting up submission and decoding workflow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-a-CUDA-Q-noisy-simulation">Running a CUDA-Q noisy simulation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../applications/python/logical_aim_sqale.html#Running-logical-AIM-on-Infleqtion's-hardware">Running logical AIM on Infleqtion’s hardware</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="backends/backends.html">   Backends</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="backends/simulators.html">   Simulation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="backends/simulators.html#state-vector-simulators">State Vector Simulators</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="backends/hardware.html">   Quantum Hardware</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#setting-credentials">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#submission-from-c">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#submission-from-python">Submission from Python</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id1">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id2">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id3">Submission from Python</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#ionq">IonQ</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#ionq-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id5">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id6">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#anyon-technologies-anyon-computing">Anyon Technologies/Anyon Computing</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id8">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id9">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#iqm">IQM</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id10">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id11">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id12">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#oqc">OQC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id13">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id14">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id15">Submission from Python</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#orca-computing">ORCA Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id16">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id17">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id18">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#quantinuum">Quantinuum</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#quantinuum-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id20">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id21">Submission from Python</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/hardware.html#quera-computing">QuEra Computing</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#quera-backend">Setting Credentials</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id23">Submission from C++</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/hardware.html#id24">Submission from Python</a></li>
-</ul>
-</li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="backends/nvqc.html">   NVIDIA Quantum Cloud</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/nvqc.html#multiple-qpus-asynchronous-execution">Multiple QPUs Asynchronous Execution</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="backends/platform.html#remote-mqpu-platform">Remote <code class="code docutils literal notranslate"><span class="pre">MQPU</span></code> Platform</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="backends/platform.html#supported-kernel-arguments">Supported Kernel Arguments</a></li>
-<li class="toctree-l4"><a class="reference internal" href="backends/platform.html#accessing-simulated-quantum-state">Accessing Simulated Quantum State</a></li>
-</ul>
-</li>
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-</li>
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-<li class="toctree-l1"><a class="reference internal" href="backends/dynamics.html">   Dynamics</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="backends/dynamics.html#quick-start">Quick Start</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="backends/dynamics.html#numerical-integrators">Numerical Integrators</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="install/install.html">   Installation</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#using-a-singularity-container">Using a Singularity container</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#connecting-to-a-remote-host">Connecting to a Remote Host</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#dgx-cloud">DGX Cloud</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#get-started">Get Started</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#use-jupyterlab">Use JupyterLab</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#use-vs-code">Use VS Code</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#additional-cuda-tools">Additional CUDA Tools</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="install/local_installation.html#installing-pre-built-binaries">Installing Pre-built Binaries</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#distributed-computing-with-mpi">Distributed Computing with MPI</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#dependencies-and-compatibility">Dependencies and Compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install/local_installation.html#next-steps">Next Steps</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="install/data_center_install.html#installation-on-the-host">Installation on the Host</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="install/data_center_install.html#cuda-runtime-libraries">CUDA Runtime Libraries</a></li>
-<li class="toctree-l4"><a class="reference internal" href="install/data_center_install.html#mpi">MPI</a></li>
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-</li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="integration/integration.html">   Integration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="integration/cmake_app.html">Downstream CMake Integration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="integration/cuda_gpu.html">Combining CUDA with CUDA-Q</a></li>
-<li class="toctree-l2"><a class="reference internal" href="integration/libraries.html">Integrating with Third-Party Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="integration/libraries.html#calling-a-cuda-q-library-from-c">Calling a CUDA-Q library from C++</a></li>
-<li class="toctree-l3"><a class="reference internal" href="integration/libraries.html#calling-an-c-library-from-cuda-q">Calling an C++ library from CUDA-Q</a></li>
-<li class="toctree-l3"><a class="reference internal" href="integration/libraries.html#interfacing-between-binaries-compiled-with-a-different-toolchains">Interfacing between binaries compiled with a different toolchains</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="extending/extending.html">   Extending</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="extending/nvqir_simulator.html">Create a new NVQIR Simulator</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="extending/nvqir_simulator.html#circuitsimulator"><code class="code docutils literal notranslate"><span class="pre">CircuitSimulator</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="extending/nvqir_simulator.html#let-s-see-this-in-action">Let’s see this in action</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="extending/cudaq_ir.html">Working with CUDA-Q IR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="extending/mlir_pass.html">Create an MLIR Pass for CUDA-Q</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../specification/index.html">   Specifications</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../specification/cudaq.html">   Language Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/machine_model.html">1. Machine Model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/namespace.html">2. Namespace and Standard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/types.html">3. Quantum Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qudit-levels">3.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::qudit&lt;Levels&gt;</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#cudaq-qubit">3.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/types.html#quantum-containers">3.3. Quantum Containers</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operators.html">4. Quantum Operators</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operators.html#cudaq-spin-op">4.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::spin_op</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/operations.html">5. Quantum Operations</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/operations.html#operations-on-cudaq-qubit">5.1. Operations on <code class="code docutils literal notranslate"><span class="pre">cudaq::qubit</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/kernels.html">6. Quantum Kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/synthesis.html">7. Sub-circuit Synthesis</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/control_flow.html">8. Control Flow</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/dynamic_kernels.html">9. Just-in-Time Kernel Creation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/patterns.html">10. Quantum Patterns</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/patterns.html#compute-action-uncompute">10.1. Compute-Action-Uncompute</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/platform.html">11. Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html">12. Algorithmic Primitives</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-sample">12.1. <code class="code docutils literal notranslate"><span class="pre">cudaq::sample</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-observe">12.2. <code class="code docutils literal notranslate"><span class="pre">cudaq::observe</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-optimizer-deprecated-functionality-moved-to-cuda-q-libraries">12.3. <code class="code docutils literal notranslate"><span class="pre">cudaq::optimizer</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/algorithmic_primitives.html#cudaq-gradient-deprecated-functionality-moved-to-cuda-q-libraries">12.4. <code class="code docutils literal notranslate"><span class="pre">cudaq::gradient</span></code> (deprecated, functionality moved to CUDA-Q libraries)</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/cudaq/examples.html">13. Example Programs</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#hello-world-simple-bell-state">13.1. Hello World - Simple Bell State</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#ghz-state-preparation-and-sampling">13.2. GHZ State Preparation and Sampling</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#quantum-phase-estimation">13.3. Quantum Phase Estimation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#deuteron-binding-energy-parameter-sweep">13.4. Deuteron Binding Energy Parameter Sweep</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#grover-s-algorithm">13.5. Grover’s Algorithm</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../specification/cudaq/examples.html#iterative-phase-estimation">13.6. Iterative Phase Estimation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../specification/quake-dialect.html">   Quake Specification</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#general-introduction">General Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../specification/quake-dialect.html#motivation">Motivation</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../api/api.html">   API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../api/languages/cpp_api.html">C++ API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#operators">Operators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#quantum">Quantum</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#common">Common</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#noise-modeling">Noise Modeling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#kernel-builder">Kernel Builder</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#algorithms">Algorithms</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#platform">Platform</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#utilities">Utilities</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/cpp_api.html#namespaces">Namespaces</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../api/languages/python_api.html">Python API</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#program-construction">Program Construction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.make_kernel"><code class="docutils literal notranslate"><span class="pre">make_kernel()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.PyKernel"><code class="docutils literal notranslate"><span class="pre">PyKernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.PyKernelDecorator"><code class="docutils literal notranslate"><span class="pre">PyKernelDecorator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.kernel"><code class="docutils literal notranslate"><span class="pre">kernel()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#kernel-execution">Kernel Execution</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.sample_async"><code class="docutils literal notranslate"><span class="pre">sample_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe"><code class="docutils literal notranslate"><span class="pre">observe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.observe_async"><code class="docutils literal notranslate"><span class="pre">observe_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state"><code class="docutils literal notranslate"><span class="pre">get_state()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_state_async"><code class="docutils literal notranslate"><span class="pre">get_state_async()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.vqe"><code class="docutils literal notranslate"><span class="pre">vqe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.draw"><code class="docutils literal notranslate"><span class="pre">draw()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.translate"><code class="docutils literal notranslate"><span class="pre">translate()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#dynamics">Dynamics</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.evolve"><code class="docutils literal notranslate"><span class="pre">evolve()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.evolve_async"><code class="docutils literal notranslate"><span class="pre">evolve_async()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#backend-configuration">Backend Configuration</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.has_target"><code class="docutils literal notranslate"><span class="pre">has_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_target"><code class="docutils literal notranslate"><span class="pre">get_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.get_targets"><code class="docutils literal notranslate"><span class="pre">get_targets()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.set_target"><code class="docutils literal notranslate"><span class="pre">set_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.reset_target"><code class="docutils literal notranslate"><span class="pre">reset_target()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.set_noise"><code class="docutils literal notranslate"><span class="pre">set_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.unset_noise"><code class="docutils literal notranslate"><span class="pre">unset_noise()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.initialize_cudaq"><code class="docutils literal notranslate"><span class="pre">initialize_cudaq()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.num_available_gpus"><code class="docutils literal notranslate"><span class="pre">num_available_gpus()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.set_random_seed"><code class="docutils literal notranslate"><span class="pre">set_random_seed()</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#data-types">Data Types</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.SimulationPrecision"><code class="docutils literal notranslate"><span class="pre">SimulationPrecision</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Target"><code class="docutils literal notranslate"><span class="pre">Target</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.State"><code class="docutils literal notranslate"><span class="pre">State</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Tensor"><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.QuakeValue"><code class="docutils literal notranslate"><span class="pre">QuakeValue</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qubit"><code class="docutils literal notranslate"><span class="pre">qubit</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qreg"><code class="docutils literal notranslate"><span class="pre">qreg</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.qvector"><code class="docutils literal notranslate"><span class="pre">qvector</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.ComplexMatrix"><code class="docutils literal notranslate"><span class="pre">ComplexMatrix</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.Schedule"><code class="docutils literal notranslate"><span class="pre">Schedule</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.integrator.BaseIntegrator"><code class="docutils literal notranslate"><span class="pre">BaseIntegrator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.EvolveResult"><code class="docutils literal notranslate"><span class="pre">EvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.AsyncEvolveResult"><code class="docutils literal notranslate"><span class="pre">AsyncEvolveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.OperatorSum"><code class="docutils literal notranslate"><span class="pre">OperatorSum</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ElementaryOperator"><code class="docutils literal notranslate"><span class="pre">ElementaryOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ProductOperator"><code class="docutils literal notranslate"><span class="pre">ProductOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.RydbergHamiltonian"><code class="docutils literal notranslate"><span class="pre">RydbergHamiltonian</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.expressions.ScalarOperator"><code class="docutils literal notranslate"><span class="pre">ScalarOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.definitions.SpinOperator"><code class="docutils literal notranslate"><span class="pre">SpinOperator</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.i"><code class="docutils literal notranslate"><span class="pre">spin.i()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.y"><code class="docutils literal notranslate"><span class="pre">spin.y()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.spin.z"><code class="docutils literal notranslate"><span class="pre">spin.z()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.cudm_state.CuDensityMatState"><code class="docutils literal notranslate"><span class="pre">CuDensityMatState</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.operator.cudm_state.to_cupy_array"><code class="docutils literal notranslate"><span class="pre">to_cupy_array()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.SampleResult"><code class="docutils literal notranslate"><span class="pre">SampleResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.AsyncSampleResult"><code class="docutils literal notranslate"><span class="pre">AsyncSampleResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.ObserveResult"><code class="docutils literal notranslate"><span class="pre">ObserveResult</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.AsyncObserveResult"><code class="docutils literal notranslate"><span class="pre">AsyncObserveResult</span></code></a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#mpi-submodule">MPI Submodule</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.initialize"><code class="docutils literal notranslate"><span class="pre">initialize()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.rank"><code class="docutils literal notranslate"><span class="pre">rank()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.num_ranks"><code class="docutils literal notranslate"><span class="pre">num_ranks()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.all_gather"><code class="docutils literal notranslate"><span class="pre">all_gather()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.broadcast"><code class="docutils literal notranslate"><span class="pre">broadcast()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.is_initialized"><code class="docutils literal notranslate"><span class="pre">is_initialized()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.mpi.finalize"><code class="docutils literal notranslate"><span class="pre">finalize()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../api/languages/python_api.html#orca-submodule">ORCA Submodule</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/languages/python_api.html#cudaq.orca.sample"><code class="docutils literal notranslate"><span class="pre">sample()</span></code></a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../api/default_ops.html">Quantum Operations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api/default_ops.html#unitary-operations-on-qubits">Unitary Operations on Qubits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#x"><code class="code docutils literal notranslate"><span class="pre">x</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#y"><code class="code docutils literal notranslate"><span class="pre">y</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#z"><code class="code docutils literal notranslate"><span class="pre">z</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#h"><code class="code docutils literal notranslate"><span class="pre">h</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#r1"><code class="code docutils literal notranslate"><span class="pre">r1</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#rx"><code class="code docutils literal notranslate"><span class="pre">rx</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#ry"><code class="code docutils literal notranslate"><span class="pre">ry</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#rz"><code class="code docutils literal notranslate"><span class="pre">rz</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#s"><code class="code docutils literal notranslate"><span class="pre">s</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#t"><code class="code docutils literal notranslate"><span class="pre">t</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#swap"><code class="code docutils literal notranslate"><span class="pre">swap</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#u3"><code class="code docutils literal notranslate"><span class="pre">u3</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../api/default_ops.html#adjoint-and-controlled-operations">Adjoint and Controlled Operations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/default_ops.html#measurements-on-qubits">Measurements on Qubits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#mz"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#mx"><code class="code docutils literal notranslate"><span class="pre">mx</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#my"><code class="code docutils literal notranslate"><span class="pre">my</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../api/default_ops.html#user-defined-custom-operations">User-Defined Custom Operations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api/default_ops.html#photonic-operations-on-qudits">Photonic Operations on Qudits</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#create"><code class="code docutils literal notranslate"><span class="pre">create</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#annihilate"><code class="code docutils literal notranslate"><span class="pre">annihilate</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#phase-shift"><code class="code docutils literal notranslate"><span class="pre">phase_shift</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#beam-splitter"><code class="code docutils literal notranslate"><span class="pre">beam_splitter</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api/default_ops.html#id1"><code class="code docutils literal notranslate"><span class="pre">mz</span></code></a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../versions.html">   Other Versions</a></li>
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-  <section id="quick-start">
-<h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading">¶</a></h1>
-<p><strong>NVIDIA CUDA-Q</strong></p>
-<p>CUDA-Q streamlines hybrid application development and promotes productivity and scalability
-in quantum computing. It offers a unified programming model designed for a hybrid
-setting—that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for
-programming in Python and in C++. Learn more about the <a class="reference external" href="https://developer.nvidia.com/cuda-q">key benefits of CUDA-Q</a>.</p>
-<p>This Quick Start page guides you through installing CUDA-Q and running your first program.
-If you have already installed and configured CUDA-Q, or if you are using our
-<a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/quantum/containers/cuda-quantum">Docker image</a>, you can move directly to our
-<a class="reference internal" href="basics/basics.html"><span class="doc">Basics Section</span></a>. More information about working with containers and Docker alternatives can be
-found in our complete <a class="reference internal" href="install/install.html"><span class="doc">Installation Guide</span></a>.</p>
-<section id="install-cuda-q">
-<h2>Install CUDA-Q<a class="headerlink" href="#install-cuda-q" title="Permalink to this heading">¶</a></h2>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">Python</label><div class="tab-content docutils">
-<p>To develop CUDA-Q applications using Python,
-please follow the instructions for <a class="reference external" href="https://pypi.org/project/cuda-quantum/">installing CUDA-Q</a> from PyPI.
-If you have an NVIDIA GPU, make sure to also follow the instructions for enabling GPU-acceleration.</p>
-<p>CUDA-Q does not require a GPU to use, but some components are GPU-accelerated.</p>
-<p>Getting started with CUDA-Q on <code class="docutils literal notranslate"><span class="pre">x86_64</span></code> platforms simply requires <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">cudaq</span></code>. Please make sure your <code class="docutils literal notranslate"><span class="pre">pip</span></code> version is &gt;= 24.0.
-If you have an NVIDIA GPU on your host system, you will be
-able to use it without any further installation steps.</p>
-<blockquote>
-<div><p><strong>Important:</strong>
-Please check if you have an existing installation of the <code class="docutils literal notranslate"><span class="pre">cuda-quantum</span></code>, <code class="docutils literal notranslate"><span class="pre">cudaq-quantum-cu11</span></code>, or <code class="docutils literal notranslate"><span class="pre">cuda-quantum-cu12</span></code> package, and uninstall it prior to installation. Different CUDA-Q binary distributions may conflict with each other causing issues.</p>
-</div></blockquote>
-<p>If you want to perform multi-GPU simulations, or if you are using
-<code class="docutils literal notranslate"><span class="pre">aarch64</span></code>/<code class="docutils literal notranslate"><span class="pre">arm64</span></code> processors, additional components must be installed. We
-recommend using <a class="reference external" href="https://docs.conda.io/en/latest/">Conda</a> to do so. If you are
-not already using Conda, you can install a minimal version following the
-instructions <a class="reference external" href="https://docs.anaconda.com/miniconda/">here</a>. The following
-commands will create and activate a complete environment for CUDA-Q with all its
-dependencies:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">cuda_version=11.8.0 # set this variable to version 11.x (where x &gt;= 8) or 12.x</span>
-<span class="go">conda create -y -n cudaq-env python=3.11 pip</span>
-<span class="go">conda install -y -n cudaq-env -c &quot;nvidia/label/cuda-${cuda_version}&quot; cuda</span>
-<span class="go">conda install -y -n cudaq-env -c conda-forge mpi4py openmpi cxx-compiler</span>
-<span class="go">conda env config vars set -n cudaq-env LD_LIBRARY_PATH=&quot;$CONDA_PREFIX/envs/cudaq-env/lib:$LD_LIBRARY_PATH&quot;</span>
-<span class="go">conda env config vars set -n cudaq-env MPI_PATH=$CONDA_PREFIX/envs/cudaq-env</span>
-<span class="go">conda activate cudaq-env</span>
-<span class="go">pip install cudaq</span>
-<span class="go">source $CONDA_PREFIX/lib/python3.11/site-packages/distributed_interfaces/activate_custom_mpi.sh</span>
-</pre></div>
-</div>
-<p>You must configure MPI by setting the following environment variables:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">export OMPI_MCA_opal_cuda_support=true OMPI_MCA_btl=&#39;^openib&#39;</span>
-</pre></div>
-</div>
-<p><em>If you do not set these variables you may encounter a segmentation fault.</em></p>
-<p><strong>Important</strong>: It is <em>not</em> sufficient to set these variable within the Conda
-environment, like the commands above do for <code class="docutils literal notranslate"><span class="pre">LD_LIBRARY_PATH</span></code>. To avoid having
-to set them every time you launch a new shell, we recommend adding them to
-<code class="docutils literal notranslate"><span class="pre">~/.profile</span></code> (create the file if it does not exist), and to <code class="docutils literal notranslate"><span class="pre">~/.bash_profile</span></code> or
-<code class="docutils literal notranslate"><span class="pre">~/.bash_login</span></code> if such a file exists.</p>
-<p>Once you completed the installation, please follow the instructions
-<a class="reference internal" href="#validate-installation"><span class="std std-ref">below</span></a> to run your first CUDA-Q program!</p>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">C++</label><div class="tab-content docutils">
-<p>To develop CUDA-Q applications using C++, please make sure you have a C++ toolchain installed
-that supports C++20, for example <code class="code docutils literal notranslate"><span class="pre">g++</span></code> version 11 or newer.
-Download the <code class="code docutils literal notranslate"><span class="pre">install_cuda_quantum</span></code> file for your processor architecture and CUDA version (<code class="code docutils literal notranslate"><span class="pre">_cu11</span></code> suffix for CUDA 11 and <code class="code docutils literal notranslate"><span class="pre">_cu12</span></code> suffix for CUDA 12)
-from the assets of the respective <a class="reference external" href="https://github.com/NVIDIA/cuda-quantum/releases">GitHub release</a>;
-hat is the file with the <code class="code docutils literal notranslate"><span class="pre">aarch64</span></code> extension for ARM processors, and the one with <code class="code docutils literal notranslate"><span class="pre">x86_64</span></code> for, e.g., Intel and AMD processors.</p>
-<p>To install CUDA-Q, execute the commands</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>-E<span class="w"> </span>bash<span class="w"> </span>install_cuda_quantum*.<span class="k">$(</span>uname<span class="w"> </span>-m<span class="k">)</span><span class="w"> </span>--accept
-.<span class="w"> </span>/etc/profile
-</pre></div>
-</div>
-<p>If you have an NVIDIA GPU, please also install the <a class="reference external" href="https://developer.nvidia.com/cuda-downloads">CUDA Toolkit</a> to enable GPU-acceleration within CUDA-Q.</p>
-<p>Please see the complete <a class="reference internal" href="install/local_installation.html#install-prebuilt-binaries"><span class="std std-ref">installation guide</span></a> for more details, including</p>
-<ul class="simple">
-<li><p>a list of <a class="reference internal" href="install/local_installation.html#dependencies-and-compatibility"><span class="std std-ref">supported operating systems</span></a>,</p></li>
-<li><p>instructions on how to <a class="reference internal" href="install/local_installation.html#distributed-computing-with-mpi"><span class="std std-ref">enable MPI parallelization</span></a> within CUDA-Q, and</p></li>
-<li><p>information about <a class="reference internal" href="install/local_installation.html#updating-cuda-quantum"><span class="std std-ref">updating CUDA-Q</span></a>.</p></li>
-</ul>
-<p>Once you completed the installation, please follow the instructions
-<a class="reference internal" href="#validate-installation"><span class="std std-ref">below</span></a> to run your first CUDA-Q program!</p>
-</div>
-</div>
-</section>
-<section id="validate-your-installation">
-<span id="validate-installation"></span><h2>Validate your Installation<a class="headerlink" href="#validate-your-installation" title="Permalink to this heading">¶</a></h2>
-<p>Let’s run a simple program to validate your installation.
-The quantum kernel in the following program creates and measures the state
-<span class="math notranslate nohighlight">\((|00\rangle + |11\rangle) / \sqrt{2}\)</span>. That means each kernel execution should
-either yield <code class="code docutils literal notranslate"><span class="pre">00</span></code> or <code class="code docutils literal notranslate"><span class="pre">11</span></code>. The program samples, meaning it executes, the kernel 1000 times
-and prints how many times each output was measured. On average, the values <code class="code docutils literal notranslate"><span class="pre">00</span></code> and <code class="code docutils literal notranslate"><span class="pre">11</span></code>
-should be observed around 500 times each.</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--1-input--1" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--1">Python</label><div class="tab-content docutils">
-<p>Create a file titled <code class="code docutils literal notranslate"><span class="pre">program.py</span></code>, containing the following code:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">cudaq</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Running on target </span><span class="si">{</span><span class="n">cudaq</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="n">qubit_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="mi">1</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="k">else</span> <span class="mi">2</span>
-
-
-<span class="nd">@cudaq</span><span class="o">.</span><span class="n">kernel</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">():</span>
-    <span class="n">qubits</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">qvector</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">)</span>
-    <span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">qubit_count</span><span class="p">):</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">ctrl</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-    <span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span>
-
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">cudaq</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>  <span class="c1"># Example: { 11:500 00:500 }</span>
-</pre></div>
-</div>
-<p>Run this program as you do any other Python program, for example:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python3 program.py</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--1-input--2" name="tab-set--1" type="radio"><label class="tab-label" for="tab-set--1-input--2">C++</label><div class="tab-content docutils">
-<p>Create a file titled <code class="code docutils literal notranslate"><span class="pre">program.cpp</span></code>, containing the following code:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cudaq.h&gt;</span>
-
-<span class="n">__qpu__</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">kernel</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="n">cudaq</span><span class="o">::</span><span class="n">qvector</span><span class="w"> </span><span class="nf">qubits</span><span class="p">(</span><span class="n">qubit_count</span><span class="p">);</span>
-<span class="w">  </span><span class="n">h</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
-<span class="w">  </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
-<span class="w">    </span><span class="n">cx</span><span class="p">(</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">qubits</span><span class="p">[</span><span class="n">i</span><span class="p">]);</span>
-<span class="w">  </span><span class="p">}</span>
-<span class="w">  </span><span class="n">mz</span><span class="p">(</span><span class="n">qubits</span><span class="p">);</span>
-<span class="p">}</span>
-
-<span class="kt">int</span><span class="w"> </span><span class="n">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="w"> </span><span class="o">*</span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">qubit_count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">argc</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">atoi</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
-<span class="w">  </span><span class="k">auto</span><span class="w"> </span><span class="n">result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cudaq</span><span class="o">::</span><span class="n">sample</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="w"> </span><span class="n">qubit_count</span><span class="p">);</span>
-<span class="w">  </span><span class="n">result</span><span class="p">.</span><span class="n">dump</span><span class="p">();</span><span class="w"> </span><span class="c1">// Example: { 11:500 00:500 }</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>Compile the program using the <code class="code docutils literal notranslate"><span class="pre">nvq++</span></code> compiler and run the built application with the following command:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ program.cpp -o program.x &amp;&amp; ./program.x</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>If you have an NVIDIA GPU the program uses GPU acceleration by default.
-To confirm that this works as expected and to see the effects of GPU acceleration, you can
-increase the numbers of qubits the program uses to 28 and
-compare the time to execute the program on the
-<code class="code docutils literal notranslate"><span class="pre">nvidia</span></code> target (<a class="reference internal" href="backends/simulators.html#cuquantum-single-gpu"><span class="std std-ref">GPU-accelerated statevector simulator</span></a>) to the time when setting the target to <code class="code docutils literal notranslate"><span class="pre">qpp-cpu</span></code> (<a class="reference internal" href="backends/simulators.html#openmp-cpu-only"><span class="std std-ref">OpenMP parallelized CPU-only statevector simulator</span></a>):</p>
-<div class="tab-set docutils">
-<input checked="True" class="tab-input" id="tab-set--2-input--1" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--1">Python</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python3 program.py 28 --target nvidia</span>
-</pre></div>
-</div>
-</div>
-<input class="tab-input" id="tab-set--2-input--2" name="tab-set--2" type="radio"><label class="tab-label" for="tab-set--2-input--2">C++</label><div class="tab-content docutils">
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">nvq++ program.cpp -o program.x --target nvidia &amp;&amp; ./program.x 28</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>When you change the target to <code class="code docutils literal notranslate"><span class="pre">qpp-cpu</span></code>, the program simply seems to hang; that is because it takes a long time for the CPU-only backend to simulate 28+ qubits! Cancel the execution with <code class="code docutils literal notranslate"><span class="pre">Ctrl+C</span></code>.</p>
-<p>For more information about enabling GPU-acceleration, please see
-our complete <a class="reference internal" href="install/local_installation.html#additional-cuda-tools"><span class="std std-ref">Installation Guide</span></a>.
-For further information on available targets, see <a class="reference internal" href="backends/backends.html"><span class="doc">Backends</span></a>.</p>
-<p>You are now all set to start developing quantum applications using CUDA-Q!
-Please proceed to <a class="reference internal" href="basics/basics.html"><span class="doc">Basics</span></a> for an introduction
-to the fundamental features of CUDA-Q.</p>
-</section>
-</section>
-
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